Multiple Environments Research Articles

Genome-wide association study of bacterial blight resistance in soybean.

Bacterial blight caused by Pseudomonas syringae pv. glycines (Psg) is a widespread foliar disease. Although four Resistance to Pseudomonas syringae pv. glycinea (Rpg) 1 ~ 4 (Rpg1~4) genes that have been observed to segregate in a Mendelian pattern have been reported to confer resistance to Psg in soybean, the genetic basis of quantitative resistance to bacterial blight in soybean remains unclear. In the present study, the Psg resistance of two soybean association panels consisting of 573 and 213 lines, respectively, were phenotyped in multiple environments in 2014 - 2016. Genome-wide association study (GWAS) were performed using 2 models FarmCPU and BLINK to identify Psg resistance loci. A total of 40 soybean varieties with high level of Psg resistance were identified, and 14 quantitative trait loci (QTLs) were detected on 12 soybean chromosomes. These QTLs were identified for the first time. The majority of the QTLs were only detected in one or the other association panels, while qRPG-18-1 was detected in both association panels for at least one growing season. A total of 46 candidate Psg resistance genes were identified from the qRpg_13_1, qRPG-15-1, and qRPG-18-1 loci based on gene function annotation. In addition, we found the genomic region covering rpg1-b and rpg1-r harbored the synteny with a genomic region on chromosome 15, and identified 16 nucleotide binding site - leucine-rich repeat (NBS-LRR) genes as the candidate Psg resistance genes from the synteny blocks. This study provides new information for dissecting the genetic control of Psg resistance in soybean.

Maternal And preGnancy hEalth duriNg elevaTed heAt – MAGENTA: Novel data linkages and methods to understand the impacts of climate change on deprived communities in Wales and London.

Background and ObjectivesThe health impacts from changing climate, including increasing frequency, severity, and duration of heat are projected to worsen. The MAGENTA study will explore the impact of heat on pregnancy-related outcomes for deprived communities in Wales and London. ApproachMAGENTA will create linked data cohorts capturing some of the most deprived and diverse communities in Europe to analyse the impact deprivation, ethnicity, heatwaves, and urban heat islands have on pregnancy-related health. A consented data-linked cohort of pregnant women will provide biological samples and undergo direct measurement of themselves and their home environments to understand the heat stress response of pregnant women not acclimatised to heat during their pregnancy. Core to the study is our public involvement strategy which includes pregnant women and women who have recently delivered a baby, co-producing the research and how results are communicated. ResultsOur electronic cohorts cover >10 years of climate and health data and capture all births in Wales >300,000 and >1.1 million in London. We will present how the novel data linkages can work in multiple trusted research environments, approaches for sharing methods in geospatial modelling and statistics across the project and preliminary findings from the study. Findings and ImplicationsThe MAGENTA study will help inform expectant parents, families and policy makers how to prepare for climate change in temperate regions. The comparison between different populations in Wales and London will give insight on where policies and interventions can be generally applied and where they need to be contextually adapted.

A novel tree-based method for interpretable reinforcement learning

Deep reinforcement learning (DRL) has garnered remarkable success across various domains, propelled by advancements in deep learning (DL) technologies. However, the opacity of DL presents significant challenges, limiting the application of DRL in critical systems. In response, decision tree (DT)-based methods, known for their transparent decision-making mechanisms, have shown promise in making interpretable policies for decision-making problems. Existing methods often employ differential DTs to model RL policies and discretize them to conventional DTs for higher interpretability. Yet, this method leads to discrepancies between the trained differential DTs and the discretized DTs. To address this issue, we introduce Generative Consistent Trees (GCTs), a novel solution that circumvents the information loss typically associated with the argmax operation in prior research. By implementing a reparameterization technique to approximate the categorical distribution, GCTs ensure the consistencies between trained GCTs and discretized counterparts. Moreover, we have developed an imitation-learning-based framework for interpretable reinforcement learning. This framework is designed to train GCTs by efficiently mimicking expert policies. Our extensive experiments across multiple environments have validated the effectiveness of this approach, highlighting the potential of GCTs in enhancing the interpretability and applicability of DRL.

