Broad Patterns Research Articles

Bacteriophage-driven DNA inversions shape bacterial functionality and long-term co-existence in Bacteroides fragilis

ABSTRACT Bacterial genomic DNA inversions, which govern molecular phase-variations, provide the bacteria with functional plasticity and phenotypic diversity. These targeted rearrangements enable bacteria to respond to environmental challenges, such as bacteriophage predation, evading immune detection or gut colonization. This study investigated the short- and long-term effects of the lytic phage Barc2635 on the functional plasticity of Bacteroides fragilis, a gut commensal. Germ-free mice were colonized with B. fragilis and exposed to Barc2635 to identify genomic alterations driving phenotypic changes. Phage exposure triggered dynamic and prolonged bacterial responses, including significant shifts in phase-variable regions (PVRs), particularly in promoter orientations of polysaccharide biosynthesis loci. These shifts coincided with increased entropy in PVR inversion ratios, reflecting heightened genomic variability. In contrast, B. fragilis in control mice exhibited stable genomic configurations after gut adaptation. The phase-variable Type 1 restriction-modification system, which affects broad gene expression patterns, showed variability in both groups. However, phage-exposed bacteria displayed more restrained variability, suggesting phage-derived selection pressures. Our findings reveal that B. fragilis employs DNA inversions to adapt rapidly to phage exposure and colonization, highlighting a potential mechanism by which genomic variability contributes to its response to phage. This study demonstrates gut bacterial genomic and phenotypic plasticity upon exposure to the mammalian host and to bacteriophages.

Family size and cardiovascular disease incidence: a population-level association study.

To investigate the population-level association between family size and cardiovascular disease (CVD) incidence, focusing on broad patterns rather than causal mechanisms or individual-level effects. Population level correlations of family size to CVD incidence were analyzed with scatter plots, simple regression, partial correlation and multivariate regression separately. Aging, economic affluence, obesity and urbanization were incorporated in models as potential confounders. Globally, family size negatively correlated to CVD incidence rate. This relationship remained in partial correlation analyses when controlling for confounders. Stepwise multiple regression revealed that family size may be the most significant predictor of CVD incidence. Large family size is significantly associated with lower cardiovascular disease (CVD) incidence, potentially due to biological, psychological, and social factors. However, as the data are cross-sectional, this relationship should be interpreted as correlational rather than causal. The association appears more pronounced in developing countries, where contextual factors may amplify its effects.

Strength in numbers: Combining small pockets of opportunistic sampling for Australian seabird plastic ingestion.

Strength in numbers: Combining small pockets of opportunistic sampling for Australian seabird plastic ingestion.

Residual Overturning Circulation and Associated Water-Mass Transformation in the East/Japan Sea

Abstract The East/Japan Sea (EJS), a marginal sea in the northwest Pacific, has marked isopycnal slopes, especially during winter, implying a store of potential energy available for baroclinic instability and hence, perhaps, a significant role for eddy processes in shaping the residual (mean plus eddy) overturning circulation. Here, for the first time, we compute the residual overturning circulation in the EJS in a high-resolution simulation and relate it to water-mass transformation processes due to air–sea fluxes and interior mixing. A sizable eddy-driven circulation is indeed found in the vicinity of tilted isopycnals. Wintertime surface buoyancy loss facilitates a volume flux toward higher-density classes, with latent heat loss being the main contributor and sensible heat loss also playing a role. The densification of northward subsurface flow is associated with diapycnal mixing. The water-mass formation rate, derived from the transformation rate, identifies upwelling and downwelling to the south and north of the subpolar front near 39.5°N, respectively, consistent with the broad pattern of residual overturning circulation. Significance Statement In the East/Japan Sea (EJS), we computed the residual overturning circulation for the first time and linked it to water-mass transformation through the air–sea fluxes and interior mixing. We found a significant eddy-driven circulation with tilted isopycnals, especially during winter. The residual overturning circulation orchestrates with the surface buoyancy flux. In particular, the wintertime buoyancy loss, mainly driven by latent heat, relates the volume flux to higher-density classes. Furthermore, we identify upwelling and downwelling patterns around the subpolar front based on the water-mass formation rate, which is consistent with the overall residual overturning circulation. These findings are crucial for understanding the role of eddies and air–sea buoyancy fluxes in the EJS circulation and provide a guide to the projected changes in the ocean circulation under a warming climate.

