Seed dispersal by pulp consumers, not ‘‘legitimate’’ seed dispersers, increases Guettarda viburnoides population growth

We examined the effect of seed dispersal by Purplish Jays (Cyanocorax cyanomelas; pulp consumers) and the Chestnut-eared Araçari (Pteroglossus castanotis; legitimate seed dispersers) on population growth of the small tree Guettarda viburnoides (Rubiaceae) in northeastern Bolivian savannas. Because each bird species differs with respect to feeding and post-feeding behavior, we hypothesized that seed dispersal by each species will contribute differently to the rate of increase of G. viburnoides, but that seed dispersal by either species will increase population growth when compared to a scenario with no seed dispersal. To examine the effects of individual dispersers on the future population size of G. viburnoides, we projected population growth rate using demographic models for G. viburnoides that explicitly incorporate data on quantitative and qualitative aspects of seed dispersal by each frugivore species. Our model suggests that seed dispersal by C. cyanomelas leads to positive population growth of G. viburnoides, whereas seed dispersal by P. castanotis has a detrimental effect on the population growth of this species. To our knowledge, this is the first study to report negative effects of a legitimate seed disperser on the population dynamics of the plant it consumes. Our results stress the importance of incorporating frugivore effects into population projection matrices, to allow a comprehensive analysis of the effectiveness of different dispersers for plant population dynamics

Predators and dispersers: context-dependent outcomes of the interactions between rodents and a megafaunal fruit plant

Many plant species bear fruits that suggest adaptation to seed dispersal by extinct megafauna. Present-day seed dispersal of these megafaunal plants is carried out by rodents, which can act as predators or dispersers; whether this interaction is primarily positive or negative can depend on the context. Here, we parameterized a stochastic model using data from the field and experimental arenas to estimate the effect of rodents on the recruitment of Myrcianthes coquimbensis -an Atacama Desert shrub with megafaunal fruits- and examine whether environmental conditions can alter the sign and strength of these rodent-plant interactions. We show that the outcome of these interactions is context-dependent: in wet conditions seed removal by rodents negatively impacts the recruitment probability of M. coquimbensis; in contrast, in dry conditions, the interaction with rodents increases recruitment success. In all cases, the strength of the effect of rodents on the recruitment success was determined mainly by their role as dispersers, which could be positive or negative. This study demonstrates that by caching seeds, rodents can be effective dispersers of a megafaunal fruit plant, but that the sign and magnitude of their effect on recruitment changes as a function of the environmental context in which the interaction occursInstituto de Ecología y Biodiversidad, Chile | Ref. P05-002Universidad de La Serena, Chile | Ref. PT14122Ministerio de Ciencia e Innovación, España | Ref. PGC2018-096656-B-I00FONDECYT, Chile | Ref. 11140400Conicyt, Chile | Ref. AFB17000

Diagnostic Accuracy of Anthropometric Markers of Obesity for Prediabetes: A Systematic Review and Meta-Analysis

Introduction: Prediabetes is a significant public health concern due to its high risk of progressing to diabetes. Anthropometric measures of obesity, including body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) have been demonstrated as key risk factors in the development of prediabetes. However, there is a lack of clarity on the diagnostic accuracy and cut-off points of these measures. Objective: To determine the diagnostic accuracy of these anthropometric measures for their most effective use in identifying prediabetes. Methodology: A systematic review (SR) with metanalysis of observational studies was carried out. The search was conducted in four databases: Pubmed/Medline, SCOPUS, Web of Science, and EMBASE. For the meta-analysis, sensitivity and specificity, together with their 95% confidence intervals (CI 95%) were calculated. Results: Among all the manuscripts chosen for review, we had four cross-sectional studies, and three were classified as cohort studies. The forest plots showed the combined sensitivity and specificity for both cross-sectional and cohort studies. For cross-sectional studies, the values were as follows: BMI had a sensitivity of 0.63 and specificity of 0.56, WC had a sensitivity of 0.59 and specificity of 0.58, and WHtR had a sensitivity of 0.63 and specificity of 0.73. In the cohort studies, the combined sensitivity and specificity were: BMI at 0.70 and 0.45, WC at 0.68 and 0.56, and WHtR at 0.68 and 0.56, respectively. All values are provided with 95% confidence intervals. Conclusions: This systematic review and meta-analysis evaluated the diagnostic accuracy of BMI, WC, and WHtR in identifying prediabetes. The results showed variations in sensitivity and specificity, with WHtR having the highest specificity in cross-sectional studies and BMI having improved sensitivity in cohort studies

An estimate of the number of tropical tree species

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher’s alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼40,000 and ∼53,000, i.e. at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼19,000–25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼4,500–6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Evenness mediates the global relationship between forest productivity and richness

1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive. 4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions

Desarrollo tecnológico en ingeniería automotriz

El proceso de investigación y desarrollo tecnológico está directamente relacionado con una adecuada metodología de procesos industriales, que cada vez son más exigentes en competitividad, eficiencia energética y de normativas ambientales. Este libro contempla resultados de un proceso de investigación y desarrollo de nuevas técnicas aplicadas en el campo de la Ingeniería Automotriz desde cuatro aristas: eficiencia energética y contaminación ambiental, planificación del transporte, ingeniería del mantenimiento aplicada al transporte y desagregación tecnológica. Este libro conmemora 20 años de formación universitaria salesiana en el sector de transporte y recoge las experiencias y resultados obtenidos asociados con el desarrollo tecnológico en ingeniería automotriz. Para lograr este objetivo, se ha convocado a la comunidad científica, académica y profesionales de la industria automotriz a participar en la publicación. Cada capítulo fue sometido a revisión, evaluación y aprobación por un comité científico altamente calificado, proveniente de seis países: Colombia, Ecuador, España, Guinea Ecuatorial, México y Venezuela. Este trabajo ha sido posible gracias al gran apoyo de la Universidad Politécnica Salesiana (UPS sede Cuenca), Ecuador y Universidad de Los Andes (ULA)

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing