Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Study of Short Computing Video Tutorials in a Graduate Public Health Biostatistics Course

Traditional lecture-centered classrooms are being challenged by active learning hybrid curricula. In small graduate programs with limited resources and primarily non-traditional students, exploring how to use online technology to optimize the role of the professor in the classroom is imperative. However, very little research exists in this area. In this study, the use of short statistical computing video tutorials was explored using a pilot study in a small Public Health Program at the University of New Mexico. The videos were implemented in two Master's-level biostatistics courses and student perception of the videos was assessed using quantitative surveys and qualitative focus groups. The results from 16 survey respondents and 12 focus group participants are presented across the two courses. Viewing rates for the videos were high, with 15 out of 16 respondents reporting usually or always viewing the videos. Overall perception of the videos as a learning tool was positive, with 14 out of 16 respondents agreeing that the videos offer advantages to them. Two prominent themes emerged in our analysis: (1) the usability and convenience of the videos and (2) the deeper learning facilitated by having the videos available. We conclude that the short video tutorials were a useful learning tool in our study population.
Viscoelastic effects on the deformation and breakup of a droplet on a solid wall in Couette flow

Abstract The deformation, movement and breakup of a wall-attached droplet subject to Couette flow are systematically investigated using an enhanced lattice Boltzmann colour-gradient model, which accounts for not only the viscoelasticity (described by the Oldroyd-B constitutive equation) of either droplet (V/N) or matrix fluid (N/V) but also the surface wettability. We first focus on the steady-state deformation of a sliding droplet for varying values of capillary number ($Ca$), Weissenberg number ($Wi$) and solvent viscosity ratio ($\beta$). Results show that the relative wetting area $A_r$ in the N/V system is increased by either increasing $Ca$, or by increasing $Wi$ or decreasing $\beta$, where the former is attributed to the increased viscous force and the latter to the enhanced elastic effects. In the V/N system, however, $A_r$ is restrained by the droplet elasticity, especially at higher $Wi$ or lower $\beta$, and the inhibiting effect strengthens with an increase of $Ca$. Decreasing $\beta$ always reduces droplet deformation when either fluid is viscoelastic. The steady-state droplet motion is quantified by the contact-line capillary number $Ca_{cl}$, and a force balance is established to successfully predict the variations of $Ca_{cl}/Ca$ with $\beta$ for each two-phase viscosity ratio in both N/V and V/N systems. The droplet breakup is then studied for varying $Wi$. The critical capillary number of droplet breakup monotonically increases with $Wi$ in the N/V system, while it first increases, then decreases and finally reaches a plateau in the V/N system.
Sensing within Smart Buildings: A Survey

Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g., building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.
Musculoskeletal pain is not clearly associated with the risk of anterior cruciate ligament reconstruction in adolescents

Objectives The purpose of this study was to investigate whether self-reported musculoskeletal pain (MSP) was associated with a future anterior cruciate ligament reconstruction (ACLR). Methods In this population-based prospective cohort study, we included 8087 participants from the adolescent part of the Trøndelag Health Study (Young-HUNT) in Norway. The exposure was self-reported MSP from the Young-HUNT3 study (2006–2008), which was categorised into two MSP load groups (high MSP and low MSP) based on frequency and number of pain sites. The outcome was ACLRs recorded in the Norwegian Knee Ligament Register between 2006 and 2019. Logistic regression was used to investigate association between MSP load and ACLR, given as ORs with 95% CIs. All tests were two-sided and p values of ≤0.05 were considered statistically significant. Results 8087 adolescents were included. We identified a total of 99 ACLRs, with 6 ACLRs (0.9%) in adolescents who reported high MSP load and 93 ACLRs (1.3%) among those who reported low MSP load. Adolescents reporting high MSP load had 23% lower odds of an ACLR (OR 0.77, 95% CI 0.31 to 1.91) compared with adolescents with low MSP load. However, the CIs were very wide. Conclusion Self-reported high MSP load in adolescents was not associated with increased risk of future ACLR. Although the number of participants was high, the relatively few cases of ACLR mean that we cannot be conclusive about the presence or absence of an association.
The aberrant hamster Melissiodon (Cricetidae, Rodentia) from the early Miocene of Echzell and other German and French localities

ABSTRACT Melissiodon is a rare cricetid with a long stratigraphic range, present from the Oligocene until the Miocene, only found in western and central Europe except some specimens recovered in Anatolia. What makes Melissiodon special is its unique dental and mandible morphology that has led to many questions regarding its relationship to other cricetid genera and its type of diet. In this work, we have studied new material attributed to Melissiodon from the German localities Echzell (MN4) and Petersbuch (MN3 and MN4), and from the French locality Beaulieu (MN3). Moreover, we compared these specimens with the already published material from other localities across western and central Europe during the early Miocene (MN3 and MN4). In conclusion, the studied specimens and the comparison with other material from different European localities allow us to ascribe this new material as Melissiodon dominans, a widely dispersed species across Europe during the early Miocene
Digital Transition and Readjustment on EU Tourism Industry

