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This study delves into the long-term atmospheric transport and deposition of 210Pb in the Northern Hemisphere by using the atmospheric transport model HIRAT. The calculation for the four-year (2012–2015) period showed an average deposition flux of 13.0 Bq m−2 month−1 with significant seasonal variations characterized by higher deposition rates during summer and lower during winter. High deposition was found in the Northern Bay of Bengal and Bangladesh regions, Southern China, the Western Philippine Sea, the Eastern Japan Sea, the Northwestern Pacific region, the Eastern and Western coasts of North America, the Caribbean Sea, the Eastern Pacific region off of Central America, the Central Atlantic region between Central America and Africa, and the Northwestern Atlantic Ocean. Deposition patterns varied across latitudinal zones, with tropical areas experiencing the highest deposition and polar/subpolar zones the lowest. This study emphasized the impact of monsoons on the significantly large 210Pb deposition in the Japan Sea region. Furthermore, this study showed that the lower troposphere (0 to 3 km) dominates with about 53%, and the middle troposphere (3 to 6 km) and upper troposphere (above 6 km) also contribute significantly to the total 210Pb inventory with 37% and 10%, respectively. These findings provide essential insights into the characteristics of atmospheric transport and deposition of 210Pb, and their mechanisms. [ABSTRACT FROM AUTHOR]

Pituitary gonadotropins perform essential functions in mammalian reproduction by stimulating gametogenesis and steroidogenesis in the ovaries and testicles. EZH2 is a histone methyltransferase that inhibits proliferation and aggravates apoptosis in stem cells subjected to pathological stimuli. However, the expression and molecular mechanisms of EZH2 in pituitary cells in vitro have not been extensively studied. In this study, the relative abundances of EZH2 mRNA (p < 0.01) and protein (p < 0.05) expression were larger in the pituitary cells of Hu sheep with relatively greater fecundity (GF) compared to those with lesser fecundity (LF). Loss-of-function examinations demonstrated that EZH2 gene knockdown led to an earlier induction of apoptosis in sheep pituitary cells (PCs). The relative abundance of CASP3, CASP9, and BAX was increased (p < 0.01), while BCL2's abundance was less decreased (p < 0.01) in PCs where there was EZH2 gene knockdown. Additionally, cell proliferation (p < 0.01) and viability (p < 0.01) were decreased in EZH2-knockdown sheep PCs, and the cell cycle was blocked compared to a negative control (NC). Notably, EZH2 gene knockdown led to reduced abundances of gonadotropin subunit gene transcripts (FSHβ, p < 0.05) and reduced FSH release (p < 0.01) from PCs. EZH2 gene knockdown led to reduced phosphorylation of AKT, ERK, and mTOR (p < 0.01). The results suggest that EZH2 regulates pituitary cell proliferation, apoptosis, and FSH secretion through modulation of the AKT/ERK signaling pathway, providing a foundation for further study of pituitary cell functions. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) play an important role in the diagnosis and treatment of diseases because of their rich molecular contents involved in intercellular communication, regulation, and other functions. With increasing efforts to move the field of EVs to clinical applications, the lack of a practical EV isolation method from circulating biofluids with high throughput and good reproducibility has become one of the biggest barriers. Here, we introduce a magnetic bead-based EV enrichment approach (EVrich) for automated and high-throughput processing of urine samples. Parallel enrichments can be performed in 96-well plates for downstream cargo analysis, including EV characterization, miRNA, proteomics, and phosphoproteomics analysis. We applied the instrument to a cohort of clinical urine samples to achieve reproducible identification of an average of 17,000 unique EV peptides and an average of 2800 EV proteins in each 1 mL urine sample. Quantitative phosphoproteomics revealed 186 unique phosphopeptides corresponding to 48 proteins that were significantly elevated in prostate cancer patients. Among them, multiple phosphoproteins were previously reported to associate with prostate cancer. Together, EVrich represents a universal, scalable, and simple platform for EV isolation, enabling downstream EV cargo analyses for a broad range of research and clinical applications. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper focuses on the roles of lysine-specific demethylase 4D (Kdm4d) regarding its antioxidant capacity in goat fibroblast cells (GFCs). We identified 1013 down-regulated genes following the knockdown of Kdm4d in GFCs, and these genes were enriched in the cell cycle, DNA replication, mitotic processes, and the oxidative phosphorylation pathway. Consistently, we found the cell proliferation rate was significantly decreased after the knockdown of Kdm4d in the GFCs. Of note, the mRNA and protein expression levels of superoxide dismutase 2 (SOD2), one of the major antioxidant enzymes, was decreased, while the reactive oxygen species (ROS) level was significantly increased in the Kdm4d knockdown GFCs. The expression of γH2A histone family member X (γH2AX) was increased significantly, indicating the presence of DNA double-strand breaks after the knockdown of the Kdm4d enzyme. Taken together, Kdm4d is essential for DNA replication, the cell cycle, and antioxidant capacity. The knockdown of Kdm4d repressed the expression of SOD2 and increased the generation of ROS, which led to DNA damage in GFCs. Our data will be helpful for understanding the mechanism underlying antioxidant capacity regulation in fibroblast cells. Oxidative damage to skin fibroblast cells is a causative factor in many skin diseases. Previous studies have reported that lysine-specific demethylase 4D (Kdm4d) is involved in DNA replication, but its role on antioxidant capacity remains unclear. In the present study, we used goat fibroblast cells (GFCs) as the research model and identified 504 up-regulated and 1013 down-regulated genes following the knockdown of Kdm4d, respectively. The down-regulated genes of this enzyme were found to be enriched in the cell cycle, DNA replication, mitotic processes, and the oxidative phosphorylation pathway, as previously revealed from gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA), suggesting vital roles of the Kdm4d enzyme in the cell cycle and in antioxidant regulation. To this end, we found the cell proliferation rate was significantly decreased after the knockdown of Kdm4d. Moreover, both the mRNA and protein expression levels of superoxide dismutase 2 (SOD2), one of the major antioxidant enzymes, was decreased, while the reactive oxygen species (ROS) level was significantly increased in Kdm4d knocked-down cells. In addition, the expression of γH2A histone family member X (γH2AX) increased significantly, indicating the presence of DNA double-strand breaks after the knockdown of the Kdm4d enzyme. In conclusion, the knockdown of Kdm4d inhibited DNA replication and the cell cycle, repressed the expression of SOD2, and increased the generation of ROS, which led to the production of DNA damage in GFCs. Our data will be helpful for understanding the mechanism underlying antioxidant capacity regulation in fibroblast cells. [ABSTRACT FROM AUTHOR]

