Telomere DNA length is a complex trait controlled both by multiple loci and environmental factors. Even though the use of telomere DNA length measurement, as a method of assessing stress accumulation and predicting how this will influence survival, is currently being studied in numerous human cohort studies, the importance of telomere length for stress response in ecological studies remains at its infancy. Here, we investigated the telomere changes occurring in the symbiotic coral Stylophora pistillata that has experienced a continuous darkness over six months. This stress condition led to the loss of its symbionts, as what is also observed when bleaching occurs in the field at a large-scale due to climate changes and anthropogenic activities, threatening the worldwide reef ecosystem. We found that the continuous darkness condition was associated with telomere DNA length shortening and a downregulation of the expression of the telomere-associated protein Pot2. These results pave the way for future studies on the role of telomere in coral stress response and the importance of telomere dysregulation in endangered coral species.

The recognition of Cannabis as a source of new compounds suitable for medical use has attracted strong interest from the scientific community in its research, and substantial progress has accumulated regarding cannabinoids’ activity; however, a thorough description of their molecular mechanisms of action remains a task to complete. Highlighting their complex pharmacology, the list of cannabinoids’ interactors has vastly expanded beyond the canonical cannabinoid receptors. Among those, we have focused our study on the glycine receptor (GlyR), an ion channel involved in the modulation of nervous system responses, including, to our interest, sensitivity to peripheral pain. Here, we report the use of computational methods to investigate possible binding modes between the GlyR and Δ 9-tetrahydrocannabinol (THC). After obtaining a first pose for the THC binding from a biased molecular docking simulation and subsequently evaluating it by molecular dynamic simulations, we found a dynamic system with an identifiable representative binding mode characterized by the specific interaction with two transmembrane residues (Phe293 and Ser296). Complementarily, we assessed the role of membrane cholesterol in this interaction and positively established its relevance for THC binding to GlyR. Lastly, the use of restrained molecular dynamics simulations allowed us to refine the description of the binding mode and of the cholesterol effect. Altogether, our findings contribute to the current knowledge about the GlyR-THC mode of binding and propose a new starting point for future research on how cannabinoids in general, and THC in particular, modulate pain perception in view of its possible clinical applications.

The collembolan Folsomia candida Willem, 1902, is an important representative soil arthropod that is widely distributed throughout the world and has been frequently used as a test organism in soil ecology and ecotoxicology studies. However, it is questioned as an ideal “standard” because of differences in reproductive modes and cryptic genetic diversity between strains from various geographical origins. In this study, we present two high-quality chromosome-level genomes of F. candida, for the parthenogenetic Danish strain (FCDK, 219.08 Mb, N50 of 38.47 Mb, 25,139 protein-coding genes) and the sexual Shanghai strain (FCSH, 153.09 Mb, N50 of 25.75 Mb, 21,609 protein-coding genes). The seven chromosomes of FCDK are each 25–54% larger than the corresponding chromosomes of FCSH, showing obvious repetitive element expansions and large-scale inversions and translocations but no whole-genome duplication. The strain-specific genes, expanded gene families and genes in nonsyntenic chromosomal regions identified in FCDK are highly related to its broader environmental adaptation. In addition, the overall sequence identity of the two mitogenomes is only 78.2%, and FCDK has fewer strain-specific microRNAs than FCSH. In conclusion, FCDK and FCSH have accumulated independent genetic changes and evolved into distinct species since diverging 10 Mya. Our work shows that F. candida represents a good model of rapidly cryptic speciation. Moreover, it provides important genomic resources for studying the mechanisms of species differentiation, soil arthropod adaptation to soil ecosystems, and Wolbachia-induced parthenogenesis as well as the evolution of Collembola, a pivotal phylogenetic clade between Crustacea and Insecta.

