Explore the vast range of titles available.

Efficient foraging plays a critical role in fitness, yet food choices and underlying nutritional goals vary among animals. To understand those choices and therefore the importance of different food resources, many studies estimate food preferences by applying electivity indices that account for resource availabilities. However, the general applicability of electivity indices in biologically relevant foraging scenarios is unclear. Our major aims were to find effective methods to estimate animals' food choices and to investigate long‐term food choices and underlying nutritional goals of the red colobus monkey (Piliocolobus tephrosceles) in Kibale National Park, Uganda, an endangered folivore. We used simulations of different foraging conditions to evaluate the applicability of electivity indices in biologically relevant scenarios to help interpret our results. Then, we used long‐term data collected between 2006 and 2016 on the feeding behavior and ecology of red colobus to determine the consumption frequencies of different foods and their food preferences. Based on these results and nutritional concentrations of young leaves of frequently consumed tree species, we investigated the importance of the protein‐to‐fiber ratio in their diet. Our simulations highlight limitations of electivity indices in biologically relevant foraging scenarios. Further, red colobus clearly chose young leaves over other plant parts, and, considering species and plant part, red colobus fed on many different items, but few dominated their diet. The availability and spatial distribution varied across the most consumed foods, but red colobus preferences remained mostly stable over time. Protein‐to‐fiber ratio had no association with preference but with consumption frequencies of different young leaves. The limitations of electivity indices in different foraging conditions underline the importance of comparing food preferences with consumption frequencies to assess the importance of different food resources. Our results provide a robust understanding of the food choices and nutritional goals of a leaf‐eating animal that can ultimately be used for implementing more effective conservation measures by directing habitat protection or restoration efforts toward these resources.

Simulation of pyrolysis involves the knowledge of reaction kinetics and thermo-physical parameters, which are in general not directly measurable. Inverse modelling provides means to deduce the needed parameters from experimental data. This complex process involves the coupling of a simulation model with an optimisation method as well as the handling of a large amount of data. All of these processes are prone to errors and therefore a unified and automated implementation is beneficial for the whole community. In this contribution, a software to carry out this process is proposed. PROPTI is an open source tool written in Python, that is meant to provide a framework for inverse modelling of parameters in computer simulation, with emphasis on pyrolysis modelling in fire simulation. Its generalised formulation allows the usage of any simulation model in combination with various experimental data. The underlying optimisation library allows the utilisation of HPC systems. After presenting the concept of this framework, two examples are shown to illustrate the process and demonstrate the capabilities.

1. Climate change is altering plant phenology globally with potential deleterious impacts on animal species and entire ecosystems, yet the long-term effects of climate change on tropical leaf production remain poorly understood. 2. We analyzed 26 years of young leaf phenology field data from Kibale National Park, Uganda, focusing on 12 tree species consumed by leaf-eating mammals. We examined seasonal and long-term patterns and how they are related to climatic variables using Bayesian hierarchical generalized additive mixed models (GAMMs). 3. The tree community and most species exhibited peaks in young leaf production during the two annual rain seasons, with seasonal changes primarily associated with diffuse light availability through solar radiation and cloud cover, as well as rainfall and minimum temperature. Long-term variations in leaf production was primarily linked to long-term changes in atmospheric CO2, solar radiation, and cloud cover. 4. Our results support the role of CO2 fertilization, though decreasing levels of solar radiation resulting from the ending of the recent solar cycle may be slowing this effect. 5. Synthesis: This study highlights the critical role of diffuse light, solar radiation, and the solar cycle in predicting tropical leaf production, emphasizing that interpretations of greening trends must consider solar radiation alongside atmospheric CO2 levels. Furthermore, our findings emphasize the complex relationship between climate and young leaf phenology, highlighting the importance of integrating species-specific long-term data to better understand the effects of climate change on food availability for tropical folivores and tropical forest ecosystems in general.

Climate change impacts ecosystems worldwide, affecting animal behaviour and survival both directly and indirectly through changes such as the availability of food. For animals reliant on leaves as a primary food source, understanding how climate change influences leaf production of trees is crucial, yet this is understudied, especially in moist evergreen tropical forests. We analyzed a 23-year dataset of young leaf phenology from a moist tropical forest in Kibale National Park, Uganda, to examine seasonal and long-term patterns of 12 key tree species consumed by folivorous primates. We described phenological patterns and explored relationships between young leaf production of different tree species and climate variables. We also assessed the suitability of the Enhanced Vegetation Index (EVI) as a proxy for young leaf production in moist evergreen tropical forests. Our results showed that tree species exhibited distinct phenological patterns, with most species producing young leaves during two seasonal peaks aligned with the rainy seasons. Rainfall, cloud cover, and maximum temperature were the most informative predictors of seasonal variation in young leaf production. However, solar radiation and atmospheric CO\\(_2\\) were most informative regarding long-term trends. EVI was strongly correlated with young leaf production within years but less effective for capturing inter-annual trends. These findings highlight the complex relationship between climate and young leaf phenology in moist evergreen tropical forests, and helps us understand the changes in food availability for tropical folivores.

