Proximity-induced topological phases in bilayer graphene

We study the band structure of phases induced by depositing bilayer graphene on a transition metal dichalcogenide monolayer. Tight-binding and low-energy effective Hamiltonian calculations show that it is possible to induce topologically nontrivial phases that should exhibit spin Hall effect in these systems. We classify bulk insulating phases through calculation of the Z$_2$ invariant, which unequivocally identifies the topology of the structure. The study of these and similar hybrid systems under applied gate voltage opens the possibility for tunable topological structures in real experimental systems.Comment: 4 pages, 4 figure

Natural Resources, Institutions, and Poverty: The Case of the MENA Region

While several Middle East and North Africa countries have a huge endowment of natural resources such as oil and natural gas, poverty indicators, as expressed by well-being indicators and headcount, are no better in these countries than others that are relatively poor in natural resources. For instance, oil rich countries, such as Syria and Sudan, have a lower life expectancy and school enrollment than poor-resources countries like Lebanon and Israel. This study thereby, investigates the cross-country differences of poverty response to changes in natural resources wealth. The paper utilizes a panel data model for the period from 1985 to 2014 based on five-year intervals. The measurement of poverty consists of five different indicators, which are the human development index, three well-being indicators and poverty headcount. Results indicate that natural resources abundance does not directly impact the well-being of the people. The results are consistent with the growth-resources literature that links natural resources abundance to slow economic growth. That is, the presence of natural resources within developing countries exacerbates the risk of political instability, corruption, and poor governance, which we further examine in the second model

Cancer risk prediction with whole exome sequencing and machine learning

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known mutated genes for specific cancers. However, previous studies have not explored the predictive ability of collective genomic variants from whole-exome sequencing data. It is crucial to train a model in one study and predict another related independent study to ensure that the predictive model generalizes to other datasets. Survival time prediction allows patients and physicians to evaluate the treatment feasibility and helps chart health treatment plans. Many studies have concluded that clinicians are inaccurate and often optimistic in predicting patients’ survival time; therefore, the need increases for automated survival time prediction from genomic and medical imaging data. For cancer risk prediction, this dissertation explores the effectiveness of ranking genomic variants in whole-exome sequencing data with univariate features selection methods on the predictive capability of machine learning classifiers. The dissertation performs cross-study in chronic lymphocytic leukemia, glioma, and kidney cancers that show that the top-ranked variants achieve better accuracy than the whole genomic variants. For survival time prediction, many studies have devised 3D convolutional neural networks (CNNs) to improve the accuracy of structural magnetic resonance imaging (MRI) volumes to classify glioma patients into survival categories. This dissertation proposes a new multi-path convolutional neural network with SNP and demographic features to predict glioblastoma survival groups with a one-year threshold that improves upon existing machine learning methods. The dissertation also proposes a multi-path neural network system to predict glioblastoma survival categories with a 14-year threshold from a heterogeneous combination of genomic variations, messenger ribonucleic acid (RNA) expressions, 3D post-contrast T1 MRI volumes, and 2D post-contrast T1 MRI modality scans that show the malignancy. In 10-fold cross-validation, the mean 10-fold accuracy of the proposed network with handpicked 2D MRI slices (that manifest the tumor), mRNA expressions, and SNPs slightly improves upon each data source individually

Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression

Quantitative phenotypes prediction from genotype data is significant for pathogenesis, crop yields, and immunity tests. The scientific community conducted many studies to find unobserved quantitative phenotype high predictive ability models. Early genome-wide association studies (GWAS) focused on genetic variants that are associated with disease or phenotype, however, these variants manly covers small portion of the whole genetic variance, and therefore, the effectiveness of predictions obtained using this information may possibly be circumscribed [ 1 ]. Instead, this study shows prediction ability from whole genome single nucleotide polymorphisms (SNPs) data of 1940 genotyped stoke mouse with - 12k SNPs, and 413 genotyped rice inbred lines with - 40k SNPs. The predictive accuracy measured as the Pearson coefficient correlation between predicted phenotype and actual phenotype values using cross validation (CV), and found a predictive ability for mouse phenotypes MCH, CD8 to be 0.64 and 0.72, respectively. The study compares whole genome SNPs data prediction methods built using Support Vector Regression (SVR) and Pearson Correlation Coefficient (PCC) to perform SNPs selection and then predict unobserved phenotype using ridge regression and SVR. The investigation shows that ranking SNPs by SVR significantly increases predictive accuracy than ranking with PCC. In general, Ridge Regression perform slightly better prediction ability than predicting with SVR

