Miracle-dz/UniFAR-FTUs huggingface dataset:
Ten simple rules for leveraging virtual interaction to build higher-level learning into bioinformatics short courses

The emergence of the Coronavirus Disease 2019 (COVID-19) crisis forced training providers worldwide to move their face-to-face (F2F) courses to virtual environments, challenging course organisers to transfer successful F2F concepts into a virtual format. While recording traditional stand-and-deliver lectures was simple enough, facilitating interaction among course participants proved the key challenge, as virtual environments were new to trainees, trainers, and organisers. Interaction in learning always carries an element of risk, whether F2F or virtual. Sometimes, groups do not get along, a strong personality takes over a course discussion, or trainees get lost in activities as they do not feel comfortable asking for help. Interaction in a virtual environment amplifies this problem as disengaging is easier. Trainees can leave their camera and mic off, leave meetings, or simply not attend more easily than in a F2F setting. Trainers too can struggle, as they are unable to walk around the room peering at screens, easily monitoring trainee progress, and initiating casual chats. Trainees can then become lost and feel excluded. Creating a comfortable and efficient interactive learning atmosphere that satisfies a wide variety of learning preferences and home/office-working settings is challenging but crucial for an efficient learning experience in a classroom setting [1]. Indeed, well-planned and well-facilitated interactive learning ensures that trainees feel comfortable and included, allowing them to fully engage with each other, the trainers and the training material. In addition to learning, training courses provide networking opportunities—particularly crucial for early career researchers hoping to find collaborators. This networking can happen organically in a F2F setting during poster sessions, over coffee or during dinner. However, this is not without its challenges—more introverted trainees may feel uncomfortable in these networking situations. Entire books are dedicated to networking for those that dislike networking [2]. Delivering networking virtually requires extensive restructuring—simply leaving nibbles in a seminar room is no longer feasible—but also provides an opportunity to better capture those who feel alienated in F2F networking events. PLOS COMPUTATIONAL BIOLOGY
Bias in Closed Population Capture-Recapture

Our primary question was the effect of departures from assumptions on population estimates obtained using three maximum likelihood population size estimators: M0 (all individuals have the same capture probability on all occasions), Mb (behavior is affected by prior capture) and Mt (all individuals have the same capture probability that varies by occasion). After examining the initial results and observing substantial negative bias (underestimates), we attempted to model the bias for M0 and Mb capture scenarios as these situations had consistent patterns. The Mt scenario with its erratic behaviors was not modeled. We noted that M0 and Mt performed equally well for the M0 and Mb captures. Mb did better as an estimator for the Mb capture scenario than for the M0 scenario. The Mt estimator for Mt captures did not perform well. Depending on actual capture probabilities, either of the other two estimators may give better, less biased
Bayesian analysis for partly linear Cox model with measurement error and time‐varying covariate effect

The Cox proportional hazards model is commonly used to estimate the association between time‐to‐event and covariates. Under the proportional hazards assumption, covariate effects are assumed to be constant in the follow‐up period of study. When measurement error presents, common estimation methods that adjust for an error‐contaminated covariate in the Cox proportional hazards model assume that the true function on the covariate is parametric and specified. We consider a semiparametric partly linear Cox model that allows the hazard to depend on an unspecified function of an error‐contaminated covariate and an error‐free covariate with time‐varying effect, which simultaneously relaxes the assumption on the functional form of the error‐contaminated covariate and allows for nonconstant effect of the error‐free covariate. We take a Bayesian approach and approximate the unspecified function by a B‐spline. Simulation studies are conducted to assess the finite sample performance of the proposed approach. The results demonstrate that our proposed method has favorable statistical performance. The proposed method is also illustrated by an application to data from the AIDS Clinical Trials Group Protocol 175.
Simulating the Dicke lattice model and quantum phase transitions using an array of coupled resonators

