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Atypical neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) can alter the cortex morphology at different levels: (i) a low-order level where cortical regions are examined individually, (ii) a high-order level where the relationship between two cortical regions is considered, and (iii) a multi-view high-order level where the relationship between regions is examined across multiple brain views. In this study, we propose to use the emerging multi-view cortical morphological network (CMN), which is derived from T1-w magnetic resonance imaging (MRI), to profile autistic and typical brains and pursue new ways of fingerprinting 'cortical morphology' at the intersection of 'network neuroscience'. Each CMN view models the pairwise morphological dissimilarity at the connection level using a specific cortical attribute (e.g., thickness). Specifically, we set out to identify the inherently most representative morphological connectivities shared across different views of the cortex in both autistic and normal control (NC) populations using tensor component analysis. We thus discover the connectional profiles of both populations shared across different CMNs of the left and right hemispheres, respectively. One of the most representative morphological cortical attributes for assessing the abnormal brain structures in patients with ASD is cortical thickness. The most representative morphological connectivities in multi-view CMN population of normal control and ASD subjects, respectively, and in both left and right hemispheres within the temporal, frontal, and insular lobes of individuals with ASD. These representative connectivities are corresponded to specific clinical features observed in individuals with ASD., 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., (Copyright © 2024 Cengiz and Rekik.)

In this study, we explored the potential of fruit fly regurgitation as a window to understand complex behaviors, such as predation and defense mechanisms, with implications for species-specific control measures that can enhance fruit quality and yield. We leverage deep learning and computer vision technologies to propose three distinct methodologies that advance the recognition, extraction, and trajectory tracking of fruit fly regurgitation. These methods show promise for broader applications in insect behavioral studies. Our evaluations indicate that the I3D model achieved a Top-1 Accuracy of 96.3% in regurgitation recognition, which is a notable improvement over the C3D and X3D models. The segmentation of the regurgitated substance via a combined U-Net and CBAM framework attains an MIOU of 90.96%, outperforming standard network models. Furthermore, we utilized threshold segmentation and OpenCV for precise quantification of the regurgitation liquid, while the integration of the Yolov5 and DeepSort algorithms provided 99.8% accuracy in fruit fly detection and tracking. The success of these methods suggests their efficacy in fruit fly regurgitation research and their potential as a comprehensive tool for interdisciplinary insect behavior analysis, leading to more efficient and non-destructive insect control strategies in agricultural settings., 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., (Copyright © 2024 Zhou, Zhan and Xiong.)

Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services., 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., (Copyright © 2023 Guo, Liu and Niu.)

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis., 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., (Copyright © 2023 Marcos-Zambrano, López-Molina, Bakir-Gungor, Frohme, Karaduzovic-Hadziabdic, Klammsteiner, Ibrahimi, Lahti, Loncar Turukalo, Dhamo, Simeon, Nechyporenko, Pio, Przymus, Sampri, Trajkovik, Lacruz-Pleguezuelos, Aasmets, Araujo, Anagnostopoulos, Aydemir, Berland, Calle, Ceci, Duman, Gündoğdu, Havulinna, Kaka Bra, Kalluci, Karav, Lode, Lopes, May, Nap, Nedyalkova, Paciência, Pasic, Pujolassos, Shigdel, Susín, Thiele, Truică, Wilmes, Yilmaz, Yousef, Claesson, Truu, Carrillo de Santa Pau.)

Introduction: Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction., Methods: In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series., Results and Discussion: The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization., 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., (Copyright © 2023 Shi, Han, González-Moreno, Dancey, Huang, Zhang, Liu, Huang, Miao and Dai.)

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices., Competing Interests: CJ is employed by Biome diagnostics GmbH. The remaining 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 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson.)

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications., Competing Interests: GP was directly affiliated with JADBio—Gnosis DA, S.A., which offers the JADBio service commercially. The remaining 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., (Copyright © 2023 Papoutsoglou, Tarazona, Lopes, Klammsteiner, Ibrahimi, Eckenberger, Novielli, Tonda, Simeon, Shigdel, Béreux, Vitali, Tangaro, Lahti, Temko, Claesson and Berland.)

Introduction: Medical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support., Methods: In order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets., Results and Discussion: We conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data., 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., (Copyright © 2023 Guo, Liu and Niu.)

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers' time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model's ability to intelligently monitor the irrigation system, which contributes to higher tomato yields., 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., (Copyright © 2023 Singh, Biswal, Samanta, Singh, Kadry, Khan and Nam.)

