Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data

Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge. A promising approach is to use probabilistic regression methods to estimate normative models of brain function using (f)MRI data then use these to map variation across individuals in clinical populations (e.g., via anomaly detection). To fully capture individual differences, it is crucial to statistically model the patterns of correlation across different brain regions and individuals. However, this is very challenging for neuroimaging data because of high-dimensionality and highly structured patterns of correlation across multiple axes. Here, we propose a general and flexible multi-task learning framework to address this problem. Our model uses a tensor-variate Gaussian process in a Bayesian mixed-effects model and makes use of Kronecker algebra and a low-rank approximation to scale efficiently to multi-way neuroimaging data at the whole brain level. On a publicly available clinical fMRI dataset, we show that our computationally affordable approach substantially improves detection sensitivity over both a mass-univariate normative model and a classifier that --unlike our approach-- has full access to the clinical labels.
Understanding human-human interactions: a survey

Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video. The main challenges stem from dealing with the considerable variation in recording settings, the appearance of the people depicted and the performance of their interaction. This survey provides a summary of these challenges and datasets, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art.
Effects of Music Intervention on the Physical and Mental Status of Patients with Breast Cancer: A Systematic Review and Meta-Analysis

Background: The aim of this review was to evaluate the effects of music intervention on the physical and mental status of patients with breast cancer. Methods: 9 databases were searched; 30 randomized controlled trials that compared the effects of music plus standard care and standard care alone in patients with breast cancer were included. Methodological quality was assessed using the Jadad scale. Results: The results of the meta-analysis suggested that music intervention was significantly effective in lowering systolic blood pressure (standardized mean difference (SMD) -0.63, 95% confidence interval (CI) -0.85 to -0.42; p < 0.00001), diastolic blood pressure (SMD -0.64, 95% CI -1.06 to -0.22; p = 0.003), and heart rate (SMD -0.45, 95% CI -0.66 to -0.24; p < 0.0001), and in relieving anxiety (Hamilton Scale: mean difference (MD) -7.04, 95% CI -9.31 to -4.78; p < 0.00001; Self-Rating Anxiety Scale: MD -7.40, 95% CI -10.28 to -4.52; p < 0.00001; State Anxiety Inventory: MD -12.40, 95% CI -21.86 to -2.94; p = 0.01), and depression (MD -7.39, 95% CI -8.35 to -6.43; p < 0.00001). Conclusion: This review provides clues that music intervention might partially improve the physiological and psychological health of patients with breast cancer.
A smartphone-based solution to monitor daily physical activity in a care home

Introduction In an ageing population, increasing chronic disease prevalence puts a high economic burden on society. Physical activity plays an important role in disease prevention and should therefore be promoted in the elderly. Methods In this study, a mobile health (mHealth) system was implemented in a care home setting to monitor and promote elderly peoples’ daily activity. The physical activity of 20 elderly people (8 female and 12 male, aged 81 ± 9 years old) was monitored over 10 weeks using the mHealth system, consisting of a smartphone and heart rate belt. Feedback on physical activity was provided weekly. A reference performance test battery derived from the Senior Fitness Test determined the participants’ physical fitness. Results Activity levels increased from week 1 onwards, peaking at week 5, and decreasing slightly until week 10. This illustrates that the use of mHealth and feedback on physical activity can motivate the elderly to become more active, but that the effect is transient without other incentives. Bio-data from the mHealth system were translated into a fitness score explaining 65% of the test battery’s variance. After separating the elderly into three groups depending on physical fitness determined from the test battery, classification based on the fitness score resulted in a correct classification rate of 67.3%. Discussion This study demonstrates that an mHealth system can be implemented in a care home setting to motivate activity of the elderly, and that the bio-data can be translated in a fitness score predicting the outcome of labour-intensive tests.
Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

ABSTRACT Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, preclinical health effects assessments, pharmacological drug development and testing, and medical diagnostics. Within this framework, the intra-class correlation coefficient (ICC) serves as an important tool for gaining insight into human variability and responses and for developing risk-based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide data for clinical and public health efforts are continually evolving to expand our knowledge base of the many thousands of environmental and biomarker chemicals that define human systems biology. These chemicals range from the smallest molecules from energy metabolism (i.e., the metabolome), through larger molecules including enzymes, proteins, RNA, DNA, and adducts. In additiona, the human body contains exogenous environmental chemicals and contributions from the microbiome from gastrointestinal, pulmonary, urogenital, naso-pharyngeal, and skin sources. This complex mixture of biomarker chemicals from environmental, human, and microbiotic sources comprise the human exposome and generally accessed through sampling of blood, breath, and urine. One of the most difficult problems in biomarker assessment is assigning probative value to any given set of measurements as there are generally insufficient data to distinguish among sources of chemicals such as environmental, microbiotic, or human metabolism and also deciding which measurements are remarkable from those that are within normal human variability. The implementation of longitudinal (repeat) measurement strategies has provided new statistical approaches for interpreting such complexities, and use of descriptive statistics based upon intra-class correlation coefficients (ICC) has become a powerful tool in these efforts. This review has two parts; the first focuses on the history of repeat measures of human biomarkers starting with occupational toxicology of the early 1950s through modern applications in interpretation of the human exposome and metabolic adverse outcome pathways (AOPs). The second part reviews different methods for calculating the ICC and explores the strategies and applications in light of different data structures.
A diagnostic instrument to help field graders evaluate active trachoma

