Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimension Reduction for Spatially Correlated Data: Spatial Predictor Envelope

Dimension reduction is an important tool for analyzing high-dimensional data. The predictor envelope is a method of dimension reduction for regression that assumes certain linear combinations of the predictors are immaterial to the regression. The method can result in substantial gains in estimation efficiency and prediction accuracy over traditional maximum likelihood and least squares estimates. While predictor envelopes have been developed and studied for independent data, no work has been done adapting predictor envelopes to spatial data. In this work, the predictor envelope is adapted to a popular spatial model to form the spatial predictor envelope (SPE). Maximum likelihood estimates for the SPE are derived, along with asymptotic distributions for the estimates given certain assumptions, showing the SPE estimates to be asymptotically more efficient than estimates of the original spatial model. The effectiveness of the proposed model is illustrated through simulation studies and the analysis of a geochemical data set. Keyword: Dimension Reduction, Envelope model, Major chemical elements, Rare earth elements, Spatial correlation. 1 ar X iv :2 20 1. 01 91 9v 1 [ st at . M E ] 6 J an 2 02 2
Ekar: An Explainable Method for Knowledge Aware Recommendation

This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations. Though these methods have been shown quite effective, they lack good explanations, which are critical to recommender systems. In this paper, we take a different route and propose generating recommendations by finding meaningful paths from users to items. Specifically, we formulate the problem as a sequential decision process, where the target user is defined as the initial state, and the edges on the graphs are defined as actions. We shape the rewards according to existing state-of-the-art methods and then train a policy function with policy gradient methods. Experimental results on three real-world datasets show that our proposed method not only provides effective recommendations but also offers good explanations.
Computational investigation of blood cell transport in retinal microaneurysms

Microaneurysms (MAs) are one of the earliest clinically visible signs of diabetic retinopathy (DR). MA leakage or rupture may precipitate local pathology in the surrounding neural retina that impacts visual function. Thrombosis in MAs may affect their turnover time, an indicator associated with visual and anatomic outcomes in the diabetic eyes. In this work, we perform computational modeling of blood flow in microchannels containing various MAs to investigate the pathologies of MAs in DR. The particle-based model employed in this study can explicitly represent red blood cells (RBCs) and platelets as well as their interaction in the blood flow, a process that is very difficult to observe in vivo. Our simulations illustrate that while the main blood flow from the parent vessels can perfuse the entire lumen of MAs with small body-to-neck ratio (BNR), it can only perfuse part of the lumen in MAs with large BNR, particularly at a low hematocrit level, leading to possible hypoxic conditions inside MAs. We also quantify the impacts of the size of MAs, blood flow velocity, hematocrit and RBC stiffness and adhesion on the likelihood of platelets entering MAs as well as their residence time inside, two factors that are thought to be associated with thrombus formation in MAs. Our results show that enlarged MA size, increased blood velocity and hematocrit in the parent vessel of MAs as well as the RBC-RBC adhesion promote the migration of platelets into MAs and also prolong their residence time, thereby increasing the propensity of thrombosis within MAs. Overall, our work suggests that computational simulations using particle-based models can help to understand the microvascular pathology pertaining to MAs in DR and provide insights to stimulate and steer new experimental and computational studies in this area.
Single-cell transcriptome analysis identifies a unique tumor cell type producing multiple hormones in ectopic ACTH and CRH secreting pheochromocytoma

Ectopic Cushing’s syndrome due to ectopic ACTH&CRH-secreting by pheochromocytoma is extremely rare and can be fatal if not properly diagnosed. It remains unclear whether a unique cell type is responsible for multiple hormones secreting. In this work, we performed single-cell RNA sequencing to three different anatomic tumor tissues and one peritumoral tissue based on a rare case with ectopic ACTH&CRH-secreting pheochromocytoma. And in addition to that, three adrenal tumor specimens from common pheochromocytoma and adrenocortical adenomas were also involved in the comparison of tumor cellular heterogeneity. A total of 16 cell types in the tumor microenvironment were identified by unbiased cell clustering of single-cell transcriptomic profiles from all specimens. Notably, we identified a novel multi-functionally chromaffin-like cell type with high expression of both POMC (the precursor of ACTH) and CRH, called ACTH+&CRH + pheochromocyte. We hypothesized that the molecular mechanism of the rare case harbor Cushing’s syndrome is due to the identified novel tumor cell type, that is, the secretion of ACTH had a direct effect on the adrenal gland to produce cortisol, while the secretion of CRH can indirectly stimulate the secretion of ACTH from the anterior pituitary. Besides, a new potential marker (GAL) co-expressed with ACTH and CRH might be involved in the regulation of ACTH secretion. The immunohistochemistry results confirmed its multi-functionally chromaffin-like properties with positive staining for CRH, POMC, ACTH, GAL, TH, and CgA. Our findings also proved to some extent the heterogeneity of endothelial and immune microenvironment in different adrenal tumor subtypes.
Amanida: an R package for meta-analysis of metabolomics non-integral data

