Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimativa da resistência e da elasticidade à compressão paralela às fibras da madeira de Eucalyptus grandis e E. urophylla usando a espectroscopia no infravermelho próximo

O emprego da madeira para finalidade estrutural, com seguranca e economia, exige o conhecimento de suas propriedades mecânicas. A realizacao do ensaio e relativamente rapida, entretanto a preparacao dos corpos-de-prova e uma etapa demorada e trabalhosa e frequentemente causa desperdicio de madeira devido a dificuldade de obtencao de corpos-de-prova livres de defeitos nas dimensoes normalizadas. Alem disso, ha a necessidade de utilizacao de maquina de ensaio e de instrumentos de medicao de deformacoes caso se deseje obter dados de rigidez. Uma das alternativas para contornar esses problemas e a espectroscopia no infravermelho proximo ou NIRS (Near Infrared Spectroscopy), que e um metodo indireto, rapido e preciso para a predicao de muitas propriedades da madeira. A utilizacao desta tecnica na avaliacao de algumas propriedades mecânicas pode reduzir o tempo, o custo e as perdas de material, principalmente em pecas com alta incidencia de defeitos, mas para isso, e necessario efetuar uma etapa de calibracao a partir de dados de referencia. O objetivo deste trabalho foi avaliar a eficiencia da espectroscopia no infravermelho proximo para estimar a resistencia e a elasticidade a compressao paralela as fibras da madeira de Eucalyptus grandis e E. urophylla. Os ensaios de compressao paralela as fibras foram executados em Maquina Universal de Ensaio e seguiram os procedimentos da norma ASTM D-143 - 94 (1997). Os espectros no infravermelho proximo foram adquiridos em um espectrometro com varredura de 4.000 a 12.500 cm-1 em modo de reflexao difusa. Assim, a informacao espectral obtida sobre a face transversal dos corpos-de-prova foi correlacionada com os valores das propriedades mecânicas pela regressao dos minimos quadrados parciais. A resistencia e a elasticidade a compressao paralela as fibras da madeira de Eucalyptus grandis e E. urophylla podem ser estimadas por modelos que apresentam coeficientes de determinacao na validacao cruzada de 0,78 e 0,75 respectivamente, que sao valores satisfatorios considerando a simplicidade e a rapidez na execucao da analise pelo NIRS.
The Role of Flax in the Diet — Learn About Its Nutritional Content and Potential to Help Prevent and Manage Various Medical Conditions

One tablespoon of ground flaxseeds provides 37 calories, 1.28 g protein, 1.9 g fiber, 2 g carbohydrate, and 2.95 g fat (0.3 g saturated, 0.5 g monounsaturated, and 2 g polyunsaturated), as well as 1,597 mg omega-3 fatty acids and 414 mg omega-6 fatty acids. It also provides a good source of thiamin (8% DV), magnesium (7% DV), and manganese (9% DV). In addition, flax is one of the richest known sources of the dietary lignan secoisolariciresinol diglucoside (SDG). 3
Genetically and dietary induced obesity associate differently with gut microbiota in a murine intestinal tumorigenesis model

Background: Overweight and obesity are risk factors for human colorectal cancer. Growing evidence suggests that the gut microbiome affects both obesity and cancer. In this study, we examined how the murine microbiota composition correlated with obesity, intestinal tumorigenesis, glucose regulation, and inflammation. Materials and Methods: We used 16S ribosomal RNA gene analyses of feces and data obtained from a double-mutant mouse model; multiple intestinal neoplasia (Min), mice, which spontaneously develop intestinal tumors, crossed with obesity (ob), mice, which develop obesity, fed 10% or 45% fat diet. Results: We found that diet and genotypes imposed a major impact on the gut microbiota composition. Likewise, we found strong associations of the microbiota composition with obesity, number of small intestinal tumors, and blood glucose levels. Specifically, bacteria related to Clostridium perfringens and Lactobacillus showed strong positive associations with both dietary induced and genetically induced obesity, while Bacteroidales showed strong negative associations. Representatives of Lachnospiraceae and Peptostreptococcaceae only showed significant negative associations with genetically induced obesity and no associations with dietary induced obesity. Conclusions: We found complex associations between the microbiota and genetic background, diet, obesity, glucose levels, inflammation, and intestinal tumorigenesis. This could contribute to the lack of consensus between the results in previous studies regarding correlations of microbiota with obesity and cancer.
Assimilation of GNSS Reflectometry Delay-Doppler Maps into Global Ocean SurfaceWind Analyses

