MiR-379 inhibits proliferation and induces apoptosis in multiple myeloma by targeting Y-box binding protein 1

Purpose: To determine the effect of miR-379 in multiple myeloma.Methods: Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to evaluate the expression of miR-379 in multiple myeloma cells. The effect of miR-379 on multiple myeloma progression was investigated by cell counting, bromodeoxyuridine staining, flow cytometry and Western blot analysis. A potential target for miR-379 was determined using a luciferase reporter assay.Results: MiR-379 expression was reduced in multiple myeloma cells, while over-expression of miR-379 increased both cell viability and proliferation of these cells (p < 0.05). Moreover, miR-379 blocked cell cycle multiple myeloma cells and promoted apoptosis by decreasing Bcl-2 expression, and increasing the expression of cleaved caspase-3 and Bax. MiR-379 bound to Y-box binding protein 1 (YBX1) and reduced YBX1 mRNA and protein expression in multiple myeloma cells (p < 0.05).Conclusion: A YBX1-mediated tumor-suppressive role for miR-379 in multiple myeloma cells has been identified, suggesting a potential strategy for the treatment of multiple myeloma. Keywords: MiR-379, Y-box binding protein 1, Multiple myeloma, Proliferation, Apoptosi

A Nonlinear Lagrange Algorithm for Stochastic Minimax Problems Based on Sample Average Approximation Method

An implementable nonlinear Lagrange algorithm for stochastic minimax problems is presented based on sample average approximation method in this paper, in which the second step minimizes a nonlinear Lagrange function with sample average approximation functions of original functions and the sample average approximation of the Lagrange multiplier is adopted. Under a set of mild assumptions, it is proven that the sequences of solution and multiplier obtained by the proposed algorithm converge to the Kuhn-Tucker pair of the original problem with probability one as the sample size increases. At last, the numerical experiments for five test examples are performed and the numerical results indicate that the algorithm is promising

Global exponential stabilization of language constrained switched linear discrete-time system based on the s-procedure approach

This paper considers global exponential stabilization (GES) of switched linear discrete-time system under language constraint which is generated by non-deterministic finite state automata. A technique in linear matrix inequalities called S-procedure is employed to provide sufficient conditions of GES which are less conservative than the existing Lyapunov-Metzler condition. Moreover, by revising the construction of Lyapunov matrices and the corresponding switching control policy, a more flexible result is obtained such that stabilization path at each moment might be multiple. Finally, a numerical example is given to illustrate the effectiveness of the proposed results

Diffusion of Colloidal Rods in Corrugated Channels

In many natural and artificial devices diffusive transport takes place in confined geometries with corrugated boundaries. Such boundaries cause both entropic and hydrodynamic effects, which have been studied only for the case of spherical particles. Here we experimentally investigate diffusion of particles of elongated shape confined into a corrugated quasi-two-dimensional channel. Elongated shape causes complex excluded-volume interactions between particle and channel walls which reduce the accessible configuration space and lead to novel entropic free energy effects. The extra rotational degree of freedom also gives rise to a complex diffusivity matrix that depends on both the particle location and its orientation. We further show how to extend the standard Fick-Jacobs theory to incorporate combined hydrodynamic and entropic effects, so as, for instance, to accurately predict experimentally measured mean first passage times along the channel. Our approach can be used as a generic method to describe translational diffusion of anisotropic particles in corrugated channels.Comment: 12 pages and 4 figure

Transitional fossil earwigs - a missing link in Dermaptera evolution

<p>Abstract</p> <p>Background</p> <p>The Dermaptera belongs to a group of winged insects of uncertain relationship within Polyneoptera, which has expanded anal region and adds numerous anal veins in the hind wing. Evolutional history and origin of Dermaptera have been in contention.</p> <p>Results</p> <p>In this paper, we report two new fossil earwigs in a new family of Bellodermatidae fam. nov. The fossils were collected from the Jiulongshan Formation (Middle Jurassic) in Inner Mongolia, northeast China. This new family, characterized by an unexpected combination of primitive and derived characters, is bridging the missing link between suborders of Archidermaptera and Eodermaptera. Phylogenetic analyses support the new family to be a new clade at the base of previously defined Eodermaptera and to be a stem group of (Eodermaptera+Neodermaptera).</p> <p>Conclusion</p> <p>Evolutional history and origin of Dermaptera have been in contention, with dramatically different viewpoints by contemporary authors. It is suggested that the oldest Dermaptera might possibly be traced back to the Late Triassic-Early Jurassic and they had divided into Archidermaptera and (Eodermaptera+Neodermaptera) in the Middle Jurassic.</p

Safety-oriented planning of expressway truck service areas based on driver demand

Funding This study was supported by the National Natural Science Foundation of China (51978522).Peer reviewedPublisher PD

Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

We hypothesize that large language models (LLMs) based on the transformer architecture can enable automated detection of clinical phenotype terms, including terms not documented in the HPO. In this study, we developed two types of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT as its pre-trained model, and PhenoGPT, a GPT-based model that can be initialized from diverse GPT models, including open-source versions such as GPT-J, Falcon, and LLaMA, as well as closed-source versions such as GPT-3 and GPT-3.5. We compared our methods with PhenoTagger, a recently developed HPO recognition tool that combines rule-based and deep learning methods. We found that our methods can extract more phenotype concepts, including novel ones not characterized by HPO. We also performed case studies on biomedical literature to illustrate how new phenotype information can be recognized and extracted. We compared current BERT-based versus GPT-based models for phenotype tagging, in multiple aspects including model architecture, memory usage, speed, accuracy, and privacy protection. We also discussed the addition of a negation step and an HPO normalization layer to the transformer models for improved HPO term tagging. In conclusion, PhenoBCBERT and PhenoGPT enable the automated discovery of phenotype terms from clinical notes and biomedical literature, facilitating automated downstream tasks to derive new biological insights on human diseases

Comprehensive Network Analysis Reveals Alternative Splicing-Related lncRNAs in Hepatocellular Carcinoma

© Copyright © 2020 Wang, Wang, Bhat, Chen, Xu, Mo, Yi and Zhou. It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs