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Musculoskeletal modeling, typically implemented using marker-based systems in laboratory environments, is commonly used for noninvasive estimations of loads. Inertial measurement units (IMUs) have become an alternative for the evaluation of kinematics. However, estimates of spine joint contact forces using IMUs have yet to be thoroughly evaluated. Dynamics tasks and static postures from activities of daily living were captured on 11 healthy subjects using both systems simultaneously. Spine kinematics obtained from IMU- and marker-based systems and L4–L5 joint contact forces were compared. Lateral bending resulted in a weak agreement with significant differences between the 2 systems (P =.02, average root mean-squared error = 4.81), whereas flexion–extension and axial rotation exhibited the highest agreement with no significant differences (P <.05, average root mean-squared error = 5.51 and P <.31, average root mean-squared error = 5.08, respectively). All tasks showed excellent correlations (R2 =.76–.99) in estimated loads between systems. Differences in predicted loads at the L4–L5 were only observed during flexion–extension (1041 N vs 947 N, P =.0004) and walking with weights (814 N vs 727 N, P =.004). Different joint reaction force outcomes were obtained in 2 of the 8 tasks between systems, suggesting that IMUs can be robust tools allowing for convenient and less expensive evaluations and for longitudinal assessments inside and outside the laboratory setting. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we compared the influence of movement specificity during velocity–load jump testing to predict on-ice acceleration performance in elite speed skaters. Methods: Elite long-track speed skaters (N = 27) performed velocity–load testing with 3 external loads during unilateral horizontal jumping, lateral jumping, and bilateral vertical countermovement jumping. For the unilateral tests, external load conditions were set to 10 N, 7.5% and 15% of external load relative to body weight. For the countermovement jumping, load conditions were body weight and 30% and 60% of external load relative to body weight. On-ice performance measures were obtained during maximal 50-m accelerations from a standing start, including maximal skating speed, maximal acceleration capacity, and maximum horizontal power. The 100-m split time from a 500-m race was also obtained. Regularized regression models were used to identify the most important predictors of on-ice acceleration performance. In addition to regularized regression coefficients, Pearson correlation coefficients (r) were calculated for all variables retained by the model to assess interrelationships between single predictors and on-ice performance measures. Results: The countermovement jump with 30% of body mass demonstrated the strongest association with maximal skating speed, maximum horizontal power, and 100-m time (regularized regression coefficient =.16−.49, r =.84−.97, P <.001). Horizontal jump with 15% of body mass was the strongest predictor of maximal acceleration capacity performance (regularized regression coefficient =.08, r =.83, P <.001). Conclusions: The findings of this study suggest that mechanical specificity rather than movement specificity was more relevant for predicting on-ice acceleration performance. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a theory on the frequent observation that cyclists prefer cadences (RPMs) higher than those considered most economical at submaximal exercise intensities via modeling and simulation of its mathematical description. Methods: The theory combines the parabolic power-to-velocity (v) relationship, where v is defined by crank length, RPM-dependent ankle velocity, and gear ratio, RPM effects on the maximal lactate steady state (MLSS), and lactate-dependent carbohydrate oxidation (CHO). It was tested against recent experimental results of 12 healthy male recreational cyclists determining the v-dependent peak oxygen uptake (VO2PEAKv), MLSS (MLSSv), corresponding power output (PMLSSv), oxygen uptake at PMLSSv (VO2MLSSv), and CHOMLSSv-management at 100 versus 50 per minute, respectively. Maximum RPM (RPMMAX) attained at minimized pedal torque was measured. RPM-specific maximum sprint power output (PMAXv) was estimated at RPMs of 100 and 50, respectively. Results: Modeling identified that MLSSv and PMLSSv related to PMAXv (IPMLSSv) promote CHO and that VO2MLSSv related to VO2PEAKv inhibits CHO. It shows that cycling at higher RPM reduces IPMLSSv. It suggests that high cycling RPMs minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. Conclusions: The present theory-guided modeling approach is exclusively based on data routinely measured in high-performance testing. It implies a higher performance reserve above IPMLSSv at higher RPM. Cyclists may prefer high cycling RPMs because they appear to minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. [ABSTRACT FROM AUTHOR]

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

To understand the extent of anthropogenic noise in the ocean, it is essential to compare the differences between modern noise environments and their pre-industrial equivalents. The Santa Barbara Channel, off the coast of Southern California, is a corridor for the transportation of goods to and from the busiest shipping ports in the Western hemisphere. Commercial ships introduce high levels of underwater noise into the marine environment. To quantify the extent of noise in the region, we modeled pre-industrial ocean noise levels, driven by wind, and modern ocean noise levels, resulting from the presence of both ships and wind. By comparing pre-industrial and modern underwater noise levels, the low-frequency (50 Hz) acoustic environment was found to be degraded by more than 15 dB. These results can be used to identify regions for noise reduction efforts, as well as to model scenarios to identify those with the greatest potential to support marine conservation efforts.

Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a "top 10" list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Purpose: To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games. Methods: The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships. Results: The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r =.99 for both approaches). Conclusions: These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games. [ABSTRACT FROM AUTHOR]

There is a powerful global trend toward deeper integration of digital twins into modern life driven by Industry 4.0 and 5.0. Defense, agriculture, engineering, manufacturing, and urban planning sectors have thoroughly incorporated digital twins to great benefit across their respective product lifecycles. Despite clear benefits, a digital twin framework for health and medical sectors is yet to emerge. This paper proposes a digital twin framework for precision neuromusculoskeletal health care. We build upon the International Standards Organization framework for digital twins for manufacturing by presenting best available computational models within a digital twin framework for clinical application. We map a use case for modeling Achilles tendon mechanobiology, highlighting how current modeling practices align with our proposed digital twin framework. Similarly, we map a use case for advanced neurorehabilitation technology, highlighting the role of a digital twin in control of systems where human and machine are interfaced. Future work must now focus on creating an informatic representation to govern how digital data are passed to, from, and within the digital twin, as well as specific standards to declare which measurement systems and modeling methods are acceptable to move toward widespread use of the digital twin framework for precision neuromusculoskeletal health care. [ABSTRACT FROM AUTHOR]

In this review, we elaborate on how musculoskeletal (MSK) modeling combined with dynamic movement simulation is gradually evolving from a research tool to a promising in silico tool to assist medical doctors and physical therapists in decision making by providing parameters relating to dynamic MSK function and loading. This review primarily focuses on our own and related work to illustrate the framework and the interpretation of MSK model-based parameters in patients with 3 different conditions, that is, degenerative joint disease, cerebral palsy, and adult spinal deformities. By selecting these 3 clinical applications, we also aim to demonstrate the differing levels of clinical readiness of the different simulation frameworks introducing in silico model-based biomarkers of motor function to inform MSK rehabilitation and treatment, with the application for adult spinal deformities being the most recent of the 3. Based on these applications, barriers to clinical integration and positioning of these in silico technologies within standard clinical practice are discussed in the light of specific challenges related to model assumptions, required level of complexity and personalization, and clinical implementation. [ABSTRACT FROM AUTHOR]

POREnet, a novel approach to model effective properties of thin porous media, TPM, is presented. The methodology allows the extraction of local effective property tensors by volume averaging from discrete pore networks, PNs, built on the tessellated continuum space of a TPM. The gradient theorem is used to describe 3D transport in bulk tessellated space, providing an appropriate metric to normalize network fluxes. Implemented effective transport properties include diffusivity, permeability, solid-phase conductivity, and entry capillary pressure and contact angle under two-phase conditions, considering multi-component materials with several solid phases and local contact resistances. Calculated property tensors can be saved on 3D image stacks, where interfacial and sub-CV scale features can be added before exporting data to CFD meshes for simulation. Overall, POREnet provides a general-purpose, versatile methodology for modeling TPM in an ample range of conditions within a single CFD framework. Among other advantages, coupling of PN and continuum models at TPM-channel interfaces is simplified, interfacial contact resistances can be included using robin boundary conditions, and transient multiphysics simulations can be implemented more easily using CFD. The code is tested against a miscellaneousness of examples extracted from electrochemical applications.

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas-driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor-holding capacity of a warmer atmosphere. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. It indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward.

Triangular cross-section color center photonics in silicon carbide is a leading candidate for scalable implementation of quantum hardware. Within this geometry, we model low-loss beam splitters for applications in key quantum optical operations such as entanglement and single-photon interferometry. We consider triangular cross-section single-mode waveguides for the design of a directional coupler. We optimize parameters for a 50:50 beam splitter. Finally, we test the experimental feasibility of the designs by fabricating triangular waveguides in an ion beam etching process and identify suitable designs for short-term implementation.

