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Background Late-life exposure to ambient air pollution is a modifiable risk factor for dementia, but epidemiological studies have shown inconsistent evidence for cognitive decline. Air quality (AQ) improvement has been associated with improved cardiopulmonary health and decreased mortality, but to the best of our knowledge, no studies have examined the association with cognitive function. We examined whether AQ improvement was associated with slower rate of cognitive decline in older women aged 74 to 92 years. Methods and findings We studied a cohort of 2,232 women residing in the 48 contiguous US states that were recruited from more than 40 study sites located in 24 states and Washington, DC from the Women’s Health Initiative (WHI) Memory Study (WHIMS)-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) study. They were predominantly non-Hispanic White women and were dementia free at baseline in 2008 to 2012. Measures of annual (2008 to 2018) cognitive function included the modified Telephone Interview for Cognitive Status (TICSm) and the telephone-based California Verbal Learning Test (CVLT). We used regionalized universal kriging models to estimate annual concentrations (1996 to 2012) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) at residential locations. Estimates were aggregated to the 3-year average immediately preceding (recent exposure) and 10 years prior to (remote exposure) WHIMS-ECHO enrollment. Individual-level improved AQ was calculated as the reduction from remote to recent exposures. Linear mixed effect models were used to examine the associations between improved AQ and the rates of cognitive declines in TICSm and CVLT trajectories, adjusting for sociodemographic (age; geographic region; race/ethnicity; education; income; and employment), lifestyle (physical activity; smoking; and alcohol), and clinical characteristics (prior hormone use; hormone therapy assignment; depression; cardiovascular disease (CVD); hypercholesterolemia; hypertension; diabetes; and body mass index [BMI]). For both PM2.5 and NO2, AQ improved significantly over the 10 years before WHIMS-ECHO enrollment. During a median of 6.2 (interquartile range [IQR] = 5.0) years of follow-up, declines in both general cognitive status (β = −0.42/year, 95% CI: −0.44, −0.40) and episodic memory (β = −0.59/year, 95% CI: −0.64, −0.54) were observed. Greater AQ improvement was associated with slower decline in TICSm (βPM2.5improvement = 0.026 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.001, 0.05; βNO2improvement = 0.034 per year for improved NO2 by each IQR = 3.92 parts per billion [ppb] reduction, 95% CI: 0.01, 0.06) and CVLT (βPM2.5 improvement = 0.070 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.02, 0.12; βNO2improvement = 0.060 per year for improved NO2 by each IQR = 3.97 ppb reduction, 95% CI: 0.005, 0.12) after adjusting for covariates. The respective associations with TICSm and CVLT were equivalent to the slower decline rate found with 0.9 to 1.2 and1.4 to 1.6 years of younger age and did not significantly differ by age, region, education, Apolipoprotein E (ApoE) e4 genotypes, or cardiovascular risk factors. The main limitations of this study include measurement error in exposure estimates, potential unmeasured confounding, and limited generalizability. Conclusions In this study, we found that greater improvement in long-term AQ in late life was associated with slower cognitive declines in older women. This novel observation strengthens the epidemiologic evidence of an association between air pollution and cognitive aging., Diana Younan and colleagues investigate whether air quality improvement is associated with rate of cognitive decline in community-dwelling older women in the United States., Author summary Why was this study done? Although studies have shown that late-life exposure to outdoor air pollution is a modifiable risk factor for dementia, the epidemiological evidence for cognitive decline has been inconsistent. Epidemiological studies have demonstrated that improved air quality (AQ) may decrease mortality and improve respiratory health, strengthening the evidence of a relationship between ambient air pollution and these health outcomes. To our knowledge, no previous studies have examined the potential benefit of slowing cognitive aging by AQ improvement. What did the researchers do and find? Using a US cohort of 2,232 older women followed up to 20 years, we explored whether women living in locations with greater AQ improvement had slower decline in their cognitive function. AQ improvement was defined as the difference in air pollution levels over 2 time points that were 10 years apart. Living in locations with greater AQ improvement was associated with slower cognitive declines in older women, equivalent to women who were 0.9 to 1.6 years younger. The associations were similar across age groups, geographic region, education, Apolipoprotein E (ApoE) e4 genotypes, and cardiovascular risk factors. What do these findings mean? These findings strengthen the contribution of outdoor air pollution on cognitive aging. These results highlight the potential benefits of reducing outdoor air pollution levels. Key study limitations include measurement error in exposure estimates, potential unmeasured confounders, and limited generalizability.

Psychological and neural distinctions between the technical concepts of “liking” and “wanting” pose important problems for motivated choice for goods. Why could we “want” something that we do not “like,” or “like” something but be unwilling to exert effort to acquire it? Here, we suggest a framework for answering these questions through the medium of reinforcement learning. We consider “liking” to provide immediate, but preliminary and ultimately cancellable, information about the true, long-run worth of a good. Such initial estimates, viewed through the lens of what is known as potential-based shaping, help solve the temporally complex learning problems faced by animals., What is the distinction between ’liking’ and ’wanting’? Why could we ’want’ something that we do not ’like,’ or ’like’ something but be unwilling to exert effort to acquire it? This Essay argues that the primary hedonic phenomenon called ’liking’ might solve the temporal credit assignment problem for learning that arises when true reinforcement values are available slowly or late.

Visual Question Answering (VQA) is a learning task that combines computer vision with natural language processing. In VQA, it is important to understand the alignment between visual concepts and linguistic semantics. In this paper, we proposed a Pre-training Model Based on Parallel Cross-Modality Fusion Layer (P-PCFL) to learn the fine-grained relationship between vision and language. The P-PCFL model is composed of three Encoders: Object Encoder, Language Encoder, and Parallel Cross-Modality Fusion Encoder, with Transformer as the core. We use four different Pre-training missions, namely, Cross-Modality Mask Language Modeling, Cross-Modality Mask Region Modeling, Image-Text Matching, and Image-Text Q&A, to pre-train the P-PCFL model and improve its reasoning and universality, which help to learn the relationship between Intra-modality and Inter-modality. Experimental results on the platform of Visual Question Answering dataset VQA v2.0 show that the Pre-trained P-PCFL model has a good effect after fine-tuning the parameters. In addition, we also conduct ablation experiments and provide some results of Attention visualization to verify the effectiveness of P-PCFL model.

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning., What makes cognition ‘advanced’ is an open and not precisely defined question. In this study, a cognitive model of cleaner fish learning the ephemeral-reward task demonstrates how a critical step in cognitive evolution may be understood as the evolution of chunking and its tuning to fit ecological conditions.

