Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing the emergence of pathogen resistance via spatially targeted antimicrobial use

From agriculture to public health to civil engineering, managing antimicrobial resistance presents a considerable challenge. The dynamics underlying resistance evolution reflect inherently spatial processes. Resistant pathogen strains increase in frequency when a strain that emerges in one locale can spread and replace pathogen subpopulations formerly sensitive to the antimicrobial agent. Moreover, the strength of selection for antimicrobial resistance is in part governed by the extent of antimicrobial use. Thus, altering how antimicrobials are used across a landscape can potentially shift the spatial context governing the dynamics of antimicrobial resistance and provide a potent management tool. Here, we model how the efficacy of adjusting antimicrobial use over space to manage antimicrobial resistance is mediated by competition among pathogen strains and the topology of pathogen metapopulations. For several pathogen migration scenarios, we derive critical thresholds for the spatial extent of antimicrobial use below which resistance cannot emerge, and relate these thresholds to (a) the ability to eradicate antimicrobial‐sensitive pathogens locally and (b) the strength of the trade‐off between resistance ability and competitive performance where antimicrobial use is absent. We find that in metapopulations where patches differ in connectedness, constraining antimicrobial use across space to mitigate resistance evolution only works if the migration of the resistant pathogen is modest; yet, this situation is reversed if the resistant strain has a high colonization rate, with variably connected metapopulations exhibiting less sensitivity to reducing antimicrobial use across space. Furthermore, when pathogens are alternately exposed to sites with and without the antimicrobial, bottlenecking resistant strains through sites without an antimicrobial is only likely to be effective under a strong competition–resistance trade‐off. We therefore identify life‐history constraints that are likely to suggest which pathogens can most effectively be controlled by a spatially targeted antimicrobial regime. We discuss implications of our results for managing and thinking about antimicrobial resistance evolution in spatially heterogeneous contexts.
Improvement in hypercapnia does not predict survival in COPD patients on chronic noninvasive ventilation

Purpose It has recently been shown that chronic noninvasive ventilation (NIV) improves a number of outcomes including survival, in patients with stable hypercapnic COPD. However, the mechanisms responsible for these improved outcomes are still unknown. The aim of the present study was to identify parameters associated with: 1) an improved arterial partial pressure of carbon dioxide (PaCO2) and 2) survival, in a cohort of hypercapnic COPD patients treated with chronic NIV. Patients and methods Data from 240 COPD patients treated with chronic NIV were analyzed. Predictors for the change in PaCO2 and survival were investigated using multivariate linear and Cox regression models, respectively. Results A higher level of bicarbonate before NIV initiation, the use of higher inspiratory ventilator pressures, the presence of anxiety symptoms, and NIV initiated following an exacerbation compared to NIV initiated in stable disease were associated with a larger reduction in PaCO2. A higher body mass index, a higher FEV1, a lower bicarbonate before NIV initiation, and younger age and NIV initiation in stable condition were independently associated with better survival. The change in PaCO2 was not associated with survival, neither in a subgroup of patients with a PaCO2 >7.0 kPa before the initiation of NIV. Conclusion Patients with anxiety symptoms and a high bicarbonate level at NIV initiation are potentially good responders in terms of an improvement in hypercapnia. Also, higher inspiratory ventilator pressures are associated with a larger reduction in PaCO2. However, the improvement in hypercapnia does not seem to be associated with an improved survival and emphasizes the need to look beyond PaCO2 when considering NIV initiation.
Bidirectional Association between Nonalcoholic Fatty Liver Disease and Gallstone Disease: A Cohort Study

