Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data Visualization and Dissemination Resource to Support HIV Prevention and Care at the Local Level: Analysis and Uses of the AIDSVu Public Data Resource

Background AIDSVu is a public resource for visualizing HIV surveillance data and other population-based information relevant to HIV prevention, care, policy, and impact assessment. Objective The site, AIDSVu.org, aims to make data about the US HIV epidemic widely available, easily accessible, and locally relevant to inform public health decision making. Methods AIDSVu develops visualizations, maps, and downloadable datasets using results from HIV surveillance systems, other population-based sources of information (eg, US Census and national probability surveys), and other data developed specifically for display and dissemination through the website (eg, pre-exposure prophylaxis [PrEP] prescriptions). Other types of content are developed to translate surveillance data into summarized content for diverse audiences using infographic panels, interactive maps, local and state fact sheets, and narrative blog posts. Results Over 10 years, AIDSVu.org has used an expanded number of data sources and has progressively provided HIV surveillance and related data at finer geographic levels, with current data resources providing HIV prevalence data down to the census tract level in many of the largest US cities. Data are available at the county level in 48 US states and at the ZIP Code level in more than 50 US cities. In 2019, over 500,000 unique users consumed AIDSVu data and resources, and HIV-related data and insights were disseminated through nearly 4,000,000 social media posts. Since AIDSVu’s inception, at least 249 peer-reviewed publications have used AIDSVu data for analyses or referenced AIDSVu resources. Data uses have included targeting of HIV testing programs, identifying areas with inequitable PrEP uptake, including maps and data in academic and community grant applications, and strategically selecting locations for new HIV treatment and care facilities to serve high-need areas. Conclusions Surveillance data should be actively used to guide and evaluate public health programs; AIDSVu translates high-quality, population-based data about the US HIV epidemic and makes that information available in formats that are not consistently available in surveillance reports. Bringing public health surveillance data to an online resource is a democratization of data, and presenting information about the HIV epidemic in more visual formats allows diverse stakeholders to engage with, understand, and use these important public health data to inform public health decision making.
Nervous System Sampling for General Toxicity and Neurotoxicity Studies in Rabbits

Although manuscripts for multiple species recommending nervous system sampling for histopathology evaluation in safety assessment have been published in the past 15 years, none have addressed the laboratory rabbit. Here, we describe 2 trimming schemes for evaluating the rabbit brain in nonclinical toxicity studies. In both schemes, the intact brain is cut in the coronal plane to permit bilateral assessment. The first scheme is recommended for general toxicity studies (tier 1) in screening agents where there is no anticipated neurotoxic potential; this 6-section approach is consistent with the Society of Toxicologic Pathology (STP) “best practice” recommendations for brain sampling in nonrodents (Toxicol Pathol 41: 1028-1048, 20131). The second trimming scheme is intended for dedicated neurotoxicity studies (tier 2) to characterize known or suspected neurotoxicants where the nervous system is a key target organ. This tier 2 strategy relies on coronal trimming of the whole brain into 3-mm-thick slices and then evaluating 12 sections. Collection of spinal cord, ganglia, and nerve specimens for rabbits during nonclinical studies should follow published STP “best practice” recommendations for sampling the central nervous system1 and peripheral nervous system (Toxicol Pathol 46: 372-402, 20182).
Gut dysbiosis associated with the rats' responses in methamphetamine‐induced conditioned place preference

Methamphetamine (MA) is a potent stimulant and notoriously addictive. Individuals respond to MA effects differently and thus have a varying susceptible risk of developing MA use disorder. Cumulative evidence has indicated that gut dysbiosis contributes to behavioral response to drug effects. However, the role of gut microbiota in the susceptible risk of developing MA use disorder has remained elusive. Using an MA‐induced conditioned place preference (CPP) rat model, we administrated the same dose of MA to rats, which then showed distinct preferences in drug‐related place, indicating their different responses to MA. From all of the MA‐exposed rats, the eight with the highest CPP scores were labeled as group high CPP (H‐CPP), and the eight with the lowest were labeled as group low CPP (L‐CPP). By 16S ribosomal RNA (rRNA) sequencing, we found that the gut microbiota compositions differed between H‐CPP and L‐CPP. Specifically, Akkermansia was significantly higher in H‐CPP and positively correlated with the CPP scores. Notably, H‐CPP and L‐CPP differed in the gut microbiota composition prior to the CPP training; Ruminococcus was the dominant phylotype in H‐CPP at baseline. More importantly, rats pretreated by antibiotics showed a significantly stronger MA‐induced CPP than did the controls. Our study demonstrates that the gut dysbiosis was associated with the MA‐induced CPP, indicating that the gut microbiota might be important modulators for MA‐induced behavior and vulnerability to MA use disorder.
Ongoing Growth Challenges Fruit Skin Integrity