A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images

Recent advancements in deep learning have significantly impacted medical image processing domain, enabling sophisticated and accurate diagnostic tools. This paper presents a novel hybrid deep learning framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for diabetic retinopathy (DR) early detection and progression monitoring using retinal fundus images. Utilizing the sequential nature of disease progression, the proposed method integrates temporal information across multiple retinal scans to enhance detection accuracy. The proposed model utilizes publicly available DRIVE and Kaggle diabetic retinopathy datasets to evaluate the performance. The benchmark datasets provide a diverse set of annotated retinal images and the proposed hybrid model employs a CNN to extract spatial features from retinal images. The spatial feature extraction is enhanced by multi-scale feature extraction to capture fine details and broader patterns. These enriched spatial features are then fed into an RNN with attention mechanism to capture temporal dependencies so that most relevant data aspects can be considered for analysis. This combined approach enables the model to consider both current and previous states of the retina, improving its ability to detect subtle changes indicative of early-stage DR. Proposed model experimental evaluation demonstrate the superior performance over traditional deep learning models like CNN, RNN, InceptionV3, VGG19 and LSTM in terms of both sensitivity and specificity, achieving 97.5% accuracy on the DRIVE dataset, 94.04% on the Kaggle dataset, 96.9% on the Eyepacs Dataset. This research work not only advances the field of automated DR detection but also provides a framework for utilizing temporal information in medical image analysis.

Web Design Analysis of Fisheries Startups in Indonesia: A Study of Aruna, Minapoli, and eFishery

The success level of startup companies that have just been started and are digitally based—is significantly influenced by their website design. The website design for fisheries startups in Indonesia should refer to the book 'The Principles of Beautiful Web Design' by Jason Beaird, commonly used by leading companies worldwide. This research aims to analyze the website designs of three leading fisheries startups in Indonesia—Aruna, Minapoli, and eFishery—based on six main design principles from the book: web page anatomy, grid theory, balance, unity, bread and butter layout, and fresh trends. The research employs a qualitative descriptive method to explore the application of these design components on the three websites and present the results descriptively. The research findings indicate that the website designs of the three startups have implemented all principles of web page anatomy, with the exception of the sidebar component. They have also applied the principle of fresh trends by adopting a broad footer navigation pattern. Regarding the principle of balance, the Aruna and Minapoli websites use a symmetrical balance pattern, whereas eFishery employs an asymmetrical balance pattern. In terms of unity, the designs of Aruna and eFishery incorporate a repetition pattern, while Minapoli uses a proximity pattern. However, none of the websites apply the principles of grid theory; instead, they feature free and modern designs. Furthermore, none of them follow the bread-and-butter layout principle; rather, they opt for a more minimalist design.

High-resolution habitat suitability maps for all widespread Italian breeding bird species

Tackling the current global biodiversity crisis requires large-scale spatially accurate biodiversity data to rapidly assess knowledge gaps and set conservation priorities. Obtaining such data is often challenging because surveying biodiversity across broad spatial scales requires massive logistical and economic efforts. Here, we provide high-resolution (0.81 to 81 km2, depending on species ecology) habitat suitability raster maps for all 225 widespread breeding bird species in Italy. Maps were generated by means of species distribution models based on ~2.5 million spatially accurate (≤1 km-scale) and expert-validated occurrence records. Occurrence data were collected during the breeding seasons 2010–2016 by over 3000 skilled observers, mostly through the Ornitho.it web platform, with the aim of realizing the second Atlas of Breeding Birds in Italy, released in 2022. These raster maps will be useful to ecologists, conservation scientists and practitioners for investigating broad spatial patterns in avian diversity and identifying conservation priorities. We discuss potential applications of this dataset for inferring the composition of ecological communities and species distributions at the Italian scale.