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples' underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly., Competing Interests: AH is owner and director of Future Cognition LTD and H2 Cognitive Designs LTD, which support online studies and develop custom cognitive assessment software, respectively. PH is owner and director of H2 Cognitive Designs LTD and reports personal fees from H2 Cognitive Designs LTD outside the submitted work. The remaining 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., (Copyright © 2023 Bălăeț, Kurtin, Gruia, Lerede, Custovic, Trender, Jolly, Hellyer and Hampshire.)

Background: Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not., Method: Participants were 48 well-characterized PD patients ( M baseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation., Results: An excellent RF model discriminated baseline PDID from PDND ( AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains., Conclusion: Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains., 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., (Copyright © 2023 McFall, Bohn, Gee, Drouin, Fah, Han, Li, Camicioli and Dixon.)

Despite the widespread interest in leveraging non-player characters (NPCs) to enhance gameplay experiences, there is a gap in understanding of how NPC spectators (i.e., those virtual characters in the scene that watch users' actions) affect players. For instance, the impact of NPC spectators' presence and feedback on players' performance and experience has not been studied, especially in virtual reality (VR) exergames. This paper aims to fill this gap and reports two user studies that assess their effect on such games. Study 1 explored the impact of having NPC spectators present and their feedback available in a gesture-based VR exergame and found having NPC spectators and their feedback could improve players' game performance, experience, and exertion. Based on Study 1's results, we further explored two characteristics of the spectators-their group size (small/large) and their feedback (with/without). The results show that (1) a large spectator number is more helpful since it improves the overall game experience (higher competence, flow, immersion), increases AvgHR% (the average heart rate percentage divided by the maximum heart rate), and enhances performance (improved players' combo performance and increased gesture success rate for particular gesture); (2) spectator feedback is instrumental in improving players' performance (higher gesture success rates, more combos performed successfully, more monster's combos prevented), enhancing game experience (positive affect, competence, flow, and immersion), and reducing negative game experience, increasing exertion (AvgHR% and burned more calories). Based on the results, we derived two main design recommendations for VR exergames that could pave the way for improving gameplay performance and game experience, especially among young adults., 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., (Copyright © 2023 Xu, Yu, Meng, Monteiro, Kao and Liang.)

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.

In this article, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step toward a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word "resilience": (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy., 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., (Copyright © 2022 Miller, Albarracin, Pitliya, Kiefer, Mago, Gorman, Friston and Ramstead.)

Multiple sclerosis (MS) is an inflammatory and demyelinating neurological disease of the central nervous system. Image-based biomarkers, such as lesions defined on magnetic resonance imaging (MRI), play an important role in MS diagnosis and patient monitoring. The detection of newly formed lesions provides crucial information for assessing disease progression and treatment outcome. Here, we propose a deep learning-based pipeline for new MS lesion detection and segmentation, which is built upon the nnU-Net framework. In addition to conventional data augmentation, we employ imaging and lesion-aware data augmentation methods, axial subsampling and CarveMix, to generate diverse samples and improve segmentation performance. The proposed pipeline is evaluated on the MICCAI 2021 MS new lesion segmentation challenge (MSSEG-2) dataset. It achieves an average Dice score of 0.510 and F score of 0.552 on cases with new lesions, and an average false positive lesion number 1 of 0.036 and false positive lesion volume n of 0.192 FP on cases with no new lesions. Our method outperforms other participating methods in the challenge and several state-of-the-art network architectures.V FP of 0.192 mm 3 on cases with no new lesions. Our method outperforms other participating methods in the challenge and several state-of-the-art network architectures., 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., (Copyright © 2022 Basaran, Matthews and Bai.)

An important step toward delivering an accurate connectome of the human brain is robust segmentation of 3D Magnetic Resonance Imaging (MRI) scans, which is particularly challenging when carried out on perinatal data. In this paper, we present an automated, deep learning-based pipeline for accurate segmentation of tissues from neonatal brain MRI and extend it by introducing an age prediction pathway. A major constraint to using deep learning techniques on developing brain data is the need to collect large numbers of ground truth labels. We therefore also investigate two practical approaches that can help alleviate the problem of label scarcity without loss of segmentation performance. First, we examine the efficiency of different strategies of distributing a limited budget of annotated 2D slices over 3D training images. In the second approach, we compare the segmentation performance of pre-trained models with different strategies of fine-tuning on a small subset of preterm infants. Our results indicate that distributing labels over a larger number of brain scans can improve segmentation performance. We also show that even partial fine-tuning can be superior in performance to a model trained from scratch, highlighting the relevance of transfer learning strategies under conditions of label scarcity. We illustrate our findings on large, publicly available T1- and T2-weighted MRI scans ( n = 709, range of ages at scan: 26-45 weeks) obtained retrospectively from the Developing Human Connectome Project (dHCP) cohort., 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., (Copyright © 2022 Richter and Fetit.)

Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs., 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., (Copyright © 2022 Herrero-Huerta, Raumonen and Gonzalez-Aguilera.)

Background: COVID-19 was named a global pandemic by the World Health Organization in March 2020. Governments across the world issued various restrictions such as staying at home. These restrictions significantly influenced mental health worldwide. This study aims to document the prevalence of mental health problems and their relationship with the quality and quantity of social relationships affected by the pandemic during the United States national lockdown., Methods: Sample data was employed from the COVID-19 Impact Survey on April 20-26, 2020, May 4-10, 2020, and May 30-June 8, 2020 from United States Dataset. A total number of 8790, 8975, and 7506 adults participated in this study for April, May and June, respectively. Participants' mental health evaluations were compared clinically by looking at the quantity and quality of their social ties before and during the pandemic using machine learning techniques. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, Naive Bayes, and logistic regression., Results: The result for each contributing feature has been analyzed separately in detail. On the other hand, the influence of each feature was studied to evaluate the impact of COVID-19 on mental health. The overall result of our research indicates that people who had previously been diagnosed with any type of mental illness were most affected by the new constraints during the pandemic. These people were among the most vulnerable due to the imposed changes in lifestyle., Conclusion: This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions., 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., (Copyright © 2022 Sadegh-Zadeh, Bahrami, Najafi, Asgari-Ahi, Campion and Hajiyavand.)

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy., 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., (Copyright © 2022 Paul, Bose, Kalmady, Shivakumar, Sreeraj, Parlikar, Narayanaswamy, Dursun, Greenshaw, Greiner and Venkatasubramanian.)

Accurately detecting and segmenting grape cluster in the field is fundamental for precision viticulture. In this paper, a new backbone network, ResNet50-FPN-ED, was proposed to improve Mask R-CNN instance segmentation so that the detection and segmentation performance can be improved under complex environments, cluster shape variations, leaf shading, trunk occlusion, and grapes overlapping. An Efficient Channel Attention (ECA) mechanism was first introduced in the backbone network to correct the extracted features for better grape cluster detection. To obtain detailed feature map information, Dense Upsampling Convolution (DUC) was used in feature pyramid fusion to improve model segmentation accuracy. Moreover, model generalization performance was also improved by training the model on two different datasets. The developed algorithm was validated on a large dataset with 682 annotated images, where the experimental results indicate that the model achieves an Average Precision (AP) of 60.1% on object detection and 59.5% on instance segmentation. Particularly, on object detection task, the AP improved by 1.4% and 1.8% over the original Mask R-CNN (ResNet50-FPN) and Faster R-CNN (ResNet50-FPN). For the instance segmentation, the AP improved by 1.6% and 2.2% over the original Mask R-CNN and SOLOv2. When tested on different datasets, the improved model had high detection and segmentation accuracy and inter-varietal generalization performance in complex growth environments, which is able to provide technical support for intelligent vineyard management., 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., (Copyright © 2022 Shen, Su, Huang, Quan, Song, Fang and Su.)

Purpose: This study aims to develop and validate a stigma scale for Chinese patients with breast cancer., Methods: Patients admitted to the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, for breast cancer treatment participated in this study. Development of the Breast Cancer Stigma Scale involved the following procedures: literature review, interview, and applying a theoretical model to generate items; the Breast Cancer Stigma Scale's content validity was assessed by a Delphi study ( n = 15) and feedback from patients with breast cancer ( n = 10); exploratory factor analysis ( n = 200) was used to assess the construct validity; convergent validity was assessed with the Social Impact Scale ( n = 50); internal consistency Cronbach's α ( n = 200), split-half reliability ( n = 200), and test-retest reliability ( N = 50) were used to identify the reliability of the scale., Results: The final version of the Breast Cancer Stigma Scale consisted of 15 items and showed positive correlations with the Social Impact Scale (ρ = 0.641, P < 0.001). Exploratory factor analysis (EFA) revealed four components of the Breast Cancer Stigma Scale: self-image impairment, social isolation, discrimination, and internalized stigma, which were strongly related to our perceived breast cancer stigma model and accounted for 69.443% of the total variance. Cronbach's α for the total scale was 0.86, and each subscale was 0.75-0.882. The test-retest reliability with intra-class correlation coefficients of the total scale was 0.947 ( P < 0.001), and split-half reliability with intra-class correlation coefficients of the total scale was 0.911 ( P < 0.001). The content validity index (CVI) was 0.73-1.0., Conclusion: The newly developed Breast Cancer Stigma Scale offers a valid and reliable instrument for assessing the perceived stigma of patients with breast cancer in clinical and research settings. It may be helpful for stigma prevention in China., 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., (Copyright © 2022 Bu, Li, Cheng, Ng, Xu, Xia and Liu.)