ABSTRACT As a result of continuing volume and hence surface-area growth, the skins of most fruit species suffer ongoing strain throughout development. Maintenance of surface integrity is essential to protect the underlying tissues from desiccation and pathogen attack. Fruit skins are commonly “primary” in structure. They comprise a polymeric cuticle overlying an epidermis and a hypodermis. The cuticle is responsible for the skin's barrier function and the cellular layers for the skin's load-bearing functions. Skin failure can be just of the cuticle layer (microcracking) resulting in barrier impairment or it can involve cuticle and cellular layers (macrocracking) resulting in both barrier and structural impairment. Fruit skin failure is associated with a number of disorders including shriveling, cracking, russeting, and skin spots. All result in reduced market value. Our objective is to review the literature on the strategies adopted by fruit to cope with the challenge of continuing skin expansion. We uncover a multistep strategy to prevent or minimize the risk of fruit skin failure. This comprises: (1) area expansion of the load-bearing skin-cell layer(s) by ongoing cell division and (2) the avoidance of skin stress or strain concentrations by matching patterns of skin-cell division to those of area expansion. Also involved, (3) are the partitioning of cuticle strain into plastic and viscoelastic components at the expense of the elastic one. For this, wax and cutin are deposited in the cuticle during growth. Wax and cutin deposition “fix” the strain in the cuticle. Cutin is preferentially deposited on the inner surface of the cuticle, which fixes the strain, but it leaves the outer cuticle surface more strained. Last, (4) if the primary skin is damaged, the barrier functions are restored by the formation of a “secondary” fruit surface (periderm). Lignin can also be used to strengthen the underlying cells following structural failure.
Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development.
Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

Knowledge of optimal technical performance is used to determine match strategy and the design of training programs. Previous studies in men’s soccer have identified certain technical characteristics that are related to success. These studies however, have relative limited sample sizes or limited ranges of performance indicators, which may have limited the analytical approaches that were used. Research in women’s soccer and our understanding of optimal technical performance, is even more limited (n = 3). Therefore, the aim of this study was to identify technical determinants of match outcome in the women’s game and to compare analytical approaches using a large sample size (n = 1390 team performances) and range of variables (n = 450). Three different analytical approaches (i.e. combinations of technical performance variables) were used, a data-driven approach, a rational approach and an approach based on the literature in men’s soccer. Match outcome was modelled using variables from each analytical approach, using generalised linear modelling and decision trees. It was found that the rational and data-driven approaches outperformed the literature-driven approach in predicting match outcome. The strongest determinants of match outcome were; scoring first, intentional assists relative to the opponent, the percentage of shots on goal saved by the goalkeeper relative to the opponent, shots on goal relative to the opponent and the percentage of duels that are successful. Moreover the rational and data-driven approach achieved higher prediction accuracies than comparable studies about men’s soccer.
Agreement Between Spatiotemporal Gait Parameters Measured by a Markerless Motion Capture System and Two Reference Systems—a Treadmill-Based Photoelectric Cell and High-Speed Video Analyses: Comparative Study

Background Markerless systems to capture body motion require no markers to be attached to the body, thereby improving clinical feasibility and testing time. However, the lack of markers might affect the accuracy of measurements. Objective This study aimed to determine the absolute reliability and concurrent validity of the Kinect system with MotionMetrix software for spatiotemporal variables during running at a comfortable velocity, by comparing data between the combination system and two widely used systems—OptoGait and high-speed video analysis at 1000 Hz. Methods In total, 25 runners followed a running protocol on a treadmill at a speed of 12 km/h. The Kinect+MotionMetrix combination measured spatiotemporal parameters during running (ie, contact time, flight time, step frequency, and step length), which were compared to those obtained from two reference systems. Results Regardless of the system, flight time had the highest coefficients of variation (OptoGait: 16.4%; video analysis: 17.3%; Kinect+MotionMetrix: 23.2%). The rest of the coefficients of variation reported were lower than 8.1%. Correlation analysis showed very high correlations (r>0.8; P<.001) and almost perfect associations (intraclass correlation coefficient>0.81) between systems for all the spatiotemporal parameters except contact time, which had lower values. Bland-Altman plots revealed smaller systematic biases and random errors for step frequency and step length and larger systematic biases and random errors for temporal parameters with the Kinect+MotionMetrix system as compared to OptoGait (difference: contact time +3.0%, flight time −7.9%) and high-speed video analysis at 1000 Hz (difference: contact time +4.2%, flight time −11.3%). Accordingly, heteroscedasticity was found between systems for temporal parameters (r2>0.1). Conclusions The results indicate that the Kinect+MotionMetrix combination slightly overestimates contact time and strongly underestimates flight time as compared to the OptoGait system and high-speed video analysis at 1000 Hz. However, it is a valid tool for measuring step frequency and step length when compared to reference systems. Future studies should determine the reliability of this system for determining temporal parameters.
Realization of Inverted Organic Solar Cells by Using Sol-Gel Synthesized ZnO/Y2O3 Core/Shell Nanoparticles as Electron Transport Layer

In this article, we describe a simple, low-cost, and environment friendly sol-gel technique to form an effective protective Y2O3 layer over the core ZnO surface. The synthesized ZnO/Y2O3 core/shell nanoparticles have been studied for the investigation of morphology, purity, structure, luminescence, and electronic properties using different characterization techniques. The average particle size has been calculated to be 40 nm (approx). Scanning electron microscopy images show that the synthesized nanomaterials have particle-like morphology with spherical shape while high-resolution transmission electron microscopy analysis confirms their core/shell structure. Furthermore, ZnO/Y2O3 sample has been investigated as an electron transport layer (ETL) in inverted organic solar cell (OSC) with a blend of poly{4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b’]dithio phene-2,6-diyl-alt-3-fluoro-2-[(2ethylhexyl)carbonyl]thieno [3,4-b]thiophene-4,6-diyl} (PTB7), and [6,6]-phenyl-C71-butyric acid methyl ester (PC71BM) as an active layer. On comparing with the standard device having pure ZnO as ETL, the power conversion efficiency of PTB7:PC71BM based device with ZnO/Y2O3 as ETL is efficiently improved from 5.77% to 6.22%. This study provides an economical method to increase the efficiency of OSCs.
On the Effect of the Vertical Axis Alignment on Cybersickness and Game Experience in a Supine Posture

Cybersickness is still an inevitable adverse effect when using VR systems, resulting in different levels of discomfort, and potentially breaking the immersive experience. To date, few studies have focused on exploring VR experience in the supine posture. Recent research indicated that simple adoption of VR game initially designed for a seated posture (by rotating 90 degrees) could lead to more severe cybersickness, even to experienced users. Following the insights from previous literature and the widely-accepted sensory conflict theory, we proposed an assumption that might explain such a phenomenon. The hypothesis is that when the perceived virtual coordinate system contradicts the received real-world coordinate through our vestibular system, the conflict appears, which can lead to a sense of discomfort. Hence, the primary goal of the study is to evaluate whether such conflict has an impact on cybersickness. Furthermore, we explored methods of mitigating this conflict through different game designs so as to improve the experience for the supine posture. The final results show that the design that aligned with the real vertical axis is effective in mitigating cybersickness, especially for games that present an acceleration sensation.
Filgrastim associations with CAR T‐cell therapy

Little is known about the benefits and risks of myeloid growth factor administration after chimeric antigen receptor (CAR) T‐cell therapy for diffuse large B‐cell lymphoma (DLBCL). We present a retrospective analysis among 22 relapsed/refractory DLBCL patients who received CAR T‐cell therapy with axicabtagene ciloleucel. Filgrastim was administered by physician discretion to seven patients (31.8%), and the median duration of neutropenia after lymphodepleting therapy was significantly shorter for those patients who received filgrastim (5 vs 15 days, P = .016). Five patients (22.7%) developed infection in the 30 days post‐CAR T‐cell therapy with three patients being Grade 3 or higher. There was no difference in the incidence and severity of infection based on filgrastim use (P = .274, P = .138). Among the seven patients that received filgrastim, six patients (85.7%) and four patients (57.1%) had evidence of cytokine release syndrome (CRS) and immune effector cell‐associated neurotoxicity syndrome (ICANS), respectively. Among the 15 patients that did not receive filgrastim, 8 patients (53.3%) and 7 patients (46.7%) had evidence of CRS and ICANS, respectively. There was no significant difference in the incidence of developing CRS or ICANS between the group of patients that received filgrastim and those that did not (P = .193, P = .647). However, there was a significant increase in the severity of CRS for patients that received filgrastim compared to those that did not (P = .042). Filgrastim administration after CAR T‐cell therapy may lead to an increase in severity of CRS without decreasing infection rates.
Quality adjusted life years based on health and consumption: A summary wellbeing measure for cross‐sectoral economic evaluation

