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Abstract We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation. 

Abstract In recent decades, cathode materials, significant in both liquid and solid-state lithium-ion and beyond-lithium batteries, are essential for global sustainability due to their unique redox and ionic transport properties. The mass production of cathodes to keep pace with electrochemical energy storage demand has increasingly come under scrutiny. However, the environmental impacts, specifically emissions and waste produced during the synthesis and surface treatment of these materials, have largely been overlooked, even in laboratory settings. This perspective addresses this gap by discussing the importance of adopting entirely dry, waste-free processes for cathode material production. We summarize recent advances in both physical and chemical dry processing techniques and outline potential future research directions in this domain, emphasizing their significance for sustainable battery manufacturing. 

Abstract BackgroundGraduate‐level education is gaining attention in engineering education scholarship. While “socialization” is a key term in doctoral literature, little is known about how socialization occurs over time. One common assumption asserts that socialization increases over time, encompassing factors such as belongingness, research ability, and advisor relationship as students acclimate to the norms and values of their advisors, departments, universities, and disciplines. We investigate engineering doctoral student socialization trends: students likely to complete their degrees and those who have questioned whether to persist in their programs. Understanding these trends is essential, as many students consider leaving their programs. Purpose/HypothesisThis paper aims to understand how socialization processes occur over several years in engineering students who questioned leaving their PhD programs. Design/MethodWe present longitudinal survey data collected from two cohorts (N_A = 113 andN_B = 355) of engineering doctoral students at R1 universities in the United States. Data were collected over 2 years through SMS surveys with participants receiving text messages three times per week. We analyzed data using descriptive and time series analysis methods. ResultsBoth cohorts showed lower levels of belongingness over time, reported declining advisor relationships, and experienced higher levels of stress. Students later in their programs also reported deteriorating overall social relationships. These findings contradict canonical socialization theory, which expects socialization to naturally improve over time. ConclusionWhile many assume socialization occurs passively and students acculturate into their department and research team over time, our results show students who question whether to persist are de‐socializing from graduate school. 

Abstract PurposeRecent studies show that glioblastoma (GBM) is more sensitive to temozolomide (TMZ) in the morning. In cells, inhibiting O6-Methylguanine-DNA-Methyltransferase (MGMT) abolished time-dependent TMZ efficacy, suggesting that circadian regulation of this DNA repair enzyme underlies daily TMZ sensitivity. Here, we tested the hypotheses thatMGMTpromoter methylation and protein abundance vary with time-of-day in GBM, resulting in daily rhythms in TMZ efficacy. MethodsWe assessed daily rhythms inMGMTpromoter methylation in GBM in vitro and retrospectively analyzedMGMTmethylation status in human GBM biopsies collected at different times of day. Next, we measured MGMT and BMAL1 protein abundances in GBM cells collected at four-hour intervals. To understand the therapeutic implications of circadian variations in MGMT, we incorporated its daily rhythms into an in vitro mathematical model capturing interactions between MGMT, TMZ, and GBM DNA. ResultsWe found daily rhythms inMGMTpromoter methylation and protein levels in GBM in vitro, and in patient biopsies peaking at midday. Further, MGMT protein levels peaked at CT4, corresponding to the time of maximal TMZ efficacy in vitro. When we incorporated cell-intrinsic circadian rhythms in MGMT protein into a mathematical model for GBM chemotherapy, we found that dosing when daily MGMT levels peaked and began to decline produced maximum DNA damage. ConclusionOur findings suggest that the likelihood of diagnosis ofMGMTpromoter methylation may vary with time of biopsy in GBM. Furthermore, theoretical modeling predicts that efforts to deliver TMZ after the daily peak of MGMT activity, with exact time being dose-dependent, may significantly enhance its therapeutic efficacy. Graphical Abstract 

ABSTRACT BackgroundIn recent years, the study of animal personality has gained significant attention in ecology and evolutionary biology. Small mammals are one of the most frequently studied mammalian taxa in this field, and their personality significantly impacts ecological outcomes. However, a review focused on the materials and methods to study wild small mammal personality is lacking. AimsTo address this gap, we aim to (1) identify the most consistent assays for measuring specific personality traits in wild species and (2) propose a standardised experimental design, detailing optimal arena size, shape and material, as well as standardised testing conditions and experimental procedures and highlighting critical aspects which require validation. Moreover, we (3) report a clear interpretation of the most commonly measured behavioural traits and the methods employed for their analysis. Material and MethodsOur review synthesises findings from 133 articles covering 54 species in a variety of habitats, ranging from the Canadian boreal forests to the semi‐desert regions of South Africa. We found a concerning lack of standardisation in research methodologies, especially for key features such as the shape and size of arenas for behavioural assays and test duration. We observed considerable variability in how behavioural traits were interpreted. Nevertheless, we identified a suite of tests and interpretations of behaviours that allow for efficient processing of animals and produce consistent results in both field and laboratory settings. ConclusionWe conclude with five recommendations for a standardised approach to enhance the comparability of results and advance the field of wild small mammal personality research. 

ABSTRACT AimTest the response of mesopelagic zooplankton community composition and distributional ranges to dispersal potential and environment, in comparison with the epipelagic zooplankton community. LocationEpipelagic (0–200 m) and mesopelagic (200–1000 m) depth zones of the North Pacific Ocean. TaxonMulticellular zooplankton. MethodsMetabarcoding of two molecular markers (18S and COI) in combination with a global ocean circulation model, analysed by General Dissimilarity Modelling. ResultsWe found no significant difference in beta‐diversity across three depth strata (0–200, 200–500, and 500–1000 m), calculated from the nMDS dispersion of samples within each stratum. Similarity in beta‐diversity within the three depth strata indicates that epipelagic and mesopelagic zooplankton communities have similar levels of spatial turnover in species composition despite differences in the magnitude of environmental gradients and dispersal potential. There were no differences in the biogeographic ranges of taxa associated with each depth zone, but we observed larger temperature, salinity, and dissolved oxygen habitat envelopes as well as narrower potential food ranges for deeper‐dwelling taxa. Ocean basin‐scale community dissimilarity was correlated with dispersal distance, as well as with changes in temperature, salinity, dissolved oxygen concentration, and food flux. Combined Generalised Dissimilarity Models incorporating both dispersal potential and environmental habitat variables revealed that the environmental variables temperature and food flux had the strongest predictive power to explain community dissimilarity. 

ABSTRACT ObjectiveThe objective of this study was to examine the movements of Common Snook Centropomus undecimalis, a tropical euryhaline species, out of the Shark River, Everglades National Park, Florida, USA, and gain a better understanding of their long-distance regional movements. MethodsThis study used 7 years (2017–2023) of acoustic telemetry data from the Shark River Florida Coastal Everglades Array and from five other collaborative telemetry arrays in southwestern Florida to assess out-of-system movements for 119 Common Snook. Generalized linear models were used to assess the relationships between out-of-system movements and biological and environmental variables. ResultsMost Common Snook departures took place during the spawning season, with 121 departures (80.1%). Of 67 departed individuals, 26 were detected by the collaborative arrays (both north and south of our array, 38.8%). Of the departed Common Snook, 35 returned to the Shark River (52.2%), with most returning to the lower river (62.7%), and with a trend for larger fish to have a higher probability of return. Last, Common Snook that spent less time outside were more likely to return to the upper part of the Shark River. ConclusionsOur study leveraged five collaborative acoustic telemetry arrays to investigate long-distance movements of Common Snook across southwest Florida, revealing that the true scales of movement and population connectivity remain largely unknown. Despite limited receiver coverage, detections of Common Snook outside the Shark River were higher than expected, with half of the individuals exhibiting site fidelity by returning to the river. These findings underscore that a single river system may only represent a portion of an organism’s home range, highlighting the need for further studies and expanded receiver coverage to elucidate broader patterns of snook movement and population connectivity. 