Abstract: The aim of this study is to enable the hydrogen economy and decarbonize various sectors in our environment that requires less expensive and more durable water electrolyzers, which can meet the Hydrogen-Shot target. The key is to improve the ionomer interfaces in low-temperature water electrolyzers as rapidly as possible, but to do so, it requires a systematic and holistic campaign combining both experiments and theory. In this perspective, we discuss the issues of electrolyzers and needs for translational science. We then present the approach that the Energy EarthShot Research Center: Center for Ionomer-based Water Electrolysis is taking in hopes of inspiring the community with this approach that can be leveraged to multiple problems and technologies. Graphical abstract: (Figure presented.) Highlights: One way to achieve the Hydrogen-Shot goal of low-cost, clean hydrogen, is advancing research and development on the interfaces of water electrolyzers for both performance and lifetime. The Center for Ionomer-based Water Electrolysis is exploring new techniques and strategies to not only interrogate interfacial phenomena in water electrolyzers to increase efficiency and durability, but also a new paradigm related to synergistic, cojoined experimental and theoretical research. Discussion: Catalyst\ionomer interfaces are complex and not fully understood, but through investigating different interfaces and utilizing digital and physical twins, we can elucidate key mechanisms and understanding. Understanding the dynamic double layer in electrochemical systems that use solid electrolytes is crucial to identifying and mitigating the controlling phenomena to enable increased performance and durability at the technology level. Studying the time and length scales of interfacial changes can be a powerful tool to understand reaction mechanisms and changes in the electrolyzer performance and durability.

Climate change has rapidly altered marine ecosystems and is expected to continue to push systems and species beyond historical baselines into novel conditions. Projecting responses of organisms and populations to these novel environmental conditions often requires extrapolations beyond observed conditions, challenging the predictive limits of statistical modeling capabilities. Bioenergetics modeling provides the mechanistic basis for projecting climate change effects on marine living resources in novel conditions, has a long history of development, and has been applied widely to fish and other taxa. We provide our perspective on 4 opportunities that will advance the ability of bioenergetics-based models to depict changes in the productivity and distribution of fishes and other marine organisms, leading to more robust projections of climate impacts. These are (1) improved depiction of bioenergetics processes to derive realistic individual-level response(s) to complex changes in environmental conditions, (2) innovations in scaling individual-level bioenergetics to project responses at the population and food web levels, (3) more realistic coupling between spatial dynamics and bioenergetics to better represent the local- to regional-scale differences in the effects of climate change on the spatial distributions of organisms, and (4) innovations in model validation to ensure that the next generation of bioenergetics-based models can be used with known and sufficient confidence. Our focus on specific opportunities will enable critical advancements in bioenergetics modeling and position the modeling community to make more accurate and robust projections of the effects of climate change on individuals, populations, food webs, and ecosystems.

Understanding factors that affect the clustering and association of antibodies molecules in solution is critical to their development as therapeutics. For 19 different monoclonal antibody (mAb) solutions, we measured the viscosities, the second virial coefficients, the Kirkwood-Buff integrals, and the cluster distributions of the antibody molecules as functions of protein concentration. Solutions were modeled using the statistical-physics Wertheim liquid-solution theory, representing antibodies as Y-shaped molecular structures of seven beads each. We found that high-viscosity solutions result from more antibody molecules per cluster. Multi-body properties such as viscosity are well predicted experimentally by the 2-body Kirkwood-Buff quantity, G22, but not by the second virial coefficient, B22, and well-predicted theoretically from the Wertheim protein-protein sticking energy. Weakly interacting antibodies are rate-limited by nucleation; strongly interacting ones by propagation. This approach gives a way to relate micro to macro properties of solutions of associating proteins.

We investigate the degradation mechanisms of In0.2Ga0.8N/GaN light emitting diodes through combined experimental analysis and simulations. The devices were submitted to constant current stress at 100 mA. Depending on the measuring current level, two degradation trends were observed: at high test currents (e.g. 200 mA), a monotonic decrease in optical power was observed; at low test currents (e.g. 5 mA), an initial degradation was observed, followed by an improvement in device efficiency (positive ageing). For the first time, such recovery effect was analyzed and modeled, as due to the generation of charged defects at the InGaN/GaN interface, resulting in the increase in the injection efficiency at low bias levels. The role of interface defects was validated by means of numerical simulations, with good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition‐state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non‐linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint‐, descriptor‐, and graph neural network‐based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists. [ABSTRACT FROM AUTHOR]

Summary Policymakers require health economic modeling to guide their decision‐making over the choice of interventions for obesity. This scoping review was undertaken to report on the health economic models in use for estimating the value of behavioral interventions (individual or population level) for obesity reduction. Electronic databases (MEDLINE, Embase, PsycINFO, EconLit, and Web of Science) were searched for publications meeting inclusion criteria from January 2015 to May 2023. Seventy‐three studies were included, using 44 health economic models between them. When considered against the expert recommendations for modeling of this type, only four models (9%) met all five key elements. The element most commonly unfulfilled was the use of a microsimulation modeling approach (41%, n = 18), followed by model validation (46%, n = 20). A majority of models met each of the other elements: use of a lifetime horizon (59%, n = 26), inclusion of key health events (66%, n = 29), and a risk equation approach to event simulation (71%, n = 31). In addition, under half of the studies considered health inequalities in their reporting. Continued proliferation of models with inadequate time horizons, breadth of obesity‐related health conditions, and perspectives on costs and outcomes risks underestimation of the benefits of longer term interventions and impacts on health inequalities. [ABSTRACT FROM AUTHOR]

