Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Autophagy-Related lncRNAs and Potential ceRNA Networks in NAFLD

Nonalcoholic fatty liver disease (NAFLD) is a common chronic disease with complex pathogenesis, which brings economic burden to the society, and there is still no effective therapy. Impaired autophagy has been implicated in the development of NAFLD. Long noncoding RNAs (lncRNAs) are also reported to play a role in the pathogenesis of NAFLD. However, the role of autophagy-related lncRNAs in NAFLD disease has not been elucidated. Here, we mined GSE135251, GSE160016, GSE130970 and GSE185062 datasets from the Gene Expression Omnibus database (GEO) and obtained the human autophagy-related gene list from the Human Autophagy Database (HADb) for in-depth bioinformatic analysis. Following differential expression analysis and intersection of the datasets, Pearson correlation analysis was performed on DElncRNAs and autophagy-related DEmRNAs to obtain autophagy-related lncRNAs, and then Starbase3.0 and TargetScan7.2 were used to construct competing endogenous RNAs (ceRNA) regulatory networks. We constructed four lncRNA-dominated ceRNA regulatory networks (PSMG3-AS1, MIRLET7BHG, RP11-136K7.2, LINC00925), and visualized with Cytoscape. Then we performed co-expression analysis of the ceRNA networks and autophagy-related genes, and functionally annotated them with Metascape. Finally, we performed receiver operating characteristic curve (ROC) analysis on lncRNAs and mRNAs within the ceRNA networks. Conclusively, our project is the first to study autophagy-related lncRNAs in NAFLD and finally mined four autophagy-related lncRNAs (PSMG3-AS1, MIRLET7BHG, RP11-136K7.2, LINC00925). We suggested that the four autophagy-related lncRNAs may be closely associated with the occurrence and development of NAFLD through the corresponding ceRNA regulatory networks. This research brings new horizons to the study of NAFLD.
An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.
The Nexus of Service Quality, Customer Experience, and Customer Commitment: The Neglected Mediating Role of Corporate Image

Quality of service is a major determinant of customer commitment to the organization. Therefore, it is important to understand the importance of service quality for the corporate image as well. In this study, the predicting roles of quality of service and customer experience have been unveiled in customer commitment through the mediating effect of corporate image. The population frame used in this study is the customers of logistic services providers in China. Total data from the 366 customers have been used to analyze the hypotheses formulated. The sample has been selected using convenience sampling and the software used for data analysis is Smart-PLS. The analytical technique used is partial least square structural equation modeling. Results of the study show that service quality and customer experience have a significant role in the customer commitment to the suppliers. In addition, it has also been found that service quality and customer experience have a major contribution to building the corporate image of the services suppliers. Further, corporate image played a significant mediating role in the relationship between service quality and customer commitment. The study has theoretically contributed to the body of literature by finding the importance of service quality for predicting customer commitment to the suppliers.
Evolutionary Dynamics of the Repeatome Explains Contrasting Differences in Genome Sizes and Hybrid and Polyploid Origins of Grass Loliinae Lineages

The repeatome is composed of diverse families of repetitive DNA that keep signatures on the historical events that shaped the evolution of their hosting species. The cold seasonal Loliinae subtribe includes worldwide distributed taxa, some of which are the most important forage and lawn species (fescues and ray-grasses). The Loliinae are prone to hybridization and polyploidization. It has been observed a striking two-fold difference in genome size between the broad-leaved (BL) and fine-leaved (FL) Loliinae diploids and a general trend of genome reduction of some high polyploids. We have used genome skimming data to uncover the composition, abundance, and potential phylogenetic signal of repetitive elements across 47 representatives of the main Loliinae lineages. Independent and comparative analyses of repetitive sequences and of 5S rDNA loci were performed for all taxa under study and for four evolutionary Loliinae groups [Loliinae, Broad-leaved (BL), Fine-leaved (FL), and Schedonorus lineages]. Our data showed that the proportion of the genome covered by the repeatome in the Loliinae species was relatively high (average ∼ 51.8%), ranging from high percentages in some diploids (68.7%) to low percentages in some high-polyploids (30.7%), and that changes in their genome sizes were likely caused by gains or losses in their repeat elements. Ty3-gypsy Retand and Ty1-copia Angela retrotransposons were the most frequent repeat families in the Loliinae although the relatively more conservative Angela repeats presented the highest correlation of repeat content with genome size variation and the highest phylogenetic signal of the whole repeatome. By contrast, Athila retrotransposons presented evidence of recent proliferations almost exclusively in the Lolium clade. The repeatome evolutionary networks showed an overall topological congruence with the nuclear 35S rDNA phylogeny and a geographic-based structure for some lineages. The evolution of the Loliinae repeatome suggests a plausible scenario of recurrent allopolyploidizations followed by diploidizations that generated the large genome sizes of BL diploids as well as large genomic rearrangements in highly hybridogenous lineages that caused massive repeatome and genome contractions in the Schedonorus and Aulaxyper polyploids. Our study has contributed to disentangling the impact of the repeatome dynamics on the genome diversification and evolution of the Loliinae grasses.
Environmental Factors Associated With Soil Prevalence of the Melioidosis Pathogen Burkholderia pseudomallei: A Longitudinal Seasonal Study From South West India

