Miracle-dz/UniFAR-FTUs huggingface dataset:
The Interactions between COVID-19 Cases in the USA, the VIX Index and Major Stock Markets

With this study, we aimed to determine (1) the effect of the daily new cases and deaths due to the COVID-19 pandemic in the United States on the CBOE volatility index (VIX index) and (2) the effect of the VIX index on the major stock markets during the early stage of the pandemic period. To do this, we collected and analysed the daily new cases and death numbers during the COVID-19 pandemic period in the United States and the country indexes of the USA (DJI), Germany (DAX), France (CAC40), England (FTSE100), Italy (MIB), China (SSEC) and Japan (Nikkei225) to determine the impact of the VIX index on the major stock markets. We then subjected this data to the Johansen co-integration test and the fully modified least-squares (FMOLS) method. The results indicated that there was co-integration between the VIX and the COVID-19 pandemic and that there was co-integration between the VIX index and major indexes, except for the CAC 40 and MIB. Moreover, the results showed that the new COVID-19 cases in the USA had a higher impact on the VIX than cases of deaths during the same period.
Water Filtration Membranes Based on Non-Woven Cellulose Fabrics: Effect of Nanopolysaccharide Coatings on Selective Particle Rejection, Antifouling, and Antibacterial Properties

This article presents a comparative study of the surface characteristics and water purification performance of commercially available cellulose nonwoven fabrics modified, via cast coating, with different nano-dimensioned bio-based carbohydrate polymers, viz. cellulose nanocrystals (CNC), TEMPO-oxidized cellulose nanofibers (T-CNF), and chitin nanocrystals (ChNC). The surface-modified nonwoven fabrics showed an improvement in wettability, surface charge modification, and a slight decrease of maximum pore size. The modification improved the water permeance in most of the cases, enhanced the particle separation performance in a wide range of sizes, upgraded the mechanical properties in dry conditions, and showed abiotic antifouling capability against proteins. In addition, T-CNF and ChNC coatings proved to be harmful to the bacteria colonizing on the membranes. This simple surface impregnation approach based on green nanotechnology resulted in highly efficient and fully bio-based high-flux water filtration membranes based on commercially available nonwoven fabrics, with distinct performance for particle rejection, antifouling and antibacterial properties.
Decompose Boolean Matrices with Correlation Clustering

One of the tasks of data science is the decomposition of large matrices in order to understand their structures. A special case of this is when we decompose relations, i.e., logical matrices. In this paper, we present a method based on the similarity of rows and columns, which uses correlation clustering to cluster the rows and columns of the matrix, facilitating the visualization of the relation by rearranging the rows and columns. In this article, we compare our method with Gunther Schmidt’s problems and solutions. Our method produces the original solutions by selecting its parameters from a small set. However, with other parameters, it provides solutions with even lower entropy.
Specialization : Transportation Engineering and Logistics Report number : 2017 . TEL . 8163 Title : Collision Detection for Automated Guided Vehicles

This report consists of 44 pages and 3 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice. Specialization: Transportation Engineering and Logistics
Fe b 20 19 UNIFYING ISOLATED AND OVERLAPPING AUDIO EVENT DETECTION WITH MULTI-LABEL MULTITASK CONVOLUTIONAL RECURRENT NEURAL NETWORKS

We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurrent layers perform sequential modelling. Furthermore, the output layer is designed to handle arbitrary degrees of event overlap. At each time step in the recurrent output sequence, an output triple is dedicated to each event category of interest to jointly model event occurrence and temporal boundaries. That is, the network jointly determines whether an event of this category occurs, and when it occurs, by estimating onset and offset positions at each recurrent time step. We then introduce three sequential losses for network training: multi-label classification loss, distance estimation loss, and confidence loss. We demonstrate good generalization on two datasets: ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for overlapping audio event detection.
Global mean frequency increases of daily and sub-daily heavy precipitation in ERA5

Changes in heavy precipitation frequency can be viewed as a change in the event return period, a common metric of risk. Compared with intensity, frequency changes are less well-studied and past work has largely been constrained to analysis of well-instrumented regions. We exploit the latest ERA5 reanalysis and its global hourly accumulations at 1/4° spatial resolution and apply a metric that captures frequency changes across both wet and dry regions. According to ERA5 and in a global average sense, during 1989–2018, hourly events that occurred once per year in 1979–1988 increased in frequency by 71 (53–93, 95% range) %, while the one day per year heavy event frequency increased by 44 (37–54) %. Thus, hourly events that occurred once per year in the baseline decade are on track to double in frequency by 2021–2030, and the daily events by 2047–2056. Furthermore, our results replicate prior findings that relative frequency increases are larger for increasingly rare events, and for the first time we quantify that mean frequency increases have been greater over ocean than land. Ocean increases are larger by factors of 3.0 and 2.1 for the hourly and daily events that occurred once per year in 1979–1988, respectively.
Laboratory Detection of Cyanoacetic Acid: A Jet-cooled Rotational Study

