Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Factors Associated With Positive Stool PCR for Gastrointestinal Pathogens in Celiac and Inflammatory Bowel Disease

Goals: We aimed to identify the clinical factors of celiac disease (CeD) and inflammatory bowel disease (IBD) associated with positive stool gastrointestinal (GI) polymerase chain reaction (PCR) test. Background: Understanding the pattern of enteric infections in CeD and IBD may allow further insight into microbiome-mediated pathogeneses. Study: This was a retrospective study of adult patients (age 18 y or above) with CeD and IBD at a large quaternary care institution. We identified patients with CeD or IBD who underwent stool GIPCR evaluation as outpatients (office visit or at endoscopy) between March 2015 and March 2019. Patients with a negative GIPCR test within the study time frame were randomly chosen as controls (1 : 1). The independent relationship between clinical characteristics and positive GIPCR was evaluated using multivariable logistic regression. Results: A total of 266 patients met criteria for the study, including 92 (35%) with CeD and 174 (65%) with IBD. On multivariable analysis of factors associated with positive GIPCR test, CeD patients were more likely to have diarrheal presentation of illness [odds ratio (OR): 2.61, 95% confidence interval (CI) 1.05-6.72], experience extraintestinal manifestations (OR: 2.49, 95% CI: 1.01-6.31), and practice a gluten-free diet for at least 5 years (OR: 4.00, 95% CI: 1.36-11.67), relative to those with a negative GIPCR test. IBD patients with positive GIPCR were more likely to be on corticosteroids (OR: 2.23, 95% CI: 1.02-5.4.84), experience extraintestinal manifestations (OR: 2.60, 95% CI: 1.22-5.53), and use proton-pump inhibitors (OR: 4.07, 95% CI: 1.69-9.77). Conclusions: Intestinal infections in CeD and IBD are associated with important disease-specific characteristics.
COMPARATIVE STUDY BETWEEN THREE EXTRA-GLOTTIC AIRWAY DEVICES WITH GASTRIC ACCESS IN GENERAL SURGERY

Background: Extraglottic airway devices (EGADs) – synonyms may be supraglottic airway devices (SADs, SGAs) or supra-laryngeal airways (SLA), are an integral part of modern anesthetic practice. These devices are inserted into the oral cavity after induction to anesthesia and help to provide patent airways during surgery. Objective: To compare the efficacy of three extra-glottic airway devices with gastric access tube namely: Proseal LMA, Air-Q, and Baska Mask in providing an adequate airway seal for general surgery in paralyzed patients with apparent normal airways, receiving controlled ventilation. Patients and Methods: This study was carried out in Al-Azhar University Hospitals, designed prospective, randomized, single-blinded study, included 90 adult male and female patients with ASA physical status class I or II, aged 18 – 44 years who were scheduled to have elective surgeries under general anesthesia constituted the population of the present study. Inclusion criteria of this study obtained after approval of the Anesthesia Department’s Research/Ethics Committee and written informed consents were obtained from the patients. Results: As regard EGAD insertion, there were no cases of failed insertion of all airway devices. The Baska Mask was the easiest device to be inserted, while the PLMA was easier than Air Q. Both Baska Mask and PLMA showed a higher rate
Prevalence of anxiety symptoms in a sample of college students at a faculty of health sciences in Mexico

Background: Anxiety symptoms are one of the more frequent mental health related issues prevalent on the Mexican population. College students are at risk of developing anxiety symptoms because of the challenges related to starting college studies and pursuing a college degree. Objective: Levels of anxiety symptoms in a sample of college students were assessed to examine their prevalence among the different university school programs. Participants: 270 college students at the National Autonomus University of Mexico, FES Iztacala, Faculty of Health Sciences. Method: Beck Anxiety Inventory was completed by the balanced sample at University campus. Results: Almost one quarter of the population has symptoms of anxiety above the cut point. Female students showed to be more anxious than male students. Younger students showed higher prevalence of anxiety symptoms. Conclusions: Findings provide evidence that females may be at a higher risk of developing anxiety symptoms, which could impact their academic performance. Likewise, the youngest students are an area of opportunity to be detected and channeled to specialized services on campus.
Fast Query and Interactively Explore Data from Indexed Genomic Files

