Miracle-dz/UniFAR-FTUs huggingface dataset:
PharmGScore scores of compound genetic variant burden for psychiatric treatment optimization

The acceptability of antidepressant drugs partly depends on genetic factors. The list of genes involved in antidepressant response, including Adverse Drug Reactions (ADRs) is broad and contains both drug-metabolizing enzymes (pharmacogenes) and genes involved in pharmacodynamics. Variants in pharmacogenes are traditionally reported in the form of star alleles and are partially annotated with known phenotypic consequences. As it is unfeasible to analyze all genotype-phenotype pairs, computational approaches remain the practical solution. A pharmacogenetic framework to predict responses to antidepressant drug treatment would provide great benefit to patients. In this study, we present a scoring system (PharmGScore) to assess both rare and common genetic variant burden across multiple genes. The PharmGScore is constructed by normalizing and aggregating existing, well-established computational variant predictors (CADD, Fathmm-xf, PROVEAN, Mutation Assessor). We show that this score effectively distinguishes no and decreased function from normal and increased function pharmacogenetic variants reported in PharmVar (PharmGScore AUC = 0.86). PharmGScore has improved performance when compared to its component scores (AUCs: CADD = 0.79; FATHMM-XF = 0.81; PROVEAN = 0.81; Mutation Assessor = 0.75). We then apply the PharmGScore to the 200k exome sequences of the UK Biobank (UKB). We report the overrepresentation of UKB participants with high (>50) gene PharmGScore for CYP2C19 and CYP2C9 and with high (>100) compound PharmGScore from nine pharmacogenes within a group with an antidepressant toxicity diagnostic code (T43.2). We then analyze all UKB participants that received any antidepressant toxicity or ADR diagnosis (n = 602). We indicate genes for which a higher burden may be associated with antidepressant toxicity or ADRs and confirm the known roles of CYP2C19 and CYP2D6 in this process. Finally, we show that patients who experienced ADRs to antidepressants in the therapeutic process or accidental poisoning with antidepressants have a higher PharmGScore composed of nine cytochrome P450 genes. Our study proposes a novel paradigm to assess the compound genetic variant burden associated with antidepressant response from exome sequencing data. This approach can be further applied to a user-defined set of genes to investigate other pharmacological traits.
Giving birth is like going to war: Obstetric violence in public maternity centers in Niger

Obstetric violence has been recognized as a significant risk factor for maternal morbidity and mortality globally. However, there is lack of literature on the abuse of birthing women and their maternal rights in formal healthcare settings in Niger. This paper explores women's experiences of violence in public maternity care in Niger to identify drivers, facilitators, manifestations, and consequences of the maltreatment of women during labor and delivery. We qualitatively explored the experiences of women who gave birth in a public maternity hospital or clinic within the 5 years preceding the study. We conducted five key informant interviews to help inform recruitment and interview protocols, a listening session with eight mothers, and individual interviews with 50 women from four areas of the urban region of Niamey. The 58 participating mothers shared experiences that included physical abuse (slapping), verbal abuse (insults), psychological abuse (mockery), discrimination (social status/ economic status), financial coercion (demanding money prior to care), and harmful birthing practices (forcing the baby out with elbows). Socio-cultural aspects driving maltreatment of women included beliefs about expression of pain during labor and delivery, while social connection and the presence of the physician during delivery were protective factors. Our study findings reflect global concerns about obstetric violence and its consequences. Given the high burden of maternal and infant morbidity and mortality in Niger it is critical to train clinical staff in safe and respectful maternity care, to improve supervision of care, to institute legal recourse for women, and to consider the integration of traditional birth attendants in clinical settings.
Brazilian Jiu Jitsu Training for US Service Members and Veterans with Symptoms of PTSD

Abstract Introduction The United States has been actively involved in major armed conflicts over the last 15 years. As a result, a significant proportion of active duty service personnel and returning veterans have endured combat, putting them at risk for developing post-traumatic stress disorder (PTSD), a disabling disorder that may occur after exposure to a traumatic event. Current therapies often require long-term, time-intensive and costly commitment from the patient and have variable degrees of success. There remains an ongoing need for better therapies, including complementary medicine approaches that can effectively reduce PTSD symptoms. While anecdotal evidence suggests that routine practice of Brazilian Jiu Jitsu (BJJ) can reduce symptoms of PTSD, there have been no formal studies to address this. Materials and Methods This study was approved by the University of South Florida Institutional Review Board (#PRO00019430). Male US active duty service members and veterans from the Tampa area participated in a 5-month (40 sessions) BJJ training program. Before beginning and again midway through and upon completion of training the participants completed several validated self-report measures that addressed symptoms of PTSD and other co-morbid conditions. Effect size and 95% confidence intervals were determined using a within-person single-group pretest–posttest design. Results Study participants demonstrated clinically meaningful improvements in their PTSD symptoms as well as decreased symptoms of major depressive disorder, generalized anxiety and decreased alcohol use; effect sizes varied from 0.80 to 1.85. Conclusions The results from this first-of-kind pilot study suggest that including BJJ as a complementary treatment to standard therapy for PTSD may be of value. It will be necessary to validate these promising results with a larger subject cohort and a more rigorous experimental design before routinely recommending this complementary therapy.
Systolic Blood Pressure and Survival to Very Old Age. Results from the Women’s Health Initiative

