Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Cell Analysis Identifies the miRNA Expression Profile of a Subpopulation of Muscle Precursor Cells Unique to Humans With Type 2 Diabetes

MicroRNAs (miRNAs) take part in regulating central cellular processes such as differentiation and metabolism. We have previously shown that muscle progenitor cells derived from individuals with type 2 diabetes (T2DM) have a dysregulated miRNA profile. We hypothesized that the T2DM muscle progenitor population is heterogeneous in its miRNA expression and differs from the progenitor population of healthy controls. MiRNA expression profiles of CD56+ muscle progenitor cells from people with T2DM and from healthy controls were therefore investigated at a single cell level. Single-cell analysis revealed three subpopulations expressing distinct miRNA profiles: two subpopulations including both T2DM and healthy control muscle precursors presented miRNA expression profiles mostly overlapping between groups. A distinct third subpopulation consisted solely of cells from donors with T2DM and showed enriched expression of miRNAs previously shown to be associated with type 2 diabetes. Among the enriched miRNAs was miR-29, a regulator of GLUT4 mRNA expression. Interestingly, this subpopulation also revealed several miRNAs with predicted targets in the PI3K/Akt pathway, not previously described in relation to T2DM muscle dysfunction. We concluded that a subpopulation of T2DM muscle precursor cells is severely dysregulated in terms of their miRNA expression, and accumulation of this population might thus contribute to the dysfunctional muscular phenotype in type 2 diabetes.
Down-Regulation of PpBGAL10 and PpBGAL16 Delays Fruit Softening in Peach by Reducing Polygalacturonase and Pectin Methylesterase Activity

β-galactosidases are cell wall hydrolases that play an important role in fruit softening. However, PpBGALs mechanism impacting on ethylene-dependent peach fruit softening was still unclear. In this study, we found that PpBGAL4, -6, -8, -10, -16, and -17 may be required for ethylene-dependent peach softening and PpBGAL10, -16 may make a main contribution to it among 17 PpBGALs. Utilization of virus-induced gene silencing (VIGS) showed that fruits were firmer than those of the control at 4 and 6 days after harvest (DAH) when PpBGAL10 and PpBGAL16 expression was down-regulated. Suppression of PpBGAL10 and PpBGAL16 expression also reduced PpPG21 and PpPME3 transcription, and polygalacturonase (PG) and pectinmethylesterases (PME) activity. Overall, total cell wall material and protopectin slowly declined, water-soluble pectin slowly increased, and cellulose and hemicellulose was altered significantly at 4 DAH, relative to control fruit. In addition, PpACO1 expression and ethylene production were also suppressed at 4 DAH because of inhibiting PpBGAL10 and PpBGAL16 expression. These results suggested that down-regulation of PpBGAL10 and PpBGAL16 expression delays peach fruit softening by decreasing PG and PME activity, which inhibits cell wall degradation and ethylene production.
Towards Flexible Indices for Distributed Graph Data: The Formal Schema-level Index Model FLuID

Graph indices are a key to manage huge amounts of distributed graph data. Instance-level indices are available that focus on the fast retrieval of nodes. Furthermore, there are so-called schema-level indices focusing on summarizing nodes sharing common characteristics, i. e. , the combination of attached types and used property-labels. We argue that there is not a one-size-fits-all schema-level index. Rather, a parameterized, formal model is needed that allows to quickly design, tailor, and compare different schema-level indices. We abstract from related works and provide the formal model FLuID using basic building blocks to flexibly define different schema-level indices. The FLuID model provides parameterized simple and complex schema elements together with four parameters. We show that all indices modeled in FLuID can be computed in O(n). Thus, FLuID enables us to efficiently implement, compare, and validate variants of schema-level indices tailored for specific application scenarios.
Distribution of Y chromosomal haplotypes in Japanese native horse populations

