High degree of BMI misclassification of malnutrition among Swedish elderly population: age-adjusted height estimation using knee height and demispan

BACKGROUND/OBJECTIVES: The degree of misclassification of obesity and undernutrition among elders owing to inaccurate height measurements is investigated using height predicted by knee height (KH) and demispan equations. SUBJECTS/METHODS: Cross-sectional investigation was done among a random heterogeneous sample from five municipalities in Southern Sweden from a general population study 'Good Aging in Skane' (GAS). The sample comprised two groups: group 1 (KH) including 2839 GAS baseline participants aged 60-93 years with a valid KH measurement and group 2 (demispan) including 2871 GAS follow-up examination participants (1573 baseline; 1298 new), aged 60-99 years, with a valid demispan measurement. Participation rate was 80%. Height, weight, KH and demispan were measured. KH and demispan equations were formulated using linear regression analysis among participants aged 60-64 years as reference. Body mass index (BMI) was calculated in kg/m2. RESULTS: Undernutrition prevalences in men and women were 3.9 and 8.6% by KH, compared with 2.4 and 5.4% by standard BMI, and more pronounced for all women aged 85+ years (21% vs 11.3%). The corresponding value in women aged 85+ years by demispan was 16.5% vs 10% by standard BMI. Obesity prevalences in men and women were 17.5 and 14.6% by KH, compared with 19.0 and 20.03% by standard BMI. Values among women aged 85+ years were 3.7% vs 10.4% by KH and 6.5% vs 12.7% by demispan compared with the standard. CONCLUSIONS: There is an age-related misclassification of undernutrition and obesity attributed to inaccurate height estimation among the elderly. This could affect the management of patients at true risk. We therefore propose using KH- and demispan-based formulae to address this issue

INVESTIGATION OF THE QUANTITATIVE AND QUALITATIVE STATUS OF COASTAL AQUIFER IN KALLIKRATIA-FLOGITA, CHALKIDIKI, GREECE

Σκοπός της παρούσας εργασίας αποτελεί η διερεύνηση της ποσοτικής και ποιοτικής κατάστασης του παράκτιου υδροφορέα στην περιοχή Καλλικράτειας–Φλογητών. Για την υλοποίηση της, αξιοποιήθηκαν υδροχημικά και κλιματικά δεδομένα, καθώς και μετρήσεις στάθμης του υπόγειου νερού. Η συνεχής πτώση της στάθμης του υπόγειου νερού, με ρυθμό 0,76 m/έτος, στην περιοχή έρευνας έχει συντελέσει στην υφαλμύριση αφενός εξαιτίας της διείσδυσης του θαλασσινού νερού, αφετέρου στην ανάμειξη γεωθερμικών ρευστών με τους ψυχρότερους επιφανειακούς υδροφορείς. Επιπλέον πίεση μπορεί να θεωρηθεί η νιτρορύπανση του υπόγειου νερού λόγω της εντατικής χρήσης λιπασμάτων. Συμπερασματικά, διαπιστώνεται πως η κακή διαχείριση του υπόγειου νερού αποτελεί τη σημαντικότερη αιτία για την ποσοτική μείωση και ποιοτική υποβάθμιση του υπόγειου νερού έναντι των αναφερόμενων κλιματικών μεταβολών/αλλαγών. Η ποσοτικοποίηση της συνεισφοράς των δύο κυρίαρχων αιτιών της υποβάθμισης του υπόγειου νερού χρήζει περαιτέρω έρευνας. The aim of this study was the determination of quantitative and qualitative status of the coastal aquifer in Kallikratia-Flogita area (Chalkidiki, North Greece). Hence, the hydrochemical data, water level measurements and climatic data were elaborated and evaluated. Groundwater decline occur with a mean rate up to 0.76 m/year which has led to salinization of the aquifer due to seawater intrusion and mixing of geothermal fluids with the upper fresh aquifers. Nitrate pollution is a further stressor that is attributed to fertilizers. Mismanagement of groundwater is the dominant cause of groundwater deterioration, while the referred climate changes follows. Quantification of the two stressors requires further and deeper analysis

Publisher Correction: Biodiversity, environmental drivers, and sustainability of the global deep-sea sponge microbiome

The original version of the Description of Additional Supplementary Files associated with this Article contained errors in the legends of Supplementary Data 5–8 and omitted legends for the Source Data. The HTML has been updated to include a corrected version of the Description of Additional Supplementary Files; the original incorrect version of this file can be found as Supplementary Information associated with this Correction

Biodiversity, environmental drivers, and sustainability of the global deep-sea sponge microbiome

In the deep ocean symbioses between microbes and invertebrates are emerging as key drivers of ecosystem health and services. We present a large-scale analysis of microbial diversity in deep-sea sponges (Porifera) from scales of sponge individuals to ocean basins, covering 52 locations, 1077 host individuals translating into 169 sponge species (including understudied glass sponges), and 469 reference samples, collected anew during 21 ship-based expeditions. We demonstrate the impacts of the sponge microbial abundance status, geographic distance, sponge phylogeny, and the physical-biogeochemical environment as drivers of microbiome composition, in descending order of relevance. Our study further discloses that fundamental concepts of sponge microbiology apply robustly to sponges from the deep-sea across distances of >10,000 km. Deep-sea sponge microbiomes are less complex, yet more heterogeneous, than their shallow-water counterparts. Our analysis underscores the uniqueness of each deep-sea sponge ground based on which we provide critical knowledge for conservation of these vulnerable ecosystems

The European language technology landscape in 2020 : language-centric and human-centric AI for cross-cultural communication in multilingual Europe

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

Microbial Biotechnolog

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/