Proximity-induced topological transition and strain-induced charge transfer in graphene/MoS2 bilayer heterostructures

Graphene/MoS2 heterostructures are formed by combining the nanosheets of graphene and monolayer MoS2. The electronic features of both constituent monolayers are rather well-preserved in the resultant heterostructure due to the weak van der Waals interaction between the layers. However, the proximity of MoS2 induces strong spin orbit coupling effect of strength ~1 meV in graphene, which is nearly three orders of magnitude larger than the intrinsic spin orbit coupling of pristine graphene. This opens a bandgap in graphene and further causes anticrossings of the spin-nondegenerate bands near the Dirac point. Lattice incommensurate graphene/MoS2 heterostructure exhibits interesting moire' patterns which have been observed in experiments. The electronic bandstructure of heterostructure is very sensitive to biaxial strain and interlayer twist. Although the Dirac cone of graphene remains intact and no charge-transfer between graphene and MoS2 layers occurs at ambient conditions, a strain-induced charge-transfer can be realized in graphene/MoS2 heterostructure. Application of a gate voltage reveals the occurrence of a topological phase transition in graphene/MoS2 heterostructure. In this chapter, we discuss the crystal structure, interlayer effects, electronic structure, spin states, and effects due to strain and substrate proximity on the electronic properties of graphene/MoS2 heterostructure. We further present an overview of the distinct topological quantum phases of graphene/MoS2 heterostructure and review the recent advancements in this field.Comment: 31 pages, 12 figure

Inducing chiral superconductivity on honeycomb lattice systems

Superconductivity in graphene-based systems has recently attracted much attention, as either intrinsic behavior or induced by proximity to a superconductor may lead to interesting topological phases and symmetries of the pairing function. A prominent system considers the pairing to have chiral symmetry. The question arises as to the effect of possible spin-orbit coupling on the resulting superconducting quasiparticle spectrum. Utilizing a Bogolyubov-de Gennes (BdG) Hamiltonian, we explore the interplay of different interaction terms in the system, and their role in generating complex Berry curvatures in the quasiparticle spectrum, as well as non-trivial topological behavior. We demonstrate that the topology of the BdG Hamiltonian in these systems may result in the appearance of edge states along the zigzag edges of nanoribbons in the appropriate regime.Comment: 8 pages, 4 figure

Community Mental Health Nursing in Saudi Arabia: Current and Future Challenges.

This study aimed to explore the current and future challenges that facing the application of community mental health nursing in Saudi Arabia. A consensus obtained by the experts. A Delphi method was used in this research. This approach has prepared ground work for educators, researchers and practitioners in future understanding perspectives of expertise about the current and future challenges for community mental health nursing in Saudi Arabia. The data was collected over three rounds, the first round the researcher utilized three open-ended questions questionnaire. Fallowing the content analysis of the open-ended questions (86) were elicited 34 on the current challenges, 26 on the potential future challenges and 26 on the suggested methods on how to start implementing it. A 29 items questionnaire was then constructed with the three categories and were utilized during the second and third round of the study. The initial study sample included 9 males and 6 females, Saudi and non-Saudi, clinical or academic workers of those who hold master degree or PHD in psychiatric nursing. During the second and third round only N= 10 and N= 8 of the experts agreed to continue in participating in the study.  The results of the study showed that consensus among the experts were reached on 18 elements with agreement level of 70% or more. 5 of them on the current challenges, 3 on the potential future challenges and 10 on the suggested methods on how to start implementing the community mental health nursing. In conclusion, it was apparent that the expert panel believes there are many elements that shape the challenges facing the community mental health nursing and other elements that should be considered when applying the program

Leeds sleep evaluation questionnaire in Jordanian university students: A psychometric investigation using comparative confirmatory factor analysis

