Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics and outcomes of 627 044 COVID-19 patients with and without obesity in the United States, Spain, and the United Kingdom

Background: COVID-19 may differentially impact people with obesity. We aimed to describe and compare the demographics, comorbidities, and outcomes of obese patients with COVID-19 to those of non-obese patients with COVID-19, or obese patients with seasonal influenza. Methods: We conducted a cohort study based on outpatient/inpatient care, and claims data from January to June 2020 from the US, Spain, and the UK. We used six databases standardized to the OMOP common data model. We defined two cohorts of patients diagnosed and/or hospitalized with COVID-19. We created corresponding cohorts for patients with influenza in 2017-2018. We followed patients from index date to 30 days or death. We report the frequency of socio-demographics, prior comorbidities, and 30-days outcomes (hospitalization, events, and death) by obesity status. Findings: We included 627 044 COVID-19 (US: 502 650, Spain: 122 058, UK: 2336) and 4 549 568 influenza (US: 4 431 801, Spain: 115 224, UK: 2543) patients. The prevalence of obesity was higher among hospitalized COVID-19 (range: 38% to 54%) than diagnosed COVID-19 (30% to 47%), or diagnosed/hospitalized influenza (15% to 48%) patients. Obese hospitalized COVID-19 patients were more often female and younger than non-obese COVID-19 patients or obese influenza patients. Obese COVID-19 patients were more likely to have prior comorbidities, present with cardiovascular and respiratory events during hospitalization, require intensive services, or die compared to non-obese COVID-19 patients. Obese COVID-19 patients were also more likely to require intensive services or die compared to obese influenza patients, despite presenting with fewer comorbidities. Interpretation: We show that obesity is more common among COVID-19 than influenza patients, and that obese patients present with more severe forms of COVID-19 with higher hospitalization, intensive services, and fatality than non-obese patients. These data are instrumental for guiding preventive strategies of COVID-19 infection and complications
Causal Gene Network Inference from Genetical Genomics Experiments via Structural Equation Modeling

The goal of this research is to construct causal gene networks for genetical genomics experiments using expression Quantitative Trait Loci (eQTL) mapping and Structural Equation Modeling (SEM). Unlike Bayesian Networks, this approach is able to construct cyclic networks, while cyclic relationships are expected to be common in gene networks. Reconstruction of gene networks provides important knowledge about the molecular basis of complex human diseases and generally about living systems. In genetical genomics, a segregating population is expression profiled and DNA marker genotyped. An Encompassing Directed Network (EDN) of causal regulatory relationships among genes can be constructed with eQTL mapping and selection of candidate causal regulators. Several eQTL mapping approaches and local structural models were evaluated in their ability to construct an EDN. The edges in an EDN correspond to either direct or indirect causal relationships, and the EDN is likely to contain cycles or feedback loops. We implemented SEM with genetics algorithms to produce sub-models of the EDN containing fewer edges and being well supported by the data. The EDN construction and sparsification methods were tested on a yeast genetical genomics data set, as well as the simulated data. For the simulated networks, the SEM approach has an average detection power of around ninety percent, and an average false discovery rate of around ten percent. iii Acknowledgements I would like to thank my advisor, Dr. Ina Hoeschele, for her time, patience, guidance, and encouragement during my doctoral study. Without her effort and support I would not have been able to finish this. I am very fortunate to have had the opportunity to work with her. I also would like to thank Dr. serving on my committee, sharing their knowledge, and providing guidance and support. Thank them for taking the time to read my dissertation and for their critical assessment of my research. I appreciate the opportunity of having studied in their classrooms. A special thank goes to my colleague Dr. Alberto de la Fuente. We have worked closely on this project, and we have some nice discussions almost everyday. I also extend my gratitude to my other colleagues in Dr. Hoeschele's group: Drs. Bing. I am very grateful to them for their friendship, valuable technique discussions, and support. I would also like to thank my collaborators on the microarray expression analysis, Dr. It is a great honor to work with them in the past years. Finally to my parents, Zhenhua …
Trends in cardiovascular disease risk factors by body mass index category among adults in England 2003-18: analysis of repeated cross-sectional national health surveys

