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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]

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.

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.)

• 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]