Miracle-dz/UniFAR-FTUs huggingface dataset:
Gefitinib Results in Robust Host-Directed Immunity Against Salmonella Infection Through Proteo-Metabolomic Reprogramming

The global rise of antibiotic-resistant strains of Salmonella has necessitated the development of alternative therapeutic strategies. Recent studies have shown that targeting host factors may provide an alternative approach for the treatment of intracellular pathogens. Host-directed therapy (HDT) modulates host cellular factors that are essential to support the replication of the intracellular pathogens. In the current study, we identified Gefitinib as a potential host directed therapeutic drug against Salmonella. Further, using the proteome analysis of Salmonella-infected macrophages, we identified EGFR, a host factor, promoting intracellular survival of Salmonella via mTOR-HIF-1α axis. Blocking of EGFR, mTOR or HIF-1α inhibits the intracellular survival of Salmonella within the macrophages and in mice. Global proteo-metabolomics profiling indicated the upregulation of host factors predominantly associated with ATP turn over, glycolysis, urea cycle, which ultimately promote the activation of EGFR-HIF1α signaling upon infection. Importantly, inhibition of EGFR and HIF1α restored both proteomics and metabolomics changes caused by Salmonella infection. Taken together, this study identifies Gefitinib as a host directed drug that holds potential translational values against Salmonella infection and might be useful for the treatment of other intracellular infections.
Human Induced Pluripotent Stem Cell-Derived Brain Endothelial Cells: Current Controversies

Brain microvascular endothelial cells (BMECs) possess unique properties that are crucial for many functions of the blood-brain-barrier (BBB) including maintenance of brain homeostasis and regulation of interactions between the brain and immune system. The generation of a pure population of putative brain microvascular endothelial cells from human pluripotent stem cell sources (iBMECs) has been described to meet the need for reliable and reproducible brain endothelial cells in vitro. Human pluripotent stem cells (hPSCs), embryonic or induced, can be differentiated into large quantities of specialized cells in order to study development and model disease. These hPSC-derived iBMECs display endothelial-like properties, such as tube formation and low-density lipoprotein uptake, high transendothelial electrical resistance (TEER), and barrier-like efflux transporter activities. Over time, the de novo generation of an organotypic endothelial cell from hPSCs has aroused controversies. This perspective article highlights the developments made in the field of hPSC derived brain endothelial cells as well as where experimental data are lacking, and what concerns have emerged since their initial description.
Association of Complement-Related Proteins in Subjects With and Without Second Trimester Gestational Diabetes

Introduction Gestational Diabetes Mellitus (GDM) development is related to underlying metabolic syndrome that is associated with elevated complement C3 and C4. Elevated C3 levels have been associated with preeclampsia and the development of macrosomia. Methods This case-control study included 34 pregnant women with GDM and 16 non-diabetic (ND) women in their second trimester. Complement-related proteins were measured and correlated with demographic, biochemical, and pregnancy outcome data. Results GDM women were older with a higher BMI (p<0.001); complement C3, C4 and Factor-H were significantly elevated (p=0.001, p=0.05, p=0.01, respectively). When adjusted for age and BMI, Complement C3 (p=0.04) and Factor-H (p=0.04) remained significant. Partial correlation showed significant correlation between C4 with serum alanine aminotransferase (ALT) (p<0.05) and 2nd term diastolic blood pressure (p<0.05); Factor-H and C-reactive protein (CRP; p<0.05). Pearson bivariate analysis revealed significant correlations between C3, C4, and Factor-H and CRP; p<0.05; C3 and gestational age at delivery (GA; p<0.05); C4 and ALT and second-trimester systolic blood pressure (STBP) (p=0.008 and p<0.05, respectively); Factor-H and glycated hemoglobin (HbA1c) (p<0.05). Regression analysis showed that the elevation of C3 could be accounted for by age, BMI, GA and CRP, with CRP being the most important predictor (p=0.02). C4 elevation could be accounted for by ALT, CRP and STBP. CRP predicted Factor-H elevation. Conclusion The increased C3, C4 and Factor-H during the second trimester of pregnancy in GDM are not independently associated with GDM; inflammation and high BMI may be responsible for their elevation. The elevation of second trimester C3 in GDM is associated with earlier delivery and further work is needed to determine if this is predictive.
Thermal properties of wood reacted with a phosphorus pentoxide-amine system

