Miracle-dz/UniFAR-FTUs huggingface dataset:
Plastic Chemical Bisphenol A Dampens Our Cardiovascular System: Evidence from Clinical and Animal Studies

The environmental exposure of Bisphenol A (BPA) is a pervasive and growing concern. BPA is a high-volume industrial chemical that possesses estrogen-like properties and functions as an environmental endocrine disruptor. It is used extensively in the production of polycarbonate plastics and epoxy resins for food and beverage packages and hygienic products. An increasing amount of plastic pollution prevalent throughout the world has resulted in the nearly ubiquitous exposure of BPA towards humans and animals alike. Concerns have surfaced accordingly, surrounding the potentially detrimental effects that might result from BPA leaching into foods and beverages. The increase in epidemiological studies related to BPA have since been able to conclude links between BPA-induced oxidative stress, cardiovascular diseases, and hypertension. This review will incorporate current literature examining BPA exposure through clinical and epidemiological trials; these trials will encompass the physiological and toxicological effects that BPA can impose on the human cardiovascular system.
Development of the Coaxiality Calibration System Based on the American Standard ASTME1012

This paper describes the composition of the Coaxiality Calibration System based on the American Standard ASTME1012. The design of the test bar with different cross-section shape, the paste method of the strain gauge and formation of bridge are illustrated in the paper. The paper also introduces the signal amplifier, data collection system and measurement software in detail. The measurement precision of the coaxiality calibration device is up to grade 0.5. The device has successfully conducted coaxiality measurement and verification in fatigue testing machine, universal testing machine and material testing machine and achieved good test effect.
A REVIEW ON CONTINUOUS AUTHENTICATION USING MULTIMODAL BIOMETRICS

Most existing computer and network systems authenticate a user only at the initial login session. This could be a critical security weakness, especially for high-security systems because it enables an impostor to access the system resources until the initial user logs out. This situation is encountered when the logged in user takes a short break without logging out or an impostor coerces the valid user to allow access to the system. Thus to ensure authenticity of the user during their entire active login period, a continuous verification is required. The current paper considers continuous unobstrusive biometric authentication as an approach to eliminate this problem. This paper discusses about the number of research works introduced to overcome the difficulties faced in continuous authentication.
Neuro-Fuzzy BasedModeling forPhotovoltaic Power Supply System

Duetotheincreasing needforintelligent systems, theAdaptive Neuro-fuzzy Inference System(ANFIS)has recently attracted theattention ofresearchers invarious scientific andengineering areas. Thepurpose ofthis workisto present themodeling ofaPhotovoltaic PowerSupply(PVPS) systemusing anANFIS.Forthemodeling ofthePVPSsystem, itisrequired to findsuitable modelsforitsdifferent components (ANFISPV-generator, ANFISbattery andANFIS regulator) undervariable climatic conditions. A database of measuredweatherdata(global radiation, temperature and humidity) andelectrical signals (photovoltaic, battery and regulator voltage andcurrent) ofaPVPSsysteminstalled in Tahifet (south ofAlgeria) hasbeenrecorded fortheperiod from1992to1997using adataacquisition system. Thesedata havebeenusedforthemodeling andsimulation ofthePVPS system.The ANFIS forthePV-generator, battery and regulator havebeentrained byusing 10signals recorded from thedifferent components ofthePVPSsystem. Eachsignal is represented by365*5values (complete 5-years). A setofdata for4-years havebeenusedforthetraining oftheANFISand datafor1-year hasbeenusedforthetesting oftheANFIS.In thisway,theANFISwas trained toacceptandhandlea numberofunusual cases. Thecomparison between actual and estimated values obtained fromtheANFISgavesatisfactory results. Thecorrelation coefficient betweenmeasured values andthoseestimated by theANFISgavegoodprediction accuracy of98%. Inaddition, testresults showthattheANFIS performed better thantheArtificial NeuralNetworks (ANN). Predicted electrical signals bytheANFIScanbeusedfor several applications inPVsystems.
Changes in Sleep Quality Following Treatment for Chronic Rhinosinusitis: A Systematic Review and Meta-Analysis

