Significance Amazonia is not only the world’s most diverse rainforest but is also the region in tropical America that has contributed most to its total biodiversity. We show this by estimating and comparing the evolutionary history of a large number of animal and plant species. We find that there has been extensive interchange of evolutionary lineages among different regions and biomes, over the course of tens of millions of years. Amazonia stands out as the primary source of diversity, which can be mainly explained by the total amount of time Amazonian lineages have occupied the region. The exceedingly rich and heterogeneous diversity of the American tropics could only be achieved by high rates of dispersal events across the continent. The American tropics (the Neotropics) are the most species-rich realm on Earth, and for centuries, scientists have attempted to understand the origins and evolution of their biodiversity. It is now clear that different regions and taxonomic groups have responded differently to geological and climatic changes. However, we still lack a basic understanding of how Neotropical biodiversity was assembled over evolutionary timescales. Here we infer the timing and origin of the living biota in all major Neotropical regions by performing a cross-taxonomic biogeographic analysis based on 4,450 species from six major clades across the tree of life (angiosperms, birds, ferns, frogs, mammals, and squamates), and integrate >1.3 million species occurrences with large-scale phylogenies. We report an unprecedented level of biotic interchange among all Neotropical regions, totaling 4,525 dispersal events. About half of these events involved transitions between major environmental types, with a predominant directionality from forested to open biomes. For all taxonomic groups surveyed here, Amazonia is the primary source of Neotropical diversity, providing >2,800 lineages to other regions. Most of these dispersal events were to Mesoamerica (∼1,500 lineages), followed by dispersals into open regions of northern South America and the Cerrado and Chaco biomes. Biotic interchange has taken place for >60 million years and generally increased toward the present. The total amount of time lineages spend in a region appears to be the strongest predictor of migration events. These results demonstrate the complex origin of tropical ecosystems and the key role of biotic interchange for the assembly of regional biotas.
We investigate how spin polarization of the initial atomic state affects the tunneling delay in the attoclock settings. Using the time dependent Schrödinger equation and the Breit–Pauli Hamiltonian we demonstrate that spin polarization can produce measurable corrections to tunneling delays in attoclock spectroscopy. Our findings indicate that spin–orbit coupling introduces an additional spin-dependent degree of freedom into under-the- barrier motion, suggesting new opportunities for spin-resolved attosecond measurements. We show that the electron’s spin orientation during tunneling produces a contribution to the attoclock offset angle. Our results are interpreted within the framework of the strong field approximation and the imaginary time methods. We show that this approach provides a rare opportunity to get an insight into the details of the sub-barrier electron motion during the tunneling process.
Accurate upsampling of Head-Related Transfer Functions (HRTFs) from sparse measurements is crucial for personalized spatial audio rendering. Traditional interpolation methods, such as kernel-based weighting or basis function expansions, rely on measurements from a single subject and are limited by the spatial sampling theorem, resulting in significant performance degradation under sparse sampling. Recent learning-based methods alleviate this limitation by leveraging cross-subject information, yet most existing neural architectures primarily focus on modeling spatial relationships across directions, while spectral dependencies along the frequency dimension are often modeled implicitly or treated independently. However, HRTF magnitude responses exhibit strong local continuity and long-range structure in the frequency domain, which are not fully exploited. This work investigates frequency-domain feature modeling by examining how different architectural choices, ranging from per-frequency multilayer perceptrons to convolutional, dilated convolutional, and attention-based models, affect performance under varying sparsity levels, showing that explicit spectral modeling consistently improves reconstruction accuracy, particularly under severe sparsity. Motivated by this observation, a frequency-domain Conformer-based architecture is adopted to jointly capture local spectral continuity and long-range frequency correlations. Experimental results on the SONICOM and HUTUBS datasets demonstrate that the proposed method achieves state-of-the-art performance in terms of interaural level difference and log-spectral distortion.