The objective of this research was to improve the fire-retardant properties of wood in one treatment using a phosphorus pentoxide-amine system. Phosphorus pentox- ide and 16 amines including alkyl, halophenyl, and phenyl amines were compounded in N,N-dimethylformamide and the resulting solutions containing phosphoramides were re- acted with wood. The characteristics of phosphoramide- reacted wood were analyzed by FTIR, energy-dispersive X-ray analysis, and elemental analysis. Fire retardancy of phosphoramide-reacted woods was evaluated by DSC and thermogravimetric analysis (TGA). DSC demonstrated that the modification can lower the onset of an endotherm and decrease the heat of combustion and heat flow. TGA showed that most of the phosphoramide-reacted woods had higher char yields than that of wood impregnated with diammo- nium phosphate. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 2465-2481, 2004
A New Method for Detecting Myocardial Ischemia Based on ECG T-Wave Area Curve (TWAC)

In recent years, coronary heart disease (CHD) has become one of the main diseases that endanger human health, with a high mortality and disability rate. Myocardial ischemia (MI) is the main symptom in the development of CHD. Continuous and severe myocardial ischemia will lead to myocardial infarction. The clinical manifestations of MI are mainly the changes of ST-T segment of ECG, that is, ST segment and T wave. Nearly one third of patients with CHD, however, has no obvious ECG changes. In this paper, a new method for detecting MI based on the T-wave area curve (TWAC) was proposed. Through observation and analysis of clinical data, it was found that there exist significant correlation between the morphology of TWAC and MI. The TWAC morphology of normal subject is smooth and gentle, while the TWAC morphology of patients with coronary stenosis is mostly jagged, and the curve becomes more severe with more severe stenosis. The preliminary test results show that the sensitivity, specificity, and accuracy of the proposed method for detecting MI are 84.3, 83.6, and 84%, respectively. This study shows that the TWAC based approach may be an effective method for detecting MI, especially for the CHD patients with no obvious ECG changes.
Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Cooking at Home and Adherence to the Mediterranean Diet During the COVID-19 Confinement: The Experience From the Croatian COVIDiet Study

Introduction: The primary aims of this study were to evaluate the changes in dietary behavior among the Croatian adult population during the COVID-19 outbreak and to explore the impact of confinement on cooking habits. Methods: The study was based on results from COVIDiet_Int cross-sectional study—a part of COVIDiet project (NCT04449731). A self-administered online questionnaire was used to assess the frequency of food consumption, eating habits, and sociodemographic information. A total number of 4,281 participants (80.5% females and 19.4% males) completed the questionnaire. Results: The Mediterranean Diet Adherence Screener (MEDAS) score before the confinement was 5.02 ± 1.97, while during the confinement, the MEDAS score increased to 5.85 ± 2.04. Participants who had higher adherence to the Mediterranean diet (MedDiet) during the confinement were mostly females (88.8%), aged between 20 and 50 years, with the highest level of education (66.3%) and normal BMI (70.6%). The majority of participants maintained their dietary behavior as it was before COVID-19 confinement, while 36.9% decreased their physical activity. Participants with higher MEDAS score were more eager to increase their physical activity. Additionally, higher median values of MEDAS score were noted for participants with body mass index values below 24.9 kg/m2 (6.0 vs. 5.0 for participants with BMI above 25 kg/m2). Participants in all residence places increased their cooking frequency during the confinement (53.8%), which was associated with an increase in vegetables, legumes, as well as fish and seafood consumption. Conclusions: According to our findings, Croatian adults exhibited medium adherence to the MedDiet during the COVID-19 confinement. The results suggest that cooking frequency could be positively associated with overall dietary quality, which is of utmost importance in these demanding times.
Metabolic Reprogramming and Reactive Oxygen Species in T Cell Immunity

T cells undergo metabolic reprogramming and multiple biological processes to satisfy their energetic and biosynthetic demands throughout their lifespan. Several of these metabolic pathways result in the generation of reactive oxygen species (ROS). The imbalance between ROS generation and scavenging could result in severe damage to the cells and potential cell death, ultimately leading to T cell-related diseases. Interestingly, ROS play an essential role in T cell immunity. Here, we introduce the important connectivity between T cell lifespan and the metabolic reprogramming among distinct T cell subsets. We also discuss the generation and sources of ROS production within T cell immunity as well as highlight recent research concerning the effects of ROS on T cell activities.
Listen2Cough

