Antimony germanium sulphide amorphous thin films fabricated by chemical vapour deposition

Antimony germanium sulphide (Sb-Ge-S) amorphous thin films have been directly fabricated on both silica on silicon and commercial glass substrates by means of chemical vapour deposition. These Sb-Ge-S films have been characterized by micro-Raman, scanning electron microscopy and energy dispersive X-ray analysis techniques. The analysis results for these amorphous films indicate the composition of Sb-Ge-S can be varied by changing the deposition temperatures. The quality of these Sb-Ge-S amorphous thin films gives them high potential for the chalcogenide optical waveguide and device applications

Epidemiology and Diagnosis of Feline Intestinal Lymphosarcomas in Egypt

Feline intestinal lymphosarcomas are mostly caused by Feline Leukemia Virus (FeLV). Unfortunately, there is no available vaccine for FeLV in Egypt. The diagnosis of feline intestinal lymphosarcomas depends upon abdominal palpation, x-rays examination, ultrasonography, direct ELISA and histopathology of masses excised during laparotomy. The recorded clinical signs in intestinal lymphosarcoma affected cats were variable including vomiting, fever, anorexia, ascites, anemia, dyspnea, constipation and emaciation. The affected lymph nodes were mesenteric, mediastinal and retropharyngeal. The prevalence of intestinal lymphosarcomas in the examined cats was 4.03 % (11 out of 273 cats). The prevalence was higher in queens than toms (2.93 % and 0.73 % respectively). The Siamese cats had higher prevalence than the Sherazy ones (2.56 % and 1.47 % respectively). X-ray films and ultrasonographic images performed on the eleven cats suffered from intestinal lymphosarcomas revealed ascities and abdominal masses. The comparison of ELISA and histopathology (of excised masses) results showed that 9 out of 11 intestinal lymphosarcoma affected cats were infected with FeLV that proved not all cases of intestinal lymphosarcoma were caused by FeLV. The sensitivity, specificity and accuracy of ELISA to diagnose intestinal lymphosarcoma in cats were 81.81 %, 100 % and 92 % respectively. Gross autopsy of the collected lymph nodes, livers, kidneys revealed that gross lymphadenopathy involving one or more nodes, hepatomegaly and kidney enlargement. Microscopically, the examined tissues specimens showed that the normal architecture of the examined lymph nodes, livers, and kidneys has been replaced by a diffuse infiltrate of both lymphocytes and lymphoblasts. The vast majority of the cells are small lymphocyte-type cells with round basophilic nuclei and a sparse rim of cytoplasm. The eleven intestinal lymphosarcoma affected cats exposed to abdominal exploratory surgery (laparotomy) died at one to three months post-surgery. It is concluded that the vaccination of kittens and cats against FeLV in Egypt is very important to prevent the highly fatal intestinal lymphosarcomas

Data-driven and task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment

Molecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation (docking task), binary activity label (screening task), and binding affinity (scoring task) of ligands against a critical protein in the disease's pathway. In most molecular docking software packages available today, a generic binding affinity-based (BA-based) SF is invoked for the three tasks to solve three different, but related, prediction problems. The vast majority of these predictive models are knowledge-based, empirical, or force-field scoring functions. The fourth family of SFs that has gained popularity recently and showed potential of improved accuracy is based on machine-learning (ML) approaches. Despite intense efforts in developing conventional and current ML SFs, their limited predictive accuracies in these three tasks have been a major roadblock toward cost-effective drug discovery. Therefore, in this work we present (i) novel task- specific and multi-task SFs employing large ensembles of deep neural networks (NN) and other state-of-the-art ML algorithms in conjunction with (ii) data-driven multi-perspective descriptors (features) for accurate characterization of protein-ligand complexes (PLCs) extracted using our Descriptor Data Bank (DDB) platform.We assess the docking, screening, scoring, and ranking accuracies of the proposed task-specific SFs with DDB descriptors as well as several conventional approaches in the context of the 2007 and 2014 PDBbind benchmark that encompasses a diverse set of high-quality PLCs. Our approaches substantially outperform conventional SFs based on BA and single-perspective descriptors in all tests. In terms of scoring accuracy, we find that the ensemble NN SFs, BsN-Score and BgN-Score, have more than 34% better correlation (0.844 and 0.840 vs. 0.627) between predicted and measured BAs compared to that achieved by X-Score, a top performing conventional SF. We further find that ensemble NN models surpass SFs based on other state-of-the-art ML algorithms. Similar results have been obtained for the ranking task. Within clusters of PLCs with different ligands bound to the same target protein, we find that the best ensemble NN SF is able to rank the ligands correctly 64.6% of the time compared to 57.8% obtained by X-Score. A substantial improvement in the docking task has also been achieved by our proposed docking-specific SFs. We find that the docking NN SF, BsN-Dock, has a success rate of 95% in identifying poses that are within 2 \uc5 RMSD from the native poses of 65 different protein families. This is in comparison to a success rate of only 82% achieved by the best conventional SF, ChemPLP, employed in the commercial docking software GOLD. As for the ability to distinguish active molecules from inactives, our screening-specific SFs showed excellent improvements over the conventional approaches. The proposed SF BsN-Screen achieved a screening enrichment factor of 33.90 as opposed to 19.54 obtained from the best conventional SF, GlideScore, employed in the docking software Glide. For all tasks, we observed that the proposed task-specific SFs benefit more than their conventional counterparts from increases in the number of descriptors and training PLCs. They also perform better on novel proteins that they were never trained on before. In addition to the three task-specific SFs, we propose a novel multi-task deep neural network (MT-Net) that is trained on data from three tasks to simultaneously predict binding poses, affinities, and activity labels. MT-Net is composed of shared hidden layers for the three tasks to learn common features, task-specific hidden layers for higher feature representation, and three outputs for the three tasks. We show that the performance of MT-Net is superior to conventional SFs and competitive with other ML approaches. Based on current results and potential improvements, we believe our proposed ideas will have a transformative impact on the accuracy and outcomes of molecular docking and virtual screening.Thesis (Ph. D.)--Michigan State University. Electrical Engineering, 2017Includes bibliographical references (pages 180-188