Abstract Summary The combination, analysis and evaluation of different studies which try to answer or solve the same scientific question, also known as a meta-analysis, plays a crucial role in answering relevant clinical relevant questions. Unfortunately, metabolomics studies rarely disclose all the statistical information needed to perform a meta-analysis. Here, we present a meta-analysis approach using only the most reported statistical parameters in this field: P-value and fold-change. The P-values are combined via Fisher’s method and fold-changes by averaging, both weighted by the study size (n). The amanida package includes several visualization options: a volcano plot for quantitative results, a vote plot for total regulation behaviours (up/down regulations) for each compound, and a explore plot of the vote-counting results with the number of times a compound is found upregulated or downregulated. In this way, it is very easy to detect discrepancies between studies at a first glance. Availability and implementation Amanida code and documentation are at CRAN and https://github.com/mariallr/amanida. Supplementary information Supplementary data are available at Bioinformatics online.
Variation of phenotypic and physiological traits of Robinia pseudoacacia L. from 20 provenances

To select elite Robinia pseudoacacia L. germplasm resources for production, 13 phenotypes and three physiological indicators of 214 seedlings from 20 provenances were systematically evaluated and analyzed. The leaf phenotypic and physiological coefficients of variation among the genotypes ranged from 3.741% to 19.599% and from 8.260% to 42.363%, respectively. The Kentucky provenance had the largest coefficient of variation (18.541%). The average differentiation coefficients between and within provenances were 34.161% and 38.756%, respectively. These close percentages showed that R. pseudoacacia presented high genetic variation among and within provenances, which can be useful for assisted migration and breeding programs. Furthermore, based on the results of correlations, principal component analysis and cluster analysis, breeding improvements targeting R. pseudoacacia’s ornamental value, food value, and stress resistance of were performed. Forty and 30 excellent individuals, accounting for 18.692% and 14.019%, respectively, of the total resources. They were ultimately screened, after comprehensively taking into considering leaf phenotypic traits including compound leaf length, leaflet number and leaflet area and physiological characteristics including proline and soluble protein contents. These selected individuals could provide a base material for improved variety conservation and selection.
Mycobacterial MMAR_2193 catalyzes O-methylation of diverse polyketide cores

O-methylation of small molecules is a common modification widely present in most organisms. Type III polyketides undergo O-methylation at hydroxyl end to play a wide spectrum of roles in bacteria, plants, algae, and fungi. Mycobacterium marinum harbours a distinctive genomic cluster with a type III pks gene and genes for several polyketide modifiers including a methyltransferase gene, mmar_2193. This study reports functional analyses of MMAR_2193 and reveals multi-methylating potential of the protein. Comparative sequence analyses revealed conservation of catalytically important motifs in MMAR_2193 protein. Homology-based structure-function and molecular docking studies suggested type III polyketide cores as possible substrates for MMAR_2193 catalysis. In vitro enzymatic characterization revealed the capability of MMAR_2193 protein to utilize diverse polyphenolic substrates to methylate several hydroxyl positions on a single substrate molecule. High-resolution mass spectrometric analyses identified multi-methylations of type III polyketides in cell-free reconstitution assays. Notably, our metabolomics analyses identified some of these methylated molecules in biofilms of wild type Mycobacterium marinum. This study characterizes a novel mycobacterial O-methyltransferase protein with multi-methylating enzymatic ability that could be exploited to generate a palette of structurally distinct bioactive molecules.
High expression of lncRNA HSD11B1-AS1 indicates favorable prognosis and is associated with immune infiltration in cutaneous melanoma

