Miracle-dz/UniFAR-FTUs huggingface dataset:
Upregulation of miR-181a-5p and miR-125b-2-3p in the Maternal Circulation of Fetuses with Rh-Negative Hemolytic Disease of the Fetus and Newborn Could Be Related to Dysfunction of Placental Function

The transplacental transfer of maternal antibodies to the fetus is a critical mechanism for infant protection and perinatal disease. Hemolytic disease of the fetus and newborn (HDFN) is a representative fetal disease caused by transplacental transfer of maternal IgG antibodies. However, it is unclear whether placental-related miRNAs are expressed in Rh-HDFN. Through the investigation of the miR-181a-5p and miR-125b-2-3p levels in maternal plasma using qPCR, we found that both miR-181a-5p and miR-125b-2-3p were highly expressed in maternal plasma of newborns with Rh-HDFN compared with healthy controls, indicating the potential roles of these two miRNAs in Rh-HDFN. To demonstrate whether dysregulation of miR-125b-2-3p and miR-181a-5p contributes to Rh-HDFN development, we analyze the placental miRNA-/mRNA sequencing data (GSE73714) using weighted gene coexpression network analysis (WGCNA), miRNA target predictive databases, and DAVID (Database for Annotation, Visualization, and Integrated Discovery). The results showed that miR-125b-2-3p and miR-181a-5p could regulate several biological processes including cytoplasmic microtubule organization and angiogenesis. Moreover, core promoter sequence-specific DNA binding and protein binding were highly enriched molecular functions, indicating the potential roles of transcriptional regulation. Further pathway enrichment showed that miR-181a-5p and miR-125b-2-3p could regulate several biological pathways that were closely related to placental function, including the FoxO signaling pathway, focal adhesion, mTOR signaling pathway, and central carbon metabolism in cancer. In conclusion, the present results first revealed miRNA expression in the maternal circulation of newborns with Rh-HDFN, which could be caused by dysfunction of the placenta.
The Sum of Standard Generalized Frames in Hilbert W ∗ -Module

In this paper, the sum of standard generalized frames of Hilbert W � -module is studied intensively by using operator-theoretic-methods, and some conditions are given to assure that the sum of two or more standard generalized frames is a standard generalized frame. Keywords generalized frame in Hilbert W � -module; analyzing operator; dual generalized frame; frame operator; disjointness; strong disjointness.
Grüneisen approach for universal scaling of the Brillouin shift in gases

A Grüneisen relationship is defined for gases, following the formulation of the original microscopic Grüneisen ratio γ = (d ln ω)/(d ln V) for solids. In the case of gases acoustic excitations represent the modes at frequency ω to be considered. By comparing to measured Brillouin shifts in various gases (SF6, N2O, and CO2) under various conditions of pressure and temperature, a specific value of the defined ratio γ 0 = 0.064 ± 0.004 is found to provide a universal description of the active modes in a gas. This finding of such universal gas law may find application in extrapolation of properties of ideal gases to regimes where those cannot be measured easily, like the acoustics and shocks at extremely high temperatures.
They're not buying what we're selling: comparing student-requested supports with instructional practice during group work

Studio physics is an interactive teaching method that benefits students’ learning over traditional physics teaching approaches. A hallmark of studio physics is that instructors minimize lecture time and maximize time spent with students working in groups. However, students need to be well-supported in the group discussions to achieve optimal learning outcomes. In this study, we distributed a student survey and conducted instructor interviews to investigate the support(s) students requested so they could actively engage in group work. The student survey data revealed that students wanted instructors to be approachable during group work and to have a mechanism that guarantees equal group contribution. However, there is a mismatch between the support provided by instructors and the detailed support requested by students. Unified and rigorous guidance for instructors is a necessity for students to actively engage in effective and inclusive group work.
Effects of Hydrogen and Nitrogen Concentration on Laminar Burning Velocities and NO, CO Formation of Propane-Air Mixtures

With the development of the uses of hydrocarbon compounds in many industrial fields, especially in the field of energy liberation through oxidation of gases, as previous research specialized in improving the combustion processes of gases by adding particles of other gases that contribute to improving the combustion of these compounds in terms of temperatures and speed of flame combustion. The previous study aimed to improve the flame speed by adding different gases to several compounds. In this research paper, propane gas C3H8 is used as the main gas to improve its combustion properties. N2 and H2 gas were added in different proportions, through which a clear effect on the combustion properties can be seen in terms of combustion speed. Flames and exhaust as CO and NOx. As the auxiliary gases were added in this improvement at rates of 20%, 30% and 40%, and then these tests were analyzed, where it was concluded that H2 gas clearly contributed to improving the combustion speed of the flame, as the flame combustion velocity reached 58.3 cm/s by 40%.
Nutritional assessment in patients with liver cirrhosis

Malnutrition is a liver cirrhosis complication affecting more than 20%-50% of patients. Although the term can refer to either nutrient deficiency or excess, it usually relates to undernutrition in cirrhosis settings. Frailty is defined as limited physical function due to muscle weakness, whereas sarcopenia is defined as muscle mass loss and an advanced malnutrition stage. The pathogenesis of malnutrition in liver cirrhosis is multifactorial, including decreased oral intake, maldigestion/malabsorption, physical inactivity, hyperammonemia, hypermetabolism, altered macronutrient metabolism and gut microbiome dysbiosis. Patients with chronic liver disease with a Body Mass Index of < 18.5 kg/m2 and/or decompensated cirrhosis or Child-Pugh class C are at the highest risk of malnutrition. For patients at risk of malnutrition, a detailed nutritional assessment is required, typically including a history and physical examination, laboratory testing, global assessment tools and body composition testing. The latter can be done using anthropometry, cross-sectional imaging including computed tomography or magnetic resonance, bioelectrical impedance analysis and dual-energy X-ray absorptiometry. A multidisciplinary team should screen for and treat malnutrition in patients with cirrhosis. Malnutrition and sarcopenia are associated with an increased risk of complications and a poor prognosis in patients with liver cirrhosis; thus, it is critical to diagnose these conditions early and initiate the appropriate nutritional therapy. In this review, we describe the prevalence and pathogenesis of malnutrition in liver cirrhosis patients and discuss the best diagnostic approach to nutritional assessment for them.
Vertex‐disjoint cycles of the same length in tournaments

A tournament is a digraph which has exactly one arc between any two vertices. Let k $k$ and q $q$ be two positive integers with q ≥ 4 $q\ge 4$ . A q $q$ ‐cycle is a cycle of length q $q$ . We prove that for any real number α > q ∕ 2 $\alpha \gt q\unicode{x02215}2$ , there exists a constant k α ${k}_{\alpha }$ such that for every k ≥ k α $k\ge {k}_{\alpha }$ , any tournament T $T$ with minimum outdegree at least α k $\alpha k$ contains k $k$ disjoint q $q$ ‐cycles. This generalizes a result by Bang‐Jensen, Bessy, and Thomassé for disjoint 3‐cycles. The linear factor q ∕ 2 $q\unicode{x02215}2$ of minimum outdegree is the best possible.
Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing.
Social Network Analysis of a Physics Faculty Online Learning Community

We analyze the results of two surveys administered to a Faculty Online Learning Community teaching a common physics curriculum designed primarily for pre-service elementary teachers. We use Social Network Analysis to represent the faculty network and compare members’ closeness, a measure of how closely connected a person is with every other person in their network, to their reported experience in the community. We find that participants’ self-efficacy, as well as their teaching and sense of benefitting from the community, are predictors of their centrality in the network as measured by closeness with other participants
Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis

must be constrained (Example 7.26), use of the step function in computing the probability that a team would rank best or worst in a league (Example 5.10), and implementation of a Dirichlet process prior (Example 6.27). The comprehensive 19-page reference list consists primarily of statistics books and journals (both applied and methodological), and also includes journals in applications areas from which worked examples are drawn (e.g., Cognitive Science, British Journal of Cancer, Virology, Health Economics, and Scienti c American). Unfortunately, due possibly to poor proofreading, the book contains so many confusing misstatements that its usefulness as an introductory text is very limited. The following are two representative examples. In Chapter 2, in which Congdon presents the basic Bayesian framework in the context of preparing for a Bayesian test of hypotheses, he states (p. 15):
Development and validation of a system for the prediction of excipient-excipient incompatibility using machine learning tools

The importance of pharmaceutical excipients in the creation of any dosage form is critical. These excipients are occasionally to blame for product underperformance and dosage form deterioration. Product deterioration and underperformance could be attributed to incompatibilities between drug and excipient or sometimes excipient and excipient either due to the presence of reactive impurities in the excipients or a reaction between the functional groups present on the excipients. Although, the drug and excipient incompatibilities are monitored and reported, excipient-excipient incompatibilities are overlooked due to a paucity of literature. Pharmaceutical companies used to work in a controlled environment (compatibility tests between excipients to determine the best excipients for dosage form creation) and utilize mitigation measures to suppress any incompatibilities between excipients when necessary. In the current paper, we propose a system to predict excipient-excipient incompatibilities that can occur during dosage form development, possible incompatibility reactions, as well as a potential way to counter these incompatibilities by suggesting alternative excipients based on the descriptor knowledge of the excipients. The system was developed and validated using SOM and random forest algorithms. Two systems were developed based on incompatibility and property relationship, this approach provides a wide scope of our system to select compatible excipients in place of incompatible ones. Two systems predicted top 10 key incompatibilities and properties which cover the maximum range of the pharmaceutical research and development. After the validation using random forest, our system was able to find the structural components responsible for these top incompatibilities and properties. The prediction accuracy was found to be >85%. This was confirmed by preparing confusion matrix of the top incompatibilities and properties and assigning values of 0.5 to predict the outcome. The excipients with probability of ≥0.5 were considered active (possessing incompatibility or property) and the excipients with probability
Vocabulary learning at first exposure: Replication of Gullberg et al. (2012) and Shoemaker and Rast (2013)

Abstract This article puts forward several proposals for replicating two well-known First Exposure studies dealing with the earliest stages of adult second language acquisition. Both of them enquire into the word-level knowledge that complete beginners are able to extract from minimal input when exposed to a new language for the first time. They also focus on several input variables that may enhance learning from minimal input. However, the first, by Gullberg et al. (2012), uses audiovisual input in Dutch learners of Chinese to assess word recognition and word meaning after watching a short video; while the second, by Shoemaker and Rast (2013), uses oral input with French learners of Polish to measure word recognition before and after 6.5 hours of intensive classroom exposure. Close and approximate replications of these studies can help to re-evaluate and generalise the findings, as well as contributing additional relevant data to the field.
Efficient Importance Sampling for Rare Event Simulation with Applications

Importance sampling has been known as a powerful tool to reduce the variance of Monte Carlo estimator for rare event simulation. Based on the criterion of minimizing the variance of Monte Carlo estimator within a parametric family, we propose a general account for finding the optimal tilting measure. To this end, when the moment generating function of the underlying distribution exists, we obtain a simple and explicit expression of the optimal alternative distribution. The proposed algorithm is quite general to cover many interesting examples, such as normal distribution, noncentral $\chi^2$ distribution, and compound Poisson processes. To illustrate the broad applicability of our method, we study value-at-risk (VaR) computation in financial risk management and bootstrap confidence regions in statistical inferences.
Gen Z Consumers’ Willingness To Pay Delivery Fee: An Indian Perspective

The Internet has become a global marketplace over the past few decades for buying or selling goods and services. E-commerce is an irreplaceable business channel for retail. With the rapid growth of internet users and organized online infrastructures in India, it was obvious that the online marketplace will get a boost. The digital population (the number of people who have internet access) in India is 687.6 million with an estimated market size of 60 billion USD in 2020 (Statista, 2021). The number of online shoppers was estimated to go up from 75 million in 2017 to over 220 million in 2025. As of January 2021, 76.7% of active internet users in India shop online (Statista, 2021). Among the internet users in India, 57 percent of Gen Z people spent four or more hours a week on the internet which is higher than any other generation.
A Functional Filtering and Neighborhood Truncation Approach to Integrated Quarticity Estimation

We provide a first in-depth look at robust estimation of integrated quarticity (IQ) based on high frequency data. IQ is the key ingredient enabling inference about volatility and the presence of jumps in financial time series and is thus of considerable interest in applications. We document the significant empirical challenges for IQ estimation posed by commonly encountered data imperfections and set forth three complementary approaches for improving IQ based inference. First, we show that many common deviations from the jump diffusive null can be dealt with by a novel filtering scheme that generalizes truncation of individual returns to truncation of arbitrary functionals on return blocks. Second, we propose a new family of efficient robust neighborhood truncation (RNT) estimators for integrated power variation based on order statistics of a set of unbiased local power variation estimators on a block of returns. Third, we find that ratio-based inference, originally proposed by Barndorff-Nielsen and Shephard, has desirable robustness properties and is well suited for our empirical applications. We confirm that the proposed filtering scheme and the RNT estimators perform well in our extensive simulation designs and in an application to the individual Dow Jones 30 stocks.
A review on developments of in-vitro and in-vivo evaluation of hybrid PCL-based natural polymers nanofibers scaffolds for vascular tissue engineering

The main objective of the present review article is to investigate the in-vitro and in-vivo biocompatibility behavior of hybrid PCL-based scaffolds with collagen, gelatin, and chitosan to improve endothelialization for VTE applications. It is reported that the high-rate failure of small diameters vascular grafts (SDVGs, <6 mm) due to adhesion of platelets and plasma protein and aggregation, over-proliferation of smooth muscle cells (SMCs), and neointimal hyperplasia at the implantation site is main challenge in vascular tissue engineering (VTE). The fast re-establishment of a functional endothelial cell (EC) layer representing a crucial importance strategy has been proposed to reduce these adverse outcomes. Polycaprolactone (PCL), with optimum mechanical properties, it showed great potential in biomedical applications, but its biocompatibility is still highly concerned in VTE. Modifications of the PCL vascular grafts by developing the hybrid structures using natural polymers with optimum hydrophilicity and biocompatibility properties in order to speed up the re-endothelialization process have been proposed over the last years. Analyzing the mentioned results in the present study can offer a better understanding of the benefits and challenges of applying the natural polymer as a thrombotic response upon implantation in clinical trials. Also, it can suggest the PCL-Collagen as the best framework for the fabrication of rationally designed SDVGs for VTE.
On the topology of stationary black hole event horizons in higher dimensions

In four dimensions the topology of the event horizon of an asymptotically flat stationary black hole is uniquely determined to be the two-sphere S2. We consider the topology of event horizons in higher dimensions. First, we reconsider Hawking's theorem and show that the integrated Ricci scalar curvature with respect to the induced metric on the event horizon is positive also in higher dimensions. Using this and Thurston's geometric types classification of three-manifolds, we find that the only possible geometric types of event horizons in five dimensions are S3 and S2 × S1. In six dimensions we use the requirement that the horizon is cobordant to a four-sphere (topological censorship), Friedman's classification of topological four-manifolds and Donaldson's results on smooth four-manifolds, and show that simply connected event horizons are homeomorphic to S4 or S2 × S2. We show that the non-simply connected event horizons S3 × S1 and S2 × Σg and event horizons with finite non-abelian first homotopy group whose universal cover is S4, are possible. Finally, we discuss the classification in dimensions higher than six.
Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques

Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more recent completed studies in the topography and surface feature identification domain. Four areas, with respect to the suggested approaches, have been analyzed and discussed: the input data, the concepts of point cloud structure for applying ML, the ML techniques used, and the applications of ML on LiDAR data. Then, an overview is provided to underline the advantages and the disadvantages of this research axis. Despite the training data labelling problem, the calculation cost, and the undesirable shortcutting due to data downsampling, most of the proposed methods use supervised ML concepts to classify the downsampled LiDAR data. Furthermore, despite the occasional highly accurate results, in most cases the results still require filtering. In fact, a considerable number of adopted approaches use the same data structure concepts employed in image processing to profit from available informatics tools. Knowing that the LiDAR point clouds represent rich 3D data, more effort is needed to develop specialized processing tools.
Molecular Mechanisms Underlying Ca2+/Calmodulin-Dependent Protein Kinase Kinase Signal Transduction

Ca2+/calmodulin-dependent protein kinase kinase (CaMKK) is the activating kinase for multiple downstream kinases, including CaM-kinase I (CaMKI), CaM-kinase IV (CaMKIV), protein kinase B (PKB/Akt), and 5′AMP-kinase (AMPK), through the phosphorylation of their activation-loop Thr residues in response to increasing the intracellular Ca2+ concentration, as CaMKK itself is a Ca2+/CaM-dependent enzyme. The CaMKK-mediated kinase cascade plays important roles in a number of Ca2+-dependent pathways, such as neuronal morphogenesis and plasticity, transcriptional activation, autophagy, and metabolic regulation, as well as in pathophysiological pathways, including cancer progression, metabolic syndrome, and mental disorders. This review focuses on the molecular mechanism underlying CaMKK-mediated signal transduction in normal and pathophysiological conditions. We summarize the current knowledge of the structural, functional, and physiological properties of the regulatory kinase, CaMKK, and the development and application of its pharmacological inhibitors.