With the promotion of cold recycled mixture (CRM) in cold areas and the improvement of its application layer, the enhancement of the low-temperature performance of mixtures is particularly important. The applicability of the current low-temperature bending test method to CRM is controversial. Firstly, the low-temperature crack resistance of CRM with different gradations and emulsified asphalt contents was studied by the indirect tensile (IDT) test and the semi-circular bending (SCB) test. Thereafter, the low-temperature performance evaluation index suitable for CRM was put forward. Then, the low-temperature performance of CRM with different gradations, fiber types, and contents was evaluated by using the above low-temperature evaluation index. The results show that the low-temperature performance of CRM with different gradations and emulsified asphalt contents can be distinguished by fracture work (W) and fracture energy (Gf). Not only do the test results have little variability (about 12% and 15%, respectively), but also the correlation coefficient with the new asphalt film thickness is the highest (0.8595 and 0.8939, respectively). Compared with coarse gradation (AC-25) and fine gradation (AC-13), medium-gradation (AC-20) CRM has higher low-temperature performance, and polyester fiber can significantly improve the low-temperature performance of CRM. Compared with non-fiber, the W and Gf of CRM of polyester fiber (0.3% content) can be increased by at least 42% and 30%, respectively. [ABSTRACT FROM AUTHOR]

Cancer is the leading cause of death in the world. Development of minimally invasive biomarkers for early detection of cancer is urgently needed to reduce high morbidity and mortality associated with malignancy. MicroRNAs (miRNAs) are small regulatory RNAs that modulate the activity of specific mRNA targets and play important roles in a wide range of physiologic and pathologic processes. Recently, miRNAs were found to be dysregulated in a variety of diseases including cancer. Emerging evidence suggests that miRNAs are involved in tumor initiation and progression. Together, the different expression profiles of miRNAs in cancer, and the stability of circulating miRNAs, make them new potentially clinical biomarkers for cancer diagnosis, classification, therapeutic decisions, and prognosis. [ABSTRACT FROM AUTHOR]