Shared medical appointments (SMAs) offer a means for providing knowledge and skills needed for chronic disease management to patients. However, SMAs require a time and attention investment from health care providers, who must understand the goals and potential benefits of SMAs from the perspective of patients and providers. To better understand how to gain provider engagement and inform future SMA implementation, qualitative inquiry of provider experience based on a knowledge-attitude-practice model was explored. Semi-structured interviews were conducted with 24 health care providers leading SMAs for heart failure at three Veterans Administration Medical Centers. Rapid matrix analysis process techniques including team-based qualitative inquiry followed by stakeholder validation was employed. The interview guide followed a knowledge-attitude-practice model with a priori domains of knowledge of SMA structure and content (understanding of how SMAs were structured), SMA attitude/beliefs (general expectations about SMA use), attitudes regarding how leading SMAs affected patients, and providers. Data regarding the patient referral process (organizational processes for referring patients to SMAs) and suggested improvements were collected to further inform the development of SMA implementation best practices. Providers from all three sites reported similar knowledge, attitude and beliefs of SMAs. In general, providers reported that the multi-disciplinary structure of SMAs was an effective strategy towards improving clinical outcomes for patients. Emergent themes regarding experiences with SMAs included improved self-efficacy gained from real-time collaboration with providers from multiple disciplines, perceived decrease in patient re-hospitalizations, and promotion of self-management skills for patients with HF. Most providers reported that the SMA-setting facilitated patient learning by providing opportunities for the sharing of experiences and knowledge. This was associated with the perception of increased comradery and support among patients. Future research is needed to test suggested improvements and to develop best practices for training additional sites to implement HF SMA.

The replay of task-relevant trajectories is known to contribute to memory consolidation and improved task performance. A wide variety of experimental data show that the content of replayed sequences is highly specific and can be modulated by reward as well as other prominent task variables. However, the rules governing the choice of sequences to be replayed still remain poorly understood. One recent theoretical suggestion is that the prioritization of replay experiences in decision-making problems is based on their effect on the choice of action. We show that this implies that subjects should replay sub-optimal actions that they dysfunctionally choose rather than optimal ones, when, by being forgetful, they experience large amounts of uncertainty in their internal models of the world. We use this to account for recent experimental data demonstrating exactly pessimal replay, fitting model parameters to the individual subjects’ choices., Author summary When animals are asleep or restfully awake, populations of neurons in their brains recapitulate activity associated with extended behaviourally-relevant experiences. This process is called replay, and it has been established for a long time in rodents, and very recently in humans, to be important for good performance in decision-making tasks. The specific experiences which are replayed during those epochs follow highly ordered patterns, but the mechanisms which establish their priority are still not fully understood. One promising theoretical suggestion is that each replay experience is chosen in such a way that the learning that ensues is most helpful for the subsequent performance of the animal. A very recent study reported a surprising result that humans who achieved high performance in a planning task tended to replay actions they found to be sub-optimal, and that this was associated with a useful deprecation of those actions in subsequent performance. In this study, we examine the nature of this pessimized form of replay and show that it is exactly appropriate for forgetful agents. We analyse the role of forgetting for replay choices of our model, and verify our predictions using human subject data.

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in ‘runs’ of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area., Author summary Foraging has been well-studied in many species that rely on widely distributed food sources, such as bees and birds. Less well understood is how humans approach foraging tasks, and whether there are general policies we can identify that describe how we search for different categories of objects that can vary in quantity and distribution. We present a way to model foraging behaviour as a generative sampling without replacement procedure, implemented in a Bayesian multilevel model. This allows us to break down behaviour into a number of independent biases that influence target selection, including the proximity of targets, a bias for selecting targets in runs and a bias for a particular target type, in a way that is not dependent on the number of targets present. We believe this tool can open the door for foraging to become a standard task for refining our understanding of attention, working memory, prospective memory, learning, planning and decision-making.

Concept learning have been studied widely in non-human animal species within or not an ecological context. Here we tested whether cleaner fish Labroides dimidiatus, which show generalised rule learning in an ecologically relevant context; they generalise that any predator may provide protection from being chased by other fish; can also learn a general concept when presented with abstract cues. We tested for this ability in the matching-to-sample task. In this task, a sample is shown first, and then the subject needs to choose the matching sample over a simultaneously presented different one in order to obtain a food reward. We used the most general form of the task, using each stimulus only once in a total of 200 trials. As a group, the six subjects performed above chance, and four individuals eventually reached learning criteria. However, individual performance was rather unstable, yielding overall only 57% correct choices. These results add to the growing literature that ectotherms show the ability of abstract concept learning, though the lack of stable high performance may indicate quantitative performance differences to endotherms.

We devised and assessed open recruitment of host families for medical student homestays in a rural area of Hyogo Prefecture, Japan, so that program organizers would not have to depend on professional and personal connections. The duration of the homestays was one night and two days, and they were conducted in August 2016, 2017, and 2018. The purpose of this community-based medical education program was to promote interactions between medical students and residents of Tamba area. The study asked one family member from each host family to complete a questionnaire after the homestay, and their experiences were evaluated in the study. The questionnaire results were analyzed using a visual analog scale (VAS; 0–100 mm). Thirty-three host families participated in the homestay program over three years. Results showed that VAS scores were high for enjoyment of homestays (VAS; 92.4 ± 13.0), continuation of the homestay program (91.7 ± 12.7), continuation of participation in the homestay program (89.2 ± 16.2), and desire for the homestay students to work in the area in the future (95.4 ± 6.3). The recruitment of host families through advertising was an efficient method for this community-based medical education homestay program. The results indicate that it is possible to attract more host families through open recruitment, which will contribute to the sustainability of the homestay program. Further research, including a follow-up of the students who participated and whether they chose a rural area or Tamba to practice is needed in the future. Since this is an ongoing program, further research in a similar format can be conducted in the future.

High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on an ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We find that the learned embeddings predict expression intensity and pattern. To test generalization, we generated embeddings in a second dataset that assayed the expression of 78 genes in 53 individuals. In this set of images, 60.2% of genes are re-identified, suggesting the model is robust. Importantly, this dataset assayed expression in individuals diagnosed with schizophrenia. Gene and donor-specific embeddings from the model predict schizophrenia diagnosis at levels similar to that reached with demographic information. Mutations in the most discriminative gene, Sodium Voltage-Gated Channel Beta Subunit 4 (SCN4B), may help understand cardiovascular associations with schizophrenia and its treatment. We have publicly released our source code, embeddings, and models to spur further application to spatial transcriptomics. In summary, we propose and evaluate gene re-identification as a machine learning task to represent ISH gene expression images.

Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still exists in these models. Considering this, a mixed filter pruning approach based on principal component analysis (PCA) and geometric median is presented. First, a pre-trained model is analyzed by using PCA to identify the important filters for every layer. These important filters are then used to reconstruct the network with a fewer number of layers and a fewer number of filters per layer. A new network with optimized structure is constructed and trained on the data. Secondly, the trained model is then analyzed using geometric median as a base. The redundant filters are identified and removed which results in further compression of the network. Finally, the pruned model is fine tuned to regain the accuracy. Experiments on CIFAR-10, CIFAR-100 and ILSVRC 2017 datasets show that the proposed mixed pruning approach is feasible and can compress the network to greater extent without any significant loss to accuracy. With VGG-16 on CIFAR-10, the number of operations and parameters are reduced to 18.56× and 3.33×, respectively, with almost 1% loss in the accuracy. The compression rate for AlexNet on CIFAR-10 dataset is 2.61× and 4.85× in terms of number of operations and number of parameters, respectively, with a gain of 1.2% in the accuracy. On CIFAR-100, VGG-19 is compressed by 16.02 X in terms of number of operations and 36× in terms of number of parameters with a 2.6% loss of accuracy. Similarly, the compression rate for VGG-19 network on ILSVRC 2017 dataset is 1.87× and 2.4× for operations and parameters with 0.5% loss in accuracy.