The psychosocial and educational burden of obsessive-compulsive disorder in youth

BackgroundPediatric OCD is associated with functional impairment in multiple environments. However, relatively little is known about the impact of comorbid conditions, as well as OCD severity on psychosocial functioning in this population. Furthermore, most studies did not include a control sample, nor examined differences between children and adolescents. The goal of this investigation was to assess psychosocial functioning and its associations with age, symptom severity, and comorbid conditions in a large well characterized sample of pediatric OCD probands, and controls. MethodsParticipants included 117 pediatric OCD probands and 147 controls, that underwent a careful diagnostic process, and completed several questionnaires and interviews. ResultsResults revealed significant psychosocial impairments across multiple domains/settings, some of which were affected by symptom severity as well as by conduct related comorbidities and to a lesser extent affective disorders. In addition, different aspects of psychosocial impairments were found between children and adolescents. ConclusionThis study provides high resolution information regarding the types and extent of psychosocial dysfunction in youth with OCD, as well as its relationship with clinical and diagnostic correlates. It is recommended that evaluation and management of OCD in youth in research and clinical settings regularly include qualitative and semi-quantitative assessment of function across these domains.

Incorporating Privacy by Design Principles in the Modification of AI Systems in Preventing Breaches across Multiple Environments, Including Public Cloud, Private Cloud, and On-prem

The rapid integration of artificial intelligence (AI) across various sectors has significantly amplified privacy concerns, particularly with the growing reliance on cloud environments. Existing methods often fall short of effectively preventing privacy breaches due to inadequate risk assessment and mitigation strategies. These limitations highlight the necessity for more robust solutions, indicating the importance of Privacy by Design (PbD) principles. This study addresses these gaps by proposing a comprehensive approach to incorporating PbD principles into AI systems to prevent breaches across public, private, and on-prem environments. The proposed work utilizes logistic regression analysis to identify significant predictors of privacy breaches, revealing that both the environment (B = -1.142, p < .001) and severity of vulnerabilities (B = 0.932, p < .01) play crucial roles. Additionally, a strong positive correlation (r = 0.791) between breach detection rates and PbD effectiveness is observed, indicating the need for enhanced detection mechanisms. To support the empirical findings, this study also reviews existing case studies. It conducts a thematic analysis to provide a deeper understanding of the practical challenges and solutions associated with PbD implementation, particularly in healthcare and smart city applications. These analyses serve to supplement the empirical evidence and demonstrate the effectiveness of PbD over other existing methods. The study concludes that implementing PbD principles is critical for achieving robust privacy protection, and the study recommends prioritizing advanced breach detection mechanisms, comprehensive privacy impact assessments, continuous stakeholder engagement, and investment in privacy-enhancing technologies to address privacy risks effectively.

What are the underlying causes and dynamics of land use conflicts in metropolitan junction areas? A case study of the central Chengdu– Chongqing region in China

Land use conflicts (LUCs), as a spatial manifestation of the conflicts in the human-land relationships, have a profound impact on regional sustainable development. For China’s metropolitan junction areas (MJAs), the existence of “administrative district economies” has made the issue of LUCs more prominent. Based on a case study of the central Chengdu–Chongqing region, we conducted an exploratory spatial data analysis of the evolutionary process of regional LUCs. Furthermore, structural equation modeling was utilized to analyze the dynamic mechanism of LUCs in MJAs, with a particular emphasis on exploring the influences of administrative boundary. The results showed that from 2010 to 2020, LUCs in the central Chengdu–Chongqing region continued to worsen, and the spatial process conflict and spatial structure conflict indices increased by more than 30.0%. The intensification of LUCs in the central Chengdu–Chongqing region from 2010 to 2020 was mainly the result of the deterioration of conflicts in evaluation units with low conflict levels. LUCs in China’s metropolitan areas generally presented a circular gradient distribution, weakening from the core to the periphery, but there were some strong isolated conflict zones in the outer regions. LUCs in China’s MJAs were the result of interactions among multiple factors, e.g., natural environment, socio-economic development, policy and institutional processes, and administrative boundary effects. Administrative boundary affected the flow of socio-economic elements, changing the supply-and-demand competition of stakeholders for land resources, consequently exerting an indirect influence on LUCs. This study advances the theory of the dynamic mechanism of LUCs, and provides theoretical support for the governance of these conflicts in transboundary areas.