Silphium integrifolium Michx. (Silflower) has been a promising subject for domestication as a perennial oilseed crop. This work was carried out to investigate the seed processing aspect of this effort. Selected physical properties of the seed were evaluated, seed milling to obtain enriched kernel fraction was conducted, and initial characterization of the seed protein was performed. There was wide variation in flat seed length (11.54 to 20.75 mm), width (4.61 to 11.76 mm), and thickness (0.92 to 1.63 mm). The thousand seed weight was 23.8 g but the tapped bulk density was only 189.58 g/L due to the presence of wing around the seed’s periphery. The kernel accounted for 56.14% of the seed weight and contained 31.00% oil. An enriched kernel fraction with 79.6% purity was obtained by roller-milling, sifting, and air classification. Linoleic (62.3%) and oleic (19.62%) acids were the major fatty acids in the oil. The defatted enriched kernel fraction contained 63.41% crude protein. Globulin, glutelin, albumin, and prolamin accounted for 55.63%, 19.28%, 16.38%, and 8.71% of the soluble proteins, respectively. At an extraction pH of 9, protein solubility was 62%. Maximum solubility (70%) was obtained at pH 10 while minimum solubility of 9% occurred between pH 4 and 5.5. Aside from the oil, the dehulling of silflower seeds also produced a high-protein defatted meal, which may be used as is or as a starting material for enriching the protein further into a protein isolate.

Background Right ventricular dysfunction is a major cause of heart failure and mortality in end-stage renal disease patients. Scarce data is available regarding the comparison of echocardiographic right ventricular function in end-stage renal disease patients on hemodialysis (HD) and peritoneal dialysis (PD). The aim of the study was to evaluate the long-term impacts of different dialysis modalities on right ventricular function assessed by conventional echocardiography, in end-stage renal disease patients with preserved left ventricular function. Methods The study included 120 patients grouped as follows: PD(n=40), HD with arterio-venous fistula (n=40) and healthy control subjects (n=40). Conventional echocardiography was performed in all patients. A classification of right ventricular function was defined in HD patients by using tricuspid annular plane systolic excursion (TAPSE), right ventricular myocardial performance index (RV-MPI), fractional area change (FAC) and tricuspid lateral annulus systolic velocity (Sa) values. Correlation analysis was performed by using right ventricular dysfunction score, clinical and echocardiographic parameters. Results The mean age of the study population was 51.9±13.1 years and 47.5% were females. TAPSE and Sa velocity were found to be significantly lower and RV-MPI was significantly higher in patients undergoing HD, compared with control and PD patients. Logistic regression analysis showed that HD treatment was an independent risk factor for developing right ventricular dysfunction. Conclusion RV function was impaired in patients undergoing HD compared with patients on PD.

AIM: To explore the level of agreement on drug-drug interaction (DDI) information listed in three major online drug information resources (DIRs) in terms of: (1) interacting drug pairs; (2) severity rating; (3) evidence rating and (4) clinical management recommendations. METHODS: We extracted DDI information from the British National Formulary (BNF), Thesaurus, and Micromedex. Following drug name normalisation, we estimated the overlap of the DIRs. We annotated clinical management recommendations either manually, where possible, or through application of a machine learning algorithm. RESULTS: The DIRs contained 51,481 (BNF), 38,037 (Thesaurus), and 65,446 (Micromedex) drug pairs involved in DDIs. The number of common DDIs across the three DIRs was 6,970 (13.54% of BNF, 18.32% of Thesaurus, and 10.65% of Micromedex). Micromedex and Thesaurus overall showed higher levels of similarity in their severity ratings, while the BNF agreed more with Micromedex on the critical severity ratings and with Thesaurus on the least significant ones. Evidence rating agreement between BNF and Micromedex was generally poor. Variation in clinical management recommendations was also identified, with some categories (i.e. Monitor and Adjust dose) showing higher levels of agreement compared to others (i.e. Use with caution, Wash-out, Modify administration). CONCLUSIONS: There is considerable variation in the DDIs included in the examined DIRs, together with variability in categorisation of severity and clinical advice given. DDIs labelled as critical are more likely to appear in multiple DIRs. Such variability in information could have deleterious consequences for patient safety, and there is a need for harmonisation and standardisation.

Molecular diagnosis is an essential means to detect pathogens. The portable nucleic acid detection chip has excellent prospects in places where medical resources are scarce, and it is also of research interest in the field of microfluidic chips. Here, the paper developed a new type of microfluidic chip for nucleic acid detection where stretching acts as the driving force. The sample entered the chip by applying capillary force. The strain valve was opened under the action of tensile force, and the spring pump generated the power to drive the fluid to flow to the detection chamber in a specific direction. The detection of COVID-19 was realized on the chip. The RT-LAMP amplification system was adopted to observe the liquid color in the detection chamber to decide whether the sample tested positive or negative qualitatively.