© 2020 Saudi Arabian Armed Forces Hospital. All rights reserved. Objectives: To analyze the fit of different competing factor models (a one-factor model, 3 2-factor models, and 2 4-factor models) of the Leeds sleep evaluation questionnaire (LSEQ) in the data from a Jordanian student population. Methods: A cross-sectional study was conducted on university students, with 2 sleep-related tools - the LSEQ and the sleep hygiene index (SHI). The students (n=166) at Jordan University of Science and Technology, Irbid, Jordan participated in this study from January-April, 2019. A total of 12 LSEQ models (6 models with all 10-items, and 6 models with one item deleted) were evaluated by using confirmatory factor analysis. The summary statistics of correlation coefficients, descriptive measures of item analysis, the model fit, and Cronbach’s alpha were determined. Results: The findings show that a 4-factor correlated solution was a plausible model for the LSEQ with 9-items, compared to a one-factor, 2-factor, and other 4-factor variant models. The deletion of one item from the original LSEQ improved the data fit significantly in the studied population. Moreover, correlation analysis between the LSEQ and SHI confirmed the divergent validity of the LSEQ. Conclusion: The results support the validity of a 4-factor structure of the LSEQ with 9-items with adequate internal consistency and divergent validity

Cucurbitacin D exhibits potent anticancer activity in cervical cancer

In this study, we for the first time, investigated the potential anti-cancer effects of a novel analogue of cucurbitacin (Cucurbitacin D) against cervical cancer in vitro and in vivo. Cucurbitacin D inhibited viability and growth of cervical cancer cells (CaSki and SiHa) in a dose-dependent manner. IC50 of Cucurbitacin D was recorded at 400 nM and 250 nM in CaSki and SiHa cells, respectively. Induction of apoptosis was observed in Cucurbitacin D treated cervical cancer cells as measured by enhanced Annexin V staining and cleavage in PARP protein. Cucurbitacin D treatment of cervical cancer cells arrested the cell cycle in G1/S phase, inhibited constitutive expression of E6, Cyclin D1, CDK4, pRb, and Rb and induced the protein levels of p21 and p27. Cucurbitacin D also inhibited phosphorylation of STAT3 at Ser727 and Tyr705 residues as well as its downstream target genes c-Myc, and MMP9. Cucurbitacin D enhanced the expression of tumor suppressor microRNAs (miR-145, miRNA-143, and miRNA34a) in cervical cancer cells. Cucurbitacin D treatment (1 mg/kg body weight) effectively inhibited growth of cervical cancer cells derived orthotopic xenograft tumors in athymic nude mice. These results demonstrate the potential therapeutic efficacy of Cucurbitacin D against cervical cancer

Extraintestinal Manifestations Of Ulcerative Colitis In Saudi Arabia: Systematic Review

Background: Inflammatory bowel disease (IBD), particularly ulcerative colitis (UC), exhibits varied clinical presentations and extraintestinal manifestations (EIMs) that impact the overall well-being of affected individuals. This systematic review aims to consolidate recent studies conducted in Saudi Arabia to comprehensively analyze the sociodemographic characteristics and clinical features of UC patients, with a specific focus on bone-related complications. Objective: To systematically review Extraintestinal Manifestations of Ulcerative Colitis in Saudi Arabia Methodology: Using the PRISMA guidelines, a comprehensive A systematic search was conducted to identify relevant studies published between 2014 and 2023 on PubMed in English that investigated UC in Saudi Arabia. resulting in the inclusion of seven studies with a collective participant count of 1580. Sociodemographic characteristics & Clinical characteristics, particularly the prevalence of bone-related complications, were examined across these studies. Results: The sociodemographic analysis of 1580 participants from seven studies highlighted variations in extraintestinal manifestations in IBD. Due to the inflammatory nature of the UC disease, and increased glucocorticoids concentrations, bone-related complications, including osteoporosis and osteopenia, were prevalent in UC patients, with distinct patterns observed in different studies. Arthropathy emerged as one of the most common extraintestinal manifestation. Moreover, renal stones are another issue for these patients. Finally, all of these manifestations contribute to the prevalence of anxiety and depression symptoms that was identified among UC patients, that indicated that fifth of these cohort suffer from, psychological disease. Conclusion: This systematic review provides a comprehensive overview of recent studies on UC in Saudi Arabia, emphasizing the prevalence of bone-related complications as predominant extra intestinal manifestations. The findings underscore the importance of addressing these complications in the management of UC patients, necessitating regular testing of the bone density in these patients and provide supplements and other necessary treatments for these patients. Moreover, it is important to consider the psychological impact of such disease on the quality of life of patients. Comprehensive multi-disciplinary medical teams need to work together to address various clinical aspects regarding Ulcerative colitis. This does not only include gastroenterologist, nephrologists and general internists, but also include psychologists/therapists to ensure all patients needs are addressed. Finally, further research is needed to have comprehensive view of UC in Saudi Populations and improve the overall quality of care