Introduction The kingdom Fungi is one of the most diverse groups of organisms on Earth, and they are integral ecosystem agents that govern soil carbon cycling, plant nutrition, and pathology. Fungi are widely distributed in all terrestrial ecosystems, but the distribution of species, phyla, and functional groups has been poorly documented. On the basis of 365 global soil samples from natural ecosystems, we determined the main drivers and biogeographic patterns of fungal diversity and community composition. Direct and indirect effects of climatic and edaphic variables on plant and fungal richness. Line thickness corresponds to the relative strength of the relationships between the variables that affect species richness. Dashed lines indicate negative relationships. MAP, mean annual precipitation; Fire, time since last fire; Dist. equator, distance from the equator; Ca, soil calcium concentration; P, soil phosphorus concentration; pH, soil pH. Rationale We identified soil-inhabiting fungi using 454 Life Sciences (Branford, CN) pyrosequencing and through comparison against taxonomically and functionally annotated sequence databases. Multiple regression models were used to disentangle the roles of climatic, spatial, edaphic, and floristic parameters on fungal diversity and community composition. Structural equation models were used to determine the direct and indirect effects of climate on fungal diversity, soil chemistry, and vegetation. We also examined whether fungal biogeographic patterns matched paradigms derived from plants and animals—namely, that species’ latitudinal ranges increase toward the poles (Rapoport’s rule) and diversity increases toward the equator. Last, we sought group-specific global biogeographic links among major biogeographic regions and biomes using a network approach and area-based clustering. Results Metabarcoding analysis of global soils revealed fungal richness estimates approaching the number of species recorded to date. Distance from equator and mean annual precipitation had the strongest effects on richness of fungi, including most fungal taxonomic and functional groups. Diversity of most fungal groups peaked in tropical ecosystems, but ectomycorrhizal fungi and several fungal classes were most diverse in temperate or boreal ecosystems, and many fungal groups exhibited distinct preferences for specific edaphic conditions (such as pH, calcium, or phosphorus). Consistent with Rapoport’s rule, the geographic range of fungal taxa increased toward the poles. Fungal endemicity was particularly strong in tropical regions, but multiple fungal taxa had cosmopolitan distribution. Conclusions Climatic factors, followed by edaphic and spatial patterning, are the best predictors of soil fungal richness and community composition at the global scale. Richness of all fungi and functional groups is causally unrelated to plant diversity, with the exception of ectomycorrhizal root symbionts, suggesting that plant-soil feedbacks do not influence the diversity of soil fungi at the global scale. The plant-to-fungi richness ratio declined exponentially toward the poles, indicating that current predictions—assuming globally constant ratios—overestimate fungal richness by 1.5- to 2.5-fold. Fungi follow similar biogeographic patterns as plants and animals, with the exception of several major taxonomic and functional groups that run counter to overall patterns. Strong biogeographic links among distant continents reflect relatively efficient long-distance dispersal compared with macro-organisms. Fungi play major roles in ecosystem processes, but the determinants of fungal diversity and biogeographic patterns remain poorly understood. Using DNA metabarcoding data from hundreds of globally distributed soil samples, we demonstrate that fungal richness is decoupled from plant diversity. The plant-to-fungus richness ratio declines exponentially toward the poles. Climatic factors, followed by edaphic and spatial variables, constitute the best predictors of fungal richness and community composition at the global scale. Fungi show similar latitudinal diversity gradients to other organisms, with several notable exceptions. These findings advance our understanding of global fungal diversity patterns and permit integration of fungi into a general macroecological framework. Global metagenomics detects hotspots of fungal diversity and macroecological patterns and indicates that plant and fungal diversity are uncoupled. [Also see Perspective by Wardle and Lindahl] Assessing fungal diversity worldwide Fungi are hyperdiverse but poorly known, despite their ecological and economic impacts. Tedersoo et al. collected nearly 15,000 topsoil samples from 365 sites worldwide and sequenced their genomes (see the Perspective by Wardle and Lindahl). Overall, they found a striking decline in fungal species richness with distance from the equator. For some specialist groups though, diversity depended more on the abundance of host plants than host diversity or geography. The findings reveal a huge gap between known and described species and the actual numbers of distinct fungi in the world's soils. Science, this issue 10.1126/science.1256688; see also p. 1052
