Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Mode Decomposition with Missing Data

Abstract Dragomiretskiy and Zosso (2014) developed a new decomposition method, termed variational mode decom-position (VMD), which is eﬃcient for handling the tone detection and separation of signals. However, VMDmay be ineﬃcient in the presence of missing data since it is based on a fast Fourier transform (FFT) algo-rithm. To overcome this problem, we propose a new approach based on a novel combination of VMD andhierarchical (or h )-likelihood method. The h -likelihood provides an eﬀective imputation methodology formissing data when VMD decomposes the signal into several meaningful modes. A simulation study and realdata analysis demonstrates that the proposed method can produce substantially eﬀective results.Keywords: Empirical mode decomposition, FFT, h-likelihood, missing data, variational mode decomposi-tion. 1. 서론 신호(signal)란 시간 또는 공간에 따라서값이변화하는 물리적인변수이다. 신호의종류에는 온도, 전류, 속도, 음성, 이미지 등이있으며 이러한 신호에 포함된다양한 주파수를 분석하여 주파수별로 신호를 분리하고자하는 연구가 지속적으로 진행되었다. Huang 등(1998)은특정 신호를 채거름과정을통하여 고주파 함수부터 저주파 함수까지 순차적으로 추출하는 경험적모드분해법(empirical mode de-composition)을제안하였다. 최근에 Dragomiretskiy와 Zosso (2014)는 경험적모드분해법의단점을보완한 변동모드분해법(variational mode decomposition; VMD)을제시하였다. 변동모드분해법은신호를 여러 개의모드의합으로 이루어져 있다고 가정하고, 각각의모드를 분해하는 방법이다. 변동모드분해법은경험적모드분해법에 비하여 주파수탐색 및 분리(tone detection andtone separation)에 탁월한 성능을보인다. 또한 고속퓨리에변환(fast fourier transform)을기반으로한 알고리즘을사용하여 상대적으로 경험적모드분해법보다 잡음에 민감하지 않다. 하지만 고속퓨리에변환은기본적으로 동일한 시간간격(혹은공간적 간격)으로 측정된등간격 신호자료를 분석하기 위한목적으로 설계된변환이기 때문에, 특정 시간 혹은공간에 결측이있을경우 자료를 적절히 분석하지
Bayesian Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm

Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of piecewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC (Marcov Chain Monte Carlo) algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of piecewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise linear regression models.
Determining the Effective Dose Equivalent for External Photon Radiation: Calculational Results for Beam and Point Source Geometries

In 1994, US nuclear plants must implement a new Code of Federal Regulations which adopts the methodology of ICRP 26 for effective dose equivalent (H E ) from internally deposited radionuclides. For exposure to photons from sources outside the body, the new regulations propose an ad hoc extension of the ICRP 26 methodology by defining a new tissue ― «the whole body» ― and assigning it a weighting factor of 1. This study provides basic data to build a methodology for assessing H E based on the original ICRP 26 organ dose methods. Specifically, Monte Carlo photon transport calculations were done with anthropomorphic male and female phantoms and a hermaphroditic phantom to calculate organ doses, and thereby H E , for each gender, for selected photon energies, for photon beams at any three-dimensional angle, and for point sources anywhere external to the torso
Differentiated expressed miRNAs in splenic monocyte induced by burn injury in mice