AMMI and GGE biplot analysis for barley genotype yield performance and stability under multi environment condition in southern Ethiopia

AbstractBarley (Hordeum vulgare L.) is a major grain crop farmed in Ethiopia throughout the long rainy season (Meher) and the short rainy season (Belg) of the year. Barley genotypes were subjected to multi‐environment experiments in six different settings to identify stable genotypes and estimate the impact of genotype × environment interaction (GEI) on grain production. In each area, the field experiment was conducted from mid‐July to January during the primary cropping season of 2021. Three replications of a randomized complete block design were used to set up the trials. According to the additive main effects and multiplicative interaction (AMMI) study, genotype (18.19%), GEI (22.98%), and environment (58.83%) all had an impact on the major treatment sum of squares. The more variance attributed to the environments is a sign of environmental diversity. Given that the two interaction principal component analysis (IPCAs) accounted for 76.94% of the interaction sum of squares, they were sufficient for cross‐validation of the grain yield variance explained by GEI. In contrast to the GGE biplot approaches, which indicated genotypes G12, G3, and G9 as stable and high‐yielding genotypes throughout the environments, the AMMI stability value identified genotypes G3, G12, and G9 as high yielding with stable performance across environments. In general, the GGE biplot and AMMI analysis models demonstrated that genotypes G12, G3, and G9 were stable and yielded well, making G3 acceptable for cultivation in a wider range of environments and G12 and G9 suitable for release.

MPSDynamics.jl: Tensor network simulations for finite-temperature (non-Markovian) open quantum system dynamics.

The MPSDynamics.jl package provides an easy-to-use interface for performing open quantum systems simulations at zero and finite temperatures. The package has been developed with the aim of studying non-Markovian open system dynamics using the state-of-the-art numerically exact Thermalized-Time Evolving Density operator with Orthonormal Polynomials Algorithm based on environment chain mapping. The simulations rely on a tensor network representation of the quantum states as matrix product states (MPS) and tree tensor network states. Written in the Julia programming language, MPSDynamics.jl is a versatile open-source package providing a choice of several variants of the Time-Dependent Variational Principle method for time evolution (including novel bond-adaptive one-site algorithms). The package also provides strong support for the measurement of single and multi-site observables, as well as the storing and logging of data, which makes it a useful tool for the study of many-body physics. It currently handles long-range interactions, time-dependent Hamiltonians, multiple environments, bosonic and fermionic environments, and joint system-environment observables.

Efficient Jamming Policy Generation Method Based on Multi-Timescale Ensemble Q-Learning

With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate.

High-precision estimation of pan-Arctic soil surface temperature from MODIS LST by incorporating multiple environment factors and monthly-based modeling

Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.

Synthesis of MnFe2O4 Spinel on Rusted Q235 Steel Surface and Its Corrosion Resistance Properties.

Corrosion of steel is an issue that cannot be ignored in contemporary society. Due to large-scale corrosion, it is urgent to develop a surface treatment process that enhances the corrosion resistance of steel, allowing for application in various scenarios as needed. This study aims to investigate a novel surface treatment process to extend the service life of corroded Q235 steel, reduce its sensitivity to corrosion, and enable its use in multiple environments. This study employs the sol-gel method, using manganese nitrate solutions of varying concentrations to treat the surface of Q235 steel after different electrolysis times. The optimal conditions for precursor preparation were found to be a Mn2+ concentration of 0.1 mol/L and an electrolysis time of 2 h. Electrochemical tests using NaCl solutions of different concentrations revealed a significant reduction in the corrosion current for the composite coating based on Q235 steel treated with this method in NaCl solutions with wt.% = 1, 2, 3, 4, 5. Furthermore, the resistance to corrosion was strongest in the NaCl solution with a concentration of 1 wt.% where the corrosion current decreased from 24.8 µA/cm2 to 6.79 µA/cm2. Additionally, the coating was found to be diffusion-controlled in the early stages of the corrosion process and charge transfer-controlled in the later stages. The MnFe2O4 spinel coating demonstrated the greatest enhancement in corrosion resistance in the wt.% = 1 NaCl solution.

