Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Explanation on Possibility of Hazard Detection Failure on Driver Intervention under Partial Driving Automation

The purpose of this study is to clarify how to explain the system limitations in terms of possibility of hazard detection failure under partial driving automation, which is the level 2 of driving automation defined by Society of Automotive Engineers (SAE). Different from level 3 of driving automation which is supposed to issue Request to Intervene (RTI) to the driver whenever system limitations, an RTI may not be issued by the level 2 system because it is possible that the level 2 system fails to detect a hazard or understand the situation accurately which system cannot deal with. Under level 2, a driver therefore needs to understand the situation accurately and intervene in driving operation when necessary. This study focuses on whether explanation on the traffic scenes where the driving automation system may fail to detect a hazard has an impact on driver intervention or not. An experiment is conducted with a driving simulator (DS) in which the host vehicle is equipped with a driving automation system at level 2. A between-participants design is employed in our experiment. We have 30 participants, half of them are explained the possible traffic scenes and the other half are not. The participants are expected to find out the hazard which the system cannot detect a hazard by itself and to intervene in driving operation. The results show that drivers who were explained the possible scenes can deal well when the automation fails. According to the results, it may be claimed that it is necessary for car makers/dealers to explain to drivers on possible scenes where the driving automation system may fail to detect a hazard.
Bite force and cranial bone strain in four species of lizards

ABSTRACT In vivo bone strain data provide direct evidence of strain patterns in the cranium during biting. Compared with those in mammals, in vivo bone strains in lizard skulls are poorly documented. This paper presents strain data from the skulls of Anolis equestris, Gekko gecko, Iguana iguana and Salvator merianae during transducer biting. Analysis of variance was used to investigate effects of bite force, bite point, diet, cranial morphology and cranial kinesis on strain magnitude. Within individuals, the most consistent determinants of variance in bone strain magnitude were gauge location and bite point, with the importance of bite force varying between individuals. Inter-site variance in strain magnitude – strain gradient – was present in all individuals and varied with bite point. Between individuals within species, variance in strain magnitude was driven primarily by variation in bite force, not gauge location or bite point, suggesting that inter-individual variation in patterns of strain magnitude is minimal. Between species, variation in strain magnitude was significantly impacted by bite force and species membership, as well as by interactions between gauge location, species and bite point. Independent of bite force, species differences in cranial strain magnitude may reflect selection for different cranial morphology in relation to feeding function, but what these performance criteria are is not clear. The relatively low strain magnitudes in Iguana and Uromastyx compared with those in other lizards may be related to their herbivorous diet. Cranial kinesis and the presence or absence of postorbital and supratemporal bars are not important determinants of inter-specific variation in strain magnitude. Summary: Cranial bone strain data from lizard crania reveal species-specific strain gradients that are not impacted by cranial kinesis or the presence/absence of postorbital and supratemporal bars, but by dietary effects.
Cooperative Lane Change Motion Planning of Connected and Automated Vehicles: A Stepwise Computational Framework

This paper focuses on the scheme of cooperative lane change motion planning of multiple connected and automated vehicles, so as to minimize the time for lane change while penalizing large steering angles subject to hard collision avoidance constraints. Nominally this scheme should be formulated in a centralized way with the constraints of all the vehicles considered simultaneously. In order to facilitate the numerical solving process of this centralized optimization problem, we propose a stepwise computation framework. Starting with a sub-problem with all of the collision avoidance constraints removed, a sequence of sub-problems are defined by adding back the removed collision avoidance constraints gradually until the original problem takes shape in the end. The optimum of one sub-problem is always used as the initial guess when solving the next sub-problem. This iterative process continues until the optimum of the original problem is obtained. In this way, the difficulties in the original centralized problem are divided into multiple parts, and every progress made to address the partial difficulties is “solidified” by the initial guess.
CPR Training in the United States: The Need for a New Gold Standard (and the Gold to Create It).