Introduction Globally, medical students have demonstrated knowledge gaps in emergency care and acute stabilization. In Colombia, new graduates provide care for vulnerable populations. The World Health Organization (WHO) Basic Emergency Care (BEC) course trains frontline providers with limited resources in the management of acute illness and injury. While this course may serve medical students as adjunct to current curriculum, its utility in this learner group has not been investigated. This study performs a baseline assessment of knowledge and confidence in emergency management taught in the BEC amongst medical students in Colombia. Methods A validated, cross-sectional survey assessing knowledge and confidence of emergency care congruent with BEC content was electronically administered to graduating medical students across Colombia. Knowledge was evaluated via 15 multiple choice questions and confidence via 13 questions using 100 mm visual analog scales. Mean knowledge and confidence scores were compared across demographics, geography and prior training using Chi-Squared or one-way ANOVA analyses. Results Data were gathered from 468 graduating medical students at 36 institutions. The mean knowledge score was 59.9% ± 23% (95% CI 57.8–62.0%); the mean confidence score was 59.6 mm ±16.7 mm (95% CI 58.1–61.2). Increasing knowledge and confidence scores were associated with prior completion of emergency management training courses (p Conclusion Knowledge and confidence levels of emergency care management for graduating medical students across Colombia demonstrated room for additional, specialized training. Higher scores were seen in groups that had completed emergency care courses. Implementation of the BEC as an adjunct to current curriculum may serve a valuable addition.

Background Adverse childhood experiences (ACEs) are linked to numerous health conditions but understudied in multiple sclerosis (MS). This study’s objective was to test for the association between ACEs and MS risk and several clinical outcomes. Methods We used a sample of adult, non-Hispanic MS cases (n = 1422) and controls (n = 1185) from Northern California. Eighteen ACEs were assessed including parent divorce, parent death, and abuse. Outcomes included MS risk, age of MS onset, Multiple Sclerosis Severity Scale score, and use of a walking aid. Logistic and linear regression estimated odds ratios (ORs) (and beta coefficients) and 95% confidence intervals (CIs) for ACEs operationalized as any/none, counts, individual events, and latent factors/patterns. Results Overall, more MS cases experienced ≥1 ACE compared to controls (54.5% and 53.8%, respectively). After adjusting for sex, birthyear, and race, this small difference was attenuated (OR = 1.01, 95% CI: 0.87, 1.18). There were no trends of increasing or decreasing odds of MS across ACE count categories. Consistent associations between individual ACEs between ages 0–10 and 11–20 years and MS risk were not detected. Factor analysis identified five latent ACE factors, but their associations with MS risk were approximately null. Age of MS onset and other clinical outcomes were not associated with ACEs after multiple testing correction. Conclusion Despite rich data and multiple approaches to operationalizing ACEs, no consistent and statistically significant effects were observed between ACEs with MS. This highlights the challenges of studying sensitive, retrospective events among adults that occurred decades before data collection.

Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this process. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.

Problem-based learning (PBL) allows students to learn medical statistics through problem solving experience. The aim of this study was to assess the efficiency of PBL modules implemented in the blended learning courses in medical statistics through knowledge outcomes and student satisfaction. The pilot study was designed as a randomized controlled trial that included 53 medical students who had completed all course activities. The students were randomized in two groups: the group with access to PBL modules within the blended learning course (hPBL group) and the group without access to PBL modules–only blended learning course (BL group). There were no significant differences between the groups concerning socio-demographic characteristics, previous academic success and modality of access to course materials. Students from hPBL group had a significantly higher problem solving score (p = 0.012; effect size 0.69) and the total medical statistics score (p = 0,046; effect size 0.57). Multivariate regression analysis with problem solving as an outcome variable showed that problem solving was associated with being in hPBL group (p = 0.010) and having higher grade point average (p = 0.037). Multivariate regression analysis with the medical statistics score as an outcome variable showed the association between a higher score on medical statistics with access to PBL modules (p = 0.045) and a higher grade point average (p = 0.021). All students in hPBL group (100.0%) considered PBL modules useful for learning medical statistics. PBL modules can be easily implemented in the existing courses within medical statistics using the Moodle platform, they have high applicability and can complement, but not replace other forms of teaching. These modules were shown to be efficient in learning, to be well accepted among students and to be a potential missing link between teaching and learning medical statistics. The authors of this study are planning to create PBL modules for advanced courses in medical statistics and to conduct this study on other universities with a more representative study sample, with the aim to overcome the limitations of the existing study and confirm its results.

Appropriate antibiotic prescription contributes to reducing bacterial resistance; therefore, it is critical to provide training regarding this challenge. The objective of this study was to develop a virtual learning environment for antibiotic prescription and to determine its impact on dentists’ awareness, attitudes, and intention to practice. First, the learning content on multimedia resources was developed and distributed into three challenges that participants had to overcome. Then, a quasi-experimental study was performed in which the virtual learning environment was implemented on dentists from seven Colombian cities. The median of correct answers and the levels of awareness, attitudes, and intention to practice were compared before, immediately after, and 6-months post-intervention. Wilcoxon signed-rank and McNemar’s tests were used to determine the differences. A total of 206 participants who finished the virtual learning environment activities exhibited a favorable and statistically significant impact on the median of correct answers of awareness (p < 0.001), attitudes (p < 0.001), and intention to practice (p = 0.042). A significant increase occurred in the number of participants with a high level of awareness (p < 0.001) and a non-significant increase in participants with high levels of attitudes (p = 0.230) and intention to practice (p = 0.286). At 6 months, the positive effect on the median of correct answers on awareness and intention to practice persisted (p < 0.001); however, this was not evident for attitudes (p = 0.105). Moreover, there was a significant decrease in the number of participants who showed low levels of awareness (p = 0.019) and a slight increase in those with high levels of the same component (p = 0.161). The use of a virtual learning environment designed for dentists contributed to a rapid improvement in awareness and intention to practice antibiotic prescription; however, their attitudes and information retention need reinforcement.

There is growing interest in the role that morphological knowledge plays in literacy acquisition, but there is no research directly comparing the efficacy of different forms of morphological instruction. Here we compare two methods of teaching English morphology in the context of a memory experiment when words were organized by affix during study (e.g., a list of words was presented that all share an affix, such as , , , , etc.) or by base during study (e.g., a list of words was presented that all share a base, such as , , , ). We show that memory for morphologically complex words is better in both conditions compared to a control condition that does not highlight the morphological composition of words, and most importantly, show that studying words in a base-centric format improves memory further still. We argue that the morphological matrix that organizes words around a common base may provide an important new tool for literacy instruction.

