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PurposeWe examined the relationship between glycemic control and urinary tract infections in women with type 1 diabetes mellitus.Materials and methodsWomen enrolled in the Epidemiology of Diabetes Interventions and Complications study, the observational followup of the Diabetes Control and Complications Trial, were surveyed to assess the rate of physician diagnosed urinary tract infections in the preceding 12 months. The relationship between glycated hemoglobin levels and number of urinary tract infections in the previous 12 months was assessed using a multivariable Poisson regression model.ResultsA total of 572 women were evaluated at year 17. Mean age was 50.7 ± 7.2 years, mean body mass index was 28.6 ± 5.9 kg/m(2), mean type 1 diabetes duration was 29.8 ± 5.0 years and mean glycated hemoglobin was 8.0% ± 0.9%. Of these women 86 (15.0%) reported at least 1 physician diagnosed urinary tract infection during the last 12 months. Higher glycated hemoglobin levels were significantly associated with number of urinary tract infections such that for every unit increase (1%) in recent glycated hemoglobin level, there was a 21% (p=0.02) increase in urinary tract infection frequency in the previous 12 months after adjusting for race, hysterectomy status, urinary incontinence, sexual activity in the last 12 months, peripheral and autonomic neuropathy, and nephropathy.ConclusionsThe frequency of urinary tract infections increases with poor glycemic control in women with type 1 diabetes. This relationship is independent of other well described predictors of urinary tract infections and suggests that factors directly related to glycemic control may influence the risk of lower urinary tract infections.

We examined whether persistence of epigenetic DNA methylation (DNA-me) alterations at specific loci over two different time points in people with diabetes are associated with metabolic memory, the prolonged beneficial effects of intensive vs. conventional therapy during the Diabetes Control and Complications Trial (DCCT) on the progression of microvascular outcomes in the long-term follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) Study. We compared DNA-me profiles in genomic DNA of whole blood (WB) isolated at EDIC Study baseline from 32 cases (DCCT conventional therapy group subjects showing retinopathy or albuminuria progression by EDIC Study year 10) vs. 31 controls (DCCT intensive therapy group subjects without complication progression by EDIC year 10). DNA-me was also profiled in blood monocytes (Monos) of the same patients obtained during EDIC Study years 16-17. In WB, 153 loci depicted hypomethylation, and 225 depicted hypermethylation, whereas in Monos, 155 hypomethylated loci and 247 hypermethylated loci were found (fold change ≥1.3; P < 0.005; cases vs. controls). Twelve annotated differentially methylated loci were common in both WB and Monos, including thioredoxin-interacting protein (TXNIP), known to be associated with hyperglycemia and related complications. A set of differentially methylated loci depicted similar trends of associations with prior HbA1c in both WB and Monos. In vitro, high glucose induced similar persistent hypomethylation at TXNIP in cultured THP1 Monos. These results show that DNA-me differences during the DCCT persist at certain loci associated with glycemia for several years during the EDIC Study and support an epigenetic explanation for metabolic memory.

Competing Interests: Declarations Conflict of interest The authors declare the following other interests: none.

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes., (© 2024. The Author(s).)

The integration of IoT systems into automotive vehicles has raised concerns associated with intrusion detection within these systems. Vehicles equipped with a controller area network (CAN) control several systems within a vehicle where disruptions in function can lead to significant malfunctions, injuries, and even loss of life. Detecting disruption is a primary concern as vehicles move to higher degrees of autonomy and the possibility of self-driving is explored. Tackling cyber-security challenges within CAN is essential to improve vehicle and road safety. Standard differences between different manufacturers make the implementation of a discreet system difficult; therefore, data-driven techniques are needed to tackle the ever-evolving landscape of cyber security within the automotive field. This paper examines the possibility of using machine learning classifiers to identify cyber assaults in CAN systems. To achieve applicability, we cover two classifiers: extreme gradient boost and K-nearest neighbor algorithms. However, as their performance hinges on proper parameter selection, a modified metaheuristic optimizer is introduced as well to tackle parameter optimization. The proposed approach is tested on a publicly available dataset with the best-performing models exceeding 89% accuracy. Optimizer outcomes have undergone rigorous statistical analysis, and the best-performing models were subjected to analysis using explainable artificial intelligence techniques to determine feature impacts on the best-performing model., (© 2024. The Author(s).)

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life., Competing Interests: Robertas Damaševičius is an Academic Editor for PeerJ Computer Science., (© 2024 Jovanovic et al.)