Alzheimer's disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer's disease permits the provision of preventive medication to slow the disease's progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer's disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases., 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., (Copyright © 2022 Lim, Lai, Haiskin, Kulathilake, Ong, Hum, Dhanalakshmi, Wu and Zuo.)

Purpose: To develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI)., Methods: MRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall. Two-dimensional (2D) cross-sectional slices reconstructed from all plaques and 7 main arterial segments of 115 patients were used to build and optimize the deep learning model. The model performance was evaluated on the remaining nine-patient test set using the Dice similarity coefficient (DSC) and average surface distance (ASD)., Results: The proposed automatic segmentation method demonstrated satisfactory agreement with the manual method, with DSCs of 93.8% for lumen contours and 86.0% for outer wall contours, which were higher than those obtained from the traditional U-Net, Attention U-Net, and Inception U-Net on the same nine-subject test set. And all the ASD values were less than 0.198 mm. The Bland-Altman plots and scatter plots also showed that there was a good agreement between the methods. All intraclass correlation coefficient values between the automatic method and manual method were greater than 0.780, and greater than that between two manual reads., Conclusion: The proposed deep learning-based automatic segmentation method achieved good consistency with the manual methods in the segmentation of arterial vessel wall and plaque and is even more accurate than manual results, hence improved the convenience of arterial morphological quantification., Competing Interests: XY, ZG, YM, SZ, YT, JZ, and QH were employed by United Imaging Healthcare Co., Ltd. The remaining 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., (Copyright © 2022 Xu, Yang, Li, Jiang, Jia, Gong, Mao, Zhang, Teng, Zhu, He, Wan, Liang, Li, Hu, Zheng, Liu and Zhang.)

Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero , is evident at birth. What is less clear is whether rhythm supports infants' earliest links between language and cognition. Prior evidence has documented that for infants as young as 3 and 4 months, listening to their native language (English) supports the core cognitive capacity of object categorization. This precocious link is initially part of a broader template: listening to a non-native language from the same rhythmic class as (e.g., German, but not Cantonese) and to vocalizations of non-human primates (e.g., lemur, Eulemur macaco flavifrons , but not birds e.g., zebra-finches, Taeniopygia guttata ) provide English-acquiring infants the same cognitive advantage as does listening to their native language. Here, we implement a machine-learning (ML) approach to ask whether there are acoustic properties, available on the surface of these vocalizations, that permit infants' to identify which vocalizations are candidate links to cognition. We provided the model with a robust sample of vocalizations that, from the vantage point of English-acquiring 4-month-olds, either support object categorization (English, German, lemur vocalizations) or fail to do so (Cantonese, zebra-finch vocalizations). We assess (a) whether supervised ML classification models can distinguish those vocalizations that support cognition from those that do not, and (b) which class(es) of acoustic features (including rhythmic, spectral envelope, and pitch features) best support that classification. Our analysis reveals that principal components derived from rhythm-relevant acoustic features were among the most robust in supporting the classification. Classifications performed using temporal envelope components were also robust. These new findings provide in principle evidence that infants' earliest links between vocalizations and cognition may be subserved by their perceptual sensitivity to rhythmic and spectral elements available on the surface of these vocalizations, and that these may guide infants' identification of candidate links to cognition., 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., (Copyright © 2022 Lau, Fyshe and Waxman.)

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing (such as Chess and Go) towards ability -oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6-10 ( n  = 52). We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, "ironbar" and "Trrrrr," from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6-10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines., Competing Interests: MC, BB and MS are employed by DeepMind Technologies Limited. The remaining 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., (Copyright © 2022 Voudouris, Crosby, Beyret, Hernández-Orallo, Shanahan, Halina and Cheke.)

