Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Real-Time and Post Hoc Feedback to Improve Driving Safety for Novice Drivers

Introduction. Motor vehicle crashes remain the leading cause of fatalities among teens in the U.S. (National Center for Injury Prevention and Control, 2013). Prior research suggests that real-time and post hoc feedback can improve teen driver behavior. The Driver Coach Study (DCS) aimed to improve teens’ safe driving habits by providing them real-time feedback and post hoc feedback to a broader range of risky driving behaviors that have never been used in previous studies. Exposure data were also collected so that rates of risky driving behaviors over time could be assessed. Post hoc feedback, which included an electronic report card of risky driving behavior as well as video clips, was provided to both teens and parents via email and secure website link. Method. Ninety-two teen/parent dyads were recruited in southwest Virginia to have a data acquisition system (DAS) installed in their vehicles within two weeks of receiving their learner’s permit. Data were collected through the nine-month (minimum) learner’s permit phase plus seven months of provisional licensure. Feedback was only provided for the first six months of post licensure, then turned off to assess whether teenagers returned to unsafe driving behavior. Trained data coders reviewed 15 seconds of video surrounding each risky driving maneuver, and recorded driver errors such as poor vehicle control, poor speed selection, drowsiness, etc., for each event. Results. In this paper, the relationship between driver coaching and driver errors will be examined across the six-month feedback phase and also compared to the seventh month when feedback was turned off. Conclusions. This study has implications for the design of future monitoring and feedback systems, as it is currently unknown whether these devices can improve novice drivers’ crash rates.
GBA Variants and Parkinson Disease: Mechanisms and Treatments

The GBA gene encodes for the lysosomal enzyme glucocerebrosidase (GCase), which maintains glycosphingolipid homeostasis. Approximately 5–15% of PD patients have mutations in the GBA gene, making it numerically the most important genetic risk factor for Parkinson disease (PD). Clinically, GBA-associated PD is identical to sporadic PD, aside from the earlier age at onset (AAO), more frequent cognitive impairment and more rapid progression. Mutations in GBA can be associated with loss- and gain-of-function mechanisms. A key hallmark of PD is the presence of intraneuronal proteinaceous inclusions named Lewy bodies, which are made up primarily of alpha-synuclein. Mutations in the GBA gene may lead to loss of GCase activity and lysosomal dysfunction, which may impair alpha-synuclein metabolism. Models of GCase deficiency demonstrate dysfunction of the autophagic-lysosomal pathway and subsequent accumulation of alpha-synuclein. This dysfunction can also lead to aberrant lipid metabolism, including the accumulation of glycosphingolipids, glucosylceramide and glucosylsphingosine. Certain mutations cause GCase to be misfolded and retained in the endoplasmic reticulum (ER), activating stress responses including the unfolded protein response (UPR), which may contribute to neurodegeneration. In addition to these mechanisms, a GCase deficiency has also been associated with mitochondrial dysfunction and neuroinflammation, which have been implicated in the pathogenesis of PD. This review discusses the pathways associated with GBA-PD and highlights potential treatments which may act to target GCase and prevent neurodegeneration.
Sex Differences in Biological Systems and the Conundrum of Menopause: Potential Commonalities in Post-Menopausal Disease Mechanisms

Sex-specific differences in biology and physiology likely start at the time of conception and progress and mature during the pre-puberty time frame and then during the transitions accompanying puberty. These sex differences are impacted by both genetics and epigenetic alterations during the maturation process, likely for the purpose of preparing for successful reproduction. For females, later in life (~45–50) they undergo another transition leading to a loss of ovarian hormone production at menopause. The reasons for menopause are not clear, but for a subset of females, menopause is accompanied by an increased risk of a number of diseases or conditions that impact a variety of tissues. Most research has mainly focused on the target cells in each of the affected tissues rather than pursue the alternative option that there may be commonalities in the development of these post-menopausal conditions in addition to influences on specific target cells. This review will address some of the potential commonalities presented by an integration of the literature regarding tissue-specific aspects of these post-menopausal conditions and data presented by space flight/microgravity (a condition not anticipated by evolution) that could implicate a loss of a regulatory function of the microvasculature in the risk attached to the affected tissues. Thus, the loss of the integration of the paracrine relationships between endothelial cells of the microvasculature of the tissues affected in the post-menopausal environment could contribute to the risk for post-menopausal diseases/conditions. The validation of this concept could lead to new approaches for interventions to treat post-menopausal conditions, as well as provide new understanding regarding sex-specific biological regulation.
Conceptualising domestic servitude as a violation of the human right to housing and reframing Australian policy responses

ABSTRACT This article makes a twofold contribution to a human rights-centred response to domestic servitude as a modern form of slavery. First, it offers a conceptualisation of domestic servitude as a comprehensive and specific violation of the human right to housing, based on a reading of the right to housing as sitting at a crucial juncture of the public and private. Victims of domestic servitude experience a violation of the right to housing, the nature of which strikingly demonstrates both how housing sits at the nexus of the public and the private; and that the enjoyment of rights in both those spheres is crucial to a person’s experience of dignity, peace and security. This conceptualisation deepens our understanding of the right to housing, and the condition of domestic servitude as a violation of human rights. This article’s second aim is to demonstrate how the commitment to fulfilling the human right to housing underpins a better policy response to modern slavery in Australia. It opens a conversation on how a social rights-based response is one that would better serve victim/survivors, offering a more meaningful and targeted response to the harms they suffer.
Does the Duration of the Illness Affect the Severity of Negative Symptoms of Schizophrenia?

Background: The negative symptoms of schizophrenia are less known aspects of the illness although they often mark its course and outcome. Negative symptoms refer to loss of function, and they are associated with poor outcomes. It is considered that they are more prominent with the longer duration of illness. Objective: To determine the negative symptoms in the patients with schizophrenia with regard to the duration of illness. Methods: A cross-sectional study was conducted in 60 consecutive outpatients with schizophrenia. Two groups were formed regarding the duration of illness (⩽2 years, and >3 years). The negative symptoms were established with the Brief Negative Symptom Assessment – BNSA. Results: Average score of negative symptoms in the group with the shorter duration of illness was 8.37±2.94, and in the group with longer duration was 10.73±2.86. Independent Samples Test was significant p=0.003, t-2.367, and therefore the difference between scores on BNSA within groups was significant. Moderate size effect was found (p = 0.69). Conclusion: Negative symptoms of schizophrenia are more prominent with the longer duration of illness.
SIMULATION METHOD OF A PINNED PHOTODIODE WITH NO IMAGE LAG AND DARK CURRENT

In this paper, the simulation method of a pinned photodiode (PD) with no image lag and dark current is proposed. The pinned PD has a shallow p+ layer on top of the conventional PD to create the pinning effect. The simulated PD has a one fifth of image lag compared with the conventional one, with much less dark current as well. Silvaco TCAD simulators, mostly ATLAS device simulator, have been used for simulation.
Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input–output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit’s application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.
Agent-Based Medical Health Monitoring System

One of the leading healthcare concerns worldwide is the aging population. Aged patients require more significant healthcare resources because they are more likely to have chronic diseases that result in higher healthcare expenses. The design and implementation of e-health solutions, which offer patients mobile services to assist and enhance their treatment based on monitoring specific physiological data, is one of the key achievements in medical information technology. In the last few decades, there have been tremendous advancements in healthcare technology regarding mobility, size, speed, and communication. However, the critical drawback of today’s e-Health monitoring systems is that patients are confined to smart rooms and beds with monitoring gadgets. Such tracking is not widespread due to chronic patients’ mobility, privacy, and flexibility issues. Further, health monitoring devices that are fastened to a patient’s body do not give any analysis or advice. To improve the health monitoring process, a multi-agent-based system for health monitoring is provided in this study, which entails a group of intelligent agents that gather patient data, reason together, and propose actions to patients and medical professionals in a mobile context. A multi-agent-based framework presented in this study is evaluated through a case study. The results show that the proposed system provides an efficient health monitoring system for chronic, aged, and remote patients. Further, the proposed approach outperforms the existing mHealth system, allowing for timely health facilities for remote patients using 5G technology.
Emerging Artificial Intelligence–Empowered mHealth: Scoping Review

Background Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. Objective Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years. Methods Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI. ” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain. Results We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research. Conclusions The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.
The Anti-Inflammatory Properties of Licorice (Glycyrrhiza glabra)-Derived Compounds in Intestinal Disorders

Intestinal diseases, such as inflammatory bowel diseases (IBDs) and colorectal cancer (CRC), are a significant source of morbidity and mortality worldwide. Epidemiological data have shown that IBD patients are at an increased risk for the development of CRC. IBD-associated cancer develops against a background of chronic inflammation and oxidative stress, and their products contribute to cancer development and progression. Therefore, the discovery of novel drugs for the treatment of intestinal diseases is urgently needed. Licorice (Glycyrrhiza glabra) has been largely used for thousands of years in traditional Chinese medicine. Licorice and its derived compounds possess antiallergic, antibacterial, antiviral, anti-inflammatory, and antitumor effects. These pharmacological properties aid in the treatment of inflammatory diseases. In this review, we discuss the pharmacological potential of bioactive compounds derived from Licorice and addresses their anti-inflammatory and antioxidant properties. We also discuss how the mechanisms of action in these compounds can influence their effectiveness and lead to therapeutic effects on intestinal disorders.
Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.
Fault Tolerant Perception for Urban Autonomous Vehicles