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models., (© 2024. The Author(s).)

Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations., Competing Interests: Robertas Damaševičius is an Academic Editor for PeerJ., (© 2024 Damaševičius et al.)

Objectives: The objective of this study was to investigate whether there are differences between brain natriuretic peptide (BNP) levels and computed tomography pulmonary angiography (CTPA) parameters, in patients with acute PE, with respect of sex., Background: Acute pulmonary embolism (PE) may provoke sudden right ventricle overload and stretching of their thin walls, causing significant raise of BNP blood levels, which correlates to acute PE severity. The properties of RV are different between sexes., Methods: This retrospective analysis was gained from the data of 1612 PE patients from the regional PE register. The patients have had CTPA verification of PE, with described localization of thrombus masses, as well as the ratio between RV and left ventricle (RV/LV), and BNP as biomarker, measured during the first 24 hours upon admission., Results: Out of 96 male patients with detected central thrombus, 75.0% patients had an increase in BNP level compared to 25.0% patients with normal BNP value (p<0.001). Of the 94 female patients with central thrombus, 85.1% patients had an elevated BNP level, compared to 14.9% patients, with BNP normal values (p<0.001). Of the 135 male patients with RV/LV˃1, 79.3% of them, had elevated BNP, compared to 20.7% patients whose BNP level was normal (p<0.001). Out of 123 female patients with RV/LV˃1, 91.1% patients had elevated BNP compared to 8.9%, whose BNP was normal (p<0.001)., Conclusion: Elevated BNP blood level correlates with CTPA parameters, such as the presence of central thrombus and the ratio between right and left ventricles greater than 1, in patients with acute PE, regardless of sex (Tab. 2, Fig. 2, Ref. 23). Text in PDF www.elis.sk Keywords: acute pulmonary embolism, computed tomography pulmonary angiography, brain natriuretic peptide, right ventricle.

Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.

Dopamine affects processing of temporal information, but most previous work has tested its role in prospective tasks, where participants know in advance when the event to be timed starts. However, we are often exposed to events whose onset we do not know in advance. We can evaluate their duration after they have elapsed, but mechanisms underlying this ability are still elusive. Here we contrasted effects of acute phenylalanine and tyrosine depletion (APTD) on both forms of timing in healthy volunteers, in a within-subject, placebo-controlled design. Participants were presented with a disc moving around a circular path and asked to reproduce the duration of one full revolution and to judge their confidence in performance. The onset of the revolution was either known in advance (explicit onset) or revealed only at the end of the trial (implicit onset). We found that APTD shortened reproduced durations in the explicit onset task but had no effect on temporal performance in the implicit onset task. This dissociation is corroborated by effects of APTD on confidence judgements in the explicit task only. Our findings suggest that dopamine has a specific role in prospective encoding of temporal intervals, rather than the processing of temporal information in general., (© 2023. The Author(s).)

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Pilcevic, Djuric Jovicic, Antonijevic, Bacanin, Jovanovic, Zivkovic, Dragovic and Bisevac.)

Processing a sequence of events is different from encoding the relative order of the elements composing the sequence. Whether order processing arises automatically from the sequential processing of events is yet unknown, however the literature suggests that order processing can occur at an automatic level when the order of stimuli is not detected consciously. In the present study, we aimed to investigate the question of automatic order processing in a difficult visual task where participants identified one among two possible target luminances. The luminance of the targets was contingent on the order of presentation of two visual cues separated by a subthreshold asynchrony. Participants' performance was compared to that in a control condition where the cues were presented synchronously. In a first experiment, participants' performance benefited from the use of subthreshold order information compared to the control condition, however this facilitation effect was transient and disappeared over the course of the experiment. In a second experiment, we investigated and confirmed the role of motivation, via a monetary incentive, on the previously observed effect. Taken together, our results suggest that the processing of temporal order of sub-threshold asynchronies is possible, although fragile and likely dependent on task requirements., (© 2023. The Author(s).)