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed., 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., (Copyright © 2022 Edwards, Rueckert, Smith, Abo Seada, Alansary, Almalbis, Allsop, Andersson, Arichi, Arulkumaran, Bastiani, Batalle, Baxter, Bozek, Braithwaite, Brandon, Carney, Chew, Christiaens, Chung, Colford, Cordero-Grande, Counsell, Cullen, Cupitt, Curtis, Davidson, Deprez, Dillon, Dimitrakopoulou, Dimitrova, Duff, Falconer, Farahibozorg, Fitzgibbon, Gao, Gaspar, Harper, Harrison, Hughes, Hutter, Jenkinson, Jbabdi, Jones, Karolis, Kyriakopoulou, Lenz, Makropoulos, Malik, Mason, Mortari, Nosarti, Nunes, O’Keeffe, O’Muircheartaigh, Patel, Passerat-Palmbach, Pietsch, Price, Robinson, Rutherford, Schuh, Sotiropoulos, Steinweg, Teixeira, Tenev, Tournier, Tusor, Uus, Vecchiato, Williams, Wright, Wurie and Hajnal.)

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes., 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., (Copyright © 2022 Kornaropoulos, Winzeck, Rumetshofer, Wikstrom, Knutsson, Correia, Sundgren and Nilsson.)

The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hamper learning a model. To mitigate this issue, techniques such as multi-source domain adaptation [Multi-source Domain Adversarial Networks (MSDA)] aim at learning an effective classification function that uses (learned) domain-invariant latent features. This article analyzes and compares the performance of various popular MSDA methods [MDAN, Domain AggRegation Networks (DARN), Multi-Domain Matching Networks (MDMN), and Moment Matching for MSDA (M 3 SDA)] at predicting different labels (illness, age, and sex) of images from two public rs-fMRI datasets: ABIDE 1and ADHD-200. It also evaluates the impact of various conditions such as class imbalance, the number of sites along with a comparison of the degree of adaptation of each of the methods, thereby presenting the effectiveness of MSDA models in neuroimaging-based applications., 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., (Copyright © 2022 Panda, Kalmady and Greiner.)

Guided by the expectancy-value theory of motivation in learning, we explored the causal relationship between students' learning experiences, motivation, and cognitive learning outcome in academic service-learning. Based on a sample of 2,056 college students from a university in Hong Kong, the findings affirm that learning experiences and motivation are key factors determining cognitive learning outcome, affording a better understanding of student learning behavior and the impact in service-learning. This research provides an insight into the impact of motivation and learning experiences on students' cognitive learning outcome while engaging in academic service-learning. This not only can discover the intermediate factors of the learning process but also provides insights to educators on how to enhance their teaching pedagogy., 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., (Copyright © 2022 Lo, Ngai, Chan and Kwan.)

Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts (https://osf.io/3nxfa/) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively., 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 reviewer AC declared a shared affiliation, with several of the authors, HK-R, AW, and RH, to the handling editor at the time of the review., (Copyright © 2022 Karimi-Rouzbahani, Woolgar, Henson and Nili.)

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures ( n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG., 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., (Copyright © 2022 Karimi-Rouzbahani and Woolgar.)

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic., 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., (Copyright © 2022 Sawalha, Yousefnezhad, Shah, Brown, Greenshaw and Greiner.)

Understanding speech becomes a demanding task when the environment is noisy. Comprehension of speech in noise can be substantially improved by looking at the speaker's face, and this audiovisual benefit is even more pronounced in people with hearing impairment. Recent advances in AI have allowed to synthesize photorealistic talking faces from a speech recording and a still image of a person's face in an end-to-end manner. However, it has remained unknown whether such facial animations improve speech-in-noise comprehension. Here we consider facial animations produced by a recently introduced generative adversarial network (GAN), and show that humans cannot distinguish between the synthesized and the natural videos. Importantly, we then show that the end-to-end synthesized videos significantly aid humans in understanding speech in noise, although the natural facial motions yield a yet higher audiovisual benefit. We further find that an audiovisual speech recognizer (AVSR) benefits from the synthesized facial animations as well. Our results suggest that synthesizing facial motions from speech can be used to aid speech comprehension in difficult listening environments., 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., (Copyright © 2022 Varano, Vougioukas, Ma, Petridis, Pantic and Reichenbach.)