A proposal for simulating the Dicke-Lattice model in a mechanics-controlled hybrid quantum system is studied here. An array of coupled mechanical resonators (MRs) can homogeneously interact with a group of trapped Bose–Einstein condensates (BECs) via the gradient magnetic field induced by the oscillating resonators. Assisted by the classical dichromatic radio-wave fields, each subsystem with the BEC-MR interaction can mimic the Dicke type spin-phonon interaction, and the whole system is therefore extended to a lattice of Dicke models with the additional adjacent phonon-phonon hopping couplings. In view of this lattice model with the Z 2 symmetry, its quantum phase transitions behavior can be controlled by this periodic phonon-phonon interactions in the momentum space. This investigation may be considered as a fresh attempt on manipulating the critical behaviors of the collective spins through the external mechanical method.
BioCaster in 2021: automatic disease outbreaks detection from global news media

Abstract Summary BioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community. Availability and implementation BioCaster web-portal is freely accessible at http://biocaster.org.
Understanding Design Approaches and Evaluation Methods in mHealth Apps Targeting Substance Use: Protocol for a Systematic Review

Background Substance use and use disorders in the United States have had significant and devastating impacts on individuals and communities. This escalating substance use crisis calls for urgent and innovative solutions to effectively detect and provide interventions for individuals in times of need. Recent mobile health (mHealth)–based approaches offer promising new opportunities to address these issues through ubiquitous devices. However, the design rationales, theoretical frameworks, and mechanisms through which users’ perspectives and experiences guide the design and deployment of such systems have not been analyzed in any prior systematic reviews. Objective In this paper, we systematically review these approaches and apps for their feasibility, efficacy, and usability. Further, we evaluate whether human-centered research principles and techniques guide the design and development of these systems and examine how the current state-of-the-art systems apply to real-world contexts. In an effort to gauge the applicability of these systems, we also investigate whether these approaches consider the effects of stigma and privacy concerns related to collecting data on substance use. Lastly, we examine persistent challenges in the design and large-scale adoption of substance use intervention apps and draw inspiration from other domains of mHealth to suggest actionable reforms for the design and deployment of these apps. Methods Four databases (PubMed, IEEE Xplore, JMIR, and ACM Digital Library) were searched over a 5-year period (2016-2021) for articles evaluating mHealth approaches for substance use (alcohol use, marijuana use, opioid use, tobacco use, and substance co-use). Articles that will be included describe an mHealth detection or intervention targeting substance use, provide outcomes data, and include a discussion of design techniques and user perspectives. Independent evaluation will be conducted by one author, followed by secondary reviewer(s) who will check and validate themes and data. Results This is a protocol for a systematic review; therefore, results are not yet available. We are currently in the process of selecting the studies for inclusion in the final analysis. Conclusions To the best of our knowledge, this is the first systematic review to assess real-world applicability, scalability, and use of human-centered design and evaluation techniques in mHealth approaches targeting substance use. This study is expected to identify gaps and opportunities in current approaches used to develop and assess mHealth technologies for substance use detection and intervention. Further, this review also aims to highlight various design processes and components that result in engaging, usable, and effective systems for substance use, informing and motivating the future development of such systems. International Registered Report Identifier (IRRID) DERR1-10.2196/35749
Evaluation of Depression, Anxiety, and Sleep Quality in Children Diagnosed With COVID-19

Abstract COVID-19 has affected individuals of all age groups, both physically and mentally. We aimed to determine anxiety and depression in children diagnosed with COVID-19. Fifty children aged 8 to 18 years diagnosed with COVID-19 were included in the study. The children were evaluated the revised Child Anxiety and Depression Scale, STAI form TX-I (state anxiety inventory), and STAI form TX-II (trait anxiety inventory). Child Sleep Habits Questionnaire was applied for one of the parents according to three different periods. The periods were established as before the pandemic, during the illness-quarantine process, and after the quarantine. We observed the highest scores for depression-anxiety and the poorest sleep quality during the quarantine period. The scores for depression-anxiety were lower, and sleep quality scores were higher in the prepandemic period compared with after the quarantine period. Measures should be taken to protect mental health for children with COVID-19.
LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.
Use of a telovelar approach for complete resection of a choroid plexus tumor in a dog