Melioidosis is a seasonal infectious disease in tropical and subtropical areas caused by the soil bacterium Burkholderia pseudomallei. In many parts of the world, including South West India, most cases of human infections are reported during times of heavy rainfall, but the underlying causes of this phenomenon are not fully understood. India is among the countries with the highest predicted melioidosis burden globally, but there is very little information on the environmental distribution of B. pseudomallei and its determining factors. The present study aimed (i) to investigate the prevalence of B. pseudomallei in soil in South West India, (ii) determine geochemical factors associated with B. pseudomallei presence and (iii) look for potential seasonal patterns of B. pseudomallei soil abundance. Environmental samplings were performed in two regions during the monsoon and post-monsoon season and summer from July 2016 to November 2018. We applied direct quantitative real time PCR (qPCR) together with culture protocols to overcome the insufficient sensitivity of solely culture-based B. pseudomallei detection from soil. A total of 1,704 soil samples from 20 different agricultural sites were screened for the presence of B. pseudomallei. Direct qPCR detected B. pseudomallei in all 20 sites and in 30.2% (517/1,704) of all soil samples, whereas only two samples from two sites were culture-positive. B. pseudomallei DNA-positive samples were negatively associated with the concentration of iron, manganese and nitrogen in a binomial logistic regression model. The highest number of B. pseudomallei-positive samples (42.6%, p < 0.0001) and the highest B. pseudomallei loads in positive samples [median 4.45 × 103 genome equivalents (GE)/g, p < 0.0001] were observed during the monsoon season and eventually declined to 18.9% and a median of 1.47 × 103 GE/g in summer. In conclusion, our study from South West India shows a wide environmental distribution of B. pseudomallei, but also considerable differences in the abundance between sites and within single sites. Our results support the hypothesis that nutrient-depleted habitats promote the presence of B. pseudomallei. Most importantly, the highest B. pseudomallei abundance in soil is seen during the rainy season, when melioidosis cases occur.
Impact of Early Childhood Malnutrition on Adult Brain Function: An Evoked-Related Potentials Study

More than 200 million children under the age of 5 years are affected by malnutrition worldwide according to the World Health Organization. The Barbados Nutrition Study (BNS) is a 55-year longitudinal study on a Barbadian cohort with histories of moderate to severe protein-energy malnutrition (PEM) limited to the first year of life and a healthy comparison group. Using quantitative electroencephalography (EEG), differences in brain function during childhood (lower alpha1 activity and higher theta, alpha2 and beta activity) have previously been highlighted between participants who suffered from early PEM and controls. In order to determine whether similar differences persisted into adulthood, our current study used recordings obtained during a Go-No-Go task in a subsample of the original BNS cohort [population size (N) = 53] at ages 45–51 years. We found that previously malnourished adults [sample size (n) = 24] had a higher rate of omission errors on the task relative to controls (n = 29). Evoked-Related Potentials (ERP) were significantly different in participants with histories of early PEM, who presented with lower N2 amplitudes. These findings are typically associated with impaired conflict monitoring and/or attention deficits and may therefore be linked to the attentional and executive function deficits that have been previously reported in this cohort in childhood and again in middle adulthood.
Associations Between Incident Asthma With Comorbidity Profiles, Night Sleep Duration, and Napping Duration Trajectories: A 7-Year Prospective Study