Granulometric and Mineralogical Characterization of Sands from the Middle Course of the Kasai River (Ilebo Territory, Kasai Province, DRC)

This study addresses sediment dynamics within the fluvial system of the middle Kasai Basin, specifically between Ilebo town (pk605) and the confluence with the Loange River (pk525). The primary aim is to characterize the granulometric and mineralogical evolution of sand bars in the Kasai River, which pose navigational challenges in this section of the basin. The study seeks to answer the following question: How do the granulometric and mineralogical characteristics of sands transported by the middle course of the Kasai River evolve? The research employs laboratory granulometric techniques. Twenty sand samples were collected from sand bars along the middle and navigable course of the Kasai River during the flood recession period, ranging from upstream to downstream. Sieving was conducted using an AFNOR-type sieve column, followed by sedimentometric analyses and the calculation of various Folk & Ward textural parameters using the Excel program Gradistat. Additionally, six samples underwent automated mineralogical analysis using a QEMSCAN FEG Quanta 650. Granulometric analysis revealed that the sands from the middle course of the Kasai River are unimodal, with fine to medium grains in the granulometric fraction ranging from 186.2 µm to 426.8 µm. Most of these sands are moderately to well graded, with grains showing granulometric symmetry and, less commonly, fine asymmetry. Their kurtosis is largely mesokurtic, with occasional leptokurtic and platykurtic characteristics, indicating multiple depositional environments. The study demonstrates that the evolution of these granulometric parameters is uneven along the middle course of the Kasai River, though overall, the parameters exhibit limited variation. This suggests minimal and regular sediment inputs, consistent with the relative regularity of granulometric variations in environments characterized by free sedimentation. Automated mineralogical analysis identified a diverse mineralogical assemblage, dominated by quartz, followed by calcite, iron oxides, orthoclase, plagioclase, and kaolinite. Additionally, a group of minerals that do not exceed the 0.55% threshold—such as illite, apatite, ilmenite, muscovite, chlorite, biotite, montmorillonite, rutile, pyrophyllite, siderite, zircon, and dolomite—was identified. Consequently, the mineralogical evolution is not uniform throughout the middle course of the Kasai River, showing a sawtooth variation. This study clarifies the evolution of the granulometric parameters of Kasai River sands, which are influenced by the river's hydrodynamic regime. It also elucidates the mineralogical evolution, linked to the petrographic nature of source areas, which are distributed based on their mechanical resistance to abrasion, chemical alteration, and the morphology of the riverbed. The findings from this research contribute significantly to the field of sedimentology and integrated river management

Quantifying the resistance of Australian wheat genotypes to Pratylenchus thornei based on a continuous metric from a factor analytic linear mixed model

Genetic resistance to the parasitic root-lesion nematode, Pratylenchus thornei, is one of the main management strategies cereal growers can use to minimise the impact of nematodes on winter cereal cropping. Screening of genotypes in the presence of P. thornei populations must provide reliable resistance measures that are realised under field conditions. Adoption of the latest statistical methodologies can help to better differentiate between resistant and susceptible genotypes. In this study, post-harvest P. thornei population densities were measured from a collection of 17 field experiments, with varying starting P. thornei population densities, conducted between 2011 and 2018 in locations across the northern grain growing region of eastern Australia. The experiments primarily consisted of wheat genotypes. The post-harvest P. thornei population densities were analysed across multiple environments in a linear mixed model framework, with a factor analytic structure used to model genotype by environment (G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E) interaction effects exclusively for wheat genotypes. In general, genetic correlations between environments were found to be high, indicating limited G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E interaction for resistance to P. thornei. Post-processing of results using the factor analytic selection tools (FAST) method provided a measure of the overall performance for each wheat genotype, as well as a stability measure reflecting the consistency of the resistance status across environments. The FAST method quantified genotype resistance on a continuous scale, better reflecting the nature of genetic resistance based on a quantitative variable such as nematode population density, and provided a statistically robust and informative means of aiding selection decisions for resistance to P. thornei.