Environmental DNA for biomonitoringJan Pawlowski1,2,3, Aurélie Bonin4, Frédéric Boyer5, Tristan Cordier1,6, Pierre Taberlet5,71 Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland2 Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland3 ID-Gene Ecodiagnostics, Geneva, Switzerland4 Department of Environmental Science and Policy, Università degli Studi di Milano, Milano, Italy5 Laboratoire d’Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, France6 NORCE Climate, NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, Norway7 Tromsø Museum, UiT – The Arctic University of Norway, Tromsø, NorwayCorresponding [email protected] 2012, Molecular Ecology published a special issue on environmental DNA, which provided an overview of the field of eDNA research and presented a selection of papers on eDNA studies (Taberlet, Coissac, Hajibabaei, & Rieseberg, 2012). This special issue also introduced the concept of Biomonitoring 2.0, advocating for the use of DNA-based identification of taxa in biodiversity surveys and ecosystem assessment (Baird & Hajibabaei, 2012). Since then, hundreds of papers have been published covering various aspects of eDNA-based biomonitoring from single-species detection to community studies and environmental impact assessments. Numerous reviews have summarized these studies for both freshwater and marine environments (e.g. Bohmann et al., 2014; Thomsen & Willerslev, 2015).The progress made in the eDNA field during these last ten years has been spectacular (Taberlet, Bonin, Zinger, & Coissac, 2018). Although the basic concepts and workflow of DNA barcoding and metabarcoding have not changed, the technological advances in high-throughput sequencing have greatly facilitated the access to eDNA data. It has become possible to monitor biodiversity with unprecedented precision and depth. Massive environmental genomic datasets have been rapidly generated at relatively low cost. The analysis of these datasets using machine learning and other taxonomy-free approaches opened wide the doors for using new groups of bioindicators to infer ecological status (Cordier et al., 2018; Cordier, Lanzén, Apothéloz-Perret-Gentil, Stoeck, & Pawlowski, 2019; Pawlowski et al., 2018). At the same time, constant efforts to fill gaps in barcoding reference databases considerably increased the effectiveness of taxonomic identification of eDNA data (Weigand et al., 2019).Astonishingly, these rapid advances in eDNA-based technologies are rather timidly implemented in routine biomonitoring (Hering et al., 2018; Shackleton et al., 2021). Although the concept of Biomonitoring 2.0 is widely endorsed, its acceptance in practice is hampered for various reasons. There is no consensus whether eDNA-based biomonitoring should only apply to conventional bioindicators (Renovate) or should also include new bioindicators (Rebuild) or new taxonomy-free approaches (Revolutionize) (see Fig. 1). Moreover, three main steps on the roadmap from eDNA to biomonitoring are not developed equally. The main attention is given to the development and optimization of eDNA data generation and analysis. The standardization of eDNA methods and their translation into legislatory framework remain at a very early stage. One of the main issues impeding the application of eDNA-based tools concerns the lack of congruence between the results of traditional and molecular analyses (Aylagas et al., 2020). It is expected that the new method is “safe to use” only if it provides the same or almost same results as the conventional one. However, obtaining such perfect congruence is often impossible because the character of data is very different (e.g., abundance of individuals vs abundance of eDNA reads). Moreover, the eDNA “ecology” can hardly be translated directly into species ecology. There are also numerous biological and technical biases that can affect the generation and processing of eDNA data, impacting their interpretation.This special issue addresses some of these challenges by presenting the latest advances in eDNA field and discussing their strengths and limitations when applied to routine biomonitoring. The issue comprises 29 papers grouped into four sections and covering different aspects of eDNA applications. It is accompanied by an opinion paper, which clarifies the eDNA terminology in relation to its use in biomonitoring (Pawlowski, Apothéloz-Perret-Gentil, & Altermatt, 2020). The first section comprises a series of studies using new analytical tools (e.g. machine learning), new types of bioindicators and genomic data (e.g. shotgun sequencing) for the assessment of ecological status. It is followed by a section dedicated to fish eDNA, whose application in biomonitoring is the most advanced. The third section comprises papers dealing with various methodological aspects and the comparison between conventional and molecular methods. The final section presents few examples of eDNA applications for biodiversity surveys and population genetics.Novel approaches to monitor ecosystemsThe development of environmental genomics enables monitoring of microbial and meiofaunal communities that were previously inaccessible when using conventional methods. However, our knowledge of the ecology of these communities is very limited and therefore new analytic approaches are necessary to integrate them into routine bioassessment. This section begins with a review of implementation strategies for the application of environmental genomics in ecological diagnostics (Cordier et al., 2021). The authors introduce four broad categories of possible strategies, including (1) DNA-based taxonomic identification of known bioindicators, (2) taxonomy-free discovery of new bioindicators, (3) structural community metrics, and (4) functional community metrics. Each of these strategies is adapted to a particular type of data (metabarcoding, metagenomics, metatranscriptomics) and rely on different computational analyses in order to provide an assessment of the ecological status.Among the different analytical tools, machine learning seems to be the most promising way to predict the ecological status (Cordier et al., 2018, 2019). In this issue, its performance is tested in the case of the benthic diatoms index widely used in the assessment of ecological quality of rivers and streams (Apothéloz-Perret-Gentil et al., 2021). This study shows that supervised machine learning performs better than the taxonomic assignment, but its predictions are similar to those obtained using a taxonomy-free molecular assignment approach. Moreover, the efficiency of a taxonomic assignment method strongly depends on the completeness of the reference database, highlighting the need to fill in the existing gaps, particularly in the case of bioindicator taxa.The ability of de novo prokaryotic bioindicators to predict multiple anthropogenic impacts on estuarine and coastal benthic communities is demonstrated by Lanzén, Mendibil, Borja, & Alonso-Sáez (2021). The authors compare their results to the traditional macrofauna-based indices and discuss various advantages of using microbial bioindicators as they are more sensitive to different abiotic pressures. Similar conclusions were reached in the case of environmental impact assessment of marine aquaculture (Frühe et al., 2021) and the oil and gas industry (Mauffrey et al., 2021). Both studies demonstrate the effectiveness of machine learning andde novo microbial bioindicators and promote their use for benthic monitoring in marine environments.The last two papers in this series explore new directions for the further development of ecogenomic diagnostics. Broman et al. (2021) use environmental RNA (eRNA) shotgun sequencing to analyse the impact of organic enrichment on benthic micro-eukaryotic communities. Compared to eDNA metabarcoding that is used in the majority of studies, eRNA shotgun data has the advantage to overcome the potential biases of PCR amplification and to better capture the organismic response to environmental pressures by targeting predominantly active cells. Ibrahim et al. (2021) use historical eDNA metabarcoding data to analyze the impact of eutrophication on lake phytoplankton in the 20thcentury. This study demonstrates the potential of paleo-metabarcoding to characterize past biodiversity and establish reference conditions for future monitoring.Refining fish eDNA surveysThe second series of papers concerns the use of eDNA to monitor fish diversity. We focus on fish because they are among the most important groups of bioindicators and also because their study from an eDNA perspective is the most advanced (Pont et al., 2021). The barcoding reference database of common fish species in some regions is close to completeness (Knebelsberger, Dunz, Neumann, & Geiger, 2015), fish-specific markers are well defined (M. Miya et al., 2015; Valentini et al., 2016; Zhang, Zhao, & Yao, 2020) and protocols for fish eDNA sampling and processing are well established (Masaki Miya, Gotoh, & Sado, 2020; Valentini et al., 2016). Currently, considerable efforts are directed to solve the most challenging issue, which is related to quantitative fish eDNA data and its application for inferring fish indices in routine biomonitoring.Two papers address this issue by proposing different approaches to estimate fish abundance from eDNA data. Fukaya et al. (2021) use numerical hydrodynamic models to simulate the spatial and temporal distribution of fish eDNA in aquatic environments. By integrating the models to the measures of eDNA concentration, the authors obtained estimates of fish population abundance comparable to those obtained by the quantitative echo sounder method. Yates et al. (2021) improve the correlation between eDNA concentration and fish abundance by integrating allometric scaling coefficients. Such coefficients can help adjust the values of eDNA production taking in