Lysophosphatidic acid (LPA), which is proposed to play an important role in normal physiological situations such as wound healing, vascular tone, vascular integrity and reproduction, may be involved in the etiology of some diseases such as atherosclerosis, cancer, obesity or myocardial infarction. Abnormal findings, including silent brain infarction (SBI), are frequently observed by magnetic resonance imaging (MRI) in patients with nonvalvular atrial fibrillation (NVAF). However, whether there is a relationship between LPA level and the prevalence of SBI has not been extensively studied. In the present study, the association between them was investigated. 235 patients with NVAF, 116 cases of SBI without NVAF and 120 cases of healthy volunteers (control group), who did not receive any antithrombotic therapy, were enrolled in this study. Plasma LPA levels in the NVAF with SBI group were significantly higher than that in the control group (p < 0.01), NVAF without SBI group (p < 0.01) and SBI without NVAF group (p < 0.01). The LPA levels are lower in the control group than in the NVAF without SBI and SBI without NVAF groups (p < 0.01), however, the latter two groups did not significantly differ from each other for LPA levels (p > 0.05) There were significant differences in the positive rate of platelet activation between each of the groups (p < 0.01). The positive rate of platelet activation was significantly higher in the NVAF with SBI group. We suggest that LPA might be a novel marker for estimation of the status of platelet activation and the risk factor for SBI onset in NVAF patients. We expected that plasma LPA levels could predict the occurrence of SBI in NVAF patients. [ABSTRACT FROM AUTHOR]

Our previous investigations showed that mobilized endothelial progenitor cells (EPCs) are enriched in non-tumor tissues (NT) surrounding hepatocellular carcinoma (HCC), compared to in tumor tissues (TT). This particular recruitment of EPCs is worth investigating further. The mobilization, recruitment, homing, and incorporation of EPCs into tumors require the participation of multiple factors, including angiogenic factors, adherent molecules, endothelial cells, hypoxic environment, etc. Therefore, we hypothesized that NT might be a hypoxic and highly angiogenic area, into which many more EPCs are recruited and homed. In the last three years, we evaluated the hypoxic condition, angiogenic factors and angiogenic index using frozen tissues or tissue microarrays from 105 patients who had undergone hepatectomy for HCC, and here we review our results and the studies of others. All results showed the expression of Hypoxia-inducible factor-1α was higher in NT than in TT. The expression of VEGFA, bFGF, TGF-β, MCP-1, MMP-9, TIMP-2, and endostatin in NT was significantly higher than in normal liver and TT. Meanwhile, the expression of CD105-the surface marker of activated endothelial cells-was also higher in NT than in TT at the protein and mRNA levels. These investigations showed that NT is a hypoxic and highly angiogenic area, which may be the 'niche' of EPCs. The particular background in HCC may be related to liver cirrhosis. Therefore, non-tumor tissues surrounding HCC may be the 'niche' of endothelial progenitor cells. [ABSTRACT FROM AUTHOR]

The pituitary gland directly regulates the reproduction of domestic animals. Research has increasingly focused on the potential regulatory mechanism of non-coding RNA in pituitary development. Little is known about the differential expression pattern of lncRNAs in Hu sheep, a famous sheep breed with high fecundity, and its role in the pituitary gland between the follicular phase and luteal phase. Herein, to identify the transcriptomic differences of the sheep pituitary gland during the estrus cycle, RNA sequencing (RNA-Seq) was performed. The results showed that 3529 lncRNAs and 16,651 mRNAs were identified in the pituitary gland. Among of them, 144 differentially expressed (DE) lncRNA transcripts and 557 DE mRNA transcripts were screened in the follicular and luteal phases. Moreover, GO and KEGG analyses demonstrated that 39 downregulated and 22 upregulated genes interacted with pituitary functions and reproduction. Lastly, the interaction of the candidate lncRNA XR_001039544.4 and its targeted gene LHB were validated in sheep pituitary cells in vitro. LncRNA XR_001039544.4 and LHB showed high expression levels in the luteal phase in Hu sheep. LncRNA XR_001039544.4 is mainly located in the cytoplasm, as determined by FISH analysis, indicating that XR_001039544.4 might act as competing endogenous RNAs for miRNAs to regulate LHB. LncRNA XR_001039544.4 knockdown significantly inhibited LH secretion and cell proliferation. LncRNA XR_001039544.4 may regulate the secretion of LH in the luteal-phase pituitary gland via affecting cell proliferation. Taken together, these findings provided genome-wide lncRNA- and mRNA-expression profiles for the sheep pituitary gland between the follicular and luteal phases, thereby contributing to the elucidation of the molecular mechanisms of pituitary function. [ABSTRACT FROM AUTHOR]

