Miracle-dz/UniFAR-FTUs huggingface dataset:
Online symptom checker diagnostic and triage accuracy for HIV and hepatitis C

Abstract We sought to address the prior limitations of symptom checker accuracy by analysing the diagnostic and triage feasibility of online symptom checkers using a consecutive series of real-life emergency department (ED) patient encounters, and addressing a complex patient population – those with hepatitis C or HIV. We aimed to study the diagnostic and triage accuracy of these symptom checkers in relation to an emergency room physician-determined diagnosis. An ED retrospective analysis was performed on 8363 consecutive adult patients. Eligible patients included: 90 HIV, 67 hepatitis C, 11 both HIV and hepatitis C. Five online symptom checkers were utilised for diagnosis (Mayo Clinic, WebMD, Symptomate, Symcat, Isabel), three with triage capabilities. Symptom checker output was compared with ED physician-determined diagnosis data in regards to diagnostic accuracy and differential diagnosis listing, along with triage advice. All symptom checkers, whether for combined HIV and hepatitis C, HIV alone or hepatitis C alone had poor diagnostic accuracy in regards to Top1 (<20%), Top3 (<35%), Top10 (<40%), Listed at All (<45%). Significant variations existed for each individual symptom checker, as some appeared more accurate for listing the diagnosis in the top of the differential, vs. others more apt to list the diagnosis at all. In regards to ED triage data, a significantly higher percentage of hepatitis C patients (59.7%; 40/67) were found to have an initial diagnosis with emergent criteria than HIV patients (35.6%; 32/90). Symptom checker diagnostic capabilities are quite inferior to physician diagnostic capabilities. Complex patients such as those with HIV or hepatitis C may carry a more specific differential diagnosis, warranting symptom checkers to have diagnostic algorithms accounting for such complexity. Symptom checkers carry the potential for real-time epidemiologic monitoring of patient symptoms, as symptom entries and subsequent symptom checker diagnosis could allow health officials a means to track illnesses in specific patient populations and geographic regions. In order to do this, accurate and reliable symptom checkers are warranted.
Demarcating Geographic Regions using Community Detection in Commuting Networks

We find agglomerations of U.S. counties that are partitioned by commuting patterns by representing inter-county commuting patterns as a weighted network. To do so, we develop a community detection method based on the configuration model to identify significant clusters of nodes in a weighted network that prominently feature self-loops which represent same-county commuting. Application of this method to county level commuting data from 2010 yielded regions that are significantly different from existing delineations such as Metropolitian Statistical Areas and Megaregions. Our method identifies regions with varying sizes as well as highly overlapping regions. Some counties may singularly define a region, while others may be part of multiple regions. Our results offer an alternative way of categorizing economic regions from existing methods and suggest that geographical delineations should be rethought.
Drugging in the absence of p53

Abstract Inactivation of the p53 gene is a key driver of tumorigenesis in various cancer cohorts and types. The quest for a successful p53-based therapy that holds the promise of treating more than half of the cancer population has culminated in extensive knowledge about the role and function of p53 and led to new proposed innovative strategies against p53-defective cancers. We will discuss some of these latest studies with a focus on metabolic regulation and DNA damage response and also highlight novel functions of p53 in these pathways that may provide a contemporary rationale for targeting p53 loss in tumors.
3D Localization for Sub-Centimeter Sized Devices

Recent years have seen significant advances in wireless localization [1,2]. However, existing solutions do not meet the requirements for size-constrained IoT applications. The battery life of common radio technologies, such as BLE, LoRa, ultra-wideband (UWB) and Wi-Fi, is less than 5 months when run with small coin and button cell batteries. This shorter battery life limits the adoption of tracking solutions based on these radio technologies by making them inconvenient for consumer applications and infeasible for large scale commercial deployments. Requiring large batteries, on the other hand, prevents scaling down the size of IoT devices. While RFID tags are attractive from a power and size perspective, they have a limited range and do not work consistently through walls and other barriers. Consumers often deploy devices in rooms throughout homes and, similarly, commercial deployments in settings like hospitals require covering multiple patient rooms with a variety of obstructions and walls. Achieving localization in these scenarios would therefore require readers in every room, which significantly increases deployment cost.
Discovery reliability