Every year over 350 000 Americans experience an out-of-hospital cardiac arrest; most will not survive. Provision of bystander cardiopulmonary resuscitation improves mortality after cardiac arrest. Yet, our current methods for public cardiopulmonary resuscitation training are burdensome and ineffective. Now is the time for funding agencies to prioritize the rigorous scientific study of established and novel cardiopulmonary resuscitation training methods to create a more efficient and effective program. In so doing, we can arm the public with this vital skill and empower them to save lives.
Automated feature selection of predictors in electronic medical records data

The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule‐based or trained with billing codes and gold standard labels curated via labor intensive medical chart review. These simplistic algorithms tend to have unpredictable portability across institutions and low accuracy for many disease phenotypes due to imprecise billing codes. Recently, more sophisticated machine learning algorithms have been developed to improve the robustness and accuracy of EHR phenotyping algorithms. These algorithms are typically trained via supervised learning, relating gold standard labels to a wide range of candidate features including billing codes, procedure codes, medication prescriptions and relevant clinical concepts extracted from narrative notes via Natural Language Processing (NLP). However, due to the time intensiveness of gold standard labeling, the size of the training set is often insufficient to build a generalizable algorithm with the large number of candidate features extracted from EHR. To reduce the number of candidate predictors and in turn improve model performance, we present an automated feature selection method based entirely on unlabeled observations. The proposed method generates a comprehensive surrogate for the underlying phenotype with an unsupervised clustering of disease status based on several highly predictive features such as diagnosis codes and mentions of the disease in text fields available in the entire set of EHR data. A sparse regression model is then built with the estimated outcomes and remaining covariates to identify those features most informative of the phenotype of interest. Relying on the results of Li and Duan (1989), we demonstrate that variable selection for the underlying phenotype model can be achieved by fitting the surrogate‐based model. We explore the performance of our methods in numerical simulations and present the results of a prediction model for Rheumatoid Arthritis (RA) built on a large EHR data mart from the Partners Health System consisting of billing codes and NLP terms. Empirical results suggest that our procedure reduces the number of gold‐standard labels necessary for phenotyping thereby harnessing the automated power of EHR data and improving efficiency.
Temporal Frequency Abnormalities in Early-Stage Diabetic Retinopathy Assessed by Electroretinography

Purpose To define the nature and extent of temporal frequency abnormalities in diabetics who have mild or no nonproliferative diabetic retinopathy (NPDR) by using the flicker electroretinogram (ERG). Methods Light-adapted flicker ERGs were recorded from 20 diabetics who have no clinically apparent retinopathy, 20 diabetics who have mild NPDR, and 20 nondiabetic, age-equivalent controls. ERGs were elicited by full-field sinusoidal flicker across the temporal frequency range of 6 to 100 Hz and were recorded using conventional techniques. The amplitude and phase of the fundamental and harmonic response components were derived by Fourier analysis and compared among the groups. Results Analysis of variance indicated that compared with the controls, both patient groups had significant amplitude reductions of the fundamental ERG component for temporal frequencies greater than 56 Hz (all P ≤ 0.03). Modeling the amplitude measurements indicated that both patient groups had significant reductions in the high-frequency response cutoff. Response phase, however, did not differ significantly among the groups at any frequency. The amplitude and phase of the high-frequency harmonics (32–96 Hz) of the patients' responses to a low-frequency stimulus (16 Hz) were normal over the temporal frequency range that the fundamental response was abnormal. Conclusions Taken together, the diabetics' fundamental amplitude attenuation for rapid flicker combined with their normal high-frequency harmonic responses generated by slow flicker suggest that the likely site of the abnormal temporal filtering occurs prior to the nonlinearity that generates the harmonic components of the ERG, implicating a photoreceptor origin.
An Efficient Hierarchical Convolutional Neural Network for Traffic Object Detection

