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A comprehensive understanding of the prevailing pollution and other impairments to reservoirs is necessary at the national level to analyze patterns and causes as well as allocate national resources effectively. Most of these impairments are associated with sedimentation, nutrient contamination, and other pollution factors. The costs involved in conducting on-site surveys at multiple locations are potential obstacles for obtaining empirical data on large-scale spatial impairments. In such cases, inputs from specialists may be required to offset the absence of empirical data for determining the impairment statuses of thousands of geographically dispersed reservoirs. Thus, our aim was to examine whether expert opinion could offer a comprehensive review of the impairment statuses of numerous reservoirs in the United States. We designed and executed an internet-based survey of reservoir specialists to gather their ratings on visually evident elements of reservoir impairment. To evaluate the ratings, we searched for correlations with factors known to have impacts on reservoir impairment. Canonical correlation analyses indicated that nine metrics used by experts to rate impairment levels on an ordinal scale were correlated with metrics descriptive of the physical qualities of reservoirs, land use in catchments, and prevailing local climate. We thus conclude that expert opinions may facilitate assessments of impairment levels over large geographical areas. Lastly, we define the circumstances under which it is permissible to rely on expert opinions and propose criteria that could improve the quality of the data collected. [ABSTRACT FROM AUTHOR]

Abstract Internet-related issues have influenced how civic knowledge is educated and measured. The International Civic and Citizenship Education Study (ICCS) is a well-known large-scale assessment concerning how civic knowledge is educated and measured globally. Regardless of the emerging roles of internet access and usage, the influences of internet access on civic knowledge have yet to be investigated in ICCS research. Hence, this study aims to study whether the multiple-choice items in the civic knowledge test of ICCS 2016 are affected by internet access, what causes the effect, and to what degree Internet access influences the measurement. Results indicated that the ICCS civic knowledge test included six differential item functioning (DIF) items on internet access, of which five favored families with internet access, and one favored families without internet access. In addition, the primary source of DIF items was highly related to socioeconomic status. Overall, the students in families where the internet was available possessed higher civic knowledge than the students who were not. In conclusion, this study provides evidence of item inequivalence and identifies suspicious sources. These results can be used as a basis to investigate related research on digital learning, online teaching, and social media engagement in civic literacy.

Internet-related issues have influenced how civic knowledge is educated and measured. The International Civic and Citizenship Education Study (ICCS) is a well-known large-scale assessment concerning how civic knowledge is educated and measured globally. Regardless of the emerging roles of internet access and usage, the influences of internet access on civic knowledge have yet to be investigated in ICCS research. Hence, this study aims to study whether the multiple-choice items in the civic knowledge test of ICCS 2016 are affected by internet access, what causes the effect, and to what degree Internet access influences the measurement. Results indicated that the ICCS civic knowledge test included six differential item functioning (DIF) items on internet access, of which five favored families with internet access, and one favored families without internet access. In addition, the primary source of DIF items was highly related to socioeconomic status. Overall, the students in families where the internet was available possessed higher civic knowledge than the students who were not. In conclusion, this study provides evidence of item inequivalence and identifies suspicious sources. These results can be used as a basis to investigate related research on digital learning, online teaching, and social media engagement in civic literacy. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the prevailing pollution and other impairments to reservoirs is necessary at the national level to analyze patterns and causes as well as allocate national resources effectively. Most of these impairments are associated with sedimentation, nutrient contamination, and other pollution factors. The costs involved in conducting on-site surveys at multiple locations are potential obstacles for obtaining empirical data on large-scale spatial impairments. In such cases, inputs from specialists may be required to offset the absence of empirical data for determining the impairment statuses of thousands of geographically dispersed reservoirs. Thus, our aim was to examine whether expert opinion could offer a comprehensive review of the impairment statuses of numerous reservoirs in the United States. We designed and executed an internet-based survey of reservoir specialists to gather their ratings on visually evident elements of reservoir impairment. To evaluate the ratings, we searched for correlations with factors known to have impacts on reservoir impairment. Canonical correlation analyses indicated that nine metrics used by experts to rate impairment levels on an ordinal scale were correlated with metrics descriptive of the physical qualities of reservoirs, land use in catchments, and prevailing local climate. We thus conclude that expert opinions may facilitate assessments of impairment levels over large geographical areas. Lastly, we define the circumstances under which it is permissible to rely on expert opinions and propose criteria that could improve the quality of the data collected.

Abstract The auxin response factor (ARF) and auxin/indole-3-acetic acid (Aux/IAA) family of genes are central components of the auxin signaling pathway and play essential roles in plant growth and development. Their large-scale analysis and evolutionary trajectory of origin are currently not known. Here, we identified the corresponding ARF and Aux/IAA family members and performed a large-scale analysis by scanning 406 plant genomes. The results showed that the ARF and Aux/IAA gene families originated from charophytes. The ARF family sequences were more conserved than the Aux/IAA family sequences. Dispersed duplications were the common expansion mode of ARF and Aux/IAA families in bryophytes, ferns, and gymnosperms; however, whole-genome duplication was the common expansion mode of the ARF and Aux/IAA families in basal angiosperms, magnoliids, monocots, and dicots. Expression and regulatory network analyses revealed that the Arabidopsis thaliana ARF and Aux/IAA families responded to multiple hormone, biotic, and abiotic stresses. The APETALA2 and serum response factor-transcription factor gene families were commonly enriched in the upstream and downstream genes of the ARF and Aux/IAA gene families. Our study provides a comprehensive overview of the evolutionary trajectories, structural functions, expansion mechanisms, expression patterns, and regulatory networks of these two gene families.