Direct remote sensing observations (e.g. radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g. ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS-R), the lower-level delay-Doppler map (DDM) observable shows a complicated relationship to the ocean surface wind field. Prior studies have demonstrated DA using GNSS-R wind retrievals produced from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of directly ingesting DDM observables into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two-dimensional variational analysis method. Bias correction and quality control methods are described. Several models for the required observation error covariance matrix are developed and evaluated, concluding that a diagonal matrix scaled with DDM magnitude performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. 10-meter surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast are used as the background. Collocated scatterometer (ASCAT, OSCAT) winds are considered the truth for comparison. Results using one month (June 2017) of data show a reduction in the root-mean-square error (RMSE) from 1.17 to 1.07 m/s and bias from -0.14 to -0.08 m/s for the wind speed at the specular point. Within a 150-km-wide swath along the specular point track, the RMSE was reduced from 1.20 to 1.13 m/s. Wind speed results from DA show smaller RMSE and bias than other CYGNSS wind products available at this time.
Characterization of the effect of dopamine on the neural coding of reward-based learning and decision-making

Dopamine has an important role in normal cognition and reward processing, both of which are impaired in disorders involving dopamine dysfunction such as addiction, schizophrenia, and Parkinson’s disease. However, our understanding of the interplay between different aspects of the dopamine system and reward-guided behavior in humans is limited. Food is an important type of reward that is critical for survival and impacts the decisions we make every day. Here, we characterize the relationship between two food-reward related phenotypes and dopamine synthesis capacity (related to tonic dopamine) as well as dopamine D1 and D2 receptor availability in healthy humans. First, we examined the link between dopamine synthesis and receptor availability and body mass regulation in 117 individuals with body mass index (BMI) values ranging from normal to obese. We found that current BMI was related to a pattern of increased dopamine synthesis in the hypothalamus, a region important for homeostatic control of appetite, but decreased dopamine D 2 receptor availability in the midbrain, where D 2 autoreceptors regulate dopamine release throughout the brain. This suggests that increased BMI is related to a dopamine imbalance between homeostatic drivers of appetite and reward system regulatory control mechanisms that could result in an overactive, unregulated intake of food. Building on this finding, we studied the link between dopamine synthesis capacity and receptor availability and an important food-reward related behavior, foraging. Fifty-seven healthy volunteers completed a computer-based foraging task where we measured their threshold for leaving one group of rewards to search for another in four different reward environments varying from a low to high rate of reward receipt. We found that two particular patterns of dopamine synthesis and receptor availability in the anterior cingulate cortex and basal ganglia were linked to the amount that individuals changed their threshold based on the reward rate of the environment. Finally, since the prefrontal cortex is known to be important for reward-guided behavior, we implemented two methodological advancements aimed to address limitations that make it difficult to measure cortical dopamine in humans with PET imaging. The first method involves partial volume correction and surface-based smoothing in order to increase the signal to noise in the cortex. The second method is a data-driven PET data parcellation and automated reference region selection algorithm to optimize the voxels included in the reference region. In conclusion, we have characterized the dopaminergic contribution of two different foodreward guided phenotypes and have developed two techniques that will aid future research on the role of cortical dopamine. Understanding the neural mechanisms underlying these rewardguided behaviors helps us to not only understand normal behavior, but also serves as a reference for comparison when studying related pathological states.
A Novel Self-Triggered MPC Scheme for Constrained Input-Affine Nonlinear Systems

This brief develops a novel self-triggered model predictive control algorithm based on time delay estimation for input-affine nonlinear systems. At each triggering instant, the algorithm determines simultaneously the predictive control sequence to feedforward compensate for the disturbance and the next triggering instant. As a result, the unnecessary samplings and transmissions are suppressed, and the frequency of solving the optimal control problem is reduced. The feasibility as well as the associated stability are verified, with a numerical example illustrating the effectiveness of the proposed scheme.
Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis

Background Computed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic. Objective The aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative. Methods A retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis. Results A total of 94 (56%) patients were confirmed positive for COVID-19 from the suspected 167 patients. We found that elderly people were more likely to be infected with COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit. No patients died during the study period. We found that the presence, distribution, and location of CT lesions were associated with the presence of COVID-19. White blood cell count, cough, and a travel history to Wuhan were also the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (95% CI 0.93-1.00). Conclusions Taken together with nucleic acid testing, we found that CT scans can allow for the rapid diagnosis of COVID-19. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms.
Sciandrone ON THE CONVERGENCE OF THE BLOCK NONLINEAR GAUSS-SEIDEL METHOD UNDER CONVEX CONSTRAINTS R . 467 Settembre 1998