Introduction to the Bloomsbury Revelations Edition Preface to the First Edition Preface to the Second Edition Preface to the Third Edition Editor's Introduction, Roy Harris Introduction 1. A Brief Survey of the History of Linguistics 2. Data and Aims of Linguistics: Connexions with Related Sciences 3. The Object of Study 4. Linguistics of Language Structure and Linguistics of Speech 5. Internal and External Elements of a Language 6. Representation of a Language by Writing 7. Physiological Phonetics Appendix: Principles of Physiological Phonetics 1. Sound Types 2. Sounds in Spoken Sequences Part One: General Principles 1. Nature of the Linguistic Sign 2. Invariability and Variability of the Sign 3. Static Linguistics and Evolutionary Linguistics Part Two: Synchronic Linguistics 1. General Observations 2. Concrete Entities of a Language 3. Identities, Realities, Values 4. Linguistic Value 5. Syntagmatic Relations and Associative Relations 6. The Language Mechanism 7. Grammar and Its Subdivisions 8. Abstract Entities in Grammar Part Three: Diachronic Linguistics 1. General Observations 2. Sound Changes 3. Grammatical Consequences of Phonetic Evolution 4. Analogy 5. Analogy and Evolution 6. Popular Etymology 7. Agglutination 8. Diachronic Units,Identities and Realities Appendices Part Four: Geographical Linguistics 1. On the Diversity of Languages 2. Geographical Diversity: Its Complexity 3. Causes of Geographical Diversity 4. Propagation of Linguistic Waves Part Five: Questions of Retrospective Linguistics Conclusion 1. The Two Perspectives of Diachronic Linguistics 2. Earliest Languages and Prototypes 3. Reconstructions 4. Linguistic Evidence in Anthropology and Prehistory 5. Language Families and Linguistic Types Index
Jean Jacques Noubiap, Jobert Richie Nansseu, E. Lontchi-Yimagou
et al.
AIMS Better knowledge of the global metabolic syndrome (MetS) prevalence and its components is a prerequisite to curb the related burden. METHODS We systematically searched PubMed, EMBASE, Web of Knowledge, Africa Journal Online, Global Index Medicus up to May 23, 2021. Prevalence pooling was done with a random-effects meta-analysis. RESULTS In total, 1,129 prevalence data (28,193,768 participants) were included. The MetS global prevalence varied from 12.5% (95%CI: 10.2-15.0) to 31.4% (29.8-33.0) according to the definition considered. The prevalence was significantly higher in Eastern Mediterranean Region and Americas and increased with country's level of income. The global prevalence was 45.1% (95%CI: 42.1-48.2) for ethnic-specific central obesity, 42.6% (40.3-44.9) for systolic blood pressure (BP) ≥ 130 mmHg and/or diastolic BP ≥ 85 mmHg, 40.2% (37.8-42.5) for HDL-cholesterol < 1.03 for men or < 1.29 mmol/L for women, 28.9% (27.4-30.5) for serum triglycerides ≥ 1.7 mmol/L, and 24.5% (22.5-26.6) for fasting plasma glucose ≥ 5.6 mmol/L. CONCLUSIONS This study reveals that MetS and its related cardiometabolic components are highly prevalent worldwide. This study calls for more aggressive and contextualized public health interventions to tackle these conditions.
Anna C. Davis, Jackson L Voelkel, Carol L Remmers
et al.
Introduction Insight into the characteristics of populations from which research samples are drawn is essential to understanding the generalizability of research findings. This study characterizes the membership of Kaiser Permanente and compares members to the population of the communities in which they live. Methods This study is a descriptive comparison of population distributions for Kaiser Permanente members vs the general population within counties in which Kaiser Permanente operates. Kaiser Permanente data on demographics, membership, geographically linked census data, and chronic condition prevalence were compared with community data drawn from the US Census and the Behavioral Risk Factor Surveillance System. Results Overall, Kaiser Permanente members were older (50% aged 40 or older compared to 45.8% of the general population) and more likely to be female (51.8% vs 50.5% of the general population). Distribution by race and ethnicity was similar for all Regions combined but varied somewhat within Regions. Distribution by neighborhood-linked income, education, and social vulnerability was similar between Kaiser Permanente and the community. Prevalence of 6 of 7 chronic conditions was higher in the community than in Kaiser Permanente, with differences ranging from 0.5% for depression to 7.7% for hyperlipidemia. Conclusion The demographic characteristics of Kaiser Permanente members are similar to the general population within each of the Kaiser Permanente Regions. Overall, the size and diversity of the Kaiser Permanente membership offers an effective platform for research. This approach to comparing health system members with the larger community provides valuable context for interpreting real-world evidence, including understanding the generalizability of research and of measures of system performance.