Over large regions exposed to natural disasters, cascading effects resulting from complex or concatenated natural processes may represent a large portion of total risk. Populated high‐mountain environments are a major concern, and methods for large‐scale quantitative risk analyses are urgently required to improve risk mitigation. This article presents a comprehensive quantitative rockfall risk assessment over a large archetypal valley of the Andean mountains, in Central Chile, which integrates a wide spectrum of elements at risk. Risk is expressed as an expected damage both in monetary terms and casualties, at different scales relevant for decision making. Notably, total rockfall risk is divided into its main drivers, which allows quantifying seismically induced rockfall risk. For this purpose, the local seismic hazard is quantified and the yield acceleration, that is, acceleration required to initiate rockfall, is determined at the regional scale. The probability of failure is thereafter derived in terms of annual frequency of rockfall initiation and integrated in the quantitative risk assessment (QRA) process. Our results show the significant role of seismic activity as the triggering mechanism of rockfalls, and highlight elements at risk that have a major contribution to the total risk. Eventually a sensitivity analysis is conducted to (i) assess the robustness of obtained risk estimates to the data and modeling choices and (ii) identify the most influential assumptions. Our approach evidences the feasibility of large‐scale QRAs in sensitive environments and opens perspectives for refining QRAs in similar territories significantly affected by cascading effects and multihazards. [ABSTRACT FROM AUTHOR]

The auxin response factor (ARF) and auxin/indole-3-acetic acid (Aux/IAA) family of genes are central components of the auxin signaling pathway and play essential roles in plant growth and development. Their large-scale analysis and evolutionary trajectory of origin are currently not known. Here, we identified the corresponding ARF and Aux/IAA family members and performed a large-scale analysis by scanning 406 plant genomes. The results showed that the ARF and Aux/IAA gene families originated from charophytes. The ARF family sequences were more conserved than the Aux/IAA family sequences. Dispersed duplications were the common expansion mode of ARF and Aux/IAA families in bryophytes, ferns, and gymnosperms; however, whole-genome duplication was the common expansion mode of the ARF and Aux/IAA families in basal angiosperms, magnoliids, monocots, and dicots. Expression and regulatory network analyses revealed that the Arabidopsis thaliana ARF and Aux/IAA families responded to multiple hormone, biotic, and abiotic stresses. The APETALA2 and serum response factor-transcription factor gene families were commonly enriched in the upstream and downstream genes of the ARF and Aux/IAA gene families. Our study provides a comprehensive overview of the evolutionary trajectories, structural functions, expansion mechanisms, expression patterns, and regulatory networks of these two gene families. [ABSTRACT FROM AUTHOR]

Single-molecule imaging in living cells is an effective tool for elucidating the mechanisms of cellular phenomena at the molecular level. However, the analysis was not designed for throughput and requires high expertise, preventing it from reaching large scale, which is necessary when searching for rare cells that induce singularity phenomena. To overcome this limitation, we have automated the imaging procedures by combining our own focusing device, artificial intelligence, and robotics. The apparatus, called automated in-cell single-molecule imaging system (AiSIS), achieves a throughput that is a hundred-fold higher than conventional manual imaging operations, enabling the analysis of molecular events by individual cells across a large population. Here, using AiSIS, we demonstrate the single-molecule imaging of molecular behaviors and reactions related to tau protein aggregation, which is considered a singularity phenomenon in neurological disorders. Changes in the dynamics and kinetics of molecular events were observed inside and on the basal membrane of cells after the induction of aggregation. Additionally, to detect rare cells based on the molecular behavior, we developed a method to identify the state of individual cells defined by the quantitative distribution of molecular mobility and clustering. Using this method, cellular variations in receptor behavior were shown to decrease following ligand stimulation. This cell state analysis based on large-scale single-molecule imaging by AiSIS will advance the study of molecular mechanisms causing singularity phenomena. [ABSTRACT FROM AUTHOR]

Transfer RNA-derived fragments (tRFs) are noncoding RNAs that arise from either mature transfer RNAs (tRNAs) or their precursors. One important category of tRFs comprises the tRNA halves, which are generated through cleavage at the anticodon. A given tRNA typically gives rise to several co-expressed 5'-tRNA halves (5′-tRHs) that differ in the location of their 3′ ends. These 5′-tRHs, even though distinct, have traditionally been treated as indistinguishable from one another due to their near-identical sequences and lengths. We focused on co-expressed 5′-tRHs that arise from the same tRNA and systematically examined their exact sequences and abundances across 10 different human tissues. To this end, we manually curated and analyzed several hundred human RNA-seq datasets from NCBI's Sequence Run Archive (SRA). We grouped datasets from the same tissue into their own collection and examined each group separately. We found that a given tRNA produces different groups of co-expressed 5′-tRHs in different tissues, different cell lines, and different diseases. Importantly, the co-expressed 5′-tRHs differ in their sequences, absolute abundances, and relative abundances, even among tRNAs with near-identical sequences from the same isodecoder or isoacceptor group. The findings suggest that co-expressed 5′-tRHs that are produced from the same tRNA or closely related tRNAs have distinct, context-dependent roles. Moreover, our analyses show that cell lines modeling the same tissue type and disease may not be interchangeable when it comes to experimenting with tRFs. [ABSTRACT FROM AUTHOR]