To find potential biomarkers based on miRNA and their potential targets in splenic monocytes in burn‐injured mice. Male Balb/c mice were subjected to sham or scalding injury of 15% total body surface area. Spenic CD11b+ monocytes were purified with magnetic beads. The monocytes were cultured in the presence of lipopolysaccharide. The proliferation of monocytes was detected by MTT assay, and the cytokines in the supernatant were examined by enzyme linked immunosorbent assay. The purified monocytes were also under total RNA extraction. The differential monocytic miRNAs expression between the sham and burn‐injured mice was analysed by miRNA microarray. The activity of monocytes was comparable between the two groups (p > 0.05). However, monocytes from burn‐injured mice secreted higher levels of tumour necrosis factor (TNF)‐α and transforming growth factor‐β, but lower level of monocyte chemoattratctant protein‐1. A total of 54 miRNAs were differentially expressed in monocytes from burn relative to sham‐injured mice (fold >3). Further quantitative reverse transcription polymerase chain reaction confirmed that the expression of miR‐146a was significantly down‐regulated, while miR‐3091‐6p was up‐regulated after burn injury. Using the combination of Miranda and TargetScan softwares, we found that mir‐146a may regulate 180 potential target genes including TNF receptor related factor 6 (TRAF6), interleukin‐1 receptor related kinase 1 (IRAK1) and CD28. Mir‐3091‐6p may regulate 39 potential targets, including SOCS7 (cytokine signal transduction inhibitor 7) and ARRB2 (arrestin, β 2). The miRNAs expressed by monocytes after burn injury may be involved in the regulation of innate immune response in burn injury.
RPL27 contributes to colorectal cancer proliferation and stemness via PLK1 signaling

Although expression of ribosomal protein L27 (RPL27) is upregulated in clinical colorectal cancer (CRC) tissue, to the best of our knowledge, the oncogenic role of RPL27 has not yet been defined. The present study aimed to investigate whether targeting RPL27 could alter CRC progression and determine whether RPL27 gains an extra-ribosomal function during CRC development. Human CRC cell lines HCT116 and HT29 were transfected with RPL27-specific small interfering RNA and proliferation was assessed in vitro and in vivo using proliferation assays, fluorescence-activated cell sorting (FACS) and a xenograft mouse model. Furthermore, RNA sequencing, bioinformatic analysis and western blotting were conducted to explore the underlying mechanisms responsible for RPL27 silencing-induced CRC phenotypical changes. Inhibiting RPL27 expression suppressed CRC cell proliferation and cell cycle progression and induced apoptotic cell death. Targeting RPL27 significantly inhibited growth of human CRC xenografts in nude mice. Notably, polo-like kinase 1 (PLK1), which serves an important role in mitotic cell cycle progression and stemness, was downregulated in both HCT116 and HT29 cells following RPL27 silencing. RPL27 silencing reduced the levels of PLK1 protein and G2/M-associated regulators such as phosphorylated cell division cycle 25C, CDK1 and cyclin B1. Silencing of RPL27 reduced the migration and invasion abilities and sphere-forming capacity of the parental CRC cell population. In terms of phenotypical changes in cancer stem cells (CSCs), RPL27 silencing suppressed the sphere-forming capacity of the isolated CD133+ CSC population, which was accompanied by decreased CD133 and PLK1 levels. Taken together, these findings indicated that RPL27 contributed to the promotion of CRC proliferation and stemness via PLK1 signaling and RPL27 may be a useful target in a next-generation therapeutic strategy for both primary CRC treatment and metastasis prevention.
Evolution of cyclic di-GMP signalling on a short and long term time scale

Diversifying radiation of domain families within specific lineages of life indicates the importance of their functionality for the organisms. The foundation for the diversifying radiation of the cyclic di-GMP signalling network that occurred within the bacterial kingdom is most likely based in the outmost adaptability, flexibility and plasticity of the system. Integrative sensing of multiple diverse extra- and intracellular signals is made possible by the N-terminal sensory domains of the modular cyclic di-GMP turnover proteins, mutations in the protein scaffolds and subsequent signal reception by diverse receptors, which eventually rewires opposite host-associated as well as environmental life styles including parallel regulated target outputs. Natural, laboratory and microcosm derived microbial variants often with an altered multicellular biofilm behaviour as reading output demonstrated single amino acid substitutions to substantially alter catalytic activity including substrate specificity. Truncations and domain swapping of cyclic di-GMP signalling genes and horizontal gene transfer suggest rewiring of the network. Presence of cyclic di-GMP signalling genes on horizontally transferable elements in particular observed in extreme acidophilic bacteria indicates that cyclic di-GMP signalling and biofilm components are under selective pressure in these types of environments. On a short and long term evolutionary scale, within a species and in families within bacterial orders, respectively, the cyclic di-GMP signalling network can also rapidly disappear. To investigate variability of the cyclic di-GMP signalling system on various levels will give clues about evolutionary forces and discover novel physiological and metabolic pathways affected by this intriguing second messenger signalling system.
CCD Star Image Rapid Locating Method Based on Binarization Difference