Due to the prolonged COVID-19 pandemic, wearing masks has become essential for social interaction, disturbing emotion recognition in daily life. In the present study, a total of 39 Korean participants (female = 20, mean age = 24.2 years) inferred seven emotions (happiness, surprise, fear, sadness, disgust, anger, surprise, and neutral) from uncovered, mask-covered, sunglasses-covered faces. The recognition rates were the lowest under mask conditions, followed by the sunglasses and uncovered conditions. In identifying emotions, different emotion types were associated with different areas of the face. Specifically, the mouth was the most critical area for happiness, surprise, sadness, disgust, and anger recognition, but fear was most recognized from the eyes. By simultaneously comparing faces with different parts covered, we were able to more accurately examine the impact of different facial areas on emotion recognition. We discuss the potential cultural differences and the ways in which individuals can cope with communication in which facial expressions are paramount.

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer’s diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

Introduction Recent reports indicate that COVID-19 pandemic has significant influence on medical professionals’ mental health. Strict limitations in clinical practice and social interactions within academic community, which had to be introduced, could lead to significant psychological distress in medical students. The aim of the study was to assess resilience, well-being and burnout among Polish medical students in the COVID-19 era. Methods The online survey consisting of validated questionnaires assessing resilience (Resilience Scale 14; RS-14), well-being (Medical Student Well-Being Index) and burnout (Maslach Burnout Inventory) as well as self-created survey concerning mental health problems, use of stimulants, SARS-CoV-2 infection, work in COVID-19 units, medical education and social attitude towards health care professionals in the pandemic era was distributed via Facebook and other online students’ platforms. 1858 MSs from all polish medical schools agreed to fill in the survey. Results ‘Very low’, ‘low’ and ‘on the low end’ levels of resilience were found in 26%, 19.1% and 26.9% of the study group, respectively. Students with higher resilience level presented better attitude towards online and hybrid classes. 16.8% of respondents stated that they worked, currently work or plan to work voluntarily at the pandemic frontline. In terms of burnout, these respondents presented lower exhaustion (p = 0.003) and cynicism (p = 0.02), and higher academic efficacy (p = 0.002). That group also showed greater resilience (p = 0.046). The SARS-CoV-2 infection among respondents, their relatives and friends did not influence the results. 39.1% of respondents declared the need of the psychological or psychiatric consultation in relation to pandemic challenges. 231 (26.4%) participants previously diagnosed with mental health disorders noticed worsening of their symptoms. Increased intake of alcohol, cigarettes or other stimulants was noticed by 340 (28.6%) respondents. 80.2% of respondents thought that social aversion and mistrust towards doctors increased during the pandemic and part of them claimed it affected their enthusiasm toward medical career. Conclusions The majority of medical students presented low levels of resilience and high burnout at the time of pandemic. Providing necessary support especially in terms of mental health and building up the resilience of this vulnerable group seems crucial to minimize harm of current pandemic and similar future challenges.

Objectives This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. Methods Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability. Results ARIMA (2, 1, 1) (0, 1, 1)12, ARIMA (1, 1, 3) (1, 1, 1)52 and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models. Conclusions Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

The hippocampal formation (HF) is well documented as having a feedforward, unidirectional circuit organization termed the trisynaptic pathway. This circuit organization exists along the septotemporal axis of the HF, but the circuit connectivity across septal to temporal regions is less well described. The emergence of viral genetic mapping techniques enhances our ability to determine the detailed complexity of HF circuitry. In earlier work, we mapped a subiculum (SUB) back projection to CA1 prompted by the discovery of theta wave back propagation from the SUB to CA1 and CA3. We reason that this circuitry may represent multiple extended noncanonical pathways involving the subicular complex and hippocampal subregions CA1 and CA3. In the present study, multiple retrograde viral tracing approaches produced robust mapping results, which supports this prediction. We find significant noncanonical synaptic inputs to dorsal hippocampal CA3 from ventral CA1 (vCA1), perirhinal cortex (Prh), and the subicular complex. Thus, CA1 inputs to CA3 run opposite the trisynaptic pathway and in a temporal to septal direction. Our retrograde viral tracing results are confirmed by anterograde-directed viral mapping of projections from input mapped regions to hippocampal dorsal CA3 (dCA3). We find that genetic inactivation of the projection of vCA1 to dCA3 impairs object-related spatial learning and memory but does not modulate anxiety-related behaviors. Our data provide a circuit foundation to explore novel functional roles contributed by these noncanonical hippocampal circuit connections to hippocampal circuit dynamics and learning and memory behaviors., This study reveals extensive non-canonical synaptic inputs to dorsal hippocampal CA3 from ventral CA1, perirhinal cortex and subicular complex, and shows that genetic inactivation of projection from ventral CA1 to dorsal CA3 impairs object-related spatial learning and memory.

To invest effort into any cognitive task, people must be sufficiently motivated. Whereas prior research has focused primarily on how the cognitive control required to complete these tasks is motivated by the potential rewards for success, it is also known that control investment can be equally motivated by the potential negative consequence for failure. Previous theoretical and experimental work has yet to examine how positive and negative incentives differentially influence the manner and intensity with which people allocate control. Here, we develop and test a normative model of control allocation under conditions of varying positive and negative performance incentives. Our model predicts, and our empirical findings confirm, that rewards for success and punishment for failure should differentially influence adjustments to the evidence accumulation rate versus response threshold, respectively. This dissociation further enabled us to infer how motivated a given person was by the consequences of success versus failure., Author summary From the school to the workplace, whether someone achieves their goals is determined largely by the mental effort they invest in their tasks. Recent work has demonstrated both why and how people adjust the amount of effort they invest in response to variability in the rewards expected for achieving that goal. However, in the real world, we are motivated both by the positive outcomes our efforts can achieve (e.g., praise) and the negative outcomes they can avoid (e.g., rejection), and these two types of incentives can motivate adjustments not only in the amount of effort we invest but also the types of effort we invest (e.g., whether to prioritize performing the task efficiently or cautiously). Using a combination of computational modeling and a novel task that measures voluntary effort allocation under varying incentive conditions, we show that people should and do engage dissociable forms of mental effort in response to positive versus negative incentives. With increasing rewards for achieving their goal, they prioritize efficient performance, whereas with increasing penalties for failure they prioritize cautious performance. We further show that these dissociable strategies enable us to infer how motivated a given person was based on the positive consequences of success relative to the negative consequences of failure.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year’s experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants’ assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners’ feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term., Author summary Bioscientists analyzing research data deal with challenges because most lack computer science background, such as programming skills, making it difficult to process their data and communicate with data analysts. Over the last few years (2017 to 2020), we assembled interdisciplinary teams of graduate and undergraduate students to develop the Brazilian Python Workshop for Biological Data. These short courses aimed to teach programming skills in a real-world setting. They were offered in Portuguese to facilitate accessibility to both Brazilians and foreigners doing research in Brazil. We accomplished these goals by emphasizing both basic programming skills and foundational concepts, alongside hands-on activities designed with biological datasets. Importantly, we were supported by experienced faculty. Although the first editions were in-person, we reformulated the 2020 edition to an online version due to the Coronavirus Disease 2019 (COVID-19) pandemic. During the online 2020 edition, we taught using a variety of tools to facilitate synchronous and asynchronous communication between participants and organization and to engage participants in activities that promoted their active participation and networking. We used digital notebooks and encouraged students to put into practice shareable and reproducible research. In 2020, we also performed online surveys with participants that helped us to implement real-time improvements and perspectives of future changes based on the students’ feedback. Our workshop comprises a model for future initiatives.