This study aimed to improve the safety and economy of cement production and to investigate the gasification performance and tar properties of wheat straw in a small electrically heated bubbling fluidized bed by varying three factors, namely, gasification reaction conditions, fuel quality and type, and the natural environment, so as to promote the application of the low-temperature gasification of biomass in the cement industry. The gasification experiment was carried out at temperatures of 550–700 °C, air equivalence ratios of 0.1–0.2, moisture contents of 5.25–24%, blended rubber ratios of 0–100%, and furnace vacuums of 0–0.03 within the parameter ranges, and the component analyses of the produced gases and tars were carried out by gas chromatography (GC) and gas chromatography–mass spectrometry (GC-MS). The experimental findings revealed that the optimal operating conditions for gasification were attained at a temperature of 650 °C, an equivalence ratio of 0.15, a moisture content of 5.25%, a rubber blending ratio of 0, and a vacuum degree of 0. Under these conditions, the concentrations of combustible components (H2, CH4, and CO) in the produced gas were 4.01%, 4.60%, and 21.05%, respectively. The carbon conversion rate was 62.40%, with the cold gas efficiency of 39.37%. The lower heating value of the produced gas was 5.915 MJ/Nm3, accompanied by a tar yield of 118.15 g/Nm3 and lower heating value of 3.385 MJ/Nm3. [ABSTRACT FROM AUTHOR]

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on "qualitatively tissue-specific expressed genes" which are highly enriched in one or a group of tissues but paid less attention to "quantitatively tissue-specific expressed genes", which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying "quantitatively tissue-specific expressed genes" capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function. [ABSTRACT FROM AUTHOR]

Simple Summary: It is known that COVID-19 causes dynamic changes in the immune system. At different stages of the course of COVID-19, the immune cells may exhibit different patterns, which have not been fully uncovered. In this study, a machine-learning-based method was designed to deeply analyze the scRNA-seq data of three types of immune cells from patients with COVID-19, including B cells, T cells, and myeloid cells. Four levels of COVID-19 severity/outcome were involved for each cell type. As a result, several essential genes were obtained, and some of them could be confirmed to be related to SARS-CoV-2 infection. As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system. [ABSTRACT FROM AUTHOR]

Vaccines trigger an immunological response that includes B and T cells, with B cells producing antibodies. SARS-CoV-2 immunity weakens over time after vaccination. Discovering key changes in antigen-reactive antibodies over time after vaccination could help improve vaccine efficiency. In this study, we collected data on blood antibody levels in a cohort of healthcare workers vaccinated for COVID-19 and obtained 73 antigens in samples from four groups according to the duration after vaccination, including 104 unvaccinated healthcare workers, 534 healthcare workers within 60 days after vaccination, 594 healthcare workers between 60 and 180 days after vaccination, and 141 healthcare workers over 180 days after vaccination. Our work was a reanalysis of the data originally collected at Irvine University. This data was obtained in Orange County, California, USA, with the collection process commencing in December 2020. British variant (B.1.1.7), South African variant (B.1.351), and Brazilian/Japanese variant (P.1) were the most prevalent strains during the sampling period. An efficient machine learning based framework containing four feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and maximum relevance minimum redundancy) and four classification algorithms (decision tree, k-nearest neighbor, random forest, and support vector machine) was designed to select essential antibodies against specific antigens. Several efficient classifiers with a weighted F1 value around 0.75 were constructed. The antigen microarray used for identifying antibody levels in the coronavirus features ten distinct SARS-CoV-2 antigens, comprising various segments of both nucleocapsid protein (NP) and spike protein (S). This study revealed that S1 + S2, S1.mFcTag, S1.HisTag, S1, S2, Spike.RBD.His.Bac, Spike.RBD.rFc, and S1.RBD.mFc were most highly ranked among all features, where S1 and S2 are the subunits of Spike, and the suffixes represent the tagging information of different recombinant proteins. Meanwhile, the classification rules were obtained from the optimal decision tree to explain quantitatively the roles of antigens in the classification. This study identified antibodies associated with decreased clinical immunity based on populations with different time spans after vaccination. These antibodies have important implications for maintaining long-term immunity to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20–85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications. [ABSTRACT FROM AUTHOR]