Genomic data repositories like The Cancer Genome Atlas (TCGA), Encyclopedia of DNA Elements (ENCODE), Bioconductor’s Anno-tationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of data files from these repositories to perform exploratory data analysis. We developed Epiviz File Server, a Python library that implements an in-situ data
h5bench: HDF5 I/O Kernel Suite for Exercising HPC I/O Patterns

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputing systems. With massive amounts of data being produced or consumed by compute nodes, high performant parallel I/O is essential. I/O benchmarks play an important role in this process, however, there is a scarcity of I/O benchmarks that are representative of current workloads on HPC systems. Towards creating representative I/O kernels from real world applications, we have created h5bench a set of I/O kernels that exercise HDF5 I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is because of the parallel I/O library’s heavy usage in a wide variety of scientific applications running on supercomputing systems. The various dimensions of h5bench include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (1D arrays, 2D meshes, 3D cubes) and I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on a Cray system: Cori at NERSC using both the DataWarp burst buffer and a Lustre file system and Summit at Oak Ridge Leadership Computing Facility (OLCF) using a SpectrumScale file system. These performance measurements are using find performance bottlenecks, identify root causes of any poor performance, and optimize I/O performance. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful not only to the CUG community but also to the broader supercomputing community. Keywords—HDF5 benchmarks,
Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

Importance: Results from observational studies and randomized clinical trials (RCTs) have led to the consensus that hydroxychloroquine (HCQ) and chloroquine (CQ) are not effective for COVID-19 prevention or treatment. Pooling individual participant data (IPD), including unanalyzed data from trials terminated early, enables further investigation of the efficacy and safety of HCQ/CQ. Objective: To assess efficacy of HCQ/CQ in patients hospitalized with COVID-19, both overall and in prespecified subgroups. Data Sources: ClinicalTrials.gov was searched multiple times in May-June 2020. Principal investigators of US-based RCTs evaluating HCQ/CQ in hospitalized COVID-19 patients were invited to collaborate in this IPD meta-analysis. Study Selection: RCTs in which: (1) HCQ/CQ was a treatment arm; (2) patient informed consent and/or individual study IRB approval allowed for data sharing; (3) principal investigators/their institutions signed a data use agreement for the present study; and (4) the outcomes defined in this study were recorded or could be extrapolated. Data Extraction and Synthesis: Wherever possible, harmonized de-identified data were collected via a common template spreadsheet sent to each principal investigator, then shared via a secure online data sharing platform to create a pooled data set. When this was not possible, individual study data were harmonized and merged manually. Data were analyzed by fitting a prespecified Bayesian ordinal regression model and standardizing the resulting predictions. Main Outcome(s) and Measure(s): 7-point ordinal scale, measured between day 28 and 35 post-enrollment. Results: Eight of 19 trials met eligibility criteria and agreed to participate. Patient-level data were available from 770 participants (412 HCQ/CQ vs 358 control). Baseline characteristics were similar between groups. We found no evidence of a difference in ordinal scores between days 28 and 35 post-enrollment in the pooled patient population (odds ratio, 0.97; 95% credible interval, 0.76-1.24; higher favors HCQ/CQ), and no convincing evidence of meaningful treatment effect heterogeneity among prespecified subgroups. Adverse event and serious adverse event rates were numerically higher with HCQ/CQ vs control (0.39 vs 0.29 and 0.13 vs 0.09 per patient, respectively). Conclusions and Relevance: The findings of this IPD meta-analysis reinforce those of individual RCTs that HCQ/CQ is not efficacious for treatment of COVID-19 in hospitalized patients.
Smoking in social housing among adults in England, 2015–2020: a nationally representative survey