Zea mays L is a crucial crop for Brazil, ranking second in terms of production and sixth in terms of exports. In Brazil, the second season, or off-season, accounts for 80 % of the overall maize output, which primarily occurs after the soybean main season. A maize yield forecast model for the off-season was developed and implemented throughout Brazilian territory due to its importance to the country's economy and food security. The model was built using multiple linear regressions that connected outputs simulated from a land surface model used in large-scale analysis for agriculture (JULES-crop), to agrometeorological indicators. The application of the developed model occurred every 10 days from the sowing until the maturation. A comparison of the forecasting model was verified with the official off-season maize yields for the years 2003–2016. Agrometeorological indicators during the reproductive phase accounted for 60 % of the interannual variability in maize production. When outputs simulated by JULES-crop were included, the forecasting model achieved Nash-Sutcliffe modeling efficiency (EF) of 0.77 in the maturation and EF = 0.72 in the filling-grain stage, suggesting that this approach can generate useful predictions for final maize yield beginning on the 80th day of the cycle. Outputs of JULES crop enhanced modeling performance during the vegetative stage, reducing the standard deviation error in prediction from 0.59 to 0.49 Mg ha−1.

State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions.

The SARS-CoV-2 pandemic had huge impacts on global urban populations, activity and health, yet little is known about attendant consequences for urban river ecosystems. We detected significant changes in occurrence and risks from contaminants of emerging concern (CECs) in waterways across Greater London (UK) during the pandemic. We were able to rapidly identify and monitor large numbers of CECs in n = 390 samples across 2019–2021 using novel direct-injection liquid chromatography-mass spectrometry methods for scalable targeted analysis, suspect screening and prioritisation of CEC risks. A total of 10,029 measured environmental concentrations (MECs) were obtained for 66 unique CECs. Pharmaceutical MECs decreased during lockdown in 2020 in the R. Thames (p ≤ 0.001), but then increased significantly in 2021 (p ≤ 0.01). For the tributary rivers, the R. Lee, Beverley Brook, R. Wandle and R. Hogsmill were the most impacted, primarily via wastewater treatment plant effluent and combined sewer overflows. In the R. Hogsmill in particular, pharmaceutical MEC trends were generally correlated with NHS prescription statistics, likely reflecting limited wastewater dilution. Suspect screening of ∼ 1,200 compounds tentatively identified 25 additional CECs at the five most impacted sites, including metabolites such as O-desmethylvenlafaxine, an EU Watch List compound. Lastly, risk quotients (RQs) ≥ 0.1 were calculated for 21 compounds across the whole Greater London freshwater catchment, of which seven were of medium risk (RQ ≥ 1.0) and three were in the high-risk category (RQ ≥ 10), including imidacloprid (RQ = 19.6), azithromycin (15.7) and diclofenac (10.5). This is the largest spatiotemporal dataset of its kind for any major capital city globally and the first for Greater London, representing ∼ 16 % of the population of England, and delivering a foundational One-Health case study in the third largest city in Europe across a global pandemic.

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation. [ABSTRACT FROM AUTHOR]

Abstract Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers. Graphical Abstract

Declines in European bird populations are reported for decades but the direct effect of major anthropogenic pressures on such declines remains unquantified. Causal relationships between pressures and bird population responses are difficult to identify as pressures interact at different spatial scales and responses vary among species. Here, we uncover direct relationships between population time-series of 170 common bird species, monitored at more than 20,000 sites in 28 European countries, over 37 y, and four widespread anthropogenic pressures: agricultural intensification, change in forest cover, urbanisation and temperature change over the last decades. We quantify the influence of each pressure on population time-series and its importance relative to other pressures, and we identify traits of most affected species. We find that agricultural intensification, in particular pesticides and fertiliser use, is the main pressure for most bird population declines, especially for invertebrate feeders. Responses to changes in forest cover, urbanisation and temperature are more species-specific. Specifically, forest cover is associated with a positive effect and growing urbanisation with a negative effect on population dynamics, while temperature change has an effect on the dynamics of a large number of bird populations, the magnitude and direction of which depend on species' thermal preferences. Our results not only confirm the pervasive and strong effects of anthropogenic pressures on common breeding birds, but quantify the relative strength of these effects stressing the urgent need for transformative changes in the way of inhabiting the world in European countries, if bird populations shall have a chance of recovering. [ABSTRACT FROM AUTHOR]

Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers. [ABSTRACT FROM AUTHOR]

Large-scale assessment of crop yields plays a fundamental role for agricultural planning and to achieve food security goals. In this study, we evaluated the robustness of data-driven models for estimating soybean yields at 120 days after sow (DAS) in the main producing regions in Brazil; and evaluated the reliability of the "best" data-driven model as a tool for early prediction of soybean yields for an independent year. Our methodology explicitly describes a general approach for wrapping up publicly available databases and build data-driven models (multiple linear regression—MLR; random forests—RF; and support vector machines—SVM) to predict yields at large scales using gridded data of weather and soil information. We filtered out counties with missing or suspicious yield records, resulting on a crop yield database containing 3450 records (23 years × 150 "high-quality" counties). RF and SVM had similar results for calibration and validation steps, whereas MLR showed the poorest performance. Our analysis revealed a potential use of data-driven models for predict soybean yields at large scales in Brazil with around one month before harvest (i.e. 90 DAS). Using a well-trained RF model for predicting crop yield during a specific year at 90 DAS, the RMSE ranged from 303.9 to 1055.7 kg ha–1 representing a relative error (rRMSE) between 9.2 and 41.5%. Although we showed up robust data-driven models for yield prediction at large scales in Brazil, there are still a room for improving its accuracy. The inclusion of explanatory variables related to crop (e.g. growing degree-days, flowering dates), environment (e.g. remotely-sensed vegetation indices, number of dry and heat days during the cycle) and outputs from process-based crop simulation models (e.g. biomass, leaf area index and plant phenology), are potential strategies to improve model accuracy. [ABSTRACT FROM AUTHOR]

Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles. [ABSTRACT FROM AUTHOR]

Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development of instrumentation and measurement techniques. We developed Toolbox Accelerating Glycomics (TAG), in which glycans can be added manually to the glycan list that can be freely designed with labels and sialic acid modifications, and fast processing is possible. In the present work, we improved TAG for large-scale analysis such as cohort analysis of serum samples. The sialic acid linkage-specific alkylamidation (SALSA) method converts differences in linkages such as α2,3- and α2,6-linkages of sialic acids into differences in mass. Glycans modified by SALSA and several structures discovered in recent years were added to the glycan list. A routine to generate calibration curves has been implemented to explore quantitation. These improvements are based on redefinitions of residues and glycans in the TAG List to incorporate information on glycans that could not be attributed because it was not assumed in the previous version of TAG. These functions were verified through analysis of purchased sera and 74 spectra with linearity at the level of R2 > 0.8 with 81 estimated glycan structures obtained including some candidate of rare glycans such as those with the N,N'-diacetyllactosediamine structure, suggesting they can be applied to large-scale analyses. [ABSTRACT FROM AUTHOR]

Tigecycline is one of important antimicrobial agents for the treatment of infections caused by multidrug-resistant (MDR) Gram-negative bacteria. However, the emergence and prevalence of plasmid-mediated tigecycline resistance gene tet (X4) are threatening human and animal health. Fitness cost elicited by resistance plasmids is a key factor affecting the maintenance and transmission of antibiotic resistance genes (ARGs) in the host. A comparative analysis of the fitness cost of different types of tet (X4)-positive plasmids is helpful to understand and predict the prevalence of dominant plasmids. In this study, we performed a large-scale analysis of fitness cost of tet (X4)-positive plasmids origin from clinical isolates. These plasmids were successfully electroporated into a reference strain Escherichia coli TOP10, and a series of transformants carrying the tet (X) gene were obtained. The effects of tet (X4)-positive plasmids on the growth rate, plasmid stability, relative fitness, biofilm formation, and virulence in a Galleria mellonella model were evaluated. Consequently, we found that these plasmids resulted in varying degrees of fitness cost on TOP10, including delayed bacterial growth and attenuated virulence. Out of these plasmids, tet (X4)-harboring IncFII plasmids showed the lowest fitness cost on the host. Furthermore, by means of experimental evolution in the presence of commonly used drugs in clinic, the fitness cost of tet (X4)-positive plasmids was substantially alleviated, accompanied by increased plasmid stability. Collectively, our data reveal the differential fitness cost caused by different types of tet (X4)-positive plasmids and suggest that the wide use of tetracycline antibiotics may promote the evolution of plasmids. [ABSTRACT FROM AUTHOR]