Bread wheat cultivar Popo harbors QTLs for seedling and adult plant resistance to leaf rust in South African and Argentine environments

Leaf rust, caused by the fungus Puccinia triticina Eriks (Pt), is a destructive disease affecting wheat (Triticum aestivum L.) production in many countries, and a serious threat to food security. As a result, several breeding programs have included leaf rust resistance as an important trait. The discovery and identification of new resistance genes that could aid in incorporating durable or long-lasting leaf rust resistance into wheat is fundamental in these breeding programs. The present study aimed to identify quantitative trait loci (QTLs) for leaf rust resistance in 127 recombinant inbred lines (RIL) developed from the cross between the resistant wheat cultivar Popo and the susceptible cultivar Kariega. The RIL population and parental lines were phenotyped for leaf rust infection type and severity at seedling and adult plant stage, respectively. The former in the greenhouse (in Argentina) and the latter in multiple field test environments comprising 3 locations in South Africa (in Tygerhoek in the Western Cape Province during the 2014, 2015, 2017 and 2018 cropping seasons; Clarens during 2014, 2016 and 2017 cropping seasons and in Bethlehem in the Free State Province during 2017 cropping season) and in 1 location in Argentina (during the 2017 and 2018 cropping seasons in Marcos Juárez, Córdoba Province). The population was genotyped using genotyping-by-sequencing. A total of 12,080 silicoDArT and 2,669 SNP markers were used for QTL analysis. In total, 25 putative QTLs for resistance to leaf rust at seedling and adult plant stages were identified, including 5 QTLs for seedling and 20 QTLs for adult plant resistance (APR). Interestingly, both Popo and Kariega contributed with alleles for resistance. Significant loci for reducing leaf rust infection at seedling stage were designated QLr.arc-1A, QLr.arc-2B, QLr.arc-5B, QLr.arc-6A and QLr.arc-6D. Three minor QTLs derived from Popo designated as QLr.arc-1B1, QLr.arc-2D and QLr.arc-3D were also detected from the field tests, explaining 5–10%, 10–16% and 5–7% of the phenotypic variance, respectively. The identified QTLs and their closely linked silicoDArT and SNP-based markers can be used for fine mapping and candidate gene discovery in wheat breeding programs targeting durable leaf rust resistance.

Improved Unsupervised Stitching Algorithm for Multiple Environments SuperUDIS.

Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that combines the powerful feature extraction capabilities of the Superpoint algorithm and the exact feature matching capabilities of the Lightglue algorithm with the image fusion algorithm of Unsupervised Deep Image Stitching (UDIS). Our proposed method effectively improves the situation where the linear structure is distorted and the resolution is low in the stitching results of the UDIS algorithm. On this basis, we make up for the shortcomings of the UDIS fusion algorithm. For stitching fractures of UDIS in some complex situations, we optimize the loss function of UDIS. We use a second-order differential Laplacian operator to replace the difference in the horizontal and vertical directions to emphasize the continuity of the structural edges during training. Combined with the above improvements, the Super Unsupervised Deep Image Stitching (SuperUDIS) algorithm is finally formed. SuperUDIS has better performance in both qualitative and quantitative evaluations compared to the UDIS algorithm, with the PSNR index increasing by 0.5 on average and the SSIM index increasing by 0.02 on average. Moreover, the proposed method is more robust in complex environments with large color differences or multi-linear structures.

Electrothermal-Assisted Photothermal Lubrication Surfaces for Continuous Anti-Icing/Deicing in Multiple Low-Temperature Environments.