Nonalcoholic fatty liver disease (NAFLD) and gallstone disease (GD) are often found to coexist but the sequential relationship of NAFLD and GD to each other remains controversial. We prospectively evaluated the bidirectional relationship of NAFLD with GD. A cohort study was performed on Korean adults who underwent a health checkup and were followed annually or biennially for a mean of 6.0 years. Fatty liver and gallstones were diagnosed by ultrasound. NAFLD was defined as hepatic steatosis on ultrasonography in the absence of excessive alcohol use or other identifiable causes. The NAFLD severity was determined by non-invasive fibrosis markers. Among 283,446 participants without either gallstones or cholecystectomy at baseline, 6440 participants developed gallstones. Among 219,641 participants without NAFLD at baseline, 49,301 participants developed NAFLD. The multivariable-adjusted hazard ratio (95% confidence interval) for incident gallstone comparing the NAFLD group vs. the non-NAFLD group was 1.26 (1.17–1.35). Increased non-invasive fibrosis markers of NAFLD were positively associated with an increased incidence of gallstones in a graded and dose-responsive manner (p-trend < 0.01). The multivariable-adjusted hazard ratios (95% confidence intervals) for incident NAFLD comparing gallstone and cholecystectomy to no GD were 1.14 (1.07–1.22) and 1.17 (1.03–1.33), respectively. This large-scale cohort study of young and middle-aged individuals demonstrated a bidirectional association between NAFLD and GD. NAFLD and its severity were independently associated with an increased incidence of gallstones, while GD and cholecystectomy were also associated with incident NAFLD. Our findings indicate that the conditions may affect each other, requiring further studies to elucidate the potential mechanisms underlying this association.
Compliance with international guidelines for chronic inflammatory neuropathies

ERare diseases' management guidelines are produced with the primary aim of improving practice and standards of care for patients and may represent a useful framework for clinical practice. The EFNS/PNS (European Federation of Neurological Societies/Peripheral Nerve Society) guidelines for CIDP (chronic inflammatory demyelinating polyneuropathy) and MMN (multifocal motor neuropathy) were last published in 2010 (1, 2). Enthusiasm of the audience for whom they are produced, arguably primarily non-sub-specialists, is however largely unexplored. Compliance to these guidelines by neuromuscular and/or peripheral nerve specialists has not been investigated. This article is protected by copyright. All rights reserved.
Dengue seroprevalence in a cohort of schoolchildren and their siblings in Yucatan, Mexico (2015-2016)

Background The implementation of vector control interventions and potential introduction new tools requires baseline data to evaluate their direct and indirect effects. The objective of the study is to present the seroprevalence of dengue infection in a cohort of children 0 to 15 years old followed during 2015 to 2016, the risk factors and the role of enhanced surveillance strategies in three urban sites (Merida, Ticul and Progreso) in Yucatan, Mexico. Methods A cohort of school children and their family members was randomly selected in three urban areas with different demographic, social conditions and levels of transmission. We included results from 1,844 children aged 0 to 15 years. Serum samples were tested for IgG, NS1 and IgM. Enhanced surveillance strategies were established in schools (absenteeism) and cohort families (toll-free number). Results Seroprevalence in children 0 to 15 years old was 46.8 (CI 95% 44.1–49.6) with no difference by sex except in Ticul. Prevalence increased with age and was significantly lower in 0 to 5 years old (26.9%, 95% CI:18.4–35.4) compared with 6 to 8 years old (43.9%, 95% CI:40.1–47.7) and 9 to 15 years old (61.4%, 95% CI:58.0–64.8). Sharing the domestic space with other families increased the risk 1.7 times over the individual families that own or rented their house, while risk was significantly higher when kitchen and bathroom were outside. Complete protection with screens in doors and windows decreased risk of infection. Seroprevalence was significantly higher in the medium and high risk areas. Conclusions The prevalence of antibodies in children 0 to 15 years in three urban settings in the state of Yucatan describe the high exposure and the heterogenous transmission of dengue virus by risk areas and between schools in the study sites. The enhanced surveillance strategy was useful to improve detection of dengue cases with the coincident transmission of chikungunya and Zika viruses.
Inhibition of Lithium Sensitive Orai1/ STIM1 Expression and Store Operated Ca2+ Entry in Chorea-Acanthocytosis Neurons by NF-κB Inhibitor Wogonin

