Plant Homeo Domain Finger Protein 8 Regulates Mesodermal and Cardiac Differentiation of Embryonic Stem Cells Through Mediating the Histone Demethylation of pmaip1

published_or_final_versio

Blockchain-based privacy preservation for 5G-enabled drone communications

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record5G-enabled drones have potential applications in a variety of both military and civilian settings (e.g., monitoring and tracking of individuals in demonstrations and/or enforcing of social / physical distancing during pandemics such as COVID-19). Such applications generally involve the collection and dissemination of (massive) data from the drones to remote data centres for storage and analysis, for example via 5G networks. Consequently, there are security and privacy considerations underpinning 5G-enabled drone communications. We posit the potential of leveraging blockchain to facilitate privacy preservation, and therefore in this article we will review existing blockchain-based solutions after introducing the architecture for 5G-enabled drone communications and blockchain. We will also review existing legislation and data privacy regulations that need to be considered in the design of blockchain-based solutions, as well as identifying potential challenges and open issues which will hopefully inform future research agenda

Functional Mapping of Dynamic Traits with Robust t-Distribution

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate -distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis

Recent research on changes in genomic regulation and protein expression in intracerebral haemorrhage

Intracerebral haemorrhage (ICH) is a devastating form of stroke that accounts for roughly 10% of all strokes and the effects on those that survive are often debilitating. To date, no suitable therapy exists. Recent work has examined alterations in gene and protein expression after ICH. The focus of this review is to outline the current knowledge of changes in genetic and protein expression after ICH and how those changes may affect the course of brain injury. Both animal and human data are reviewed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73607/1/j.1747-4949.2007.00160.x.pd

Why do Particle Clouds Generate Electric Charges?

Grains in desert sandstorms spontaneously generate strong electrical charges; likewise volcanic dust plumes produce spectacular lightning displays. Charged particle clouds also cause devastating explosions in food, drug and coal processing industries. Despite the wide-ranging importance of granular charging in both nature and industry, even the simplest aspects of its causes remain elusive, because it is difficult to understand how inert grains in contact with little more than other inert grains can generate the large charges observed. Here, we present a simple yet predictive explanation for the charging of granular materials in collisional flows. We argue from very basic considerations that charge transfer can be expected in collisions of identical dielectric grains in the presence of an electric field, and we confirm the model's predictions using discrete-element simulations and a tabletop granular experiment

The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis.

Ferroptosis is a form of regulated cell death that is caused by the iron-dependent peroxidation of lipids1,2. The glutathione-dependent lipid hydroperoxidase glutathione peroxidase 4 (GPX4) prevents ferroptosis by converting lipid hydroperoxides into non-toxic lipid alcohols3,4. Ferroptosis has previously been implicated in the cell death that underlies several degenerative conditions2, and induction of ferroptosis by the inhibition of GPX4 has emerged as a therapeutic strategy to trigger cancer cell death5. However, sensitivity to GPX4 inhibitors varies greatly across cancer cell lines6, which suggests that additional factors govern resistance to ferroptosis. Here, using a synthetic lethal CRISPR-Cas9 screen, we identify ferroptosis suppressor protein 1 (FSP1) (previously known as apoptosis-inducing factor mitochondrial 2 (AIFM2)) as a potent ferroptosis-resistance factor. Our data indicate that myristoylation recruits FSP1 to the plasma membrane where it functions as an oxidoreductase that reduces coenzyme Q10 (CoQ) (also known as ubiquinone-10), which acts as a lipophilic radical-trapping antioxidant that halts the propagation of lipid peroxides. We further find that FSP1 expression positively correlates with ferroptosis resistance across hundreds of cancer cell lines, and that FSP1 mediates resistance to ferroptosis in lung cancer cells in culture and in mouse tumour xenografts. Thus, our data identify FSP1 as a key component of a non-mitochondrial CoQ antioxidant system that acts in parallel to the canonical glutathione-based GPX4 pathway. These findings define a ferroptosis suppression pathway and indicate that pharmacological inhibition of FSP1 may provide an effective strategy to sensitize cancer cells to ferroptosis-inducing chemotherapeutic agents

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links

Background: Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes. Results: We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone. Conclusions: The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization. © 2013 Kimmel, Visweswaran

miR-100-5p inhibition induces apoptosis in dormant prostate cancer cells and prevents the emergence of castration-resistant prostate cancer

Carcinoma of the prostate is the most common cancer in men. Treatment of aggressive prostate cancer involves a regiment of radical prostectomy, radiation therapy, chemotherapy and hormonal therapy. Despite significant improvements in the last decade, the treatment of prostate cancer remains unsatisfactory, because a significant fraction of prostate cancers develop resistance to multiple treatments and become incurable. This prompts an urgent need to investigate the molecular mechanisms underlying the evolution of therapy-induced resistance of prostate cancer either in the form of castration-resistant prostate cancer (CRPC) or transdifferentiated neuroendocrine prostate cancer (NEPC). By analyzing micro-RNA expression profiles in a set of patient-derived prostate cancer xenograft tumor lines, we identified miR-100-5p as one of the key molecular components in the initiation and evolution of androgen ablation therapy resistance in prostate cancer. In vitro results showed that miR-100-5p is required for hormone-independent survival and proliferation of prostate cancer cells post androgen ablation. In Silico target predictions revealed that miR-100-5p target genes are involved in key aspects of cancer progression, and are associated with clinical outcome. Our results suggest that mir-100-5p is a possible therapeutic target involved in prostate cancer progression and relapse post androgen ablation therapy