Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining Physiological and Psychological Predictors of Time to Task Failure on a Virtual Reality Sørensen Test in Participants With and Without Recurrent Low Back Pain: Exploratory Study

Background Sørensen trunk extension endurance test performance predicts the development of low back pain and is a strong discriminator of those with and without low back pain. Performance may greatly depend on psychological factors, such as kinesiophobia, self-efficacy, and motivation. Virtual reality video games have been used in people with low back pain to encourage physical activity that would otherwise be avoided out of fear of pain or harm. Accordingly, we developed a virtual reality video game to assess the influence of immersive gaming on the Sørensen test performance. Objective The objective of our study was to determine the physiological and psychological predictors of time to task failure (TTF) on a virtual reality Sørensen test in participants with and without a history of recurrent low back pain. Methods We recruited 24 individuals with a history of recurrent low back pain and 24 sex-, age-, and body mass index–matched individuals without a history of low back pain. Participants completed a series of psychological measures, including the Center for Epidemiological Studies-Depression Scale, Pain Resilience Scale, Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia, and a self-efficacy measure. The maximal isometric strength of trunk and hip extensors and TTF on a virtual reality Sørensen test were measured. Electromyography of the erector spinae, gluteus maximus, and biceps femoris was recorded during the strength and endurance trials. Results A two-way analysis of variance revealed no significant difference in TTF between groups (P=.99), but there was a trend for longer TTF in females on the virtual reality Sørensen test (P=.06). Linear regression analyses were performed to determine predictors of TTF in each group. In healthy participants, the normalized median power frequency slope of erector spinae (beta=.450, P=.01), biceps femoris (beta=.400, P=.01), and trunk mass (beta=−.32, P=.02) predicted TTF. In participants with recurrent low back pain, trunk mass (beta=−.67, P<.001), Tampa Scale for Kinesiophobia (beta=−.43, P=.01), and self-efficacy (beta=.35, P=.03) predicted TTF. Conclusions Trunk mass appears to be a consistent predictor of performance. Kinesiophobia appears to negatively influence TTF for those with a history of recurrent low back pain, but does not influence healthy individuals. Self-efficacy is associated with better performance in individuals with a history of recurrent low back pain, whereas a less steep median power frequency slope of the trunk and hip extensors is associated with better performance in individuals without a history of low back pain.
Beyond Greedy Search: Pruned Exhaustive Search for Diversified Result Ranking

As a search query can correspond to multiple intents, search result diversification aims at returning a single result list that could satisfy as many users' information needs as possible. However, determining the optimal ranking list is NP-hard. Several algorithms have been proposed to obtain a local optimal ranking with greedy approximations. In this paper, we propose a pruned exhaustive method to generate better solutions than the greedy search. Our approach is based on the observations that there are fewer than ten subtopics for most queries, most relevant results cover only a few subtopics, and most search users only focus on the top results. The proposed pruned exhaustive search algorithm based on ordered pairs (PesOP) finds the optimal solution efficiently. Experimental results based on TREC Diversity and NTCIR Intent task datasets show that PesOP outperforms greedy strategies with better diversification performance. Compared with the original non-pruned exhaustive search, the PesOP algorithm decreases the computational cost while maintaining optimality.
Regulation of Ovarian Cancer Prognosis by Immune Cells in the Tumor Microenvironment

It is estimated that in the United States in 2018 there will be 22,240 new cases of ovarian cancer and 14,070 deaths due to this malignancy. The most common subgroup of this disease is high-grade serous ovarian cancer (HGSOC), which is known for its aggressiveness, high recurrence rate, metastasis to other sites, and the development of resistance to conventional therapy. It is important to understand the ovarian cancer tumor microenvironment (TME) from the viewpoint of the function of pre-existing immune cells, as immunocompetent cells are crucial to mounting robust antitumor responses to prevent visible tumor lesions, disease progression, or recurrence. Networks consisting of innate and adaptive immune cells, metabolic pathways, intracellular signaling molecules, and a vast array of soluble factors, shape the pathogenic nature of the TME and are useful prognostic indicators of responses to conventional therapy and immunotherapy, and subsequent survival rates. This review highlights key immune cells and soluble molecules in the TME of ovarian cancer, which are important in the development of effective antitumor immunity, as well as those that impair effector T cell activity. A more insightful knowledge of the HGSOC TME will reveal potential immune biomarkers to aid in the early detection of this disease, as well as biomarkers that may be targeted to advance the design of novel therapies that induce potent antitumor immunity and survival benefit.
Novel Synthetic Opioids: The Pathologist’s Point of View