Abstract Objective To describe a telovelar approach to the fourth ventricle for excision of a choroid plexus tumor within the ventricle. Animal A 3‐year‐old entire male Chihuahua. Study design Case report Methods A 3‐year‐old dog with two‐month history of progressive vestibular signs and subdued mentation was diagnosed with a fourth ventricle tumor. Gross total resection of the tumor was achieved through a telovelar approach to the fourth ventricle. Results Complete removal of the tumor was confirmed on immediate postoperative MRI. The dog recovered from the surgical procedure without complications, displaying some neurological deficits as preoperatively. His neurological examination was normal 2 weeks after surgery and remained so until the time of writing this case report (28 months) without additional treatment. Conclusion The telovelar approach allowed complete excision of a choroid plexus tumor located in the fourth ventricle of the dog reported here.
Autism presentation in female and Black populations: Examining the roles of identity, theory, and systemic inequalities

The prevalence of autism is rising, a dynamic attributed to numerous explanations (e.g. better diagnostic practices, decreased stigma). As this neurodevelopmental disability increases in visibility, disparities in its diagnostic rate grow. Female and Black populations in the United States have historically lower prevalence, are diagnosed later, are more likely to have co-occurring intellectual disability, and experience exclusion from research. Autistic Black girls are effectively invisible in the literature. To combat this exclusion, this narrative review discusses intersectionality theory and how it may be used to provide integrated models that are inclusive toward diverse gender, ability, and racial/ethnic backgrounds. Systemic inequalities, including prenatal care and misdiagnosis, disproportionately negatively impact the quality of care available to female and Black autistic populations. The authors recommend that research include autistic populations with co-occurring intellectual disability, include a diagnostic evaluation as part of protocols, and clinically, universal screening and improved provider competence and confidence in working with diverse autistic populations. Lay abstract Although the prevalence of autism has been rising in recent years, disparities in diagnosis still remain. Female and Black populations in the United States are diagnosed later, are more likely to have an intellectual disability, and are excluded from research as well as services designed for autistic individuals. Autistic Black girls are effectively invisible in the current scientific literature. Intersectional theory, which looks at a person as a whole, examines models that are inclusive toward diverse gender, ability, and racial/ethnic backgrounds. This theory may be a useful approach to clinical and research work with autism so that practitioners may be most effective for the whole population of autistic people. The authors recommend research focusing on inclusion of autistic populations with intellectual disability and research studies that include evaluations as part of the procedure. Clinically, the authors recommend a focus on screening all young children for autism and improving provider knowledge in working with diverse autistic populations.
Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms

Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting postures can help reduce health risks induced by a modern lifestyle. This paper presents the design, realization and evaluation of a new smart chair sensors system capable of sitting postures identification. The system consists of eight pressure sensors placed on the chair’s sitting cushion and the backrest. A signal acquisition board was designed from scratch to acquire data generated by the pressure sensors and transmit them via a Wi-Fi network to a purposely developed graphical user interface which monitors and stores the acquired sensors’ data on a computer. The designed system was tested by means of an extensive sitting experiment involving 40 subjects, and from the acquired data, the classification of the respective sitting postures out of eight possible postures was performed. Hereby, the performance of seven deep-learning algorithms was assessed. The best accuracy of 91.68% was achieved by an echo memory network model. The designed smart chair sensors system is simple and versatile, low cost and accurate, and it can easily be deployed in several smart chair environments, both for public and private contexts.
Older Adults’ Views on Social Interactions and Online Socializing Games – A Qualitative Study