consideration density, biomass and metabolic rates characteristic to a given taxon.A better understanding of the “ecology” of fish eDNA, and particularly how its temporal and spatial distribution is shaped by abiotic and biotic factors, is the subject of the following papers. Littlefair et al. (2021) tested how seasonal variations in thermal stratification influence the distribution of fish eDNA in lakes. The authors show that eDNA distribution follows lake stratification and the thermal niche of the species, which in turn may affect its detection in certain seasons. The distribution of fish and amphibian eDNA in a lentic system was investigated experimentally by Brys et al. (2021). This study indicates high eDNA decay rates and limited dispersal, reinforcing the accuracy of eDNA-based monitoring for retrieving the spatiotemporal occupancy patterns. The advantages of using eDNA for survey of fish populations were also demonstrated by other papers in this section. McColl-Gausden et al. (2021) showed that eDNA metabarcoding is generally more sensitive than electrofishing for conducting fish surveys in freshwater streams, while Aglieri et al. (2021) demonstrate strong complementarity of eDNA-based analysis with visual and capture-based methods in the survey of coastal fish communities.Methodology and comparison with conventional methodsGeneral acceptance of molecular methods in biomonitoring requires their benchmarking against conventional morphotaxonomy-based approaches. This is commonly achieved by processing the same samples in parallel using different methods and by assessing how the molecular data fit to the results of traditional approaches, considered as a ground truth. The papers of this section compare the results of eDNA metabarcoding vs bulk DNA metabarcoding vs different morphology-based approaches. They also present and discuss the biases of molecular methods and propose solutions to improve the outcomes of molecular data generation and processing.The section begins with the three comparative studies of marine biomonitoring. Suter et al. (2021) evaluate the performance of water eDNA and bulk DNA metabarcoding in assessing the biodiversity of zooplankton in open ocean, currently monitored by using continuous plankton recorders. The study shows that both methods recover more species than morphological analyses, however, their efficiency depends on the sampling method and selected marker. They conclude that eDNA metabarcoding is very promising, but it still requires some refinement and standardization before it can be routinely used for zooplankton biomonitoring. Similar conclusions are drawn from the comparison of sediment DNA metabarcoding and macrofauna surveys applied to monitor benthic impacts of salmon farms (He et al., 2021). Although the authors found a certain coherence in relative abundance of common macrofauna bioindicators inferred from morphological and eDNA data, they observed that the correlation with organic enrichment was much stronger for meiofauna, which is not usually included in biomonitoring studies. Significant differences were also found between water eDNA samples and bulk DNA extracts from adjacent benthic communities (Antich et al., 2021). The authors concluded that water eDNA is a poor proxy for the analysis of benthic communities, although they do not exclude that the use of taxon-specific markers could improve the congruence between eDNA and bulk DNA metabarcoding data.The importance of marker selection has also been emphasized in the case of freshwater macrobenthos metabarcoding. The performance of different markers, with focus on key insect orders (Ephemeroptera, Plecoptera and Trichoptera) was tested by Ficetola et al. (2021). The authors demonstrate the complexity of the marker selection process and advocate for the use of multiple markers to cover the widest range of taxa. Combining data from different markers was shown to considerably improve the match between macrobenthic indices inferred from bulk DNA and morphotaxonomic surveys (Meyer et al., 2021). A multimarker approach was also recommended for the assessment of macroinvertebrate communities from the bulk preservative (Martins et al., 2021). Despite the importance of using multiple markers, the authors also demonstrate that the presence of heavily sclerotized exoskeleton can act as a limiting factor for the detection of some taxa.The comparison of bulk DNA vs water eDNA metabarcoding has been reported by two papers. Gleason et al. (2021) show that bulk DNA metabarcoding more accurately represents the local stream macroinvertebrate community, with water eDNA data being overwhelmed by non-metazoan sequences. The same difference was observed when comparing bulk DNA to water eDNA and morphological inventories of pond macroinvertebrates (Harper et al., 2021). However, the authors consider both approaches as complementary and suggest that they should be combined for comprehensive assessment of the invertebrate community. The importance of bulk DNA metabarcoding as a tool for the assessment of marine ecosystems is also highlighted by van de Loos and Nijland (2021). The authors review various technical biases affecting bulk DNA metabarcoding workflow and discuss possible improvements that could help overcoming these biases in the future.The analysis of water samples from five sites in the Brazilian Atlantic forest and one adjacent site in Cerrado grasslands allowed Lopes et al. (2021) to demonstrate that eDNA metabarcoding significantly improves traditional monitoring methods, confirming the presence of frog species undetected by traditional methods. For a few years, invertebrate-derived DNA (iDNA) from leech blood-meal have been used to track mammalian species (Schnell et al., 2012). Here, Drinkwater et al. (2021) apply this approach to assess differences in mammalian diversity across a gradient of forest degradation in Borneo. For monitoring elusive mammals, the iDNA method complements the more traditional and widely used camera trapping.The last two papers in this section provide examples of metabarcoding optimizations aiming at improving its effectiveness in biomonitoring surveys. Guerrieri et al. (2021) show how soil preservation methods can affect estimates of taxonomic richness and community composition. The authors propose guidelines for optimizing soil preservation conditions in agreement with the objectives and practical constraints of the research project. On the other hand, Mächler et al. (2021) address the optimization of data analysis, by investigating how stringency filtering can affect eDNA diversity estimates. The authors conclude that the use of Hill numbers can help in comparisons of eDNA datasets that strongly differ in diversity.Other perspectives for eDNA-based biomonitoringThe last three articles in this special issue present ground-breaking approaches to monitoring biodiversity. Martel et al. (2021) clearly show that eDNA surveys paired with occupancy modelling can uncover metapopulation dynamics and their drivers. Such type of information is important for monitoring endangered species distributed in metapopulations and is quite difficult to obtain via traditional inventories. Shum and Palumbi (2021) reanalyzed a published marine metabarcoding dataset concerning cobble communities found within kelp forest ecosystems. They focussed on diversity data at the intraspecific level to infer population structure and demographic trends. This type of approach greatly increases the scope and value of metabarcoding studies, also opening the way towards metaphylogeography (Turon, Antich, Palacín, Praebel, & Wangensteen, 2020). Finally, Sigsgaard et al. (2021) successfully tracked insects from cow dungs from different environments, and showed that eDNA metabarcoding represents an efficient method for assessing insect diversity, with potential for biomonitoring in relation with the relatively easy standardization of such an approach.ConclusionAs shown by the collection of papers published in this issue, potential applications of eDNA in biomonitoring are highly diverse. Their scope ranges from tracking endangered species to surveying biodiversity or assessing environmental impact. Some papers focus on integrating eDNA into existing bioindication systems, whereas others use eDNA to expand the range of bioindicators and include inconspicuous, commonly overlooked microbial and meiofaunal taxa. All these papers attest to major efforts that have been done to improve eDNA methodology at every step of the workflow from sampling to data analysis. They also contribute to better understand the biological and technical factors impacting the eDNA analyses. Yet, despite this huge new knowledge and numerous practical advantages, the implementation of eDNA in routine biomonitoring still has not taken off.It is now high time to move on and to transform the eDNA field into a truly applied science. The biodiversity crisis and global environmental changes call for an urgent modernization of the tools to monitor biodiversity and assess the ecological status of our environment. As shown by the papers published here, the eDNA methodology achieved top levels of technical and scientific excellence in many areas. Certainly, there are some biases and limitations inherent to eDNA specificity, but there is no reason to consider that the technology is less “safe to use” than the conventional morpho-taxonomic approaches. There are also actions to be taken to ensure the quality and to build confidence in eDNA analyses through standardization of technical protocols and intercalibration tests. However, in view of the substantial efforts that have been made by the scientific community and illustrated by the content of this special issue, it is reasonable to expect that the implementation of eDNA-based tools in biomonitoring will not be long in coming