Predicting information is considered to be an efficient strategy to minimise processing costs by exploiting regularities in the environment, and to allow for adaptation in case of irregularities, i.e. prediction errors. How such errors impact conscious perception is unclear, especially when predictions concern elementary visual features. Here we present results from a novel experimental approach allowing us to investigate the perceptual consequences of violated low-level predictions about moving objects. Observers were presented with two squares moving towards each other with a constant speed, and reported whether they were in contact or not before they disappeared. A compelling illusion of a gap between the squares occurred when the leading edges of those squares contacted briefly. The apparent gap was larger than a physical and stable separation of 2.6 min of arc between the squares. The illusion disappeared only when the contact did not violate extrapolations of the contrast edge between the moving object and the background. The pattern of results is consistent with an early locus of the effect and cannot be explained by decisional biases, guesses, top-down, attentional or masking effects. We suggest that violations of the contrast edge extrapolation in the direction of motion have strong perceptual consequences., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To analyze phylogenetic relations and assess the role of cross-border clusters in the spread of HIV-1 subtype B across the Balkans, given the general trends of new HIV diagnoses in seven Balkan countries., Design: Retrospective phylogenetic and trend analysis., Methods: In-depth phylogenetic, phylodynamic and phylogeographic analysis performed on 2415 HIV-1 subtype B sequences from 1999 to 2019 using maximal likelihood and Bayesian methods. The joinpoint regression analysis of new HIV diagnoses by country and modes of transmission using 2004-2019 ECDC data., Results: Ninety-three HIV-1 Subtype B transmission clusters (68% of studied sequences) were detected of which four cross-border clusters (11% of studied sequences). Phylodynamic analysis showed activity of cross-border clusters up until the mid-2000s, with a subsequent stationary growth phase. Phylogeography analyses revealed reciprocal spread patterns between Serbia, Slovenia and Montenegro and several introductions to Romania from these countries and Croatia. The joinpoint analysis revealed a reduction in new HIV diagnoses in Romania, Greece and Slovenia, whereas an increase in Serbia, Bulgaria, Croatia and Montenegro, predominantly among MSM., Conclusion: Differing trends of new HIV diagnoses in the Balkans mirror differences in preventive policies implemented in participating countries. Regional spread of HIV within the countries of former Yugoslavia has continued to play an important role even after country break-up, whereas the spread of subtype B through multiple introductions to Romania suggested the changing pattern of travel and migration linked to European integration of Balkan countries in the early 2000s., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

The HIV/AIDS epidemic in Russia is among the fastest growing in the world. HIV epidemic burden is non-uniform in different Russian regions and diverse key populations. An explosive epidemic has been documented among people who inject drugs (PWID) starting from the mid-1990s, whereas presently, the majority of new infections are linked to sexual transmission. Nationwide, HIV sub-subtype A6 (previously called A FSU ) predominates, with the increasing presence of other subtypes, namely subtype B and CRF063&#95;02A. This study explores HIV subtype B sequences from St. Petersburg, collected from 2006 to 2020, in order to phylogenetically investigate and characterize transmission clusters, focusing on their evolutionary dynamics and potential for further growth, along with a socio-demographic analysis of the available metadata. In total, 54% (107/198) of analyzed subtype B sequences were found grouped in 17 clusters, with four transmission clusters with the number of sequences above 10. Using Bayesian MCMC inference, tMRCA of HIV-1 subtype B was estimated to be around 1986 (95% HPD 1984-1991), whereas the estimated temporal origin for the four large clusters was found to be more recent, between 2001 and 2005. The results of our study imply a complex pattern of the epidemic spread of HIV subtype B in St. Petersburg, Russia, still in the exponential growth phase, and in connection to the men who have sex with men (MSM) transmission, providing a useful insight needed for the design of public health priorities and interventions.

The COVID-19 pandemic and associated lockdowns triggered worldwide changes in the daily routines of human experience. The Blursday database provides repeated measures of subjective time and related processes from participants in nine countries tested on 14 questionnaires and 15 behavioural tasks during the COVID-19 pandemic. A total of 2,840 participants completed at least one task, and 439 participants completed all tasks in the first session. The database and all data collection tools are accessible to researchers for studying the effects of social isolation on temporal information processing, time perspective, decision-making, sleep, metacognition, attention, memory, self-perception and mindfulness. Blursday includes quantitative statistics such as sleep patterns, personality traits, psychological well-being and lockdown indices. The database provides quantitative insights on the effects of lockdown (stringency and mobility) and subjective confinement on time perception (duration, passage of time and temporal distances). Perceived isolation affects time perception, and we report an inter-individual central tendency effect in retrospective duration estimation., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Metacognition is the ability to weigh the quality of our own cognition, such as the confidence that our perceptual decisions are correct. Here we ask whether metacognitive performance can itself be evaluated or else metacognition is the ultimate reflective human faculty. Building upon a classic visual perception task, we show that human observers are able to produce nested, above-chance judgements on the quality of their decisions at least up to the fourth order (i.e. meta-meta-meta-cognition). A computational model can account for this nested cognitive ability if evidence has a high-resolution representation, and if there are two kinds of noise, including recursive evidence degradation. The existence of fourth-order sensitivity suggests that the neural mechanisms responsible for second-order metacognition can be flexibly generalized to evaluate any cognitive process, including metacognitive evaluations themselves. We define the theoretical and practical limits of nested cognition and discuss how this approach paves the way for a better understanding of human self-regulation., (© The Author(s) 2022. Published by Oxford University Press.)