Abstract This is a heterodox review of Political Economy, Macroeconomics and Business Management on the readjustment effect in the tourism sector due to the impact of the digital transition and its aggravation with the COVID-19 crisis and the Ukraine war. A synthesis of the paradigmatic change with the digital economy and how it affects labor and professional relations is proposed, requiring a transformation of the economic system and the production process. Attention is focused on the tourism sector and its paradox in the European Union: despite its growing importance, however, its recognition and transformation is insufficient. This paradox is intensified in the Spanish case, where more European recovery funds are being allocated and yet the vulnerability of its SMEs and tourism entrepreneurs is growing.
On norm equivalence between the displacement and velocity vectors for free linear dynamical systems

As the main new result, under certain hypotheses, for free vibration problems, the norm equivalence of the displacement vector and the velocity vector is proven. The pertinent inequalities are applied to derive some two-sided bounds on and that are known so far only for the state vector . Sufficient algebraic conditions are given such that norm equivalence between and holds, respectively, does not hold, as the case may be. Numerical examples illustrate the results for vibration problems of n degrees of freedom with by computing the mentioned algebraic conditions and by plotting the graphs of and . Some notations and definitions of References Kohaupt (2008b, 2011) are necessary and are therefore recapitulated. The paper is of interest to Mathematicians and Engineers.
W2V-SA: A Deep Neural Network-based Approach to Smart Contract Vulnerability Detection

As blockchain applications increase continuously, the number of smart contracts has exploded. However, an increasing number of hackers are mining the vulnerabilities hidden in smart contracts and using them to attack blockchain networks, causing serious consequences. To solve the problem of smart contract vulnerability, we propose W2V-SA, a static analysis method based on deep neural networks for vulnerability detection in smart contracts. This approach appraises the smart contracts before they are deployed to the blockchain network, to detect vulnerabilities timely. Firstly, converts smart contracts into vectors as input data by the word embedding method. Secondly, the hybrid deep neural network model is used to extract and classify features from the input data. Finally, efficient and accurate detection is achieved for six vulnerability types on real smart contracts. The experimental results indicate that the average accuracy of this method is more than 94% in smart contract vulnerability detection. The experimental results demonstrate the effectiveness of W2V-SA.
Comparison of Object Detection Algorithms for the Task of Detecting Possible Road Accident

This article is an investigation of the best suited algorithm for object detection in terms of finding a potential road accident case. The determination of the best algorithm is based on comparative analysis of evaluation and testing results and metrics. Also, the max FPS of processing video during detection will be considered. There are a lot of examples of road accident situations, but in this study the turn left across one and more oncoming road lanes will be explained. There are two classes for recognition: danger and not-danger which explain case. Other accident types have not been considered due to wish for making simple models with further sophistication. The Yolov7 and Detectron2 algorithms are compared.
Extraction of analogies between sentences on the level of syntax using parse trees

Example-based machine translation by analogy is an alternative approach to machine translation. Its principle is relatively simple, but the absolute number of analogies between sentences contained in the corpus is crucial for the overall quality of translation. The relative number of analogies is called the analogical density. The goal of this paper is to measure the analogical density of different aligned corpora. To this end, we extract analogies between sentences. Now, we use parse trees to represent sentences on the level of syntax. We report analogical densities for five different languages in an aligned multilingual corpus extracted from the Tatoeba resource, at the level of characters, words or parse trees.
Technology of Ukrainian-English Machine Translation Based on Recursive Neural Network as LSTM

The paper presents a novel approach to developing Ukrainian-English machine translation technology using neural machine translation. A comparative analysis of the developed software product with analogues such as Google Neural Machine Translation, Microsoft Translator and OpenNMT is conducted. The study focuses on the analysis of their main advantages and disadvantages. Here, we introduce an effective solution for the typical architecture of the intelligent Ukrainian-English machine translation system based on recurrent neural networks. The main functional requirements for similar systems are determined, and the technical task is developed. The neural network model test results have shown that the confirmation loss stopped decreasing after 25 epochs.
Mutational scanning pinpoints distinct binding sites of key ATGL regulators in lipolysis

ATGL is the key enzyme in intracellular lipolysis playing a critical role in metabolic and cardiovascular diseases. ATGL is tightly regulated through a known set of protein-protein interaction partners with activating or inhibiting functions in control of lipolysis. However, the binding mode and protein interaction sites of ATGL and its partners are unknown. Using deep mutational protein interaction perturbation scanning we generated comprehensive profiles of single amino acid variants effecting the interactions of ATGL with its regulatory partners: CGI-58, G0S2, PLIN1, PLIN5 and CIDEC. Twenty-three ATGL variants gave a specific interaction perturbation pattern when validated in co-immunoprecipitation experiments in mammalian cells. We identified and characterized eleven, highly selective ATGL “switch” mutations which affect the interaction of one of the five partners without affecting the others. Switch mutations thus provided distinct interaction determinants for ATGL’s key regulatory proteins at an amino acid resolution. When tested for triglyceride hydrolase activity in vitro and lipolysis in cells, the activity patterns of the ATGL switch variants traced to their protein interaction profile. In the context of structural data, the integration of variant binding and activity profiles provided important insights into lipolysis regulation and the impact of mutations in human disease.
Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues

Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Beginning with the discovery of insulin over a century ago, characterization of molecules responsible for signal between tissues has required careful and elegant experimentation where these observations have been integral to deciphering physiology and disease. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. For example, physiologic dissection of the actions of soluble proteins such as proprotein convertase subtilisin/kexin type 9 (PCSK9) and glucagon-like peptide 1 (GLP1) have yielded among the most promising therapeutics to treat cardiovascular disease and obesity, respectively1–4. A major obstacle in the characterization of such soluble factors is that defining their tissues and pathways of action requires extensive experimental testing in cells and animal models. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by “brute-force” surveys of all genes within RNA-sequencing measures across tissues within a population5–9. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or HFHS diet. Variation of genes such as FGF21, ADIPOQ, GCG and IL6 showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver PCSK9) as well as genes encoding enzymes producing metabolites (adipose PNPLA2), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as Gene-Derived Correlations Across Tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways and network architectures across metabolic organs.
Feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.
Leveraging SHAP and CBR for Dimensionality Reduction on the Psychology Prediction Dataset

Effective dimensionality reduction for feature spaces can benefit the accuracy, efficiency, and/or explain-ability of models using the features. One task of the Explainable AI Challenge of the 2022 International Conference on Case-Based Reasoning is to apply explanation methods for informed feature pruning to refine an artificial neural network model for depression screening. This paper explores how explanations provided by SHAP values can guide feature pruning. It presents and evaluates four approaches developed for this task: 1) iterative feature pruning based on SHAP values, 2) using case-based reasoning to guide search through potential feature prunings, 3) pruning based on using Hamming distance between a reference case and patient cases to partition the case base, and 4) evaluating feature prunings in light of semi-factual and counterfactual cases. Results show that both using case-based reasoning and absolute SHAP values can guide feature pruning to improve model accuracy, with best performance occurring when SHAP values inform feature pruning selection for case-based reasoning-based methods.
Thrombocytopenia Independently Leads to Monocyte Immune Dysfunction

In addition to their well-studied hemostatic functions, platelets are immune cells. Platelets circulate at the interface between the vascular wall and leukocytes, and transient platelet-leukocyte complexes are found in both healthy and disease states, positioning platelets to provide physiologic cues of vascular health and injury. Roles for activated platelets in inducing and amplifying immune responses have received an increasing amount of research attention, but our past studies also showed that normal platelet counts are needed in healthy conditions to maintain immune homeostasis. We have now found that thrombocytopenia (a low platelet count) leads to monocyte dysfunction, independent of the cause of thrombocytopenia, in a manner that is dependent on direct platelet-monocyte CD47 interactions that regulate monocyte immunometabolism and gene expression. Compared to monocytes from mice with normal platelet counts, monocytes from thrombocytopenic mice had increased toll-like receptor (TLR) responses, including increased IL-6 production. Furthermore, ex vivo co-incubation of resting platelets with platelet naïve bone marrow monocytes, induced monocyte metabolic programming and durable changes in TLR agonist responses. Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-Seq) on monocytes from thrombocytopenic mice showed persistently open chromatin at LPS response genes and resting platelet interactions with monocytes induced histone methylation in a CD47 dependent manner. Using mouse models of thrombocytopenia and sepsis, normal platelet numbers were needed to limit monocyte immune dysregulation and IL6 expression in monocytes from human patients with sepsis also inversely correlated with patient platelet counts. Our studies demonstrate that in healthy conditions, resting platelets maintain monocyte immune tolerance by regulating monocyte immunometabolic processes that lead to epigenetic changes in TLR-related genes. This is also the first demonstration of sterile cell interactions that regulate of innate immune-metabolism and monocyte pathogen responses.
When Do Visual Category Representations Emerge in Infants’ Brains?

Visual categorization structures infants’ interactions with their environment. However, it is unknown when visual category representations emerge during the first year of life. Here we measured cortical representations of faces, limbs, corridors, characters, and cars using an electroencephalography (EEG) frequency-tagging paradigm in infants aged 3-4 months (N = 17), 4-6 months (N = 14), 6-8 months (N= 15), and 12-15 months (N = 15). We discovered a staggered development of category representations. Representations of faces emerge first, between 4-6 months and strengthen thereafter. Whereby representations for limbs, corridors, and characters emerge later, between 6-15 months. Not only do these data suggest that category representations are learned, but also that representations of everyday categories take different durations to develop. Thus, these findings necessitate a rethinking of the development of category representations in infancy and, have important implications for assessing atypical cortical development.