Objectives To analyse associations between living in social housing and smoking in England and to evaluate progress towards reducing disparities in smoking prevalence among residents of social housing compared with other housing types. Design Cross-sectional analysis of nationally representative data collected between January 2015 and February 2020. Setting England. Participants 105 562 adults (≥16 years). Primary and secondary outcome measures Linear and logistic regression were used to analyse associations between living in social housing (vs other housing types) and smoking status, cigarettes per day, time to first cigarette, exposure to others’ smoking, motivation to stop smoking, quit attempts and use of cessation support. Analyses were adjusted for sex, age, social grade, region and year. Results Adults living in social housing had two times the odds of being a smoker (ORadj=2.17, 95% CI 2.08 to 2.27), and the decline in smoking prevalence between 2015 and 2020 was less pronounced in this high-risk group (−7%; ORadj=0.98, 95% CI 0.96 to 1.01) than among adults living in other housing types (−24%; ORadj=0.95, 95% CI 0.94 to 0.96; housing tenure–survey year interaction p=0.020). Smokers living in social housing were more addicted than those in other housing types (smoking within 30 min of waking: ORadj=1.50, 95% CI 1.39 to 1.61), but were no less motivated to stop smoking (ORadj=1.06, 95% CI 0.96 to 1.17) and had higher odds of having made a serious attempt to quit in the past year (ORadj=1.16, 95% CI 1.07 to 1.25). Among smokers who had tried to quit, those living in social housing had higher odds of using evidence-based cessation support (ORadj=1.22, 95% CI 1.07 to 1.39) but lower odds of remaining abstinent (ORadj=0.63, 95% CI 0.52 to 0.76). Conclusions There remain stark inequalities in smoking and quitting behaviour by housing tenure in England, with declines in prevalence stalling between 2015 and 2020 despite progress in the rest of the population. In the absence of targeted interventions to boost quitting among social housing residents, inequalities in health are likely to worsen.
A general class of surrogate functions for stable and efficient reinforcement learning

Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong theoretical guarantees, leading to algorithms such as TRPO, PPO or MPO. Rather than design yet another surrogate function, we instead propose a general framework (FMA-PG) based on functional mirror ascent that gives rise to an entire family of surrogate functions. We construct surrogate functions that enable policy improvement guarantees, a property not shared by most existing surrogate functions. Crucially, these guarantees hold regardless of the choice of policy parameterization. Moreover, a particular instantiation of FMA-PG recovers important implementation heuristics (e.g., using forward vs reverse KL divergence) resulting in a variant of TRPO with additional desirable properties. Via experiments on simple bandit problems, we evaluate the algorithms instantiated by FMA-PG. The proposed framework also suggests an improved variant of PPO, whose robustness and efficiency we empirically demonstrate on the MuJoCo suite.
MobilePhys

Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement. The current state-of-the-art neural models are typically trained in a supervised manner using videos accompanied by gold standard physiological measurements. However, they often generalize poorly out-of-domain examples (i.e., videos that are unlike those in the training set). Personalizing models can help improve model generalizability, but many personalization techniques still require some gold standard data. To help alleviate this dependency, in this paper, we present a novel mobile sensing system called MobilePhys, the first mobile personalized remote physiological sensing system, that leverages both front and rear cameras on a smartphone to generate high-quality self-supervised labels for training personalized contactless camera-based PPG models. To evaluate the robustness of MobilePhys, we conducted a user study with 39 participants who completed a set of tasks under different mobile devices, lighting conditions/intensities, motion tasks, and skin types. Our results show that MobilePhys significantly outperforms the state-of-the-art on-device supervised training and few-shot adaptation methods. Through extensive user studies, we further examine how does MobilePhys perform in complex real-world settings. We envision that calibrated or personalized camera-based contactless PPG models generated from our proposed dual-camera mobile sensing system will open the door for numerous future applications such as smart mirrors, fitness and mobile health applications.
Emotion Intensity and its Control for Emotional Voice Conversion

Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In EVC, emotions are usually treated as discrete categories overlooking the fact that speech also conveys emotions with various intensity levels that the listener can perceive. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding. We further learn the actual emotion encoder from an emotion-labelled database and study the use of relative attributes to represent fine-grained emotion intensity. To ensure emotional intelligibility, we incorporate emotion classification loss and emotion embedding similarity loss into the training of the EVC network. As desired, the proposed network controls the fine-grained emotion intensity in the output speech. Through both objective and subjective evaluations, we validate the effectiveness of the proposed network for emotional expressiveness and emotion intensity control.
State Estimation in Electric Power Systems Leveraging Graph Neural Networks

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.
Demystifying the Vulnerability Propagation and Its Evolution via Dependency Trees in the NPM Ecosystem