In this paper we state some new convergence results on the minimization version of the blocknonlinear Gauss-Seidel (GS) method for problems with feasible set defined as the Cartesian product of closed convex sets. First we derive some general properties of the limit points produced by the GS method. Using these results we show that in the case of a two-block decomposition, the method is globally convergent towards critical points of the constrained problem even in the absence of any convexity assumption on the objective function f . Then we analyze the convergence properties of the method with a m−block decomposition, under convexity assumptions on f . We show that the GS method is globally convergent both when f is componentwise strictly pseudo-convex with respect to m−2 blocks, and when f is pseudoconvex and has bounded level sets in the feasible region. We also discuss a constrained version of a counterexample of Powell, which shows that the GS method may fail for m ≥ 3 when these assumptions are not satisfied. Finally, in the general case of nonconvex objective function and arbitrary decomposition, we prove the global convergence of a proximal point modification of the GS method.
Serologic SARS-CoV-2 testing in healthcare workers with positive RT-PCR test or Covid-19 related symptoms

Background. Limited information is available on prevalence and determinants of serologic response to SARS-CoV-2 infection among healthcare workers (HCWs). Methods. We analyzed the results of serologic testing with chemiluminescence immunoassay analyzer (CLIA), lateral flow immunoassay (LFIA) and enzyme-linked immunosorbent assay (ELISA) test among 544 HCWs with at least one positive RT-PCR test and 157 HCWs with Covid-19 related symptoms without a positive RT-PCR test from public hospitals in Bologna, Northern Italy. Tests were performed between March and August 2020. We fitted multivariate logistic regression models to identify determinants of positive serology. Results. The sensitivity of SARS-CoV-2 was 75.2% (LFIA) and 90.6% (CLIA). No differences in seropositivity were observed by sex, while older HCWs had higher positivity than other groups, and nurses had higher positivity compared to physicians, but not other HCWs. An estimated 73.4% of HCWs with Covid-19 symptoms without RT-PCR test were not infected with SARS-CoV-2. Conclusions. Our study provides the best available data on sensitivity of serologic tests and on determinants of serologic response among HCWs positive for SARS-CoV-2, and provide evidence on the low specificity of Covid-19 related symptoms to identify infected HCWs.
Clinical Outcome of Asymptomatic COVID-19 Infection Among a Large Nationwide Cohort of 5,621 Hospitalized Patients in Korea

We investigated clinical outcome of asymptomatic coronavirus disease 2019 (COVID-19) and identified risk factors associated with high patient mortality using Korean nationwide public database of 5,621 hospitalized patients. The mortality rate and admission rate to intensive care unit were compared between asymptomatic and symptomatic patients. The prediction model for patient mortality was developed through risk factor analysis among asymptomatic patients. The prevalence of asymptomatic COVID-19 infection was 25.8%. The mortality rates were not different between groups (3.3% vs. 4.5%, p=0.17). However, symptomatic patients were more likely to receive ICU care compared to asymptomatic patients (4.1% vs. 1.0%, p<0.0001). The age-adjusted Charlson comorbidity index score (CCIS) was the most potent predictor for patient mortality in asymptomatic patients. The clinicians should predict the risk of death by evaluating age and comorbidities but not the presence of symptoms.
Structured Visual Search via Composition-aware Learning

This paper studies visual search using structured queries. The structure is in the form of a 2D composition that encodes the position and the category of the objects. The transformation of the position and the category of the objects leads to a continuous-valued relationship between visual compositions, which carries highly beneficial information, although not leveraged by previous techniques. To that end, in this work, our goal is to leverage these continuous relationships by using the notion of symmetry in equivariance. Our model output is trained to change symmetrically with respect to the input transformations, leading to a sensitive feature space. Doing so leads to a highly efficient search technique, as our approach learns from fewer data using a smaller feature space. Experiments on two large-scale benchmarks of MS-COCO [29] and HICO-DET [4] demonstrates that our approach leads to a considerable gain in the performance against competing techniques.
Decline in mortality among hospitalised covid-19 patients in Sweden: a nationwide observational study

