Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ubiquitin Switch in Plant Stress Response

Ubiquitin is a 76 amino acid polypeptide common to all eukaryotic organisms. It functions as a post-translationally modifying mark covalently linked to a large cohort of yet poorly defined protein substrates. The resulting ubiquitylated proteins can rapidly change their activities, cellular localization, or turnover through the 26S proteasome if they are no longer needed or are abnormal. Such a selective modification is essential to many signal transduction pathways particularly in those related to stress responses by rapidly enhancing or quenching output. Hence, this modification system, the so-called ubiquitin-26S proteasome system (UPS), has caught the attention in the plant research community over the last two decades for its roles in plant abiotic and biotic stress responses. Through direct or indirect mediation of plant hormones, the UPS selectively degrades key components in stress signaling to either negatively or positively regulate plant response to a given stimulus. As a result, a tightly regulated signaling network has become of much interest over the years. The ever-increasing changes of the global climate require both the development of new crops to cope with rapid changing environment and new knowledge to survey the dynamics of ecosystem. This review examines how the ubiquitin can switch and tune plant stress response and poses potential avenues to further explore this system.
Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types

COVID‐19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID‐19 was emerged due to the SARS‐CoV‐2 that is highly infectious pandemic. Every country tried to control the COVID‐19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time‐series and machine learning models, named as random forests, K‐nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID‐19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top‐ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out‐of‐sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID‐19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID‐19.
The correlation of salivary telomere length and single nucleotide polymorphisms of the ADIPOQ, SIRT1 and FOXO3A genes with lifestyle-related diseases in a Japanese population

Background It has been reported that genetic factors are associated with risk factors and onset of lifestyle-related diseases, but this finding is still the subject of much debate. Objective The aim of the present study was to investigate the correlation of genetic factors, including salivary telomere length and three single nucleotide polymorphisms (SNPs) that may influence lifestyle-related diseases, with lifestyle-related diseases themselves. Methods In one year at a single facility, relative telomere length and SNPs were determined by using monochrome multiplex quantitative polymerase chain reaction and TaqMan SNP Genotyping Assays, respectively, and were compared with lifestyle-related diseases in 120 Japanese individuals near our university. Results In men and all participants, age was inversely correlated with relative telomere length with respective p values of 0.049 and 0.034. In men, the frequency of hypertension was significantly higher in the short relative telomere length group than in the long group with unadjusted p value of 0.039, and the difference in the frequency of hypertension between the two groups was of borderline statistical significance after adjustment for age (p = 0.057). Furthermore, in men and all participants, the sum of the number of affected lifestyle-related diseases, including hypertension, was significantly higher in the short relative telomere length group than in the long group, with p values of 0.004 and 0.029, respectively. For ADIPOQ rs1501299, men’s ankle brachial index was higher in the T/T genotype than in the G/G and G/T genotypes, with p values of 0.001 and 0.000, respectively. For SIRT1 rs7895833, men’s body mass index and waist circumference and all participants’ brachial-ankle pulse wave velocity were higher in the A/G genotype than in the G/G genotype, with respective p values of 0.048, 0.032 and 0.035. For FOXO3A rs2802292, women’s body temperature and all participants’ saturation of peripheral oxygen were lower in the G/T genotype than in the T/T genotype, with respective p values of 0.039 and 0.032. However, relative telomere length was not associated with physiological or anthropometric measurements except for height in men (p = 0.016). ADIPOQ rs1501299 in men, but not the other two SNPs, was significantly associated with the sum of the number of affected lifestyle-related diseases (p = 0.013), by genotype. For each SNPs, there was no significant difference in the frequency of hypertension or relative telomere length by genotype. Conclusion Relative telomere length and the three types of SNPs determined using saliva have been shown to be differentially associated with onset of and measured risk factors for lifestyle-related diseases consisting mainly of cardiovascular diseases and cancer.
Variations in gene expression levels with severity of synovitis in dogs with naturally occurring stifle osteoarthritis