Background: New Psychoactive Substances (NPS) constitute a broad range of hundreds of natural and synthetic drugs, including synthetic opioids, synthetic cannabinoids, synthetic cathinones, and other NPS classes, which were not controlled from 1961 to 1971 by the United Nations drug control conventions. Among these, synthetic opioids represent a major threat to public health. Methods: A literature search was carried out using public databases (such as PubMed, Google Scholar, and Scopus) to survey fentanyl-, fentanyl analogs-, and other synthetic opioid-related deaths. Keywords including “fentanyl”, “fentanyl analogs”, “death”, “overdose”, “intoxication”, “synthetic opioids”, “Novel Psychoactive Substances”, “MT-45”, “AH-7921”, and “U-47700” were used for the inquiry. Results: From our literature examination, we inferred the frequent implication of fentanyls and synthetic opioids in side effects, which primarily affected the central nervous system and the cardiovascular and pulmonary systems. The data showed a great variety of substances and lethal concentrations. Multidrug-related deaths appeared very common, in most reported cases. Conclusions: The investigation of the contribution of novel synthetic opioid intoxication to death should be based on a multidisciplinary approach aimed at framing each case and directing the investigation towards targeted toxicological analyses.
Cardiotoxicity of Uremic Toxins: A Driver of Cardiorenal Syndrome

Cardiovascular disease (CVD) is highly prevalent in the setting of chronic kidney disease (CKD). Such coexistence of CVD and CKD—the so-called “cardiorenal or renocardiac syndrome”—contributes to exponentially increased risk of cardiovascular (CV) mortality. Uremic cardiomyopathy is a characteristic cardiac pathology commonly found in CKD. CKD patients are also predisposed to heart rhythm disorders especially atrial fibrillation. Traditional CV risk factors as well as known CKD-associated CV risk factors such as anemia are insufficient to explain CV complications in the CKD population. Accumulation of uremic retention solutes is a hallmark of impaired renal excretory function. Many of them have been considered inert solutes until their biological toxicity is unraveled and they become accepted as “uremic toxins”. Direct cardiotoxicity of uremic toxins has been increasingly demonstrated in recent years. This review offers a mechanistic insight into the pathological cardiac remodeling and dysfunction contributed by uremic toxins with a main focus on fibroblastic growth factor-23, an emerging toxin playing a central role in the chronic kidney disease–mineral bone disorder, and the two most investigated non-dialyzable protein-bound uremic toxins, indoxyl sulfate and p-cresyl sulfate. Potential therapeutic strategies that could address these toxins and their relevant mediated pathways since pre-dialysis stages are also discussed.
Microglia Play an Active Role in Obesity-Associated Cognitive Decline

Obesity affects >600 million people worldwide, a staggering number that appears to be on the rise. One of the lesser known consequences of obesity is its deleterious effects on cognition, which have been well documented across many cognitive domains and age groups. To investigate the cellular mechanisms that underlie obesity-associated cognitive decline, we used diet-induced obesity in male mice and found memory impairments along with reductions in dendritic spines, sites of excitatory synapses, increases in the activation of microglia, the brain's resident immune cells, and increases in synaptic profiles within microglia, in the hippocampus, a brain region linked to cognition. We found that partial knockdown of the receptor for fractalkine, a chemokine that can serve as a “find me” cue for microglia, prevented microglial activation and cognitive decline induced by obesity. Furthermore, we found that pharmacological inhibition of microglial activation in obese mice was associated with prevention of both dendritic spine loss and cognitive degradation. Finally, we observed that pharmacological blockade of microglial phagocytosis lessened obesity-associated cognitive decline. These findings suggest that microglia play an active role in obesity-associated cognitive decline by phagocytosis of synapses that are important for optimal function. SIGNIFICANCE STATEMENT Obesity in humans correlates with reduced cognitive function. To investigate the cellular mechanisms underlying this, we used diet-induced obesity in mice and found impaired performance on cognitive tests of hippocampal function. These deficits were accompanied by reduced numbers of dendritic spines, increased microglial activation, and increased synaptic profiles within microglia. Inhibition of microglial activation by transgenic and pharmacological methods prevented cognitive decline and dendritic spine loss in obese mice. Moreover, pharmacological inhibition of the phagocytic activity of microglia was also sufficient to prevent cognitive degradation. This work suggests that microglia may be responsible for obesity-associated cognitive decline and dendritic spine loss.
Pushing the Limits of Encrypted Databases with Secure Hardware

Encrypted databases have been studied for more than 10 years and are quickly emerging as a critical technology for the cloud. The current state of the art is to use property-preserving encrypting techniques (e.g., deterministic encryption) to protect the confidentiality of the data and support query processing at the same time. Unfortunately, these techniques have many limitations. Recently, trusted computing platforms (e.g., Intel SGX) have emerged as an alternative to implement encrypted databases. This paper demonstrates some vulnerabilities and the limitations of this technology, but it also shows how to make best use of it in order to improve on confidentiality, functionality, and performance.
Nonpharmacological Management of Gout and Hyperuricemia: Hints for Better Lifestyle