Whenever an experiment yields a statistically significant outcome you should ask yourself: To what extent can I trust this result? This is especially important for pre-clinical drug studies because of the frequent failures of phase III clinical trials of neurological diseases, which has put the reliability of pre-clinical research into question. Two important factors, the pre-study likelihood of treatment benefit, and statistical power, affects the reliability of the result in a quantifiable way. This can be used to assess to what extent the result of a study can be trusted (discovery reliability), and to guide the design of pre-clinical research.
Predicting Human Mobility via Attentive Convolutional Network

Predicting human mobility is an important trajectory mining task for various applications, ranging from smart city planning to personalized recommendation system. While most of previous works adopt GPS tracking data to model human mobility, the recent fast-growing geo-tagged social media (GTSM) data brings new opportunities to this task. However, predicting human mobility on GTSM data is not trivial because of three challenges: 1) extreme data sparsity; 2) high order sequential patterns of human mobility and 3) evolving preference of users for tagging. In this paper, we propose ACN, an attentive convolutional network model for predicting human mobility from sparse and complex GTSM data. In ACN, we firstly design a multi-dimension embedding layer which jointly embeds key features (i.e., spatial, temporal and user features) that govern human mobility. Then, we regard the embedded trajectory as an "image" and learn short-term sequential patterns as local features of the image using convolution filters. Instead of directly using convention filters, we design hybrid dilated and separable convolution filters to effectively capture high order sequential patterns from lengthy trajectory. In addition, we propose an attention mechanism which learns the user long-term preference to augment convolutional network for mobility prediction. We conduct extensive experiments on three publicly available GTSM datasets to evaluate the effectiveness of our model. The results demonstrate that ACN consistently outperforms existing state-of-art mobility prediction approaches on a variety of common evaluation metrics.
Conservation practitioners’ understanding of how to manage evolutionary processes

Both academics and practitioners consider a lack of knowledge about evolutionary theory to be a general barrier to effectively managing genetic diversity. However, it is challenging to judge practitioners’ level of understanding and how this influences their management decisions. Knowledge built through experience may be difficult for practitioners to articulate, but could nonetheless result in appropriate management strategies. To date, researchers have assessed only the explicit (formal) knowledge practitioners have of evolutionary concepts. To explore practitioners’ understanding of evolutionary concepts, it is necessary to consider how they might apply explicit and implicit knowledge to their management decisions. Using an online survey, we asked Australian practitioners to respond to 2 common management scenarios in which there is strong evidence that managing genetic diversity can improve outcomes: managing small, isolated populations and sourcing seeds for restoration projects. In describing their approach to these scenarios, practitioners demonstrated a stronger understanding of the effective management of genetic diversity than the definitions of the relevant concepts. However, their management of genetic diversity within small populations was closer to best practice than for restoration projects. Moreover, the risks practitioners described in implementing best practice management were more likely to affect their approach to restoration than translocation projects. These findings provide evidence that strategies to build the capacity of practitioners to manage genetic diversity should focus on realistic management scenarios. Given that practitioners recognize the importance of adapting their practices and the strong evidence for the benefits of actively managing genetic diversity, there is hope that better engagement by evolutionary biologists with practitioners could facilitate significant shifts toward evolutionarily enlightened management.
Kinetics of Early Innate Immune Activation during HIV-1 Infection of Humanized Mice

