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Water reuse and sustainable urban drainage systems

Antibiotics are a factor in developing antibiotic resistance in the environment. Outbreaks due to pathogens and resistant bacteria are an emerging issue in this decade. Resistance of Escherichia coli to two groups of antibiotics has been revised recently by the World Health Organization (WHO). These data showed that bacteria have already developed resistance to third and fourth group of antibiotics. The WHO report on surveillance and antibiotics consumption evaluation showed that antibiotic consumption varies in EU countries. Outbreaks have increased in parallel to these data depending on country, season, sex, and age group. This chapter revises the routes of spreading and surveillance of E. coli. There is a particular focus on water sources including hospitals, urban wastewater treatment plants (UWTPs), diffuse sources, and water reuse. Extensively revised data are given on the control techniques by biological and advanced processes. The emerging issue of gene transfer control in parallel to the control of bacteria is expressed. A detailed literature survey of emerging technologies of photocatalysis and nanoparticles is given.

Sustained changes in body composition during 6 months follow-up after combined lifestyle intervention in older adults with obesity and type 2 diabetes

Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.

CoPub update

In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences. CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri. In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions. CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur. It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates. With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology. Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows. CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org. © 2011 The Author(s).

Reduced seed germination in Arabidopsis over-expressing SWI/SNF2 ATPase genes

In the life of flowering plants, seed germination is a critical step to ensure survival into the next generation. Generally the seed prior to germination has been in a dormant state with a low rate of metabolism. In the transition from a dormant seed to a germinating seed, various epigenetic mechanisms play a regulatory role. Here, we demonstrate that the over-expression of chromatin remodeling ATPase genes (AtCHR12 or AtCHR23) reduced the frequency of seed germination in Arabidopsis thaliana up to 30% relative to the wild-type seeds. On the other hand, single loss-of-function mutations of the two genes did not affect seed germination. The reduction of germination in over-expressing mutants was more pronounced in stress conditions (salt or high temperature), showing the impact of the environment. Reduced germinations upon over-expression coincided with increased transcript levels of seed maturation genes and with reduced degradation of their mRNAs stored in dry seeds. Our results indicate that repression of AtCHR12/23 gene expression in germinating wild-type Arabidopsis seeds is required for full germination. This establishes a functional link between chromatin modifiers and regulatory networks towards seed maturation and germination.

Influence of online collaborative learning on social network and academic performance of medical students: lessons learned from the COVID-19 pandemic

Introduction: The social distancing restrictions due to the COVID-19 pandemic have changed students’ learning environment and limited their social interactions. Therefore, the objective of this study was to investigate the influence of the social distancing restrictions on students’ social networks, wellbeing, and academic performance. Methods: We performed a questionnaire study in which 102 students participated before and 167 students during the pandemic. They completed an online questionnaire about how they formed their five peer social networks (study-related support, collaboration, friendship, share information, and learn-from) out-of-class. We performed social network analysis to compare the sizes, structures, and compositions of students’ five social networks before and during the pandemic, between first- and second-year students, and between international and domestic students. Additionally, we performed Kruskal–Wallis H test to compare students’ academic performance before and during the pandemic. We performed thematic analysis to answers for two open-end questions in the online questionnaire to explore what difficulties students encountered during the COVID-19 pandemic and what support they needed. Results: The results showed that the size of students’ social networks during the pandemic was significantly smaller than before the pandemic. Besides, the formation of social networks differed between first- and second-year students, and between domestic and international students. However, academic performance did not decline during the COVID-19 pandemic. Furthermore, we identified three key areas in which students experienced difficulties and needed support by thematic analysis: social connections and interactions, learning and studying, and physical and mental wellbeing. Conclusion: When institutions implement learning with social distancing, such as online learning, they need to consider changes in students’ social networks and provide appropriate support.