Third-party libraries with rich functionalities facilitate the fast development of JavaScript software, leading to the explosive growth of the NPM ecosystem. However, it also brings new security threats that vulnerabilities could be introduced through dependencies from third-party libraries. In particular, the threats could be excessively amplified by transitive dependencies. Existing research only considers direct dependencies or reasoning transitive dependencies based on reachability analysis, which neglects the NPM-specific dependency resolution rules as adapted during real installation, resulting in wrongly resolved dependencies. Consequently, further fine-grained analysis, such as precise vulnerability propagation and their evolution over time in dependencies, cannot be carried out precisely at a large scale, as well as deriving ecosystem-wide solutions for vulnerabilities in dependencies. To fill this gap, we propose a knowledge graph-based dependency resolution, which resolves the inner dependency relations of dependencies as trees (i.e., dependency trees), and investigates the security threats from vulnerabilities in dependency trees at a large scale. Specifically, we first construct a complete dependency-vulnerability knowledge graph (DVGraph) that captures the whole NPM ecosystem (over 10 million library versions and 60 million well-resolved dependency relations). Based on it, we propose a novel algorithm (DTResolver) to statically and precisely resolve dependency trees, as well as transitive vulnerability propagation paths, for each package by taking the official dependency resolution rules into account. Based on that, we carry out an ecosystem-wide empirical study on vulnerability propagation and its evolution in dependency trees. Our study unveils lots of useful findings, and we further discuss the lessons learned and solutions for different stakeholders to mitigate the vulnerability impact in NPM based on our findings. For example, we implement a dependency tree based vulnerability remediation method (DTReme) for NPM packages, and receive much better performance than the official tool (npm audit fix).
Nanometer-scale engineering of shallow spins in diamond

Author(s): Ohno, Kenichi | Advisor(s): Awschalom, David D; Palmstrom, Christopher J | Abstract: A crystal growth technique enabling to control the depth of a single nitro\-gen-vacancy (NV) center at nanometer scale in diamond is developed. This nitrogen delta-doping technique during the plasma-enhanced chemical vapor deposition (PE-CVD) of diamond enables to create near-surface NV centers whose depths ranging from about 100 nm down to less than 2 nm while preserving their spin coherence times. These shallowly doped, long-coherence NV centers are used as an atomic-scale magnetic sensor that enables to detect nuclear spin signal from an organic sample of a nanometer-scale volume {\it external} to the diamond crystal. Extension of this nanometer-scale nuclear magnetic resonance (nanoNMR) to two-dimensional nanometer-scale magnetic resonance imaging (2D nanoMRI) is also presented. The nitrogen delta-doping technique is combined with shallow 12C ion implantation through lithographically-patterned apertures to demonstrate three-dimensional (3D) localization of single NV centers at nanometer scale. The demonstrated long spin coherence times of 3D-localized NV centers pave a way towards quantum applications by maximizing their interactions to the diamond-based nanostructures.
Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.
Ancestral instrument method for causal inference without a causal graph

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement of standard IV is strict and untestable. Conditional IV has been proposed to relax the requirement of standard IV by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for ﬁnding a conditioning set for a conditional IV needs complete causal structure knowledge or a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it impossible to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) in causal inference with latent variables, we propose a new type of IV, ancestral IV in MAG, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in MAG. Based on the theory, we develop an al-gorithm for unbiased causal effect estimation with an ancestral IV in MAG and observational data. Extensive experiments on synthetic and real-world datasets have demonstrated the performance of the algorithm in comparison with existing IV methods.
Stability Properties of Moduli Spaces

1. Moduli Spaces and Stability Moduli spaces are spaces that parameterize topological or geometric data. They often appear in families; for example, the configuration spaces of n points in a fixed manifold, the Grassmannians of linear subspaces of dimension d in R∞, and the moduli spaces Mg of Riemann surfaces of genus g. These families are usually indexed by some geometrically defined quantity, such as the number n of points in a configuration, the dimension d of the linear subspaces, or the genus g of a Riemann surface. Understanding the topology of these spaces has been a subject of intense interest for the last 60 years. For a family of moduli spaces {Xn}n we ask: