Search for: All records

Cortical thickness is a widely used biomarker of brain morphology and health, yet it is dependent on local cortical folding. Because gyral crowns are consistently thicker than sulcal fundi and cortical folds vary widely across individuals, these fluctuations introduce unmodeled nuisance variance that can obscure meaningful biological effects of interest. Previous global methods of folding compensation incompletely compensate for folding effects on cortical thickness. Spatial smoothing is commonly used to reduce these effects in the literature, but this markedly degrades spatial localization precision. To address these limitations, we developed a novel method for folding-compensated cortical thickness estimation that uses nonlinear local multiple regression with five folding measures to model and more completely remove folding-related variance from cortical thickness. This approach estimates what cortical thickness would have been in the absence of folding, yielding a more biologically interpretable measure of cortical architecture. We applied this new approach to data from the Young Adult Human Connectome Project (HCP-YA) and Aging Human Connectome Project (HCA), demonstrating substantial reductions in intra-areal and inter-individual variability, substantially increasing standardized effect sizes of age on cortical thickness (41% increase) while preserving neurobiologically expected patterns, and avoiding the loss of spatial precision that occurs with the spatial smoothing that has traditionally been used in the literature. The method has been integrated into the HCP pipelines, facilitating its widespread use. By attenuating folding-induced variability, this technique enhances cortical thickness as a structural phenotype and may support more accurate cortical parcellation, longitudinal tracking, and biomarker discovery in brain health and disease. 

In recent years, there has been a rise in recognition of the need for computing education to bridge the gap between academia and industry. In addition, educational researchers are also interested in increasing student engagement by grounding learning experiences in real-life concerns, community issues, or personal interests. Unfortunately, traditional lecture-based teaching techniques often fail to prepare students for the challenges they will face in real-world software development scenarios. Project-Based Learning (PjBL) takes a different approach by immersing students in real-world software engineering projects, allowing them to apply theoretical knowledge in practical contexts, building practical skills, fostering critical thinking, and improving problem-solving abilities. Prior literature reviews have explored aspects of PjBL in computing education, such as communication support, educational effectiveness, sprint organization, and capstone course design. However, no literature review extensively and comprehensively examines the following questions as a whole: where PjBL is used, how it is taught, why it should be used, and what challenges to expect in software-related computing courses. The review takes a systematic approach, incorporating a thorough search strategy across four academic databases and targeting keywords associated with PjBL and software computing in higher education. A total of 34 PjBL course attributes were extracted from 184 selected primary studies, which contributed to answering six research questions: (1) What computing courses use PjBL? (2) What is the nature of software projects used? (3) How are these projects organized? (4) How are students assessed and evaluated? (5) What are the reported impacts of PjBL? and (6) How are students supported throughout the projects? The literature review makes four key contributions: a description of the nature of software projects used and how these projects are organized, a highlight of the impacts of PjBL and the methods used to measure those impacts, a summary of the various forms of support provided to students throughout their projects, and the list of challenges encountered in implementing PjBL and recommendations to alleviate those challenges. This comprehensive review offers new insights and serves as a catalog of best practices for computing educators. 

Traceability is critical for achieving seafood sustainability goals. However, trade restrictions highlight challenges for identifying basic information, including country of harvest. We use new seafood trade data to illustrate how trade can elude enforcement using the case of responses following Russian sanctions and quantify pathways through which the US imports Russian-harvested products. We then discuss the current policy landscape for enforcing trade restrictions and highlight priorities for improving seafood traceability. 

Field-theoretic simulations that rely on a partial saddle-point approximation have become powerful tools for studying complex polymer materials. The computational cost of such simulations depends critically upon the efficiency of the iterative algorithm used to identify a partial saddle-point field configuration during each step of a stochastic simulation. We introduce a new algorithm for this purpose that relies on a physically motivated approximation in which the linear response of the density to a small change in a pressure-like field is approximated by the response of a hypothetical homogeneous system. The computational cost of the resulting algorithm is significantly less than that of the commonly used Anderson mixing algorithm. 