Developments of large FOV(field of view) star sensor and opto-electronic telescope observation mode need a rapider CCD star image processing speed than before.This paper provides an improved method of the cross projection algorithm for star point rapid locating.It includes following steps: first,preprocessing the star image to binarization by determining a threshold,then,making horizontal and vertical projections of the binarized image.In each projection direction,extracting the domain bound of the star point object using the method of binarization difference,which is different from the traditional marker method,finally,implementing the star image rapid locating,using the centroid method to calculate the precise position of the star point.The experiment shows that the improved method has a higher processing speed than the cross projection algorithm.
GNPNAT1 promotes the stemness of breast cancer and serves as a potential prognostic biomarker

Glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is a member of the acetyltransferase superfamily, related to general control non-depressible 5 (GCN5). It has been documented that GNPNAT1 expression is increased in lung cancer, whereas its involvement in breast cancer (BC) remains to be further investigated. The present study aimed to evaluate the expression levels of GNPNAT1 in BC and its effect on BC stem cells (BCSCs). The Cancer Genome Atlas (TCGA) database was used for the analysis of the expression of GNPNAT1 and its clinical significance. Cox regression and logistic regression analyses were used to evaluate prognosis-related factors. The GNPNAT1-binding protein network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) application. The biological signaling pathways implicated in GNPNAT1 were investigated through function enrichment analysis including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis. The single-sample GSEA method was used to investigate the connection between the level of immune infiltration and GNPNAT1 expression in BC. GNPNAT1 expression was upregulated in patients with BC and was significantly associated with a poor prognosis. GNPNAT1 and its co-expressed genes were mostly enriched in nuclear transport, Golgi vesicle transport, ubiquitin-like protein transferase activity and ribonucleoprotein complex binding, as determined using functional enrichment analysis. GNPNAT1 expression was positively associated with Th2 cells and T-helper cells, and negatively associated with plasmacytoid dendritic cells, CD8+ T-cells and cytotoxic cells. Additionally, the GNPNAT1 expression levels were considerably increased in BCSCs. GNPNAT1 knockdown markedly decreased the stemness ability of SKBR3 and Hs578T cells, including the production of CSC markers and mammosphere or clone formation, while GNPNAT1 overexpression increased the stemness level. Hence, the findings of the present study demonstrate that GNPNAT1 may be exploited as a novel prognostic biomarker and therapeutic target for BC.
GAN-based data augmentation for transcriptomics: survey and comparative assessment

Abstract Motivation Transcriptomics data are becoming more accessible due to high-throughput and less costly sequencing methods. However, data scarcity prevents exploiting deep learning models’ full predictive power for phenotypes prediction. Artificially enhancing the training sets, namely data augmentation, is suggested as a regularization strategy. Data augmentation corresponds to label-invariant transformations of the training set (e.g. geometric transformations on images and syntax parsing on text data). Such transformations are, unfortunately, unknown in the transcriptomic field. Therefore, deep generative models such as generative adversarial networks (GANs) have been proposed to generate additional samples. In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes. Results This work highlights a significant boost in binary and multiclass classification performances due to augmentation strategies. Without augmentation, training a classifier on only 50 RNA-seq samples yields an accuracy of, respectively, 94% and 70% for binary and tissue classification. In comparison, we achieved 98% and 94% of accuracy when adding 1000 augmented samples. Richer architectures and more expensive training of the GAN return better augmentation performances and generated data quality overall. Further analysis of the generated data shows that several performance indicators are needed to assess its quality correctly. Availability and implementation All data used for this research are publicly available and comes from The Cancer Genome Atlas. Reproducible code is available on the GitLab repository: https://forge.ibisc. univ-evry.fr/alacan/GANs-for-transcriptomics
Identification of Critical Lines and Nodes in Power Grid Based on Maximum Flow Transmission Contribution Degree