Despite many studies explored the effect of livestock grazing on plant communities, the response of species composition and diversity to livestock grazing in arid rangelands remain ambiguous. This study examined the effects of livestock grazing on plant communities in arid steppe rangelands of North Africa. Plant diversity of annual species, perennial species and all species combined was measured and compared between grazed and grazing-excluded areas. We also examined the relative importance of species turnover and community nestedness. Moreover, the effects of livestock grazing on beta diversity at local among transects and landscape among sites scales were examined using the multiplicative diversity partitioning. Results revealed that livestock grazing significantly decreased the alpha diversity of all species combined and the diversity of annual plants. Livestock grazing induced a shift in plant community composition where most of species composition variation (~74%) was due to infrequent species replacement ‘turnover’ between the two management types rather than nestedness (~26%). Results revealed also that among transects, beta diversity was higher in grazed steppes than in grazing-excluded steppes. Whereas, among sites, beta diversity was lower in grazed steppes compared to grazing-excluded steppes. These findings suggest that livestock grazing in arid steppe rangelands increases the variation in plant species composition at a local spatial scale and engenders vegetation homogeneity at landscape spatial scale. Therefore, the implementation of appropriate management practices such as short-term grazing exclusion is mandatory to prevent these ecosystems from large scale biotic homogenization.