Approximately 20.5 million infants were born weighing <2500 g (defined as low birthweight or LBW) in 2015, primarily in low- and middle-income countries (LMICs). Infants born LBW, including those born preterm (<37 weeks gestation), are at increased risk for numerous consequences, including neonatal mortality and morbidity as well as suboptimal health and nutritional status later in life. The objective of this study was to identify predictors of LBW and preterm birth among infants in rural Uganda. Data were derived from a prospective birth cohort study conducted from 2014-2016 in 12 districts across northern and southwestern Uganda. Birth weights were measured in triplicate to the nearest 0.1 kg by trained enumerators within 72 hours of delivery. Gestational age was calculated from the first day of last menstrual period (LMP). Associations between household, maternal, and infant characteristics and birth outcomes (LBW and preterm birth) were assessed using bivariate and multivariable logistic regression with stepwise, backward selection analyses. Among infants in the study, 4.3% were born LBW (143/3,337), and 19.4% were born preterm (744/3,841). In multivariable analysis, mothers who were taller (>150 cm) (adjusted Odds Ratio (aOR) = 0.42 (95% CI = 0.24, 0.72)), multigravida (aOR = 0.62 (95% CI = 0.39, 0.97)), or with adequate birth spacing (>24 months) (aOR = 0.60 (95% CI = 0.39, 0.92)) had lower odds of delivering a LBW infant Mothers with severe household food insecurity (aOR = 1.84 (95% CI = 1.22, 2.79)) or who tested positive for malaria during pregnancy (aOR = 2.06 (95% CI = 1.10, 3.85)) had higher odds of delivering a LBW infant. In addition, in multivariable analysis, mothers who resided in the Southwest (aOR = 0.64 (95% CI = 0.54, 0.76)), were ≥20 years old (aOR = 0.76 (95% CI = 0.61, 0.94)), with adequate birth spacing (aOR = 0.76 (95% CI = 0.63, 0.93)), or attended ≥4 antenatal care (ANC) visits (aOR = 0.56 (95% CI = 0.47, 0.67)) had lower odds of delivering a preterm infant; mothers who were neither married nor cohabitating (aOR = 1.42 (95% CI = 1.00, 2.00)) or delivered at home (aOR = 1.25 (95% CI = 1.04, 1.51)) had higher odds. In rural Uganda, severe household food insecurity, adolescent pregnancy, inadequate birth spacing, malaria infection, suboptimal ANC attendance, and home delivery represent modifiable risk factors associated with higher rates of LBW and/or preterm birth. Future studies on interventions to address these risk factors may be warranted.

Ribosome biogenesis is a fundamental multi-step cellular process in all domains of life that involves the production, processing, folding, and modification of ribosomal RNAs (rRNAs) and ribosomal proteins. To obtain insights into the still unexplored early assembly phase of the bacterial 50S subunit, we exploited a minimal in vitro reconstitution system using purified ribosomal components and scalable reaction conditions. Time-limited assembly assays combined with cryo-EM analysis visualizes the structurally complex assembly pathway starting with a particle consisting of ordered density for only ~500 nucleotides of 23S rRNA domain I and three ribosomal proteins. In addition, our structural analysis reveals that early 50S assembly occurs in a domain-wise fashion, while late 50S assembly proceeds incrementally. Furthermore, we find that both ribosomal proteins and folded rRNA helices, occupying surface exposed regions on pre-50S particles, induce, or stabilize rRNA folds within adjacent regions, thereby creating cooperativity. The production of ribosomes is a precisely orchestrated energy consuming cellular process of highest priority. Here, the authors use cryo-EM to show that bacterial ribosomal subunits, self-assembled from their purified RNA and protein components, mature along parallel pathways.

This multiagency report developed by the Interagency Collaboration for Genomics for Food and Feed Safety provides an overview of the use of and transition to whole genome sequencing (WGS) technology for detection and characterization of pathogens transmitted commonly by food and for identification of their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among the following federal agencies: National Institutes of Health; Department of Health and Human Services, Centers for Disease Control and Prevention (CDC) and U.S. Food and Drug Administration (FDA); and the U.S. Department of Agriculture, Food Safety and Inspection Service, Agricultural Research Service, and Animal and Plant Health Inspection Service. We describe single nucleotide polymorphism, core-genome, and whole genome multilocus sequence typing data analysis methods as used in the PulseNet (CDC) and GenomeTrakr (FDA) networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Interagency Collaboration partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles) and source attribution efforts and increase transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information. We also highlight the impact of current trends in the use of culture-independent diagnostic tests for human diagnostic testing on analytical approaches related to food safety and what is next for the use of WGS in the area of food safety.

