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The major histocompatibility complex (MHC) is a genetic region in jawed vertebrates that contains key genes involved in the immune response. Associations between the MHC and avian malaria infections in wild birds have been observed and mainly explored in the Northern Hemisphere, while a general lack of information remains in the Southern Hemisphere. Here, we investigated the associations between the MHC genes and infections with Plasmodium and Haemoproteus blood parasites along a latitudinal gradient in South America. We sampled 93 rufous-collared sparrows (Zonotrichia capensis) individuals from four countries, Colombia, Ecuador, Peru, and Chile, and estimated MHC-I and MHC-II allele diversity. We detected between 1-4 (MHC-I) and 1-6 (MHC-II) amino acidic alleles per individual, with signs of positive selection. We obtained generalized additive mixed models to explore the associations between MHC-I and MHC-II diversity and latitude. We also explored the relationship between infection status and latitude/biome. We found a non-linear association between the MHC-II amino acidic allele diversity and latitude. Individuals from north Chile presented a lower MHC genetic diversity than those from other locations. We also found an association between deserts and xeric shrublands and a lower prevalence of Haemoproteus parasites. Our results support a lower MHC genetic in arid or semi-arid habitats in the region with the lower prevalence of Haemoproteus parasites.

Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

Plant-associated microbes contribute to crop health and resilience, potentially extending or complementing plant traits under abiotic stress. Here we tested a series of hypotheses centered on the fungal mycobiome and bacterial microbiome of field-grown sorghum (Sorghum bicolor), an emerging model crop for drought resilience, which we cultivated under arid conditions. Overall, the sorghum mycobiome and microbiome differed in composition between the exterior and interior of plant tissues, between roots and leaves, and among sorghum genotypes. We did not observe variation in fungal and bacterial endophytes among performance classes of sorghum when water was plentiful, but the root endophytic mycobiome and microbiome both shifted markedly under water limitation, with similar shifts in composition observed among multiple plant genotypes. The root endophytic microbiome of high-performing sorghum was especially responsive to water limitation. Network analysis suggested that water limitation did not directly remodel these root endophytic microbiomes, such that interactions of the microbiome with the host plant, rather than interactions among microbes within the microbiome, may be the most dynamic element of change when water is limited. Overall, our study points to shifts in the prevalence of particular taxa under abiotic stress and suggests that high-performing lines may have distinctive features as substrates or strategies for actively recruiting diverse, abundant, and distinctive microbial communities to roots under water limitation. Such findings are important in the arid lands that frame marginal agriculture today and comprise an increasing proportion of agriculture in a changing world.

The challenge of energy storage is a pivotal consideration in renewable energy-based power systems. Hydrogen emerges as a highly promising alternative or complementary solution to electric batteries, showcasing its potential for long-term and high-capacity storage. In this context, energy system modeling and optimization has gained prominence as an indispensable research tool, aiding in the processes of designing, sizing, and managing the day-to-day operations of renewable energy systems integrated with a hydrogen storage unit. However, the gathering of reliable and accurate techno-economic data emerges as time-consuming tasks, and the lack of standardized reference data introduces variability in model results. This variability arises from inconsistent input parameters rather than the physics or complexity of energy systems, leading to potentially erroneous results and misguided policy recommendations. Recognizing the need for comprehensive and transparent datasets, we introduce this open data techno-economic repository. The dataset is meticulously designed to encompass key technologies essential for hydrogen production, compression, storage, and utilization within a power-to-power system. Specifically, techno-economic data are reported for electrolysers, fuel cells, battery energy storage systems, hydrogen compression units, and hydrogen storage vessels. The learning curves and cost functions embedded in this paper, delineating investment costs as a function of production scale up and size, are derived directly from the raw data, providing a nuanced understanding of the economic landscape.

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

Abstract The major histocompatibility complex (MHC) is a genetic region in jawed vertebrates that contains key genes involved in the immune response. Associations between the MHC and avian malaria infections in wild birds have been observed and mainly explored in the Northern Hemisphere, while a general lack of information remains in the Southern Hemisphere. Here, we investigated the associations between the MHC genes and infections with Plasmodium and Haemoproteus blood parasites along a latitudinal gradient in South America. We sampled 93 rufous‐collared sparrows (Zonotrichia capensis) individuals from four countries, Colombia, Ecuador, Peru, and Chile, and estimated MHC‐I and MHC‐II allele diversity. We detected between 1–4 (MHC‐I) and 1–6 (MHC‐II) amino acidic alleles per individual, with signs of positive selection. We obtained generalized additive mixed models to explore the associations between MHC‐I and MHC‐II diversity and latitude. We also explored the relationship between infection status and latitude/biome. We found a non‐linear association between the MHC‐II amino acidic allele diversity and latitude. Individuals from north Chile presented a lower MHC genetic diversity than those from other locations. We also found an association between deserts and xeric shrublands and a lower prevalence of Haemoproteus parasites. Our results support a lower MHC genetic in arid or semi‐arid habitats in the region with the lower prevalence of Haemoproteus parasites.

Understanding photoinjection in semiconductors—a fundamental physical process—represents the first step toward devising new opto-electronic devices, capable of operating on unprecedented time scales. Fostered by the development of few-femtosecond, intense infrared pulses, and attosecond spectroscopy techniques, ultrafast charge injection in solids has been the subject of intense theoretical and experimental investigation. Recent results have shown that while under certain conditions photoinjection can be ascribed to a single, well-defined phenomenon, in a realistic multi-band semiconductor like Ge, several competing mechanisms determine the sub-cycle interaction of an intense light field with the atomic and electronic structure of matter. In this latter case, it is yet unclear how the complex balance between the different physical mechanisms is altered by the chosen interaction geometry, dictated by the relative orientation between the crystal lattice and the laser electric field direction. In this work, we investigate ultrafast photoinjection in a Ge monocrystalline sample with attosecond temporal resolution under two distinct orientations. Our combined theoretical and experimental effort suggests that the physical mechanisms determining carrier excitation in Ge are largely robust against crystal rotation. Nevertheless, the different alignment between the laser field and the crystal unit cell causes non-negligible changes in the momentum distribution of the excited carriers and their injection yield. Further experiments are needed to clarify whether the crystal orientation can be used to tune the photoinjection of carriers in a semiconductor at these extreme time scales.

This study analyzes and implements the Floyd Warshall algorithm using Z-Score to track alternative routes to areas in Medan City, Indonesia that are prone to mugging. The data was obtained from Porlestabes (Police station) Medan-Indonesia. This study employed the Z-Score rank method to rank specific values and determine the levels of crime-prone areas. The highest and lowest levels of crime-proneness were identified using the Z-Score method, and the Floyd Warshall Algorithm is used to identify alternative routes to avoid such areas. The language used in this study adheres to objective and formal writing principles, with value-neutral and clear terminology employed throughout. The results of this analysis showed that out of 99 roads across 18 districts, 4.04% of them were classified as very high prone, 9.09% as high prone, 11.11% as prone, and 75.76% as low prone. The search results from conducting alternative route analysis with the Floyd Warshall algorithm on Perintis Kemerdekaan street indicate the identification of the safest routes.