Camptothecin (CPT), first isolated from Chinese tree Camptotheca acuminate, produces rapid and prolonged inhibition of DNA synthesis and induction of DNA damage by targeting topoisomerase I (top1), which is highly activated in cancer cells. CPT thus exhibits remarkable anticancer activities in various cancer types, and is a promising therapeutic agent for the treatment of cancers. However, it remains to be uncovered underlying its cytotoxicity toward germ cells. In this study we found that CPT, a cell cycle-specific anticancer agent, reduced fecundity and exhibited significant cytotoxicity toward GSCs and two-cell cysts. We showed that CPT induced GSC loss and retarded two-cell cysts differentiation in a niche- or apoptosis-independent manner. Instead, CPT induced ectopic expression of a differentiation factor, bag of marbles (Bam), and regulated the expression of cyclin A, which contributed to GSC loss. In addition, CPT compromised two-cell cysts differentiation by decreasing the expression of Bam and inducing cell arrest at G1/S phase via cyclin A, eventually resulting in two-cell accumulation. Collectively, this study demonstrates, for the first time in vivo, that the Bam–cyclin A axis is involved in CPT-mediated germline stem cell loss and two-cell cysts differentiation defects via inducing cell cycle arrest, which could provide information underlying toxicological effects of CPT in the productive system, and feature its potential to develop as a pharmacology-based germline stem cell regulation agent. [ABSTRACT FROM AUTHOR]

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy. [ABSTRACT FROM AUTHOR]

Pituitary gonadotropins play a pivotal role in reproduction. Long noncoding RNAs (lncRNAs) have been identified as important regulators in the hypothalamic–pituitary–ovarian (HPO) axis associated with reproduction. However, the contributions of lncRNAs to pituitary gonadotropin secretion remain largely unknown. Therefore, this work was performed to uncover the functional mechanisms of the novel lncRNA TCONS_00083279 (lncRNA SM2) and its potential targeting pathway oar-miR-16b/TGF-beta/SMAD2, which is associated with gonadotropin secretion in sheep pituitary cells. In the present study, the lncRNA SM2 showed high expression levels in the sheep pituitary gland, and it was located in both the nucleus and the cytoplasm of pituitary cells. lncRNA SM2 knockdown inhibited pituitary cell proliferation and FSH and LH secretion. The function of the lncRNA SM2 was sponged by oar-miR-16b, and this regulated the growth and gonadotropin secretion of pituitary cells by modulating SMAD2, as shown by the dual-luciferase reporter assay. FSH and LH levels were both upregulated by SMAD2 overexpression. Moreover, the levels of the lncRNA SM2, SMAD2 and TGFR1, as well as FSH and LH, in sheep pituitary cells increased significantly under gonadotropin-releasing hormone (GnRH) stimulation (p < 0.05). This work illustrates that the lncRNA SM2 regulates gonadotropin secretion in the Hu sheep anterior pituitary by targeting the oar-miR-16b/TGF-β/SMAD2 signaling pathway, providing a valuable resource for understanding the molecular mechanisms underlying sheep reproduction. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Male germ cell development plays a crucial role in male reproduction, and gene expression also presents an essential regulatory role in its development. Many studies have been devoted to the induction and differentiation of pluripotent stem cells into germ cells in vitro. However, the culture system for pluripotent stem cells from domestic animals is not stable, especially in sheep. Our study attempted to transdifferentiate sheep somatic cells into germ cells in vitro by the overexpression of key germ cell related genes, with the aim of perfecting the construction of germ cell research models in vitro. Therefore, we explored the expression pattern of four crucial genes, bmp4, dazl, nanos3 and sycp2, in Hu sheep testicular development, and investigated the potential efficiency of overexpression of the four candidate genes using the CRISPR/dcas9 system in Leydig cells. We revealed that the overexpression of bmp4, dazl, nanos3 and sycp2 can promote the expression of male germ cell related genes. To the best of our knowledge, this is the first study to construct an overexpression induction system using CRISPR/dcas9 technology, and to induce sheep somatic cells into germ cells in vitro. Male germ cells directly affect the reproduction of males; however, their accurate isolation and culture in vitro is extremely challenging, hindering the study of germ cell development and function. CRISPR/dcas9, as an efficient gene reprogramming system, has been verified to promote the transdifferentiation of pluripotent stem cells into male germ cells by editing target genes. In our research, we explored the expression pattern of the germ cell related genes bmp4, dazl,nanos3 and sycp2 in Hu sheep testicular development and constructed the overexpression model using the CRISPR/dcas9 system. The results indicated that four genes showed more expression in testis tissue than in other tissues, and that bmp4, dazl and sycp2 present higher expression levels in nine-month-old sheep testes than in three-month-olds, while nanos3 expressed the opposite trend (p < 0.05). In addition, the expression of four potential genes in spermatogenic cells was slightly different, but they were all expressed in sheep Leydig cells. To verify the potential roles of the four genes in the process of inducing differentiation of male germ cells, we performed cell transfection in vitro. We found that the expression of the germ cell related genes Prdm1, Prdm14, Mvh and Sox17 were significantly increased after the overexpression of the four genes in Leydig cells, and the co-transfection effect was the most significant (p < 0.05). Our results illustrate the crucial functions of bmp4, dazl, nanos3 and sycp2 in Hu sheep testis development and verified the effectiveness of the overexpression model that was constructed using the CRISPR/dcas9 system, which provided a basis for further male germ cell differentiation in vitro. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew's correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study. [ABSTRACT FROM AUTHOR]