Human immunodeficiency virus type 1 (HIV-1) infection is restricted to humans and some nonhuman primates (e.g., chimpanzee and gorilla). Alternative model systems based on simian immunodeficiency virus (SIV) infection of macaques are available but do not recapitulate all aspects of HIV-1 infection and disease. Humanized mice, which contain a human immune system, can be used to study HIV-1, but only limited information on early events and immune responses is available to date. Here, we describe very early immune responses to HIV-1 and demonstrate a suppression of cell-intrinsic innate immunity. Furthermore, we show that HIV-1 infection interacts differently with innate immune responses in blood and lymphoid organs. ABSTRACT Human immunodeficiency virus type 1 (HIV-1) infection is associated with aberrant immune activation; however, most model systems for HIV-1 have been used during established infection. Here, we utilize ultrasensitive HIV-1 quantification to delineate early events during the eclipse, burst, and chronic phases of HIV-1 infection in humanized mice. We show that very early in infection, HIV-1 suppresses peripheral type I interferon (IFN) and interferon-stimulated gene (ISG) responses, including the HIV-1 restriction factor IFI44. At the peak of innate immune activation, prior to CD4 T cell loss, HIV-1 infection differentially affects peripheral and lymphoid Toll-like receptor (TLR) expression profiles in T cells and macrophages. This results in a trend toward an altered activation of nuclear factor κB (NF-κB), TANK-binding kinase 1 (TBK1), and interferon regulatory factor 3 (IRF3). The subsequent type I and III IFN responses result in preferential induction of peripheral ISG responses. Following this initial innate immune activation, peripheral expression of the HIV-1 restriction factor SAM domain- and HD domain-containing protein 1 (SAMHD1) returns to levels below those observed in uninfected mice, suggesting that HIV-1 interferes with their basal expression. However, peripheral cells still retain their responsiveness to exogenous type I IFN, whereas splenic cells show a reduction in select ISGs in response to IFN. This demonstrates the highly dynamic nature of very early HIV-1 infection and suggests that blocks to the induction of HIV-1 restriction factors contribute to the establishment of viral persistence. IMPORTANCE Human immunodeficiency virus type 1 (HIV-1) infection is restricted to humans and some nonhuman primates (e.g., chimpanzee and gorilla). Alternative model systems based on simian immunodeficiency virus (SIV) infection of macaques are available but do not recapitulate all aspects of HIV-1 infection and disease. Humanized mice, which contain a human immune system, can be used to study HIV-1, but only limited information on early events and immune responses is available to date. Here, we describe very early immune responses to HIV-1 and demonstrate a suppression of cell-intrinsic innate immunity. Furthermore, we show that HIV-1 infection interacts differently with innate immune responses in blood and lymphoid organs.
Overlapping Community Detection in Static and Dynamic Networks

Studying behavior of systems through networks is important because it allows to understand them and make decisions based on this knowledge. Community detection is one of the tools used in this sense, for detection of groups in graphs. This can be done not only considering connections between nodes, but also including their attributes. Also, objects can be part of different groups with varying degrees, so overlapping fuzzy assignment is relevant in this context. Furthermore, most networks change overtime, so including this aspect also enhance the benefits of using community detection. Hence, in this doctoral thesis we propose to design models for overlapping community detection for static and dynamic networks with node attributes. Firstly, an approach based on a nonnegative matrix factorization generative model that automatically detects the number of communities in the network, is designed. Secondly, tensor factorization is used in order to overcome some of the challenges faced in the first model.
Asymptotic and Finite-Time Cluster Synchronization of Coupled Fractional-Order Neural Networks With Time Delay

This article is devoted to the cluster synchronization issue of coupled fractional-order neural networks. By introducing the stability theory of fractional-order differential systems and the framework of Filippov regularization, some sufficient conditions are derived for ascertaining the asymptotic and finite-time cluster synchronization of coupled fractional-order neural networks, respectively. In addition, the upper bound of the settling time for finite-time cluster synchronization is estimated. Compared with the existing works, the results herein are applicable for fractional-order systems, which could be regarded as an extension of integer-order ones. A numerical example with different cases is presented to illustrate the validity of theoretical results.
Heatwaves cause fluctuations in wMel Wolbachia densities and frequencies in Aedes aegypti