X chromosome inactivation is a mammalian dosage compensation mechanism, where one of two X chromosomes is randomly inactivated in female cells. Previous studies have suggested that primed human embryonic stem cells (hESCs) maintain an eroded state of the X chromosome and do not express XIST, while in naïve transition, both XIST and the eroded X chromosome are reactivated. However, the pattern of chromosome X reactivation in naïve hESCs remains mainly unknown. In this study, we examine the variations in the status of X chromosome between primed and naïve hESCs by analyzing RNA sequencing samples from different studies. We show that most samples of naïve hESCs indeed reactivate XIST and there is an increase in gene expression levels on chromosome X. However, most of the naïve samples do not fully activate chromosome X in a uniform manner and present a distinct eroded pattern, probably as a result of XIST reactivation and initiation of re-inactivation of chromosome X. This large-scale analysis provides a higher-resolution description of the changes occurring in chromosome X during primed-to-naïve transition and emphasizes the importance of taking these variations into consideration when studying X inactivation in embryonic development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tigecycline is one of important antimicrobial agents for the treatment of infections caused by multidrug-resistant (MDR) Gram-negative bacteria. However, the emergence and prevalence of plasmid-mediated tigecycline resistance gene tet(X4) are threatening human and animal health. Fitness cost elicited by resistance plasmids is a key factor affecting the maintenance and transmission of antibiotic resistance genes (ARGs) in the host. A comparative analysis of the fitness cost of different types of tet(X4)-positive plasmids is helpful to understand and predict the prevalence of dominant plasmids. In this study, we performed a large-scale analysis of fitness cost of tet(X4)-positive plasmids origin from clinical isolates. These plasmids were successfully electroporated into a reference strain Escherichia coli TOP10, and a series of transformants carrying the tet(X) gene were obtained. The effects of tet(X4)-positive plasmids on the growth rate, plasmid stability, relative fitness, biofilm formation, and virulence in a Galleria mellonella model were evaluated. Consequently, we found that these plasmids resulted in varying degrees of fitness cost on TOP10, including delayed bacterial growth and attenuated virulence. Out of these plasmids, tet(X4)-harboring IncFII plasmids showed the lowest fitness cost on the host. Furthermore, by means of experimental evolution in the presence of commonly used drugs in clinic, the fitness cost of tet(X4)-positive plasmids was substantially alleviated, accompanied by increased plasmid stability. Collectively, our data reveal the differential fitness cost caused by different types of tet(X4)-positive plasmids and suggest that the wide use of tetracycline antibiotics may promote the evolution of plasmids.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intensification of agriculture and increased insecticide use have been implicated in global losses of farmland biodiversity and ecosystem services. We hypothesized that increased insecticide applications (proportion of area treated with insecticides) in Canada's expansive agricultural landscapes are due, in part, to shifts toward more simplified landscapes. To assess this relationship, we analyzed data from the Canadian Census of Agriculture spanning 20 years including five census periods (1996–2016) and across 225 census units within the four major agricultural regions of Pacific, Prairie, Central, and Atlantic Canada. Generalized mixed effects models were used to evaluate if changes in landscape simplification — defined as the proportion of farmland in crops (cereals, oilseeds, pulses and fruit/vegetables) — alongside other farming and climatic variables, influenced insecticide applications over time. Bayesian spatial–temporal models were further used to estimate the strength of the relationship with landscape simplification over time. We found that landscape simplification increased in 89% and insecticide applications increased in 70% of the Census Division spatial units during the 1996–2016 period. Nationally, significant increases in landscape simplification were observed in the two most agriculturally intensive regions of Prairie (from 55% to 63%) and Central (from 51% to 60%) Canada. For both regions, landscape simplification was a strong and significant predictor of higher insecticide applications, even after accounting for other factors such as climate, farm economics, farm size and land use practices (e.g., area in cash crops and tillage). If current trends continue, we estimated that insecticide applications will increase another 10%–20% by 2036 as a result of landscape simplification alone. To avoid increased reliance on toxic insecticides, agri‐environmental policies need to consider that losing diverse natural habitat can increase insect pest pressure and resistance with negative environmental consequences extending beyond the field. [ABSTRACT FROM AUTHOR]

Abstract Background Endothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood. Results To characterize the transcriptomic and epigenomic landscape in the vascular system, we cataloged gene expression and active histone marks in nine types of human ECs (generating 148 genome-wide datasets) and carried out a comprehensive analysis with chromatin interaction data. We developed a robust procedure for comparative epigenome analysis that circumvents variations at the level of the individual and technical noise derived from sample preparation under various conditions. Through this approach, we identified 3765 EC-specific enhancers, some of which were associated with disease-associated genetic variations. We also identified various candidate marker genes for each EC type. We found that the nine EC types can be divided into two subgroups, corresponding to those with upper-body origins and lower-body origins, based on their epigenomic landscape. Epigenomic variations were highly correlated with gene expression patterns, but also provided unique information. Most of the deferentially expressed genes and enhancers were cooperatively enriched in more than one EC type, suggesting that the distinct combinations of multiple genes play key roles in the diverse phenotypes across EC types. Notably, many homeobox genes were differentially expressed across EC types, and their expression was correlated with the relative position of each organ in the body. This reflects the developmental origins of ECs and their roles in angiogenesis, vasculogenesis and wound healing. Conclusions This comprehensive analysis of epigenome characterization of EC types reveals diverse transcriptional regulation across human vascular systems. These datasets provide a valuable resource for understanding the vascular system and associated diseases.

The ill-conditioned numerical system arising from an electromagnetic analysis is often due to the discretized curl–curl operator which has both zero eigenvalues and large ones inversely proportional to the square of the smallest feature size. In this work, we analytically decompose the discretized curl–curl operator into a gradient divergence operator and a Laplacian without numerical computation. We further split the field solution into a gradient field (curl-free component) and a divergence-free one to take advantage of the decomposition. The gradient divergence operator vanishes when acting on the divergence-free component of the field solution. As a result, we can replace the highly ill-conditioned curl–curl operator with a Laplacian for computing the divergence-free component regardless of inhomogeneity and source settings. Since the Laplacian is positive definite and well-conditioned, the new system matrix can converge quickly in a small number of iterations. The method is applied to accelerate time-domain solutions of Maxwell’s equations in ill-conditioned problems such as integrated circuits (ICs). Numerical results have validated its accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Australia responded to the emergence of the COVID-19 global pandemic in 2020 by initiating a border and bio-security zone lockdown and policies emphasising social distancing and hand hygiene. To understand the public response to this, Southern Queensland Rural Health commenced a two-phase research project exploring attitudes and practices towards the COVID-19 pandemic in Australia. An initial online survey foreshadowed 90 qualitative interviews with respondents to explore what the pandemic meant for everyday life. This paper details use of a qualitative approach by a national collaborative of investigators from 9 rural university departments in Australia who came together to research the qualitative phase of the project. Our methodological approach aligned with extant literature describing the management of large-scale interviewing and coding in the context of unfolding and dynamic contexts. The 'RITA' model (Rapid Identification of Themes from Audio recordings) entails a five-step process designed to progress from identifying research foci, through deductive and iterative coding to identify key concepts. We used a combination of coding templates, organisation and tagging of field notes and real-time sharing through a secure cloud drive to create a data set for immersive analysis and generation of ideas. Use of this method has added to the collective knowledge about successful rapid research investigations, recognising the inherent tension between speed and rigour. This is not a binary but a dialectic; trustworthiness is integral to qualitative research. However, use of fresh approaches is accommodated by new technologies and can preserve adequate rigour while enabling collaboration, research capacity building and increasing the pace of data collection and analysis. This project has presented methodological challenges and highlights some strengths of such an approach. It is hoped that reporting our approach and experiences is useful for the broader health and research community considering large-scale qualitative research. [ABSTRACT FROM AUTHOR]

Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles.

Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development of instrumentation and measurement techniques. We developed Toolbox Accelerating Glycomics (TAG), in which glycans can be added manually to the glycan list that can be freely designed with labels and sialic acid modifications, and fast processing is possible. In the present work, we improved TAG for large-scale analysis such as cohort analysis of serum samples. The sialic acid linkage-specific alkylamidation (SALSA) method converts differences in linkages such as α2,3- and α2,6-linkages of sialic acids into differences in mass. Glycans modified by SALSA and several structures discovered in recent years were added to the glycan list. A routine to generate calibration curves has been implemented to explore quantitation. These improvements are based on redefinitions of residues and glycans in the TAG List to incorporate information on glycans that could not be attributed because it was not assumed in the previous version of TAG. These functions were verified through analysis of purchased sera and 74 spectra with linearity at the level of R2 > 0.8 with 81 estimated glycan structures obtained including some candidate of rare glycans such as those with the N,N’-diacetyllactosediamine structure, suggesting they can be applied to large-scale analyses.

X chromosome inactivation is a mammalian dosage compensation mechanism, where one of two X chromosomes is randomly inactivated in female cells. Previous studies have suggested that primed human embryonic stem cells (hESCs) maintain an eroded state of the X chromosome and do not express XIST, while in naïve transition, both XIST and the eroded X chromosome are reactivated. However, the pattern of chromosome X reactivation in naïve hESCs remains mainly unknown. In this study, we examine the variations in the status of X chromosome between primed and naïve hESCs by analyzing RNA sequencing samples from different studies. We show that most samples of naïve hESCs indeed reactivate XIST and there is an increase in gene expression levels on chromosome X. However, most of the naïve samples do not fully activate chromosome X in a uniform manner and present a distinct eroded pattern, probably as a result of XIST reactivation and initiation of re-inactivation of chromosome X. This large-scale analysis provides a higher-resolution description of the changes occurring in chromosome X during primed-to-naïve transition and emphasizes the importance of taking these variations into consideration when studying X inactivation in embryonic development.

Single-molecule imaging in living cells is an effective tool for elucidating the mechanisms of cellular phenomena at the molecular level. However, the analysis was not designed for throughput and requires high expertise, preventing it from reaching large scale, which is necessary when searching for rare cells that induce singularity phenomena. To overcome this limitation, we have automated the imaging procedures by combining our own focusing device, artificial intelligence, and robotics. The apparatus, called automated in-cell single-molecule imaging system (AiSIS), achieves a throughput that is a hundred-fold higher than conventional manual imaging operations, enabling the analysis of molecular events by individual cells across a large population. Here, using AiSIS, we demonstrate the single-molecule imaging of molecular behaviors and reactions related to tau protein aggregation, which is considered a singularity phenomenon in neurological disorders. Changes in the dynamics and kinetics of molecular events were observed inside and on the basal membrane of cells after the induction of aggregation. Additionally, to detect rare cells based on the molecular behavior, we developed a method to identify the state of individual cells defined by the quantitative distribution of molecular mobility and clustering. Using this method, cellular variations in receptor behavior were shown to decrease following ligand stimulation. This cell state analysis based on large-scale single-molecule imaging by AiSIS will advance the study of molecular mechanisms causing singularity phenomena., Competing Interests: The authors declare no competing interests., (2024 THE BIOPHYSICAL SOCIETY OF JAPAN.)

Over large regions exposed to natural disasters, cascading effects resulting from complex or concatenated natural processes may represent a large portion of total risk. Populated high-mountain environments are a major concern, and methods for large-scale quantitative risk analyses are urgently required to improve risk mitigation. This article presents a comprehensive quantitative rockfall risk assessment over a large archetypal valley of the Andean mountains, in Central Chile, which integrates a wide spectrum of elements at risk. Risk is expressed as an expected damage both in monetary terms and casualties, at different scales relevant for decision making. Notably, total rockfall risk is divided into its main drivers, which allows quantifying seismically induced rockfall risk. For this purpose, the local seismic hazard is quantified and the yield acceleration, that is, acceleration required to initiate rockfall, is determined at the regional scale. The probability of failure is thereafter derived in terms of annual frequency of rockfall initiation and integrated in the quantitative risk assessment (QRA) process. Our results show the significant role of seismic activity as the triggering mechanism of rockfalls, and highlight elements at risk that have a major contribution to the total risk. Eventually a sensitivity analysis is conducted to (i) assess the robustness of obtained risk estimates to the data and modeling choices and (ii) identify the most influential assumptions. Our approach evidences the feasibility of large-scale QRAs in sensitive environments and opens perspectives for refining QRAs in similar territories significantly affected by cascading effects and multihazards., (© 2023 Society for Risk Analysis.)