ABSTRACT Age-related difficulties and quarantine restrictions impede the possibilities to maintain contact with one’s social network. Maintaining these contacts may be supported by digital games. To develop effective and feasible digital tools to foster social interaction, we aimed to explore what older adults find important in social contact and what barriers and enablers they foresee in digital gaming interventions as network support aids. Two focus groups and 20 semi-structured interviews (N = 29) with older adults (aged 55–87) were held to explore the research questions. Furthermore, a questionnaire was administered (N = 29) containing measures of loneliness, frailty, and social network size. Participants found ‘reciprocity’, ‘in-person contact’, and ‘personal connection’ important in contact with strong ties. Online games were not used much for socializing but may be used in the future, particularly by less mobile older adults. Future social gaming interventions should be challenging, user-friendly, and offer the possibility to communicate. Digital co-designed interventions that are feasible, challenging, intuitive, and trigger meaningful communication may strengthen social interactions in older adults. They may be a relevant social support tool in periods of interaction limitations due to functional impairment or social isolation.
Inferring the nature of linguistic computations in the brain

Sentences contain structure that determines their meaning beyond that of individual words. An influential study by Ding and colleagues (2016) used frequency tagging of phrases and sentences to show that the human brain is sensitive to structure by finding peaks of neural power at the rate at which structures were presented. Since then, there has been a rich debate on how to best explain this pattern of results with profound impact on the language sciences. Models that use hierarchical structure building, as well as models based on associative sequence processing, can predict the neural response, creating an inferential impasse as to which class of models explains the nature of the linguistic computations reflected in the neural readout. In the current manuscript, we discuss pitfalls and common fallacies seen in the conclusions drawn in the literature illustrated by various simulations. We conclude that inferring the neural operations of sentence processing based on these neural data, and any like it, alone, is insufficient. We discuss how to best evaluate models and how to approach the modeling of neural readouts to sentence processing in a manner that remains faithful to cognitive, neural, and linguistic principles.
Impact of loneliness on health‐related factors in Australia during the COVID‐19 pandemic: A retrospective study

Abstract COVID‐19 pandemic and its associated social and physical distancing restrictions may have had a severe impact on health. In the present study, we investigate the changes in physical, social and mental health, as well as the health literacy of Australians subsequent to the onset of COVID‐19 pandemic, and examine the influence of loneliness on these health‐related factors. Using a retrospective cross‐sectional study design, 607 Australian adults completed a self‐report online survey which assessed their health‐related factors before and after onset of the COVID‐19 pandemic (data collected between June 2020 to November 2020). Australians reported statistically significant increase in a number of (poorer) health‐related factors (e.g., weight gain, sleeping difficulties, poor somatic health, higher loneliness, more issues navigating the healthcare system) post onset of COVID‐19 pandemic. Further, after adjusting for covariates, higher loneliness during pandemic predicted poorer health‐related outcomes (e.g., more somatic health complaints, poorer quality of diet, poorer social support for health). The COVID‐19 pandemic and its associated social and physical distancing restrictions may have contributed towards poorer health‐related factors among Australian adults. Further, increased loneliness during the pandemic may have further worsened physical health and health literacy outcomes among Australians.
Identifying child protection workers at risk for secondary traumatization: A latent class analysis of the Professional Quality of Life Scale–5

Abstract Secondary traumatization (ST) is a topical area among trauma‐exposed employees, where many are at risk of occupational mental health problems due to their work with trauma survivors. Challenges related to the accurate operationalization of ST symptoms persist, and there is no valid method for identifying employees at risk of clinically significant symptom levels. This study aimed to test the applicability of latent class analysis (LCA) to identify employees at risk for clinically relevant ST symptoms. LCA was used to identify patterns of ST symptom endorsement using the Professional Quality of Life Scale–5 (ProQoL) in a national sample of Danish child protection workers (CPW; N = 545). Predictors and associated levels of distress and functional impairment across the symptom presentations were tested using regression analysis, and three classes were identified. A total of 3.8% (n = 21) of participants were at high risk of endorsing symptoms of ST that were associated with significant levels of distress and increased functional impairment. The LCA approach outperformed existing scoring procedures in differentiating between employees with varying levels of distress, impairment, and well‐being. A minority of Danish CPWs are at risk for levels of ST that may be considered clinically significant for their associations with anxiety and social and cognitive impairment. Although the ProQoL is not a diagnostic tool, per se, a revised scoring procedure may render the measure useful when screening for employees who are at risk for levels of distress and impairment that require intervention.