Objective. Favourable trends in cardiovascular disease (CVD) risk factors at the population level potentially mask differences within high- and low-risk groups. Data from annual, repeated cross-sectional surveys (Health Survey for England 2003-18) was used to examine trends in the prevalence of key CVD risk factors by body mass index (BMI) category among adults aged 16 years or older (n=115,860). Methods. Six risk factors were investigated: (i) current cigarette smoking; (ii) physical inactivity (<30 minutes of moderate-to-vigorous physical activity per week); (iii) drinking above recommended daily alcohol limits; (iv) hypertension (measured blood pressure [≥]140/90mmHg or use of medicine prescribed for high blood pressure); (v) total diabetes (reported diagnosed or elevated glycated haemoglobin); and (vi) raised total cholesterol ([≥]5mmol/L). Age-standardised risk factor prevalence was computed in each four-year time period (2003-06; 2007-10; 2011-14; 2015-18) in all adults and by BMI category (normal-weight; overweight; obesity). Change in risk factor prevalence on the absolute scale was computed as the difference between the first and last time-periods, expressed in percentage points (PP). Results. Risk factor change varied by BMI category in a number of cases. Current smoking prevalence fell more sharply for normal-weight men (-8.1 PP; 95% CI: -10.3, -5.8) versus men with obesity (-3.8 PP; 95% CI: -6.2, -1.4). Hypertension remained at a stable level among normal-weight men but decreased among men with obesity (-4.1 PP; 95% CI: -7.1, -1.0). Total diabetes remained at a stable level among normal-weight adults, but increased among adults with obesity (men: 3.5 PP; 95% CI: 1.2, 5.7; women: 3.6 PP; 95% CI: 1.8, 5.4). Raised total cholesterol decreased in all BMI groups, but fell more sharply among women with obesity (-21 PP; 95% CI: -25, -17) versus their normal-weight counterparts (-16 PP; 95% CI: -18, -14). Conclusions. Relative to adults with normal weight, greater reductions in hypertension and raised total cholesterol among adults with overweight and obesity reflect at least in part improvements in screening, treatment and control among those at highest cardiovascular risk. Higher levels of risk factor prevalence among adults with overweight and obesity, in parallel with rising diabetes, highlight the importance of national prevention efforts to combat the public health impact of excess adiposity.
Change of Cells and Activity of Phenoloxidase in Bombyx mori Parasitized by Larva of Exorista sorbillans Wiedemann

The number of the cell and the activity of phenoloxidase in Bombyx mori parasitized by the larva of Exorista sorbillans Wiedemann was investigated.The results showed that the amount of the granular cell and plasmatocytes of the parasitized silkworm increased significantly.They were about 6.81×10~(6) /mL in 16~24 h after parasitized,increased by 4.76×10~(6) /mL compared to control.The melaninization of the haemolymph quickened and the melanin increased.At the same time,the activity of the phenoloxidase in haemolymoph increased from 7 U/mg to 34 U/mg in six days.
A magic determinant formula for symmetric polynomials of eigenvalues

Symmetric polynomials of the roots of a polynomial can be written as polynomials of the coefficients, and by applying this to the characteristic polynomial we can write a symmetric polynomial of the eigenvalues $a_{i}$ of an $n\times n$ matrix $A$ as a polynomial of the entries of the matrix. We give a magic formula for this: symbolically substitute $a\mapsto A$ in the symmetric polynomial and replace multiplication by $\det$. For instance, for a $2\times2$ matrix $A$ with eigenvalues $a_{1},a_{2}$, \begin{align*} a_1 a_2^2 +a_1^2 a_2 & =\det(A_1, A_2^2)+ \det(A_1^2, A_2) \end{align*} where $A_i^k$ is the $i$-th column of $A^k$. One may also take negative powers, allowing us to calculate: \begin{align*} a_1a_2^{-1}+a_1^{-1}a_{2} & =\det(A_{1},A_{2}^{-1})+\det(A_1^{-1},A_{2}) \end{align*} The magic method also works for multivariate symmetric polynomials of the eigenvalues of a set of commuting matrices, e.g. for $2\times2$ matrices $A$ and $B$ with eigenvalues $a_1,a_2$ and $b_{1},b_{2}$, \begin{align*} a_1 b_1 a_2^2 + a_1^2a_2b_2 & = \det(AB_{1},A_2^2) + \det(A_1^2,AB_2) \end{align*}
Molecular signature of penumbra in acute ischemic stroke: a pilot transcriptomics study

We aimed to characterize peripheral blood gene expression profile of penumbra defined as MRI perfusion–diffusion mismatch (PD MM) in peripheral blood of patients with acute ischemic stroke. We studied 23 patients. Perfusion–diffusion mismatch volume was observed to be associated and significantly correlated with the expression of 34 genes including those related to inflammation, SUMOylation, and coagulation; while lipopolysaccharide inhibition was identified to be a candidate upstream regulator of these processes (z‐score −2.38, P = 0.04). Penumbral volume is correlated with a specific gene expression profile in the peripheral blood characterized by overlap of inflammatory and neuroprotective pathways that are regulated by lipopolysaccharide inhibition.
NagE: Non-Abelian Group Embedding for Knowledge Graphs