Zea mays L is a crucial crop for Brazil, ranking second in terms of production and sixth in terms of exports. In Brazil, the second season, or off-season, accounts for 80 % of the overall maize output, which primarily occurs after the soybean main season. A maize yield forecast model for the off-season was developed and implemented throughout Brazilian territory due to its importance to the country's economy and food security. The model was built using multiple linear regressions that connected outputs simulated from a land surface model used in large-scale analysis for agriculture (JULES-crop), to agrometeorological indicators. The application of the developed model occurred every 10 days from the sowing until the maturation. A comparison of the forecasting model was verified with the official off-season maize yields for the years 2003-2016. Agrometeorological indicators during the reproductive phase accounted for 60 % of the interannual variability in maize production. When outputs simulated by JULES-crop were included, the forecasting model achieved Nash-Sutcliffe modeling efficiency (EF) of 0.77 in the maturation and EF = 0.72 in the filling-grain stage, suggesting that this approach can generate useful predictions for final maize yield beginning on the 80th day of the cycle. Outputs of JULES crop enhanced modeling performance during the vegetative stage, reducing the standard deviation error in prediction from 0.59 to 0.49 Mg ha -1 ., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In this article, starting from full-wave partial differential equation (PDE)-based Maxwell’s equations where $\mathbf {E}$ and $\mathbf {H}$ are coupled, we derive a closed-form model of the inverse of Maxwell’s system of equations in the physical layout of an integrated circuit, package, and board. In this model, we decompose the inverse rigorously into $R$ , $C$ , $L$ , and full-wave components, with neither numerical computation nor approximation, and for an arbitrary physical layout. As a result, each component can be found independently and then superposed to obtain the total response of a layout to any circuit stimuli. The time marching and point-by-point frequency sweeping are also avoided for the $RC$ component as this component’s time and frequency dependencies are analytically revealed in the proposed model. Moreover, the full-wave component is efficiently represented by a small number of high-frequency modes. Using the proposed model, not only many accuracy issues related to existing layout modeling can be addressed but also we drastically speed up layout modeling and simulation and provide circuit designers with an effective model for layout automation. In addition, we develop fast and large-scale algorithms to find each component of the inverse rapidly, where many steps are made analytical, thus further saving CPU run time. The proposed work has been applied to large-scale layout extraction and analysis. Superior performance has been demonstrated in accuracy, efficiency, and capacity. [ABSTRACT FROM AUTHOR]

Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena, single-molecule imaging analysis has not been extended to a large scale of molecules in cells due to the low measurement throughput as well as required expertise. To overcome these problems, we have automated the imaging processes by using computer operations, robotics and artificial intelligence (AI). AI is an ideal substitute for expertise to obtain high-quality images for quantitative analysis. Our automated in-cell single-molecule imaging system, AiSIS, could analyze 1600 cells in 1 day, which corresponds to ∼ 100-fold higher efficiency than manual analysis. The large-scale analysis revealed cell-to-cell heterogeneity in the molecular behavior, which had not been recognized in previous studies. An analysis of the receptor behavior and downstream signaling was accomplished within a significantly reduced time frame and revealed the detailed activation scheme of signal transduction, advancing cell biology research. Furthermore, by combining the high-throughput analysis with our previous finding that a receptor changes its behavioral dynamics depending on the presence of a ligand/agonist or inhibitor/antagonist, we show that AiSIS is applicable to comprehensive pharmacological analysis such as drug screening. This AI-aided automation has wide applications for single-molecule analysis. [ABSTRACT FROM AUTHOR]

Background: Endothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood. Results: To characterize the transcriptomic and epigenomic landscape in the vascular system, we cataloged gene expression and active histone marks in nine types of human ECs (generating 148 genome-wide datasets) and carried out a comprehensive analysis with chromatin interaction data. We developed a robust procedure for comparative epigenome analysis that circumvents variations at the level of the individual and technical noise derived from sample preparation under various conditions. Through this approach, we identified 3765 EC-specific enhancers, some of which were associated with disease-associated genetic variations. We also identified various candidate marker genes for each EC type. We found that the nine EC types can be divided into two subgroups, corresponding to those with upper-body origins and lower-body origins, based on their epigenomic landscape. Epigenomic variations were highly correlated with gene expression patterns, but also provided unique information. Most of the deferentially expressed genes and enhancers were cooperatively enriched in more than one EC type, suggesting that the distinct combinations of multiple genes play key roles in the diverse phenotypes across EC types. Notably, many homeobox genes were differentially expressed across EC types, and their expression was correlated with the relative position of each organ in the body. This reflects the developmental origins of ECs and their roles in angiogenesis, vasculogenesis and wound healing. Conclusions: This comprehensive analysis of epigenome characterization of EC types reveals diverse transcriptional regulation across human vascular systems. These datasets provide a valuable resource for understanding the vascular system and associated diseases. [ABSTRACT FROM AUTHOR]