Abstract We introduce a summary wellbeing measure for economic evaluation of cross‐sectoral public policies with impacts on health and living standards. We show how to calculate period‐specific and lifetime wellbeing using quality‐adjusted life years based on widely available data on health‐related quality of life and consumption and normative assumptions about three parameters—minimal consumption, standard consumption, and the elasticity of the marginal value of consumption. We also illustrate how these three parameters can be tailored to the decision‐making context and varied in sensitivity analysis to provide information about the implications of alternative value judgments. As well as providing a general measure for cost‐effectiveness analysis and cost‐benefit analysis in terms of wellbeing, this approach also facilitates distributional analysis in terms of how many good years different population subgroups can expect to live under different policy scenarios.
Internalized Stigma in People With Mood Disorders

Abstract To analyze the predictors of internalized stigma among people with mood disorders, we conducted an analytical observational cross-sectional study with 108 people with mood disorders in a public service setting in Sao Paulo, Brazil. We applied a sociodemographic and clinical questionnaire, the Internalized Stigma of Mental Illness Scale, the Medication Adherence Scale, the Brief Psychiatric Rating Scale–Anchored, and the Herth Hope Index. We analyzed the data using descriptive statistics, average comparison tests, a correlation test, and multiple linear regression. Internalized stigma was associated with symptomatology, history of aggressive behavior, psychiatric hospitalizations, suicide attempts, hopelessness, nonadherence to psychotropic medications, and unemployment. The predictors of internalized stigma were unemployment, more psychiatric symptoms, history of previous suicide attempts, and less hope. Clinical interventions and investigations for stigma reduction and psychosocial rehabilitation should incorporate the factors associated with self-stigma (aggressive behavior, history of psychiatric hospitalizations, suicide attempts, hopelessness, nonadherence to medication, and unemployment).
Optimal Bidding and Operation Strategies for EV Aggegators by Regrouping Aggregated EV Batteries

We propose an optimal operation strategy for an electric vehicle (EV) aggregator (AGG), which performs energy arbitrage in the energy market and provides ancillary services from aggregated EVs, while providing charging services to EVs to maximize the profit in a future energy market. We group EV batteries as several virtual batteries (VB) with respect to their departure time and stages to implement multiple schedules simultaneously in the multi-stage stochastic optimization (MSSO). They are continuously grouped and regrouped as EVs arrive and depart. We predict VB scenario trees of stepwise EV driving routes and optimize the decisions in responding to uncertain EV movements. The VB states change as EVs enter and exit through the stages, so we should indirectly track time-varying VB characteristics at each stage. Then, we distribute the regulation bids for the VBs to the individual EVs. To reduce mismatches between the bidding amounts for VBs and actual transacted amounts for EVs, we suggest a novel binary progressive hedging algorithm to quickly determine the VB operations. Based on data from historical vehicle statistics, we verify that our strategy with the MSSO model provides a higher profit to the AGG than two-stage stochastic models.
High-resolution modeling of thermal thresholds and multiple environmental influences on coral bleaching for regional and local reef managements

Corals are one of the communities most threatened by global and local stressors. Excessive summer sea temperatures can cause coral bleaching, resulting in decreases in living coral coverage. Coral bleaching may begin with rising sea temperatures, although the widely used threshold of 1 °C over the local climatological maximum sea temperature has been reconsidered. In this study, we refine thermal indices predicting coral bleaching at high resolution (1 km) by statistically optimizing the thermal threshold and multiple environmental influences on bleaching, such as ultraviolet (UV) radiation, water turbidity, and cooling effects on corals. We use a dataset of coral bleaching events observed during 2004–2016 in Japan derived from the Web-based monitoring system, the Sango (Coral) Map Project, aiming at regional to local conservation of Japanese corals. We show how the ability to predict coral bleaching is improved by the choice of thermal index, statistical optimization of thermal thresholds, usage of multiple environmental influences, and modeling methods (generalized linear model and random forest). After optimization, the differences among the thermal indices in the ability to predict coral bleaching were slight. Among environmental influences, cooling effects, UV radiation, and water turbidity, in addition to a thermal index, well explain the occurrence of coral bleaching. Prediction based on the best explanatory model reveals that recent Japanese coral reefs are experiencing bleaching in many areas, although we show a practical way to reduce bleaching frequency significantly by screening UV radiation. Thus, our high-resolution models may provide a quantitative basis for the management of local reefs under current global and local stressors. The results of this study may be useful to other researchers for selecting a predictive method according to their needs or skills.