In this study, nanocomposites of g‐C3N4/MN4 (where M is Mn, Fe and Co) have been designed using advanced density functional theory (DFT) calculations. A comprehensive analysis was conducted on the geometry, electronic, optical properties, work function, charge transfer interaction and adhesion energy of the g‐C3N4/MN4 heterostructures and concluded that g‐C3N4/FeN4 and g‐C3N4/CoN4 heterojunctions exhibit higher photocatalytic performance than individual units. The better photocatalytic activity can be attributed mainly by two facts; (i) the visible light absorption of both g‐C3N4/FeN4 and g‐C3N4/CoN4 interfaces are higher compared to its isolated analogs and (ii) a significant enhancement of band gap energy in g‐C3N4/FeN4 and g‐C3N4/CoN4 heterostructures limited the electron–hole recombination significantly. The potential of the g‐C3N4/MN4 heterojunctions as a photocatalyst for the water splitting reaction was assessed by examining its band alignment for water splitting reaction. Importantly, while the electronic and magnetic properties of MN4 systems were studied, this is the first example of inclusion of MN4 on graphene‐based material (g‐C3N4) for studying the photocatalytic activity. The state of the art DFT calculations emphasis that g‐C3N4/FeN4 and g‐C3N4/CoN4 heterojunctions are half metallic photocatalysts, which is limited till date. [ABSTRACT FROM AUTHOR]

This article presents examples of the use of complex sheet metal in everyday life and the course of modeling a sheet bent simultaneously in two planes. The element selected to describe the modelling process is a fragment of the side wall of a lever hydroelectric power plant that operates on the Bystrzyca River in Lublin. The shape of the element is conditioned by the need to achieve a gentle change in the cross-section of the water stream during the flow between the steering system and the outflow area. The segment through which the water flows is clearly lower and wider than the other modules of the power plant, which is due to the average water level in the Bystrzyca River. Such a shape is necessary because the inflow and outflow parts of the power plant must be submerged under water all the time. This is due to the principle of operation of the power plant, which works by obtaining a vacuum created by pumping air from the inside. The article also presents the results of the strength analysis, which was performed for the segment exposed to the highest loads. The analysis and the entire design were performed in Autodesk Inventor, and the results obtained were as expected and even in the most critical places no values were reached that would require changes to the design. [ABSTRACT FROM AUTHOR]

The significant production of sewage sludge by wastewater treatment plants on a global scale and the lack of correspondence between housing development and the expansion of sanitation infrastructure indicate a genuine concern regarding environmental preservation. This study addresses the crucial issue of effective sewage sludge management and its environmental impact. In the context of searching for new drying methods that optimize energy use and effectively stabilize sewage sludge, this work investigated the drying behavior of sewage sludge from treatment plants in two northern Moroccan cities using a prototype of an indirect forced convection solar dryer. The drying experiments enabled the determination of drying kinetics as well as highlighted the influence of temperature and humidity on the drying rate. The characteristic drying curve (CDC) and its mathematical expression were determined using Van Meel’s formalism. Thermal diffusivity of wastewater sludge during drying was also investigated. Using Fick’s diffusion model, diffusion coefficients ranged between 0.59 × 10-9 m²/s and 1.43 × 10-9 m²/s, demonstrating an increase in effective diffusivity with rising temperature. The Arrhenius equation provided activation energy values of 16.80 kJ/mol for Oujda samples and 19.72 kJ/mol for Nador samples, indicating the effect of temperature on effective diffusivity. A new equation based on the Midilli-Kucuk model was proposed to predict the drying behavior under untested aerothermal conditions, considering drying temperature and the initial dryness. This study offers a comprehensive analysis of the drying kinetics and effective diffusivity of sewage sludge, providing valuable insights for designing large dryers for sludge management in WWTPs. This approach presents an optimal solution for drying and stabilizing sludge, contributing to environmental preservation efforts. [ABSTRACT FROM AUTHOR]