ABSTRACT The SAFE strategy (Surgery for trichiasis, mass treatment with Antibiotics to clear ocular Chlamydia trachomatis infection, and Facial cleanliness and Environmental improvement to reduce transmission) is being used to eliminate trachoma as a public health problem. Decisions on whether or not to implement the A, F, and E components of SAFE are made on the basis of the prevalence of trachomatous inflammation—follicular (TF) in 1–9-year-olds. TF has a precise definition: at least five follicles, each of which is at least 0.5-mm diameter, in the central part of the upper tarsal conjunctiva. Determining whether a follicle has a diameter ≥0.5mm is difficult using magnifying loupes alone. We have developed an ultra-low-cost solution: a follicle size guide that takes the form of a durable printed adhesive sticker which can be fixed to graders’ thumb nails for direct size comparison. This tool will be made available to health ministries free of charge. It is anticipated to simplify grader training, increase grader trainee pass rates, and prevent in-service diagnostic drift after training is complete.
Using Reflexive Thinking to Establish Rigor in Qualitative Research

Background Reflexivity can be helpful in developing the methodological rigor necessary to attaining trustworthy qualitative study results. Objectives The aim of this study was to evaluate strategies of critical reflexive thinking during a qualitative enquiry rooted in a mixed-methods study. Methods Guided by the questions of Rolfe and colleagues from 2001 (“what,” “so what,” and “now what”), we applied reflexive thinking to all aspects of the investigation. Results Critical reflexive thinking strongly supported our efforts to establish methodological rigor and helped reveal shortcomings. Discussion Effective strategical use of reflexive thinking takes concerted effort. Both time and space are essential to applying reflexive thinking throughout the qualitative research process.
Experienced mental workload, perception of usability, their interaction and impact on task performance

Past research in HCI has generated a number of procedures for assessing the usability of interacting systems. In these procedures there is a tendency to omit characteristics of the users, aspects of the context and peculiarities of the tasks. Building a cohesive model that incorporates these features is not obvious. A construct greatly invoked in Human Factors is human Mental Workload. Its assessment is fundamental for predicting human performance. Despite the several uses of Usability and Mental Workload, not much has been done to explore their relationship. This empirical research focused on I) the investigation of such a relationship and II) the investigation of the impact of the two constructs on human performance. A user study was carried out with participants executing a set of information-seeking tasks over three popular web-sites. A deep correlation analysis of usability and mental workload, by task, by user and by classes of objective task performance was done (I). A number of Supervised Machine Learning techniques based upon different learning strategy were employed for building models aimed at predicting classes of task performance (II). Findings strongly suggests that usability and mental workload are two non overlapping constructs and they can be jointly employed to greatly improve the prediction of human performance.
Increasing Liver Transplantation Wait‐List Dropout for Hepatocellular Carcinoma With Widening Geographical Disparities: Implications for Organ Allocation

Given the increasing incidence of hepatocellular carcinoma (HCC) and regional variation in liver transplantation (LT) rates for HCC, we investigated temporal and geographic disparities in LT and wait‐list dropout. LT candidates receiving Model for End‐Stage Liver Disease (MELD) exception from 2005 to 2014 were identified from the United Network for Organ Sharing database (n = 14,320). Temporal differences were compared across 2 eras (2005‐2009 and 2010‐2014). Regional groups were defined based on median wait time as long‐wait region (LWR; regions 1, 5, and 9), mid‐wait region (MWR; regions 2, 4, 6, 7, and 8), and short‐wait region (SWR; regions 3, 10, and 11). Fine and Gray competing risk regression estimated risk of wait‐list dropout as hazard ratios (HRs). The cumulative probability of LT within 3 years was 70% in the LWR versus 81% in the MWR and 91% in the SWR (P < 0.001). From 2005‐2009 to 2010‐2014, median time to LT increased by 6.0 months (5.6 to 11.6 months) in the LWR compared with 3.8 months (2.6 to 6.4 months) in the MWR and 1.3 months (1.0 to 2.3 months) in the SWR. The cumulative probability of dropout within 3 years was 24% in the LWR versus 16% in the MWR and 8% in the SWR (P < 0.001). From 2005‐2009 to 2010‐2014, the LWR also had the greatest increase in probability of dropout. Risk of dropout was increased in the LWR (HR, 3.5; P < 0.001) and the MWR (HR, 2.2; P < 0.001) compared with the SWR, and year of MELD exception 2010‐2014 (HR, 1.9; P < 0.001) compared with 2005‐2009. From 2005‐2009 to 2010‐2014, intention‐to‐treat 3‐year survival decreased from 69% to 63% in the LWR (P < 0.001), 72% to 69% in the MWR (P = 0.008), and remained at 74% in the SWR (P = 0.48). In conclusion, we observed a significant increase in wait‐list dropout in HCC patients in recent years that disproportionately impacted LWR patients. Widening geographical disparities call for changes in allocation policy as well as enhanced efforts at increasing organ donation and utilization.
Thoracoscopic segmentectomy for small-sized peripheral lung cancer.