Charcot–Marie–Tooth diseases are hereditary peripheral neuropathies for which there are currently no effective therapies; here the type 2D subtype of these diseases is shown to be caused by mutations impeding a signalling pathway necessary for motor neuron survival. Neuropathy link to VEGF–Nrp1 signalling defect Charcot–Marie–Tooth diseases are hereditary peripheral neuropathies for which there are currently no effective therapies. The type 2D subtype of these diseases (CMT2D) is associated with dominant mutations in the enzyme glycyl-tRNA synthetase (GlyRS). Here the molecular mechanism by which these mutations cause neuropathy is shown to involve suppression of a signalling pathway necessary for motor neuron survival. CMT2D mutations alter the conformation of GlyRS, enabling it to bind the neuropilin 1 (Nrp1) receptor. This aberrant interaction competitively interferes with the binding of the cognate ligand vascular endothelial growth factor (VEGF) to Nrp1, and indicates that the VEGF–Nrp1 signalling axis is an actionable target for treating CMT2D. Selective neuronal loss is a hallmark of neurodegenerative diseases, which, counterintuitively, are often caused by mutations in widely expressed genes 1 . Charcot–Marie–Tooth (CMT) diseases are the most common hereditary peripheral neuropathies, for which there are no effective therapies 2 , 3 . A subtype of these diseases—CMT type 2D (CMT2D)—is caused by dominant mutations in GARS , encoding the ubiquitously expressed enzyme glycyl-transfer RNA (tRNA) synthetase (GlyRS). Despite the broad requirement of GlyRS for protein biosynthesis in all cells, mutations in this gene cause a selective degeneration of peripheral axons, leading to deficits in distal motor function 4 . How mutations in GlyRS (GlyRS CMT2D ) are linked to motor neuron vulnerability has remained elusive. Here we report that GlyRS CMT2D acquires a neomorphic binding activity that directly antagonizes an essential signalling pathway for motor neuron survival. We find that CMT2D mutations alter the conformation of GlyRS, enabling GlyRS CMT2D to bind the neuropilin 1 (Nrp1) receptor. This aberrant interaction competitively interferes with the binding of the cognate ligand vascular endothelial growth factor (VEGF) to Nrp1. Genetic reduction of Nrp1 in mice worsens CMT2D symptoms, whereas enhanced expression of VEGF improves motor function. These findings link the selective pathology of CMT2D to the neomorphic binding activity of GlyRS CMT2D that antagonizes the VEGF–Nrp1 interaction, and indicate that the VEGF–Nrp1 signalling axis is an actionable target for treating CMT2D.

Earth system models (ESMs) are important tools to improve our understanding of present-day climate and to project climate change under different plausible future scenarios. Thus, ESMs are continuously improved and extended, resulting in more complex models. Particularly during the model development phase, it is important to continuously monitor how well the historical climate is reproduced and to systematically analyze, evaluate, understand, and document possible shortcomings. Hence, putting model biases relative to observations or, for example, a well-characterized pre-industrial control run, into the context of deviations shown by other state-of-the-art models greatly helps to assess which biases need to be addressed with higher priority. Here, we introduce the new capability of the open-source community-developed Earth System Model Evaluation Tool (ESMValTool) to monitor running simulations or benchmark existing simulations with observations in the context of results from the Coupled Model Intercomparison Project (CMIP). To benchmark model output, ESMValTool calculates metrics such as the root-mean-square error, the Pearson correlation coefficient, or the earth mover's distance relative to reference datasets. This is directly compared to the same metric calculated for an ensemble of models such as the one provided by Phase 6 of the CMIP (CMIP6), which provides a statistical measure for the range of values that can be considered typical of state-of-the-art ESMs. Results are displayed in different types of plots, such as map plots or time series, with different techniques such as stippling (maps) or shading (time series) used to visualize the typical range of values for a given metric from the model ensemble used for comparison. While the examples shown here focus on atmospheric variables, the new functionality can be applied to any other ESM component such as land, ocean, sea ice, or land ice. Automatic downloading of CMIP results from the Earth System Grid Federation (ESGF) makes application of ESMValTool for benchmarking of individual model simulations, for example, in preparation of Phase 7 of the CMIP (CMIP7), easy and very user-friendly.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is an established method for the interrogation of protein conformation and dynamics. While the data analysis challenge of HDX-MS has been addressed by a number of software packages, new computational tools are needed to keep pace with the improved methods and throughput of this technique. To address these needs, we report an integrated desktop program titled HDX Workbench, which facilitates automation, management, visualization, and statistical cross-comparison of large HDX data sets. Using the software, validated data analysis can be achieved at the rate of generation. The application is available at the project home page http://hdx.florida.scripps.edu .