ABSTRACT The distribution of Y chromosomal haplotypes in Japanese native horse populations was investigated to obtain genetic information on these populations. Here, 159 male/gelded horses from eight local populations were investigated, and three Y haplotypes (JHT-1, JHT-2, and JHT-3) were identified by analyzing five Y-linked loci. Five populations had only JHT-1, whereas two populations had only JHT-2. One population had JHT-1 and JHT-3. Based on the geographical distribution of these haplotypes and previously reported haplotypes for other Asian horses, JHT-1 is considered to be a major haplotype in ancestral native horses. The fixation of each haplotype suggests the influence of independent breeding and genetic drift in each population. These findings complement the results from previous genetic studies of Japanese native horses.
NUDT @ CLSciSumm-18

In this paper, we introduce the NUDT system for this year’s CLSciSumm 2018 task at the BIRNDL 2018 Workshop. For task 1a, we identify the related text spans referred to the citation with random forest model, exploring multiple features. Additionally, we integrate random forest model with BM25 and VSM model and apply a voting strategy to select the most related text spans. Besides, we explore the language model with word embeddings and integrate it into the voting system to improve the performance. For task 1b, we use multi-features random forest classifier to identify the facet of the cited sentences.
Pharmacological Inhibition of Indoleamine 2,3-Dioxygenase-2 and Kynurenine 3-Monooxygenase, Enzymes of the Kynurenine Pathway, Significantly Diminishes Neuropathic Pain in a Rat Model

Neuropathic pain caused by a primary injury or dysfunction in the peripheral or central nervous system is a tremendous therapeutic challenge. Here, we have collected the first evidence from a single study on the potential contributions to neuropathic pain development by enzymes in the kynurenine pathway [tryptophan 2,3-dioxygenase (TDO), indoleamine 2,3-dioxygenase (IDO1/2), kynurenine 3-monooxygenase (KMO); kynureninase, 3-hydroxyanthranilate-3,4-dioxygenase (HAOO)] at the spinal cord and dorsal root ganglia (DRG) levels. At the spinal cord, mRNA levels of IDO2, KMO, and HAOO were elevated as measured on day 7 after chronic constriction injury in a rat model, parallel to the C1q-positive cell activation. According to our data obtained from primary microglial cell cultures, all enzymes of the kynurenine pathway except TDO were derived from these cells; however, the activation of microglia induced stronger changes in IDO2 and KMO. Our pharmacological studies gave evidence that the repeated intraperitoneal administration of minocycline, a microglia/macrophage inhibitor, not only attenuated tactile and thermal hypersensitivity but also diminished the levels of IDO2 and KMO mRNA. Our further pharmacological studies confirmed that IDO2 and KMO enzymes take part in the development of neuropathic pain, since we observed that the repeated administration of IDO2 (1-methyl-D-tryptophan) and KMO [UPF 648 – (1S,2S)-2-(3,4-dichlorobenzoyl)cyclopropanecarboxylic acid] inhibitors diminished hypersensitivity development as measured on days 2 and 7. The results of our studies show that the kynurenine pathway is an important mediator of neuropathic pain pathology in rats and indicate that IDO2 and KMO represent novel pharmacological targets for treating neuropathy.
Changes in NR2B-NMDA Receptor Expression Relate to Individual Differences in Adaptability to Food Restriction-Stress