Solving the problem of ice accumulation on solid surfaces is of great significance to the economic development of the country and the safety of people's lives. In this work, a coating with multifunctional photothermal/electrothermal solid-state lubrication (PEL) for anti-icing/deicing was prepared in layers based on the intrinsic properties of silicone oil and paraffin wax in combination with conductive graphite and multiwalled carbon nanotubes. Silicone oils and paraffins are used as lubricating media giving the coating excellent lubricity, which results in a water sliding angle (SA) of only 12° on the PEL surface. Meanwhile, PEL shows favorable static and dynamic ice resistance at low temperatures; at -10 °C, the freezing time of water droplets on the PEL surface is extended by at least 4 times compared to the bare substrate. Furthermore, PEL also offers highly efficient photothermal and electrothermal deicing performance, which can effectively remove the accumulated ice at a light intensity of 0.6 kW/m2 or an EPD of 0.1 W/cm2. Meanwhile, the synergistic deicing mechanism of photothermal and electrothermal was verified at -20 °C. Interestingly, the coating shows heat-assisted healing ability due to the phase change characteristic of paraffin wax, which allows the coating to regain lubricating properties after mechanical abrasion. Therefore, this work provides a reliable way for the design of stable all-weather anti-icing/deicing strategies at low temperatures.

A Soft Amphibious Voxel-Type Quadruped Robot Based on Origami Flexiball of Rhombic Dodecahedron.

The research work presents a novel voxel-type soft amphibious robot based on an assembly of origami flexiballs. The geometric and elastic constitutive models of the origami flexiball are theoretically established to elucidate its intricate deformation mechanism. Especially, the zero-energy storage phenomenon and the quasi-zero-stiffness characteristic are revealed to prove that the origami flexiball is suitable for serving as soft robotic components. As a proof of concept, fourteen origami flexiballs are interconnected to form a quadruped robot capable of walking or crawling in both underwater and terrestrial environments, including flat surfaces and sandy terrain. Its adaptability across multiple environments is enhanced by the origami polyhedra-inspired hollow structure, which naturally adjusts to underwater conditions such as hydrostatic pressure and currents, improving stability and performance. Other advantages of the voxel-type soft amphibious quadruped robot include its ease of manufacture using 3D printing with accessible soft elastic materials, ensuring rapid and cost-effective fabrication. We anticipate its potentially versatile applications, including underwater pipeline inspections, offshore maintenance, seabed exploration, ecological monitoring, and marine sample collection. By leveraging metamaterial features embodied in the origami polyhedra, the presented voxel-type soft robot exemplifies an innovative approach to achieving complex functionalities in soft robotics.

Evaluation of Rice Genotypes for Yield Stability and Adaptability Across Multiple Environments Using AMMI and GGE Biplot Analysis

Evaluation of Rice Genotypes for Yield Stability and Adaptability Across Multiple Environments Using AMMI and GGE Biplot Analysis

Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study.

Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.

Robustness quantification of a mutant library screen revealed key genetic markers in yeast

BackgroundMicrobial robustness is crucial for developing cell factories that maintain consistent performance in a challenging environment such as large-scale bioreactors. Although tools exist to assess and understand robustness at a phenotypic level, the underlying metabolic and genetic mechanisms are not well defined, which limits our ability to engineer more strains with robust functions.ResultsThis study encompassed four steps. (I) Fitness and robustness were analyzed from a published dataset of yeast mutants grown in multiple environments. (II) Genes and metabolic processes affecting robustness or fitness were identified, and 14 of these genes were deleted in Saccharomyces cerevisiae CEN.PK113-7D. (III) The mutants bearing gene deletions were cultivated in three perturbation spaces mimicking typical industrial processes. (IV) Fitness and robustness were determined for each mutant in each perturbation space. We report that robustness varied according to the perturbation space. We identified genes associated with increased robustness such as MET28, linked to sulfur metabolism; as well as genes associated with decreased robustness, including TIR3 and WWM1, both involved in stress response and apoptosis.ConclusionThe present study demonstrates how phenomics datasets can be analyzed to reveal the relationship between phenotypic response and associated genes. Specifically, robustness analysis makes it possible to study the influence of single genes and metabolic processes on stable microbial performance in different perturbation spaces. Ultimately, this information can be used to enhance robustness in targeted strains.