Cutaneous melanoma is an aggressive malignant cancer associated with poor prognosis. Identification of reliable biomarkers for predicting prognosis of melanoma contributes to improved clinical outcome and disease management. Long non-coding RNAs (lncRNAs) serve a crucial regulatory role of oncogenesis and tumor suppression in melanoma. Using data from The Cancer Genome Atlas database, novel lncRNA 11β-hydroxysteroid dehydrogenase type 1-antisense RNA 1 (HSD11B1-AS1) was identified, which was significantly downregulated in malignant melanoma and its downregulation was significantly associated with poor clinicopathological characteristics, including advanced T and pathological stage, Clark level, Breslow depth and ulceration and worse prognosis. Multivariate analysis showed that HSD11B1-AS1, as well as N stage and Breslow depth, were independent prognostic factors in cutaneous melanoma, and nomograms suggested a good predictive value of 1-, 3- and 5-year overall survival, progression-free interval and disease-specific survival. In vitro experiments verified the decreased HSD11B1-AS1 expression in melanoma cell lines compared with human epidermal melanocytes. Moreover, cell experiments in vitro, including Cell Counting Kit-8, colony formation, wound healing and Transwell assay, suggested that overexpression of HSD11B1-AS1 significantly inhibited melanoma cell proliferation, migration and invasion. Functional enrichment showed significantly enriched pathways in IFN-γ and -α response, TNF-α signaling via NF-κB and IL-2/STAT-5 and IL-6/JAK/STAT-3 signaling. In addition, immune infiltration analysis demonstrated that HSD11B1-AS1 may function by accelerating immune response regulation and the immune cell infiltration of various immunocytes, especially T, T helper 1, activated dendritic and B cells. The present study revealed HSD11B1-AS1 as a potential therapeutic target and promising biomarker for diagnosis and prognosis of cutaneous melanoma.
miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions

Abstract MicroRNAs (miRNAs) are noncoding RNAs with 18–26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA–target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update’s critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3′ untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk. edu.cn/.
Motivational Factors for Smoking Behaviors in Individuals with Metabolic Syndrome

Purpose Smoking, which is one of the major risk factors for metabolic syndrome that causes cardiovascular disease or diabetes, is a crucial risk factor, which is modifiable. This study aimed to determine the factors that promote smoking behaviors according to smoking status among adults with metabolic syndrome. Patients and Methods A survey of structured self-reported questionnaires was conducted on 152 adults with metabolic syndrome. Outcome variables included cognitive motivations (self-efficacy, and perceived benefits and barriers), emotional motivation (emotional salience), and autonomous and controlled motivation based on self-determination theory. The participants were categorized by their smoking status (never smoking, quit smoking, or current smoking). Their sociodemographic and motivational factors were examined using one-way analysis of variance, analysis of covariance, and multinomial logistic regression analysis. Results Data on 152 individuals with metabolic syndrome with a mean age of 57.5 years were included. The findings indicated that the motivational factors for the never-smoking and quit-smoking groups significantly differed from those for the current-smoking group in terms of self-efficacy, perceived benefits, perceived barriers, and autonomous motivation. Based on multinomial logistic regression with current smoking as the reference group, sex (being female, OR=57.69) and perceived barriers (OR=0.39) were the significant predictors for the never-smoking group, while autonomous motivation (OR=1.96) was the significant predictor for the quit-smoking group. Conclusion The motivational factors for smoking behaviors varied according to the smoking status of adults with metabolic syndrome. Autonomous motivation was the significant predictor of smoking cessation for individuals who successfully quit, whereas cognitive motivation was influential in the prevention of smoking by individuals who have never smoked. Further studies are warranted to develop smoking cessation strategies, which should focus on specific motivational factors to lead effective smoking prevention programs in this population.
ONQUADRO: a database of experimentally determined quadruplex structures

Abstract ONQUADRO is an advanced database system that supports the study of the structures of canonical and non-canonical quadruplexes. It combines a relational database that collects comprehensive information on tetrads, quadruplexes, and G4-helices; programs to compute structure parameters and visualise the data; scripts for statistical analysis; automatic updates and newsletter modules; and a web application that provides a user interface. The database is a self-updating resource, with new information arriving once a week. The preliminary data are downloaded from the Protein Data Bank, processed, annotated, and completed. As of August 2021, ONQUADRO contains 1,661 tetrads, 518 quadruplexes, and 30 G4-helices found in 467 experimentally determined 3D structures of nucleic acids. Users can view and download their description: sequence, secondary structure (dot-bracket, classical diagram, arc diagram), tertiary structure (ball-and-stick, surface or vdw-ball model, layer diagram), planarity, twist, rise, chi angle (value and type), loop characteristics, strand directionality, metal ions, ONZ, and Webba da Silva classification (the latter by loop topology and tetrad combination), origin structure ID, assembly ID, experimental method, and molecule type. The database is freely available at https://onquadro.cs. put.poznan. pl/. It can be used on both desktop computers and mobile devices.
Attenuation of hyperglycemia and amadori products by aminoguanidine in alloxan-diabetic rabbits occurs via enhancement in antioxidant defenses and control of stress

