Graph Pangenomes Track Genetic Variants for Crop Improvement

Global climate change and the urgency to transform crops require an exhaustive genetic evaluation. The large polyploid genomes of food crops, such as cereals, make it difficult to identify candidate genes with confirmed hereditary. Although genome-wide association studies (GWAS) have been proficient in identifying genetic variants that are associated with complex traits, the resolution of acquired heritability faces several significant bottlenecks such as incomplete detection of structural variants (SV), genetic heterogeneity, and/or locus heterogeneity. Consequently, a biased estimate is generated with respect to agronomically complex traits. The graph pangenomes have resolved this missing heritability and provide significant details in terms of specific loci segregating among individuals and evolving to variations. The graph pangenome approach facilitates crop improvements through genome-linked fast breeding.     

The Effect of Intensity-Modulated Radiotherapy to the Head and Neck Region on the Oral Innate Immune Response and Oral Microbiome: A Prospective Cohort Study of Head and Neck Tumour Patients

Neutrophils, also known as polymorphonuclear leukocytes (PMNs), form a significant component of the innate host response, and the consequence of the interaction between the oral microbiota and PMNs is a crucial determinant of oral health status. The impact of radiation therapy (RT) for head and neck tumour (HNT) treatment on the oral innate immune system, neutrophils in particular, and the oral microbiome has not been thoroughly investigated. Therefore, the objective of this study was to characterize RT-mediated changes in oral neutrophils (oPMNs) and the oral microbiome in patients undergoing RT to treat HNTs. Oral rinse samples were collected prior to, during and post-RT from HNT patients receiving RT at Dental Oncology at Princess Margaret Cancer Centre. The oPMNs counts and activation states were analysed using flow cytometry, and the oral microbiome was analysed using 16S rRNA gene sequencing. Statistically significant (p < 0.05) drops in oPMN counts and the activation states of the CD11b, CD16, CD18, CD64 and H3Cit markers from pre-RT to post-RT were observed. Moreover, exposure to RT caused a significant reduction in the relative abundance of commensal Gram-negative bacteria and increased the commensal Gram-positive microbes. Ionizing radiation for the treatment of HNTs simultaneously decreased the recruitment of oPMNs into the oral cavity and suppressed their activation state. The oral microbiome composition post-RT was altered significantly due to RT which may favour the colonization of specific microbial communities unfavourable for the long-term development of a balanced oral microbiome.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

Alcohol-mediated haemolysis in yeast

Although yeast are generally non-haemolytic, we have found that addition of alcohol vapour confers haemolytic properties on many strains of yeast and other fungi. We have called this phenomenon 'microbial alcohol-conferred haemolysis' (MACH). MACH is species- and strain-specific: whereas all six Candida tropicalis strains tested were haemolytic in the presence of ethanol, none among 10 C. glabrata strains tested exhibited this phenomenon. Among 27 C. albicans strains and 11 Saccharomyces cerevisiae strains tested, ethanol-mediated haemolysis was observed in 11 and 4 strains, respectively. Haemolysis is also dependent on the alcohol moiety: n-butanol and n-pentanol could also confer haemolysis, whereas methanol and 2-propanol did not. Haemolysis was found to be dependent on initial oxidation of the alcohol. Reduced haemolysis was observed in specific alcohol dehydrogenase mutants of both Aspergillus nidulans and S. cerevisiae. MACH was not observed during anaerobic growth, and was reduced in the presence of pararosaniline, an aldehyde scavenger. Results suggest that initial oxidation of the alcohol to the corresponding aldehyde is an essential step in the observed phenomenon.     

Efficacy of synbiotic supplementation in patients with nonalcoholic fatty liver disease: A systematic review and meta-analysis of clinical trials: Synbiotic supplementation and NAFLD

ABSTRACT

Objective: We systematically reviewed available randomized clinical trials (RCTs) to elucidate the overall effects of synbiotic supplementation in patients with nonalcoholic fatty liver disease (NAFLD).

Methods: PubMed, Scopus, ISI Web of science and Google Scholar were searched up to December, 2017. All RCTs using synbiotic supplements to treat NAFLD included in this systematic review and meta-analysis. Mean Difference (MD) was pooled using a random-effects model.

Results: Eleven eligible databases from seven RCTs were identified for the present meta-analysis. Our results showed that synbiotic supplementation can decrease body weight, fasting blood sugar, insulin, low density lipoprotein cholesterol, total cholesterol, triglyceride, high-sensitivity C-reactive protein, tumor necrosis factor alpha, alanine transaminase and aspartate transaminase levels among patients with NAFLD. In contrast, synbiotic did not have favorable effects on body mass index (BMI), waist circumference, homeostasis model assessment for insulin resistance (HOMA-IR), and high density lipoprotein cholesterol (HDL) levels compared with the placebo group.

Conclusion: The current study revealed that synbiotic supplementation has favorable effect on inflammatory factors, liver enzymes and some anthropometric indices, lipid profiles and glucose homeostasis parameters in patients with NAFLD.     

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population''s activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.     

Study on morphology,thermal, mechanical and Cd(II) adsorption properties of PVC/α-MnO<Subscript>2</Subscript>-stearic acid nanocomposites: production and application

Alpha manganese dioxide nanorods (α-MnO₂) were successfully functionalized with stearic acid (SA) by solvothermal method to prevent agglomeration. The α-MnO₂-SA nanorods were employed as a filler for the preparation of poly(vinyl chloride) (PVC) nanocomposite (NC) films with different percentages (1, 3 and 5 wt%). The morphology, mechanical and thermal properties of the obtained NCs were investigated. The results showed that α-MnO₂-SA can effectively improve the properties of PVC. The enhancement in properties of the NCs was attributed to the improved interfacial bonding by modification. Also, these NCs were used as adsorbent for removal of cadmium ions. Our finding suggests that the PVC/α-MnO₂-SA NCs are good candidates for efficient Cd(??) removal from the wastewater.