Objectives: We aim to determine whether comorbidity profiles, night sleep duration, and napping duration trajectories were associated with incident asthma in Chinese adults. Methods: A total of 7,655 community-dwelling individuals were included in this study. Latent class/profile analysis(LCA/LPA) identified comorbidity profiles, night sleep duration, and napping duration trajectories. A generalized additive model with binomial regression assessed the associations between incident asthma with sleep trajectories. Results: During a 7-year follow-up period, 205 individuals were newly diagnosed with asthma. LPA identified four trajectories of night sleep duration: dominant short (n = 2,480), dominant healthy-long (n = 1,405), long decreasing (n = 1875), and short increasing (n = 1895). We also found three trajectories of napping duration: short increasing (n = 3,746), stable normal (n = 1,379), and long decreasing (n = 2,530). We found three comorbidity profiles: dominant heart diseases or risks (n = 766), multiple disorders (n = 758), and minimal or least disorders (n = 6,131). Compared with dominant short night sleep duration, three other trajectories were associated with significantly decreasing incident asthma. Minimal or least disorders profile was associated with a significant reduction of new-onset asthma than two other comorbidity profiles in dominant short night sleep duration. Conclusion: Our findings suggested that a dominant short night sleep duration trajectory potentially increases incident asthma in Chinese adults.
Bioinformatics Analysis Reveals FOXM1/BUB1B Signaling Pathway as a Key Target of Neosetophomone B in Human Leukemic Cells: A Gene Network-Based Microarray Analysis

Abnormal expression of Forkhead box protein M1 (FOXM1) and serine/threonine kinase Budding uninhibited by benzimidazoles 1 (BUB1B) contributes to the development and progression of several cancers, including chronic myelogenous leukemia (CML). However, the molecular mechanism of the FOXM1/BUB1B regulatory network and the role of Neosetophomone-B (NSP-B) in leukemia remains unclear. NSP-B, a meroterpenoid fungal secondary metabolite, possesses anticancer potential in human leukemic cells lines; however, the underlying mechanism has not been elucidated. The present study aimed to explore the role of NSP-B on FOXM1/BUB1B signaling and the underlying molecular mechanism of apoptosis induction in leukemic cells. We performed gene expression profiling of NSP-B-treated and untreated leukemic cells to search for differentially expressed genes (DEGs). Interestingly BUB1B was found to be significantly downregulated (logFC -2.60, adjusted p = 0.001) in the treated cell line with the highest connectivity score among cancer genes. Analysis of TCGA data revealed overexpression of BUB1B compared to normal in most cancers and overexpression was associated with poor prognosis. BUB1B also showed a highly significant positive correlation with FOXM1 in all the TCGA cancer types. We used human leukemic cell lines (K562 and U937) as an in vitro study model to validate our findings. We found that NSP-B treatment of leukemic cells suppressed the expression of FOXM1 and BUB1B in a dose-dependent manner. In addition, NSP-B also resulted in the downregulation of FOXM1-regulated genes such as Aurora kinase A, Aurora kinase B, CDK4, and CDK6. Suppression of FOXM1 either by siRNA or NSP-B reduced BUB1B expression and enhanced cell survival inhibition and induction of apoptosis. Interestingly combination treatment of thiostrepton and NSP-B suppressed of cell viability and inducted apoptosis in leukemic cells via enhancing the activation of caspase-3 and caspase-8 compared with single-agent treatment. These results demonstrate the important role of the FOXM1/BUB1B pathway in leukemia and thus a potential therapeutic target.
Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation

Abstract Meaning Representation is a sentence-level meaning representation, which abstracts the meaning of sentences into a rooted acyclic directed graph. With the continuous expansion of Chinese AMR corpus, more and more scholars have developed parsing systems to automatically parse sentences into Chinese AMR. However, the current parsers can’t deal with concept alignment and relation alignment, let alone the evaluation methods for AMR parsing. Therefore, to make up for the vacancy of Chinese AMR parsing evaluation methods, based on AMR evaluation metric smatch, we have improved the algorithm of generating triples so that to make it compatible with concept alignment and relation alignment. Finally, we obtain a new integrity metric align-smatch for paring evaluation. A comparative research then was conducted on 20 manually annotated AMR and gold AMR, with the result that align-smatch works well in alignments and more robust in evaluating arcs. We also put forward some fine-grained metric for evaluating concept alignment, relation alignment and implicit concepts, in order to further measure parsers’ performance in subtasks.
C USTOMER E NGAGEMENT IN AN O NLINE S OCIAL P LATFORM : A C ONCEPTUAL M ODEL AND S CALE D EVELOPMENT Research-in-Progress

The rise of new media channels in the last few years has changed the ways customers communicate and exchange information. Managing customer relationship through social media engagement has become an important component of successful electronic marketing strategies. Though the concept of customer engagement has received considerable attention in the business community, there is a paucity of theory-guided empirical research in customer engagement in an online social platform. More importantly, there is a lack of consensus regarding its definition, forms, dimensionality, and operationalization. This study addresses these limitations in part by theoretically conceptualizing and empirically validating a scale to measure customer engagement in an online social platform. In this research-in-progress paper, we present our conceptual model of customer engagement and propose a rigorous approach in scale development and validation. The authors believe that this research will contribute to the development of online community literature and add to the repository of rigorous research instruments for researchers to use.
Using Sentence-level Classification Helps Entity Extraction from Material Science Literature

In the last few years, several attempts have been made on extracting information from material science research domain. Material Science research articles are a rich source of information about various entities related to material science such as names of the materials used for experiments, the computational software used along with its parameters, the method used in the experiments, etc. But the distribution of these entities is not uniform across different sections of research articles. Most of the sentences in the research articles do not contain any entity. In this work, we first use a sentence-level classifier to identify sentences containing at least one entity mention. Next, we apply the information extraction models only on the filtered sentences, to extract various entities of interest. Our experiments for named entity recognition in the material science research articles show that this additional sentence-level classification step helps to improve the F1 score by more than 4%.
Quantification Annotation in ISO 24617-12, Second Draft

This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework. After a break, caused by the Covid-19 pandemic, the project was relaunched in early 2022 with a second working draft of an annotation scheme, which is discussed in this paper. Keywords: semantic annotation, quantification, interoperability, annotation schema, ISO standard
Making Sense of Stop

The instruction to “stop” in human-robot interactions is packed with multiple interpretations. “Stop” can convey the operator’s intent to indicate where the robot should halt motion, or it can convey the operator’s realization that the robot is not executing an instruction satisfactorily and begin the process of repair. We analyze cases of “stop” in a corpus of human-robot dialogue, characterizing them along the dimensions of repair status and timing within the interaction, in order to discover patterns and develop design recommendations for how robots should make sense of “stop. ”
Leadership skills propagated by Pt. Shriram Sharma Acharya for sustainable development with special reference to all world Gayatri Pariwar

Leadership skills are essential for all human beings to live a life full of enthusiasm, holistic vision, and contribute for sustainable development. Leaders perform a crucial role and share responsibility in the paradigm shift towards a civilised, peaceful, developed and happy society. This research article focused on presenting key leadership skills in the context of Pt Shriram Sharma Acharya. It is based on a literature review about leadership, leadership skills and All World Gayatri Pariwar (AWGP). This work results in leadership skills found. Those who want to become a holistic and perfect leader might adopt these skills as necessary for the future leaders.
Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources

This article presents the current outcomes of the CURLICAT CEF Telecom project, which aims to collect and deeply annotate a set of large corpora from selected domains. The CURLICAT corpus includes 7 monolingual corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing selected samples from respective national corpora. These corpora are automatically tokenized, lemmatized and morphologically analysed and the named entities annotated. The annotations are uniformly provided for each language specific corpus while the common metadata schema is harmonised across the languages. Additionally, the corpora are annotated for IATE terms in all languages. The file format is CoNLL-U Plus format, containing the ten columns specific to the CoNLL-U format and three extra columns specific to our corpora as defined by Varádi et al. (2020). The CURLICAT corpora represent a rich and valuable source not just for training NMT models, but also for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.