Assessing grand narratives of economic inequality across time

Long-entrenched grand narratives have tied inequality in large human aggregations to generally linear trends, a direct outcome of domestication, then fostered by population growth and/or stepped scalar transitions in the hierarchical complexity of human institutions. This general pattern has been argued to short-circuit or reverse only in the context of cataclysmic disasters or societal breakdowns. Yet, for the most part, these universal deterministic frameworks have been constructed from historical or ethnographic snapshots in time and afford little systematic attention to human institutions or agency. Here, we leverage quantitative, temporally defined archaeological, and ethnographic data from a suite of global regions, most of which transitioned through the process of urbanism and complex hierarchy formation, to examine shifts in degrees of inequality over time. Although broad temporal patterns are evidenced, the regional trends in inequality are neither linear, uniform, nor triggered immediately or mechanically by Malthusian dynamics or scalar increases.

An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification

Cassava is a tuberous edible plant native to the American tropics and is essential for its versatile applications including cassava flour, bread, tapioca, and laundry starch. Cassava leaf diseases reduce crop yields, elevate production costs, and disrupt market stability. This places significant burdens on farmers and economies while highlighting the need for effective management strategies. Traditional methods of manual disease diagnosis are costly, labor-intensive, and time-consuming. This research aims to address the challenge of accurate disease classification by overcoming the limitations of existing methods, which encounter difficulties with the complexity and variability of leaf disease symptoms. To the best of our knowledge, this is the first study to propose a novel dual-track feature aggregation architecture that integrates the Residual Inception Positional Encoding Attention (RIPEA) Network with EfficientNet for the classification of cassava leaf diseases. The proposed model employs a dual-track feature aggregation architecture which integrates the RIPEA Network with EfficientNet. The RIPEA track extracts significant features by leveraging residual connections for preserving gradients and uses multi-scale feature fusion for combining fine-grained details with broader patterns. It also incorporates Coordinate and Mixed Attention mechanisms which focus on cross-channel and long-range dependencies. The extracted features from both tracks are aggregated for classification. Furthermore, it incorporates an image augmentation method and a cosine decay learning rate schedule to improve model training. This improves the ability of the model to accurately differentiate between Cassava Bacterial Blight (CBB), Brown Streak Disease (CBSD), Green Mottle (CGM), Mosaic Disease (CMD), and healthy leaves, addressing both local textures and global structures. Additionally, to enhance the interpretability of the model, we apply Grad-CAM to provide visual explanations for the model’s decision-making process, helping to understand which regions of the leaf images contribute to the classification results. The proposed network achieved a classification accuracy of 93.06%.

The Scale‐Dependency in Freshwater Habitat Regionalisation Analyses

ABSTRACTFreshwater ecosystems need efficient protection which requires detailed information regarding the spatial distribution of its environmental characteristics, which allows simple habitat suitability assessments for freshwater species. Such characteristics can be assessed with regionalisation analyses, where environmental characteristics are spatially clustered to highlight similarities or disparities across a given study area. While large drainage basins are useful for large‐scale estimates, it is equally important to address small streams which contribute most to the stream network length. The question however remains, what is the relative impact of the spatial scale and the choice of variables on regionalisation analyses? We tested for scale‐ and variable‐contingent effects in freshwater habitat clusters using three analysis designs. We used the Hydrography90m high‐resolution stream network dataset and aggregated land cover, hydro‐geomorphological and climatic variables across the sub‐catchments of six drainage basins distributed across continents and climatic zones. We then employed k‐means cluster analyses and tested the effect of (i) spatial scale, (ii) the choice of environmental variables and (iii) the combination of scale and variables on the resulting habitat regionalisation. Our results show that similar broad habitat cluster patterns emerged regardless of the analysis design, whereas basin‐specific analyses uncovered new smaller habitat clusters. Land cover stood out as the most influential variable regardless of the analysis design. Our findings highlight the importance of addressing the spatial scale in freshwater regionalisation analyses for assessing environmental characteristics that are unique to a given drainage basin, which could provide guidance for an improved mapping of high‐resolution freshwater habitat patterns globally.