Background: The association between systolic blood pressure (SBP) and longevity is not fully understood. We aimed to determine survival probabilities to age 90 for various SBP levels among women aged [≥] 65 years with or without BP medication. Methods: We analyzed blood pressure data from participants in the Women's Health Initiative (n=16,570) who were aged 65 or older and without history of cardiovascular disease, diabetes or cancer. Blood pressure was measured at baseline (1993-1998) and then annually through 2005. The outcome was defined as survival to age 90 with follow-up until February 28, 2020. Results: During a follow-up of 18 years, 9,723 (59%) of 16,570 women survived to age 90. The SBP associated with the highest probability of survival was about 120mmHg regardless of age. Compared to an SBP between 110 and 130 mmHg, women with uncontrolled SBP had a lower survival probability across all age groups and with or without BP medication. A 65-year-old women on BP medication with an interpolated SBP between 110 and 130 mmHg in 80% of the first 5 years of follow-up had a 31% (95% confidence interval, 24%, 38%) absolute survival probability. For those with 20% time in range, the probability was 21% (95% confidence interval, 16%, 26%). Conclusions: An SBP level below 130 mmHg was found to be associated with longevity among older women. The longer SBP was controlled at a level between 110 and 130 mmHg, the higher the survival probability to age 90. Preventing age-related rises in SBP and increasing the time with controlled BP levels constitute important measures for achieving longevity.
Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

Recent advancements in target tracking using Wi-Fi signals and channel state information (CSI) have significantly improved the accuracy and efficiency of tracking mobile targets. However, there remains a gap in developing a comprehensive approach that combines CSI, an unscented Kalman filter (UKF), and a sole self-attention mechanism to accurately estimate the position, velocity, and acceleration of targets in real-time. Furthermore, optimizing the computational efficiency of such approaches is necessary for their applicability in resource-constrained environments. To bridge this gap, this research study proposes a novel approach that addresses these challenges. The approach leverages CSI data collected from commodity Wi-Fi devices and incorporates a combination of the UKF and a sole self-attention mechanism. By fusing these elements, the proposed model provides instantaneous and precise estimates of the target’s position while considering factors such as acceleration and network information. The effectiveness of the proposed approach is demonstrated through extensive experiments conducted in a controlled test bed environment. The results exhibit a remarkable tracking accuracy level of 97%, affirming the model’s ability to successfully track mobile targets. The achieved accuracy showcases the potential of the proposed approach for applications in human-computer interactions, surveillance, and security.
Applicability of the Meniscus-Removal Method for Q-Band Liquid Characterization in Semi-Open Waveguide Cell

We present the broadband transmission-reflection meniscus-removal method for liquid characterization in a semi-open rectangular waveguide. The algorithm utilizes 2-port scattering parameters measured with a calibrated vector network analyzer for three states of the measurement cell: empty and filled with two liquid levels. The method enables the mathematical de-embedding of a symmetrical sample of a liquid, not distorted with a meniscus, and provision of its permittivity and permeability, as well as its height. We validate the method for propan-2-ol (IPA), a 50% aqueous solution of IPA, and distilled water in the Q-band (33–50 GHz). We investigate typical problems for in-waveguide measurements, such as phase ambiguity.
A Holistic 4D Approach to Optimize Intrinsic and Extrinsic Factors Contributing to Variability in Microarray Biosensing in Glycomics

Protein–carbohydrate interactions happen to be a crucial facet of biology, discharging a myriad of functions. Microarrays have become a premier choice to discern the selectivity, sensitivity and breadth of these interactions in a high-throughput manner. The precise recognition of target glycan ligands among the plethora of others is central for any glycan-targeting probe being tested by microarray analyses. Ever since the introduction of the microarray as an elemental tool for high-throughput glycoprofiling, numerous distinct array platforms possessing different customizations and assemblies have been developed. Accompanying these customizations are various factors ushering variances across array platforms. In this primer, we investigate the influence of various extrinsic factors, namely printing parameters, incubation procedures, analyses and array storage conditions on the protein–carbohydrate interactions and evaluate these factors for the optimal performance of microarray glycomics analysis. We hereby propose a 4D approach (Design–Dispense–Detect–Deduce) to minimize the effect of these extrinsic factors on glycomics microarray analyses and thereby streamline cross-platform analyses and comparisons. This work will aid in optimizing microarray analyses for glycomics, minimize cross-platform disparities and bolster the further development of this technology.
Perceived Causal Networks: Clinical Utility Evaluated by Therapists and Patients