Introduction: Military members and Veterans are at risk of developing combat-related, treatment-resistant posttraumatic stress disorder (TR-PTSD) and moral injury (MI). Conventional trauma-focused therapies (TFTs) have shown limited success. Novel interventions including Multi-modal Motion-assisted Memory Desensitization and Reconsolidation therapy (3MDR) may prove successful in treating TR-PTSD. Objective: To qualitatively study the experiences of Canadian military members and Veterans with TR-PTSD who received the 3MDR intervention. Methods: This study explored qualitative data from a larger mixed-method waitlist control trial testing the efficacy of 3MDR in military members and veterans. Qualitative data were recorded and collected from 3MDR sessions, session debriefings and follow-up interviews up to 6 months post-intervention; the data were then thematically analyzed. Results: Three themes emerged from the data: (1) the participants' experiences with 3MDR; (2) perceived outcomes of 3MDR; and (3) keys to successful 3MDR treatment. Participants expressed that 3MDR provided an immersive environment, active engagement and empowerment. The role of the therapist as a coach and "fireteam partner" supports the participants' control over their therapy. The multi-modal nature of 3MDR, combining treadmill-walking toward self-selected trauma imagery with components of multiple conventional TFTs, was key to helping participants engage with and attribute new meaning to the memory of the traumatic experience. Discussion: Preliminary thematic analysis of participant experiences of 3MDR indicate that 3MDR has potential as an effective intervention for combat-related TR-PTSD, with significant functional, well-being and relational improvements reported post-intervention. Conclusion: Military members and Veterans are at risk of developing TR-PTSD, with worse outcomes than in civilians. Further research is needed into 3MDR and its use with other trauma-affected populations., 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., (Copyright © 2021 Hamilton, Burback, Smith-MacDonald, Jones, Brown, Mikolas, Tang, O'Toole, Vergis, Merino, Weiman, Vermetten and Brémault-Phillips.)

Approximately 70% of the cost of beef production is impacted by dietary intake. Maximizing production efficiency of beef cattle requires not only genetic selection to maximize feed efficiency (i.e., residual feed intake (RFI)), but also adequate nutrition throughout all stages of growth and development to maximize efficiency of growth and reproductive capacity, even during gestation. RFI as a measure of feed efficiency in cattle has been recently accepted and used in the beef industry, but the effect of selection for RFI upon the dynamics of gestation has not been extensively studied, especially in the context of fluctuating energy supply to the dam and fetus. Nutrient restriction during gestation has been shown to negatively affect postnatal growth and development as well as fertility of beef cattle offspring. This, when combined with the genetic potential for RFI, may significantly affect energy partitioning in the offspring and subsequently important performance traits. In this review, we discuss: 1) the importance of RFI as a measure of feed efficiency and how it can affect other economic traits in beef cattle; 2) the influence of prenatal nutrition on physiological phenotypes in calves; 3) the benefits of investigating the interaction of genetic selection for RFI and prenatal nutrition; 4) how metabolomics, transcriptomics, and epigenomics have been employed to investigate the underlying biology associated with prenatal nutrition, RFI, or their interactions in beef cattle; and 5) how the integration of omics information is adding a level of deeper understanding of the genetic architecture of phenotypic traits in cattle., 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., (Copyright © 2021 Foroutan, Wishart and Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada for the contribution of Carolyn Fitzsimmons.)

This paper proposes a cooperative learning method for use in physical education, involving two different grouping methods: S-type heterogeneous grouping and "free" grouping. Cooperative learning was found to enhance the effectiveness of basketball skills learning and learning motivation. A comparison was made of the differences between action skills grouping (the control group) and "free" grouping (the experimental group). The ARCS Motivation Scale and Basketball Action Skills Test were used to measure results, and SPSS statistical analysis software was used for relevant statistical processing (with α set to.05). The results showed that overall skills, dribbling and passing among the action skills groups and "free" groupings significantly improved, but results for shooting were not significant; motivation levels for the two grouping methods significantly improved overall, and no significant differences in learning motivation and learning effectiveness were found between the different grouping methods. It is clear that teachers should first establish a good relationship between and with students, and free grouping methods can be used to good effect. Teachers using cooperative learning should intervene in a timely manner and choose suitable grouping methods according to the teaching goals., 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., (Copyright © 2021 Yang, Chen, Chen and Lu.)