StoichLife: A Global Dataset of Plant and Animal Elemental Content

The elemental content of life is a key trait shaping ecology and evolution, yet organismal stoichiometry has largely been studied on a case-by-case basis. This limitation has hindered our ability to identify broad patterns and mechanisms across taxa and ecosystems. To address this, we present StoichLife, a global dataset of 28,049 records from 5,876 species spanning terrestrial, freshwater, and marine realms. Compiled from published and unpublished sources, StoichLife documents elemental content and stoichiometric ratios (%C, %N, %P, C:N, C:P, and N:P) for individual plants and animals. The dataset is standardized and, where available, includes information on taxonomy, habitat, body mass (for animals), geography, and environmental conditions such as temperature, solar radiation, and nutrient availability. By providing an unprecedented breadth of organismal stoichiometry, StoichLife enables the exploration of global patterns, ecological and evolutionary drivers, and context-dependent variations. This resource advances our understanding of the chemical makeup of life and its responses to environmental change, supporting progress in ecological stoichiometry and related fields.

Windblown dust in the Tarim basin, Northwest China

Tarim Basin in western China is home to the world’s second-largest mobile dune desert, Taklimakan Desert, and it’s one of Asia’s primary sources of sand and dust storm. Observations of windblown dust are insufficient over this hyper-dry inland region. Here we present a comprehensive study based on consecutive in-situ field observations, meteorological records, environmental monitoring data and satellite measurements over the Tarim Basin for a full year in 2015. The results show that during the severe sand and dust storm events, the observed ambient PM10 (particulate matter with an aerodynamic diameter ≤ 10 μm) concentration rises rapidly, with a maximum value exceeding 10,000 µg/m3 per hour, while wind speeds reach 10–30 m/s and visibility is reduced to less than 10 m. Soil particulates can be blown vertically into the atmosphere at a height of 3–12 km. High volumes of dust deposition were measured at environmental monitoring stations, ranging from 1764 to 3800 g/m2 yr. Those significant flux levels of ambient particulate matter (PM) concentrations and dust depositions are strongly associated with frequent dust occurrence in the arid environment of the Tarim Basin. Satellite measurements of aerosol optical depths (AOD) show a broad spatial pattern of dust aerosols distribution over the basin, with dense dust remaining suspended for long periods of time (3–5 months in spring and summer seasons). The wind regimes, basin-like topography, thermodynamic condition, and loose sandy surfaces greatly affect the regional aeolian dust environment in the Tarim Basin, which lead to a significantly high dust emission, ambient PM concentration and dust deposition.

Evolutionary consequences of long-distance dispersal in mosquitoes.

Long-distance dispersal (LDD) provides a means for mosquitoes to invade new regions and spread adaptive alleles, including those conferring insecticide resistance. Most LDD takes place on human transport vessels and will typically be rarer and more directionally constrained than active flight but can connect populations and regions that are otherwise mutually inaccessible. These features make LDD worthy of specific consideration in mosquito research. This paper reviews recent evolutionary research on LDD and its consequences for mosquito populations and mosquito control. LDD is the main source of mosquito range expansions, and genomic methods can now trace the origins of new invasions to specific towns or cities. Genomic methods can also give a rough indication of the number of invaders, which if very small may lead to the stochastic loss of advantageous alleles during invasion bottlenecks. Once invasions are established, LDD spreads adaptive alleles between populations. Emerging insights into insecticide resistance evolution indicate that LDD has repeatedly spread resistance mutations across global species ranges, but these broad patterns are convoluted by two other evolutionary processes: parallel adaptation at the same gene or gene cluster and polygenic adaptation at different genes in different populations. Together, these processes have produced patterns of similarity and dissimilarity at resistance genes that are decoupled from geographical distance. LDD within cities is less well studied but is important for planning and evaluating local control efforts. Urban investigations of LDD may help identify areas experiencing weaker selection pressures from insecticides and isolated areas to target for control.

Collective Action Under Repressive Conditions: Integration of Individual, Group, and Structural Level Research, Recommendations, and Reflections

ABSTRACTSocial scientific research from different traditions on collective action under repressive conditions is fragmented across different levels of analysis. The current paper takes a first step toward remedying this fragmentation by reviewing research findings on repression and collective action and organizing them into a multilevel framework. We describe the impact of repression on antecedents of collective action at the (a) individual level (including grievances, emotions, efficacy beliefs, politicized identity, and individual differences), (b) group level (including community cohesion and norms), and (c) structural level (including political opportunities and socioecological conditions). We then present an integrative summary reflecting on the broad patterns we observed in the literature. We conclude with policy implications of this work, suggesting recommendations for activists to overcome repression, for authorities to foster pluralist political participation while maintaining stability, and for researchers to further advance knowledge on repression and collective action.