Working memory (WM) deficits have been widely documented in schizophrenia (SZ), and almost all existing studies attributed the deficits to decreased capacity as compared to healthy control (HC) subjects. Recent developments in WM research suggest that other components, such as precision, also mediate behavioral performance. It remains unclear how different WM components jointly contribute to deficits in schizophrenia. We measured the performance of 60 SZ (31 females) and 61 HC (29 females) in a classical delay-estimation visual working memory (VWM) task and evaluated several influential computational models proposed in basic science of VWM to disentangle the effect of various memory components. We show that the model assuming variable precision (VP) across items and trials is the best model to explain the performance of both groups. According to the VP model, SZ exhibited abnormally larger variability of allocating memory resources rather than resources or capacity per se. Finally, individual differences in the resource allocation variability predicted variation of symptom severity in SZ, highlighting its functional relevance to schizophrenic pathology. This finding was further verified using distinct visual features and subject cohorts. These results provide an alternative view instead of the widely accepted decreased-capacity theory and highlight the key role of elevated resource allocation variability in generating atypical VWM behavior in schizophrenia. Our findings also shed new light on the utility of Bayesian observer models to characterize mechanisms of mental deficits in clinical neuroscience., Author summary Working memory is a core cognitive function related to a broad range of cognitive domains such as problem-solving, attention, executive control, and IQ. Although working memory deficits have been well-documented in schizophrenia, the underlying mechanisms remain unclear. Conventional working memory theories attribute working memory deficits in schizophrenia to their reduced memory capacity, overlooking the potential roles of other memory components, such as precision. In this study, we take the approach of computational psychiatry and use computational modeling to uncover the major determinants of working memory deficits. We assess working memory performance of a large cohort of participants (60 schizophrenia patients and 61 demographic matched healthy controls) and evaluate multiple mainstream computational models of visual working memory. The variable precision model turns out to be the best model for both groups. We further find that the poorer performance of schizophrenia patients arises from heterogeneous distribution of memory resources when encoding items in memory. This resource allocation variability can also predict symptom severity in schizophrenia. Our study highlights the use of computational models in psychiatric researches.

Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one’s influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one’s own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one’s genuine controllability requires exclusion of other agents’ possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression., Author summary How we perceive controllability over an outcome if there are multiple other agents who can simultaneously influence that outcome? Previous ‘single-agent’ studies showed that an agents’ inferred controllability depends on contingency between its own action and following outcome and this inference involves striatum. Here, we show that in a multi-agent setting, other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which was explained as a biased Bayesian inference. Notably, bias in inference played an adaptive role under volatile controllability and was associated with a perception of guilt. Striatum and temporoparietal junction (TPJ) were involved in this multi-agent Bayesian controllability inference and this controllability information was leveraged to increase motivated behavior in the vmPFC. Our results first provide a neurocomputational mechanism of multi-agent controllability inference.

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use. ispartof: Plos One vol:16 issue:10 pages:1-16 ispartof: location:United States status: published

The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable., Author summary The spread of infectious diseases presents many challenges to healthcare systems and infrastructures across the world. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to the dangers of disease spread. This study seeks to understand how COVID-19 spreads in settlements, focusing on the Cox’s Bazar refugee settlement in Bangladesh. Our model simulates the movements and interactions of each individual in the settlement, incorporating information about family structures and demographic attributes, to understand how COVID-19 might spread under various intervention strategies. Our analysis suggests that mask wearing in indoor locations can have a significant effect on disease spread, even when wearing reusable cotton masks, which the people in the settlement can make themselves. We also look at different ways to treat individuals who only have milder symptoms and don’t yet require hospitalisation, as well as various scenarios which might allow for the safe reopening of schools in the settlement. With almost 80 million forcibly displaced people in the world, we hope that this work will inspire more modeling groups to focus on these vulnerable populations, which have been traditionally under-served by such efforts, to ensure no one is left behind.

International audience; The reduced specificity of positive and negative autobiographical memories observed in anorexic (AN) patients may reflect a global disturbance in their emotional information processing. However, their emotional difficulties may differ according to the subtype of AN, implying possible differences in the manifestation of autobiographical memory impairments. The aims of the study were (1) to confirm the autobiographical memory deficits in AN patients in terms of specificity and wealth of memories, and (2) to compare autobiographical deficits according to the AN subtype: restrictive type (AR) or binge/purging type (AB). Ninety-five non-clinical (NC) individuals and 95 AN patients including 69 AR and 22 AB patients were administered the Williams’ and Scott’s Autobiographical Memory Test. The results confirmed a lack of specificity regardless of emotional valence in the overall AN patient group without any distinction of subtype, which was linked to the number of hospitalizations. When the AN subtype was considered, AR patients demonstrated reduced specificity for negative memories only, suggesting differences in emotional functioning or in the mechanisms underlying reduced specificity between AR and AB patients. Furthermore, the overall AN group demonstrated lower variability and complexity in their memory content than the NC group. However, this difference in the complexity of recalled memories was only found in response to negative cues. When AN subtypes were considered, AR patients showed fewer complex memories than NC individuals. Beyond a reduced specificity, AN patients also depict a poverty in the range of event recall and a difficulty in developing narrative content. The clinical implications of such autobiographical memory deficits need to be further investigated.

Several lines of study suggest that peripheral metabolism of amyloid beta (Aß) is associated with risk for Alzheimer disease (AD). In blood, greater than 90% of Aß is complexed as an apolipoprotein, raising the possibility of a lipoprotein-mediated axis for AD risk. In this study, we report that genetic modification of C57BL/6J mice engineered to synthesise human Aß only in liver (hepatocyte-specific human amyloid (HSHA) strain) has marked neurodegeneration concomitant with capillary dysfunction, parenchymal extravasation of lipoprotein-Aß, and neurovascular inflammation. Moreover, the HSHA mice showed impaired performance in the passive avoidance test, suggesting impairment in hippocampal-dependent learning. Transmission electron microscopy shows marked neurovascular disruption in HSHA mice. This study provides causal evidence of a lipoprotein-Aß /capillary axis for onset and progression of a neurodegenerative process.

Background Approximately 40% of children 3 to 4 years of age in low- and middle-income countries have suboptimal development and growth. Women’s empowerment may help provide inputs of nurturing care for early development and growth by building caregiver capacity and family support. We examined the associations between women’s empowerment and child development, growth, early learning, and nutrition in sub-Saharan Africa (SSA). Methods and findings We pooled data on married women (15 to 49 years) and their children (36 to 59 months) from Demographic and Health Surveys that collected data on child development (2011 to 2018) in 9 SSA countries (N = 21,434): Benin, Burundi, Cameroon, Chad, Congo, Rwanda, Senegal, Togo, and Uganda. We constructed a women’s empowerment score using factor analysis and assigned women to country-specific quintile categories. The child outcomes included cognitive, socioemotional, literacy–numeracy, and physical development (Early Childhood Development Index), linear growth (height-for-age Z-score (HAZ) and stunting (HAZ, Lilia Bliznashka and co-workers study empowerment of women and associated child health outcomes in sub-Saharan Africa., Author summary Why was this study done? Nearly 40% of children 3 to 4 years of age in low- and middle-income countries have suboptimal development and growth. Children require multiple health, nutrition, early learning, and care inputs to reach their full developmental and growth potential. Women’s empowerment may help provide these inputs by increasing shared caregiving, building caregiver capacity, and improving family support. Even though women’s empowerment is predictive of better child health, nutrition, and growth, less is known about the relationship between women’s empowerment and child development and early learning outcomes. What did the researchers do and find? We pooled Demographic and Health Surveys data from married women (15 to 49 years) and their children (36 to 59 months) in 9 sub-Saharan African countries to investigate the association between women’s empowerment and child development, growth, early learning, and nutrition outcomes. Our results showed that higher women’s empowerment was predictive of better child cognitive development, growth, and nutrition. More empowered women and their partners also had access to more learning resources and provided more stimulation activities to their child. What do these findings mean? Our findings indicate that improving women’s empowerment is one potential strategy to help provide the nutrition and early learning inputs children require to reach their full developmental and growth potential. Efforts to improve early child development and growth should consider the role of women’s empowerment in improving early learning and nutrition outcomes. Future research should assess the longitudinal and causal relationships between women’s empowerment and child development, growth, early learning, and nutrition outcomes.

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories., Author summary We show that neuromodulatory changes during non-rapid eye movement (NREM) sleep generate stable spike timing relationships between neurons, the ordering of which reflects the neurons’ relative firing rates during wake. Learning-dependent ordering of firing in the hippocampus during NREM, acting in tandem with spike timing-dependent plasticity, reconfigures neuronal firing rates across the hippocampal network. This “rescaling” of neuronal firing rates has recently been reported in multiple brain circuits across periods of sleep. Together, our results suggest that the brain is remapping frequency-biased representations of information formed during wake into timing biased-representations during NREM sleep.

© 2021 Moore et al., Bioinformatics training is required at every stage of a scientist’s research career. Continual bioinformatics training allows exposure to an ever-changing and growing repertoire of techniques and databases, and so biologists, computational scientists, and healthcare practitioners are all seeking learning opportunities in the use of computational resources and tools designed for data storage, retrieval, and analysis. There are abundant opportunities for accessing bioinformatics training for scientists in high-income countries (HICs), with well-equipped facilities and participants and trainers requiring minimal travel and financial costs alongside a range of general advice for developing short bioinformatics training courses [1–3]. However, regionally targeted bioinformatics training in low- and middle-income countries (LMICs) often requires more extensive local and external support, organization, and travel. Due to the limited expertise in bioinformatics in LMICs in general, most bioinformatics training requires a fair amount of collaboration with experts beyond the local community, country, or region. A common model of training, used as the basis of this article, includes a local host collaborating with local, regional, and international experts gathering to train local or regional participants. Recently, there has been a growth of capacity strengthening initiatives in LMICs, such as the Pan African Bioinformatics Network for Human Heredity and Health in Africa (H3ABioNet) Initiative [4–6], the Capacity Building for Bioinformatics in Latin America (CABANA) Project [7], the Asia Pacific BioInformatics Network (APBioNet) [8], and the Wellcome Connecting Science Courses and Conferences program [9]. One of the important strands of these initiatives is a drive to organize and deliver valuable bioinformatics training, but organizing and delivering short bioinformatics training workshops in an LMIC present a unique set of challenges. This paper attempts to build upon the sage advice for organizing bioinformatics workshops with specific guidance for organizing and delivering them in LMICs. It describes the processes to follow in organizing courses taking into consideration the low-resource setting. We should also note that LMICs are not a monolithic group and that setting, context, temporality, and specific location matters. LMICs are a complex regional grouping [10] and should be treated as such; however, we will present some common lessons that we hope will help organizers and trainers of bioinformatics training events in LMICs to navigate the often different, challenging, and rewarding experience., The authors who contributed to this manuscript are funded as follows: BM receives salary support from Wellcome Trust grants [WT108749/Z/15/Z, WT108749/Z/15/A], PC, VR, NM, AG’s salaries are funded in whole, or in part, by the NIH Common Fund H3ABioNet grant [U24HG006941], MC, SLFV, AR, PG, PCL’s salaries were partly funded by the UKRI-BBSRC ‘Capacity building for bioinformatics in Latin America’ (CABANA) grant, on behalf of the Global Challenges Research Fund [BB/P027849/1], JDLR is funded by ISCiii AES [ref. PI18/00591] at the CSIC/USAL (Spain) and by CYTED, RIABIO (Red Iberoamericana 521RT0118), AM’s salary is funded by [WT206194/Z/17/Z], GO is funded by the CABANA grant and SM is funded by the EMBL-EBI.

Several supervillains have higher degrees—why don’t you? There can be a variety of reasons for wanting to go to grad school and for applying to a particular school and program. But often, one can only tell apart good and bad reasons from hindsight. Failing at something is perhaps the best way to know what can go wrong and what advice would have been useful when considering graduate school applications. We should know one of us started grad school 4 separate times, and another learned what a PhD was only after having started one. One of us lost 2 supervisors before even starting to write her thesis, and yet another accepted a PhD offer from the lab where she was working, without considering any alternatives; finally, one of us had applied to graduate schools for 5 years (with 19 rejections) before finally landing a PhD offer from their dream school. We hope that our hard-earned lessons will help you to avoid some of the pitfalls that we ourselves fell prey to. In this article, we address how to choose a graduate program, how to apply strategically, and some of the key challenges that may arise along the way toward graduate school. Conveniently, our advice can be summarized as 10 simple rules … so here they are. (Extracted from Introduction)

Like many indigenous populations worldwide, Yucatec Maya communities are rapidly undergoing change as they become more connected with urban centers and access to formal education, wage labour, and market goods became more accessible to their inhabitants. However, little is known about how these changes affect children’s language input. Here, we provide the first systematic assessment of the quantity, type, source, and language of the input received by 29 Yucatec Maya infants born six years apart in communities where increased contact with urban centres has resulted in a greater exposure to the dominant surrounding language, Spanish. Results show that infants from the second cohort received less directed input than infants in the first and, when directly addressed, most of their input was in Spanish. To investigate the mechanisms driving the observed patterns, we interviewed 126 adults from the communities. Against common assumptions, we showed that reductions in Mayan input did not simply result from speakers devaluing the Maya language. Instead, changes in input could be attributed to changes in childcare practices, as well as caregiver ethnotheories regarding the relative acquisition difficulty of each of the languages. Our study highlights the need for understanding the drivers of individual behaviour in the face of socio-demographic and economic changes as it is key for determining the fate of linguistic diversity.