In our interconnected world, Systems Thinking (ST) is increasingly being recognized as a key learning goal for science education to help students make sense of complex phenomena. To support students in mastering ST, educators are advocating for using computational modeling programs. However, studies suggest that students often have challenges with using ST in the context of computational modeling. While previous studies have suggested that students have challenges modeling change over time through collector and flow structures and representing iterative processes through feedback loops, most of these studies investigated student ST through pre and post tests or through interviews. As such there is a gap in the literature regarding how student ST approaches develop and change throughout a computational modeling unit. In this case study, we aimed to determine which aspects of ST students found challenging during a computational modeling unit, how their approaches to ST changed over time, and how the learning environment was supporting students with ST. Building on prior frameworks, we developed a seven-category analysis tool that enabled us to use a mixture of student discourse, writing, and screen actions to categorize seven ST behaviors in real time. Through using this semi-quantitative tool and subsequent narrative analysis, we found evidence for all seven behavior categories, but not all categories were equally represented. Meanwhile our results suggest that opportunities for students to engage in discourse with both their peers and their teacher supported them with ST. Overall, this study demonstrates how student discourse and student writing can be important evidence of ST and serve as a potential factor to evaluate ST application as part of students' learning progression. The case study also provides evidence for the positive impact that the implementation of a social constructivist approach has in the context of constructing computational system models. [ABSTRACT FROM AUTHOR]

Among pathogens that affect tomato, Alternaria spp. are important due to their implications in yield losses and the contamination of tomato products by mycotoxins. In this study, a systematic literature review was conducted to retrieve and analyze available data on the Alternaria– tomato pathosystem, with particular attention focused on the main biological processes included in the pathogen life cycle and mycotoxin production. We considered 110 papers (selected from initial 2,138 papers) on five Alternaria species that were historically related or recently identified to cause damage to tomato leafage and fruits. Published mathematical models related to Alternaria diseases in tomato were also screened based on their aim and development methods, highlighting the wide use of empirical approach. Retrieved information was also evaluated for applications in building a mechanistic, weather-driven model that incorporates the key steps of the pathogen life cycle. This systematic review highlights several knowledge gaps, including the effect of wetness on infection and environmental requirements for mycotoxin production, and suggests paths for further research especially for recently isolated species. [ABSTRACT FROM AUTHOR]

Hox transcription factors play crucial roles in organizing developmental patterning across metazoa, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of intestinal subregions early in embryogenesis, which ultimately leads to their specialization in both form and function. Although the midgut forms villi, the hindgut develops sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements of the embryonic chick intestine and mathematical modeling, we demonstrate that the posterior Hox gene HOXD13 regulates biophysical phenomena that shape the hindgut lumen. We further show that HOXD13 acts through the transforming growth factor β (TGF-β) pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme—together, these features lead to hindgut-specific surface buckling. TGF-β, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional identity that direct posterior intestinal morphogenesis. [Display omitted] • Mechanical properties define epithelial buckling in the developing chick intestine • HOXD13 promotes hindgut mechanics through increased mesenchymal TGF-β signaling • Hindgut and ectopic midgut HOXD13- expressing smooth muscle upregulates INHBA • Mesenchymal TGF-β activation tunes mechanics via extracellular matrix remodeling Gill et al. mechanistically link HOXD13 expression to morphogenesis of the chick hindgut epithelium. HOXD13 activity within smooth muscle induces INHBA expression, which causes adjacent mesenchymal cells to remodel extracellular matrix (ECM) in response to TGF-β upregulation, thus promoting the biomechanical parameters that define the hindgut morphological trajectory. [ABSTRACT FROM AUTHOR]

Through alteration of the polarity of DC plasma during the growth of carbon nanotubes in a PECVD reactor, significantly different morphologies of such species have been achieved. By using this approach, for the first time, both Half‐aligned and Entangled structures were synthesized, along with Full‐aligned carbon nanotubes, introducing three binder‐free electrodes with various levels of tortuosity. The crucial parameter and influential effect of tortuosity in these three‐dimensional nanostructure scaffolds for application in lithium‐ion batteries were investigated. Previous research findings suggested that increasing the tortuosity of the conductive scaffolds leads to preferential accumulation of lithium at the top surface and causes the loss of capacity in subsequent charge‐discharge cycles. Our finding reveals that there exists a trade‐off between lithium‐demand, capacity, and preferential accumulation of lithium at the top surface. Among the presented scaffolds, the Half‐aligned MWCNTs was able to maintain a high capacity of 876.9 mAh/g over more than 300 cycles, and demonstrate capacity improvement during this period and excellent rate capability, even at a rate of 5 C. This capacity is almost three times that can be achieved with graphite, showcasing promising and outstanding results for carbon nanotubes. [ABSTRACT FROM AUTHOR]

This study presents a simplified numerical modeling approach that estimates the local scour depths around bridge supports based on the bed shear stresses. It is developed using 3D computational fluid dynamics and coding through ANSYS workbench scripting along with C++ language. The proposed model is called SCFDS (Simplified Computational Fluid Dynamics Scour). The model's main idea is to lower the bed bathymetry at the locations affected by flow obstruction until the shear stresses reach stable target values. The developed model is assessed by applying it to a complex pier of a real bridge over the Nile-River, and its scour data is calculated using the Hec-18 empirical equations. The outcomes of the model's assessment show that it can depict the local scour around complex bridge supports. The developed approach is a novel and efficient tool that can help the designers in simulating local scour in the vicinity of flow obstructions. However, more verification studies are necessary to reach a generalized conclusion. [ABSTRACT FROM AUTHOR]