Road driving environments are complex, unstructured and highly changeable. A safe driving is, thus, becoming quite challenging task, in particular from the view point of development and deployment of autonomous vehicles-based urban transport systems. And, in that context, the reliable perception appears as a one of the main enabling strategies in developing safe autonomous driving. Currently, many autonomous vehicles are being tested on public roads with the objective of demonstrating the capability of operating in real world situations. A big effort has been focused towards creating fault-free autonomous vehicles. Nevertheless, fault tolerant perception for autonomous vehicles still needs to be further developed in order to create autonomous vehicles capable of driving under real road traffic conditions since on-board vehicle sensors may fail due to bad calibration, erroneous readings, physical or electrical failures, etc. A multi-sensor based vehicle architecture is a logical response to this issue. While the multi- sensor concept often relates to the strategy of using a variety of sensor types, this research has been focused on to the case when all sensors are vision sensors, either identical or different from each other. This thesis proposes a Fault Tolerant Perception paradigm that deals with possible sensor faults by defining the Federated Data Fusion Architecture designed to detect a faulty sensor and reduce its impact on to the safe autonomous driving. The proposed architecture minimises the influence of faulty data allowing the system to enter in a tolerated error state, where a recovery action can be performed to avoid failures. The developed architecture was then adapted towards meeting requirements of the KITTI Vision Benchmark Suite. Experimental results demonstrated the feasibility of the developed fault tolerant perception paradigm to successfully detect early faulty data from a singular sensor and to minimise the influence of that faulty sensor in the fusion process.
Alleviation of Salt Stress via Habitat-Adapted Symbiosis

Halotolerant plant-growth-promoting rhizobacteria (PGPR) could not only promote plant growth, but also help in counteracting the detrimental effects of salt stress. In the present study, a total of 76 bacteria were isolated from the rhizosphere, non-rhizospheric soil and endophytes of the halophyte Salsola tetrandra, collected from natural saline soils in Algeria. Phylogenetic analysis based on the 16S rDNA sequence of Gram-negative bacteria (n = 51) identified, showed seventeen representative isolates grouped into four genera (Pseudomonas, Acinetobacter, Enterobacter, and Providencia). These bacterial isolates that exhibited different PGPR traits were selected and tested for their ability to tolerate different abiotic stress (NaCl, PEG8000, and pH). The majority of isolates were drought tolerant (60% of PEG8000) and had an optimal growth at high pH values (pH 9 and 11) and some strains tolerated 2 M of NaCl. Strains identified as Enterobacter xiangfangensis BE1, Providencia rettgeri BR5 and Pseudomonas stutzeri MLR6 showed high capacity of adaptation on their PGP traits. The salt-tolerant isolates were finally chosen to promote growth and enhance salt tolerance, separately or combined, of Arabidopsis thaliana (Col-0) exposed or not to 0.1 M NaCl, by following fresh and root weight, primary root elongation and lateral root number. The best bacterial effect was recorded for the MLR6 strain in increasing shoot fresh weight and for BE1 in terms of root fresh weight in the absence of salt stress. At stressed conditions, all growth parameters declined. However, inoculation of Arabidopsis thaliana with the three bacterial strains (MLR6, BE1 and BR5), single or in co-culture, conferred an increase in the shoot weight, primary root length and lateral root number. The use of these strains separately or combined as biofertilizers seems to be a powerful tool in the development of sustainable agriculture in saline soils.
Rational Design and Numerical Analysis of a Hybrid Floating cIDE Separator for Continuous Dielectrophoretic Separation of Microparticles at High Throughput

Dielectrophoresis (DEP) enables continuous and label-free separation of (bio)microparticles with high sensitivity and selectivity, whereas the low throughput issue greatly confines its clinical application. Herein, we report a novel design of the DEP separator embedded with cylindrical interdigitated electrodes that incorporate hybrid floating electrode layout for (bio)microparticle separation at favorable throughput. To better predict microparticle trajectory in the scaled-up DEP platform, a theoretical model based on coupling of electrostatic, fluid and temperature fields is established, in which the effects of Joule heating-induced electrothermal and buoyancy flows on particles are considered. Size-based fractionation of polystyrene microspheres and dielectric properties-based isolation of MDA-MB-231 from blood cells are numerically realized, respectively, by the proposed separator with sample throughputs up to 2.6 mL/min. Notably, the induced flows can promote DEP discrimination of heterogeneous cells. This work provides a reference on tailoring design of enlarged DEP platforms for highly efficient separation of (bio)samples at high throughput.