The coastal heathlands of North-west Europe are valuable cultural landscapes, created and maintained over millennia by a land-use regime involving burning and grazing. These heathlands are now critically threatened throughout their range by land-use change and, increasingly, climatic changes. The climatic change impacts are complex, as the coastal heathland regions are experiencing increased temperature and precipitation, but also increased frequency and severity of extreme events, such as drought. Previous studies reveal that established heathland vegetation, including Calluna, are vulnerable to drought, but also that these vulnerabilities vary throughout the range, and with successional stage after fire. Recruitment from seed is an important regeneration strategy for Calluna heathland vegetation after burning, and our study is the first to assess how the seed germination and early seedling growth of Calluna respond to drought. We will do this in a lab germination experiment, where we will expose Calluna seeds to five different drought treatments, from -0.25 MPa to -1.7 MPa, and measure germination, and record germination percentage, germination rates, and seedling growth, below-ground allocation, and functional traits (Specific Leaf Area, Specific Root Length). To allow assessment of variation in drought responses due to geographic origin, successional stage, and the maternal plants’ drought exposure, we will conduct this experiment on seeds from 540 Calluna plants sampled from across three drought treatments (control, 50%, and 90% coverage), in three successional stages after fire (pioneer, building, mature), in two regions (60N, 65N), using a factorial design.