Exposure to a dynamic texture reduces the perceived separation between objects, altering the mapping between physical relations in the environment and their neural representations. Here we investigated the spatial tuning and spatial frame of reference of this aftereffect to understand the stage(s) of processing where adaptation-induced changes occur. In Experiment 1, we measured apparent separation at different positions relative to the adapted area, revealing a strong but tightly tuned compression effect. We next tested the spatial frame of reference of the effect, either by introducing a gaze shift between adaptation and test phase (Experiment 2) or by decoupling the spatial selectivity of adaptation in retinotopic and world-centered coordinates (Experiment 3). Results across the two experiments indicated that both retinotopic and world-centered adaptation effects can occur independently. Spatial attention to the location of the adaptor alone could not account for the world-centered transfer we observed, and retinotopic adaptation did not transfer to world-centered coordinates after a saccade (Experiment 4). Finally, we found that aftereffects in different reference frames have a similar, narrow spatial tuning profile (Experiment 5). Together, our results suggest that the neural representation of local separation resides early in the visual cortex, but it can also be modulated by activity in higher visual areas.

Aims: To examine the relationship between admission glucose (AG) level and short-term in-hospital mortality and to investigate the association between hyperglycemia and major bleeding in PE patients with and without DMT2., Methods: We evaluated 1165 patients with diagnosed acute PE with multi-detector computed tomography pulmonary angiography (MDCT-PA) enrolled in the Regional multicenter PE registry (REPER). The study population was classified to patients with diabetes mellitus type 2 (DMT2) and those without diabetes. According to quartiles of AG patients, both groups separately were categorized into four subgroups (DMT2 I: < 7.5 mmol/L; II: 7.5-10.0 mmol/L; III: 10.0-15.7 mmol/L; IV: > 15.7 mmol/L and (non-DMT2 I: < 5.5 mmol/L; II: 5.5-6.3 mmol/L; III: 6.3-7.9 mmol/L; IV: > 7.9 mmol/L)., Results: All-cause mortality was higher in the DMT2 group (9.5% vs. 18.2%, p < 0.001), and PE-cause mortality was 6% for the patients without DMT2 and 12.4% for DMT2 patients (p = 0.02). The patients in the fourth AG quartiles in both groups, without DMT2 and with DMT2, had significantly higher all-cause and PE-cause in-hospital mortality compared with the first quartile. Rates of major bleeding were similar between the groups. On the multivariable analysis, after adjusting for age, gender and mortality risk, the adherence in the fourth AG quartile had an independent predictive value for all-cause death (HR 2.476, 95% CI 1.017-6.027) only in DM patients., Conclusion: In our cohort of patients with acute PE, diabetes was associated with increased rates for all-cause and PE-cause mortality., (© 2021. Springer-Verlag Italia S.r.l., part of Springer Nature.)

The determination of the abundances of the CH x , C = O and aromatic groups in chondritic Insoluble Organic Matter (IOM) and coals by Infrared (IR) spectroscopy is a challenging issue due to insufficient knowledge on the absorption cross-sections and their sensitivity to the molecular environment. Here, we report a calibration approach based on a 13 C synthetic model material whose composition was unambiguously determined by Direct-Pulse/Magic Angle Spinning Nuclear Magnetic Resonance (DP/MAS NMR). Ratios of the cross-sections of the CH x , C = O and aromatic groups have been determined, and the method has been applied to IOM samples extracted from four chondrites as Orgueil (CI), Murchison (CM), Tagish Lake (C2-ungrouped) and EET 92042 (CR2), and to a series of coals. The estimate of the aliphatic to aromatic carbon ratio (n CHx /n Aro ) in IOM samples from Orgueil, Murchison and Tagish Lake chondrites is in good agreement with Single-Pulse/NMR estimates earlier published, and is lower by a factor of 1.3 in the case of the CR chondrite EET 92042 (but the error bars overlap). In contrast, the aliphatic to carbonyl ratio (n CHx /n C=O ) is overestimated for the four chondrites. These discrepancies are likely due to the control of the absorption cross-section of the C = O and C = C bonds by the local molecular environment. Regarding coals, the use of published NMR analyses has brought to light that the integrated cross-section ratio A CHx /A Aro varies with the vitrinite reflectance over an order of magnitude. Here as well, the local oxygen speciation plays a critical control in A Aro , which decreases with increasing the vitrinite reflectance. We provide an analytical law that links A CHx /A Aro and vitrinite reflectance, which will allow the determination of n CHx /n Aro for any coal sample, provided its vitrinite reflectance is known., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Movement execution is not always optimal. Understanding how humans evaluate their own motor decisions can give us insights into their suboptimality. Here, we investigated how humans time the action of synchronizing an arm movement with a predictable visual event and how well they can evaluate the outcome of this action. On each trial, participants had to decide when to start (reaction time) and for how long to move (movement duration) to reach a target on time. After each trial, participants judged the confidence they had that their performance on that trial was better than average. We found that participants mostly varied their reaction time, keeping the average movement duration short and relatively constant across conditions. Interestingly, confidence judgements reflected deviations from the planned reaction time and were not related to planned movement duration. In two other experiments, we replicated these results in conditions where the contribution of sensory uncertainty was reduced. In contrast to confidence judgements, when asked to make an explicit estimation of their temporal error, participants' estimates were related in a similar manner to both reaction time and movement duration. In summary, humans control the timing of their actions primarily by adjusting the delay to initiate the action, and they estimate their confidence in their action from the difference between the planned and executed movement onset. Our results highlight the critical role of the internal model for the self-evaluation of one's motor performance., (© 2021 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Studies in animals and humans have implicated the neurotransmitter dopamine in duration processing. However, very few studies have examined dopamine's involvement in other forms of temporal processing such as temporal order judgments. In a randomized within-subject placebo-controlled design, we used acute phenylalanine/tyrosine depletion (APTD) to reduce availability of the dopamine precursors tyrosine and phenylalanine in healthy human volunteers. As compared to a nutritionally balanced drink, APTD significantly impaired the ability to accurately reproduce interval duration in a temporal reproduction task. In addition, and confirming previous findings, the direction of error differed as a function of individual differences in underlying dopamine function. Specifically, APTD caused participants with low baseline dopamine precursor availability to overestimate the elapse of time, whereas those with high dopamine availability underestimated time. In contrast to these effects on duration processing, there were no significant effects of APTD on the accuracy of discriminating the temporal order of visual stimuli. This pattern of results does not simply represent an effect of APTD on motor, rather than perceptual, measures of timing because APTD had no effect on participants' ability to use temporal cues to speed RT. Our results demonstrate, for the first time in healthy volunteers, a dopaminergic dissociation in judging metrical (duration) versus ordinal (temporal order) aspects of time., (© 2021 Massachusetts Institute of Technology.)

It is now appreciated that long non-coding RNAs (lncRNAs) are important players in orchestrating cancer progression. In this study we characterized GHSROS , a human lncRNA gene on the opposite DNA strand (antisense) to the ghrelin receptor gene, in prostate cancer. The lncRNA was upregulated by prostate tumors from different clinical datasets. Transcriptome data revealed that GHSROS alters the expression of cancer-associated genes. Functional analyses in vitro showed that GHSROS mediates tumor growth, migration and survival, and resistance to the cytotoxic drug docetaxel. Increased cellular proliferation of GHSROS -overexpressing PC3, DU145, and LNCaP prostate cancer cell lines in vitro was recapitulated in a subcutaneous xenograft model. Conversely, in vitro antisense oligonucleotide inhibition of the lncRNA reciprocally regulated cell growth and migration, and gene expression. Notably, GHSROS modulates the expression of PPP2R2C , the loss of which may drive androgen receptor pathway-independent prostate tumor progression in a subset of prostate cancers. Collectively, our findings suggest that GHSROS can reprogram prostate cancer cells toward a more aggressive phenotype and that this lncRNA may represent a potential therapeutic target., Competing Interests: The authors declare there are no competing interests., (©2021 Thomas et al.)