Background/Aims: The neurodegenerative disease Chorea-Acanthocytosis (ChAc) is caused by loss-of-function-mutations of the chorein-encoding gene VPS13A. In ChAc neurons transcript levels and protein abundance of Ca2+ release activated channel moiety (CRAC) Orai1 as well as its regulator STIM1/2 are decreased, resulting in blunted store operated Ca2+-entry (SOCE) and enhanced suicidal cell death. SOCE is up-regulated and cell death decreased by lithium. The effects of lithium are paralleled by upregulation of serum & glucocorticoid inducible kinase SGK1 and abrogated by pharmacological SGK1 inhibition. In other cell types SGK1 has been shown to be partially effective by upregulation of NFκB, a transcription factor stimulating the expression of Orai1 and STIM. The present study explored whether pharmacological inhibition of NFκB interferes with Orai1/STIM1/2 expression and SOCE and their upregulation by lithium in ChAc neurons. Methods: Cortical neurons were differentiated from induced pluripotent stem cells generated from fibroblasts of ChAc patients and healthy volunteers. Orai1 and STIM1 transcript levels and protein abundance were estimated from qRT-PCR and Western blotting, respectively, cytosolic Ca2+-activity ([Ca2+]i) from Fura-2-fluorescence, SOCE from increase of [Ca2+]i following Ca2+ re-addition after Ca2+-store depletion with sarco-endoplasmatic Ca2+-ATPase inhibitor thapsigargin (1µM), as well as CRAC current utilizing whole cell patch clamp recording. Results: Orai1 and STIM1 transcript levels and protein abundance as well as SOCE and CRAC current were significantly enhanced by lithium treatment (2 mM, 24 hours). These effects were reversed by NFκB inhibitor wogonin (50 µM). Conclusion: The stimulation of expression and function of Orai1/STIM1/2 by lithium in ChAc neurons are disrupted by pharmacological NFκB inhibition.
Actin Bundles in The Pollen Tube

The angiosperm pollen tube delivers two sperm cells into the embryo sac through a unique growth strategy, named tip growth, to accomplish fertilization. A great deal of experiments have demonstrated that actin bundles play a pivotal role in pollen tube tip growth. There are two distinct actin bundle populations in pollen tubes: the long, rather thick actin bundles in the shank and the short, highly dynamic bundles near the apex. With the development of imaging techniques over the last decade, great breakthroughs have been made in understanding the function of actin bundles in pollen tubes, especially short subapical actin bundles. Here, we tried to draw an overall picture of the architecture, functions and underlying regulation mechanism of actin bundles in plant pollen tubes.
Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals

Background The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results. Materials and methods To construct an ADR reference dataset, we extracted known drug–laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug–laboratory event pairs, except known ones, are considered unknown. To detect a known drug–laboratory event pair, three existing algorithms—CERT, CLEAR, and PACE—were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug–laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC). Results All measures of ML models outperformed those of existing algorithms with sensitivity of 0.593–0.793, specificity of 0.619–0.796, NPV of 0.645–0.727, PPV of 0.680–0.777, F1-measure of 0.629–0.709, and AUROC of 0.737–0.816. Features related to change or distribution of shape were considered important for detecting ADR signals. Conclusions Improved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.
Metrics of progress in the understanding and management of threats to Australian birds