OBJECTIVE It is important to know if mortality among hospitalised covid-19 patients has changed as the pandemic has progressed. The aim of this study was to describe the dynamics of mortality among patients hospitalised for covid-19 in a nationwide study. DESIGN Nationwide observational cohort study of all patients hospitalised in Sweden 1 March to 30 June 2020 with SARS-CoV-2 RNA positivity 14 days before to 5 days after admission, and a discharge code for covid-19. SETTING All hospitals in Sweden. PARTICIPANTS 15 761 hospitalised patients with covid-19, with data compiled by the Swedish National Board of Health and Welfare. MAIN OUTCOME MEASURES Outcome was 60-day all-cause mortality. Patients were stratified according to month of hospital admission. Poisson regression was used to estimate the relative risk of death by month of admission, adjusting for pre-existing conditions, age, sex, care dependency, and severity of illness (Simplified Acute Physiology, version 3), for patients in intensive care units (ICU). RESULTS The overall 60-day mortality was 17.8% (95% confidence interval (CI), 17.2% to 18.4%), and it decreased from 24.7% (95% CI, 23.0% to 26.5%) in March to 13.3% (95% CI, 12.1% to 14.7%) in June. Adjusted relative risk (RR) of death was 0.56 (95% CI, 0.51 to 0.63) for June, using March as reference. Corresponding RR for patients not admitted to ICU and those admitted to ICU were 0.60 (95% CI, 0.53 to 0.67) and 0.61 (95% CI, 0.48 to 0.79), 3 respectively. The proportion of patients admitted to ICU decreased from 19.5% (95% CI, 17.9% to 21.0%) in the March cohort to 11.0% (95% CI, 9.9% to 12.2%) in the June cohort. CONCLUSIONS There was a gradual decline in mortality from March to June 2020 in Swedish hospitalised covid-19 patients, which was independent of pre-existing conditions, age, and sex. Future research is needed to explain the reasons for this decline. The changing covid-19 mortality should be taken into account when management and results of studies from the first pandemic wave are evaluated.
Mitochondrial genome copy number in human blood-derived DNA is strongly associated with insulin levels and related metabolic traits and primarily reflects cell-type composition differences

Mitochondrial copy number is known to vary among humans and across tissues, and a population-based study of mitochondrial genome copy number (MT-CN) in blood - as estimated from genome sequencing data - observed strong (~54%) heritability. However, the genetic causes and phenotypic consequences of MT-CN variation in humans are not well-studied. Here, we studied MT-CN variation in blood-derived DNA from 19,184 Finnish individuals with deep cardiometabolic trait measurements using a combination of whole genome (N = 4,163) and exome sequencing (N = 19,034) data as well as imputed array genotypes (N = 17,718). We confirmed that MT-CN in blood is highly heritable (31% by GREML). We identified two loci in the nuclear genome that are significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6x10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMB1M1 gene (P = 3.0x10-8), which has been reported to protect against non-alcoholic fatty liver disease in model organisms. We also found that MT-CN is strongly associated with insulin levels (P = 2.0x10-21), fat mass (P = 4.5x10-16), and other related traits. Using a Mendelian randomization framework, we constructed a genetic instrument for MT-CN using penalized regression with adjustment for potentially confounding covariates and found a significant association with insulin levels, which suggests that our MT-CN measurement in blood may be causally related to metabolic syndrome. Finally, we computed our genetic instrument in UK Biobank participants and tested it against a set of cell count and cardiometabolic traits. We found significant associations between MT-CN and both neutrophil and platelet counts (P = 1.8x10-8 and P = 1.2x10-3). While the association between MT-CN and metabolic syndrome traits was replicated in the UK Biobank, adjusting for cell counts largely eliminated these signals, suggesting that MT-CN is actually a proxy measurement for neutrophil and platelet counts in its effect on metabolic syndrome. Taken together, these results suggest that measurements of MT-CN in blood-derived DNA may primarily reflect differences in cell-type composition, and that these differences may be causally linked to insulin and related traits.
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.
Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model

Recently, online end-to-end ASR has gained increasing attention. However, the performance of online systems still lags far behind that of offline systems, with a large gap in quality of recognition. For specific scenarios, we can trade-off between performance and latency, and can train multiple systems with different delays to match the performance and latency requirements of various application scenarios. In this work, in contrast to trading-off between performance and latency, we envisage a single system that can match the needs of different scenarios. We propose a novel architecture, termed Universal ASR that can unify streaming and non-streaming ASR models into one system. The embedded streaming ASR model can configure different delays according to requirements to obtain real-time recognition results, while the non-streaming model is able to refresh the final recognition result for better performance. We have evaluated our approach on the public AISHELL-2 benchmark and an industrial-level 20,000-hour Mandarin speech recognition task. The experimental results show that the Universal ASR provides an efficient mechanism to integrate streaming and non-streaming models that can recognize speech quickly and accurately. On the AISHELL-2 task, Universal ASR comfortably outperforms other state-of-the-art systems.