Herein we present a laboratory rotational study of cyanoacetic acid (CH2(CN)C(O)OH), an organic acid as well as a –CN-bearing molecule, which is a candidate molecular system to be detected in the interstellar medium (ISM). Our investigation aims to provide direct experimental frequencies of cyanoacetic acid to guide its eventual astronomical search in low-frequency surveys. Using different jet-cooled rotational spectroscopic techniques in the time domain, we have determined a precise set of the relevant rotational spectroscopic constants, including the 14N nuclear quadrupole coupling constants for the two distinct structures, cis- and gauche-cyanoacetic acid. We believe this work will potentially allow the detection of cyanoacetic acid in the ISM, whose rotational features have remained unknown until now.
J ul 2 02 1 Uncertainty in Ranking

Ranks estimated from data are uncertain and this poses a challenge in many applications. The need to measure the uncertainty in sample ranks has been recognized for some time, but the previous literature considering this problem has been concentrated on measuring the uncertainty of individual ranks and not the joint uncertainty. We characterize the relationship between parameter uncertainty and rank uncertainty in terms of linear extensions of a partial order and use this characterization to propose a measure of the joint uncertainty in a sample ranking. We provide efficient algorithms for several questions of interest and also derive valid simultaneous confidence intervals for the individual ranks. We apply our methods to both simulated and real data and make them available through the R package rankUncertainty.
Collective Reputations, Trust Premium, and Corporate Misconduct

We examine the relationship between collective country reputations and foreign stock returns following news of corporate scandals. We find that investors punish not only the scandalous foreign firm but also other firms from the same country of origin, especially those coming from more trustworthy countries. The effect is more salient for firms with more imperfect information as measured by higher information asymmetry, lower reporting quality, and higher bankruptcy risk. Our findings suggest that investors incorporate a trust premium into stock prices based on foreign firms’ country-of-origin. However, they reclaim such a premium when a country’s trustworthiness is compromised by its affinity firm’s misconduct. Overall, we provide novel evidence that collective country reputations affect the pricing of foreign firms listed on the U.S. stock market.
The Global Financial Crisis 2007-2009: A Survey

The goal of this survey is to summarize the 2007–2009 Global Financial Crisis (GFC) main reasons and consequences. The GFC originated in developed countries and it was the first major financial crisis of the twenty-first century. From the United States perspective, the following four major events determined the GFC course: (1) the increase in subprime delinquency rates in the spring of 2007, (2) the liquidity crunch in late 2007, (3) the liquidation of Bear Stearns in March 2008, and (4) the bankruptcy of Lehman Brothers in September 2008. The GFC began in the U.S. but not all financial markets were affected by the crisis at the same time and to the same extent. The GFC periods were different in various countries. Therefore, the additional aim of this study is to gather the research results regarding the GFC periods within the world. Moreover, the research topics concerning stock market cross-correlations, contagion, integration, and globalization effects are often attributed to the impact of the GFC. Unfortunately, financial crises tend to repeat. For instance, the European markets have suffered from the subsequent financial crises since late 2009. Recently, financial markets in the world have been deeply affected by the COVID-19 pandemic crisis period.
Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean (Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.
Evolutionary relationships among the snakelike pygopodid lizards: a review of phylogenetic studies of an enigmatic Australian adaptive radiation