Background Chronic rhinosinusitis (CRS) presents with broad and systemic manifestations, including impaired sleep; however, the impact of CRS treatments upon sleep is unknown. Objective To establish the effect of medical or surgical CRS treatment on subjective and objective sleep metrics for patients not previously diagnosed with sleep apnea. Methods Review of PubMed, Scopus, Web of Science, and the Cochrane Library was performed from the databases’ date of inception through August 13, 2020, for studies evaluating the effect of CRS treatment on sleep quality. All studies reporting on subjective and objective sleep parameters for patients with CRS, with completed pre- and posttreatment data were included. Studies composed of patients with diagnosed sleep apnea were excluded. Results Sixteen unique studies reporting data on a total of 1770 patients (mean age, 50.6 ± 15.6 (n = 1675) years) following treatment for CRS were included. Patient-reported outcome measures, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, and Fatigue Severity Scale, demonstrated mean posttreatment differences of −2.8 (95% CI: −4.9 to −0.7), −2.4 (95% CI: −3.7 to −1.2), and −1.2 (95% CI: −1.6 to −0.7), respectively. The SNOT-22 and its sleep domain demonstrated a mean posttreatment difference of −23.5 (95% CI: −31.7 to −15.3) and −5.4 (95% CI: −6.8 to −4.0), respectively. EpSS, FSS and SNOT-22 exceeded their respective reported MCID values. Objective findings did not significantly change with treatment; mean difference: AHI: 0.7 (95% CI: −1.5 to 2.9), oxygen nadir: 0.3 (95% CI: −0.4 to 0.9). Conclusions Treatment of CRS may lead to clinically meaningful reduction in disease burden and improvements in both overall sleep quality and patient-reported fatigue. Despite clinically meaningful quality of life improvements, objective sleep parameters did not demonstrate corresponding posttreatment improvements.
Advances in distributed sensing techniques using Brillouin scattering

This paper provides an overview of distributed strain and temperature sensing techniques that employ Brillouin scattering in single-mode optical fibers. These techniques are based on strain- and temperature- induced changes in the Brillouin frequency shift. Emphasis will be placed on recent progress in performance such as improvements in dynamic range, measurement accuracy, and spatial resolution.
Meridians: Emergent Lines of Shape Control

The Chinese concept of meridians has proved to be resistant to a bioscience understanding. Many investigative techniques and technologies have been applied to the human body in the search for a material substrate that is meridian-like. Using evolutionary biomechanics and embryology, the author has constructed a new model of human movement. The model, called 'Contractile Fields', has flexion/extension, side-bending, twisting, squeezing, limb, and visceral fields of contractility. That model was then turned towards the enigmatic meridians mapped over 2000 years ago in China. Recoil from a noxious stimulus is a reflex that all animals develop as a way of avoiding hurt. Acupuncture and moxibustion can elicit this reflex. Lines emerge on the body-wall and limbs that elicit similar biomechanical recoil vectors. The neurophysiology of recoil allied to the concept of 'border control' predicts 12 + 2 meridians as being the theoretical minimum needed to predictably control subtle human shape. Shape and function are closely coupled. Meridians are hypothesised to be 'emergent lines of shape control'.
Unified and Fast Human Trajectory Prediction Via Conditionally Parameterized Normalizing Flow

Human trajectory prediction is crucial for service robots, autonomous driving and advanced driver assistant systems. Current top-performing methods mainly rely on intractable generative models to learn a distribution of future trajectories, and sample multiple plausible ones as prediction results. In this letter, we propose a novel perspective for trajectory prediction, called Predicting Flow, which models the probability distribution of future trajectories explicitly and tractably by a conditionally parameterized normalizing flow. Compared with previous methods, our prediction framework is unified and faster by enjoying three advantages: i) Both future trajectories and probability maps of future positions can be predicted with our network, bringing more comprehensive prediction results. ii) For future trajectories, exact probabilities for predicted samples can be directly calculated without the aid of extra modules. iii) Our model is adaptive and compact with the conditional parameterization mechanism, leading to significantly stronger expression capability and higher inference speed. Exhaustive experiments on widely-used benchmarks demonstrate that our approach outperforms state-of-the-art performance in both accuracy and inference speed.
Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer

ABSTRACT Deep learning methods allow computational models involving multiple processing layers to discover intricate structures in data sets. Classifying an image is one such problem where these methods are found to be very useful. Although different approaches have been proposed in the literature, this paper illustrates a successful implementation of the Bayesian Convolution Neural Networks (BCNN)-based classification procedure to classify microscopic images of blood samples (lymphocyte cells) without involving manual feature extractions. The data set contains 260 microscopic images of cancerous and noncancerous lymphocyte cells. We experiment with different network structures and obtain the model that returns the lowest error rate in classifying the images. Our developed models not only produce high accuracy in classifying cancerous and noncancerous lymphocyte cells but also provide useful information regarding uncertainty in predictions.
Regenerative responses of rabbit corneal endothelial cells to stimulation by fibroblast growth factor 1 (FGF1) derivatives, TTHX1001 and TTHX1114