Osteoarthritis (OA) is one of the major causes of chronic pain in dogs. However, the pathogenesis of OA has not been fully understood in dogs. The objective of this study was to comprehensively investigate the mRNA expression levels of proinflammatory cytokines, inflammatory mediators, nerve growth factor and its receptor, and matrix metalloproteinases in the synovium of dogs with spontaneous OA as well as to elucidate their relationships with the severity of synovitis. Dogs that were diagnosed with stifle OA on the basis of radiographic findings were included, and the degree of synovitis was observed using stifle arthroscopy. The dogs were assigned to two different groups depending on their synovitis scores: the low-grade group (score of 1 or 2; n = 8) and high-grade group (score of 3 to 5; n = 18). The dogs showing no evidence of orthopedic disease were included in the control group (n = 6). Synovial tissue samples were collected from the sites at which synovitis scores were assessed using arthroscopy. Total RNA was extracted from the collected synovial tissue, and cDNA was synthesized. Subsequently, RT-qPCR were performed using canine-specific primer sets for IL1B, IL6, CXCL8, TNF, TGFB1, PTGS2, PTGES, MMP3, MMP13, NGF, NTRK1, and PTGER4. Expression levels of IL1B, IL6, CXCL8, and MMP13 were significantly higher in the high-grade group than in the control group. In addition, expression levels of IL1B, CXCL8, TNF, and PTGS2 were significantly higher in the high-grade group than in the low-grade group. Expression levels of IL1B, IL6, CXCL8, TNF, PTGS2, and PTGER4 showed significant positive correlation with synovitis score. In conclusion, all mRNA expression levels in the synovial membrane varied according to the degree of synovitis in dogs with spontaneous OA. Thus, this study may partially elucidate the pathogenesis of synovitis in dogs with spontaneous OA.
Solanum melongena L. Extract Protects Retinal Pigment Epithelial Cells from Blue Light-Induced Phototoxicity in In Vitro and In Vivo Models

N-retinylidene-N-retinylethanolamine (A2E) accumulation in the retina is a prominent marker of retinal degenerative diseases. Blue light exposure is considered as an important factor contributing to dry age-related macular degeneration (AMD). Eggplant and its constituents have been shown to confer health benefits, but their therapeutic effects on dry AMD remain incompletely understood. In this study, we showed that an extract of Solanum melongena L. (EPX) protected A2E-laden ARPE-19 cells against blue light-induced cell death via attenuating reactive oxygen species. Transcriptomic analysis demonstrated that blue light modulated the expression of genes associated with stress response, inflammation, and cell death, and EPX suppressed the inflammatory pathway induced by blue light in A2E-laden ARPE-19 cells by inhibiting the nuclear translocation of nuclear factor kappa B and transcription of pro-inflammatory genes (CXCL8 and IL1B). The degradation of intracellular A2E was considered the major mechanism underlying the protective effect of EPX. Moreover, chlorogenic acid isolated from EPX exerted protective effects against blue light-induced cell damage in A2E-laden ARPE-19 cells. In vivo, EPX administration in BALB/c mice reduced the fundus damage and degeneration of the retinal layer in a blue light-induced retinal damage model. Collectively, our findings suggest the potential role of Solanum melongena L. extract for AMD treatment.
A Missing Link: Engagements of Dendritic Cells in the Pathogenesis of SARS-CoV-2 Infections

Dendritic cells (DC) connect the innate and adaptive arms of the immune system and carry out numerous roles that are significant in the context of viral disease. Their functions include the control of inflammatory responses, the promotion of tolerance, cross-presentation, immune cell recruitment and the production of antiviral cytokines. Based primarily on the available literature that characterizes the behaviour of many DC subsets during Severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19), we speculated possible mechanisms through which DC could contribute to COVID-19 immune responses, such as dissemination of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to lymph nodes, mounting dysfunctional inteferon responses and T cell immunity in patients. We highlighted gaps of knowledge in our understanding of DC in COVID-19 pathogenesis and discussed current pre-clinical development of therapies for COVID-19.
Accuracy of Guided Implant Surgery in the Edentulous Jaw Using Desktop 3D-Printed Mucosal Supported Guides