The micro- and macro-complications in diabetes mellitus (DM) mainly arise from the damage induced by Amadori and advanced glycation end products, as well as the released free radicals. The primary goal of DM treatment is to reduce the risk of micro- and macro-complications. In this study, we looked at the efficacy of aminoguanidine (AG) to prevent the production of early glycation products in alloxan-diabetic rabbits. Type1 DM was induced in rabbits by a single intravenous injection of alloxan (90 mg/kg body weight). Another group of rabbits was pre-treated with AG (100 mg/kg body weight) prior to alloxan injection; this was followed by weekly treatment with 100 mg/kg of AG for eight weeks. Glucose, insulin, and early glycation products (HbA1C and fructosamine) were measured in control, diabetic and AG treated diabetic rabbits. The effects of hyperglycemia on superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (Gpx), reduced glutathione (rGSH), nitric oxide, lipid peroxides, and protein carbonyl were investigated. Alloxan-diabetic rabbits had lower levels of SOD, CAT, Gpx, and rGSH than control rabbits. Nitric oxide levels were considerably greater. AG administration restored the activities of SOD, CAT, Gpx enzymes up to 70–80% and ameliorated the nitric oxide production. HbA1c and fructosamine levels were considerably lower in AG-treated diabetic rabbits. The observed control of hyperglycemia and amadori adducts in alloxan-diabetic rabbits by AG may be attributed to decrease of stress and restoration of antioxidant defenses.
Radar State Monitoring System Based on Virtual Instrument Technique

In this paper,the virtual instrument technique is put forward to set up a whole radar state monitoring system to counter that there is generally lack of monitoring points in the radar equipment and the general instrument limit in the equipment state monitoring area.The operation principles of the virtual instrument and data collection techniques are described,the hardware compositions and support software related to the virtual instrument technique are described in detail,the monitoring equipment composed of virtual instrument is compared with the general instruments.Dealing with the specific radar system——180 precision instrumentation radar,the method of establishing the monitoring system is described in detail.Based on the characteristics of the radar,the feasible monitoring points and the significance and purpose of designing the monitoring points are put forward.The monitoring results are displayed in the forms of figures and data,which are objective and convenient and this displaying method has an important meaning for increasing the feasibility of the radar system.
Psychological distress and experiences of Adolescents and Young Adults with cancer during the COVID‐19 pandemic: A cross‐sectional survey

This study investigated prevalence of psychological distress, factors associated with distress, and experiences of Adolescents and Young Adults (AYAs) with cancer during the COVID‐19 pandemic. It also compared distress in this group to previously surveyed Canadian AYAs with cancer in 2018 by the Young Adults with Cancer in their Prime (YACPRIME) study.
Effectiveness of Digital Counseling Environments on Anxiety, Depression, and Adherence to Treatment Among Patients Who Are Chronically Ill: Systematic Review

Background Patients who are chronically ill need novel patient counseling methods to support their self-care at different stages of the disease. At present, knowledge of how effective digital counseling is at managing patients’ anxiety, depression, and adherence to treatment seems to be fragmented, and the development of digital counseling will require a more comprehensive view of this subset of interventions. Objective This study aims to identify and synthesize the best available evidence on the effectiveness of digital counseling environments at improving anxiety, depression, and adherence to treatment among patients who are chronically ill. Methods Systematic searches of the EBSCO (CINAHL), PubMed, Scopus, and Web of Science databases were conducted in May 2019 and complemented in October 2020. The review considered studies that included adult patients aged ≥18 years with chronic diseases; interventions evaluating digital (mobile, web-based, and ubiquitous) counseling interventions; and anxiety, depression, and adherence to treatment, including clinical indicators related to adherence to treatment, as outcomes. Methodological quality was assessed using the standardized Joanna Briggs Institute critical appraisal tool for randomized controlled trials or quasi-experimental studies. As a meta-analysis could not be conducted because of considerable heterogeneity in the reported outcomes, narrative synthesis was used to synthesize the results. Results Of the 2056 records screened, 20 (0.97%) randomized controlled trials, 4 (0.19%) pilot randomized controlled trials, and 2 (0.09%) quasi-experimental studies were included. Among the 26 included studies, 10 (38%) digital, web-based interventions yielded significantly positive effects on anxiety, depression, adherence to treatment, and the clinical indicators related to adherence to treatment, and another 18 (69%) studies reported positive, albeit statistically nonsignificant, changes among patients who were chronically ill. The results indicate that an effective digital counseling environment comprises high-quality educational materials that are enriched with multimedia elements and activities that engage the participant in self-care. Because of the methodological heterogeneity of the included studies, it is impossible to determine which type of digital intervention is the most effective for managing anxiety, depression, and adherence to treatment. Conclusions This study provides compelling evidence that digital, web-based counseling environments for patients who are chronically ill are more effective than, or at least comparable to, standard counseling methods; this suggests that digital environments could complement standard counseling.