We reviewed lifestyle factors that influence serum uric acid levels and risk of gout flare, and how to improve their deleterious effects. Since obesity increases uric acid and weight gain increases gout risk, weight reduction by daily exercise and limiting intake of excess calories is recommended. However, strenuous exercise, which causes adenine nucleotide degradation; starvation, which decreases uric acid excretion; and dehydration may raise the level of uric acid in serum and trigger gout. Increased intake of purine-rich foods, such as meat and seafood, raise the level of uric acid in serum and is associated with increased risk of gout, whereas dairy products, especially low-fat types, are associated with a lower risk of gout. Also, heavy alcohol drinking raises the uric acid level and increases the risk of gout through adenine nucleotide degradation and lactate production. Sweet fruits and soft drinks containing fructose should be moderated, since fructose may raise uric acid and increase gout risk through uric acid production and/or decreased excretion. On the other hand, the Mediterranean diet is recommended for gout patients, since it may also help prevent hyperuricemia. Furthermore, coffee and vitamin C supplementation could be considered as preventive measures, as those can lower serum uric acid levels as well as the risk of gout.
The Value of Privacy

We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. One primary goal of the data collector is to learn some desired information from the elicited data. Specifically, this information is modeled by an underlying state, and the private data of each individual represents his of her knowledge about the state. Departing from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Further, an individual takes full control of his or her own data privacy and reports only a privacy-preserving version of his or her data. In this article, the value of ε units of privacy is measured by the minimum payment among all nonnegative payment mechanisms, under which an individual’s best response at a Nash equilibrium is to report his or her data in an ε-locally differentially private manner. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy that are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use a lower payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we design. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given accuracy target for learning the underlying state and show that the total payment of the designed mechanism is at most one individual’s payment away from the minimum.
Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach

Spectrum sensing in a large-scale heterogeneous network is very challenging as it usually requires a large number of static secondary users (SUs) to obtain the global spectrum states. To tackle this problem, in this paper, we propose a new framework based on Bayesian machine learning. We exploit the mobility of multiple SUs to simultaneously collect spectrum sensing data and cooperatively derive the global spectrum states. We first develop a novel non-parametric Bayesian learning model, referred to as beta process (BP) sticky hidden Markov model (SHMM), to capture the spatial–temporal correlation in the collected spectrum data, where the SHMM models the latent statistical correlation within each mobile SU's time series data, while the BP realizes the cooperation among multiple SUs. Bayesian inference is then carried out to automatically infer the heterogeneous spectrum states. Based on the inference results, we also develop a new algorithm with a refinement mechanism to predict the spectrum availability, which enables a newly joining SU to immediately access the unoccupied frequency band without sensing. Simulation results show that the proposed framework can significantly improve spectrum sensing performance compared with the existing spectrum sensing techniques.
Nonadiabatic Excited-State Dynamics with Machine Learning

We show that machine learning (ML) can be used to accurately reproduce nonadiabatic excited-state dynamics with decoherence-corrected fewest switches surface hopping in a 1-D model system. We propose to use ML to significantly reduce the simulation time of realistic, high-dimensional systems with good reproduction of observables obtained from reference simulations. Our approach is based on creating approximate ML potentials for each adiabatic state using a small number of training points. We investigate the feasibility of this approach by using adiabatic spin-boson Hamiltonian models of various dimensions as reference methods.
Driving cognitions, rumination, and posttraumatic stress disorder in road traffic accidents survivors.

This study evaluated the relationships between driving cognitions (i.e., panic-related cognitions, accident-related cognitions, and social-related cognitions), rumination, and posttraumatic stress disorder (PTSD) symptoms in a sample of road traffic accidents (RTAs) survivors. We also investigated the indirect effect of driving cognitions on PTSD symptoms through rumination. The sample included 633 drivers (62% were men; Mage = 36.09; standard deviation [SD] = 11.42 years). The participants completed scales measuring driving cognitions, rumination, and PTSD symptoms, as well as providing their demographic information. The results showed that all three types of driving cognitions assessed in the present study and rumination were significantly positively associated with PTSD symptoms. Furthermore, rumination mediated the relation between social-related cognitions and PTSD symptoms. The implications for PTSD treatment and future research are discussed.
Optimization of Canine Placenta Decellularization: An Alternative Source of Biological Scaffolds for Regenerative Medicine