Los autores se proponen interpretar y evidenciar la identidad lingüística de los emigrantes ecuatorianos que se han dirigido a países de mayor desarrollo, en especial Estados Unidos, Canadá, España, Alemania y Gran Bretaña. Para ello, se fundamenta en obras testimoniales escritas por los protagonistas, cuyas publicaciones oscilan entre 1996 y 2015. Con la orientación de las teorías devenidas de las ciencias sociales y humanas, particularmente de la lingüística, luego de la aplicación de la metodología y técnicas propias de la investigación bibliográfico-documental, se concluye que existe una significativa representación de cuatro elementos vinculados con el idioma y dialecto empleado por los emigrantes mientras permanecen fuera del Ecuador: problemáticas que enfrentan los ecuatorianos por desconocimiento de la lengua utilizada en los países de llegada; estrategias de comunicación interpersonal empleadas por los emigrantes hasta adquirir nociones básicas del nuevo idioma; diferencias en el uso del español, entre ecuatorianos y peninsulares; y asimilación del habla española como estrategia de integración de los ecuatorianos en España.
American literature, Latin America. Spanish America
A method is presented for the fast evaluation of the transient acoustic field generated outside a spherical surface using surface data on the sphere. The method employs Lebedev quadratures, which are optimal integration on the sphere, and Lagrange interpolation and differentiation in an advanced time algorithm for the evaluation of the transient field. Numerical testing demonstrates that the approach gives near machine-precision accuracy and a speed-up in evaluation time which depends on the order of quadrature rule employed but breaks even with direct evaluation at a number of field points about 1.15 times the number of surface quadrature nodes, making the method an efficient means of evaluating the field generated by a large number of sources.
According to the Pan American Health Organization, the number of cancer cases in Latin America was estimated at 4.2 million in 2022 and is projected to rise to 6.7 million by 2045. Osteosarcoma, one of the most common and deadly bone cancers affecting young people, is difficult to detect due to its unique texture and intensity. Surgical removal of osteosarcoma requires precise safety margins to ensure complete resection while preserving healthy tissue. Therefore, this study proposes a method for estimating the confidence interval of surgical safety margins in osteosarcoma surgery around the knee. The proposed approach uses MRI and X-ray data from open-source repositories, digital processing techniques, and unsupervised learning algorithms (such as k-means clustering) to define tumor boundaries. Experimental results highlight the potential for automated, patient-specific determination of safety margins.
Tubagus Aryandi Gunawan, Hongxi Luo, Chris Greig
et al.
The chemicals industry accounts for about 5% of global greenhouse gas emissions today and is among the most difficult industries to abate. We model decarbonization pathways for the most energy-intensive segment of the industry, the production of basic chemicals: olefins, aromatics, methanol, ammonia, and chlor-alkali. Unlike most prior pathways studies, we apply a scenario-analysis approach that recognizes the central role of corporate investment decision making for capital-intensive industries, under highly uncertain long-term future investment environments. We vary the average pace of decarbonization capital allocation allowed under plausible alternative future world contexts and construct least-cost decarbonization timelines by modeling abatement projects individually across more than 2,600 production facilities located in four major producing regions. The timeline for deeply decarbonizing production varies by chemical and region but depends importantly on the investment environment context. In the best-of-all environments, to deeply decarbonize production, annual average capital spending for abatement for the next two to three decades will need to be greater than (and in addition to) historical "business-as-usual" investments, and cumulative investment in abatement projects would exceed $1 trillion. In futures where key drivers constrain investment appetites, timelines for decarbonizing the industry extend well into the second half of the century.
Christopher Bacon, Ava Gleicher, Emma McCurry
et al.
The 60,000 food pantries in the United States are well known for charity-based emergency food assistance and edible food recovery, serving 53 million people in 2022 (Feeding America, 2023a). Thousands of urban gardens emphasize vegetable production and food justice, but lack strong connections to food pantries. We explore how food pantries and urban gardens could partner to transform pantries into distribution sites that also become food justice education and organizing spaces. To assess this potential, we engaged in participatory action research with a leading social services provider that offers programs supporting both organized urban gardeners and a large urban food pantry in San Jose, California. We conducted and analyzed 21 interviews with food pantry volunteers and urban gardeners affiliated with the same agency, and eight interviews with other urban gardeners and food pantry staff from external organizations. We found that while both food pantry volunteers and urban gardeners expressed concerns about increasing healthy food access and reducing food waste, pantry volunteers were often unfamiliar with food justice and uncomfortable talking about race and culturally rooted food preferences. These findings were similar with the informants from external organizations. To support urban gardener and food pantry volunteer collaboration, we developed a food justice approach to emergency food assistance and food waste management in which both groups co-create onsite vermicomposting infrastructure and partner with a university to design a training program focused on diversity, justice, and systemic change.
Wendy Lorena Reyes-Ardila, Paula Andrea Rugeles-Silva, Juan Diego Duque-Zapata
et al.