The effectiveness of using mobile learning to enhance UAEU students’ achievement and motivation in Emirati studies course

United Arab Emirates University adopted a particular project aiming to improve and develop the learning process. The idea of the research focused on the utilization of mobile learning in classrooms through using the iPad devices by students in different content areas. Applying such a notion helped in motivating students in learning, especially, for some courses featured as more theoretical and abstract like Emirati Studies course. The Emirati Studies, considering as a compulsory course for the students in the UAEU, addresses different aspects related to the Emirati community whether socially, historically, geographically, politically or economically. So, the purpose of this study is to explore the effectiveness of mobile leaning utilization in developing students’ achievement and motivation in Emirati Studies course. The study was conducted in the United Arab Emirates University in Al-Ain City during spring semester (2015-2016). The sample was selected through using stratified sampling technique; in which the population was divided into two subgroups including (97) male students and (104) female students studying in four sections distributed equally for both genders. The researcher employed a descriptive method to construct a list of standers for creating a digital learning unit using mobile learning in addition to using the quasi-experimental research design, the sample was selected and divided into two groups; the experimental group included (55) female students and (49) male students, and the control group contained (52) female students and (44) male students. The content was selected from the Emirati Studies’ content in which the experimental group was taught by using mobile learning, while the control group used the same content through applying the conventional way of teaching. The content was digitized by utilizing the iBook author application. Data were collected through carrying out an achievement test and adopted John Keller’s Instructional Materials Motivation Scale (IMMS) after translating it to Arabic. Data were statistically analyzed by employing t-test to show the significant differences of the means between the two groups in both; the achievement test and the motivation scale. The results revealed that there was statistic significant differences at (0.05) between the experimental group and control group in both the achievement and the motivation measurement for the benefit of the experimental group. The research used (ETA) Square to identify the practical value of the experimental method, The research findings suggest that mobile learning has minimal effectiveness in improving UAEU students’ achievement in the course “Emirati Studies”. The results, however, point out the effectiveness of using mobile learning that adopts Keller’s model in fostering UAEU students’ motivation in studying the course “Emirati Studies”. The recommendations in this study were illustrated in considering (ARCS) Model of motivation in designing any targeted content to serve the learning process, and in implementing mobile learning in other content areas

C9ORF72 interaction with cofilin modulates actin dynamics in motor neurons.

Intronic hexanucleotide expansions in C9ORF72 are common in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, but it is unknown whether loss of function, toxicity by the expanded RNA or dipeptides from non-ATG-initiated translation are responsible for the pathophysiology. We determined the interactome of C9ORF72 in motor neurons and found that C9ORF72 was present in a complex with cofilin and other actin binding proteins. Phosphorylation of cofilin was enhanced in C9ORF72-depleted motor neurons, in patient-derived lymphoblastoid cells, induced pluripotent stem cell-derived motor neurons and post-mortem brain samples from ALS patients. C9ORF72 modulates the activity of the small GTPases Arf6 and Rac1, resulting in enhanced activity of LIM-kinases 1 and 2 (LIMK1/2). This results in reduced axonal actin dynamics in C9ORF72-depleted motor neurons. Dominant negative Arf6 rescues this defect, suggesting that C9ORF72 acts as a modulator of small GTPases in a pathway that regulates axonal actin dynamics

REMOVAL OF FERRIC IONS (Fe+3) FROM NEUTRAL SOLUTIONS USING MODIFIED CHITOSAN

The object of this study is to assess the removal of Fe(III) ions from aqueous solutions onto modified chitosan. The effect of various parameters has been investigated by the following batch adsorption technique. The various variables studied include initial concentration of the adsorbate, agitation time, adsorbent dosage, kinetics, influence of temperature. The experimental data was fit well to the Freundlich isotherm. Thermodynamic parameters such as ΔH, ΔS and ΔG were calculated, indicating that the adsorption was spontaneous and endothermic nature

