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Microorganisms produce and excrete a versatile array of metabolites with different physico-chemical properties and biological activities. However, the ability of microorganisms to release volatile compounds has only attracted research attention in the past decade. Recent research has revealed that microbial volatiles are chemically very diverse and have important roles in distant interactions and communication. Microbial volatiles can diffuse fast in both gas and water phases, and thus can mediate swift chemical interactions. As well as constitutively emitted volatiles, microorganisms can emit induced volatiles that are triggered by biological interactions or environmental cues. In this Review, we highlight recent discoveries concerning microbial volatile compounds and their roles in intra-kingdom microbial interactions and inter-kingdom interactions with plants and insects. Furthermore, we indicate the potential biotechnological applications of microbial volatiles and discuss challenges and perspectives in this emerging research field.

The Concise Guide to PHARMACOLOGY 2021/22 is the fifth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of nearly 1900 human drug targets with an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes over 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/bph.15538. G protein-coupled receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2021, and supersedes data presented in the 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.

Feeding cancer registries with data extracted from textual reports, while maintaining a high level of data quality, has always been a labour-intensive task, due to the heterogeneity of the sources. The support of this task by IT solutions is expected to accelerate and optimise this process. To this end, the commercial text mining system Averbis Health Discovery was tailored to extract information from free text at the cancer registry of the federal state of Baden-Württemberg. The following entity types were extracted from German-language pathology reports: tumour localisation and morphology, pTNM, grading, (sentinel) nodes examined and affected, laterality and R-class. According to the entity type, several machine learning approaches as well as rules were used for the tumour types breast, prostate, colorectal and skin. Whereas for the pilot site, F values ranged between 0.800 and 0.996, values dropped when applying the extraction pipeline to two new sites (cancer registries Rhineland-Palatinate and Lower Saxony), for morphology from 0.950 to 0.657 and 0.933, and for localisation (topography) from 0.902 to 0.675 and 0.768. There was much less difference with R-class and lymph node counts. A thorough error analysis revealed numerous issues that explain these differences, such as different workflows between the sites, disagreements between textual and coded content as well as different handlings of missing values., Das Anreichern von Krebsregistern mit Daten aus medizinischen Texten, bei gleichzeitiger Sicherstellung der Datenqualität, ist aufgrund der Heterogenität dieser Quellen mit erheblichem Aufwand verbunden. Die Unterstützung dieser Aufgabe durch IT-Lösungen soll diesen Prozess beschleunigen und optimieren. Zu diesem Zweck wurde das kommerzielle Text-Mining-System Averbis Health Discovery darauf zugeschnitten, Freitextinformationen für das Krebsregister Baden-Württemberg zu verwerten. Die folgenden Entitätstypen wurden aus Befundberichten der Pathologie extrahiert: Tumorlokalisation und -morphologie, pTNM, Grading, untersuchte und betroffene (Sentinel-) Lymphknoten, Lateralität und R-Klassifikation. Je nach Tumortyp wurden verschiedene Ansätze des maschinellen Lernens sowie Regeln für die Tumorentitäten Brust, Prostata, Kolon/Rektum und Haut verwendet. Während für den Pilotstandort die F-Werte zwischen 0,804 und 0,996 lagen, fielen die Werte, wenn die Extraktionspipeline auf zwei neue Standorte (Krebsregister Rheinland-Pfalz und Niedersachsen) angewendet wurde, für die Morphologie von 0,950 auf 0,657 und 0,933 und für die Lokalisierung (Topographie) von 0,902 auf 0,675 und 0,768. Es gab viel weniger Unterschiede in der R-Klassifikation und bei den Lymphknoten. Eine gründliche Fehleranalyse verwies auf zahlreiche Probleme, die diese Unterschiede erklären, z.B. unterschiedliche Arbeitsabläufe zwischen den Standorten, Widersprüche zwischen Text und codiertem Inhalt sowie unterschiedlicher Umgang mit fehlenden Werten.

Information on water balance components such as evapotranspiration and groundwater recharge are crucial for water management. Due to differences in physical conditions, but also due to limited budgets, there is not one universal best practice, but a wide range of different methods with specific advantages and disadvantages. In this study, we propose an approach to quantify actual evapotranspiration, groundwater recharge and water inflow, i.e. precipitation and irrigation, that considers the specific conditions of irrigated agriculture in warm, arid environments. This approach does not require direct measurements of precipitation or irrigation quantities and is therefore suitable for sites with an uncertain data basis. For this purpose, we combine soil moisture and energy balance monitoring, remote sensing data analysis and numerical modeling using Hydrus. Energy balance data and routine weather data serve to estimate ET0. Surface reflectance data from satellite images (Sentinel‐2) are used to derive leaf area indices, which help to partition ET0 into energy limited evaporation and transpiration. Subsequently, first approximations of water inflow are derived based on observed soil moisture changes. These inflow estimates are used in a series of forward simulations that produce initial estimates of drainage and ETact, which in turn help improve the estimate of water inflow. Finally, the improved inflow estimates are incorporated into the model and then a parameter optimization is performed using the observed soil moisture as the reference figure. Forward simulations with calibrated soil parameters result in final estimates for ETact and groundwater recharge. The presented method is applied to an agricultural test site with a crop rotation of cotton and wheat in Punjab, Pakistan. The final model results, with an RMSE of 2.2% in volumetric water content, suggest a cumulative ETact and groundwater recharge of 769 mm and 297 mm over a period of 281 days, respectively. The

Although recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation have been suggested to generate a burden throughout the population. To provide scientific data to help identify risk factors for the psychosocial strain during the COVID-19 outbreak, an international cross-disciplinary online survey was circulated in April 2020. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement. The ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform and was promoted by thirty-five research organizations from Europe, North Africa, Western Asia and the Americas. Questions were presented in a differential format with questions related to responses “before” and “during” the confinement period. 1047 replies (54% women) from Western Asia (36%), North Africa (40%), Europe (21%) and other continents (3%) were analysed. The COVID-19 home confinement evoked a negative effect on mental wellbeing and emotional status (P < 0.001; 0.43 ≤ d ≤ 0.65) with a greater proportion of individuals experiencing psychosocial and emotional disorders (+10% to +16.5%). These psychosocial tolls were associated with unhealthy lifestyle behaviours with a greater proportion of individuals experiencing (i) physical (+15.2%) and social (+71.2%) inactivity, (ii) poor sleep quality (+12.8%), (iii) unhealthy diet behaviours (+10%), and (iv) unemployment (6%). Conversely, participants demonstrated a greater use (+15%) of technology during the confinement period. These findings elucidate the risk of psychosocial strain during the COVID-19 home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle AHCL).