Purpose: The aim of this in vitro study is to evaluate the accuracy of implant position using mucosal supported surgical guides, produced by a desktop 3D printer. Methods: Ninety implants (Bone Level Roxolid, 4.1 mm × 10 mm, Straumann, Villerat, Switzerland) were placed in fifteen mandibular casts (Bonemodels, Castellón de la Plana, Spain). A mucosa-supported guide was designed and printed for each of the fifteen casts. After placement of the implants, the location was assessed by scanning the cast and scan bodies with an intra-oral scanner (Primescan®, Dentsply Sirona, York, PA, USA). Two comparisons were performed: one with the mucosa as a reference, and one where only the implants were aligned. Angular, coronal and apical deviations were measured. Results: The mean implant angular deviation for tissue and implant alignment were 3.25° (SD 1.69°) and 2.39° (SD 1.42°) respectively, the coronal deviation 0.82 mm (SD 0.43 mm) and 0.45 mm (SD 0.31 mm) and the apical deviation 0.99 mm (SD 0.45 mm) and 0.71 mm (SD 0.43 mm). All three variables were significantly different between the tissue and implant alignment (p < 0.001). Conclusion: Based on the results of this study, we conclude that guided implant surgery using desktop 3D printed mucosa-supported guides has a clinically acceptable level of accuracy. The resilience of the mucosa has a negative effect on the guide stability and increases the deviation in implant position.
Multicenter Evaluation of an ELISA for the Detection of Cryptosporidium spp. Antigen in Clinical Human Stool Samples

Human cryptosporidiosis remains underdiagnosed, and rapid/accurate diagnosis is of clinical importance. Diagnosis of the Cryptosporidium oocyst in stool samples by conventional microscopy is labor-intensive, time-consuming, and requires skillful experience. Thus, we aimed to evaluate the usefulness of a coproantigen enzyme-linked immunosorbent assay (ELISA) test in detecting Cryptosporidium spp. from fecal specimens. For this aim, we evaluated the performances of a commercial ELISA (CoproELISA Cryptosporidium kit, Savyon Diagnostics, Israel) for the detection of Cryptosporidium spp. in random clinical stool samples through a multicenter study. The sensitivity and specificity for coproantigen ELISA were 98.86% and 94.32%, respectively. The coproantigen ELISA results indicate that the simple, rapid, reliable, and standardized immunoassay test is sensitive and specific for routine diagnosis, and may be useful for large-scale epidemiological studies of cryptosporidiosis.
Breast Cancer Type Classification Using Machine Learning

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.
Closed formulae for (1s|1s), Slater two‐center integrals, using the three‐center nuclear attraction integral program in spherical coordinates

The strategy of using expansions in spherical harmonics of displaced Slater-type (exp(−αx)) orbitals to evaluate multicenter molecular integrals is applied to three-centered nuclear attraction integrals (potential due to the product of separated Slater-type orbitals). Closed formulae are produced for nine terms of an infinite series for interior region, (inside − ipot) r2 a. The value a = 2 is chosen for the interior region and r = 14 for the exterior region. Our focus is to obtain a closed formula for the Slater formula. © 2004 Wiley Periodicals, Inc. Int J Quantum Chem, 2004
Reuse of Molecules for Glioblastoma Therapy

Glioblastoma multiforme (GBM) is a highly malignant primary brain tumor. The current standard of care for GBM is the Stupp protocol which includes surgical resection, followed by radiotherapy concomitant with the DNA alkylator temozolomide; however, survival under this treatment regimen is an abysmal 12–18 months. New and emerging treatments include the application of a physical device, non-invasive ‘tumor treating fields’ (TTFs), including its concomitant use with standard of care; and varied vaccines and immunotherapeutics being trialed. Some of these approaches have extended life by a few months over standard of care, but in some cases are only available for a minority of GBM patients. Extensive activity is also underway to repurpose and reposition therapeutics for GBM, either alone or in combination with the standard of care. In this review, we present select molecules that target different pathways and are at various stages of clinical translation as case studies to illustrate the rationale for their repurposing-repositioning and potential clinical use.
Fine particles in the global troposphere. A review