We discuss the evolution of a computational model of delusions, beginning with a background consideration of how computational psychiatry, with its roots firmly based in cognitive neuropsychiatry, seeks to develop descriptive and mechanistic models that reach across different levels of explanation in order to provide more comprehensive understanding of how neurobiological, cognitive, subjective and sociocultural factors may all contribute to complex psychopathology. This quest for bridging explanations – or "consilience" – across the levels is a shared goal of computational psychiatry and cognitive neuropsychiatry and is, we argue, crucial to explaining delusional beliefs. We outline how early computational models appealed to prediction error disturbances as a basis for understanding the early emergence of delusions and show that, despite empirical support, there have been certain explanatory limitations that make a simple prediction error account partially limited. Embedding the account within the increasingly influential hierarchical predictive processing framework subsequently offered a more powerful and comprehensive account, particularly by encouraging the consideration hierarchically-organized inference and its evolution over time. However, further limitations remain in its explanatory scope, most notably the fact that delusions can emerge rapidly and suddenly in a way that seems revelatory and convincing. This phenomenon is not easily encompassed by the standard predictive processing account which emphasizes an iterative process of optimizing inference. However, more recent development in the form of "Hybrid Predictive Coding" posits a complementary rapid inference mechanism. We discuss how this hybrid approach may be key to a more comprehensive computational understanding of delusions. [ABSTRACT FROM AUTHOR]

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the C lustered R egularly I nterspaced S hort P alindromic R epeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

This article presents the results of the modeling of the behavior of the titanium alloy Ti-3Al‑2.5V at the initial stage of extrusion of a tube billet. The influence of the gaps between the surfaces of the tool and the billet on the nature of its shape change and the strain state is discussed. In particular, the most deformed tube parts of the maximum (by modulus) strain tensor values are identified. The dependence of the Ti-3Al‑2.5V alloy tube extrusion force on the stroke of the pressure pad is calculated. The results of the calculation of the maximum forces and the forces at the steady-state stage are in good agreement with the data obtained under an industrial experiment. Horizontal extrusion leads to an asymmetry in the location of the deformation fields relative to the extrusion axis. Accordingly, an asymmetric distribution of properties arises in the extruded tube, which is experimentally confirmed using hardness measurements. Correspondence is established between the simulation data on the heterogeneity of the strain distribution over the wall thickness of the hot-pressed tube made of titanium alloy Ti-3Al‑2.5V, considering the pressing-out, with the results of micro- and X‑ray structural studies of the tube semifinished product obtained. [ABSTRACT FROM AUTHOR]

The School Science Activity recognises the need for science education that provides citizens with a worldview about which they can think, act and communicate. However, science teachers, especially in physics, continue to use transmissive teaching approaches and show evidence of difficulties in implementing didactic approaches such as modeling in the classroom. Some reasons include the complexity of designing sequences under this approach and the lack of comprehension of the role of modeling competences that are developed in this kind of implementation. This research analyses how the ideas of physics teachers in training in Chile change concerning modeling and its competences, after participating in a course oriented towards this approach. At the end of the course, progress is identified in comprehending the aims of the modeling competences of expression, evaluation and application of a model. The results also highlight the importance of training future teachers through classes focused on modeling. To this purpose, some design and implementation guidelines are proposed. [ABSTRACT FROM AUTHOR]

The buffer leakage phenomena have a large effect on high‐power and fast‐switching GaN high electron mobility transistor devices. Herein, the buffer leakage mechanism in a GaN‐on‐Si substrate with AlGaN buffer layers is investigated by using samples with various carbon concentrations. Comparing the leakage current with crystallinity and impurity concentration, it is found that the vertical leakage current is dominated by both threading dislocations and bulk impurities. Increasing the carbon concentration of buffer layers suppresses leakage current in the bulk conducting regime in high electric fields but increases it in the dislocation conducting regime in low electric fields. These results indicate that the optimal carbon concentration of buffer layers depends on the target electric field. [ABSTRACT FROM AUTHOR]

