miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization), this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, <it>miRFam</it>, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes.</p> <p>Results</p> <p>An existing miRNA family system prepared by miRBase was downloaded online. We first employed <it>n</it>-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown). Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members), the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples), the accuracy surprisingly reaches 90%.</p> <p>Conclusions</p> <p>Based on experimental results, we argue that <it>miRFam </it>is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA) classification methods, since it only requires primary sequence information.</p> <p>Availability</p> <p>The source code of <it>miRFam</it>, written in C++, is freely and publicly available at: <url>http://admis.fudan.edu.cn/projects/miRFam.htm</url>.</p

Dynamic Monte Carlo Simulation of Polymerization of Amphiphilic Macromers in a Selective Solvent and Associated Chemical Gelation

ABSTRACT: Gel formation via polymerization of amphiphilic macromers with a soluble central block and two insoluble but polymerizable end groups was investigated by dynamic Monte Carlo simulation. A simplified free radical polymerization of coarse-grained self-avoiding macromers was modeled on lattices. The simulation reproduced the unexpected experimental phenomenon reported in the literature that polymerization of PEOacrylate or PEO-diacrylate macromers proceeded quite fast in water in contrast to in organic solvents. The simulation confirmed that the enhancement of local concentration of the polymerizable groups in the micellar cores was responsible for the rapid polymerization of self-assembled macromers in a selective solvent. A straightforward criterion to determine an infinite gel network in a finite modeling system with the periodic boundary was also put forward. The gelling kinetics associated with polymerization of such macromers with &quot;double bonds&quot; at both ends was investigated. Fast chemical gelation of concentrated macromer solutions in a selective solvent was interpreted from both the rapid polymerization and the more bridges linking micelles. Hence, this paper illustrates a strong coupling between polymerization kinetics and self-assembled structures of amphiphilic monomers

Continual Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing a fixed document-token graph and cannot make inferences on new documents. It is a challenge to deploy them in online systems to infer steaming text data. In this work, we present a continual GCN model (ContGCN) to generalize inferences from observed documents to unobserved documents. Concretely, we propose a new all-token-any-document paradigm to dynamically update the document-token graph in every batch during both the training and testing phases of an online system. Moreover, we design an occurrence memory module and a self-supervised contrastive learning objective to update ContGCN in a label-free manner. A 3-month A/B test on Huawei public opinion analysis system shows ContGCN achieves 8.86% performance gain compared with state-of-the-art methods. Offline experiments on five public datasets also show ContGCN can improve inference quality. The source code will be released at https://github.com/Jyonn/ContGCN.Comment: 9 pages, 4 figures, AAAI 2023 accepted pape

Particulate matter pollution over China and the effects of control policies

China is one of the regions with highest PM(2.5)concentration in the world. In this study, we review the spatio-temporal distribution of PM2.5 mass concentration and components in China and the effect of control measures on PM2.5 concentrations. Annual averaged PM2.5 concentrations in Central-Eastern China reached over 100 mu g m(-3), in some regions even over 150 mu g m(-3). In 2013, only 4.1% of the cities attained the annual average standard of 35 mu g m(-3). Aitken mode particles tend to dominate the total particle number concentration. Depending on the location and time of the year, new particle formation (NPF) has been observed to take place between about 10 and 60% of the days. In most locations, NPF was less frequent at high PM mass loadings. The secondary inorganic particles (i.e., sulfate, nitrate and ammonium) ranked the highest fraction among the PM2.5 species, followed by organic matters (OM), crustal species and element carbon (EC), which accounted for 6-50%, 15-51%, 5-41% and 2-12% of PM2.5, respectively. In response to serious particulate matter pollution, China has taken aggressive steps to improve air quality in the last decade. As a result, the national emissions of primary PM2.5, sulfur dioxide (SO2), and nitrogen oxides (NOx) have been decreasing since 2005, 2006, and 2011, respectively. The emission control policies implemented in the last decade could result in noticeable reduction in PM2,(5)concentrations, contributing to the decreasing PM2.5 trends observed in Beijing, Shanghai, and Guangzhou. However, the control policies issued before 2010 are insufficient to improve PM2.5 air quality notably in future. An optimal mix of energy-saving and end-of-pipe control measures should be implemented, more ambitious control policies for NMVOC and NH3 should be enforced, and special control measures in winter should be applied. 40-70% emissions should be cut off to attain PM2.5 standard. (C) 2017 Elsevier B.V.All rights reserved.Peer reviewe

Endoscopic endonasal transsphenoidal surgery for unusual sellar lesions: eight cases and review of the literature

BackgroundPreoperative imaging for some unusual lesions in the sellar region can pose challenges in establishing a definitive diagnosis, impacting treatment strategies.MethodsThis study is a retrospective analysis of eight cases involving unusual sellar region lesions, all treated with endoscopic endonasal transsphenoidal surgery (EETS). We present the clinical, endocrine, and radiological characteristics, along with the outcomes of these cases.ResultsAmong the eight cases, the lesions were identified as follows: Solitary fibrous tumor (SFT) in one case, Lymphocytic hypophysitis (LYH) in one case, Cavernous sinus hemangiomas (CSH) in one case, Ossifying fibroma (OF) in two cases; Sphenoid sinus mucocele (SSM) in one case, Pituitary abscess (PA) in two cases. All patients underwent successful EETS, and their diagnoses were confirmed through pathological examination. Postoperatively, all patients had uneventful recoveries without occurrences of diabetes insipidus or visual impairment.ConclusionOur study retrospectively analyzed eight unusual lesions of the sellar region. Some lesions exhibit specific imaging characteristics and clinical details that can aid in preoperative diagnosis and inform treatment strategies for these unusual sellar diseases

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually

Induction of RIPK3/MLKL-mediated necroptosis by Erigeron breviscapus injection exhibits potent antitumor effect

Colorectal cancer (CRC) is the second leading cause of tumor-related deaths worldwide. Resistance of tumor cells to drug-induced apoptosis highlights the need for safe and effective antitumor alternatives. Erigeron breviscapus (Dengzhanxixin in China) injection (EBI), extracted from the natural herb Erigeron breviscapus (Vant.) Hand.-Mazz (EHM), has been widely used in clinical practice for cardiovascular diseases. Recent studies have suggested that EBI’s main active ingredients exhibit potential antitumor effects. This study aims to explore the anti-CRC effect of EBI and elucidate the underlying mechanism. The anti-CRC effect of EBI was evaluated in vitro using CCK-8, flow cytometry, and transwell analysis, and in vivo through a xenograft mice model. RNA sequencing was utilized to compare the differentially expressed genes, and the proposed mechanism was verified through in vitro and in vivo experiments. Our study demonstrates that EBI significantly inhibits the proliferation of three human CRC cell lines and effectively suppresses the migration and invasion of SW620 cells. Moreover, in the SW620 xenograft mice model, EBI markedly retards tumor growth and lung metastasis. RNA-seq analysis revealed that EBI might exert antitumor effects by inducing necroptosis of tumor cells. Additionally, EBI activates the RIPK3/MLKL signaling pathway, a classical pathway of necroptosis and greatly promotes the generation of intracellular ROS. Furthermore, the antitumor effect of EBI on SW620 is significantly alleviated after the pretreatment of GW806742X, the MLKL inhibitor. Our findings suggest that EBI is a safe and effective inducer of necroptosis for CRC treatment. Notably, necroptosis is a non-apoptotic programmed cell death pathway that can effectively circumvent resistance to apoptosis, which provides a novel approach for overcoming tumor drug resistance