We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models. Our theoretical analysis explores merely the intrinsic property of the embedding problem itself hence is model independent. Motivated by the theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework, in which relations are embedded as group elements, and entities are represented by vectors in group action spaces. We provide a generic recipe to construct embedding models associated with two instantiating examples: SO3E and SU2E, both of which apply a continuous non-Abelian group as the relation embedding. Empirical experiments using these two exampling models have shown state-of-the-art results on benchmark datasets.
Simulation of Nonradiative Energy Transfer in Photosynthetic Systems Using a Quantum Computer

Photosynthesis is an important and complex physical process in nature, whose comprehensive understanding would have many relevant industrial applications, for instance in the field of energy production. In this paper we propose a quantum algorithm for the simulation of the excitonic transport of energy, occurring in the first stage of the process of photosynthesis. The algorithm takes in account the quantum and environmental effects (pure-dephasing), influencing the quantum transport. We performed quantum simulations of such phenomena, for a proof of concept scenario, in an actual quantum computer the IBM Q, of 5 qubits. We validate the results with the Haken-Strobl model and discuss the influence of environmental parameters on the efficiency of the energy transport.
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises

The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and predicting human mobility patterns, such as destination and trajectory selection, can inform emerging congestion and road closures raised by disruptions in emergencies. Data related to human movement trajectories are scarce, especially in the context of emergencies, which places a limitation on applications of existing urban mobility models learned from empirical data. Models with the capability of learning the mobility patterns from data generated in normal situations and which can adapt to emergency situations are needed to inform emergency response and urban resilience assessments. To address this gap, this study creates and tests an adaptive reinforcement learning model that can predict the destinations of movements, estimate the trajectory for each origin and destination pair, and examine the impact of perturbations on humans' decisions related to destinations and movement trajectories. The application of the proposed model is shown in the context of Houston and the flooding scenario caused by Hurricane Harvey in August 2017. The results show that the model can achieve more than 76\% precision and recall. The results also show that the model could predict traffic patterns and congestion resulting from to urban flooding. The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility.
Established Time Goals Can Increase the Efficiency of Trauma Resuscitation

Introduction Our institution uses video review as a quality improvement tool. Starting in March 2018, we specifically focused on meeting certain time goals during trauma resuscitation aimed at decreasing time to final disposition. The purpose of this study was to evaluate the effect of establishing strict time goals on total time spent in the trauma bay by pediatric trauma patients. Materials and methods A retrospective review of all level I trauma activations at a level I pediatric trauma center between November 2017 and December 2018 was performed via manual review of the recorded trauma activations. Data on key time points such as time from arrival to transfer to gurney, to completion of primary survey, to chest x-ray, to Emergency Medical Services (EMS) report, to CT scan, and to disposition (CT or admission/operating room [OR] if no CT scan was performed) were analyzed and compared between the cohort of patients prior to implementation of the time goals with that after. The cohort of patients who presented between March 2018 and May 2018 were excluded to allow for time for the intervention to take effect. Results There were 13 level I trauma activations before implementation of the time goals and 41 after. There was a significant decrease in time to transfer to gurney (1.8 minutes vs. 1.0 minutes; p=0.02), to CT scan (18.8 minutes vs. 14.2 minutes; p=0.01), and to disposition (18.3 minutes vs. 14.9 minutes; p=0.047). There was no decrease in time to completion of primary survey, EMS report, or chest x-ray. Conclusions Utilizing video review in pediatric trauma as a quality improvement initiative with a focus on meeting specific time goals for key elements of the activation led to decreased total time in our trauma bay with critically ill patients.
Atypical Presentation of Post-Kala-Azar Dermal Leishmaniasis in Bhutan

This article describes an atypical case of post-kala-azar dermal leishmaniasis associated with complications due to delayed diagnosis and poor case management. The grave consequences of the prolonged disease process that continued for over 2 decades with eventual healing included facial disfigurement, visual impairment, and mental distress both to the patient and the family. The persistent infection within the skin over a lengthy period with likely increased risk of infection spread in the community highlights its potential negative impact on the ongoing leishmaniasis elimination program in the Indian subcontinent. Bhutan is a member of the leishmaniasis elimination network in Asia, and the government continues to invest in maintenance of the national healthcare system. The case study reveals the gaps in the healthcare system with hardships faced by a patient to access quality healthcare and poor patient outcome used as proxy indicators. It also points to the need to enhance access to healthcare to ensure early diagnosis and effective treatment for leishmaniasis patients including those who live in remote areas, in order to achieve the planned disease elimination targets. It also points towards the key challenges faced by a resource poor nation such as Bhutan in achieving universal health coverage and reaching the set goals for disease elimination. The findings underscore the need for a careful review of the national health care system and to address the deficiencies.
From Hospital to the Community: Redesigning the Human Immunodeficiency Virus (HIV) Clinical Service Model to Respond to an Outbreak of HIV Among People Who Inject Drugs

