Miracle-dz/UniFAR-FTUs huggingface dataset:
Chloroquine and Hydroxychloroquine for the Prevention and Treatment of COVID-19: A Fiction, Hope or Hype? An Updated Review

: In December 2019, the novel coronavirus disease pandemic (COVID-19) that began in China had infected so far more than 109,217,366 million individuals worldwide and accounted for more than 2,413,912 fatalities. With the dawn of this novel coronavirus (SARS-CoV-2), there was a requirement to select potential therapies that might effectively kill the virus, accelerate the recovery, or decrease the case fatality rate. Besides the currently available antiviral medications for human immunodeficiency virus (HIV) and hepatitis C virus (HCV), the chloroquine/hydroxychloroquine (CQ/HCQ) regimen with or without azithromycin has been repurposed in China and was recommended by the National Health Commission, China in mid-February 2020. By this time, the selection of this regimen was based on its efficacy against the previous SARS-CoV-1 virus and its potential to inhibit viral replication of the SARS-CoV-2 in vitro. There was a shortage of robust clinical proof about the effectiveness of this regimen against the novel SARS-CoV-2. Therefore, extensive research effort has been made by several researchers worldwide to investigate whether this regimen is safe and effective for the management of COVID-19. In this review, we provided a comprehensive overview of the CQ/HCQ regimen, summarizing data from in vitro studies and clinical trials for the protection against or the treatment of SARS-CoV-2. Despite the initial promising results from the in vitro studies and the widespread use of CQ/HCQ in clinical settings during the 1st wave of COVID-19, current data from well-designed randomized controlled trials showed no evidence of benefit from CQ/HCQ supplementation for the treatment or prophylaxis against SARS-CoV-2 infection. Particularly, the two largest randomized controlled trials to date (RECOVERY and WHO SOLIDARITY trials), both confirmed that CQ/HCQ regimen does not provide any clinical benefit for COVID-19 patients. Therefore, we do not recommend the use of this regimen in COVID-19 patients outside the context of clinical trials.
Nursing Homes in Equilibrium: Implications for Long-term Care Policies

We build an equilibrium model of the market for nursing home care with decision-makers on both sides of the market. The nursing home demand arises as a result of stochastic dynamic optimizations by households heterogeneous in age, health, wealth; and the cost of home-andcommunity-based care. On the supply side, locally competitive nursing homes decide prices and care intensity. The government pays for the long-term care of the poorest. We estimate the model parameters using Health and Retirement Survey and simulate the model to quantitatively evaluate the effects of long-term care policies on prices, intensities, care allocation, and welfare. Citation Koreshkova, Tatyana and Minjoon Lee. 2020. “Nursing Homes in Equilibrium: Implications for Long-term Care Policies. ” Ann Arbor, MI. University of Michigan Retirement and Disability Research Center (MRDRC) Working Paper; MRDRC WP 2020-414. https://mrdrc.isr. umich.edu/publications/papers/pdf/wp414.pdf Authors’ acknowledgements We thank the seminar and workshop participants at Concordia University and the Michigan Retirement and Disability Research Center Workshop.
Measure-valued Pólya urn processes

A Pólya urn process is a Markov chain that models the evolution of an urn containing some coloured balls, the set of possible colours being {1, . , d} for d ∈ N. At each time step, a random ball is chosen uniformly in the urn. It is replaced in the urn and, if its colour is c, Rc,j balls of colour j are also added (for all 1 ≤ j ≤ d). We introduce a model of measure-valued processes that generalises this construction. This generalisation includes the case when the space of colours is a (possibly infinite) Polish space P. We see the urn composition at any time step n as a measureMn – possibly non atomic – on P. In this generalisation, we choose a random colour c according to the probability distribution proportional to Mn, and add a measure Rc in the urn, where the quantity Rc(B) of a Borel set B models the added weight of “balls” with colour in B. We study the asymptotic behaviour of these measure-valued Pólya urn processes, and give some conditions on the replacements measures (Rc, c ∈ P) for the sequence of measures (Mn, n ≥ 0) to converge in distribution, possibly after rescaling. For certain models, related to branching random walks, (Mn, n ≥ 0) is shown to converge almost surely under some moment hypothesis; a particular case of this last result gives the almost sure convergence of the (renormalised) profile of the random recursive tree to a standard Gaussian.
Bright Line Eating: A Two-year Follow-up Evaluation of a Commercial Telehealth Weight Loss Program within an Abstinence-Based Food Addiction Framework