Nuclear poly(ADP-ribose) activity is a therapeutic target in amyotrophic lateral sclerosis

Abstract Amyotrophic lateral sclerosis (ALS) is a devastating and fatal motor neuron disease. Diagnosis typically occurs in the fifth decade of life and the disease progresses rapidly leading to death within ~ 2–5 years of symptomatic onset. There is no cure, and the few available treatments offer only a modest extension in patient survival. A protein central to ALS is the nuclear RNA/DNA-binding protein, TDP-43. In > 95% of ALS patients, TDP-43 is cleared from the nucleus and forms phosphorylated protein aggregates in the cytoplasm of affected neurons and glia. We recently defined that poly(ADP-ribose) (PAR) activity regulates TDP-43-associated toxicity. PAR is a posttranslational modification that is attached to target proteins by PAR polymerases (PARPs). PARP-1 and PARP-2 are the major enzymes that are active in the nucleus. Here, we uncovered that the motor neurons of the ALS spinal cord were associated with elevated nuclear PAR, suggesting elevated PARP activity. Veliparib, a small-molecule inhibitor of nuclear PARP-1/2, mitigated the formation of cytoplasmic TDP-43 aggregates in mammalian cells. In primary spinal-cord cultures from rat, Veliparib also inhibited TDP-43-associated neuronal death. These studies uncover that PAR activity is misregulated in the ALS spinal cord, and a small-molecular inhibitor of PARP-1/2 activity may have therapeutic potential in the treatment of ALS and related disorders associated with abnormal TDP-43 homeostasis

Neurodegenerative Diseases and Autophagy

Most neurodegenerative diseases are characterized by the accumulation of aggregated proteins within neurons. These aggregate-prone proteins cause toxicity, a phenomenon that is further exacerbated when there is defective protein clearance. Autophagy is an intracellular clearance pathway that can clear these protein aggregates and has been shown to be beneficial in the treatment of neurodegenerative diseases in a variety of model systems. Here, we introduce the key components of the autophagy machinery and signaling pathways that control this process and discuss the evidence that autophagic flux may be impaired and therefore a contributing factor in neurodegenerative disease pathogenesis. Finally, we review the use of autophagy upregulation as a therapeutic strategy to treat neurodegenerative disorders

Prevalence and Antibiotic Susceptibility Pattern of Pseudomonas aeruginosa Isolated from Hospital Environment in South Libya

Pseudomonas aeruginosa has been emerged as a significant pathogen and is the most common dreadful gram-negative bacilli found in various health care-associated infections all over the world due to its virulence, well-known ability to resist killing by various antibiotics and disinfectants. The aim of this study was isolation and identification of Pseudomonas aeruginosa in the hospital environment and determining the antibiotic susceptibility of the isolates to four antibiotics (Ciprofloxacin, Amikacin, Imipenem, and Piperacillin). A total of 200 sterile cotton swab samples were collected from hospital environment including ground, walls, beds, bed sheets, blankets, doors, doors handle, nurse tables, trays, chairs, electronic equipment's, medicine cabinet, windows and (operation theater) (Sabha medical center and Brack general hospital were enrolled in this cross-sectional study). Bacterial isolates were identified by standard microbiological procedures. Antibiotic susceptibility testing was carried out by disc diffusion method. Results revealed that out of the 200 collected samples, 12 Pseudomonas spp. (6%) were isolated. Other different bacterial species isolated were 148 (74%) and 40 samples (20%) were negative for growth. Most isolates were obtained from sinks 6 (50%) and then ground 2 (16.7%), Air conditions 2 (16.7%), walls 1 (8.3%), Chairs 1 (8.3%). we found that all Pseudomonas spp. isolates were sensitive to Ciprofloxacin, Amikacin, Piperacillin, and Imipenem

ENHANCING THE ESTERIFICATION CONVERSION USING PERVAPORATION

Coupling of a pervaporation membrane unit with an esterification reactor has been undertaken with a view to improve the overall efficiency of the esterification process through removal of one of the products. The esterification reaction of acetic acid with methanol in the presence of two alternative heterogeneous catalysts Nafion resin (NR) and silica sulfuric acid (SSA) is investigated on the laboratory scale. The system consists of a batch reactor externally coupled with pervaparation (PV) module containing a Nafion membrane. The effect of different parameters on the esterification / pervaporation system is explored. The studied parameters include reactants molar ratio, temperature, and catalyst weight percent. The results show that the water diffusion through the PV membrane helps to break the thermodynamic equilibrium barrier of reversible esterification reaction and improve the reaction conversion. The maximum conversion reached 96.76 % after 60 min at 60 ºC, 3% silica sulfuric acid as catalyst, with a reactant to acid molar ratio of 8:1, and a membrane surface area to reactor volume of 1.3 cm-1.