The eating disorder Anorexia Nervosa has an extremely high mortality rate of 5.9% (Arcelus et al. 2011) and a poorly understood neurobiology. Research indicates the animal model Activity Based Anorexia (ABA) may provide insight to the neural correlates of its behavioral and physical markers, hyperactivity and weight loss. Previously, female ABA rodents were shown to have increased levels of the NR2B subunit of NMDA receptor (NMDAR), an excitatory glutamate receptor, in stratum radiatum of dorsal hippocampal CA1. Increased NR2B-NMDAR levels may increase excitability in hippocampus, an anxiety-regulating brain region, possibly increasing anxiety –like behavior (Chen et al. 2017) such as hyperactivity (Wable et al. 2015). Here we tested whether adolescent female mice exposed to a second food restriction (ABA2) also demonstrate the behavioral and physical markers of ABA. NR2BNMDARs in ABA2 and control mice were immunolabeled for electron microscopy and quantified at excitatory synapses of CA1 at various pre-, post-, and peri-synaptic regions. Electron microscopy was chosen to selectively observe NR2B-immunoreactivity at distinct preand post-synaptic compartments because NMDARs can operate differently depending on how closely they are anchored to the synaptic junction immediately apposed to presynaptic release site (Hardingham and Bading 2010). We found that ABA2 animals had more NR2B-labeling in presynaptic terminals and postsynaptic cytoplasm than controls. Increased NR2Blabeling at postsynaptic density correlated with increased running activity during food access and weight loss, suggesting that NR2B-NMDARs at postsynaptic density may mediate vulnerability to ABA2. Increased levels of NR2B-labeling at extra-synaptic membrane of dendritic spines correlated with less running during ABA2, suggesting NR2B at this site may mediate ABA resilience. The results indicate localized NR2B expression correlates with adaptability to second exposure to food restriction and support a neurobiological basis for ABA.
Passive measurement of progressive mass change via bifurcation sensing with a multistable micromechanical system

Mass sensing using the onset and crossing of a dynamic bifurcation of a micromechanical system has been shown to reduce the mass threshold which may be detected and exhibited improved robustness to damping and noise compared to traditional tracking of resonant frequency shift. Previous investigations demonstrated that mass measurement over time via actively controlled strategies or explored passively operated threshold-type methods to indicate a pre-determined mass was adsorbed. Recently, an alternative idea integrating aspect of frequency shift- and bifurcation-based mass sensors and methods was proposed, providing initial illustration of a noteworthy ability to passively quantify progressive mass adsorption due to sequentially activated bifurcations. To advance the state of the art, this research provides a thorough investigation of this new sensing concept in terms of its dynamic characteristics and devises guidelines for effective, reliable operations. The conceptual foundation of the sensing method and an experimentally validated sensor model are reviewed. The results of numerous simulated operational trials and parametric investigations are detailed to reach important conclusions on sensor operations and versatility, and to uncover the influences of key operational conditions upon detection metrics. Finally, suitable microscale sensor architectures and fabrications are described to exemplify the flexibility of successfully realizing the mass sensing strategy.
GINS 2 regulates cell proliferation and apoptosis in human epithelial ovarian cancer

Go-Ichi-Ni-San 2 (GINS2), also known as partner of Sld five 2, is involved in the initiation of DNA replication and cell cycle progression. GINS2 is abundantly expressed in a number of malignant solid tumors, including breast cancer, melanoma and hepatic carcinoma. However, the functions of GINS2 in epithelial ovarian cancer (EOC) remain unclear. The aim of the present study was to investigate these functions. GINS2 expression was detected in EOC and normal ovarian tissues using immunohistochemistry. To investigate the functions of GINS2 in EOC, GINS2 expression was stably knocked down in SKOV‐3 cells using lentiviral short hairpin RNA (shRNA). The expression of GINS2 mRNA and protein in SKOV‐3 cells was examined using reverse‐transcription quantitative polymerase chain reaction (RT‐qPCR) and western blot analyses, respectively. Cell proliferation was determined using high-content screening and MTT assays. Cell cycle progression and apoptosis were detected using flow cytometry. Compared with normal ovarian tissues, EOC tissues expressed increased levels of GINS2 expression (16.7 vs. 58.3%). Increased expression of GINS2 mRNA was also observed in SKOV‐3 and OVCAR3 cells. In the investigation of GINS2 functions in EOC, GINS2 expression at the mRNA and protein levels was significantly inhibited by specific GINS2 shRNA. GINS2 knockdown significantly inhibited the proliferation and viability of SKOV‐3 cells and induced cell cycle arrest in S phase. Furthermore, GINS2 knockdown in SKOV‐3 cells significantly increased cell apoptosis. GINS2 is markedly expressed in EOC tissues and cell lines. Stable GINS2 knockdown in SKOV‐3 cells significantly inhibited cell proliferation and induced cell cycle arrest and cell apoptosis. Therefore, GINS2 may be involved in EOC progression.
MPPT and pitch control for wind turbine of DFIG with flywheel energy storage system

