Impact of Histone Modifications and Their Therapeutic Targeting in Hematological Malignancies

Hematologic malignancies are a large and heterogeneous group of neoplasms characterized by complex pathogenetic mechanisms. The abnormal regulation of epigenetic mechanisms and specifically, histone modifications, has been demonstrated to play a central role in hematological cancer pathogenesis and progression. A variety of epigenetic enzymes that affect the state of histones have been detected as deregulated, being either over- or underexpressed, which induces changes in chromatin compaction and, subsequently, affects gene expression. Recent advances in the field of epigenetics have revealed novel therapeutic targets, with many epigenetic drugs being investigated in clinical trials. The present review focuses on the biological impact of histone modifications in the pathogenesis of hematologic malignancies, describing a wide range of therapeutic agents that have been discovered to target these alterations and are currently under investigation in clinical trials.     

Synthesis and Biological Evaluation of Small Molecules as Potential Anticancer Multitarget Agents

Twenty-six triazole-based derivatives were designed for targeting both PD-L1 (programmed death receptor ligand 1) and VEGFR-2 (vascular endothelial growth factor receptor 2). These compounds were synthetized and biologically evaluated as multitarget inhibitors of VEGFR-2, PD-L1 and c-Myc proteins. The antiproliferative activity of these molecules on several tumor cell lines (HT-29, A-549, and MCF-7) and on the non-tumor cell line HEK-293 was determined. The effects on the abovementioned biological targets were evaluated for some selected compounds. Compound 23, bearing a p-chlorophenyl group, showed better results than sorafenib in regard to the downregulation of VEGFR-2 and a similar effect to BMS-8 on both PD-L1 and c-Myc proteins. The antiangiogenic and antivascular activities of chloro derivatives were also established by endothelial microtube formation assay on Matrigel^®.     

On the Tolerance to Manufacturing Defects in Molecular QCA Tiles for Processing-by-wire

Among emerging technologies, Quantum-dot Cellular Automata (QCA) relies on innovative computational paradigms. For nano-scale implementation, the so-called processing-by-wire (PBW) paradigm in QCA is very effective as processing takes place, while signal communication is accomplished. This paper analyzes the defect tolerance properties of PBW for manufacturing tiles by molecular QCA cells. Based on a 3?×?3 grid and various input/output arrangements in QCA cells, different tiles are analyzed and simulated using a coherence vector engine. The functional characterization and polarization level of these tiles for undeposited cell defects are reported and detailed profiles are provided. It is shown that novel features of PBW are possible due to the spatial redundancy of the cells in the tiles that permits to retain at high probability the fault free function in the presence of defects. Moreover, it is shown that QCA tiles are robust and inherently tolerant to cell defects (by logic equivalence, also additional cell defects can be accommodated).     

Bubble hydrodynamic influence on oxygen transfer rate at presence of cationic and anionic surfactants in electroflotation proces

In this work, the effects of the presence of surfactants in the liquid phase and the hydrodynamic regime of the bubble flow on the oxygen transfer rate were investigated in an electroflotation process in batch mode. The volumetric mass transfer coefficient KLα and the oxygenation capacity were evaluated to improve the performances of the electroflotation process in terms of oxygenation. In order to evaluate the liquid-side mass transfer coefficient KLα the volumetric mass transfer coefficient KLα was dissociated into KL and the specific interracial area （a） since the last one was obtained from the gas hold-up and the bubble diameter. The effect of Reynolds number which define the hydrodynamic of the bubble flow has been also studied. Models of KLa and KL have been established to show the effects of the hydrodynamic parameters and liquid phase characteristics on the oxygen transfer rate.     

SALINE DEPOSITS AND ALKALINE MAGMATISM: A GENETIC MODEL

The temporal and spatical coincidence of saline deposits with rifting and alkaline magmatism is now widely accepted. The congruency of these three phenomena implies, logically, their trilateral genetic inter-relationship. Conventional models for the formation of saline deposits, i.e. the "evaporation process", cannot be properly applied to giant saline deposits, such as those of the Mediterranean and Red Sea basins
The alternative "Volcanogenic Model" proposed here offers a genetic explanation for these problems. This Model is compared with both the conventional evaporation model and the deep-basin evaporation model of fSchmalz. Well-known saline-surface basins (e.g. the Red Sea, East African rift system, Dead Sea, Mediterranean, Circum-Atlantic, Zechstein, and Asian InfraCambrian — Lower Cambrian), and "Kuroko" and "Black Smoker" depostis, are briefly reviewed. The role of alkaline and calc-alkaline magmatism in producing saline-surface deposts and the application of the Volcanogenic Model in each case is discussed, and conclusions concerning the suitability of the new model are drawn.     

Complex formation of montmorillonite clay with polymers. Part 2: The use of montmorillonite clay–vinyl monomer complex as a comonomer in the copolymerization reaction of styrene–acrylonitrile monomers

The bulk copolymerization of styrene–acrylonitrile monomers using styrene‐N⁺–montmorillonite complex as a comonomer in the polymerization was studied. The X‐ray diffraction (XRD) analysis showed that part of the styrene‐N⁺–montmorillonite complex remained non‐dispersed (immiscible) and the copolymer was excluded from the interlayer of the immiscible part of the clay. The successive chemical extraction process revealed that a reasonable amount of the styrene–acrylonitrile copolymer was directly attached to the styrene‐N⁺–montmorillonite complex and enveloped the clay. Highly exfoliated clay lamella and nanospheres (3–5 nm) were observed by transmission electron microscopy (TEM). The montmorillonite clay assume two different morphologies, immiscible and exfoliated, on the basis of the XRD and TEM data. A simple method of calculation of the ratio of the exfoliated/immiscible amounts of the clay indicated that the amount of the styrene‐N⁺–montmorillonite complex exfoliated into separate lamella was 40 % (w/w) of the amount of the clay samples containing 2 % of the (styrene‐N⁺–montmorillonite complex) clay. This amount of exfoliated clay decreases with the increase of the concentration of the clay. The presence of the styrene‐N⁺–montmorillonite clay in the copolymerization reaction had a minor effect on both the copolymer composition and the molecular weight. Modification of the clay with the derivatized styrene monomer can achieve a nanocomposite using a percentage no more than 4 % (w/w) of complex in the copolymer. Copyright © 2004 Society of Chemical Industry     

Solvent effect in phase separation for fabrication of micropatterned porous scaffold sheets

Cellular orientation control is important for tissue regeneration. Design of oriented structures for cells with suitable features can be used in tissue engineering. One of the methods of cellular orientation with the aim of regenerating which damaged tissues is utilizing oriented biocompatible substrates. This paper reports a one-step method with different solvents to fabricate porous micropatterned polyhydroxybutyrate scaffold sheets. The results indicated that the porosity and pore morphology of the scaffolds are viable with respect to proliferation rate, and a micropattern for cell alignment. Stem cells culturing proved that the scaffold sheets are suitable for cell culturing. Preliminary experiments indicate that the 2-D scaffold sheets are very promising as basis for building 3-D scaffolds.     

Detecting anomalous packets in network transfers: investigations using PCA,autoencoder and isolation forest in TCP

Machine Learning - Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss...     

Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Engineering with Computers - Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce...     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.