Conceptualizing psychiatric disorders as idiosyncratic networks of mutually reinforcing behaviors and emotions has a long history in the form of psychotherapy case conceptualizations created collaboratively by therapist and patient. However, such methods are typically unsystematic and biased by therapist assumptions. An alternative is Perceived Causal Networks (PECAN), a structured online questionnaire in which patients quantify causal relations between problematic behaviors and emotions, and responses are visualized in the form of a network. In the present study, PECAN was evaluated for clinical utility at the start of therapy for five patients screening positive for depression. As expected, the five networks were found to be highly idiosyncratic, with two revealing the expected maintaining feedback loops. Both therapists and patients evaluated the method as useful in the initial phase of a therapy treatment. Although PECAN shows promise as a clinical tool, findings suggest that the method could be improved by including contextual factors maintaining depression.
Utility and Usability of Two Forms of Supplemental Vibrotactile Kinesthetic Feedback for Enhancing Movement Accuracy and Efficiency in Goal-Directed Reaching

Recent advances in wearable sensors and computing have made possible the development of novel sensory augmentation technologies that promise to enhance human motor performance and quality of life in a wide range of applications. We compared the objective utility and subjective user experience for two biologically inspired ways to encode movement-related information into supplemental feedback for the real-time control of goal-directed reaching in healthy, neurologically intact adults. One encoding scheme mimicked visual feedback encoding by converting real-time hand position in a Cartesian frame of reference into supplemental kinesthetic feedback provided by a vibrotactile display attached to the non-moving arm and hand. The other approach mimicked proprioceptive encoding by providing real-time arm joint angle information via the vibrotactile display. We found that both encoding schemes had objective utility in that after a brief training period, both forms of supplemental feedback promoted improved reach accuracy in the absence of concurrent visual feedback over performance levels achieved using proprioception alone. Cartesian encoding promoted greater reductions in target capture errors in the absence of visual feedback (Cartesian: 59% improvement; Joint Angle: 21% improvement). Accuracy gains promoted by both encoding schemes came at a cost in terms of temporal efficiency; target capture times were considerably longer (1.5 s longer) when reaching with supplemental kinesthetic feedback than without. Furthermore, neither encoding scheme yielded movements that were particularly smooth, although movements made with joint angle encoding were smoother than movements with Cartesian encoding. Participant responses on user experience surveys indicate that both encoding schemes were motivating and that both yielded passable user satisfaction scores. However, only Cartesian endpoint encoding was found to have passable usability; participants felt more competent using Cartesian encoding than joint angle encoding. These results are expected to inform future efforts to develop wearable technology to enhance the accuracy and efficiency of goal-directed actions using continuous supplemental kinesthetic feedback.
Effect of One Step Solid State Reaction Route on the Semiconductor Behavior of the Spinel (NI, Co, and Mn)O4 to Be Used as Temperature Sensor

Achieving carbon neutrality is important to solve environmental problems and thus requires decarbonizing manufacturing processes to reduce greenhouse gas emissions. The firing of ceramics, including calcination and sintering, is a typical fossil fuels-driven manufacturing process that requires large power consumption. Although the firing process in manufacturing ceramics cannot be eliminated, an effective firing strategy to reduce processing steps can be a choice to lower power consumption. Herein, we suggest a one-step solid solution reaction (SSR) route to manufacture (Ni, Co, and Mn)O4 (NMC) electroceramics for their application in temperature sensors with negative temperature coefficient (NTC). Additionally, the effect of the one-step SSR route on the electrical properties of the NMC is investigated. Similar to the NMC prepared using the two-step SSR route, spinel structures with dense microstructure are observed in the NMC prepared via the one-step SSR route. Based on the experimental results, the one-step SSR route can be considered as one of the effective processing techniques with less power consumption to manufacture electroceramics.
Relationship between Language Dominance and Stimulus-Stimulus or Stimulus-Response Inhibition in Uyghur-Chinese Bilinguals with an Investigation of Speed-Accuracy Trade-Offs