Abstract This article presents a novel approach for generating metamaterial designs by leveraging texture information learned from stochastic microstructure samples with exceptional mechanical properties. This eXplainable Artificial Intelligence (XAI)-based approach reduces the reliance on brainstorming and trial-and-error in inspiration-driven design practices. The key research question is whether the texture information extracted from stochastic microstructure samples can be used to design metamaterials with periodic structural patterns that surpass the original stochastic microstructures in mechanical properties. The proposed approach employs a pretrained supervised neural network and applies the Activation Maximization Texture Synthesis (AMTS) method to extract representative textures from high-performance stochastic microstructure samples. These textures serve as building blocks for creating novel periodic metamaterial designs. Using three benchmark cases of stochastic microstructure-inspired periodic metamaterial design, we compare the proposed approach with an earlier XAI design approach based on Gradient-weighted Regression Activation Mapping (Grad-RAM). Unlike the proposed approach, Grad-RAM extracts local microstructure patches directly from the original sample images rather than synthesizing representative textures to generate novel periodic metamaterial designs. Both XAI-based design approaches are evaluated based on the mechanical properties of the resulting designs. The relative merits of both approaches in terms of design performance and the need for human intervention are discussed. 

Abstract BackgroundTrypanosomaare protozoa parasites that infect animals and can cause economic losses in cattle production.Trypanosomalive in the blood and are transmitted by hematophagous insects, such as flies in the genusTabanus.Using ecological niche models, we explored the current geography of six commonTabanusspecies in Brazil, which are considered vectors ofTrypanosoma vivaxandTr. evansiin the Neotropics. MethodsWe used georeferenced data and biotic and abiotic variables integrated using a fundamental ecological niche modeling approach. Modeling results from sixTabanusspecies were used to identify risk areas ofTrypanosomatransmission in Latin America accounting for area predicted, landscape conditions, and density of livestock. We performed Jaccard, Schoener, and Hellinger metrics to indicate the ecological niche similarities of pairs ofTabanusspecies to identify known and likely vectors overlapping in distribution across geographies. ResultsOur results revealed significant ecological niche similarities for twoTabanusspecies (T. pungensandT. sorbillans), whereasT. triangulumandT. importunushave low ecological similarity. Ecological niche models predicted risk ofTrypanosomatransmission across Neotropical countries, with the highest risk in southern South America, Venezuela, and central Mexico. ConclusionsMore than 1.6 billion cattle and 38 million horses are under a threat category for infection risk. Furthermore, we identified specific areas and livestock populations at high risk of trypanosomiasis in Latin America. This study reveals the areas, landscapes, and populations at risk ofTrypanosomainfections in livestock in the Americas. Graphical Abstract 

Abstract BackgroundAs habitat fragmentation increases, ecological processes, including patterns of vector-borne pathogen prevalence, will likely be disrupted, but ongoing investigations are necessary to examine this relationship. Here, we report the differences in the prevalence of Lyme disease (Borrelia burgdorferisensu lato, s.l.) and haemoproteosis (Haemoproteusspp.) pathogens in avian populations of a fragmented habitat.B. burgdorferis.l. is a generalist pathogen that is transmitted byIxodes pacificusvectors in California, andHaemoproteusis an avian parasite transmitted byCulicoidesvectors. MethodsTo determine whether biotic (avian and mammalian abundance) or abiotic characteristics (patch size and water availability) correlated with infection prevalence change, we screened 176 birds sampled across seven sites in oak woodland habitat in northern California. ResultsWhile biotic factors correlated with an increase in both pathogens, infection prevalence ofHaemoproteusspp. was only associated with individual-level traits, specifically foraging substrate and diet, andB. burgdorferis.l. was associated with community-level characteristics, both total mammal and, specifically, rodent abundance. Proximity to water was the only abiotic factor found to be significant for both pathogens and reinforces the importance of water availability for transmission cycles. Larger patch sizes did not significantly affect infection prevalence ofHaemoproteus,but did increase the prevalence ofB. burgdorferi. ConclusionsThese results highlight that while environmental factors (specifically habitat fragmentation) have a limited role in vector-borne pathogen prevalence, the indirect impact to biotic factors (community composition) can have consequences for bothHaemoproteusandB. burgdorferiprevalence in birds. Given the pervasiveness of habitat fragmentation, our results are of broad significance. Graphical abstract