Although evidence‐based approaches have become commonplace for determining the success of conservation measures for the management of threatened taxa, there are no standard metrics for assessing progress in research or management. We developed 5 metrics to meet this need for threatened taxa and to quantify the need for further action and effective alleviation of threats. These metrics (research need, research achievement, management need, management achievement, and percent threat reduction) can be aggregated to examine trends for an individual taxon or for threats across multiple taxa. We tested the utility of these metrics by applying them to Australian threatened birds, which appears to be the first time that progress in research and management of threats has been assessed for all threatened taxa in a faunal group at a continental scale. Some research has been conducted on nearly three‐quarters of known threats to taxa, and there is a clear understanding of how to alleviate nearly half of the threats with the highest impact. Some management has been attempted on nearly half the threats. Management outcomes ranged from successful trials to complete mitigation of the threat, including for one‐third of high‐impact threats. Progress in both research and management tended to be greater for taxa that were monitored or occurred on oceanic islands. Predation by cats had the highest potential threat score. However, there has been some success reducing the impact of cat predation, so climate change (particularly drought), now poses the greatest threat to Australian threatened birds. Our results demonstrate the potential for the proposed metrics to encapsulate the major trends in research and management of both threats and threatened taxa and provide a basis for international comparisons of evidence‐based conservation science.
RGB-based 3D Hand Pose Estimation via Privileged Learning with Depth Images

This paper proposes a method for hand pose estimation from RGB images that uses both external large-scale depth image datasets and paired depth and RGB images as privileged information at training time. We show that providing depth information during training significantly improves performance of pose estimation from RGB images during testing. We explore different ways of using this privileged information: (1) using depth data to initially train a depth-based network, (2) using the features from the depth-based network of the paired depth images to constrain mid-level RGB network weights, and (3) using the foreground mask, obtained from the depth data, to suppress the responses from the background area. By using paired RGB and depth images, we are able to supervise the RGB-based network to learn middle layer features that mimic that of the corresponding depth-based network, which is trained on large-scale, accurately annotated depth data. During testing, when only an RGB image is available, our method produces accurate 3D hand pose predictions. Our method is also tested on 2D hand pose estimation. Experiments on three public datasets show that the method outperforms the state-of-the-art methods for hand pose estimation using RGB image input.
Testing local properties of arrays

We study testing of local properties in one-dimensional and multi-dimensional arrays. A property of $d$-dimensional arrays $f:[n]^d \to \Sigma$ is $k$-local if it can be defined by a family of $k \times \ldots \times k$ forbidden consecutive patterns. This definition captures numerous interesting properties. For example, monotonicity, Lipschitz continuity and submodularity are $2$-local; convexity is (usually) $3$-local; and many typical problems in computational biology and computer vision involve $o(n)$-local properties. In this work, we present a generic approach to test all local properties of arrays over any finite (and not necessarily bounded size) alphabet. We show that any $k$-local property of $d$-dimensional arrays is testable by a simple canonical one-sided error non-adaptive $\varepsilon$-test, whose query complexity is $O(\epsilon^{-1}k \log{\frac{\epsilon n}{k}})$ for $d = 1$ and $O(c_d \epsilon^{-1/d} k \cdot n^{d-1})$ for $d > 1$. The queries made by the canonical test constitute sphere-like structures of varying sizes, and are completely independent of the property and the alphabet $\Sigma$. The query complexity is optimal for a wide range of parameters: For $d=1$, this matches the query complexity of many previously investigated local properties, while for $d > 1$ we design and analyze new constructions of $k$-local properties whose one-sided non-adaptive query complexity matches our upper bounds. For some previously studied properties, our method provides the first known sublinear upper bound on the query complexity.
Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel

Deep Neural Network (DNN) is a widely used deep learning technique. How to ensure the safety of DNN-based system is a critical problem for the research and application of DNN. Robustness is an important safety property of DNN. However, existing work of verifying DNN's robustness is time-consuming and hard to scale to large-scale DNNs. In this paper, we propose a boosting method for DNN robustness verification, aiming to find counter-examples earlier. Our observation is DNN's different inputs have different possibilities of existing counter-examples around them, and the input with a small difference between the largest output value and the second largest output value tends to be the achilles's heel of the DNN. We have implemented our method and applied it on Reluplex, a state-of-the-art DNN verification tool, and four DNN attacking methods. The results of the extensive experiments on two benchmarks indicate the effectiveness of our boosting method.
Knowledge And Attitude Of Undergraduate Dental Students Towards Research.