Abstract Utilising rabbit corneal endothelial cells (CEC) in three different paradigms, two human FGF1 derivatives (TTHX1001 and TTHX1114), engineered to exhibit greater stability, were tested as proliferative agents. Primary CECs and mouse NIH 3T3 cells treated with the two FGF1 derivatives showed equivalent EC50 ranges (3.3–24 vs.1.9–16. ng/mL) and, in organ culture, chemically lesioned corneas regained half of the lost endothelial layer in three days after treatment with the FGF1 derivatives as compared to controls. In vivo, following cryolesioning, the CEC monolayer, as judged by specular microscopy, regenerated 10–11 days faster when treated with TTHX1001. Over two weeks, all treated eyes showed clearing of opacity about twice that of untreated controls. In all three rabbit models, both FGF1 derivatives were effective in inducing CEC proliferation over control conditions, supporting the prediction that these stabilised FGF1 derivatives can potentially regenerate corneal endothelial deficits in humans.
Effects of Motion-Relevant Knowledge From Unlabeled Video to Human–Object Interaction Detection

The existing works on human–object interaction (HOI) detection usually rely on expensive large-scale labeled image datasets. However, in real scenes, labeled data may be insufficient, and some rare HOI categories have few samples. This poses great challenges for deep-learning-based HOI detection models. Existing works tackle it by introducing compositional learning or word embedding but still need large-scale labeled data or extremely rely on the well-learned knowledge. In contrast, the freely available unlabeled videos contain rich motion-relevant information that can help infer rare HOIs. In this article, we creatively propose a multitask learning (MTL) perspective to assist in HOI detection with the aid of motion-relevant knowledge learning on unlabeled videos. Specifically, we design the appearance reconstruction loss (ARL) and sequential motion mining module in a self-supervised manner to learn more generalizable motion representations for promoting the detection of rare HOIs. Moreover, to better transfer motion-related knowledge from unlabeled videos to HOI images, a domain discriminator is introduced to decrease the domain gap between two domains. Extensive experiments on the HICO-DET dataset with rare categories and the V-COCO dataset with minimum supervision demonstrate the effectiveness of motion-aware knowledge implied in unlabeled videos for HOI detection.
Correlation between Volumes Determined by Echocardiography and Cardiac MRI in Controls and Atrial Fibrillation Patients

Aims: We aimed to compare cardiac volumes measured with echocardiography (echo) and cardiac magnetic resonance imaging (MRI) in a mixed cohort of healthy controls (controls) and patients with atrial fibrillation (AF). Materials and methods: In total, 123 subjects were included in our study; 99 full datasets were analyzed. All the participants underwent clinical evaluation, EKG, echo, and cardiac MRI acquisition. Participants with full clinical data were grouped into 63 AF patients and 36 controls for calculation of left atrial volume (LA Vol) and 51 AF patients and 30 controls for calculation of left ventricular end-diastolic volume (LV EDV), end-systolic volume (ESV), and LV ejection fraction (LV EF). Results: No significant differences in LA Vol were observed (p > 0.05) when measured by either echo or MRI. However, echo provided significantly lower values for left ventricular volume (p < 0.0001). The echo LA Vol of all the subjects correlated well with that measured by MRI (Spearmen correlation coefficient r = 0.83, p < 0.0001). When comparing the two methods, significant positive correlations of EDV (all subjects: r = 0.55; Controls: r = 0.71; and AF patients: r = 0.51) and ESV (all subjects: r = 0.62; Controls: r = 0.47; and AF patients: r = 0.66) were found, with a negative bias for values determined using echo. For a subgroup of participants with ventricular volumes smaller than 49.50 mL, this bias was missing, thus in this case echocardiography could be used as an alternative for MRI. Conclusion: Good correlation and reduced bias were observed for LA Vol and EF determined by echo as compared to cardiac MRI in a mixed cohort of patients with AF and healthy volunteers. For the determination of volume values below 49.50 mL, an excellent correlation was observed between values obtained using echo and MRI, with comparatively reduced bias for the volumes determined by echo. Therefore, in certain cases, echocardiography could be used as a less expensive, less time-consuming, and contraindication free alternative to MRI for cardiac volume determination.
Rainfall study for dry land areas of selected districts of Tamil Nadu for crop planning

Monthly rainfall data for 90 years from 1911 to 2000 and recent years weekly rainfall data from 1981 to 2010 for 30 years were collected from India Meteorological Department and used for analysis to propose alternate crops under dry land situation of three selected districts of Tamil Nadu (Dharmapuri, Tirunelveli and Villupuram). Based on the geographical location, the annual mean rainfall of the three selected districts did vary indicating spatial variability of rainfall in Tamil Nadu. The length of growing period was analysed employing three different methods namely Hargreave’s Moisture Availability Index, Jeevananda Reddy 14 weeks moving average and FAO water balance model. Conditional probability index was also computed for weeding and plant protection measures for the three districts. Under dry land situation, the shorter scale weekly rainfall analysis revealed 16 weeks growing period for Dharmapuri district and suggested crops were maize, ragi and mixture of sorghum, red gram and mochai. In respect of Tirunelveli district, the identified growing weeks were 12 and scientifically lesser Evapo-Transpiration requirement crops like pulses and minor millets had been suggested. For Villupuram district, the growing period was16 weeks and hence groundnut, sesame, maize, varagu and soybean had been suggested against cotton.
Self-rated physical health, health-risk behaviors, and disparities: A cross-sectional study of youth in the slums of Kampala, Uganda