Abstract Antifungal killer toxins are cytotoxic proteins that have the potential to combat the growing threat of fungi to human health and agriculture. A lack of empirical tertiary structures has placed limitations on understanding their mechanisms of action and targeting of pathogens. AlphaFold and molecular dynamics simulations were used to create tertiary structure models of all canonicalSaccharomyceskiller toxins. These models have enabled the prediction of the functional domains of killer toxins and postranslational modifications, including sites of proteolytic cleavage and disulfide bonds. They have also revealed unexpected homology betweenSaccharomyceskiller toxins, suggesting that all but K28 are likely ionophores. Structural homology with the well-studied killer toxins K1 and K2 enabled prior empirical data to predict the antifungal and immunity mechanisms of the K1L, K21, K45, K74, and KHS toxins. The understudied killer toxins Klus, KHR, and K62 were found to have homology to bacterial and plant toxins, including members of the aerolysin family and antifungal lectins. These structural similarities provided clues for the mechanisms of killer toxin carbohydrate binding, oligomerization, and membrane attack. Given the hundreds of sequence homologs of theSaccharomyceskiller toxin identified across fungi, modeling studies offer an exciting opportunity to characterize novel toxin-like proteins. This approach is strengthened by the continued use of the model yeastS. cerevisiaeto study killer toxins and the wealth of functional data gathered in the decades since their first discovery. 

Abstract Reliable estimates of climate sensitivity require understanding how patterns of surface temperature change influence the global radiative feedback. Here we present a theoretical basis for this pattern effect as it relates to the longwave clear sky feedback. A moist adiabatic feedback framework is developed that partitions the feedback into components associated with locally determined moist adiabatic processes and components associated with deviations therefrom, such as due to nonlocal influences and relative humidity changes. Applying this feedback framework to simulations forced by transient and equilibrium patterns of sea surface temperature change reveals that the pattern effect is driven by different physical processes in different geographic regions. In the subtropics, the more stabilizing feedback under transient climate change is explained by a more negative relative humidity feedback. Over the Southern Ocean, the less stabilizing feedback under transient climate change occurs due to the muted surface warming there, which promotes a weak surface temperature feedback; furthermore, for an idealized pattern of change in which the transient sea surface temperature change is uniformly increased but retains the same structure, the pattern effect essentially disappears. The moist adiabatic feedback framework demonstrates that the evolving zonal-mean longwave clear sky feedback—towards stabilization at high latitudes and destabilization at low latitudes, as the climate approaches equilibrium—is controlled by processes, specifically surface temperature and relative humidity feedbacks, not isolated by conventional feedback analysis. In the global mean, the destabilization effect proves larger, receiving additional contributions from small but geographically extensive differences in the fixed-relative humidity atmospheric temperature feedback. 

SMAX1-LIKE (SMXL) proteins in plants are cellular signaling hubs, many of which are posttranslationally regulated by karrikins from smoke, the plant hormones strigolactones (SLs), and/or cues such as light and nutrients. SMXL proteins control diverse aspects of growth, development, and environmental adaptation in plants through transcriptional corepression and interactions with transcriptional regulator proteins. In flowering plants, the SMXL family comprises four phylogenetic clades with different roles. Functions of the aSMAX1 clade include control of germination and seedling development, while the SMXL78 clade controls shoot architecture. We investigated how SMXL roles are specified inArabidopsis thaliana.Through promoter-swapping experiments, we found thatSMXL7can partially replicateSMAX1function, butSMAX1cannot replaceSMXL7. This implies that the distinct roles of these genes are primarily due to differences in protein sequences rather than expression patterns. To determine which part of SMXL proteins specifies downstream control, we tested a series of protein chimeras and domain deletions of SMAX1 and SMXL7. We found an N-terminal region that is necessary and sufficient to specify control of germination, seedling growth, or axillary branching. We screened 158 transcription factors (TFs) for interactions with SMAX1 and SMXL7 in yeast two-hybrid assays. The N-terminal domain was necessary and/or sufficient for most of the 33 potential protein–protein interactions that were identified for SMAX1. This finding unlocks different ways to engineer plant growth control through cross-wiring SMXL regulatory “input” and developmental “output” domains from different clades and lays a foundation for understanding how functional differences evolved in the SMXL family.