BACKGROUND Scientific research is an important part of healthcare curricula and a vital exercise to help develop clinician's research skills. The aim of this study was to evaluate quantitatively the knowledge and attitude of undergraduate dental students towards scientific research. In addition, the study explored the reasons for lack of interest in research by the dental students. METHODS This cross-sectional study was conducted among students of three dental schools in Riyadh. The required information was collected through a self-designed, pretested and validated questionnaire used for investigating the students' knowledge, attitudes and reasons for lack of interest if any in research. Descriptive, chi-square and t-tests were used for statistical analysis of the responses considering a p-value of <0.05 as the cut-off level for significance. RESULTS Out of 701 completed questionnaires (Response Rate=77.88%), 360 (51.4%) and 341 (48.6%) were for males and female students respectively. Subjects, stratified according to year of study, included students of first year=144 (20.5%); second year=151 (21.5%); third year=132 (18.8%); fourth year=145 (20.7%) and fifth year=129 (18.4%). The mean and standard of the knowledge score was 3.36±1.76 and attitude score was 3.87±2. Students' knowledge and attitude scores tended to increase as the study year went higher. CONCLUSIONS A moderate level of knowledge about research was found among dental students. However, attitude of the dental students towards research was positive. Improvements were seen in the knowledge and attitude of students over the study years. Curriculum overload, exposure, literature search, knowledge and finding a topic or mentor in the school seemed to be the barriers in pursuing research.
Methyl-β-Cyclodextrin Impairs the Phosphorylation of the β2 Subunit of L-Type Calcium Channels and Cytosolic Calcium Homeostasis in Mature Cerebellar Granule Neurons

The activation of L-type calcium channels (LTCCs) prevents cerebellar granule neurons (CGNs) from entering low-K+-induced apoptosis. In previous works, we showed that LTCCs are largely associated with caveolin-1-rich lipid rafts in the CGN plasma membrane. In this work, we show that protein kinase A (PKA) and calmodulin-dependent protein kinase II (CaMK-II) are associated with caveolin-1-rich lipid rafts of mature CGNs, and we further show that treatment with the cholesterol-trapping and lipid raft-disrupting agent methyl-β-cyclodextrin decreases the phosphorylation level of the LTCC β2 subunit and the steady-state calcium concentration in neuronal somas ([Ca2+]i) to values close to those measured in 5 mM KCl proapoptotic conditions. These effects correlate with the effects produced by a short (15 min) treatment of CGNs with H-89 and KN-93—inhibitors of PKA and CaMK-II, respectively—in 25 mM KCl medium. Moreover, only a 15 min incubation of CGNs with H-89 produces about a 90% inhibition of the calcium entry that would normally occur through LTCCs to increase [Ca2+]i upon raising the extracellular K+ from 5 to 25 mM, i.e., from proapoptotic to survival conditions. In conclusion, the results of this work suggest that caveolin-1-rich lipid rafts play a major role in the control of the PKA- and CaMK-II-induced phosphorylation level of the LTCC β2 subunit, thus preventing CGNs from entering apoptosis.
Exercise training for chronic heart failure (ExTraMATCH II): Why all data are not equal

We, the ExTraMATCH II collaborative, recently completed an individual patient data (IPD) meta-analysis on the effect of exercise-based cardiac rehabilitation (ExCR) on mortality and hospitalisation in patients with heart failure. This analysis considered 18 randomised controlled trials, which included 3912 people with heart failure who were randomised to an ExCR intervention or control group (treatment as usual). Unlike the original ExTraMATCH IPD meta-analysis published in 2004 by Piepoli et al., this latest analysis did not demonstrate mortality and hospitalisation benefits from ExCR in patients with heart failure. We believe that some of the reasons for this difference in findings could be attributed to the data provided by the individual trials. We share here some of the data problems we encountered and provide our insights. In doing so, we also propose some recommendations for both future study designs and funding bodies, so that we may move towards a more efficacious approach to managing heart failure with ExCR in the future.