The available body of data on fine particles (> 1 μm radius) in the troposphere is reviewed in relation to our understanding of sources, sinks and transformation processes of atmospheric aerosols. This review yields the following results. There are no data available to characterize the free troposphere above the boundary layer. Most of the boundary layer data suffer from the lack of a well-defined upper size limit of the samplers at about 1 μm radius and concurrent results on total fine particle mass. The fine particle composition data are consistent with our understanding of natural and anthropogenic sources of trace substances, which lead to fine particles. The chemical composition is presented for the three aerosol types: urban, non-urban continental and remote regions. Special attention needs to be focused on fine particle interaction with clouds for three reasons. Fine particles control number- and surface-distribution of the condensed matter; the state of precondensation haze and clouds provide the most important processes of fine particle elimination from the atmosphere. Furthermore, climatic effects of fine particles as expressed by their interaction with atmospheric radiation is strongest during their passage through clouds. Very little is known about the processing of aerosols through clouds. Consequently, there are large uncertainties about their climatic effect and their lifetime in the atmosphere. Finally, no single type of secular trend in particle concentration is found on a global scale. Depending on which component and which geographical region is monitored, both decreasing and increasing concentrations have been measured. The differences are consistent with our understanding of aerosol properties and their gas phase precursors. DOI: 10.1111/j.1600-0889.1989. tb00132.x
Analyzing Risk of Service Failures in Heavy Haul Rail Lines: A Hybrid Approach for Imbalanced Data

An incident in which a rail defect of size over a threshold value is noticed and the track is taken out of service is known as a service failure. This article aims at building accurate prediction models with binary outcome for risk of service failures on heavy haul rail segments. An analysis of the factors that influence the risk of a service failure is conducted and quantitative models are developed to predict locations where service failures are most likely to occur until the next inspection. To this end, data are collected from a Class I U.S. Railroads for six years from 2011 to 2016. Four prediction models (i.e., logistic regression, decision tree, multilayer perceptron, and gradient boosting classifier) are implemented and their results are compared. To account for the imbalanced classes between the normal operation and service failure, two treatments have been used including undersampling and oversampling. To improve the model performance, the parameters of each method are tuned using random search hyperparameter optimization. Later, bootstrap aggregation (or bagging) is incorporated into each method. The findings of the study show that the prediction performance is the highest when using bagging and oversampling as treatments with gradient boosting method. It was also identified that gross tonnage, presence of geometry defects, ambient temperature, segment length, and rail defect presence are the most important factors for predicting the risk of service failures. The results of this study are useful for railroads to develop effective strategies for rail inspections, preventive maintenance, and capital planning.
An In Vitro Co-Culture Model of Bone Marrow Mesenchymal Stromal Cells and Peripheral Blood Mononuclear Cells Promotes the Differentiation of Myeloid Angiogenic Cells and Pericyte-like Cells.

Mesenchymal stromal cells (MSCs) are known to stimulate the survival and growth of endothelial cells (ECs) by producing paracrine signals, as well as to differentiate into pericytes and thereby support blood vessel formation and stability. On the other hand, cells with EC-like phenotype have been found within the CD14+ and CD34+ cell populations of peripheral blood (PB) mononuclear cells (MNCs). The aim of this study was to investigate the pro-angiogenic differentiation potential of human MSC-MNC co-cultures. MSCs (2500 cells/cm2) were co-cultured with MNCs (50 000 cells/cm2), which were isolated from the peripheral blood of healthy donors. MSCs and MNCs cultured alone at same cell densities were used as controls. Cells in MNC fraction and in co-cultures were isolated for CD14, CD34 and CD31 surface markers with magnetic-activated cell sorting. Co-cultures were analyzed for cell proliferation and morphology, as well as for the expression of various hematopoietic, endothelial and pericyte markers by immunocytochemistry, qPCR and flow cytometry. VEGF expression and secretion was measured with qPCR and ELISA, respectively. Our results show that in co-cultures with MSCs, CD14+CD45+ MNCs differentiated into spindle-shaped, non-proliferative, EC-like, myeloid angiogenic cells (MACs) expressing CD31, but also into pericyte-like cells expressing NG2 and CD146. Functionality of the isolated MACs was demonstrated in co-cultures with HUVECs, where they supported the formation of tube-like structures. NG2+ cells of MNC-origin were found among both CD34-CD14+ and CD34-CD14- cell populations, indicating the existence of different subtypes of pericyte-like cells. In addition, VEGF was shown to be secreted in MSC-MNC co-cultures, mainly by MSCs. In conclusion, MSCs were shown to possess proangiogenic capacity in MSC-MNC co-cultures as they supported the differentiation of functional MACs, as well as the differentiation of pericyte-like cells of MNC-origin. This phenomenon was mediated at least partially via secreted VEGF.