Aedes aegypti mosquitoes infected with the wMel strain of Wolbachia are being released into natural mosquito populations in the tropics as a way of reducing dengue transmission. High temperatures adversely affect wMel, reducing Wolbachia density and cytoplasmic incompatibility in some larval habitats that experience large temperature fluctuations. We monitored the impact of a 43.6°C heatwave on the wMel infection in a natural population in Cairns, Australia, where wMel was first released in 2011 and has persisted at a high frequency. Wolbachia infection frequencies in the month following the heatwave were reduced to 83% in larvae sampled directly from field habitats and 88% in eggs collected from ovitraps, but recovered to be near 100% four months later. Effects of the heatwave on wMel appeared to be stage-specific and delayed, with reduced frequencies and densities in field-collected larvae and adults reared from ovitraps but higher frequencies in field-collected adults. Laboratory experiments showed that the effects of heatwaves on cytoplasmic incompatibility and density are life stage-specific, with first instar larvae being the most vulnerable to temperature effects. Our results indicate that heatwaves in wMel-infected populations will have only temporary effects on Wolbachia frequencies and density once the infection has established in the population. Our results are relevant to ongoing releases of wMel-infected Ae. aegypti in several tropical countries.
Salmonella Contamination Associated with Bacterial Soft Rot of Fresh Fruits and Vegetables in the Marketplace.

Wash water from 66% of 401 samples of fresh fruits and vegetables collected in the marketplace and affected by bacterial soft rot were positive for suspected strains of Salmonella, i.e., black, hydrogen sulfide-positive colonies on Salmonella-Shigella agar incubated for 24 h at 37°C. By comparison, 30% of 402 healthy samples were positive. Incidence of suspected Salmonella in broth enrichment cultures was 59% in 533 soft rotted samples and 33% in 781 healthy samples. Thirty percent of 166 representative strains of suspected Salmonella, selected at random from 20 different commodities, were confirmed to be Salmonella by physiological and serological tests. Adjusting incidence values accordingly, Salmonella contamination was potentially present in at least 18 to 20% of soft rotted samples and in 9 to 10% of healthy samples. Wash water from 120 paired healthy and soft rotted fruits and vegetables contained an average of 1.0 × 105 and 3.7 × 106 CFU/ml, respectively, of suspected Salmonella-a ratio of 1:37. Average concentrations of suspected Salmonella in enrichment cultures of healthy and soft rotted samples were 7.5 × 107 and 2.7 × 109 CFU/ml, respectively, also in the ratio of 1:37. Fresh potato, carrot, and pepper disks coinoculated with the soft rot bacterium Erwinia carotovora and with Salmonella typhimurium, and incubated for up to 72 h at room temperature, contained approximately 10 times the concentration of S. typhimurium as did disks inoculated with Salmonella alone. Disks coinoculated with Pseudomonas viridiflava and S. typhimurium contained approximately three times the Salmonella populations as disks inoculated with Salmonella alone.
The Mammalian Circadian Timing System and the Suprachiasmatic Nucleus as Its Pacemaker

The past twenty years have witnessed the most remarkable breakthroughs in our understanding of the molecular and cellular mechanisms that underpin circadian (approximately one day) time-keeping. Across model organisms in diverse taxa: cyanobacteria (Synechococcus), fungi (Neurospora), higher plants (Arabidopsis), insects (Drosophila) and mammals (mouse and humans), a common mechanistic motif of delayed negative feedback has emerged as the Deus ex machina for the cellular definition of ca. 24 h cycles. This review will consider, briefly, comparative circadian clock biology and will then focus on the mammalian circadian system, considering its molecular genetic basis, the properties of the suprachiasmatic nucleus (SCN) as the principal circadian clock in mammals and its role in synchronising a distributed peripheral circadian clock network. Finally, it will consider new directions in analysing the cell-autonomous and circuit-level SCN clockwork and will highlight the surprising discovery of a central role for SCN astrocytes as well as SCN neurons in controlling circadian behaviour.
Are food exposures obtained through commercial market panels representative of the general population? Implications for outbreak investigations

