A Systematic Review and Meta-Analysis of Pharmacogenetic Studies in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is an important global public health problem due to its high prevalence and morbidity. Although the treatment of nephrology patients has changed considerably, ineffectiveness and side effects of medications represent a major issue. In an effort to elucidate the contribution of genetic variants located in several genes in the response to treatment of patients with CKD, we performed a systematic review and meta-analysis of all available pharmacogenetics studies. The association between genotype distribution and response to medication was examined using the dominant, recessive, and additive inheritance models. Subgroup analysis based on ethnicity was also performed. In total, 29 studies were included in the meta-analysis, which examined the association of 11 genes (16 polymorphisms) with the response to treatment regarding CKD. Among the 29 studies, 18 studies included patients with renal transplantation, 8 involved patients with nephrotic syndrome, and 3 studies included patients with lupus nephritis. The present meta-analysis provides strong evidence for the contribution of variants harbored in the ABCB1, IL-10, ITPA, MIF, and TNF genes that creates some genetic predisposition that reduces effectiveness or is associated with adverse events of medications used in CKD.     

Cognitive and affective processes for learning science in immersive virtual reality

As immersive virtual reality (IVR) systems proliferate in classrooms, it is important to understand how they affect learning outcomes and the underlying affective and cognitive processes that may cause these outcomes. Proponents argue that IVR could improve learning by increasing positive affective and cognitive processing, thereby supporting improved performance on tests of learning outcome, whereas opponents of IVR contend that it could hurt learning by increasing distraction, thereby disrupting cognitive learning processes and leading to poorer learning outcomes. In a media comparison study, students viewed a biology lesson either as an interactive animated journey in IVR or as a slideshow on a desktop monitor. Those who viewed the IVR lesson performed significantly worse on transfer tests, reported higher emotional arousal, reported more extraneous cognitive load and showed less engagement based on EEG measures than those who viewed the slideshow lesson, with or without practice questions added to the lessons. Mediational analyses showed that the lower retention scores for the IVR lesson were related to an increase in self-reported extraneous cognitive load and emotional arousal. These results support the notion that immersive environments create high affective and cognitive distraction, which leads to poorer learning outcomes than desktop environments.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Genome-wide association study for milk infrared wavenumbers

Individual wavenumbers of the infrared (IR) spectra of bovine milk have been shown to be moderately to highly heritable. The objective of this study was to identify genomic regions associated with individual milk IR wavenumbers. This is expected to provide information about the genetic background of milk composition and give insight in the relation between IR wavenumbers and milk components. For this purpose, a genome-wide association study was performed for a selected set of 50 individual IR wavenumbers measured on 1,748 Dutch Holstein cows. Significant associations were detected for 28 of the 50 wavenumbers. In total, 24 genomic regions distributed over 16 bovine chromosomes were identified. Major genomic regions associated with milk IR wavenumbers were identified on chromosomes 1, 5, 6, 14, 19, and 20. Most of these regions also showed significant associations with fat, protein, or lactose percentage. However, we also identified some new regions that were not associated with any one of these routinely collected milk composition traits. On chromosome 1, we identified 2 new genomic regions and hypothesized that they are related to variation in milk phosphorus content and orotic acid, respectively. On chromosome 20, we identified a new genomic region that seems to be related to citric acid. Identification of genomic regions associated with milk phosphorus content, orotic acid, and citric acid suggest that the milk IR spectra contain direct information on these milk components. Consequently milk IR analyses probably can be used to predict these milk components, which have low concentrations in milk; this can lead to novel applications of milk IR spectroscopy for dairy cattle breeding and herd management.     

Examining the role of regional culture and geographical distances on the representation of unfamiliar foods in a continental-size country

This study provides an experimental-exploratory investigation about the role of regional culture and Euclidean distances on the consumers’ representation of edible insects in Brazil, a country with an extensive geographical surface. Seven hundred and eighty participants were recruited on the streets of eight cities from different Brazilian states: Manaus in Amazonas; Porto Velho in Rondônia; Macapá in Amapá; Cuiabá in Mato Grosso; Aracaju in Sergipe; Rio de Janeiro in Rio de Janeiro; Campinas in São Paulo; and Santa Maria in Rio Grande do Sul. These participating cities were considered from their cultural identity differences and geographical distances. Through a continual restricted word association task, participants were instructed to promptly verbalize the first five terms that came to their minds when stimulated with the expression “food made with edible insects”. Following, they had to score the valence of each term they produced. The dictionaries produced in each city were compared and classified into groups using the Ellegård’s index. Each group presented distinct ways of expression and attitude with respect to the inductive expression. Basically, Brazil was divided into two main groups according to their representation of edible insects: one consisted by the cities situated near the shore of the Atlantic Ocean, which present a cultural formation influenced by the European immigrants; and the other comprised the cities from the continental region that have strong cultural influence from the Amerindians. Thus, the cultural formation was more decisive to explain the similar representations among the cities than their geographical proximity. Given that, to effectively introduce a novel food in a country with varied regional culture, the marketing strategy should be focused on the values and beliefs of their culture subgroups instead of a single strategy for the whole country.     

数字花卉植物的全息可视化与交互实现

花卉植物形态、结构与生长过程的数字化和可视化是现代林业科研的重要研究内 容，数字花卉植物在科普、教育、展示等方面都有着广泛的应用与需求。全息影像技术是一种 新兴的前沿技术，其原理源自佩珀尔幻象，通常用于生成立体感强的影像，实现数字幻象与真 实世界的融合，拥有良好的观赏性，用户接受度较高。将全息影像技术与数字花卉植物相关研 究进行结合，可以加强数字花卉植物的可视化效果，从而实现更好的科普、教育、展示效果。 因此在现有的研究基础之上，考虑实现数字花卉植物的全息可视化，并在可视基础上增加了用 户交互实现，设计出一套兼具展示性和交互性的流程，力求总结出一种具有完整可行性的方案， 从而对数字花卉植物的新型表现形式进行深入研究，以期对数字植物研究与应用的进一步发展 提供参考。     

Role of Flaxseed Meal Feeding for Different Durations in the Lipid Deposition and Meat Quality in Broiler Chickens

This study investigated the role of flaxseed meal (FSM), a rich terrestrial source of ω-3 fatty acids, in the alteration of the fatty acid profile and metabolism, health indices, physicochemical properties, and sensory quality of broiler chicken meat. The broiler chickens were fed 100 g FSM kg⁻¹ diet for different time periods (1, 2, 3, 4, and 5 weeks). The results revealed that 100 g FSM feeding in broiler chickens for at least 3 weeks increased (P < 0.01) the EPA, DHA, MUFA, PUFA, ω-3 PUFA, and ω-6 PUFA of broiler chicken meat with the corresponding decrease in palmitic acid, stearic acid, and SFA content. 100 g FSM feeding up to 3 weeks has increased the Δ⁹-desaturases (P < 0.05), thioesterase index (P < 0.01), and Δ⁵-desaturase + Δ⁶-desaturase activity (P < 0.01) along with an improvement in health indices (P < 0.01) of chicken meat. Similarly, a reduction in meat cholesterol and fat content of thigh meat (P < 0.01) was observed by feeding 100 g FSM for at least 3 weeks with no effect on the pH, color scores, and sensory evaluation of broiler chicken meat. The water-holding capacity (WHC) and extract release volume (ERV) decreased, whereas, drip loss of meat increased (P < 0.01) due to the feeding of 100 g FSM beyond 3 weeks. Thus, this study concluded that 100 g FSM feeding for 3 weeks in broiler chickens significantly improves the fatty acid profile, lipid metabolism, and health indices of meat, without compromising the physicochemical properties of broiler chicken meat.     

基于改进关联规则的卸船机故障预测模型

为使桥式抓斗卸船机安全稳定运行，针对大量监测数据利用率低、故障诊断不及时等问题，提出了基于兴趣度关联规则的卸船机故障预测模型方法。采用传感器监测和时域分析方法获取卸船机运行参数空间，利用聚类离散算法将监测数据根据其属性值域离散为非线性聚类区间，获取卸船机关联规则组，提取状态数据关联维的权重系数，构建状态监测数据关联规则指向性特征约束函数模型，通过预测模型中关联规则状态的改变实现故障预测。实验结果表明，该方法能有效表征卸船机运行状态监测的关联内部特征信息，实现对卸船机故障类别的预测，降低卸船机故障发生的频率。     

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.