Background: In our analysis of adolescents affected by the 2016 Fort McMurray wildfire, we observed many negative mental health effects in individuals with a prior history of psychological trauma. Elevated rates of depression and markers of post-traumatic stress disorder (PTSD) were observed, consistent with the hypothesis that prior trauma may reduce sensitivity thresholds for later psychopathology (stress sensitization). Surprisingly, levels of anxiety did not differ based on prior trauma history, nor were retraumatized individuals at increased risk for recent (past month) suicidal ideation. These results are more suggestive of inoculation by prior trauma than stress sensitization. This led us to consider whether individuals with a prior trauma history showed evidence of Post-Traumatic Growth (PTG), a condition in which the experience of a previous trauma leads to areas of sparing or even improvement. Method: To investigate this issue, we generated a structural equation model (SEM) exploring the role of anxiety in previously traumatized ( n = 295) and wildfire trauma alone ( n = 740) groups. Specifically, models were estimated to explore the relationship between hopelessness, anxiety, PTSD symptoms, self-efficacy and potential protective factors such as friend and family support in both groups. The model was tested using a cross-sectional sample of affected youth, comparing effects between the two groups. Results: While both models produced relatively good fit, differences in the effects and chi-squared values led us to conclude that the groups are subject to different causal specifications in a number of areas, although details warrant caution pending additional investigation. Discussion: We found that adolescents with a prior trauma history appear to have a more realistic appraisal of potential difficulties associated with traumatic events, and seem less reactive to potentially unsettling PTSD symptoms. They also seemed less prone to overconfidence as they got older, an effect seen in the adolescents without a history of trauma. Our findings provide preliminary evidence that the construct of anxiety may work differently in newly traumatized and retraumatized individuals, particularly in the context of mass trauma events., 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., (Copyright © 2021 Pazderka, Brown, McDonald-Harker, Greenshaw, Agyapong, Noble, Mankowski, Lee, Omeje, Brett-MacLean, Kitching, Hayduk and Silverstone.)

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted., 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., (Copyright © 2021 Tariq, Babar, Poulin, Khattak, Alshehri and Kaleem.)

In the wake of the massive Canadian wildfire of May 2016 in the area of Fort McMurray Alberta, we observed increased rates of mental health problems, particularly post-traumatic stress disorder (PTSD), in school-aged adolescents (ages 11-19). Surprisingly, we did not see these rates decline over the 3.5-year follow-up period. Additionally, our research suggested that the impact of this mass incident resulted in other unanticipated effects, including the finding that children who were not present for and relatively unaffected by the wildfire showed a similar PTSD symptom profile to children more directly involved, suggesting some degree of spillover or stress contagion. A potential explanation for these high rates in individuals who were not present could be undiagnosed retraumatization in some of the students. To investigate this possibility, we compared two groups of students: those who reported the wildfire as their most significant trauma ( n = 740) and those who had their most significant trauma prior to the wildfire ( n = 295). Those with significant pre-existing trauma had significantly higher rates of both depression and PTSD symptoms, although, unexpectedly the groups exhibited no differences in anxiety level. Taken together, this evidence suggests retraumatization is both longer-lasting and more widespread than might be predicted on a case-by-case basis, suggesting the need to reconceptualize the role of past trauma history in present symptomatology. These findings point to the need to recognize that crises instigated by natural disasters are mass phenomena which expose those involved to numerous unanticipated risks. New trauma-informed treatment approaches are required that incorporate sensitivity to the collective impact of mass crises, and recognize the risk of poorer long-term mental health outcomes for those who experienced trauma in the past., 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., (Copyright © 2021 Pazderka, Brown, Agyapong, Greenshaw, McDonald-Harker, Noble, Mankowski, Lee, Drolet, Omeje, Brett-MacLean, Kitching and Silverstone.)

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.