Innovative methods for substrate patterning provide intriguing possibilities for the development of devices based on ordered arrays of semiconductor nanowires. Control over the nanostructures' morphology in situ can be obtained via extensive theoretical studies of their formation. In this paper, we carry out an investigation of the ordered nanowires' formation kinetics depending on the growth mask geometry. Diffusion equations for the growth species on both substrate and nanowire sidewalls depending on the spacing arrangement of the nanostructures and deposition rate are considered. The value of the pitch corresponding to the maximum diffusion flux from the substrate is obtained. The latter is assumed to be the optimum in terms of the nanowire elongation rate. Further study of the adatom kinetics demonstrates that the temporal dependence of a nanowire's length is strongly affected by the ratio of the adatom's diffusion length on the substrate and sidewalls, providing insights into the proper choice of a growth wafer. The developed model allows for customization of the growth protocols and estimation of the important diffusion parameters of the growth species. [ABSTRACT FROM AUTHOR]

Few studies have examined the N kinetics of individual feeds with stable isotope tracing. We hypothesized that N partitioning to milk and excreta pools as well as the rates of the processes that drive this partitioning would differ for alfalfa silage, corn silage, corn grain, and soybean meal. Feed ingredients were endogenously labeled with 15N and included in 4 diets to create treatments with the same dietary composition and different labeled feed. Diets were fed to 12 late-lactation dairy cows for 4 d (96 h) and feces, urine, and milk collection proceeded during the 4 d of 15N enrichment and for 3 d (80 h) after cessation of label feeding. Nonlinear models of 15N enrichment and decay were fit to milk (MN), urine (UN), and fecal N (FN) in R with the nlme package, and feed-specific parameter estimates were compared. The estimated proportions of feed N that were excreted in feces supported our understanding that N from soybean meal and corn grain is more digestible than N from alfalfa and corn silage. Estimates for the N partitioning between milk and urine from the 2 concentrate feeds (soybean meal and corn grain) indicated that UN/MN ratios were less than or equal to 1:1, indicating either more or equal nitrogen partitioning to milk compared with urine. It is important to maintain factual accuracy in representing the results rather than implying a desired outcome unsupported by the data. In contrast, UN/MN ratios for forage feeds (corn and alfalfa silage) were >1:1, indicating more N partitioning to urine than milk. The modeled proportion of total FN that originated from feed N was 82.2%, which is in line with previous research using a similar 15N measurement timeframe. However, the proportion of urinary and MN originating from feed N was much lower (60.5% for urine, 57.9% for milk), suggesting that approximately 40% of urinary and MN directly originate from body N sources related to protein turnover. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [Display omitted] [ABSTRACT FROM AUTHOR]

Handling geotechnical peculiarities in specialized ground models – an experience report Dr. Spang GmbH has developed numerous specialized ground models in recent years and has gained corresponding experience in ground modeling. Within the company, a standard procedure for creating these specialized models has been established. From a software perspective, automatic meshing (triangulated 3D surface mesh of the layer boundaries) already delivers useful results for many standard cases. However, in geotechnics, there are not only standard cases and features to assess. Geotechnical peculiarities (e. g. inhomogeneous layering) result in an increased manual modeling effort. This article aims to shed light on how to handle these peculiarities in modeling, present the additional modeling steps involved, and highlight the software limitations that arise in automatic meshing (interpolation or triangulation) in such special cases. [ABSTRACT FROM AUTHOR]