The prevalence of ubiquitous computing enables new opportunities for lung health monitoring and assessment. In the past few years, there have been extensive studies on cough detection using passively sensed audio signals. However, the generalizability of a cough detection model when applied to external datasets, especially in real-world implementation, is questionable and not explored adequately. Beyond detecting coughs, researchers have looked into how cough sounds can be used in assessing lung health. However, due to the challenges in collecting both cough sounds and lung health condition ground truth, previous studies have been hindered by the limited datasets. In this paper, we propose Listen2Cough to address these gaps. We first build an end-to-end deep learning architecture using public cough sound datasets to detect coughs within raw audio recordings. We employ a pre-trained MobileNet and integrate a number of augmentation techniques to improve the generalizability of our model. Without additional fine-tuning, our model is able to achieve an F1 score of 0.948 when tested against a new clean dataset, and 0.884 on another in-the-wild noisy dataset, leading to an advantage of 5.8% and 8.4% on average over the best baseline model, respectively. Then, to mitigate the issue of limited lung health data, we propose to transform the cough detection task to lung health assessment tasks so that the rich cough data can be leveraged. Our hypothesis is that these tasks extract and utilize similar effective representation from cough sounds. We embed the cough detection model into a multi-instance learning framework with the attention mechanism and further tune the model for lung health assessment tasks. Our final model achieves an F1-score of 0.912 on healthy v.s. unhealthy, 0.870 on obstructive v.s. non-obstructive, and 0.813 on COPD v.s. asthma classification, outperforming the baseline by 10.7%, 6.3%, and 3.7%, respectively. Moreover, the weight value in the attention layer can be used to identify important coughs highly correlated with lung health, which can potentially provide interpretability for expert diagnosis in the future.
Emission spectra of free base phthalocyanine in superfluid helium droplets

We report on the electronic emission spectra of free-base phthalocyanine doped into superfluid 4He droplets. The emission spectra of phthalocyanine obtained upon vibronic excitation show a doubling of all lines, absent in the gas phase [Fitch, Wharton, and Levy, J. Chem. Phys. 70, 2018 (1979)]. Similar features found for Mg-phthalocyanine in 4He droplets suggest that this doubling is due to the helium environment. Quantized relaxation states of the first helium layer surrounding the embedded molecule are discussed as likely causes of the doubling.
Obtenção e separação de compostos bioativos de SCHINUS TEREBINTHIFOLIUS RADDI em meio supercrítico e avaliação da atividade citotóxica em células leucêmicas.

RESUMO – Pimenta rosa (Schinus terebenthifolius Raddi), distribuida amplamente no territorio brasileiro, possui varias acoes terapeuticas, tais como: cicatrizante, antisseptica, antifungica e anti-inflamatoria. Estudos recentes mostraram que seu oleo essencial possui tambem potencial citotoxico em linhagens de celulas cancerosas. Visando a aplicacao de compostos bioativos desta planta na area biomedica, o objetivo deste trabalho foi o desenvolvimento de um protocolo para a obtencao e separacao destes compostos utilizando fluido supercritico. Para isto, diferentes condicoes operacionais de extracao foram avaliadas. Os resultados obtidos apresentaram a existencia de duas fracoes com diferente composicao quimica sendo a condicao de 80 bar de pressao e 35 °C a que mostrou uma melhor separacao dos compostos. A primeira fracao apresentou os seguintes compostos majoritarios: alfa-felandreno, delta-3-carene e limoneno. Ja a segunda apresentou: beta-Elemen-7 alfa-ol, Elemol e beta-elemene.
Some Spherically Symmetric Exact Solutions of the Metric-Affine Gravity Theory

We present four new spherically symmetric exact solutions of the Metric-Affine Gravity theory. In each case, just as in the other known spherically symmetric solutions, the metric has the Reissner-Nordstrom (anti) de-Sitter form. The nonmetricity is of the Weyl type with a Coulomb potential form due to a dilatation charge. The torsion exhibits a variety of asymptotic behaviors including asymptotically constant. For three solutions the torsion depends on an arbitrary function. Each of the solutions is rather general, requiring only 7 or 8 conditions on the 23 parameters of the theory.
Present OH measurement limits and associated uncertainties

The first ion-assisted OH measurement instrument was developed and field tested in 1989. Since that time both instrument and technique have evolved substantially and several potential measurement interferences have been investigated. Included among these are reactions that could compete with the H2O/SO3 reaction, the effect Of H2O on the NO3− · HNO3/H2SO4 reaction, and potential wall losses. These investigations have also provided data of relevance to atmospheric ion and neutral chemistry for the two reactions listed above, as well as for the OH measurement itself. Measurements made at Mauna Loa Observatory in Hawaii show for the first time a diurnal variation in tropospheric OH which spans more than 2 orders of magnitude. Nighttime measurements revealed hydroxyl radicals in the 104 molecules cm−3 concentration range well after dark.
Anthropological Perspective in HR Management: A Case Study of China-based Multi-national Corporation

Chinese companies encounter many cross cultural management problems when globalize their market. With an anthropological perspective, this empirical research taps various information by getting familiar with the people within the organization studied, and explores how organizational identification (OID) affects employees’ performance in a cross culture scenario. The research conclusion shows that OID will affect employee’s work behavior and attitude in a positive way and hence improve team performance. In the final part of the study, some cross culture management issues are discussed and advices based on the study are presented.
On the influence of body velocity in foothold adaptation for dynamic legged locomotion via CNNs