Objective: The current study evaluates two-year weight outcomes for participants of the Bright Line Eating: Boot Camp program (BLE:BC), a weight management program that teaches participants to abstain from sugar and flour within a food addiction framework. Methods: Data come from participants in the BLE:BC Follow-up Research Program. Participants were invited to complete monthly follow-up surveys. First, we examined primary outcomes of percent weight loss (%WL) and change in body mass index (BMI) for participants who completed both the BLE:BC program and at least one followup survey. Next, we examined %WL and BMI change only among participants who completed all surveys. Results: An independent samples analysis showed that, at the time of each follow-up survey (6, 12, 18, and 24 months), participants reported clinically significant weight loss (>5%WL) from baseline. Weight loss at each followup was significantly greater than at the end of the BLE:BC program (>7.9%WL). Among participants who completed all surveys (n=238), weight loss was higher among participants enrolled in the Bright Lifers continuity program at 12, 18, and 24 months. At the 24-month follow-up, participants enrolled in the Bright Lifers program experienced an average 15.3%WL and a 5.0 reduction in BMI. Conclusion: A rigorous evaluation of the efficacy of commercial weight loss programs remains imperative. As research on the construct of food addiction in humans’ increases, evaluating treatment approaches becomes increasingly important. Although the generalizability of the current study is limited due to selection bias and sample homogeneity, this study contributes significant findings to the literature showing sustained, long-term weight loss among participants in the BLE:BC program.
BRDF Importance Sampling for Linear Lights

We introduce an efficient method to sample linear lights, i.e. infinitesimally thin cylinders, proportional to projected solid angle. Our method uses inverse function sampling with a specialized iterative procedure that converges to high accuracy in only two iterations. It also allows us to sample proportional to a linearly transformed cosine. By combining both sampling techniques through suitable multiple importance sampling heuristics and by using good stratification, we achieve unbiased diffuse and specular real‐time shading with low variance outside penumbrae at two samples per pixel. Additionally, we provide a fast method for solid angle sampling.
X-ray absorption near edge structure (XANES) study of the speciation of uranium and thorium in Al-rich CaSiO3 perovskite

Abstract X-ray absorption spectroscopy was used to investigate the oxidation state of uranium in various Uand Th-bearing Al-rich CaSiO3 perovskite samples synthesized at high-pressure and high-temperature using a multi-anvil press apparatus. X-ray absorption near edge spectroscopy (XANES) spectra collected at the U LIII- and Th LIII-edges using both micro- and macro-focused beams show U4+ in the Al-rich CaSiO3 perovskite. The structure of the U- and Th-bearing Al-rich CaSiO3 perovskite samples have been cross-checked by XANES spectra collected at the Ca K-, Al K-, and Si K-edges. Al K and Si K spectra suggest that Al incorporates exclusively on the Si site of the CaSiO3 perovskite. Ca K spectra of the (U,Th)-bearing Al-rich CaSiO3 perovskite samples were succesfully compared to FEFF8.2 ab initio models of a tetragonal CaSiO3 perovskite with space group P4/mmm. Our results confirm previous assumptions of the coupled substitution of CaSi2 by UAl2 in CaSiO3 perovskite and that U and Th can be incorporated separately or together in CaSiO3 perovskite by means of this mechanism. The possible occurrence of the U- and Th-bearing Al-rich CaSiO3 perovskite are discussed as a potential candidate to locally host a large amount of actinides in the Earth’s deep mantle. The study of a phase that can act as a storage mineral for heat-producing actinide elements such as uranium and thorium is fundamental to the understanding of the geodynamics and thermal behavior of Earth.
Automated, miniaturized, and scalable screening of healthcare workers, first responders, and students for SARS-CoV-2 in San Diego County