Antifreeze protein (AFP) is a proteinaceous compound with improved antifreeze ability and binding ability to ice to prevent its growth. As a surface-active material, a small number of AFPs have a tremendous influence on the growth of ice. Therefore, identifying novel AFPs is important to understand protein–ice interactions and create novel ice-binding domains. To date, predicting AFPs is difficult due to their low sequence similarity for the ice-binding domain and the lack of common features among different AFPs. Here, a computational engine was developed to predict the features of AFPs and reveal the most important 39 features for AFP identification, such as antifreeze-like/N-acetylneuraminic acid synthase C-terminal, insect AFP motif, C-type lectin-like, and EGF-like domain. With this newly presented computational method, a group of previously confirmed functional AFP motifs was screened out. This study has identified some potential new AFP motifs and contributes to understanding biological antifreeze mechanisms. [ABSTRACT FROM AUTHOR]

The Eurasian avian-like swine (EA) H1N1 virus has affected the Chinese swine industry, and human infection cases have been reported occasionally. However, little is known about the pathogenic mechanism of EA H1N1 virus. In this study, we compared the mouse pathogenicity of A/swine/Guangdong/YJ4/2014 (YJ4) and A/swine/Guangdong/MS285/2017 (MS285) viruses, which had similar genotype to A/Hunan/42443/2015 (HuN-like). None of the mice inoculated with 106 TCID50 of YJ4 survived at 7 days post infection, while the survival rate of the MS285 group was 100%. Therefore, a series of single fragment reassortants in MS285 background and two rescued wild-type viruses were generated by using the reverse genetics method, and the pathogenicity analysis revealed that the PB2 gene contributed to the high virulence of YJ4 virus. Furthermore, there were 11 amino acid differences in PB2 between MS285 and YJ4 identified by sequence alignment, and 11 single amino acid mutant viruses were generated in the MS285 background. We found that the R251K mutation significantly increased the virulence of MS285 in mice, contributed to high polymerase activity and enhanced viral genome transcription and replication. These results indicate that PB2-R251K contributes to the virulence of the EA H1N1 virus and provide new insight into future molecular epidemiological surveillance strategies. [ABSTRACT FROM AUTHOR]

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes. [ABSTRACT FROM AUTHOR]

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew's correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules. [ABSTRACT FROM AUTHOR]

Considerable efforts have been focused on the exploitation of macromolecule ligands for synthesis of magnetic Fe3O4 nanoparticles as T1 magnetic resonance imaging (MRI) contrast agents, but studies that concern macromolecule ligands with different charges and coordination groups are still limited. Herein, we used poly(acrylic acid) (PAA), poly(allylamine hydrochloride) (PAH), and polyvinyl alcohol (PVA), which possess negative, positive and neutral charges with carboxylic acid, amino and hydroxyl groups respectively, as templates and stabilizers to fabricate Fe3O4 nanoparticles through coprecipitation reaction. The obtained Fe3O4-PAA, Fe3O4-PAH, and Fe3O4-PVA nanoparticles showed T1 contrast performance with r1 relaxivities of 23.4, 60.3, and 30.6 mM s−1 at 0.5 T (25 °C), and a r2/r1 ratio of 2.62, 3.82, and 7.26, respectively. The cell viability assay revealed that Fe3O4-PAA and Fe3O4-PVA exhibited good biocompatibility, while Fe3O4-PAH displayed high cytotoxicity. In vivo T1-weighted (1 T) mice showed that both Fe3O4-PAA and Fe3O4-PVA were able to display remarkably brighten the contrast enhancement for the mice tumor and kidney sites, but Fe3O4-PAA had better contrast performance. This work highlights that the macromolecule ligands play an important role in the biocompatibility and T1 contrast performance of magnetic Fe3O4 nanoparticles. [ABSTRACT FROM AUTHOR]

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups. [ABSTRACT FROM AUTHOR]

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles. [ABSTRACT FROM AUTHOR]