Deadwood represents a significant carbon pool and unique biodiversity reservoir in forests and savannas but has been largely overlooked until recently. Storage and release of carbon from deadwood is controlled by interacting decomposition drivers including biotic consumers (animals and microbes) and abiotic factors (water, fire, sunlight, and freeze–thaw). Although previous research has focused mainly on forests, we synthesize deadwood studies across diverse ecosystems with woody vegetation. As changing climates and land-use practices alter the landscape, we expect accelerating but variable rates of inputs and outputs from deadwood pools. Currently, Earth system models implicitly represent only microbial consumers as drivers of wood decomposition; we show that many other factors influence deadwood pools. Forest management practices increasingly recognize deadwood as an important contributor to forest dynamics, biodiversity, and carbon budgets. Together, emerging knowledge from modeling and management suggests a growing need for additional research on deadwood contributions to carbon storage and greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Aim of the study was to analyze the metabolic profile of badminton matches and training drills. Therefore, 11 male (23.2 ± 3.8 years, 182 ± 7 cm, 74.4 ± 8.4 kg) and five female (19.3 ± 1.5 years, 170 ± 6 cm, 62.6 ± 9.2 kg) elite badminton players participated in either a training match (TM; n = 7) and/or three protocols of multifeeding drills (T10, T30,T50;n = 13), that varied in interval and rest durations (10 s/10 s, 30 s/30 s, 50 s/50 s). Absolute and relative energetic costs (Wtot and Etot) and contribution to oxidative (WOxid), phosphagen (WPCr), and anaerobic glycolytic (WLa) metabolism were calculated by the three‐component PCr‐La‐O2‐method based on an indirect calorimetric approach from oxygen consumption during exercise, post exercise, and net blood lactate concentration. A novel intermittent approach was used to consider replenishment of phosphocreatine during each resting phase. Results show that during TM, Etot was 676 ± 98J·kg−1 min−1, while metabolic pathways contributed by 56.9 ± 8.6% (WOxid), 42.7 ± 8.7% (WPCr), and 0.4 ± 0.6% (WLa). In the multifeeding drills Etot was comparable between T10 (1020 ± 160J·kg−1 min−1) and T30 (985 ± 173 J·kg−1 min−1) but higher in T50 (1266 ± 194J·kg−1 min−1) (p < 0.001). Relative contribution of WOxid was lower in T10 (47.3 ± 7.7%) but similar in T30 (56.5 ± 6.2%) and T50 (57.3 ± 6.0%) (p < 0.001). WPCr was highest in T10 (51.1 ± 8.3%) followed by T30 (42.2 ± 6.9%) and lowest in T50 (31.2 ± 7.7%) (p < 0.001). WLa was similar between T10 (1.6 ± 1.0%) and T30 (2.1 ± 1.0%) but higher in T50 (11.6 ± 4.8%) (p < 0.001). Concludingly, metabolic costs in badminton are predominantly covered by oxidative and phosphagen energetic pathways. Metabolic profiles of the multifeeding drills differ depending on rally/interval duration, with increasing contribution of anaerobic glycolysis and decreasing phosphagen contribution in case of longer intervals. Plain Language Summary: Badminton stands out as a high‐paced and exceptionally demanding sport, necessitating a very complex interaction between the aerobic and anaerobic metabolic systems. Using an innovative approach of indirect caloric measurement, the main aim of the investigation was to determine the metabolic profiles of elite badminton match play and badminton‐specific training drills. Therefore, 16 elite players conducted either a training match and/or three different protocols of a multifeeding drill (with varying interval/rest durations). The study underscores a pivotal role of anaerobic alactic energy supply in badminton, highlighting the critical need for rapid phosphocreatine (PCr) replenishment to prevent performance decline attributed to increasing acidosis. While aerobic capacity remains vital for PCr replenishment, occasional reliance on lactic metabolism may be important in certain probable match deciding situations. The multifeeding drills exhibit variations in the metabolic profile depending on rally and rest durations. As the interval length increases, the dominance of aerobic and lactic metabolism rises, while the proportion of anaerobic alactic contribution decreases. Emphasizing that in on‐court drills different interval durations address different metabolic pathways, training content should be chosen wisely to accurately address individual training goals and to align with a long‐term training periodization perspective. Highlights: This is the first study using an intermittent approach of the three‐component PCr‐La‐O2‐method, to determine the metabolic profile of elite badminton match play and the impact of drill prescription on metabolic stress in badminton‐specific training drills.The present findings reveal that energy in badminton generally is predominantly derived from oxidative and phosphagen energetic pathways, with the anaerobic glycolytic system contributing only minimally.Multifeeding drills essentially mirror the metabolic demands of matches but exhibit variations depending on rally and rest periods. These findings underscore the need to select training protocols thoughtfully, tailor training according to individualized goals and to structure those goals within a long‐term training periodization. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields. [ABSTRACT FROM AUTHOR]

Adavosertib (AZD1775) is a potent small‐molecule inhibitor of Wee1 kinase. This analysis utilized pharmacokinetic data from 8 Phase I/II studies of adavosertib to characterize the population pharmacokinetics of adavosertib in patients (n = 538) with solid tumors and evaluate the impact of covariates on exposure. A nonlinear mixed‐effects modeling approach was employed to estimate population and individual parameters from the clinical trial data. The model for time dependency of apparent clearance (CL) was developed in a stepwise manner and the final model validated by visual predictive checks (VPCs). Using an adavosertib dose of 300 mg once daily on a 5 days on/2 days off dosing schedule given 2 weeks out of a 3‐week cycle, simulation analyses evaluated the impact of covariates on the following exposure metrics at steady state: maximum concentration during a 21‐day cycle, area under the curve (AUC) during a 21‐day cycle, AUC during the second week of a treatment cycle, and AUC on day 12 of a treatment cycle. The final model was a linear 2‐compartment model with lag time into the dosing compartment and first‐order absorption into the central compartment, time‐varying CL, and random effects on all model parameters. VPCs and steady‐state observations confirmed that the final model satisfied all the requirements for reliable simulation of randomly sampled Phase I and II populations with different covariate characteristics. Simulation‐based analyses revealed that body weight, renal impairment status, and race were key factors determining the variability of drug‐exposure metrics. [ABSTRACT FROM AUTHOR]