In the living cells, the majority of proteins does not work alone, but interact with other proteins or other biomolecules to maintain the cellular function, constituting a "protein community". Previous efforts on mass spectroscopy-based protein interaction networks, interactomes, have provided a picture on the protein community. However, these were static information after cells were disrupted. For a better understanding of the protein community in cells, it is important to know the properties of intracellular dynamics and interactions. Since hydrodynamic size and mobility of proteins are related into such properties, direct measurement of diffusional motion of proteins in single living cells will be helpful for uncovering the properties. Here we completed measurement of the diffusion and homo-oligomeric properties of 369 cytoplasmic GFP-fusion proteins in living yeast Saccharomyces cerevisiae cells using fluorescence correlation spectroscopy (FCS). The large-scale analysis showed that the motions of majority of proteins obeyed a two-component (i.e. slow and fast components) diffusion model. Remarkably, both of the two components diffused more slowly than expected monomeric states. In addition, further analysis suggested that more proteins existed as homo-oligomeric states in living cells than previously expected. Our study, which characterizes the dynamics of proteins in living cells on a large-scale, provided a global view on intracellular protein dynamics to understand the protein community. [ABSTRACT FROM AUTHOR]

Lipidomics studies the large-scale changes in nonwater-soluble metabolites (lipids) accompanying perturbations of biological systems. Because lipids are involved in crucial biological mechanisms, there is a growing scientific interest in using lipidomic approaches to understand the regulation of the lipid metabolism in all eukaryotic and prokaryotic organisms. Lipidomics is a powerful tool in system biology that can be used together with genomics, transcriptomics, and proteomics to answer biological questions arising from various scientific areas such as environmental sciences, pharmacology, nutrition, biophysics, cell biology, physiology, pathology, and disease diagnostics. One of the main challenges for lipidomic analysis is the range of concentrations and chemical complexity of different lipid species. In this chapter, we present a lipidomic approach that combines sample preparation, chromatographic, and intrasource ionization separation coupled to mass spectrometry for analyzing a broad-range of lipid molecules in biological samples.

• Multiple penalised state-space (MPSS) models to simultaneously solve MEG/EEG source localisation and functional connectivity problems are introduced. • We demonstrate backpropagation and state-space gradient descent algorithms to be straightforward solutions to solve MPSS models, particularly for small-scale scenarios. • An alternating least squares (ALS) algorithm, based on closed-form iterative solutions, is developed to solve MPSS models much more efficiently for large-scale scenarios, e.g., for thousands of brain dipole sources. • An extension of K -fold cross-validation to evaluate the MPSS model's regularisation parameters is introduced. • Evaluations with small- and large-scale simulations and MEG/EEG data demonstrate the potential for the MPSS-based regularisation approach to accelerate and improve data-intensive neuroscience and neuroimaging studies. State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K -fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions. [ABSTRACT FROM AUTHOR]

River impoundment constitutes one of the most important anthropogenic impacts on the World’s rivers. An increasing number of studies have tried to quantify the effects of river impoundment on riverine ecosystems over the past two decades, often focusing on the effects of individual large reservoirs. This study is one of the first to use a large-scale, multi-year diatom dataset from a routine biomonitoring network to analyse sample sites downstream of a large number of water supply reservoirs (n = 77) and to compare them with paired unregulated control sites. We analysed benthic diatom assemblage structure and a set of derived indices, including ecological guilds, in tandem with multiple spatio-temporal variables to disclose patterns of ecological responses to reservoirs beyond the site-specific scale. Diatom assemblage structure at sites downstream of water supply reservoirs was significantly different to control sites, with the effect being most evident at the regional scale. We found that regional influences were important drivers of differences in assemblage structure at the national scale, although this effect was weaker at downstream sites, indicating the homogenising effect of river impoundment on diatom assemblages. Sites downstream of reservoirs typically exhibited a higher taxonomic richness, with the strongest increases found within the motile guild. In addition, Trophic Diatom Index (TDI) values were typically higher at downstream sites. Water quality gradients appeared to be an important driver of diatom assemblages, but the influence of other abiotic factors could not be ruled out and should be investigated further. Our results demonstrate the value of diatom assemblage data from national-scale biomonitoring networks to detect the effects of water supply reservoirs on instream communities at large spatial scales. This information may assist water resource managers with the future implementation of mitigation measures such as setting environmental flow targets.

Inferring transcriptional direction (orientation) of the CRISPR array is essential for many applications, including systematically investigating non-canonical CRISPR/Cas functions. The standard method, CRISPRDirection (embedded within CRISPRCasFinder), fails to predict the orientation (ND predictions) for ∼37% of the classified CRISPR arrays (>2200 loci); this goes up to >70% for the II-B subtype where non-canonical functions were first experimentally discovered. Alternatively, Potential Orientation (also embedded within CRISPRCasFinder), has a much smaller frequency of ND predictions but might have significantly lower accuracy. We propose a novel simple criterion, where the CRISPR array direction is assigned according to the direction of its associated cas genes (Cas Orientation). We systematically assess the performance of the three methods (Cas Orientation, CRISPRDirection, and Potential Orientation) across all CRISPR/Cas subtypes, by a mutual crosscheck of their predictions, and by comparing them to the experimental dataset. Interestingly, CRISPRDirection agrees much better with Cas Orientation than with Potential Orientation, despite CRISPRDirection and Potential Orientation being mutually related – Potential Orientation corresponding to one of six (heterogeneous) predictors employed by CRISPRDirection – and being unrelated to Cas Orientation. We find that Cas Orientation has much higher accuracy compared to Potential Orientation and comparable accuracy to CRISPRDirection – while accurately assigning an orientation to ∼95% of the CRISPR arrays that are non-determined by CRISPRDirection. Cas Orientation is, at the same time, simple to employ, requiring only (routine for prokaryotes) the prediction of the associated protein coding gene direction.