Konstantin Klemmer, Esther Rolf, C. Robinson
et al.
Geographic information is essential for modeling tasks in fields ranging from ecology to epidemiology. However, extracting relevant location characteristics for a given task can be challenging, often requiring expensive data fusion or distillation from massive global imagery datasets. To address this challenge, we introduce Satellite Contrastive Location-Image Pretraining (SatCLIP). This global, general-purpose geographic location encoder learns an implicit representation of locations by matching CNN and ViT inferred visual patterns of openly available satellite imagery with their geographic coordinates. The resulting SatCLIP location encoder efficiently summarizes the characteristics of any given location for convenient use in downstream tasks. In our experiments, we use SatCLIP embeddings to improve performance on nine diverse geospatial prediction tasks including temperature prediction, animal recognition, and population density estimation. Across tasks, SatCLIP consistently outperforms alternative location encoders and shows promise for improving geographic domain adaptation. These results demonstrate the potential of vision-location models to learn meaningful representations of our planet from the vast, varied, and largely untapped modalities of geospatial data.
The male-specific part of the human Y chromosome is widely used in forensic DNA analysis, particularly in cases where standard autosomal DNA profiling is not informative. A Y-chromosomal gene fragment is applied for inferring the biological sex of a crime scene trace donor. Haplotypes composed of Y-chromosomal short tandem repeat polymorphisms (Y-STRs) are used to characterise paternal lineages of unknown male trace donors, especially suitable when males and females have contributed to the same trace, such as in sexual assault cases. Y-STR haplotyping applied in crime scene investigation can (i) exclude male suspects from involvement in crime, (ii) identify the paternal lineage of male perpetrators, (iii) highlight multiple male contributors to a trace, and (iv) provide investigative leads for finding unknown male perpetrators. Y-STR haplotype analysis is employed in paternity disputes of male offspring and other types of paternal kinship testing, including historical cases, as well as in special cases of missing person and disaster victim identification involving men. Y-chromosome polymorphisms are applied for inferring the paternal bio-geographic ancestry of unknown trace donors or missing persons, in cases where autosomal DNA profiling is uninformative. In this overview, all different forensic applications of Y-chromosome DNA are described. To illustrate the necessity of forensic Y-chromosome analysis, the investigation of a prominent murder case is described, which initiated two changes in national forensic DNA legislation both covering Y-chromosome use, and was finally solved via an innovative Y-STR dragnet involving thousands of volunteers after 14 years. Finally, expectations for the future of forensic Y-chromosome DNA analysis are discussed.
Manfred Brath, Jon Petersen, Stefan A. Buehler
et al.
Abstract Atmospheric Radiative Transfer Simulator (ARTS) is an open source general purpose radiative transfer model used for a wide range of applications from remote sensing to the interaction of climate and radiation. In the past, it was confined to the microwave and infrared spectral range, as ARTS could not simulate the interaction of solar radiation in the atmosphere. Here we close this gap and extend ARTS to the shortwave range. We introduce a solar source that can be used as a collimated beam source and as background source allowing to resolve the finite solar disc. Additionally, we implemented a new clear sky solver that supports collimated beam sources and solar background sources, and updated the ARTS DISORT interface to utilize DISORT's capability for collimated beam sources. This allows consistent line‐by‐line radiative transfer simulations from the microwave to the ultraviolet range with ARTS. We evaluated the shortwave capabilities of ARTS by comparing it with LBLRTM for clear sky simulations. The root mean square deviation between them is 0.70 Wm−2 for the upward flux at top of the atmosphere and 0.66 Wm−2 for the downward flux at the surface. Simulations of a sunrise as seen from space, of satellite observations and of full spectrum all sky radiative flux illustrate the new capabilities. With the new features, ARTS can be used for a wide range of new applications.
For a (molecular) graph $G$ and any real number $α\ne 0$ , the zero-order general Randić index , denote by $^0R_α$, is defined by the following equation: \begin{align*} {^0R_α} (G) =\sum_{v\in G}d_G (v) ^α (α\in \mathbb{R}-\left\{0\right\}) . \end{align*} In this paper, we use this index to give sufficient conditions for a graph $G$ to satisfy the Hamiltonian (or $k$-Hamiltonian) property, and show that none of these conditions can be dropped. Finally we give similar results for the case when $G$ is a balanced bipartite graph.
Digital global connectivity offers multinational enterprises new opportunities, but it also brings unique risks, an area not yet well examined in international business. This article presents an organizational information-processing framework, which elucidates what (risk types and sources), how (risk assessing and managing), and whom (vulnerable firms) these risks relate to, in a dialectical manner that seeks to combine theoretical insights and managerial actions. We focus on three types of risks (digital interdependence, information security, and regulatory complexity) that MNEs uniquely face, and explain within-country and cross-country risk drivers. We document variances among MNEs in exposure to these risks, proposing that information flow intensity, geographic diversity, international strategy, and global platform participation carry strong implications on the firm’s risk exposure and response. Finally, this framework offers actions essential for MNEs, individually and collectively, to manage digital risks, emphasizing processes of building and deploying digital intelligence in pursuit of transnational resilience for cross-border activities that become increasingly digitally connected.
The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.
Phillip J. Haubrock, Ismael Soto, Irmak Kurtul
et al.
Abstract Background Long-term biomonitoring of macroinvertebrates is a popular and valuable approach for assessing the status of freshwater ecosystems, identifying the impact of stressors, and evaluating ecosystem health. Although macroinvertebrate-based biomonitoring can be effective in detecting changes in distribution patterns and community trends over time, crayfish often remain undetected or unreported by biomonitoring efforts despite their importance in maintaining the functioning of aquatic ecosystems. Results By analyzing a comprehensive database of long-term macroinvertebrate time series, we found that most sampling methods and assessment schemes can detect both native and non-native crayfish in running waters if sites are continuously sampled. However, native crayfish were detected to a lesser extent and by fewer methods. Kick-net sampling and assessment techniques prevailed as the most efficient methods for capturing crayfish. However, the substantial number of time series lacking crayfish data calls into question whether these methods are sufficiently comprehensive to encapsulate crayfish populations accurately. The use of other targeted methods such as baited traps or hand catching may provide a more reliable estimate of their presence. Conclusions Given the detrimental impacts of non-native crayfish and the decline in native crayfish populations, we strongly recommend that stakeholders and managers incorporate a combination of these approaches into their monitoring efforts. The use of different taxonomic levels (family vs. genus vs. species level) in estimating biological indices and biomonitoring tools can cause delays in identifying new non-native specie’s occurrences, hindering effective water quality assessment and ecosystem management by governments and stakeholders. Therefore, whenever possible, we call for standardized taxonomic levels for biomonitoring studies and management strategies to accurately address these issues and make recommendations going forward.
General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems. Recently, the emergence of the Sora model has attained significant attention due to its remarkable simulation capabilities, which exhibits an incipient comprehension of physical laws. In this survey, we embark on a comprehensive exploration of the latest advancements in world models. Our analysis navigates through the forefront of generative methodologies in video generation, where world models stand as pivotal constructs facilitating the synthesis of highly realistic visual content. Additionally, we scrutinize the burgeoning field of autonomous-driving world models, meticulously delineating their indispensable role in reshaping transportation and urban mobility. Furthermore, we delve into the intricacies inherent in world models deployed within autonomous agents, shedding light on their profound significance in enabling intelligent interactions within dynamic environmental contexts. At last, we examine challenges and limitations of world models, and discuss their potential future directions. We hope this survey can serve as a foundational reference for the research community and inspire continued innovation. This survey will be regularly updated at: https://github.com/GigaAI-research/General-World-Models-Survey.
This study provided a comprehensive characterization on ingestion of different typologies of microplastics in several fish and invertebrate species from the Adriatic Sea, considered as a preferential area of plastic accumulation in the Mediterranean. Almost 500 organisms were sampled in the three sectors of Northern, Central and Southern Adriatic, testing the hypothesis that area of collection, habitat and feeding strategy might influence the occurrence of plastic particles in biota. In this study, the overall characterization considered separately plastic microparticles (MPs) from textile microfibers (MFs) which also included natural and semi-synthetic ones. Ingestion of MPs was a widespread phenomenon, but their number (typically 1 or 2) did not reveal any significant relationship with biometric values, geographical areas or ecological features of the species. Conversely, the frequency of ingestion, ranging from 13 to 35% of organisms containing MPs, appeared a more reliable index to highlight such differences, revealing higher values in species from Central and Southern basins compared to the Northern one, as well as in benthopelagic compared to benthic or pelagic organisms. Geographical differences also occurred in terms of size and typology of ingested particles, suggesting the importance of local river runoffs and surface currents dynamics. Textile microfibers (MFs) were also abundant in Adriatic food webs occurring in all the analyzed species with average numbers (3-10) and frequencies (40-70%) higher than those reported for MPs; further, an elevated percentage of MFs (>80%) was of natural or semi-synthetic origin. Overall, this study provided general insights toward the harmonization of a common biomonitoring strategy, as in the context of MSFD, including the suggestion of a frequency-based index and of a multi-species approach to increase the ecological relevance of assessment, as well as the comparability between different areas and trophic webs.
Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp.
We prove a number of results relating the concepts of Keisler measures, generic stability, randomizations, and NIP formulas. Among other things, we do the following: (1) We introduce the notion of a Keisler-Morley measure, which plays the role of a Morley sequence for a Keisler measure. We prove that if $μ$ is fim over $M$, then for any Keisler-Morley measure $λ$ in $μ$ over $M$ and any formula $\varphi(x,b)$, $\lim_{i \to \infty} λ(\varphi(x_i,b)) = μ(\varphi(x,b))$. We also show that any measure satisfying this conclusion must be fam. (2) We study the map, defined by Ben Yaacov, taking a definable measure $μ$ to a type $r_μ$ in the randomization. We prove that this map commutes with Morley products, and that if $μ$ is fim then $r_μ$ is generically stable. (3) We characterize when generically stable types are closed under Morley products by means of a variation of ict-patterns. Moreover, we show that NTP$_2$ theories satisfy this property. (4) We prove that if a local measure admits a suitably tame global extension, then it has finite packing numbers with respect to any definable family. We also characterize NIP formulas via the existence of tame extensions for local measures.
S. Colorado-Yohar, A. C. Castillo-González, J. Sánchez-Meca
et al.
BACKGROUND Human bisphenol-A (BPA) exposure has been linked to adverse health effects even at low doses, which may be of potential public health concern. OBJECTIVE To summarize BPA concentrations in general human population and their variability according to sex, geographic area, and analytical method. METHODS Systematic review and meta-analysis of studies reporting BPA concentrations in adult human populations. Separate meta-analyses of median values were carried out for BPA in serum, creatinine-adjusted urinary BPA, and unadjusted urinary BPA concentrations using a random-effects model. Cochran's Q-statistic, I2 index, 95% prediction intervals (PIs), between-studies standard deviation (τ), and forest plots were applied to verify study heterogeneity. Sensitivity and subgroup analyses and weighted ANOVAs and meta-regressions were conducted. Funnel plots and Egger's tests were used to examine publication bias. RESULTS Fifteen studies were included in the meta-analysis, totaling 28,353 participants. BPA was detected in over 90% of participants. The pooled creatinine-adjusted urinary BPA concentration was 1.76 μg/g (95% PI: 0.79-2.73), with individual estimates ranging between 1.20 and 2.41. The pooled estimate for unadjusted urinary BPA was 1.91 μg/l (95% PI: 0-3.97), ranging between 0.81 and 3.50, while the pooled estimate for serum BPA was 1.75 μg/l (95% PI: 0-10.58), ranging between 0.34 and 3.76. No differences were found by sex, geographic area or analytical technique. Larger sample sizes were associated with lower BPA concentrations. There was large heterogeneity across studies, whereas data for urinary BPA levels suggested a publication bias affecting research in low exposed populations. CONCLUSION This first meta-analysis of human BPA concentrations highlights a widespread population exposure to BPA. Although there was high heterogeneity across studies, the expected range of estimated human BPA concentrations suggests that potential health risks are unlikely. Further studies are warranted to better characterize the epidemiology of human BPA exposure, accounting for ethnic, geographic, individual and environmental variability.
The globalization of medical research and global health’s increasing popularity worldwide have resulted in greater geographic, ethnic, and socioeconomic diversity of studies published in the scientific literature. Yet the geographic distribution, authorship representation, and subject trends among Low-/Low-Middle-Income Country (LIC/LMIC)-based scientific publications remain largely unknown. This analysis assesses these gaps in knowledge. We performed a comprehensive bibliometric analysis of all scientific articles published between January 2014 and June 2016 in the four most prominent general medicine and five most prominent general global health journals based on impact factor. The African region, containing 24% of the global LIC/LMIC population, accounted for 49.9% of all publications. Corresponding authors with either exclusive or joint appointment to a LIC/ LMIC institution were present in 26.2% of all included articles. Over one-quarter (28.8%) of all publications did not list a local author. Nearly two-thirds (62.1%) of articles published in global health journals and roughly half (52.4%) in general medicine journals involved infectious diseases. Non-HIV infectious disease studies were by far the most frequent subject areas across all journals. The trends identified in this study may help to inform the evolution and prioritization of future research efforts, thereby allowing global health to remain truly global.