Abstract Current methods of control recruitment for case-control studies can be slow (a particular issue for outbreak investigations), resource-intensive and subject to a range of biases. Commercial market panels are a potential source of rapidly recruited controls. Our study evaluated food exposure data from these panel controls, compared with an established reference dataset. Market panel data were collected from two companies using retrospective internet-based surveys; these were compared with reference data from the National Diet and Nutrition Survey (NDNS). We used logistic regression to calculate adjusted odds ratios to compare exposure to each of the 71 food items between the market panel and NDNS participants. We compared 2103 panel controls with 2696 reference participants. Adjusted for socio-demographic factors, exposure to 90% of foods was statistically different between both panels and the reference data. However, these differences were likely to be of limited practical importance for 89% of Panel A foods and 79% of Panel B foods. Market panel food exposures were comparable with reference data for common food exposures but more likely to be different for uncommon exposures. This approach should be considered for outbreak investigation, in conjunction with other considerations such as population at risk, timeliness of response and study resources.
An update on the utility and safety of cholinesterase inhibitors for the treatment of Alzheimer’s disease

ABSTRACT Introduction: Alzheimer’s disease (AD) is the most common cause of major neurocognitive disorders with a prevalence in the US of about 5.7 million in 2018. With the disease burden projected to increase dramatically in the coming years, it is imperative to review the current available treatment regimens for their safety and utility. The cholinesterase inhibitors (ChEIs) have continued to play a pivotal role in managing the symptoms and possibly slowing the rate of progression of AD since 1993. Owing to their being a mainstay in the treatment of AD, the safety and efficacy of prescribing these drugs needs to be reviewed often, especially with the approval of new formulations and doses. Areas covered: The three ChEIs currently approved by the FDA are donepezil, rivastigmine and galantamine. This article will review the safety and tolerability of these ChEIs and analyze the potential disease modifying properties of these drugs. The authors have reviewed all recent literature including review articles, meta-analyzes, clinical trials and more. Expert opinion: These ChEIs differ subtly in their mechanisms of action, in their tolerability and safety and FDA-approved indications. All are considered first-line, symptomatic treatments of the various phases of AD and may even have potentially disease-modifying effects.
The wavelet power spectrum of perfusion weighted MRI correlates with tumor vascularity in biopsy-proven glioblastoma samples

Background Wavelet transformed reconstructions of dynamic susceptibility contrast (DSC) MR perfusion (wavelet-MRP) are a new and elegant way of visualizing vascularization. Wavelet-MRP maps yield a clear depiction of hypervascular tumor regions, as recently shown. Objective The aim of this study was to elucidate a possible connection of the wavelet-MRP power spectrum in glioblastoma (GBM) with local vascularity and cell proliferation. Methods For this IRB-approved study 12 patients (63.0+/-14.9y; 7m) with histologically confirmed IDH-wildtype GBM were included. Target regions for biopsies were prospectively marked on tumor regions as seen on preoperative 3T MRI. During subsequent neurosurgical tumor resection 43 targeted biopsies were taken from these target regions, of which all 27 matching samples were analyzed. All specimens were immunohistochemically analyzed for endothelial cell marker CD31 and proliferation marker Ki67 and correlated to the wavelet-MRP power spectrum as derived from DSC perfusion weighted imaging. Results There was a strong correlation between wavelet-MRP power spectrum (median = 4.41) and conventional relative cerebral blood volume (median = 5.97 ml/100g) in Spearman's rank-order correlation (κ = .83, p < .05). In a logistic regression model, the wavelet-MRP power spectrum showed a significant correlation to CD31 dichotomized to no or present staining (p = .04), while rCBV did not show a significant correlation to CD31 (p = .30). No significant association between Ki67 and rCBV or wavelet-MRP was found (p = .62 and p = .70, respectively). Conclusion The wavelet-MRP power spectrum derived from existing DSC-MRI data might be a promising new surrogate for tumor vascularity in GBM.