The type of distributed generation unit that is the subject of this paper relates to renewable energy sources, especially wind power. The wind generator used is based on a double fed induction Generator (DFIG). The stator of the DFIG is connected directly to the network and the rotor is connected to the network through the power converter with three levels. The objective of this work is to study the association a Flywheel Energy Storage System (FESS) in wind generator. This system is used to improve the quality of electricity provided by wind generator. It is composed of a flywheel; an induction machine (IM) and a power electronic converter. A maximum power tracking technique « Maximum Power Point Tracking » (MPPT) and a strategy for controlling the pitch angle is presented. The model of the complete system is developed in Matlab/Simulink environment / to analyze the results from simulation the integration of wind chain to networks. Keywords-component; doubly fed induction generator; flywheel energy storage system; induction machine; maximum power point tracking; pitch control.
Oridonin inhibits VEGF-A-associated angiogenesis and epithelial-mesenchymal transition of breast cancer in vitro and in vivo

Metastasis is the primary cause of mortality in patients with breast cancer and lacks effective therapeutic agents. Oridonin, an active diterpenoid compound isolated from Rabdosia rubescens, was identified to be the most potent anti-tumor ingredient. However, the molecular mechanisms responsible for its anti-metastatic effects remain unclear. In the present study, oridonin significantly suppressed the migration, invasion and adhesion of MDA-MB-231 and 4T1 breast cancer cells, and inhibited tube formation of human umbilical vein endothelial cells in a dose-dependent manner. The expression levels of epithelial-mesenchymal transition (EMT)-associated marker and the hypoxia inducible factor 1α (HIF-1α)/vascular endothelium growth factor (VEGF) signaling pathway mRNA and proteins were determined by reverse transcription-quantitative polymerase chain reaction and western blotting, respectively in vitro. The results demonstrated that oridonin effectively inhibited EMT as demonstrated by the significant increases in the expression levels of E-cadherin, and decreased expression of N-cadherin, Vimentin and Snail. In addition, oridonin exerted its anti-angiogenesis activity through significantly decreasing HIF-1α, VEGF-A and VEGF receptor-2 protein expression. Furthermore, oridonin was demonstrated to decrease the micro-vessel density as evidenced by the decreased expression of cluster of differentiation 31, a marker for neovasculature. In brief, oridonin inhibits tumor cell migration, invasion and adhesion, as well as tumor angiogenesis, which are mediated by suppressing EMT and the HIF-1α/VEGF signaling pathway. The results of the present study suggest that oridonin may be a promising anti-metastatic agent in breast cancer treatment.
Parameter identification of solar module model using a metaheuristic technique

In fact that the meaningful parameters of photovoltaic (PV) module model are of major importance, they could lead to carry out a monitoring tool, so the extraction method should be accurate and quick in the same time. Due to the nonlinear and implicit nature of the circuit equivalent PV module model it is preferable to use a good optimization method; in this paper we opt for a metaheuristic based parameter identification method inspired from Artificial Bee Colony (ABC) and Differential Evolutionary (DE). The proposed method is compared with other optimization methods and then is validated with a real measured data under different meteorological conditions of several PV modules. Keywords-parameter extraction; metaheuristic technique; one diode model; Differential Evolutionary.
Genomics for Ruminants in Developing Countries: From Principles to Practice

Using genomic information, local ruminant populations can be better characterized and compared to selected ones. Genetic relationships between animals can be established even without systematic pedigree recording, provided a budget is available for genotyping. Genomic selection (GS) can rely on a subset of the total population and does not require a costly national infrastructure, e.g., based on progeny testing. Yet, the use of genomic tools for animal breeding in developing countries is still limited. We identify three main reasons for this: (i) the instruments for cheap recording of phenotypes and data management are still limiting. (ii) many developing countries are recurrently exposed to unfavorable conditions (heat, diseases, poor nutrition) requiring special attention to fitness traits, (iii) a high level of expertise in quantitative genetics, modeling, and data manipulation is needed to perform genomic analyses. Yet, the potential outcomes go much beyond genetic improvements and can improve the resilience of the whole farming system. They include a better management of genetic diversity of local populations, a more balanced genetic progress and the possibility to unravel the genetic basis of adaptation of local breeds through whole genome approaches. A GS program being developed by BAIF, a large Indian NGO, is analyzed as a pilot case. It relies on the creation of a female reference population of Bos indicus and crossbreds, recorded with modern technology (e.g., smartphones) to collect performances at low cost in tiny herds on production and fertility. Finally, recommendations for the implementation of GS in developing countries are proposed.
Sports & Nutrition Data Science using Gradient Boosting Machines

Throughout1 recent years, a significant amount of research has dealt with how humans could beat time. According to the extracted outcome, the main weapons in this battle are balanced nutrition and systematic exercise. Outdoor running, ranging from short distances up to even marathons, is gaining popularity, boosting the clothing, shoes and even gadget industry as well. The current rapid development of technology leads to the supply of a vast amount of information that enables us to properly process valuable knowledge. Smart watches, wristbands, mobile phone applications, shoes and clothes sensors, etc. have provided practitioners the tools to collect numerous data regarding their fitness levels. The recent challenge data mining is about to serve, lies to the fact of how one can combine and analyze the abundance of data from daily activities, nutrition habits and fitness activities into one, unified model that could explain the underlying patterns of a subject's physical capacity. The current work deals with collecting heterogeneous fitness and nutrition facts from students of various athletic background and skills within the University of the Aegean campus, using hi-tech GPS smartwatches and calories monitoring smartwatch applications. Upon data collection, we apply Gradient Boosting Machines to forecast the finishing time of two different running tasks, namely 800m and 5000m. The algorithm is trained and evaluated using different attribute sets, focusing on features that describe either fitness skills, nutrition habits or both. The results demonstrate an impressive ability of the aforementioned method to predict the finishing time which surpasses many other Machine Learning algorithms and traditional models such as Support Vector Machines, Random Forests, Deep Neural Networks and Linear Regression. Note that, based on current literature on the task at hand, prediction is almost exclusively relied to Linear Regression models, mentioning results far worse than GBM. Finally, feature importance functionalities of GBM are also exploited to reveal the factors that are considered to be most significant during the forecasting process. Such factors could be further utilized by professional or amateur athletes (with the assistant of professional trainers and experts of course), for improving their current fitness and health levels.
Launch-Mode-Aware Context-Sensitive Activity Transition Analysis

Existing static analyses model activity transitions in Android apps context-insensitively, making it impossible to distinguish different activity launch modes, reducing the pointer analysis precision for an activity's callbacks, and potentially resulting in infeasible activity transition paths. In this paper, we introduce Chime, a launch-mode-aware context-sensitive activity transition analysis that models different instances of an activity class according to its launch mode and the transitions between activities context-sensitively, by working together with an object-sensitive pointer analysis. Our evaluation shows that our context-sensitive activity transition analysis is more precise than its context-insensitive counterpart in capturing activity transitions, facilitating GUI testing, and improving the pointer analysis precision.
Sentiment Analysis for Software Engineering: How Far Can We Go?

Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the specific usage context. We describe our experience in building a software library recommender exploiting crowdsourced opinions mined from Stack Overflow (e.g., what is the sentiment of developers about the usability of a library). To reach our goal, we retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an effort- and time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of these tools on a variety of SE datasets. Our results should warn the research community about the strong limitations of current sentiment analysis tools.