Here, I review phylogenetic studies of the lizard family Pygopodidae, a group of 47 extant species that diversified in Australia and New Guinea. The goal of this study was to examine published phylogenetic and phylogenomic hypotheses on pygopodids to identify the strengths and weaknesses in our understanding of their phylogeny. Many parts of the pygopodid family tree are well established by multiple independent tree inferences including: (1) all multispecies genera (i.e., Aprasia, Delma, Lialis, Pletholax, and Pygopus) are monophyletic groups; (2) the root of the pygopodid tree is located along the branch leading to the Delma clade, thus showing that Delma is the sister group to all other pygopodid genera; (3) the Aprasia repens group, Delma tincta group, and several other groups of closely related species are demonstrated to be monophyletic entities; and (4) the monotypic Paradelma orientalis is the sister lineage to the Pygopus clade. Based on accumulated phylogenetic evidence, two taxonomic recommendations are given: Paradelma merits generic status rather than being subsumed into Pygopus as some earlier studies had suggested, and the monotypic Aclys concinna should be recognized as a member of Delma (following current practice) until future studies clarify its placement inside or outside the Delma clade. One chronic problem with phylogenetic studies of pygopodids, which has limited the explanatory power of many tree hypotheses, concerns the undersampling of known species. Although the continual addition of newly described species, especially over the past two decades, has been a major reason for these taxon sampling gaps, deficits in species sampling for ingroups and/or outgroups in several studies of pygopodid species complexes has confounded the testing of some ingroup monophyly hypotheses. Ancient hybridization between non-sister lineages may also be confounding attempts to recover the relationships among pygopodids using molecular data. Indeed, such a phenomenon can explain at least five cases of mito-nuclear discordance and conflicts among trees based on nuclear DNA datasets. Another problem has been the lack of consensus on the relationships among most pygopodid genera, an issue that may stem from rapid diversification of these lineages early in the group’s history. Despite current weaknesses in our understanding of pygopodid phylogeny, enough evidence exists to clarify many major and minor structural parts of their family tree. Accordingly, a composite tree for the Pygopodidae was able to be synthesized. This novel tree hypothesis contains all recognized pygopodid species and reveals that about half of the clades are corroborated by multiple independent tree hypotheses, while the remaining clades have less empirical support.
Impact of Clinician Recognition of Acute Respiratory Distress Syndrome on Evidenced-Based Interventions in the Medical ICU

Supplemental Digital Content is available in the text. OBJECTIVES: Acute respiratory distress syndrome is underrecognized in the ICU, but it remains uncertain if acute respiratory distress syndrome recognition affects evidence-based acute respiratory distress syndrome care in the modern era. We sought to determine the rate of clinician-recognized acute respiratory distress syndrome in an academic medical ICU and understand how clinician-recognized-acute respiratory distress syndrome affects clinical care and patient-centered outcomes. DESIGN: Observational cohort study. SETTING: Single medical ICU at an academic tertiary-care hospital. PATIENTS: Nine hundred seventy-seven critically ill adults (381 with expert-adjudicated acute respiratory distress syndrome) enrolled from 2006 to 2015. INTERVENTIONS: Clinician-recognized-acute respiratory distress syndrome was identified using an electronic keyword search of clinical notes in the electronic health record. We assessed the classification performance of clinician-recognized acute respiratory distress syndrome for identifying expert-adjudicated acute respiratory distress syndrome. We also compared differences in ventilator settings, diuretic prescriptions, and cumulative fluid balance between clinician-recognized acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome. MEASUREMENTS AND MAIN RESULTS: Overall, clinician-recognized-acute respiratory distress syndrome had a sensitivity of 47.5%, specificity 91.1%, positive predictive value 77.4%, and negative predictive value 73.1% for expert-adjudicated acute respiratory distress syndrome. Among the 381 expert-adjudicated acute respiratory distress syndrome cases, we did not observe any differences in ventilator tidal volumes between clinician-recognized-acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome, but clinician-recognized-acute respiratory distress syndrome patients had a more negative cumulative fluid balance (mean difference, –781 mL; 95% CI, [–1,846 to +283]) and were more likely to receive diuretics (49.3% vs 35.7%, p = 0.02). There were no differences in mortality, ICU length of stay, or ventilator-free days. CONCLUSIONS: Acute respiratory distress syndrome recognition was low in this single-center study. Although acute respiratory distress syndrome recognition was not associated with lower ventilator volumes, it was associated with differences in behaviors related to fluid management. These findings have implications for the design of future studies promoting evidence-based acute respiratory distress syndrome interventions in the ICU.
Baseline Values and Kinetics of IL-6, Procalcitonin, and TNF-α in Landrace-Large White Swine Anesthetized with Propofol-Based Total Intravenous Anesthesia

The baseline levels of various inflammatory mediators and their changes during anesthesia in swine are not known. The aim of this animal study was to measure the baseline values and kinetics of interleukin-6, procalcitonin, and tumor necrosis factor-alpha in healthy Landrace-Large White swine anesthetized with propofol-based total intravenous anesthesia. We included 8 healthy male pigs with an average weight of 19 ± 2 kg (aged 10-15 weeks) that were subjected to propofol-based total intravenous anesthesia for 8 hours. Complete blood count, serum chemistry, and serum levels of interleukin-6, procalcitonin, and tumor necrosis factor-alpha were analyzed, and serum levels were quantified hourly. Blood was also collected for bacterial culturing. Baseline values of interleukin-6 and procalcitonin were 18 pg/ml and 21 ng/ml, respectively, while tumor necrosis factor-alpha was not detectable during collection of baseline samples. A statistically significant difference was observed in interleukin-6 levels between time points (p < 0.0001). Procalcitonin increased with time, but there were no significant differences between time points (p = 0.152). Tumor necrosis factor-alpha increased until the 3rd hour of propofol-based total intravenous anesthesia, while after the 4th hour, it gradually decreased, reaching its baseline undetectable values by the 7th hour (p < 0.001). Our results can serve as the basis for further translational research.
Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine

Stem cells are undifferentiated cells that can self-renew and differentiate into diverse types of mature and functional cells while maintaining their original identity. This profound potential of stem cells has been thoroughly investigated for its significance in regenerative medicine and has laid the foundation for cell-based therapies. Regenerative medicine is rapidly progressing in healthcare with the prospect of repair and restoration of specific organs or tissue injuries or chronic disease conditions where the body’s regenerative process is not sufficient to heal. In this review, the recent advances in stem cell-based therapies in regenerative medicine are discussed, emphasizing mesenchymal stem cell-based therapies as these cells have been extensively studied for clinical use. Recent applications of artificial intelligence algorithms in stem cell-based therapies, their limitation, and future prospects are highlighted.
The Aqueous Extract of Eucommia Leaves Promotes Proliferation, Differentiation, and Mineralization of Osteoblast-Like MC3T3-E1 Cells

Eucommia leaves are dry leaves of Eucommia ulmoides which have long been considered as a functional health food for the treatment of hypertension, hypercholesterolemia, fatty liver, and osteoporosis. With the recent development of Chinese medicine, Eucommia leaves are widely used for tonifying the kidneys and strengthening bone. However, the specific molecular mechanism of Eucommia leaves for strengthening bone remains largely unknown. Osteoblasts are the main functional cells of bone formation; thus, it is essential to study the effect of Eucommia leaves on osteoblasts to better understand their mechanism of action. In the present study, we prepared an aqueous extract of Eucommia leaves (ELAE) and determined its content by high-performance liquid chromatography (HPLC). The effects of ELAE on MC3T3-E1 cells were investigated by CCK-8 assay, alkaline phosphatase (ALP), and Alizarin red S staining assays, combined with RNA sequencing (RNA-seq) and qRT-PCR validation. We demonstrated that ELAE had a significant promoting effect on the proliferation of MC3T3-E1 cells and significantly enhanced extracellular matrix synthesis and mineralization, which were achieved by regulating various functional genes and related signaling pathways. ELAE significantly increased the expression level of genes promoting cell proliferation, such as Rpl10a, Adnp, Pex1, Inpp4a, Frat2, and Pcdhga1, and reduced the expression level of genes inhibiting cell proliferation, such as Npm1, Eif3e, Cbx3, Psmc6, Fgf7, Fxr1, Ddx3x, Mbnl1, and Cdc27. In addition, ELAE increased the expression level of gene markers in osteoblasts, such as Col5a2, Ubap2l, Dkk3, Foxm1, Col16a1, Col12a1, Usp7, Col4a6, Runx2, Sox4, and Bmp4. Taken together, our results suggest that ELAE could promote osteoblast proliferation, differentiation, and mineralization and prevent osteoblast apoptosis. These findings not only increase our understanding of ELAE on the regulation of bone development but also provide a possible strategy to further study the prevention and treatment of osteogenic related diseases by ELAE.
Viral transmission and evolution dynamics of SARS-CoV-2 in shipboard quarantine

Abstract Objective To examine transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in shipboard quarantine of the Diamond Princess cruise ship. Methods We obtained the full SARS-CoV-2 genome sequences of 28 samples from the Global Initiative on Sharing All Influenza Data database. The samples were collected between 10 and 25 February 2020 and came for individuals who had been tested for SARS-CoV-2 during the quarantine on the cruise ship. These samples were later sequenced in either Japan or the United States of America. We analysed evolution dynamics of SARS-CoV-2 using computational tools of phylogenetics, natural selection pressure and genetic linkage. Findings The SARS-CoV-2 outbreak in the cruise most likely originated from either a single person infected with a virus variant identical to the WIV04 isolates, or simultaneously with another primary case infected with a virus containing the 11083G > T mutation. We identified a total of 24 new viral mutations across 64.2% (18/28) of samples, and the virus evolved into at least five subgroups. Increased positive selection of SARS-CoV-2 were statistically significant during the quarantine (Tajima’s D: −2.03, P < 0.01; Fu and Li’s D: −2.66, P < 0.01; and Zeng’s E: −2.37, P < 0.01). Linkage disequilibrium analysis confirmed that ribonucleic acid (RNA) recombination with the11083G > T mutation also contributed to the increase of mutations among the viral progeny. Conclusion The findings indicate that the 11083G > T mutation of SARS-CoV-2 spread during shipboard quarantine and arose through de novo RNA recombination under positive selection pressure.