In this paper, we propose a novel hierarchical convolutional neural network for traffic object detection, which is defined as Fusion and Multi-level Alignment CNN (namely FMLA-CNN). The method extends a popular two-stage detector by incorporating a remodified feature fusion module and a multi-level alignment (MLA) strategy such that it is capable of efficiently detecting multi-scale objects in autonomous driving scenario. The feature fusion strategy in proposal generation network improves detection accuracy by inserting high-level semantics to the whole pyramidal feature hierarchy. Subsequently the MLA strategy in the second detection stage can exactly reserve spatial locations from corresponding feature layers determined by hierarchical region-of-interest proposals. In the experiments on KITTI benchmark, our FMLA-CNN achieves an impressively better trade-off between accuracy and efficiency compared with other state-of-the-art methods.
Another “great mimicker”: FDG-PET/CT imaging findings of sarcoid-like reaction

Sarcoid-like reaction (SLR) is a cause of non-caseating granulomas in some of the cancer patients with otherwise no signs or symptoms of sarcoidosis. SLR has been described in a variety of solid organ malignancies, including breast and lung cancer. SLR may result in hypermetabolic activity in 18-fludeoxyglucose positron emission tomography (PET)/CT scan, resulting in false positive reporting for malignancy. The purpose of this case series is to expose residents/practising physicians who interpret PET/CT to a series of cases illustrating findings of SLR.
Associations between maternal obesity and severe maternal morbidity: Findings from the French EPIMOMS population‐based study

BACKGROUND Maternal obesity is increasing. There is growing evidence of its effect on severe maternal morbidity. We assessed prepregnancy obesity as an independent risk factor for severe maternal morbidity by timing and cause. METHODS We designed a case-control analysis within the EPIMOMS prospective population-based study conducted in six French regions in 2012-2013 (182 309 women who delivered at ≥22 weeks). Cases were all women who experienced severe maternal morbidity during pregnancy to 42 days postpartum as per a multicriteria definition derived by national expert consensus (n = 2540, severe maternal morbidity prevalence 1.4%). Controls were randomly selected from the same health centres (n = 3651). The association between obesity and severe maternal morbidity was assessed from fitting multivariable logistic regression models: overall, by timing (antepartum and intrapartum/ postpartum), and by cause. RESULTS Prepregnancy obesity was associated with overall severe maternal morbidity (adjusted odds ratio [OR] 1.34, 95% confidence interval [CI] 1.14, 1.59) and antepartum severe maternal morbidity (OR 2.07, 95% CI 1.61, 2.65), but not with intra/postpartum severe maternal morbidity (OR 1.15, 95% CI 0.96, 1.38). Among antepartum severe maternal morbidity, severe hypertensive disorders were most strongly associated with obesity (OR 2.50, 95% CI 1.85, 3.40) but the risk of antepartum severe maternal morbidity due to other causes was also increased among obese women (OR 1.64, 95% CI 1.13, 2.37). Obesity was not associated with severe postpartum haemorrhage (OR 1.12, 95% CI 0.92, 1.37). CONCLUSION Obesity is associated with an increased risk of antepartum, but not intra/ postpartum, severe maternal morbidity.
Ideal telestroke time targets: Telestroke-based treatment times in the United States stroke belt

Background Studying critical time interval requirements can enhance thrombolytic treatment for stroke patients in telestroke networks. We retrospectively examined 12 concurrent months of targeted time interval information in the South Central US telemedicine programme, Arkansas Stroke Assistance through Virtual Emergency Support (AR SAVES). Hypothesis: We hypothesised that consult data analysis would highlight areas for improvement to shorten overall door to Intra venous (IV) tissue plasminogen activator (tPA) administration time. Methods We analysed critical time targets for 238 consecutive telestroke neurology consults obtained over 12 months from AR SAVES spoke sites when tPA was administered. The following time intervals were analysed: emergency department (ED) door to Computed Tomography (D-CT); ED door to call centre (D-CC) for initiation of consult; ED door to neurology call (D-NC); neurology call to camera (NC-Cam); tele consult time (Con); ED door to tissue plasminogen activator (tPA)/needle (DTN). Results The median times of D-CT (13 min, inter quartile range (IQR) 6–22 min), D-CC (34 min, IQR 20–45 min), D-NC (40 min, IQR 21–71 min), NC-Cam (4 min, IQR 2–8 min), and Con (25 min, IQR 17–37 min) all contributed to a DTN median time of 71 min (IQR 50–104 min). A total of 238 patients received tPA with a 29.4% treatment rate and a DTN time of ≤60 min was achieved in 25.2% of patients. Conclusions Focusing on reducing D-CC and Con times may help to achieve the DTN time of < 60 min for the majority of patients. Having ideal time targets for telestroke patients akin to traditional patients will help identify and improve the overall goal of a DTN time < 60 min.
Resistin-Induced Endoplasmic Reticulum Stress Contributes to the Impairment of Insulin Signaling in Endothelium