Stunting remains a significant public health issue in Aceh, Indonesia, and is influenced by various socio-economic and environmental factors. This study aims to address key challenges in accurately classifying stunting prevalence, predicting future trends, and optimizing clustering methods to support more effective interventions. To this end, we propose a novel hybrid machine learning framework that integrates classification, predictive modeling, and clustering optimization. Support Vector Machines (SVM) with Radial Basis Function (RBF) and Sigmoid kernels were employed to improve the classification accuracy, with the RBF kernel outperforming the Sigmoid kernel, achieving an accuracy rate of 91.3% compared with 85.6%. This provides a more reliable tool for identifying high-risk populations. Furthermore, linear regression was used for predictive modeling, yielding a low Mean Squared Error (MSE) of 0.137, demonstrating robust predictive accuracy for future stunting prevalence. Finally, the clustering process was optimized using a weighted-product approach to enhance the efficiency of K-Medoids. This optimization reduced the number of iterations from seven to three and improved the Calinski–Harabasz Index from 85.2 to 93.7. This comprehensive framework not only enhances the classification, prediction, and clustering of results but also delivers actionable insights for targeted public health interventions and policymaking aimed at reducing stunting in Aceh.

To compare preadolescents’ smile outcomes after two different orthodontic treatments: RME/Schwarz plate (RS) and clear aligners (CAs). A sample of 31 patients (13 M, 18 F, mean age 8.3 ± 1.2 y.m) treated with RS and a sample of 28 patients (12 M, 16 F, mean age 7.9 ± 1.1 y.m) treated with CAS were included. Pre-treatment and post-treatment records were taken before the treatment (T1) and at the end of treatment (T2). Seventeen variables were evaluated. Both therapies were effective in smile width increase (RS = p < 0.02; CAs = p < 0.04) and buccal corridor reduction (RS = p < 0.02; CAs = p < 0.04). The intragroup analysis showed in the RS group an increase of incisor gingival display (p < 0.02), overbite (OVB) (p < 0.04) alongside a reduction of overjet (OVJ) (p < 0.01). Conversely, the CAs group evidenced a reduction in incisor gingival display (p < 0.04) and OVB (p < 0.01) with an increase in C angulation (p < 0.02) and maxillary incisor inclination (p < 0.04). An intergroup comparison evidenced a greater improvement in smile width, smile index and buccal corridor in the RS group with respect to the CAs group (p < 0.02; p < 0.02; p < 0.03). CAs were more effective in the management of gingival display, incisor position, midline correction (incisor gingival display p < 0.01; maxillary incisor position p < 0.04; maxillary dental midline p < 0.02), with a better control of OVJ (p < 0.01) and OVB (p < 0.02). The RS approach resulted in a greater smile width and a reduction in buccal corridor with respect to the CAs. The CAs provided a better management of both gingival display and smile aesthetics.

SDG4 aims to guarantee inclusive education. However, achieving this goal is problematic in the face of biocultural homogenization. This article examines the role of education in homogenization by promoting a biased representation of biodiversity based on an analysis of the visual content of textbooks in primary schools in Mexico and Chile. The results show taxonomic biases from a limited corpus of images that do not represent unique biodiversity. In Mexico, we recorded 2.5 animals for each plant studied, with 74% of native species. In Chile, the animal plant ratio was approximately 1:1, with 68% of exotic species. We discuss these biases in relation to the educational transformation that would best serve the 2030 Agenda.

We present a reliable and robust open dynamic chamber for measuring greenhouse gas exchange in peatlands with minimal disturbance of the ground. This chamber, called the “skirt chamber”, is based on a transparent plastic film placed above an open frame made of sparse interwoven wires and expanded around the base of the chamber below a steel chain that ensures contact to the ground, avoiding damage, trenching, and cutting vegetation. Gas exchange is determined using a portable gas analyzer from a mass balance in which the imperfect sealing of the chamber to the ground is quantified through the injection of a methane pulse. The method was tested on a pristine peatland dominated by Sphagnum magellanicum located on Navarino Island in the subantarctic Magellanic ecoregion in Chile. Our results indicate that the skirt chamber allowed the determination of methane fluxes and ecosystem respiration in about 20 min, with a limit of detection of 0.185 mg CH4 m−2 h−1 and 173 mg CO2 m−2 h−1, respectively. We conclude that the skirt chamber is a minimally intrusive, fast, portable, and inexpensive method that allows the quantification of greenhouse gas emissions with high spatial resolution in remote locations and without delay.