Background Lung segmentectomy is a therapeutic option in containing pathological diagnosis and radical cure for small-sized peripheral lung cancer. We retrospectively investigated the results of thoracoscopic segmentectomy (TS-S). Methods From April 2008 to December 2016, 191 patients who underwent TS-S for small-sized peripheral lung cancer were reviewed retrospectively. Intentional indication of TS-S is peripheral radiologically noninvasive lung cancer whose tumor size is less than 2 cm in size with consolidation to tumor (C/T) ratio less than 0.5. Compromised indication is radiologically invasive lung cancer (C/T ration more than 0.5) which we can keep sufficient surgical margin. Results We performed TS-S in 191 patients (81 males and 110 females, median age 66 years). The mean diameter of the nodules was 15 mm (range, 6-46 mm), and clinical IA/IB was 184/7, respectively. Intentional indication was 145 (76%) and compromised one was 46 (24%). The mean operation time was 169 min (range, 73-319 min) and the mean blood loss was 42 g (range, 0-2,900 g). One procedure was converted to open thoracotomy due to bleeding of pulmonary artery (conversion rate, 0.5%). The median chest drainage duration was 1 day (range, 1-9 days), and the median postoperative hospital stay was 7 days (range, 3-30 days). Postoperative complications occurred in 19 patients (10%), including air leak lasting more than 7 days in 3 patients, and late phase air leak in 1 patient. There was no 30-day mortality. Median follow-up was 52 months. The 5-year overall survival (OS) rates and relapse free survival rates, including deaths from all causes, were 93.4% and 90.8%, respectively. During this period, there were 4 distal recurrences after TS-S. However, there was no local recurrence. Conclusions Our result of TS-S was an acceptable. Appropriate selection of patient and surgical procedure in TS-S is important issue.
Epigenetic immune cell counting in human blood samples for immunodiagnostics

Epigenetic immune cell counting provides a potentially more robust platform to diagnose immune defects in adults and newborns. Counting by chromatin Peripheral immune cell counts can be wielded to diagnose a variety of disorders. There are limitations to traditional methods such as flow cytometry, including the type of sample needed for analysis. Baron et al. took advantage of distinct immune cell epigenetic signatures and devised a real-time quantitative polymerase chain reaction (qPCR) method to perform immune cell counting without the requirement of viable cells. They examined different types of samples from healthy adults or those that were infected with HIV (and consequently had fewer CD4+ T cells). The epigenetic qPCR method correlated well with flow cytometry and could also be applied to dried blood spots to identify newborns with primary immunodeficiencies. Although it is not ready to be deployed clinically, the advantages of using epigenetic qPCR make this an intriguing approach to develop further. Immune cell profiles provide valuable diagnostic information for hematologic and immunologic diseases. Although it is the most widely applied analytical approach, flow cytometry is limited to liquid blood. Moreover, either analysis must be performed with fresh samples or cell integrity needs to be guaranteed during storage and transport. We developed epigenetic real-time quantitative polymerase chain reaction (qPCR) assays for analysis of human leukocyte subpopulations. After method establishment, whole blood from 25 healthy donors and 97 HIV+ patients as well as dried spots from 250 healthy newborns and 24 newborns with primary immunodeficiencies were analyzed. Concordance between flow cytometric and epigenetic data for neutrophils and B, natural killer, CD3+ T, CD8+ T, CD4+ T, and FOXP3+ regulatory T cells was evaluated, demonstrating substantial equivalence between epigenetic qPCR analysis and flow cytometry. Epigenetic qPCR achieves both relative and absolute quantifications. Applied to dried blood spots, epigenetic immune cell quantification was shown to identify newborns suffering from various primary immunodeficiencies. Using epigenetic qPCR not only provides a precise means for immune cell counting in fresh-frozen blood but also extends applicability to dried blood spots. This method could expand the ability for screening immune defects and facilitates diagnostics of unobservantly collected samples, for example, in underdeveloped areas, where logistics are major barriers to screening.
Monitoring cognition during awake brain surgery in adults: A systematic review