Abstract An outbreak of human immunodeficiency virus (HIV) among people who inject drugs in Glasgow, Scotland started in 2014. We describe 156 cases over 5 years and evaluate the impact of clinical interventions using virological and phylogenetic analysis. We established (1) HIV services within homeless health facilities, including outreach nurses, and (2) antiretroviral therapy (ART) via community pharmacies. Implementation of the new model reduced time to ART initiation from 264 to 23 days and increased community viral load suppression rates to 86%. Phylogenetic analysis demonstrated that 2019 diagnoses were concentrated within a single network. Traditional HIV care models require adaptation for this highly complex population.
Attempt to Predict A/T/N-Based Alzheimer’s Disease Cerebrospinal Fluid Biomarkers Using a Peripheral Blood DNA Methylation Clock

Background: Although aging is the strongest risk factor for the development of Alzheimer’s disease (AD), it remains uncertain if the blood DNA methylation clock, which reflects the effect of biological aging on DNA methylation (DNAme) status of blood cells, may be used as a surrogate biomarker for AD pathology in the central nervous system (CNS). Objective: We aimed to develop a practical model to predict for A/T/N-based AD biomarkers as the prediction targets using the aging acceleration of blood cells. Methods: We obtained data of North American ADNI study participants (n = 317) whose blood DNA methylation microarray (Illumina HumanMethylation EPIC Beadchips) and cerebrospinal fluid (CSF) AD biomarkers (Aβ, t-tau, and p-tau) were recorded simultaneously. Methylation clock was calculated to conduct machine learning, in order to predict binary statuses (+ or –) for A (corresponding to the lowered CSF Aβ), T (the elevated CSF p-tau), or N (the elevated CSF t-tau). The predictive performance of the models was evaluated by area under curve (AUC) in the test subset within ADNI. Results: Among the 317 included samples, 194 (61.2%) were A+, 247 (77.9%) were T+, and 104 (32.8%) were N+. The degree of blood aging acceleration showed weak positive correlation with the CSF Aβ levels, even after adjustment with APOE genotype and other covariates. However, the contribution of aging acceleration to improve the predictive performance of models was not significant for any of A+, T+, or N+. Conclusion: Our exploratory attempts could not demonstrate the substantial utility of the peripheral blood cells’ methylation clock as a predictor for A/T/N-based CSF biomarkers of AD, and further additional work should be conducted to determine whether the blood DNAme signatures including methylation clock have substantial utility in detecting underlying amyloid, tau or neurodegeneration pathology of AD.
Cartesian Control of Sit-to-Stand Motion Using Head Position Feedback

Sit-to-stand (STS) motion is an indicator of an individual's physical independence and well-being. Determination of various variables that contribute to the execution and control of STS motion is an active area of research. In this study, we evaluate the clinical hypothesis that besides numerous other factors, the central nervous system (CNS) controls STS motion by tracking a prelearned head position trajectory. Motivated by the evidence for a task-oriented encoding of motion by the CNS, we adopt a robotic approach for the synthesis of STS motion and propose this scheme as a solution to this hypothesis. We propose an analytical biomechanical human CNS modeling framework where the head position trajectory defines the high-level task control variable. The motion control is divided into low-level task generation and motor execution phases. We model CNS as STS controller and its Estimator subsystem plans joint trajectories to perform the low-level task. The motor execution is done through the Cartesian controller subsystem that generates torque commands to the joints. We do extensive motion and force capture experiments on human subjects to validate our analytical modeling scheme. We first scale our biomechanical model to match the anthropometry of the subjects. We do dynamic motion reconstruction through the control of simulated custom human CNS models to follow the captured head position trajectories in real time. We perform kinematic and kinetic analyses and comparison of experimental and simulated motions. For head position trajectories, root mean square (RMS) errors are 0.0118 m in horizontal and 0.0315 m in vertical directions. Errors in angle estimates are 0.55 rad, 0.93 rad, 0.59 rad, and 0.0442 rad for ankle, knee, hip, and head orientation, respectively. RMS error of ground reaction force (GRF) is 50.26 N, and the correlation between ground reaction torque and the support moment is 0.72. Low errors in our results validate (1) the reliability of motion/force capture methods and anthropometric technique for customization of human models and (2) high-level task control framework and human CNS modeling as a solution to the hypothesis. Accurate modeling and detailed understanding of human motion can have significant scope in the fields of rehabilitation, humanoid robotics, and virtual characters' motion planning based on high-level task control schemes.