A new method for assessing the critical lines and nodes of power grid from the structural perspective is proposed.The transmission contribution degree is defined to identify the critical lines and nodes based on the maximum flow and the complex network theories,and characterizes the ability of lines and nodes to undertake the transmission of power flow in the grid.The method can overcome the limitation of the hypothesis that power flow is transferred along the shortest path compared with the present research,and take all the possible transmission paths between different "source-load" pairs into consideration based on the whole structure of the network.Meanwhile the power gird is considered as a directed and weighted network with the power transfer ability of line taken into account,making the physical background even more suitable for the actual condition of the power system.The test on an IEEE 39-bus system demonstrates the rationality and validity in comparison with the methods available.
Patellofemoral Pain Syndrome Is Associated With Chronic Disease and Allergies in 60 997 Distance Runner Race Entrants: SAFER XXX Study

Objective: Patellofemoral pain syndrome (PFPS) is a common running-related injury. Independent risk factors associated with PFPS have not been described in a large cohort of distance runners. Design: Descriptive, cross-sectional study. Setting: 21.1 and 56 km Two Oceans Marathon races (2012-2015). Participants: 60 997 race entrants. Assessment of risk factors: Participants completed a compulsory prerace medical screening questionnaire (history of PFPS in the past 12 months, n = 362; no injury history, n = 60 635). Selected risk factors associated with a history of PFPS were explored using univariate & multivariate analyses: demographics, training/running variables, history of chronic diseases (composite chronic disease score), and any allergy. Main Outcome Measures: Prevalence ratios (PRs, 95% confidence intervals). Results: Risk factors associated with PFPS (univariate analysis) were increased years of recreational running (PR = 1.09; P = 0.0107), older age (>50 years), and chronic diseases (PR > 2): gastrointestinal disease (PR = 5.06; P < 0.0001), cardiovascular disease (CVD) (PR = 3.28; P < 0.0001), nervous system/psychiatric disease (PR = 3.04; P < 0.0001), cancer (PR = 2.83; P = 0.0005), risk factors for CVD (PR = 2.42; P < 0.0001), symptoms of CVD (PR = 2.38; P = 0.0397), and respiratory disease (PR = 2.00; P < 0.0001). Independent risk factors (multivariate analysis) associated with PFPS (adjusted for age, sex, and race distance) were a higher chronic disease composite score (PR = 2.68 increased risk for every 2 additional chronic diseases; P < 0.0001) and a history of allergies (PR = 2.33; P < 0.0001). Conclusions: Novel independent risk factors associated with PFPS in distance runners are a history of multiple chronic diseases and a history of allergies. Identification of chronic diseases and allergies should be considered as part of the clinical assessment of a runner presenting with a history of PFPS.
Development of Tuberculosis Vulnerability Assessment Conceptual Framework Using Automatic Content Analysis

The tuberculosis prevention and control model needs to be explored. This study aimed to create a conceptual framework for measuring TB vulnerability to guide the prevention program's effectiveness. SLR method was employed, resulting in 1.060 articles being analyzed with ACA Leximancer 5.0 and facet analysis. The built framework consists of five components: risk of TB transmission, damage caused by TB, health care facility, the burden of TB, and awareness of TB. Future research is required to explore variables in each component to formulate the degree of TB vulnerability.
A Study of Physical Resilience and Aging (SPRING): Conceptual framework, rationale, and study design.

