Functional Crosstalk between Lysine Methyltransferases on Histone Substrates: The Case of G9A/GLP and Polycomb Repressive Complex 2

Significance: Methylation of histone H3 on lysine 9 and 27 (H3K9 and H3K27) are two epigenetic modifications that have been linked to several crucial biological processes, among which are transcriptional silencing and cell differentiation. Recent Advances: Deposition of these marks is catalyzed by H3K9 lysine methyltransferases (KMTs) and polycomb repressive complex 2, respectively. Increasing evidence is emerging in favor of a functional crosstalk between these two major KMT families. Critical Issues: Here, we review the current knowledge on the mechanisms of action and function of these enzymes, with particular emphasis on their interplay in the regulation of chromatin states and biological processes. We outline their crucial roles played in tissue homeostasis, by controlling the fate of embryonic and tissue-specific stem cells, highlighting how their deregulation is often linked to the emergence of a number of malignancies and neurological disorders. Future Directions: Histone methyltransferases are starting to be tested as drug targets. A new generation of highly selective chemical inhibitors is starting to emerge. These hold great promise for a rapid translation of targeting epigenetic drugs into clinical practice for a number of aggressive cancers and neurological disorders

Progressiveness of Sharia Insurance as A Component of The Islamic Non-Bank Financial Industry in Indonesia

The condition of sharia insurance in Indonesia shows limited development; it ranked fourth and only rose one rank from 2019 as the country with the highest number of operators in the world. The market share of sharia insurance, when compared to conventional insurance, reached 6% at the end of 2020. This study aims to analyze the development of sharia insurance in Indonesia through statistical data analysis of sharia insurance financial performance during the 2018–2022 period. This article is classified as literature research with a qualitative approach. The methodology used is a content analysis study. The results found that sharia insurance in Indonesia experienced asset growth of 3.53%, total gross contribution year-on-year of 16.38%, and continued to record investment results in the 2018–2022 range. The growth was dominated by the capital market sector and the banking sector, which experienced an increase

MLP neural network based gas classification system on Zynq SoC

Systems based on Wireless Gas Sensor Networks (WGSN) offer a powerful tool to observe and analyse data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex Micro hotplates (MHP). The overall system acquires the gas sensor data through RFID, and processes the sensor data with the proposed MLP classifier implemented on a System on Chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and achieved results have shown that an accuracy of 97.4% has been obtained

Post-translational modifications of histones H3 and H4 associated with the histone methyltransferases Suv39h1 and G9a

Mass spectrometry analysis of the post-transcriptional modifications of histones H3 and H4 that were co-purified with histone methyltransferases Suv39h1 and G9a shows that, in HeLa cells, histone methyltransferases can be physically associated with acetylated histones, which normally mark transcriptionally active chromatin

Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization

Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done. Uncertainty quantification (UQ) is essential for qualifying NDE inspections and building trust in their predictions. Therefore, this article aims to demonstrate how UQ can best be achieved for deep learning in the context of crack sizing for inline pipe inspection. A convolutional neural network architecture is used to size surface breaking defects from plane wave imaging (PWI) images with two modern UQ methods: deep ensembles and Monte Carlo dropout. The network is trained using PWI images of surface breaking defects simulated with a hybrid finite element / ray-based model. Successful UQ is judged by calibration and anomaly detection, which refer to whether in-domain model error is proportional to uncertainty and if out of training domain data is assigned high uncertainty. Calibration is tested using simulated and experimental images of surface breaking cracks, while anomaly detection is tested using experimental side-drilled holes and simulated embedded cracks. Monte Carlo dropout demonstrates poor uncertainty quantification with little separation between in and out-of-distribution data and a weak linear fit ( R=0.84 ) between experimental root-mean-square-error and uncertainty. Deep ensembles improve upon Monte Carlo dropout in both calibration ( R=0.95 ) and anomaly detection. Adding spectral normalization and residual connections to deep ensembles slightly improves calibration ( R=0.98 ) and significantly improves the reliability of assigning high uncertainty to out-of-distribution samples

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Abstract: Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated 4×4 tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the 4×4 array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz

The Core Binding Factor CBF Negatively Regulates Skeletal Muscle Terminal Differentiation

BACKGROUND: Core Binding Factor or CBF is a transcription factor composed of two subunits, Runx1/AML-1 and CBF beta or CBFbeta. CBF was originally described as a regulator of hematopoiesis. METHODOLOGY/PRINCIPAL FINDINGS: Here we show that CBF is involved in the control of skeletal muscle terminal differentiation. Indeed, downregulation of either Runx1 or CBFbeta protein level accelerates cell cycle exit and muscle terminal differentiation. Conversely, overexpression of CBFbeta in myoblasts slows terminal differentiation. CBF interacts directly with the master myogenic transcription factor MyoD, preferentially in proliferating myoblasts, via Runx1 subunit. In addition, we show a preferential recruitment of Runx1 protein to MyoD target genes in proliferating myoblasts. The MyoD/CBF complex contains several chromatin modifying enzymes that inhibits MyoD activity, such as HDACs, Suv39h1 and HP1beta. When overexpressed, CBFbeta induced an inhibition of activating histone modification marks concomitant with an increase in repressive modifications at MyoD target promoters. CONCLUSIONS/SIGNIFICANCE: Taken together, our data show a new role for Runx1/CBFbeta in the control of the proliferation/differentiation in skeletal myoblasts

The H3K9 methylation writer SETDB1 and its reader MPP8 cooperate to silence satellite DNA repeats in mouse embryonic stem cells

SETDB1 (SET Domain Bifurcated histone lysine methyltransferase 1) is a key lysine methyltransferase (KMT) required in embryonic stem cells (ESCs), where it silences transposable elements and DNA repeats via histone H3 lysine 9 tri-methylation (H3K9me3), independently of DNA methylation. The H3K9 methylation reader M-Phase Phosphoprotein 8 (MPP8) is highly expressed in ESCs and germline cells. Although evidence of a cooperation between H3K9 KMTs and MPP8 in committed cells has emerged, the interplay between H3K9 methylation writers and MPP8 in ESCs remains elusive. Here, we show that MPP8 interacts physically and functionally with SETDB1 in ESCs. Indeed, combining biochemical, transcriptomic and genomic analyses, we found that MPP8 and SETDB1 co-regulate a significant number of common genomic targets, especially the DNA satellite repeats. Together, our data point to a model in which the silencing of a class of repeated sequences in ESCs involves the cooperation between the H3K9 methylation writer SETDB1 and its reader MPP8. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

University Sustainability in Relation to Higher Education Funding Model in Kazakhstan in the Context of Transition Period

Over the last years the Ministry of Education and Science of the Republic of Kazakhstan has started to apply new approaches to higher education funding. The attempt to try and implement new funding mechanisms is based on the fact that Kazakhstan departed from the principle of “funding to all” to the principle of “funding to everybody”. The coverage of the student’s tuition fees rather than funding an academic institution has become part of the common practice. The financial mechanism of higher educational institutions is based on the multichannel system of financing. In these conditions not only the sufficiency of financial resources but also the optimum combination of various sources of financing, their influence, both on the development of a higher educational institution and the quality of specialists training is important. To increase the level of its competitiveness a higher educational institution has to adhere to an efficient strategy of development, optimum financial policy and actual management in the implementation of own activity..