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.     

A hybrid metaheuristic approach for the capacitated p-median problem

The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.     

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.     

CFD modeling of flow and heat transfer in a thermosyphon

In the present study a gas/liquid two-phase flow and the simultaneous evaporation and condensation phenomena in a thermosyphon was modeled. The volume of fluid (VOF) technique was used to model the interaction between these phases. Experiments in a thermosyphon were carried out at different operating conditions. The CFD predicted temperature profile in the thermosyphon was compared with experimental measurements and a good agreement was observed. It was concluded that CFD is a useful tool to model and explain the complex flow and heat transfer in a thermosyphon.     

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.     

考虑电容器电压特性的配电网无功规划

传统的配电网无功规划中,补偿电容器的容量往往被当作固定值,事实上,电容器的实际补偿容量和其接入点的电压水平有着很大的关系。考虑电容器的电压特性,以年综合费用最小为目标,建立了配电网无功规划模型。针对该模型的特点,提出采用自适应遗传算法进行求解。通过33节点和69节点配电网的仿真分析对模型进行验证。