COVID-19, the illness caused by SARS-CoV-2, has a wide-ranging clinical spectrum that, in the worst-case scenario, involves a rapid progression to severe acute respiratory syndrome and even death. Epidemiological data show that “diabesity”, the association of obesity and diabetes, is among the main risk factors associated with high morbidity and mortality. The increased susceptibility to SARS-CoV-2 infection documented in diabesity argues for initial defects in defense mechanisms, most likely due to an elevated systemic metabolic inflammation (“metaflammation”). The NLRP3 inflammasome is a master regulator of metaflammation and has a pivotal role in the pathophysiology of diabesity. Here we discuss the most recent findings suggesting contribution of NLRP3 inflammasome to the increase in complications in COVID-19 patients with diabesity. We also review current pharmacological strategies for COVID-19, focusing on treatments whose efficacy could be due, at least in part, to interference with the activation of the NLRP3 inflammasome.

Monitoring of the fluctuations of groundwater storage is particularly important in arid and semi-arid regions where water scarcity brings about various challenges. Remote sensing data and techniques play a preponderant role in developing solutions to environmental problems. Emergence of GRACE satellites have eased the remote monitoring and evaluation of groundwater resources with an unprecedented precision over large scales. Within the scope of the current study, the latest release of GRACE Mascon’s dataset as well as GLDAS models of Noah and CLSM were integrated to extract groundwater storage anomalies (GWSA) over Turkey. The temporal interactions of the estimated GWSA with the climatic variables of precipitation and temperature (derived from the reanalysis datasets of CHELSA and FLDAS, respectively) were investigated statistically. The results suggest the there is a descending trend for TWSA and GWSA over Turkey with a total loss of 11 and 6 cm respectively. The statistical analysis results also indicate that the monthly variations of GWS over Turkey are highly correlated with precipitation and temperature at 2-month lag. The analysis of the climatology (long-term) values of monthly GWSA, precipitation and temperature also revealed high agreement between the variables.