Background: Successful containment strategies for SARS-CoV-2, the causative virus of the COVID-19 pandemic, have involved widespread population testing that identifies infections early and enables rapid contact tracing. In this study, we developed a rapid and inexpensive RT-qPCR testing pipeline for population-level SARS-CoV-2 detection, and used this pipeline to establish a clinical laboratory dedicated to COVID-19 testing at the University of California San Diego (UCSD) with a processing capacity of 6,000 samples per day and next-day result turnaround times. Methods and findings: Using this pipeline, we screened 6,786 healthcare workers and first responders, and 21,220 students, faculty, and staff from UCSD. Additionally, we screened 6,031 preschool-grade 12 students and staff from public and private schools across San Diego County that remained fully or partially open for in-person teaching during the pandemic. Between April 17, 2020 and February 5, 2021, participants provided 161,582 nasal swabs that were tested for the presence of SARS-CoV-2. Overall, 752 positive tests were obtained, yielding a test positivity rate of 0.47%. While the presence of symptoms was significantly correlated with higher viral load, most of the COVID-19 positive participants who participated in symptom surveys were asymptomatic at the time of testing. The positivity rate among preschool-grade 12 schools that remained open for in-person teaching was similar to the positivity rate at UCSD and lower than that of San Diego County, with the children in private schools being less likely to test positive than the adults at these schools. Conclusions: Most schools across the United States have been closed for in-person learning for much of the 2020-2021 school year, and their safe reopening is a national priority. However, as there are no vaccines against SARS-CoV-2 currently available to the majority of school-aged children, the traditional strategies of mandatory masking, physical distancing, and repeated viral testing of students and staff remain key components of risk mitigation in these settings. The data presented here suggest that the safety measures and repeated testing actions taken by participating healthcare and educational facilities were effective in preventing outbreaks, and that a similar combination of risk-mitigation strategies and repeated testing may be successfully adopted by other healthcare and educational systems.
■ RAMSES – a new general equilibrium model for monetary policy analysis

For little more than one year now a new macroeconomic general equilibrium model of the Swedish economy is being used at the Riksbank for producing forecasts, interpreting economic developments and calculating effects of monetary policy interventions, for example. The model, called RAMSES (the Riksbank Aggregate Macromodel for Studies of the Economy of Sweden), is tailored to describe the development of a number of central macroeconomic time series with the aid of the best available econometric methods. In its methodology the model is in the scientifi c front line. The model is general in the sense that in principle it aims to explain the entire economy, not just a particular component such as private consumption or the labour market. But that does not mean that RAMSES describes every major aspect of the Swedish economy equally well.2
The Effect of Augmented Reality in Improving Visual Thinking in Mathematics of 10th-Grade Students in Jordan

Augmented reality is one of the key issues in the area of improving visual thinking in science courses such as Mathematics. Augmented reality also offers a significant and effective role in the educational process. The current study aimed to investigate the effect of augmented reality in improving visual thinking of 10th-grade students in mathematics in Jordan. To achieve the objectives of the study, the methodology used includes the application of the semi-experimental approach and augmented reality technology. The methodology used also includes preparing a test to measure visual thinking comprising (20) multiple-choice items used as a pre-and post-test, and its validity and reliability are verified. The study sample consists of (57) female students purposefully selected from the 10th-grade students at the Jerash Model Schools for the first semester of 2020/2021. The study sample is divided into two groups as follows: one is an experimental group consisting of (28) female students taught by the augmented reality technology, and the second is a control group consisting of (29) female students taught in the traditional method. The results of the study show that there are statistically significant differences at the level of (α = 0.05) in the development of visual thinking in favor of the experimental group students taught by the augmented reality technology. The study also shows that there are differences in the performance of the experimental group students in each skill of
How Do Accounting Estimate Bias and Method Similarity Influence Investors’ Reliability Judgment and Investment Decisions?