Background: Endoplasmic reticulum (ER) stress plays an important role in the pathogenesis of obesity, insulin resistance and cardiovascular diseases (CVDs). Impairment of insulin vascular action may represent a mechanism linking insulin resistance and CVDs. The present study tested the hypothesis that adipocyte-derived resistin inhibits insulin-stimulated endothelial NO production through the induction of ER stress. Methods and Results: Human umbilical vein endothelial cells (HUVC) were incubated with tunicamycin (an inducer of ER stress, 1–20 μg/mL) or resistin (10–100 ng/mL) for 1 h. Either tunicamycin or resistin increased GRP78 (an ER stress marker) expression associated with the impairment of insulin-stimulated Akt/eNOS phosphorylation, which were prevented by TUDCA (an ER stress suppressor). Resistin increased reactive oxygen species (ROS) production, antioxidant treatment inhibited resistin-induced GRP78 expression and impairment of insulin Akt/eNOS signaling, suggesting that ROS may involve resistin-induced ER stress. Resistin also increased JNK phosphorylation, which was prevented by TUDCA. JNK inhibitor SP600125 relieved the resistin inhibitory effects on endothelial insulin Akt/eNOS signaling. In ex vivo experiments, the incubation of aortic rings with resistin impaired insulin- but not acetylcholine-induced vasodilation, which was restored by TUDCA. LNAME (a NOS inhibitor) abolished insulin-induced vasorelaxation in the control or the resistin-treated aortic rings. In addition, resistin increased the mRNA expressions of proinflammatory cytokines tumor nuclear factor (TNF)α and interleukin (IL)-1β, which were also prevented by TUDCA. Conclusion: Our results support the ideal that ER stress may play an important role for resistin impairment of vascular insulin signaling and insulin action. The mitigation of ER stress may represent a new strategy for prevention and treatment of CVDs in obesity and insulin resistant-related diseases.
Postmortem Toxicology of New Synthetic Opioids

One hundred fifteen Americans die every day from opioid overdose. These overdose fatalities have been augmented by the increased availability of potent synthetic opioids, such as fentanyl and its derivatives. The death rate of synthetic opioids, other than methadone, increased by 72.2% from 2014 to 2015, and doubled from 2015 to 2016, situating the USA in the midst of an opioid overdose epidemic. The analytical identification of these opioids in postmortem samples and the correct toxicological data interpretation is critical to identify and implement preventive strategies. This article reviews the current knowledge of postmortem toxicology of synthetic opioids and the chemical and pharmacological factors that may affect drug concentrations in the different postmortem matrices and therefore, their interpretation. These factors include key chemical properties, essential pharmacokinetics parameters (metabolism), postmortem redistribution and stability data in postmortem samples. Range and ratios of concentrations reported in traditional and non-traditional postmortem specimens, blood, urine, vitreous humor, liver and brain, are summarized in tables. The review is focused on fentanyl and derivatives (e.g., acetyl fentanyl, butyryl fentanyl, carfentanil, furanyl fentanyl, 4-methoxybutyrylfentanyl, 4-fluorobutyrylfentanyl, ocfentanil) and non-traditional opioid agonists (e.g., AH-7921, MT-45, U-47700). All of these data are critically compared to postmortem data, and chemical and pharmacological properties of natural opioids (morphine), semi-synthetic (oxycodone, hydrocodone, hydromorphone, and oxymorphone), and synthetic opioids (methadone and buprenorphine). The interpretation of drug intoxication in death investigation is based on the available published literature. This review serves to facilitate the evaluation of cases where synthetic opioids may be implicated in a fatality through the critical review of peer reviewed published case reports and research articles.
Neurologic Alterations Due to Respiratory Virus Infections