Understanding the physiological basis of physical resilience to clinical stressors is crucial for the well-being of older adults. This article presents a novel framework to discover the biological underpinnings of physical resilience in older adults as part of the "Characterizing Resiliencies to Physical Stressors in Older Adults: A Dynamical Physiological Systems Approach" study, also known as The Study of Physical Resilience and Aging (SPRING). Physical resilience, defined as the capacity of a person to withstand clinical stressors and quickly recover or improve upon a baseline functional level, is examined in adults aged 55 years and older by studying the dynamics of stress response systems. The hypothesis is that well-regulated stress response systems promote physical resilience. The study employs dynamic stimulation tests to assess energy metabolism, the hypothalamic-pituitary-adrenal axis, the autonomic nervous system, and the innate immune system. Baseline characteristics influencing resilience outcomes are identified through deep phenotyping of physical and cognitive function, as well as of biological, environmental, and psychosocial characteristics. SPRING aims to study participants undergoing knee replacement surgery (n = 100), bone and marrow transplantation (n = 100), or anticipating dialysis initiation (n = 60). Phenotypic and functional measures are collected pre-stressor and at multiple times after stressor for up to 12 months to examine resilience trajectories. By improving our understanding of physical resilience in older adults, SPRING has the potential to enhance resilient outcomes to major clinical stressors. The article provides an overview of the study's background, rationale, design, pilot phase, implementation, and implications for improving the health and well-being of older adults.
Data Quality in Healthcare for the Purpose of Artificial Intelligence: A Case Study on ECG Digitalization

The quantity of data generated within healthcare is increasing exponentially. Following this development, the interest of using data driven methodologies such as machine learning is on a steady rise. However, the quality of the data also needs to be considered, since information generated for human interpretation may not be optimal for quantitative computer-based analysis. This work investigates dimensions of data quality for the purpose of artificial intelligence applications in healthcare. Particularly, ECG is studied which traditionally rely on analog prints for initial examination. A digitalization process for ECG is implemented, together with a machine learning model for heart failure prediction, to quantitatively compare results based on data quality. The digital time series data provide a significant accuracy increase, compared to scans of analog plots.
Nurse Scheduling with State-of-the Art Open-Source Tools

Nurse scheduling is still an unsolved issue, as it is NP-hard and highly context-dependent. Despite this fact, the practice needs guidance on how to tackle this problem without using costly commercial tools. Concretely, we have the following use case: a Swiss hospital is planning a new station designed for nurse training. The capacity planning is finished, and the hospital wants to assess whether shift planning with known constraints leads to valid solutions. Here, a mathematical model is combined with a genetic algorithm. We trust the solution of the mathematical model more, but if it does not provide a valid solution, we try out an alternative. Our solutions indicate that actual capacity planning together with the hard constraints cannot lead to valid staff schedules. The central conclusion is that more degrees of freedom are necessary and that open-source tools OMPR and DEAP are valuable alternatives to commercial products such as Wrike or Shiftboard, in which the degree of freedom of customization is reduced in favor of easiness of use.
Circ-SNX27 sponging miR-375/RPN1 axis contributes to hepatocellular carcinoma progression

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor with high fatality. It has yet to be reported whether circ-SNX27 can affect the progression of HCC. This study attempted to analyze circ-SNX27’s precise role and underlying mechanisms in HCC. HCC cell lines and tumor specimens from HCC patients were analyzed using quantitative real-time PCR and Western blotting to quantify the expressions of circ-SNX27, miR-375, and ribophorin I (RPN1). Cell invasion and cell counting kit 8 experiments were conducted for the evaluation of HCC cell invasion and proliferation. Caspase-3 Activity Assay Kit was utilized to gauge the caspase-3 activity. Luciferase reporter and RNA immunoprecipitation assays were executed to ascertain the relationships among miR-375, circ-SNX27, and RPN1. To determine how circ-SNX27 knockdown affects the growth of HCC xenografts in vivo, tumor-bearing mouse models were constructed. Elevated expressions of circ-SNX27 and RPN1 as well as a reduced miR-375 expression were observed among HCC cells and HCC patient tumor specimens. Knocking-down circ-SNX27 in HCC cells abated their proliferative and invasive abilities but raised their caspase-3 activity. Moreover, the poor levels of circ-SNX27 inhibited HCC tumor growth among the mice. Circ-SNX27 enhanced RPN1 by competitively binding with miR-375. Silencing miR-375 in HCC cells promoted their malignant phenotypes. Nonetheless, the promotive effect of miR-375 silencing was reversible via the knockdown of circ-SNX27 or RPN1. This research demonstrated that circ-SNX27 accelerated the progression of HCC by modulating the miR-375/RPN1 axis. This is indicative of circ-SNX27’s potential as a target for the treatment of HCC.