In a multi-firm setting, we examine how accounting estimate bias and accounting method similarity jointly influence investors’ reliability and investment judgments. Applying psychology research on the similarity effect, we predict and find that the similarity of accounting method amplifies the effect of accounting estimate bias on investors’ reliability judgment. Specifically, while investors are able to detect the negative impact of a favorablybiased (versus unfavorably-biased) accounting estimate on financial reporting reliability when firms use the same accounting method, investors lose such ability when firms use different accounting methods. This finding suggests that using different accounting methods among peer firms hinders investors’ ability to make appropriate reliability judgment. We also find that favorable (versus unfavorable) accounting estimate bias has a positive direct effect on investment, and a negative indirect effect on investment via perceived reliability. Our study highlights the importance of cross-firm reporting features, such as accounting method similarity, in shaping investors’ reliability and investment judgments.
Adaptive Spatio-temporal Query Planning For Linked Sensor Data

Researchers in the Semantic Web community have proposed a substantial number of works that use Semantic Web technologies for effectively managing and querying heterogeneous IoT data. However, our survey of research work has shown that the goal of providing an intelligent processing and analysis engine for IoT has still not been fully achieved. Central to this problem is the requirement for a semantic spatio-temporal query processing engine that is able to not only analyze spatio-temporal correlations in a massive amount of IoT data but can also generate an effective query plan for a given query to execute in a timely manner. In this paper, we propose an alternative query optimization solution that uses query similarity identiﬁcation in conjunction with machine learning techniques to recommend a previously generated query plan to the optimizer for a given query. Our approach also aims to predict the query execution time for the purposes of workload management and capacity planning. Our extensive experiments indicate the efﬁciency of our learning approach with an impressive prediction accuracy on test queries.
Skillful decadal prediction of unforced southern European summer temperature variations

We assess the capability of decadal prediction simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive to predict European summer temperature during the period 1970–2014. Using a multi-model ensemble average, we show that Southern European (SEU) summer temperatures are highly predictable for up to ten years in CMIP6. Much of this predictive skill, is related to the externally forced response: historical simulations explain about 90% of observed SEU summer temperature variance. Prediction skill for the unforced signal of SEU summer temperature is low: initialized model simulations explain less than 10% of observed variance after removing the externally forced response. An observed link between unforced SEU summer temperature and preceding spring Eastern North Atlantic—Mediterranean sea surface temperature (SST) motivates the application of a dynamical-statistical model to overcome the low summer temperature skill over Europe. This dynamical-statistical model uses dynamical spring SST predictions to predict European summer temperature, and significantly increases decadal prediction skill of unforced European summer temperature variations, showing significant prediction skill for unforced Southern European summer temperature 2–9 years ahead. As a result, dynamical-statistical models can benefit the decadal prediction of variables with initially limited skill beyond the forcing, such as summer temperature over Europe.
Point2color: 3D Point Cloud Colorization Using a Conditional Generative Network and Differentiable Rendering for Airborne LiDAR

Airborne LiDAR observations are very effective for providing accurate 3D point clouds, and archived data are becoming available to the public. In many cases, only geometric information is available in the published 3D point cloud observed by airborne LiDAR (airborne 3D point cloud), and geometric information alone is not readable. Thus, it is important to colorize airborne 3D point clouds to improve visual readability. A scheme for 3D point cloud colorization using a conditional generative adversarial network (cGAN) was proposed, but it is difficult to apply to airborne LiDAR because the method is for artificial CAD models. Since airborne 3D point clouds are spread over a wider area than simple CAD models, it is important to evaluate them spatially in two-dimensional (2D) images. Currently, the differentiable renderer is the most reliable method to bridge 3D and 2D images. In this paper, we propose an airborne 3D point cloud colorization scheme called point2color using cGAN with points and rendered images. To achieve airborne 3D point cloud colorization, we estimate the color of each point with PointNet++ and render the estimated colored airborne 3D point cloud into a 2D image with a differentiable renderer. The network is then trained by minimizing the distance between real color and colorized fake color. The experimental results demonstrate the effectiveness of point2color using the IEEE GRSS 2018 Data Fusion Contest dataset with lower error than previous studies. Furthermore, an ablation study demonstrates the effectiveness of using a cGAN pipeline and 2D images via a differentiable renderer. Our code will be available at GitHub.
DMSO_A_288587 5113..5123

1Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, Yunnan 650118, People’s Republic of China; 2Department of Medicine, Dentistry and Healthy Science, The University of Melbourne, Melbourne, VIC 3010, Australia; 3Department of Endocrinology and Metabolism, The Second People’s Hospital of Yunnan Province, & the Affiliated Hospital of Yunnan University, Kunming, Yunnan 650021, People’s Republic of China; 4School of Clinical Medicine, Dali University, Dali, Yunnan 671000, People’s Republic of China
Assessing the Communicative Use of Literary Texts in EFL Coursebooks

Integrating and utilizing literary texts from coursebooks in foreign language teaching could impact the communicative competence of language learners. The study aims to scrutinize the usage of authentic and inauthentic literary texts found in 44 mainstream English as a foreign language (EFL) coursebooks. The article particularly examines how texts can facilitate communicative language learning and teaching in language classes. To do this, the study proposes a set of principles that can enable using literary texts more appropriately. The analysis of the study indicates that authentic and inauthentic literary texts are used differently, with authentic texts providing great opportunities for communication and offering a unique contribution to the EFL classroom. The study has implications for language teachers and coursebook designers in language programs. Further recommendations are made on how literature can be used communicatively.
FEA UTILIZATION FOR STUDY OF THE CONVEYOR BELTS PROPERTIES IN THE CONTEXT OF INTERNAL LOGISTICS SYSTEMS

Belt conveyors are used in numerous in-house logistics systems. It is necessary to know properties of these conveyor belts for their effective functioning. This work is focused on research forces applied in conveyor belt during contact with rollers and reversing drum according to the different material properties of conveyor belt. For this purpose was created simulation computational model by Abaqus. Effects of the material properties on the forces in the longitudinal and transverse directions were studied by this model. In the model were observed correlation between material properties of the conveyor belt and its tension-strain states in place of the first and second roller stool, at the site of reverse drum and places of troughing transformation of conveyor belt.
Neonatal and Maternal Outcomes in Pregnant Women with Rheumatoid Arthritis

Background: Rheumatoid arthritis is an autoimmune and inflammatory disease, mainly attacks the joints, usually many joints at once, and organs such as the lungs, heart, and eyes. The course of rheumatoid arthritis often changes during pregnancy. Objective: The aim of the study was to assess whether pregnant women with rheumatoid arthritis and her baby are at high risk for adverse outcomes. Methods: This a case-control study was conducted at the Duhok Obstetrics and Gynecology Teaching Hospital and Kurdistan Private Hospital in Iraqi Kurdistan between March 2016 and November 2020. The study included seventytwo pregnant women, they were divided into two groups; study group or (Rheumatoid arthritis group) and control group or (Non-Rheumatoid arthritis group). Both groups were compared regarding a baseline characteristics and maternal with neonatal outcomes. The data were statistically analyzed using a software package, current versions IBM (SPSS) Statistic. Results: During the study period, from March 2016 and November 2020, Thirty-two pregnant women had RA were compared with the control group included 40 pregnant women who had not the disease group. There was significant difference between both groups regarding the female age. Pregnant women who had RA had lower parity and a higher number of pregnancy loss as well as more likely to smoke. The RA group was highly significant had a history of a previous cesarean delivery, and history of co-morbidities. Neonates of mothers who had RA commonly had small for gestational age and preterm babies. Conclusion: Pregnant women with RA are associated with a higher risk of adverse maternal and neonatal outcomes, and she regarded as a high-risk pregnancy.