Due to the scarcity of tissues and organs for transplantation, the demand for bioengineered tissues is increasing with the advancement of technologies and new treatments in human and animal regenerative medicine. Thus, decellularized placental extracellular matrix (ECM) has emerged as a new tool for the production of biological scaffolds for subsequent recellularization and implantation for recovery of injured areas or even for replacement of organ and tissue fractions. To be classified as an ideal biological scaffold, the ECM must be acellular and preserve its proteins and physical features to be useful for cellular adhesion. In this context, we developed a process of decellularization of canine placentas with 35 and 40 days of gestation using dodecyl sulfate sodium under immersion and agitation in sterile conditions. Before use of this scaffold in recellularization processes, the decellularization efficiency needs to be confirmed by the absence of cellular content and an irrelevant amount of reminiscent DNA. Both vasculature architecture and ECM proteins, such as collagen types I, III, and IV, laminin, and fibronectin, were preserved with our method. In this way, we established a new biological scaffold model that could be used for recellularization in regenerative medicine of tissues.
Variations in pelvic dimensions: An anatomical and computed tomography study

The anterior pelvic plane (APP) is a useful anatomical reference with both clinical and research applications in orthopedic surgery and rehabilitation medicine. It is used as a marker for computer‐assisted total hip replacement and image‐guided assessment of the hip center in clinical gait analysis. Despite its common use, no published data exist on the variations in height and width in an adult population. The aim of this study was to determine the range of dimensions for the anterior pelvic plane found in the Scottish adult population. Thirty‐five human cadavers and 100 pelvic computed tomography (CT) scans were examined. Pelvic height and width were measured, and the ratios were determined. The mean width and height for combined cadaver and CT pelves were found to be 238.0 mm (SD 20.1, range 188.3–273.8) and 92.7 mm (SD 10.5, range 71.2–114.7), respectively. The mean width‐to‐height ratio for all pelves was 2.59 (SD 0.31, range 1.73–3.50). There were no statistically significant differences in means between males and females. The variations of APP dimensions within an adult population are presented. These will be of value in the validation of algorithms for computer navigation and hip joint center calculation in total hip arthroplasty and gait analysis. Furthermore, differences in dimensions between cadaveric and CT measurements have been shown which may have implications for further research and the validity of reference data dependent on data‐point acquisition. Clin. Anat. 31:981–987, 2018. © 2018 Wiley Periodicals, Inc.
Annoyance in Response to Vibrations from Railways

Rail transport is a key stepping stone in the EU’s transport policy and is pinpointed for investment and growth over the coming decades. This expanding infrastructure implies increased exposure to environmental stressors, such as noise and ground-borne vibrations. Little is known about the health impacts of exposure to these vibrations. The aim of this paper is to examine the association between annoyance from rail vibrations and the distance of residential dwelling from the railway. It reports the first results of a large epidemiological study, EpiVib, which was designed to investigate the long-term health effects of exposure to rail vibrations. The first part of this study examines a self-reported questionnaire. In total, 6894 individuals aged between 18 and 80 living within 1 km of a railway in west Sweden participated. Results presented here examine the association between distance to the railway and annoyance from vibrations and are stratified by train type. A positive association between closer distance and increased annoyance is seen. After adjustment for important modifiers, results showed that vibrations from freight trains and maintenance operations are reported to be moderately and highly annoying at distances of up to 400 m from the railway and diesel up to 300 m. Vibration from passenger and fast trains are significantly annoying up to 200 m from the track. Vibration from freight trains and maintenance operations were considered highly annoying up to 300 m from the track, diesel up to 400 m. Vibration from passenger and fast trains are not reported to be highly annoying after adjustment. Heavier, slower moving locomotives, in the form of diesel and freight trains, appear to be the source of annoyance at distances further from the railway compared to passenger and fast trains. This has implications in terms of property, transport, and infrastructure planning.
Neural Activity Predicts Reaction in Primates Long Before a Behavioral Response

How neural activity is linked to behavior is a critical question in neural engineering and cognitive neurosciences. It is crucial to predict behavior as early as possible, to plan a machine response in real-time brain computer interactions. However, previous studies have studied the neural readout of behavior only within a short time before the action is performed. This leaves unclear, if the neural activity long before a decision could predict the upcoming behavior. By recording extracellular neural activities from the visual cortex of behaving rhesus monkeys, we show that: (1) both, local field potentials (LFPs) and the rate of neural spikes long before (>2 s) a monkey responds to a change, foretell its behavioral performance in a spatially selective manner; (2) LFPs, the more accessible component of extracellular activity, are a stronger predictor of behavior; and (3) LFP amplitude is positively correlated while spiking activity is negatively correlated with behavioral reaction time (RT). These results suggest that field potentials could be used to predict behavior way before it is performed, an observation that could potentially be useful for brain computer interface applications, and that they contribute to the sensory neural circuit’s speed in information processing.