ABSTRACT Introduction: Historically, most studies about awake brain surgery have focused on language or motor functions. More recently, other cognitive functions have also been assessed. However, a clear overview of the neuropsychological tests or test paradigms that are used during such procedures is missing. The primary research question of this review is: What neuropsychological tests or paradigms are used during awake brain surgery? This review aims to give an extensive overview about the assessment of cognition during awake brain surgery. Method: A systematic search was performed in PubMed and Embase. Studies about awake surgery that mentioned a specific test or test paradigm for assessing cognition were included in this review. Results: The search yielded 4,052 articles. A manual selection for cognition in title and abstract resulted in 360 studies. Those were evaluated in full text; 212 articles described a cognitive task or paradigm. Further reference-list search yielded 20 more studies. In 207 of 232 studies, a test for assessment of language is reported. Tests for the visuospatial domain and motor and sensory functions are described in, respectively, only 23 and 20 studies. Tests for memory, calculation, emotions, or other cognitive functions are reported only in a minority of the included studies. Conclusions: Tests for assessment of language functions during awake brain surgery are widely reported. Other cognitive functions are underexposed. There is a need for development of tests or paradigms for assessment of other cognitive functions so that the broad spectrum of cognition can be monitored during awake brain surgery.
Septin‐based readout of PI(4,5)P2 incorporation into membranes of giant unilamellar vesicles

Septins constitute a novel class of cytoskeletal proteins. Budding yeast septins self‐assemble into non‐polar filaments bound to the inner plasma membrane through specific interactions with l‐α‐phosphatidylinositol‐4,5‐bisphosphate (PI(4,5)P2). Biomimetic in vitro assays using giant unilamellar vesicles (GUVs) are relevant tools to dissect and reveal insights in proteins–lipids interactions, membrane mechanics and curvature sensitivity. GUVs doped with PI(4,5)P2 are challenging to prepare. This report is dedicated to optimize the incorporation of PI(4,5)P2 lipids into GUVs by probing the proteins–PI(4,5)P2 GUVs interactions. We show that the interaction between budding yeast septins and PI(4,5)P2 is more specific than using usual reporters (phospholipase Cδ1). Septins have thus been chosen as reporters to probe the proper incorporation of PI(4,5)P2 into giant vesicles. We have shown that electro‐formation on platinum wires is the most appropriate method to achieve an optimal septin–lipid interaction resulting from an optimal PI(4,5)P2 incorporation for which, we have optimized the growth conditions. Finally, we have shown that PI(4,5)P2 GUVs have to be used within a few hours after their preparation. Indeed, over time, PI(4,5)P2 is expelled from the GUV membrane and the PI(4,5)P2 concentration in the bilayer decreases.
Wearable Bluetooth Sensors for Capturing Relational Variables and Temporal Variability in Relationships: A Construct Validation Study

The advent of wearable sensor technologies has the potential to transform organizational research by offering the unprecedented opportunity to collect continuous, objective, highly granular data over extended time periods. Recent evidence has demonstrated the potential utility of Bluetooth-enabled sensors, specifically, in identifying emergent networks via colocation signals in highly controlled contexts with known distances and groups. Although there is proof of concept that wearable Bluetooth sensors may be able to contribute to organizational research in highly controlled contexts, to date there has been no explicit psychometric construct validation effort dedicated to these sensors in field settings. Thus, the two studies described here represent the first attempt to formally evaluate longitudinal Bluetooth data streams generated in field settings, testing their ability to (a) show convergent validity with respect to traditional self-reports of relational data; (b) display discriminant validity with respect to qualitative differences in the nature of alternative relationships (i.e., advice vs. friendship); (c) document predictive validity with respect to performance; (d) decompose variance in network-related measures into meaningful within- and between-unit variability over time; and (e) complement retrospective self-reports of time spent with different groups where there is a “ground truth” criterion. Our results provide insights into the validity of Bluetooth signals with respect to capturing variables traditionally studied in organizational science and highlight how the continuous data collection capabilities made possible by wearable sensors can advance research far beyond that of the static perspectives imposed by traditional data collection strategies.
Multiparty Dependent Session Types (Extended Abstract)

Programs are more distributed and concurrent today than ever before, and structural communications are at the core. Constructing and debugging such programs are hard due to the lack of formal specification/verification of concurrency. This work formalizes the first multiparty dependent session types as an expressive and practical type discipline for enforcing communication protocols. The type system is formulated in the setting of multi-threaded $\lambda$-calculus with inspirations from multirole logic, a generalization of classical logic we discovered earlier. We prove its soundness by a novel technique called deadlock-freeness reducibility. The soundness of the type system implies communication fidelity and absence of deadlock.