Central Nervous System (CNS) infections are one of the most critical problems in public health, as frequently patients exhibit neurologic sequelae. Usually, CNS pathologies are caused by known neurotropic viruses such as measles virus (MV), herpes virus and human immunodeficiency virus (HIV), among others. However, nowadays respiratory viruses have placed themselves as relevant agents responsible for CNS pathologies. Among these neuropathological viruses are the human respiratory syncytial virus (hRSV), the influenza virus (IV), the coronavirus (CoV) and the human metapneumovirus (hMPV). These viral agents are leading causes of acute respiratory infections every year affecting mainly children under 5 years old and also the elderly. Up to date, several reports have described the association between respiratory viral infections with neurological symptoms. The most frequent clinical manifestations described in these patients are febrile or afebrile seizures, status epilepticus, encephalopathies and encephalitis. All these viruses have been found in cerebrospinal fluid (CSF), which suggests that all these pathogens, once in the lungs, can spread throughout the body and eventually reach the CNS. The current knowledge about the mechanisms and routes used by these neuro-invasive viruses remains scarce. In this review article, we describe the most recent findings associated to neurologic complications, along with data about the possible invasion routes of these viruses in humans and their various effects on the CNS, as studied in animal models.
Impact of Replication Stress in Human Papillomavirus Pathogenesis

The inactivation of critical cell cycle checkpoints by the human papillomavirus (HPV) oncoprotein E7 results in replication stress (RS) that leads to genomic instability in premalignant lesions. Intriguingly, RS tolerance is achieved through several mechanisms, enabling HPV to exploit the cellular RS response for viral replication and to facilitate viral persistence in the presence of DNA damage. ABSTRACT The inactivation of critical cell cycle checkpoints by the human papillomavirus (HPV) oncoprotein E7 results in replication stress (RS) that leads to genomic instability in premalignant lesions. Intriguingly, RS tolerance is achieved through several mechanisms, enabling HPV to exploit the cellular RS response for viral replication and to facilitate viral persistence in the presence of DNA damage. As such, inhibitors of the RS response pathway may provide a novel approach to target HPV-associated lesions and cancers.
Clinical and metabolic implications of obesity in prostate cancer: is testosterone a missing link?

Abstract Objectives: To assess sex hormones in men with obesity and prostate cancer (PCa) and to study association between androgens and the pathogenesis biology of PCa in vitro. Subjects and methods: One hundred and eighty-one men older than 45 years selected from of a population attending to Urology departments screening for PCa, (78 participants without PCa and 103 patients with PCa). All participants were assessed for body mass index (BMI), age, Gleason score, and PSA. Endocrine profile was determined for LH, total testosterone (TT), 17β-estradiol (E2), prolactin and leptin. Biochemical profile (HbA1c, triacylglycerols and lipoproteins) was also determined. In vitro experiments were also performed, involving the study of 5α-dihydrotestosterone (DHT) and E2 in the presence of adipocyte-conditioned medium (aCM). Results: All variables were continuous and described a Gaussian distribution unless mentioned. To determine the relation of aggressiveness, variable were transformed into categories. Thus, PCa aggressiveness is associated with the increase of age and BMI (p < .0001) but with is decreased with TT and E2 (p < .05). Moreover, adipocyte-secreted molecules increase aggressiveness of PCa cells in vitro. Lastly, DTH but not E2 enables invasiveness in vitro. Conclusions: It was observed a coexistence of hormone axis profile alteration with sex hormones and BMI in PCa patients, in accordance with the new perspective of PCa pathogenesis.