The European Higher Education Area (EHEA) recommends the use of new educational methodologies and the evaluation of student satisfaction. Different instruments have been developed in Spain to evaluate different aspects such as clinical decisions and teamwork, however no instruments have been found that specifically evaluate student self-confidence and satisfaction during clinical simulation. The aim was to translate the Student Satisfaction and Self-Confidence in Learning Scale (SCLS) questionnaire into Spanish and analyse its reliability and validity and understand the level of satisfaction and self-confidence of nursing students with respect to learning in clinical simulations. The study was carried out in two phases: (1) adaptation of the questionnaire into Spanish. (2) Cross-sectional study in a sample of 489 nursing students. The reliability and exploratory and confirmatory factorial analyses were performed. To analyse the relationship of the scale scores with the socio-demographic variables, the Fisher Student T-test or the ANOVA was used. The scale demonstrated high internal consistency reliability for the total scale and each of its dimensions. Cronbach's alpha was 0.88 (0.83 to 0.81) for each of the dimensions. The exploratory and confirmatory factor analysis showed that both the one-dimensional and two-dimensional models were acceptable. The results showed average scores above 4 for both dimensions. The SCLS-Spanish translation demonstrated evidence of its validity and reliability for use to understand the level of satisfaction and self-confidence of nursing students in clinical simulation. Clinical simulations help students to increase their levels of confidence and satisfaction, enabling them to face real scenarios in clinical practice.

A critical facet of adjusting one’s behaviour after succeeding or failing at a task is assigning responsibility for the ultimate outcome. Humans have trait- and state-like tendencies to implicate aspects of their own behaviour (called ‘internal’ ascriptions) or facets of the particular task or Lady Luck (’chance’). However, how these tendencies interact with actual performance is unclear. We designed a novel task in which subjects had to learn the likelihood of achieving their goals, and the extent to which this depended on their efforts. High internality (Levenson I-score) was associated with decision making patterns that are less vulnerable to failure. Our computational analyses suggested that this depended heavily on the adjustment in the perceived achievability of riskier goals following failure. We found beliefs about chance not to be explanatory of choice behaviour in our task. Beliefs about powerful others were strong predictors of behaviour, but only when subjects lacked substantial influence over the outcome. Our results provide an evidentiary basis for heuristics and learning differences that underlie the formation and maintenance of control expectations by the self., Author summary How success in tasks depends on our efforts is largely a feature of what is known as the controllability of the situation or environment. This quantity should determine the way we approach, adapt to, and perform tasks. In novel settings, it can only be our expectations about controllability that exert an effect, for instance determining the balance of our focus between features of achievability and potential reward; or affecting prior notions about what exactly is achievable or what we can or cannot do. All of these might be different between aversive and appetitive domains. To study these issues, we designed a novel task in which subjects have to learn about a new environment, and analyzed their behavior using a rich computational model. We found that expectations about controllability played a particularly important role in influencing learning, but in a way that differed between positive and negative outcomes. In particular, high expectations about controllability were tied to higher learning rates of unachievability given negative outcomes, guarding subjects from further loss, and preserving their subjective optimistic expectations about control. Our findings can be interpreted within a theoretical framework which implicates control expectations in individual learning differences, and fit well within modern theories of learning in aversive contexts and serotonergic function.

Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the “Methods” and “Results” sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration., A quantitative survey of >1 million published research articles reveals that while classical statistical methods remain in widespread use, multivariate statistical and machine-learning approaches have seen a significant increase; statistics curricula should be revised to take full advantage of these new analytical tools.

Acute stress can crucially influence learning and memory processes. One of the key processes underlying human learning and memory is the ability of our brain to rapidly detect and extract regularities from sensory input across time and space leading to effective predictive processing. Here, we aimed to get an in-depth look into the effect of stress on the acquisition of two aspects of regularity extraction. We examined whether and how stress affects the learning (1) of probability-based regularities and (2) of serial order-based regularities in the same experimental design, and (3) the explicit access to the acquired information. Considering that the acquisition of probability-based regularities is a relatively rapid process, we primarily focused on the early phase of the task. We induced stress with the Socially Evaluated Cold Pressor Test in 27 young adults, while 26 participants were enrolled in the control group. Salivary cortisol levels and subjective ratings of affective states showed successful stress induction. After the stress induction, we measured regularity extraction with the cued Alternating Serial Reaction Time task. We found that stress promoted the extraction of probability-based regularities measured by the learning performance in the early phase of the task and did not alter the learning of serial order-based regularities. Post-block reports showed weaker explicit access to the serial order-based regularities in the stress group. Our results can contribute to a process-level understanding on how stress alters learning and memory functions related to predictive processes.

Objectives To investigate the associations of plasma lamivudine (3TC), abacavir (ABC), emtricitabine (FTC) and tenofovir (TFV) concentrations with cognitive function in a cohort of treated people with HIV (PWH). Methods Pharmacokinetics (PK) and cognitive function (Cogstate, six domains) data were obtained from PWH recruited in the POPPY study on either 3TC/ABC or FTC/tenofovir disoproxil fumarate (TDF)-containing regimens. Association between PK parameters (AUC0-24: area under the concentration-time curve over 24 hours, Cmax: maximum concentration and Ctrough: trough concentration) and cognitive scores (standardized into z-scores) were evaluated using rank regression adjusting for potential confounders. Results Median (IQR) global cognitive z-scores in the 83 PWH on 3TC/ABC and 471 PWH on FTC/TDF were 0.14 (-0.27, 0.38) and 0.09 (-0.28, 0.42), respectively. Higher 3TC AUC0-24 and Ctrough were associated with better global z-scores [rho = 0.29 (p = 0.02) and 0.27 (p = 0.04), respectively], whereas higher 3TC Cmax was associated with poorer z-scores [rho = -0.31 (p0.05). None of the FTC and TFV PK parameters were associated with global or domain cognitive scores. Conclusions Whilst we found no evidence of either detrimental or beneficial effects of ABC, FTC and TFV plasma exposure on cognitive function of PWH, higher plasma 3TC exposures were generally associated with better cognitive performance although higher peak concentrations were associated with poorer performance.

Despite the clear importance of language in our life, our vital ability to quickly and effectively learn new words and meanings is neurobiologically poorly understood. Conventional knowledge maintains that language learning—especially in adulthood—is slow and laborious. Furthermore, its structural basis remains unclear. Even though behavioural manifestations of learning are evident near instantly, previous neuroimaging work across a range of semantic categories has largely studied neural changes associated with months or years of practice. Here, we address rapid neuroanatomical plasticity accompanying new lexicon acquisition, specifically focussing on the learning of action-related language, which has been linked to the brain’s motor systems. Our results show that it is possible to measure and to externally modulate (using transcranial magnetic stimulation (TMS) of motor cortex) cortical microanatomic reorganisation after mere minutes of new word learning. Learning-induced microstructural changes, as measured by diffusion kurtosis imaging (DKI) and machine learning-based analysis, were evident in prefrontal, temporal, and parietal neocortical sites, likely reflecting integrative lexico-semantic processing and formation of new memory circuits immediately during the learning tasks. These results suggest a structural basis for the rapid neocortical word encoding mechanism and reveal the causally interactive relationship of modal and associative brain regions in supporting learning and word acquisition., This combined neuroimaging and brain stimulation study reveals rapid and distributed microstructural plasticity after a single immersive language learning session, demonstrating the causal relevance of the motor cortex in encoding the meaning of novel action words.

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus—the default mode network (DMN) and a “medial temporal network” (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a “posterior medial” (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an “anterior temporal” (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a “medial prefrontal” (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory., Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. This study uses network analyses of intrinsic brain networks at rest to identify and characterize brain networks that interact with the hippocampus and have distinct functions during memory-guided decision making.

To survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a form of hippocampal Spike-Timing-Dependent-Plasticity (STDP) that is sequentially modulated by acetylcholine and dopamine. Acetylcholine facilitates synaptic depression, while dopamine retroactively converts the depression into potentiation. When these experimental findings were implemented as a learning rule in a computational model, our simulations showed that cholinergic-facilitated depression is important for reversal learning. In the present study, we tested the model’s prediction by optogenetically inactivating cholinergic neurons in mice during a hippocampus-dependent spatial learning task with changing rewards. We found that reversal learning, but not initial place learning, was impaired, verifying our computational prediction that acetylcholine-modulated plasticity promotes the unlearning of old reward locations. Further, differences in neuromodulator concentrations in the model captured mouse-by-mouse performance variability in the optogenetic experiments. Our line of work sheds light on how neuromodulators enable the learning of new contingencies., Author summary Reversal learning likely involves changes in synaptic connections, a neural mechanism known as synaptic plasticity, so old information can be updated. We previously discovered that acetylcholine, an important neuromodulator in the brain, changes synaptic connections in a way that favours reversal learning. Specifically, acetylcholine weakens active synapses in brain slices, but these synapses can later be strengthened by a reward signal. Based on this result in slices, we used a computational model to propose a behavioural function for the action of acetylcholine on synaptic connections. In the model, acetylcholine would weaken synaptic connections associated with an old reward, allowing an agent to quickly learn a new reward location. We tested this hypothesis here by silencing acetylcholine neurons in mice while they navigated a maze for food rewards. These animals were able to learn the location of the first food reward, but were impaired when the reward was shifted to a new location. The behavioural results of this study suggest that acetylcholine indeed facilitates reversal learning, which the computational model attributes to a weakening of synaptic connections that do not lead to reward. Taken together, our experimental and computational work show how synaptic strength changes, gated by neuromodulators, affect learning behaviour.

Categorical judgments can systematically bias the perceptual interpretation of stimulus features. However, it remained unclear whether categorical judgments directly modify working memory representations or, alternatively, generate these biases via an inference process down-stream from working memory. To address this question we ran two novel psychophysical experiments in which human subjects had to reverse their categorical judgments about a stimulus feature, if incorrect, before providing an estimate of the feature. If categorical judgments indeed directly altered sensory representations in working memory, subjects’ estimates should reflect some aspects of their initial (incorrect) categorical judgment in those trials. We found no traces of the initial categorical judgment. Rather, subjects seemed to be able to flexibly switch their categorical judgment if needed and use the correct corresponding categorical prior to properly perform feature inference. A cross-validated model comparison also revealed that feedback may lead to selective memory recall such that only memory samples that are consistent with the categorical judgment are accepted for the inference process. Our results suggest that categorical judgments do not modify sensory information in working memory but rather act as top-down expectations in the subsequent sensory recall and inference process., Author summary Humans have the tendency to remain consistent with their previous categorical judgments when recalling memorized evidence. This tendency gives rise to many known forms of consistency and confirmation biases. Using a detailed computational model comparison, we show that these biases do not reflect distortions in the memory representation of the evidence but rather result from a biased interpretation of the evidence during memory recall. Our results are important as they suggest that consistency biases are malleable, even reversible, which has implications for our understanding of economic and cognitive decision-making processes and even society at large.

Defendants can deny they have agency, and thus responsibility, for a crime by using a defense of mental impairment. We argue that although this strategy may help defendants evade blame, it may carry longer-term social costs, as lay people’s perceptions of a person’s agency might determine some of the moral rights they grant them. Three randomized between-group experiments (N = 1601) used online vignettes to examine lay perceptions of a hypothetical defendant using a defense of mental impairment (versus a guilty plea). We find that using a defense of mental impairment significantly reduces responsibility, blame, and punitiveness relative to a guilty plea, and these judgments are mediated by perceptions of reduced moral agency. However, after serving their respective sentences, those using the defense are sometimes conferred fewer rights, as reduced agency corresponds to an increase in perceived dangerousness. Our findings were found to be robust across different types of mental impairment, offences/sentences, and using both manipulated and measured agency. The findings have implications for defendants claiming reduced agency through legal defenses, as well as for the broader study of moral rights and mind perception.

Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via nonlinear voltage-dependent mechanisms, such as NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of “under-performing” synapses on a model dendrite during learning (“structural plasticity”), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location-dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the all-versus-all MNIST task (~85%) approaches that of logistic regression (~93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron (“functional plasticity”) and show that a G-clusteron that utilizes the weight update rule can achieve ~89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions., Author summary Artificial neural networks (ANNs) have become among the most powerful tools in artificial intelligence and machine learning, enabling computers to solve complex tasks like image recognition. Inspired by the brain, ANNs are composed of simple neuron-like units that perform a weighted sum of their synaptic inputs. Artificial neurons can “learn” by modifying the weight of each input. Biological neurons, however, are more complex. Synapses in a real neuron can interact nonlinearly with each other in a distance-dependent manner due to voltage-dependent mechanisms, such as the NMDA receptor. We created a model neuron, called the Gradient clusteron (G-clusteron) which can use distance-dependent nonlinearities to learn to solve classification tasks by making its synapses attract or repel each other. We tested the G-clusteron on its ability to recognize handwritten digits from the MNIST dataset and showed that it can achieve a high level of classification accuracy (~85%) just by updating its synaptic locations. We also derive a rule for updating the synaptic weights of the G-clusteron and show that having the ability to update both its synaptic weights and synaptic locations allows the G-clusteron to solve the “exclusive or” (XOR) problem, which is famously impossible for a linear neuron.

Depression is characterized by deficits in the reinforcement learning (RL) process. Although many computational and neural studies have extended our knowledge of the impact of depression on RL, most focus on habitual control (model-free RL), yielding a relatively poor understanding of goal-directed control (model-based RL) and arbitration control to find a balance between the two. We investigated the effects of subclinical depression on model-based and model-free learning in the prefrontal–striatal circuitry. First, we found that subclinical depression is associated with the attenuated state and reward prediction error representation in the insula and caudate. Critically, we found that it accompanies the disrupted arbitration control between model-based and model-free learning in the predominantly inferior lateral prefrontal cortex and frontopolar cortex. We also found that depression undermines the ability to exploit viable options, called exploitation sensitivity. These findings characterize how subclinical depression influences different levels of the decision-making hierarchy, advancing previous conflicting views that depression simply influences either habitual or goal-directed control. Our study creates possibilities for various clinical applications, such as early diagnosis and behavioral therapy design., Author summary Human decision making is known to be driven by at least two distinct processes, goal-directed and habitual learning. Previous studies argued that these systems and their interaction are disrupted in depression. However, we have limited understanding of the integration of the two systems and how this case extends to early or mild depression. We used a computational model and fMRI to address this issue. We found that depression-related changes were observed in the different levels of the decision making process. Notably, we found that depressive individuals have higher sensitivity of the habitual learning process, indicating the impairment of the proper integration of the two. Our findings raise the hope about developing clinical applications for the early diagnosis of this disorder, as well as using behavioral/cognitive therapy or brain stimulus techniques for restoring the balance between goal-directed and habitual learning in individuals with subclinical depression.