In Fort McMurray, Alberta, Canada, the wildfire of May 2016 forced the population of 88,000 to rapidly evacuate in a traumatic and chaotic manner. Ten percentage of the homes in the city were destroyed, and many more structures were damaged. Since youth are particularly vulnerable to negative effects of natural disasters, we examined possible long-term psychological impacts. To assess this, we partnered with Fort McMurray Public and Catholic Schools, who surveyed Grade 7-12 students (aged 11-19) in November 2017, 2018, and 2019-i.e., at 1.5, 2.5, and 3.5 years after the wildfire. The survey included validated measurement scales for post-traumatic stress disorder (PTSD), depression, anxiety, drug use, alcohol use, tobacco use, quality of life, self-esteem, and resilience. Data analysis was done on large-scale anonymous surveys including 3,070 samples in 2017; 3,265 samples in 2018; and 3,041 samples in 2019. The results were unexpected and showed that all mental health symptoms increased from 2017 to 2019, with the exception of tobacco use. Consistent with this pattern, self-esteem and quality of life scores decreased. Resilience scores did not change significantly. Thus, mental health measures worsened, in contrast to our initial hypothesis that they would improve over time. Of note, we observed higher levels of mental health distress among older students, in females compared to male students, and in individuals with a minority gender identity, including transgender and gender-non-conforming individuals. These findings demonstrate that deleterious mental health effects can persist in youth for years following a wildfire disaster. This highlights the need for multi-year mental health support programs for youth in post-disaster situations. The indication that multi-year, post-disaster support is warranted is relatively novel, although not unknown. There is a need to systematically investigate factors associated with youth recovery following a wildfire disaster, as well as efficacy of psychosocial strategies during later phases of disaster recovery relative to early post-disaster interventions., 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., (Copyright © 2021 Brown, Pazderka, Agyapong, Greenshaw, Cribben, Brett-MacLean, Drolet, McDonald-Harker, Omeje, Lee, Mankowsi, Noble, Kitching and Silverstone.)

Endowing robots with the ability to view the world the way humans do, to understand natural language and to learn novel semantic meanings when they are deployed in the physical world, is a compelling problem. Another significant aspect is linking language to action, in particular, utterances involving abstract words, in artificial agents. In this work, we propose a novel methodology, using a brain-inspired architecture, to model an appropriate mapping of language with the percept and internal motor representation in humanoid robots. This research presents the first robotic instantiation of a complex architecture based on the Baddeley's Working Memory (WM) model. Our proposed method grants a scalable knowledge representation of verbal and non-verbal signals in the cognitive architecture, which supports incremental open-ended learning. Human spoken utterances about the workspace and the task are combined with the internal knowledge map of the robot to achieve task accomplishment goals. We train the robot to understand instructions involving higher-order (abstract) linguistic concepts of developmental complexity, which cannot be directly hooked in the physical world and are not pre-defined in the robot's static self-representation. Our proposed interactive learning method grants flexible run-time acquisition of novel linguistic forms and real-world information, without training the cognitive model anew. Hence, the robot can adapt to new workspaces that include novel objects and task outcomes. We assess the potential of the proposed methodology in verification experiments with a humanoid robot. The obtained results suggest robust capabilities of the model to link language bi-directionally with the physical environment and solve a variety of manipulation tasks, starting with limited knowledge and gradually learning from the run-time interaction with the tutor, past the pre-trained stage., 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 handling Editor declared a past co-authorship with one of the authors AC., (Copyright © 2021 Giorgi, Cangelosi and Masala.)

Information on age-related differences in affective meanings of words is widely used by researchers to study emotions, word recognition, attention, memory, and text-based sentiment analysis. To date, no Chinese affective norms for older adults are available although Chinese as a spoken language has the largest population in the world. This article presents the first large-scale age-related affective norms for 2,061 four-character Chinese words (AANC). Each word in this database has rating values in the four dimensions, namely, valence, arousal, dominance, and familiarity. We found that older adults tended to perceive positive words as more arousing and less controllable and evaluate negative words as less arousing and more controllable than younger adults did. This indicates that the positivity effect is reliable for older adults who show a processing bias toward positive vs. negative words. Our AANC database supplies valuable information for researchers to study how emotional characteristics of words influence the cognitive processes and how this influence evolves with age. This age-related difference study on affective norms not only provides a tool for cognitive science, gerontology, and psychology in experimental studies but also serves as a valuable resource for affective analysis in various natural language processing applications., 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., (Copyright © 2021 Liu, Lu, Zhang, Tang and Han.)

Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g., neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically study children behaviours. However, artificial models need to incorporate realistic sensory-motor information from the body to fully mimic the children's learning behaviours, e.g., the use of fingers to learn and manipulate numbers. To this end, this article presents a database of images, focused on number representation with fingers using both human and robot hands, which can constitute the base for building new realistic models of numerical cognition in humanoid robots, enabling a grounded learning approach in developmental autonomous agents. The article provides a benchmark analysis of the datasets in the database that are used to train, validate, and test five state-of-the art deep neural networks, which are compared for classification accuracy together with an analysis of the computational requirements of each network. The discussion highlights the trade-off between speed and precision in the detection, which is required for realistic applications in robotics., 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., (Copyright © 2021 Davies, Lucas, Ricolfe-Viala and Di Nuovo.)