This paper analyzes the importance of considering the body velocity for foothold adaptation during legged locomotion. We show how this velocity aﬀects the decisions made by a foothold adaption strategy, and the number of feasible footholds computed by the approach. We extend our previous work by considering the body velocity in this foothold adaptation method and augmenting a convolutional neural network (CNN) classiﬁer to account for the current velocity of the robot. Our results suggest that the foothold evaluation strategy has a better performance with this new CNN than with architectures that assume a constant velocity and that only consider heightmaps as input for the foothold evaluation.
Application of Deep Learning in Remote Sensing Monitoring of Large Herbivores- A Case Study in Qinghai Tibet Plateau

1School of Remote Sensing Information, North China Institute of Aerospace Eingering, Langfang 065000, Hebei Province, China 2Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, Hebei Province, China 3National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, Hebei Province, China Article Information Received 05 December 2019 Revised 11 February 2020 Accepted 22 February 2020 Available online 12 March 2021
Optimal short-term scheduling of multiproduct single-stage batch plants with parallel lines

The short-term scheduling of multiproduct single-stage batch plants with parallel lines involves the constraints concerning the release times and due dates of orders, as well as the sequence-dependent setup times and forbidden subsequences of production orders and the ready times of units. Taking the above constraints into consideration, this paper develops a short-term scheduling model for the multiproduct single-stage batch plants with parallel lines using the continuous-time domain representation and the notation of time slot. When the model is developed, the allocation of orders and units to time slots is represented by two sets of binary variables. The model is formulated as a mixed-integer linear programming (MILP) problem. It not only involves fewer binary variables than any other model based on the notation of time slot (Pinto, J. M.; Grossman, I. E. Ann. Oper. Res. 1998, 81, 433) but also can be used to optimize several types of objective functions. Based on the characteristics of the model and multiproduct single-stage batch plants, some heuristic rules are introduced. The rational employment of these heuristic rules can cut down the size of the model and has no effect on the optimality of the scheduling problem. A number of computational examples show that the optimal scheduling can be obtained by solving the MILP model in reasonable CPU time, especially when the scheduling problem involving several identical orders is considered.
The Wasserstein Loss Function

In many learning scenarios, the target variable space has a natural metric associated with it that captures the notion of semantic similarity between different target values. We can utilise this metric to define better loss functions that can incorporate the information from this metric into the learning algorithm. One such loss function is the Wasserstein Loss function, which provides a notion of the distance between two measures on a target label space with a particular metric. The Wasserstein distance between two measures is defined as the amount of “mass” that has to move times the distance by which it needs to move to make the two measures the same. The inspiration for our project was the recent NIPS paper (Frogner et al. 2015), which proposes to use the Wasserstein Loss function in a supervised learning setting, specifically, for a multi-class multi-label learning problem. In this project, we would like to explore the properties of this loss function by comparing its accuracy, convergence rates etc. against other loss functions, and by evaluating how changes in parameters and the distance metric impact its performance. We also try to reproduce some of the results described in (Frogner et al. 2015). Before we go into the details, we provide a brief summary of our project work:
Cytotoxic Effects of Verbascoside on MCF-7 and MDA-MB-231

Objectives Verbascoside, also known as acteoside/kusaginin, has attracted a great attention due to its pharmacological features. In this study, we aimed to determine the cytotoxic effects of pure verbascoside isolated from Phlomis nissolii L. plant in both MCF-7 and MDA-MB-231 cell lines in vitro. Materials and Methods MCF-7 and MDA-MB 231 cells were treated with verbascoside (100, 48, 25, 10, 1, 0.5, and 0.1 μM) for 24, 48, and 72 hours. Cytotoxic effect of verbascoside in MCF-7 and MDA-MB-231 cells was assessed using TEBU-BIO cell counting kit 8. Results and Conclusion IC50 values for 24, 48, and 72 h verbascoside exposure of MCF-7 cells were determined as 0.127, 0.2174, and 0.2828 μM, respectively. R2 values were calculated as 0.9630, 0.8789 and 0.8752, respectively. Two-Way ANOVA multiple comparison test results showed that 100 μM verbascoside has the highest cytotoxic effect on MCF-7 breast cancer (BC) cells after 72 h of exposure. IC50 values for 24, 48 and 72 h verbascoside exposure of MDA-MB 231 cells were determined as 0.1597, 0.2584 and 0.2563 μM, respectively and R2 values were calculated as 0.8438, 0.5107 and 0.9203, respectively. Two-Way ANOVA multiple comparisons test results showed that 100 μM verbascoside has the highest cytotoxic effect on MDA-MB 231 BC cells after 24, 48 and 72 h of exposure.