We examined associations between mild or asymptomatic prenatal SARS-CoV-2 infection and preterm live birth in a prospective cohort study. During August 2020–October 2021, pregnant persons were followed with systematic surveillance for RT-PCR or serologically-confirmed SARS-CoV-2 infection until pregnancy end. The association between prenatal SARS-CoV-2 infection and preterm birth was assessed using Cox proportional-hazards regression. Among 954 pregnant persons with a live birth, 185 (19%) had prenatal SARS-CoV-2 infection and 123 (13%) had preterm birth. The adjusted hazard ratio for the association between SARS-CoV-2 infection and preterm birth was 1.28 (95% confidence interval 0.82-1.99, p=0.28), although results did not reach statistical significance.

While many phenylpropanoid pathway-derived molecules act as physical and chemical barriers to pests and pathogens, comparatively little is known about their role in regulating plant immunity. To explore this research field, we transiently perturbed the phenylpropanoid pathway through application of the CINNAMIC ACID-4-HYDROXYLASE (C4H) inhibitor piperonylic acid (PA). Using bioassays involving diverse pests and pathogens, we show that transient C4H inhibition triggers systemic, broad-spectrum resistance in higher plant without affecting growth. PA treatment enhances tomato (Solanum lycopersicum) resistance in field and laboratory conditions, thereby illustrating the potential of phenylpropanoid pathway perturbation in crop protection. At the molecular level, transcriptome and metabolome analyses reveal that transient C4H inhibition in tomato reprograms phenylpropanoid and flavonoid metabolism, systemically induces immune signaling and pathogenesis-related genes, and locally affects reactive oxygen species metabolism. Furthermore, C4H inhibition primes cell wall modification and phenolic compound accumulation in response to root-knot nematode infection. Although PA treatment induces local accumulation of the phytohormone salicylic acid, the PA resistance phenotype is preserved in tomato plants expressing the salicylic acid-degrading NahG construct. Together, our results demonstrate that transient phenylpropanoid pathway perturbation is a conserved inducer of plant resistance and thus highlight the crucial regulatory role of this pathway in plant immunity.

The realization of the enormous potential of stem cells requires development of efficient bioprocesses and optimization drawing drawn from mechanobiological considerations. Here, we emphasize the importance of mechanotransduction as one of the governing principles of stem cell bioprocesses, underscoring the need to further explore the behavioral mechanisms involved in sensing mechanical cues and coordinating transcriptional responses. We identify the sources of the intrinsic, extrinsic, and external noise in bioprocess under uncertainty, and discuss criteria and indicators that might assess and predict cell-to-cell variability resulting from environmental fluctuations. Specifically, we propose a conceptual framework to explain the impact of mechanical forces within cellular environment and identify key cell state determinants in bioprocess and discuss their implementation challenges.

AUTHORSDr. Saroj Poudel Clinical Pharmacist Critical Care Medicine, Nepal Mediciti Email: [email protected]. Rupak Chalise Anesthesiologist Critical Care Medicine, Nepal Mediciti Email: [email protected]. Manoj Bist Medical Officer Critical Care Medicine, Nepal Mediciti Email: [email protected]. Ashim Regmi Junior Consultant Critical Care Medicine, Nepal Mediciti Email: [email protected]. Anup Ghimire Junior Consultant Critical Care Medicine, Nepal Mediciti Email: [email protected]. Kishor Khanal Intensivist and ICU Coordinator Critical Care Medicine, Nepal Mediciti Email: [email protected]

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a flexible meta-ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. Through simulating a forest-lake-stream meta-ecosystem, our model illustrated that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross-ecosystem efficiency hypothesis’, emphasizes the importance of integrating ecosystem diversity and complementarity in meta-ecosystem models to generate empirically testable hypotheses for ecosystem functions.

Chronic myeloid leukemia (CML) is a myeloproliferative disorder due to translocation between chromosomes (9, 22), known as the “Philadelphia chromosome.” In 2016, the World health organization (WHO) introduced a new clinical entity of de novo acute myeloid leukemia (AML). Both diseases share some commonalities, therefore, create a challenge to diagnose.