Intercomparison of Antarctic Sea-Ice Thickness Estimates from Satellite Altimetry and Assessment over the 2019 Data-Rich Year

Sea-ice thickness (SIT) from satellites is an essential climate variable for characterizing the ice-covered ocean and evaluating numerical models. Although satellite altimetry is a promising option to obtain sustainable circum-Antarctic SIT estimates, its application in the Antarctic remains challenging due to the scarcity of systematic in situ observations for validation, and the most recent intercomparison exercise covered the period 2004 to 2008. In this study, we compared three empirical methods (ERM, BERM, and OLM) and one lidar-only method (ZIF) to determine SIT from lidar freeboard observations, one method combining lidar and radar freeboard observations (FDM), and one that uses both lidar freeboard observations and an independent snow depth dataset from passive microwaves (SICC). We first compared the methods in 2019, which is the only data-rich year during the overlapping period from 2019 to 2023. While the methods agreed on the broad spatial patterns of SIT, they clustered in two groups that have significant magnitude differences, with SICC and FDM estimating thicker ice and the lidar-based methods producing the thinnest estimates. Based on the limited set of available data, we did not find any single best performing method, and we recommend using the methods in a complementary way and to establish a network of concerted and continued field measurements for method assessments.

Applications of environmental DNA monitoring for seaweed reproductive phenology: A case study with giant kelp (Macrocystis pyrifera).

Monitoring the seasonal reproductive cycles of seaweeds is crucial for effective population and ecosystem management, as well as mariculture seedstock collection. Traditional methods, such as visual monitoring by SCUBA diving or snorkeling, are costly, labor-intensive, and limited in temporal and spatial coverage. This study explores substituting these methods with environmental DNA (eDNA) techniques for giant kelp (Macrocystis pyrifera, order Laminariales). This laboratory study aimed to determine the minimum detectable concentration of zoospores and sporophyte tissue needed for detecting the reproductive phenology of M. pyrifera and to assess the ability and sensitivity to discriminate between life stages. The study involved syringe-filtering seawater samples through 0.45-μm pore-size filters before quantitative polymerase chain reaction (qPCR) analysis with species-specific primers. There was a strong positive correlation between zoospore concentration and eDNA copies per μL (ρ = 0.982, p < 0.001), and a weak correlation for sporophyte wet weight (ρ = 0.367, p = 0.134). There was a significant difference between zoospore and zoospore + sporophyte treatments (p = 0.010), indicating the substantial influence of sporophyte tissue on detected eDNA quantity. Sporophyte tissue obscures the zoospore signal, especially at lower zoospore concentrations (<37 zoospores · mL-1), highlighting that eDNA analysis is suitable for monitoring reproductive peaks and broader patterns in seasonal reproduction cycles of giant kelp when zoospore concentrations are high.

Enhancing Healthcare Sustainability: Informal Caregivers Leveraging Social Media in the Caregiving Process

Introduction: Informal caregivers who offer unpaid assistance to family and friends in need navigate complex challenges during their caregiving journey. Seeking support and guidance, many caregivers turn to social media (SM) platforms, marking a crucial intersection between technology transfer, digital transformation, and sustainability in caregiving. Methods: Twenty-four caregivers from diverse regions of Saudi Arabia were interviewed, semi-structured interviews, to gauge the extent to which they utilized SM in caregiving tasks. Results: Our analysis revealed broad patterns of SM utilization among caregivers, characterized by four main themes. Most participants actively used SM platforms to seek medical information, communicate with physicians, and participate in support groups. Educational background influenced adoption patterns, with varying levels of engagement across educational attainment. While the majority leveraged multiple platforms, such as WhatsApp, Twitter, and Instagram, for medical information access and healthcare providers (HCPs) communication, some participants expressed reservations and hesitancy about SM use in caregiving. Notably, participants utilized these platforms for resource management and accessing educational materials about caregiving techniques, demonstrating the multifaceted role of SM in supporting informal caregivers. Conclusion: Our findings revealed widespread SM use among informal caregivers, depicting the adoption of new technologies and a shift toward secure, high-quality care. The caregivers leveraged SM for medical information, consultations, and support group participation. Nevertheless, skepticism exists with some caregivers not trusting SM as an enabler of caregiving duties. Future research may focus on enhancing caregivers’ experiences, facilitating their tasks, and improving their loved one’s health through wider technological deployment and digital health transformation activities.