ABSTRACT Self-rated physical health (SRPH) has been extensively used to assess health status. In this study, we examine how youth living in the slums of Kampala perceive their physical health and the psychosocial correlates of poor health. Cross-sectional data from the 2014 Kampala Youth Survey (N = 1,134) of youth ages 12–18 years was used to conduct the analyses. Chi-square tests and logistic regression analyses were conducted to determine associations between SRPH, demographic and psychosocial characteristics. Overall, 72% of youth rated their health as ‘excellent’ or ‘good. ’ Poor SRPH was associated with older age and lower education, but not with sex. Also, orphans (OR = 2.03; 95%CI:1.51–2.72), those who lived on the streets (OR=3.09; 95%CI:2.30–4.15), who did not have electricity (OR = 2.83;95%CI:2.12–3.78), who initiated alcohol use early (OR = 2.08; 95%CI:1.47–2.94), who frequently get drunk (OR = 5.67; 95%CI:2.69–11.96), who were HIV positive (OR = 2.18; 95%CI:1.47–3.23), who had been injured due to their drinking (OR = 2.09; 95%CI:1.44–3.03), who thought about hurting themselves (OR = 2.09; 95%CI:1.60–2.73), and those who often felt lonely (OR = 2.54; 95%CI:1.61–4.02) had higher odds of poor SRPH compared to their peers without these characteristics. Poor SRPH may serve as a marker for multiple health-risk behaviors and severe health disparities among youth in vulnerable and resource-limited settings.
Innovations in retinitis pigmentosa – Metabolic rescue of cones, gene therapy, retinal transplantation

T December issue of the Taiwan Journal of Ophthalmology expands on the theme introduced in September – management and treatment of inherited retinal dystrophies. Retinitis pigmentosa (RP) is a group of inherited retinal diseases with variable clinical presentations from mild nyctalopia to total blindness. The gene mutations responsible for RP occur overwhelmingly in rod photoreceptors. Although the visual disability from rod dysfunction is significant, it is the subsequent loss of central vision in later life due to cone degeneration that is catastrophic. Until recently, the reason for cone dysfunction in RP was unknown. However, it is now recognized that cones degenerate losing outer segment (OS) synthesis and inner segment disassembly because of glucose starvation. [1,2] Rod OS phagocytosis by the apical microvilli of retinal pigment epithelium (RPE) is necessary for the transport of glucose from the choriocapillaris to the subretinal space. Although cones lose OS with the onset of rod degeneration in RP, regardless of the gene mutation in rods, cone nuclei remain viable for years (i.e., enter cone dormancy) so that therapies aimed at reversing glucose starvation can prevent and/or recover cone function and central vision. [3] The Metabolic Rescue of Cone Photoreceptors in RP by Kaplan et al. explores these events in more detail.
Simple constant frequency constant current load-resonant power supply under variable load conditions

A power supply incorporating a load-resonant converter operating at a fixed frequency and capable of efficient operation with a constant current characteristic, independent of the load conditions, is presented. The simple resonant circuit is designed so that at one of its multiple resonant frequencies both the load current and the resonant frequency are independent of the value of the load. A power supply is designed and constructed which delivers over 1 kW to a variable load at a constant switching frequency of 82 kHz.
Light Rail Accident Involvement and Severity

Accident causation and accident severity analyses for a light rail transit system are presented, with a view to providing input for the identification and development of accident and severity countermeasures. Accident reports of the Santa Clara County Transit Authority were used in both analyses. In the accident causation analysis, patterns of critical events for various types of light rail accident involvement were determined. The accident severity model is a binary logit model expressing the probability of injury accident as a function of speeds before collision of both the light rail vehicle and the motor vehicle, movement of the motor vehicle before collision, and peak and off-peak period.
Success Factors in Sustainability of Academic Social Network Site in Developing Countries: The Case of Thailand

As education is the key to success in the development of any country, electronic communication technology, such as social network sites, is gaining interest and is regarded as a potential tool to provide support and even act as a tool to improve the current studying and learning environments, thereby increasing the performance of students. However, the level of success in implementation of the technologies in educational environment is limited in Thailand and the assistance of technology in teaching and learning are not widely accepted. This research revealed the success factors necessary in order to successfully implement and customize social networks for academic purposes based on the concept of data mining, using the framework which has been primarily developed based on the Unif ied Theory of Acceptance and Use of Technology (UTAUT).