<i>Bidens pilosa</i> L., native to South America and commonly used for medicinal purposes, has been understudied at molecular and genomic levels and in its relationship with soil microorganisms. In this study, restriction site-associated DNA markers (RADseq) techniques were implemented to analyze genetic diversity and population structure, and metabarcoding to examine microbial composition in soils from Palmira, Sibundoy, and Bogotá, Colombia. A total of 2,984,123 loci and 3485 single nucleotide polymorphisms (SNPs) were identified, revealing a genetic variation of 12% between populations and 88% within individuals, and distributing the population into three main genetic groups, F<sub>ST</sub> = 0.115 (<i>p</i> < 0.001) and F<sub>IT</sub> = 0.013 (<i>p</i> > 0.05). In the soil analysis, significant correlations were found between effective cation exchange capacity (ECEC) and apparent density, soil texture, and levels of Mg and Fe, as well as negative correlations between ECEC and Mg, and Mg, Fe, and Ca. Proteobacteria and Ascomycota emerged as the predominant bacterial and fungal phyla, respectively. Analyses of alpha, beta, and multifactorial diversity highlight the influence of ecological and environmental factors on these microbial communities, revealing specific patterns of clustering and association between bacteria and fungi in the studied locations.
Daniel Seal, Rossella Arcucci, Salva Rühling-Cachay
et al.
Climate change is increasing the frequency of extreme precipitation events, making weather disasters such as flooding and landslides more likely. The ability to accurately nowcast precipitation is therefore becoming more critical for safeguarding society by providing immediate, accurate information to decision makers. Motivated by the recent success of generative models at precipitation nowcasting, this paper: extends the DYffusion framework to this task and evaluates its performance at forecasting IMERG satellite precipitation data up to a 4-hour horizon; modifies the DYffusion framework to improve its ability to model rainfall data; and introduces a novel loss function that combines MSE, MAE and the LPIPS perceptual score. In a quantitative evaluation of forecasts up to a 4-hour horizon, the modified DYffusion framework trained with the novel loss outperforms four competitor models. It has the highest CSI scores for weak, moderate, and heavy rain thresholds and retains an LPIPS score $<$ 0.2 for the entire roll-out, degrading the least as lead-time increases. The proposed nowcasting model demonstrates visually stable and sharp forecasts up to a 2-hour horizon on a heavy rain case study. Code is available at https://github.com/Dseal95/DYffcast.
<p>African and South American (ASA) wildfires account for more than 70 % of
global burned areas and have strong connection to local climate for
sub-seasonal to seasonal wildfire dynamics. However, representation of the
wildfire–climate relationship remains challenging due to spatiotemporally
heterogenous responses of wildfires to climate variability and human
influences. Here, we developed an interpretable machine learning (ML) fire
model (AttentionFire_v1.0) to resolve the complex controls of
climate and human activities on burned areas and to better predict burned
areas over ASA regions. Our ML fire model substantially improved
predictability of burned areas for both spatial and temporal dynamics
compared with five commonly used machine learning models. More importantly,
the model revealed strong time-lagged control from climate wetness on the
burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse
in South America in the near future due to climate changes. Our study
provides a reliable and interpretable fire model and highlights the importance
of lagged wildfire–climate relationships in historical and future
predictions.</p>
Accounting for 41.7% of China’s total land area, grasslands are linked to the livelihoods of over 20 million people. Although grassland fires cause severe damage in China every year, their spatiotemporal patterns and climate drivers are not well understood. In this study, we used grassland fire record forms provided by the National Forestry and Grassland Administration and grassland fire location data from the Wildfire Atlas of China to examine the spatiotemporal patterns and and seasonality of fires in China for the period from 2008 to 2020. We found that most grassland fires occurred in Inner Mongolia in northern China, specifically in the Hulun Buir and Xilingol grasslands. We found distinct differences in fire seasonality in northern China, which has a major fire season in April, versus southwestern China, where the major fire season occurs in February, March and April. April grassland fires in northern China are the result of strong winds, typically from the west, and spring drought. A secondary fire season in northern China occurs in October and is also driven by strong winds. The fire season in southwestern China seems to be less shaped by climatic factors such as wind speed, precipitation, and drought. This study provides support for decision-making by fire prevention and fire management authorities in China.
Accurately classifying accents and assessing accentedness in non-native speakers are both challenging tasks due to the complexity and diversity of accent and dialect variations. In this study, embeddings from advanced pre-trained language identification (LID) and speaker identification (SID) models are leveraged to improve the accuracy of accent classification and non-native accentedness assessment. Findings demonstrate that employing pre-trained LID and SID models effectively encodes accent/dialect information in speech. Furthermore, the LID and SID encoded accent information complement an end-to-end accent identification (AID) model trained from scratch. By incorporating all three embeddings, the proposed multi-embedding AID system achieves superior accuracy in accent identification. Next, we investigate leveraging automatic speech recognition (ASR) and accent identification models to explore accentedness estimation. The ASR model is an end-to-end connectionist temporal classification (CTC) model trained exclusively with en-US utterances. The ASR error rate and en-US output of the AID model are leveraged as objective accentedness scores. Evaluation results demonstrate a strong correlation between the scores estimated by the two models. Additionally, a robust correlation between the objective accentedness scores and subjective scores based on human perception is demonstrated, providing evidence for the reliability and validity of utilizing AID-based and ASR-based systems for accentedness assessment in non-native speech.
After completing their undergraduate studies, many computer science (CS) students apply for competitive graduate programs in North America. Their long-term goal is often to be hired by one of the big five tech companies or to become a faculty member. Therefore, being aware of the role of admission criteria may help them choose the best path towards their goals. In this paper, we analyze the influence of students' previous universities on their chances of being accepted to prestigious North American universities and returning to academia as professors in the future. Our findings demonstrate that the ranking of their prior universities is a significant factor in achieving their goals. We then illustrate that there is a bias in the undergraduate institutions of students admitted to the top 25 computer science programs. Finally, we employ machine learning models to forecast the success of professors at these universities. We achieved an RMSE of 7.85 for this prediction task.
Pese a que la historiografía argentina se ocupó en las últimas décadas de revisar con renovada atención las agencias, burocracias y saberes de Estado, aún se observan vacíos significativos en ciertas áreas, como la que se vincula con la generación de conocimientos especializados y el despliegue de políticas públicas para el sector agrario. Para aportar a dicha línea de análisis, aquí proponemos focalizar en un tópico clave respecto del accionar estatal, durante el período estudiado, en una región que comprendía el oeste de la provincia de Buenos Aires, el sur de Córdoba, el este de La Pampa y una parte de San Luis. Dicho tópico era el proceso erosivo, que acarreó como consecuencia la pérdida de la fertilidad del suelo en espacios donde la producción primaria era una actividad económica importante. El artículo se concentra en la etapa que se inicia a fines de los años treinta, un momento signado por la extrema sequía, la creación de dependencias estatales y el comienzo de estudios sobre esa problemática, y culmina al promediar la década del sesenta, cuando el tema continuaba muy vigente en la agenda oficial e incluso había adquirido mayor trascendencia internacional.
S. Sudhakar, S. Vignesh Raja, A. Govindarajan
et al.
In the context of $\mathcal{PT}$-symmetric fiber Bragg gratings, tailoring the nonlinear profile along the propagation coordinate serves to be a new direction for realizing low-power all-optical switches. The scheme is fruitful only when the nonlinearity profile will be either linearly decreasing or increasing form. If the rate of variation of the nonlinearity profile is high, the critical intensities fall below the input power of value 0.01 in the unbroken regime provided that the light launching direction is right. Nowadays, every new theoretical inception into the PTFBG has started making sense of switching in the broken $\mathcal{PT}$-symmetric regime which was once believed to be the instability regime. When the inhomogeneous nonlinearity acts together with the broken $\mathcal{PT}$-symmetry and right light incidence, it leads to two peculiar settings. First, the switch-up intensities are ultra-low. Second, the switch-down action takes place at zero critical intensities. Such OB curves are unprecedented in the context of conventional gratings and found only in plasmonic devices and anti-directional couplers. Even though the nonlinearity is inhomogeneous, the ramp-like first stable states persist in the broken $\mathcal{PT}$-symmetric regime giving an additional indication that the broken PTFBG is closely associated with the plasmonic structures. In the existing PTFBG systems, the switching intensities are relatively higher in the broken regime. However, the proposed system records the lowest switching intensities in the broken regime. The reported intensities ($< 0.005$) are also the lowest ever-switching intensities recorded in the perspective of PTFBGs to date.