SETD1B-mediated broad H3K4me3 controls proper temporal patterns of gene expression critical for spermatid development

Epigenetic programming governs cell fate determination during development through intricately controlling sequential gene activation and repression. Although H3K4me3 is widely recognized as a hallmark of gene activation, its role in modulating transcription output and timing within a continuously developing system remains poorly understood. In this study, we provide a detailed characterization of the epigenomic landscapes in developing male germ cells. We identified thousands of spermatid-specific broad H3K4me3 domains regulated by the SETD1B-RFX2 axis, representing a previously underappreciated form of H3K4me3. These domains, overlapping with H3K27ac-marked enhancers and promoters, play critical roles in orchestrating robust transcription and accurate temporal control of gene expression. Mechanistically, these broad H3K4me3 compete effectively with regular H3K4me3 for transcriptional machinery, thereby ensuring robust levels and precise timing of master gene expression in mouse spermiogenesis. Disruption of this mechanism compromises the accuracy of transcription dosage and timing, ultimately impairing spermiogenesis. Additionally, we unveil remarkable changes in the distribution of heterochromatin marks, including H3K27me3 and H3K9me2, during the mitosis-to-meiosis transition and completion of meiotic recombination, which closely correlates with gene silencing. This work underscores the highly orchestrated epigenetic regulation in spermatogenesis, highlighting the previously unrecognized role of Setd1b in the formation of broad H3K4me3 domains and transcriptional control, and provides an invaluable resource for future studies toward the elucidation of spermatogenesis.

CNN AND LSTM MODELS FOR FMRI-BASED SCHIZOPHRENIA CLASSIFICATION USING C-ICA OF DFNC.

Resting-state fMRI (rs-fMRI) captures brain activity at rest, it demonstrates information on how different regions interact without explicity task-based influences. This provides insights into both healthy and disordered brain states. However, clinical application of rs-fMRI remains challenging due to the wide variability in functional connectivity across individuals. Traditional data-driven methods like independent component analysis (ICA) struggle to balance these individual differences with broader patterns. Constrained methods, such as constrained ICA (cICA), have been introduced to address this by integrating templates from multiple external datasets to enhance accuracy and consistency. In our study, we analyzed rs-fMRI data from 100,517 individuals from diverse datasets, processed through a robust quality-control dynamic connectivity pipeline established in previous work. Using the resulting brain state templates as cICA priors, we examined the effectiveness of cICA for schizophrenia classification using a combined CNN and LSTM architecture. Results showed stable classification accuracy (87.6% to 86.43%) for the CNN model, while the LSTM model performed less optimally, likely due to sequence processing, yet still yielded comparable results. These findings underscore the potential of group-informed methods and prior data templates in constrained dynamic ICA, offering improved reliability and clinical relevance in rs-fMRI analysis and advancing our understanding of brain function.

Origins of Latin American inequality

Abstract How deep are the roots of Latin America’s economic inequalities? In this article we survey both the history and the literature about the region’s extreme economic disparities, focusing on the most recent academic contributions. We begin by documenting the broad patterns of national and subnational differences in income and inequality, building on the seminal contributions of Sokoloff and Engerman (2000); Engerman and Sokoloff, 2002, Engerman and Sokoloff, 2005) and aiming to capture different dimensions of inequality. We then proceed thematically, providing empirical evidence and summarizing the key modern studies on colonial institutions, slavery, land reform, education and the role of elites. Finally, we conduct a ‘replication’ exercise with some seminal papers in the literature, extending their economic results to include different measures of inequality as outcomes.