The effect of bilingualism on inhibition control is increasingly under ongoing exploration. The present study primarily investigated the effect of within bilingual factors (i.e., dominance types of Uyghur-Chinese bilinguals) on a Stimulus-Stimulus task (Flanker) and a Stimulus-Response task (Simon). We also compared the bilinguals’ performance on each type of cognitive control task in respect to a possible trade-off between speed and accuracy. The findings showed no explicit differences on performance in response time or accuracy among balanced, L1-dominant and L2-dominant bilinguals but balanced bilinguals demonstrated a significant speed-accuracy trade-off in the overall context switching between non-conflict and conflict trials in both cognitive control tasks where monitoring process is highly demanded. Additionally, all bilinguals across all language dominance types showed a trade-off strategy in inhibition during a Stimulus-Stimulus conflict (flanker task). This evidence indicates that the differences of within bilinguals in cognitive control could lie in the monitoring process, while for all bilinguals, inhibition during a Stimulus-Stimulus conflict could be a major component in the mechanism of bilingual language processing.
Joint Fusion and Detection via Deep Learning in UAV-Borne Multispectral Sensing of Scatterable Landmine

Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine detection. Using the UAV-borne multispectral cruise platform, we establish a multispectral dataset of scatterable mines, with mine-spreading areas of the ground vegetation considered. In order to achieve the robust detection of occluded landmines, first, we employ an active learning strategy to refine the labeling of the multispectral dataset. Then, we propose an image fusion architecture driven by detection, in which we use YOLOv5 for the detection part, to improve the detection performance instructively while enhancing the quality of the fused image. Specifically, a simple and lightweight fusion network is designed to sufficiently aggregate texture details and semantic information of the source images and obtain a higher fusion speed. Moreover, we leverage detection loss as well as a joint-training algorithm to allow the semantic information to dynamically flow back into the fusion network. Extensive qualitative and quantitative experiments demonstrate that the detection-driven fusion (DDF) that we propose can effectively increase the recall rate, especially for occluded landmines, and verify the feasibility of multispectral data through reasonable processing.
Prospective study comparing hepatic steatosis assessment by magnetic resonance imaging and four ultrasound methods in 105 successive patients

BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is becoming a major health problem, resulting in hepatic, metabolic and cardio-vascular morbidity. AIM To evaluate new ultrasonographic tools to detect and measure hepatic steatosis. METHODS We prospectively included 105 patients referred to our liver unit for NAFLD suspicion or follow-up. They underwent ultrasonographic measurement of liver sound speed estimation (SSE) and attenuation coefficient (AC) using Aixplorer MACH 30 (Supersonic Imagine, France), continuous controlled attenuation parameter (cCAP) using Fibroscan (Echosens, France) and standard liver ultrasound with hepato-renal index (HRI) calculation. Hepatic steatosis was then classified according to magnetic resonance imaging proton density fat fraction (PDFF). Receiver operating curve (ROC) analysis was performed to evaluate the diagnostic performance in the diagnosis of steatosis. RESULTS Most patients were overweight or obese (90%) and had metabolic syndrome (70%). One third suffered from diabetes. Steatosis was identified in 85 patients (81%) according to PDFF. Twenty-one patients (20%) had advanced liver disease. SSE, AC, cCAP and HRI correlated with PDFF, with respective Spearman correlation coefficient of -0.39, 0.42, 0.54 and 0.59 (P < 0.01). Area under the receiver operating characteristic curve (AUROC) for detection of steatosis with HRI was 0.91 (0.83-0.99), with the best cut-off value being 1.3 (Se = 83%, Sp = 98%). The optimal cCAP threshold of 275 dB/m, corresponding to the recent EASL-suggested threshold, had a sensitivity of 72% and a specificity of 80%. Corresponding AUROC was 0.79 (0.66-0.92). The diagnostic accuracy of cCAP was more reliable when standard deviation was < 15 dB/m with an AUC of 0.91 (0.83-0.98). An AC threshold of 0.42 dB/cm/MHz had an AUROC was 0.82 (0.70-0.93). SSE performed moderately with an AUROC of 0.73 (0.62-0.84). CONCLUSION Among all ultrasonographic tools evaluated in this study, including new-generation tools such as cCAP and SSE, HRI had the best performance. It is also the simplest and most available method as most ultrasound scans are equipped with this module.
Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks

This paper compares the usability of various Apple MacBook Pro laptops were tested for basic machine learning research applications, including text-based, vision-based, and tabular data. Four tests/benchmarks were conducted using four different MacBook Pro models—M1, M1 Pro, M2, and M2 Pro. A script written in Swift was used to train and evaluate four machine learning models using the Create ML framework, and the process was repeated three times. The script also measured performance metrics, including time results. The results were presented in tables, allowing for a comparison of the performance of each device and the impact of their hardware architectures.
Shoulder Range of Motion Measurement Using Inertial Measurement Unit—Validation with a Robot Arm

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics.