The collection of microbes that live in and on the human body - the human microbiome - can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell-cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology - a systems biology approach.
Recent progress in vision language models (VLMs) has enabled remarkable perception and reasoning capabilities, yet their potential for scientific regression in Earth Observation (EO) remains largely unexplored. Existing EO datasets mainly emphasize semantic understanding tasks such as captioning or classification, lacking benchmarks that align multimodal perception with measurable biophysical variables. To fill this gap, we present REO-Instruct, the first unified benchmark designed for both descriptive and regression tasks in EO. REO-Instruct establishes a cognitively interpretable logic chain in forest ecological scenario (human activity,land-cover classification, ecological patch counting, above-ground biomass (AGB) regression), bridging qualitative understanding and quantitative prediction. The dataset integrates co-registered Sentinel-2 and ALOS-2 imagery with structured textual annotations generated and validated through a hybrid human AI pipeline. Comprehensive evaluation protocols and baseline results across generic VLMs reveal that current models struggle with numeric reasoning, highlighting an essential challenge for scientific VLMs. REO-Instruct offers a standardized foundation for developing and assessing next-generation geospatial models capable of both description and scientific inference. The project page are publicly available at \href{https://github.com/zhu-xlab/REO-Instruct}{REO-Instruct}.
Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang
et al.
Underwater Passive Acoustic Monitoring (UPAM) provides rich spatiotemporal data for long-term ecological analysis, but intrinsic noise and complex signal dependencies hinder model stability and generalization. Multilayered windowing has improved target sound localization, yet variability from shifting ambient noise, diverse propagation effects, and mixed biological and anthropogenic sources demands robust architectures and rigorous evaluation. We introduce GetNetUPAM, a hierarchical nested cross-validation framework designed to quantify model stability under ecologically realistic variability. Data are partitioned into distinct site-year segments, preserving recording heterogeneity and ensuring each validation fold reflects a unique environmental subset, reducing overfitting to localized noise and sensor artifacts. Site-year blocking enforces evaluation against genuine environmental diversity, while standard cross-validation on random subsets measures generalization across UPAM's full signal distribution, a dimension absent from current benchmarks. Using GetNetUPAM as the evaluation backbone, we propose the Adaptive Resolution Pooling and Attention Network (ARPA-N), a neural architecture for irregular spectrogram dimensions. Adaptive pooling with spatial attention extends the receptive field, capturing global context without excessive parameters. Under GetNetUPAM, ARPA-N achieves a 14.4% gain in average precision over DenseNet baselines and a log2-scale order-of-magnitude drop in variability across all metrics, enabling consistent detection across site-year folds and advancing scalable, accurate bioacoustic monitoring.
The paper derives new results on the marginal likelihood of a two-way table which clarify the conditions under which Ecological inference is possible and lead to an efficient algorithm for maximizing the exact multinomial likelihood. The first part generalizes the work of Placket(1977} on the marginal likelihood of a 2 x 2 table to a general R x C table. In doing so, new conceptual tools are introduced and new insights on the geometry of the collection of tables having fixed row and column margins and the extended hypergeometric distribution are derived. In the second part, when observations on the row and the column marginal distributions are available for a collection of two-way tables sharing the same association structure, an efficient Fisher scoring algorithm for maximizing the exact likelihood under multinomial sampling is introduced and a small simulation study is used to compare the performance of the proposed method with two well established ones.
ABSTRACT Many giant viruses replicate in the cytoplasm in viral factories. How exactly these viral factories are established and where the different steps of the replication cycle occur remain largely obscure. We have developed a single-molecule messenger RNA fluorescence in situ hybridization (smFISH) protocol for giant viruses in an Acanthamoeba host. Combined with other labeling techniques (FUNCAT, DiD, rRNA FISH, and DAPI), we show the Mimivirus transcription and translation sites during an infection cycle in the amoeba host cell. Although viral mRNA localization changes depend on the infection stage, transcription occurs at well-defined spots within the viral factory. The original viral cores released within the cytoplasm most likely define these spots. When transported outside of the viral factory, the translation of viral mRNA takes place in a well-defined ring surrounding it. With this study, we obtained novel insights into giant virus replication, of which the methods are widely applicable to other viruses for the visualization and quantification of RNA molecules.IMPORTANCEGiant viruses have massive particle and genome sizes, which are known to infect unicellular eukaryotes. Although most viruses replicate in the host cell’s nucleus, the giant Mimivirus replicates in viral factories established in the host cell’s cytoplasm. Before this study, the location of the various steps in the Mimivirus replication cycle was largely unknown. By developing new protocols to label giant virus mRNA, protein synthesis, host cell membranes and rRNA, we demonstrate that Mimivirus transcription occurs at well-defined sites within the viral factory. In contrast, translation takes place directly outside of it. This is different from other viruses known to have a cytoplasmic life cycle. These results bring us a step closer to understanding how the genome complexity of viruses influences the virus-host interactions and viral replication strategies.
Benjamin Thomas Camper, Andrew Stephen Kanes, Zachary Tyler Laughlin
et al.
Abstract Background Hybridization between evolutionary lineages has profound impacts on the fitness and ecology of hybrid progeny. In extreme cases, the effects of hybridization can transcend ecological timescales by introducing trait novelty upon which evolution can act. Indeed, hybridization can even have macroevolutionary consequences, for example, as a driver of adaptive radiations and evolutionary innovations. Accordingly, hybridization is now recognized as a motor for macrobial evolution. By contrast, there has been substantially less progress made towards understanding the positive eco-evolutionary consequences of hybridization on holobionts. Rather, the emerging paradigm in holobiont literature is that hybridization disrupts symbiosis between a host lineage and its microbiome, leaving hybrids at a fitness deficit. These conclusions, however, have been drawn based on results from predominantly low-fitness hybrid organisms. Studying “dead-end” hybrids all but guarantees finding that hybridization is detrimental. This is the pitfall that Dobzhansky fell into over 80 years ago when he used hybrid sterility and inviability to conclude that hybridization hinders evolution. Goldschmidt, however, argued that rare saltational successes—so-called hopeful monsters—disproportionately drive positive evolutionary outcomes. Goldschmidt’s view is now becoming a widely accepted explanation for the prevalence of historical hybridization in extant macrobial lineages. Aligning holobiont research with this broader evolutionary perspective requires recognizing the importance of similar patterns in host–microbiome systems. That is, rare and successful “hopeful holobionts” (i.e., hopeful monsters at the holobiont scale) might be disproportionately responsible for holobiont evolution. If true, then it is these successful systems that we should be studying to assess impacts of hybridization on the macroevolutionary trajectories of host–microbiome symbioses. Results In this paper, we explore the effects of hybridization on the gut (cloacal) and skin microbiota in an ecologically successful hybrid lizard, Aspidoscelis neomexicanus. Specifically, we test the hypothesis that hybrid lizards have host-associated (HA) microbiota traits strongly differentiated from their progenitor species. Across numerous hybrid microbiota phenotypes, we find widespread evidence of transgressive segregation. Further, microbiota restructuring broadly correlates with niche restructuring during hybridization. This suggests a relationship between HA microbiota traits and ecological success. Conclusion Transgressive segregation of HA microbiota traits is not only limited to hybrids at a fitness deficit but also occurs in ecologically successful hybrids. This suggests that hybridization may be a mechanism for generating novel and potentially beneficial holobiont phenotypes. Supporting such a conclusion, the correlations that we find between hybrid microbiota and the hybrid niche indicate that hybridization might change host microbiota in ways that promote a shift or an expansion in host niche space. If true, hybrid microbiota restructuring may underly ecological release from progenitors. This, in turn, could drive evolutionary diversification. Using our system as an example, we elaborate on the evolutionary implications of host hybridization within the context of holobiont theory and then outline the next steps for understanding the role of hybridization in holobiont research. Video Abstract
Phil Richtsmeier, Ruslan Nedielkov, Malte Haring
et al.
ABSTRACT Bile salts are steroids with distinctive hydroxylation patterns that are produced and excreted by vertebrates. In contrast to common human bile salts, ursodeoxycholate (UDCA) has a 7-hydroxy group in β-configuration and is used in large amounts for the treatment of diverse gastrointestinal diseases. We isolated the 7β-hydroxysteroid dehydratase Hsh3 that is involved in UDCA degradation by Sphingobium sp. strain Chol11. Hsh3 eliminates the 7β-hydroxy group as water, leading to a double bond in the B-ring. This is similar to 7α-hydroxysteroid dehydratases in this and other strains, but Hsh3 is evolutionarily different from these. Purified Hsh3 accepted steroids with and without side chains as substrates and had minor activity with 7α-hydroxy groups. The deletion mutant strain Chol11 Δhsh3 had impacted growth with UDCA and accumulated a novel compound. The compound was identified as 3′,5-dihydroxy-H-methyl-hexahydro-5-indene-1-one-propanoate, consisting of steroid rings C and D with a C3-side chain carrying the former 7β-hydroxy group, indicating that Hsh3 activity is important especially for the later stages of bile salt degradation. Hsh3 homologs were found in other sphingomonads that were also able to degrade UDCA as well as in environmental metagenomes. Thus, Hsh3 adds to the bacterial enzyme repertoire for degrading a variety of differently hydroxylated bile salts.IMPORTANCEThe bacterial degradation of different bile salts is not only important for the removal of these steroidal compounds from the environment but also harbors interesting enzymes for steroid biotechnology. The 7β-hydroxy bile salt ursodeoxycholate (UDCA) naturally occurs in high concentration in the feces of black bears and has important pharmaceutical relevance for the treatment of different liver-related diseases, for which it is administered in high and increasing amounts. Additionally, it is present in the bile salt pool of humans in trace amounts. While UDCA degradation is environmentally important, the enzyme Hsh3 modifies the hydroxy group that confers the medically relevant properties and thus might be interesting for microbiome analyses and biotechnological applications.
Abstract Under human care, felids are typically fed similar diets, unlike wild counterparts whose diets vary by body mass and ecology. This study evaluated fecal microbiota and fermentation products in 18 zoo felids from Pairi Daiza Zoo, Belgium, grouped by body mass: under 100 kg ("small") and over 100 kg ("large"), with 9 animals in each group. Fresh feces were collected from the rectum under anesthesia. Microbial composition was assessed via 16S rRNA gene sequencing, while the fecal volatile fatty acids were quantified using gas chromatography. At the phylum level, regardless of body mass, the gut microbiota of zoo felids was predominantly composed of Firmicutes (61.7%), Actinobacteria (16.4%) and Bacteroidetes (12.5%). At the genus level, the most abundant genus was Clostridium sensu stricto 1 (15.9%), followed by Collinsella (15.7%). Although no significant differences in microbial composition or alpha diversity were found, beta diversity showed body mass influenced overall microbial structure. Smaller felids had significantly higher acetate levels than larger felids (p < 0.01). Additionally, acetate proportions were positively correlated with Clostridium sensu stricto 13 (r = 0.6, p < 0.01) and Peptoniphilus (r = 0.5, p < 0.05). These results show particular associations between body mass and the response of the intestinal microbiome to diet, suggesting that a uniform diet may not suit all felids under human care.
It is undeniable that plastics are ubiquitous and a threat to global ecosystems. Plastic waste is transformed into microplastics (MPs) through physical and chemical disruption processes within the aquatic environment. MPs are detected in almost every environment due to their worldwide transportability through ocean currents or wind, which allows them to reach even the most remote regions of our planet. MPs colonized by biofilm-forming microbial communities are known as the ‘‘plastisphere”. The revelation that this unique substrate can aid microbial dispersal has piqued interest in the ground of microbial ecology. MPs have synergetic effects on the development, transportation, persistence, and ecology of microorganisms. This review summarizes the studies of plastisphere in recent years and the microbial community assemblage (viz . autotrophs, heterotrophs, predators, and pathogens). We also discussed plastic-microbe interactions and the potential sources of plastic degrading microorganisms. Finally, it also focuses on current technologies used to characterize those microbial inhabitants and recommendations for further research.
In this study, we explore the application of Physics-Informed Neural Networks (PINNs) to the analysis of bifurcation phenomena in ecological migration models. By integrating the fundamental principles of diffusion-advection-reaction equations with deep learning techniques, we address the complexities of species migration dynamics, particularly focusing on the detection and analysis of Hopf bifurcations. Traditional numerical methods for solving partial differential equations (PDEs) often involve intricate calculations and extensive computational resources, which can be restrictive in high-dimensional problems. In contrast, PINNs offer a more flexible and efficient alternative, bypassing the need for grid discretization and allowing for mesh-free solutions. Our approach leverages the DeepXDE framework, which enhances the computational efficiency and applicability of PINNs in solving high-dimensional PDEs. We validate our results against conventional methods and demonstrate that PINNs not only provide accurate bifurcation predictions but also offer deeper insights into the underlying dynamics of diffusion processes. Despite these advantages, the study also identifies challenges such as the high computational costs and the sensitivity of PINN performance to network architecture and hyperparameter settings. Future work will focus on optimizing these algorithms and expanding their application to other complex systems involving bifurcations. The findings from this research have significant implications for the modeling and analysis of ecological systems, providing a powerful tool for predicting and understanding complex dynamical behaviors.
We delve into the interactions between a prey-predator and a vector-borne epidemic system, driven by agro-ecological motivations. This system involves an ODE, two reaction--diffusion PDEs and one reaction--diffusion--advection PDE. It has no complete variational or monotonic structure and features spatially heterogeneous coefficients. Our initial focus is to examine the continuity of a quantity known as ''harvest'', which depends on the time-integral of infected vectors. We analyze its asymptotic behaviour as the domain becomes homogeneous. Then we tackle a non-standard optimal control problem related to the linearized harvest and conduct an analysis to establish the existence, uniqueness, and properties of optimizers. Finally, we refine the location of optimizers under specific initial conditions.
Julius W. Jaeger, Annette Brandt, Wenfang Gui
et al.
Background & Aims: Changes in gut microbiota in metabolic dysfunction-associated steatotic liver disease (MASLD) are important drivers of disease progression towards fibrosis. Therefore, reversing microbial alterations could ameliorate MASLD progression. Oat beta-glucan, a non-digestible polysaccharide, has shown promising therapeutic effects on hyperlipidemia associated with MASLD, but its impact on gut microbiota and most importantly MASLD-related fibrosis remains unknown. Methods: We performed detailed metabolic phenotyping, including assessments of body composition, glucose tolerance, and lipid metabolism, as well as comprehensive characterization of the gut-liver axis in a western-style diet (WSD)-induced model of MASLD and assessed the effect of a beta-glucan intervention on early and advanced liver disease. Gut microbiota were modulated using broad-spectrum antibiotic treatment. Results: Oat beta-glucan supplementation did not affect WSD-induced body weight gain or glucose intolerance and the metabolic phenotype remained largely unaffected. Interestingly, oat beta-glucan dampened MASLD-related inflammation, which was associated with significantly reduced monocyte-derived macrophage infiltration and fibroinflammatory gene expression, as well as strongly reduced fibrosis development. Mechanistically, this protective effect was not mediated by changes in bile acid composition or signaling, but was dependent on gut microbiota and was lost upon broad-spectrum antibiotic treatment. Specifically, oat beta-glucan partially reversed unfavorable changes in gut microbiota, resulting in an expansion of protective taxa, including Ruminococcus, and Lactobacillus followed by reduced translocation of Toll-like receptor ligands. Conclusions: Our findings identify oat beta-glucan as a highly efficacious food supplement that dampens inflammation and fibrosis development in diet-induced MASLD. These results, along with its favorable dietary profile, suggest that it may be a cost-effective and well-tolerated approach to preventing MASLD progression and should be assessed in clinical studies. Impact and Implications: Herein, we investigated the effect of oat beta-glucan on the gut-liver axis and fibrosis development in a mouse model of metabolic dysfunction-associated steatotic liver disease (MASLD). Beta-glucan significantly reduced inflammation and fibrosis in the liver, which was associated with favorable shifts in gut microbiota that protected against bacterial translocation and activation of fibroinflammatory pathways. Together, oat beta-glucan may be a cost-effective and well-tolerated approach to prevent MASLD progression and should be assessed in clinical studies.
Diseases of the digestive system. Gastroenterology
Ruth Gómez Expósito, I. de Bruijn, J. Postma
et al.
Disease suppressive soils offer effective protection to plants against infection by soil-borne pathogens, including fungi, oomycetes, bacteria, and nematodes. The specific disease suppression that operates in these soils is, in most cases, microbial in origin. Therefore, suppressive soils are considered as a rich resource for the discovery of beneficial microorganisms with novel antimicrobial and other plant protective traits. To date, several microbial genera have been proposed as key players in disease suppressiveness of soils, but the complexity of the microbial interactions as well as the underlying mechanisms and microbial traits remain elusive for most disease suppressive soils. Recent developments in next generation sequencing and other ‘omics’ technologies have provided new insights into the microbial ecology of disease suppressive soils and the identification of microbial consortia and traits involved in disease suppressiveness. Here, we review the results of recent ‘omics’-based studies on the microbial basis of disease suppressive soils, with specific emphasis on the role of rhizosphere bacteria in this intriguing microbiological phenomenon.
Microbial ecologists have made exceptional improvements in our understanding of microbiomes in the last decade due to breakthroughs in sequencing technologies. These advances have wide-ranging implications for fields ranging from agriculture to human health. Due to limitations in databases, the majority of microbial ecology studies use a binning approach to approximate taxonomy based on DNA sequence similarity. There remains extensive debate on the best way to bin and approximate this taxonomy. Here we examine two popular approaches using a large field-based data set examining both bacteria and fungi and conclude that there are not major differences in the ecological outcomes. Thus, it appears that standard microbial community analyses are not overly sensitive to the particulars of binning approaches. ABSTRACT Recent discussion focuses on the best method for delineating microbial taxa, based on either exact sequence variants (ESVs) or traditional operational taxonomic units (OTUs) of marker gene sequences. We sought to test if the binning approach (ESVs versus 97% OTUs) affected the ecological conclusions of a large field study. The data set included sequences targeting all bacteria (16S rRNA) and fungi (internal transcribed spacer [ITS]), across multiple environments diverging markedly in abiotic conditions, over three collection times. Despite quantitative differences in microbial richness, we found that all α and β diversity metrics were highly positively correlated (r > 0.90) between samples analyzed with both approaches. Moreover, the community composition of the dominant taxa did not vary between approaches. Consequently, statistical inferences were nearly indistinguishable. Furthermore, ESVs only moderately increased the genetic resolution of fungal and bacterial diversity (1.3 and 2.1 times OTU richness, respectively). We conclude that for broadscale (e.g., all bacteria or all fungi) α and β diversity analyses, ESV or OTU methods will often reveal similar ecological results. Thus, while there are good reasons to employ ESVs, we need not question the validity of results based on OTUs. IMPORTANCE Microbial ecologists have made exceptional improvements in our understanding of microbiomes in the last decade due to breakthroughs in sequencing technologies. These advances have wide-ranging implications for fields ranging from agriculture to human health. Due to limitations in databases, the majority of microbial ecology studies use a binning approach to approximate taxonomy based on DNA sequence similarity. There remains extensive debate on the best way to bin and approximate this taxonomy. Here we examine two popular approaches using a large field-based data set examining both bacteria and fungi and conclude that there are not major differences in the ecological outcomes. Thus, it appears that standard microbial community analyses are not overly sensitive to the particulars of binning approaches.
In this study, we used microscopic, spectroscopic, and molecular analysis to characterize endolithic colonization in gypsum (selenites and white crystalline gypsum) from several sites in Sicily. Our results showed that the dominant microorganisms in these environments are cyanobacteria, including: Chroococcidiopsis sp., Gloeocapsopsis pleurocapsoides, Gloeocapsa compacta, and Nostoc sp., as well as orange pigmented green microalgae from the Stephanospherinia clade. Single cell and filament sequencing coupled with 16S rRNA amplicon metagenomic profiling provided new insights into the phylogenetic and taxonomic diversity of the endolithic cyanobacteria. These organisms form differently pigmented zones within the gypsum. Our metagenomic profiling also showed differences in the taxonomic composition of endoliths in different gypsum varieties. Raman spectroscopy revealed that carotenoids were the most common pigments present in the samples. Other pigments such as gloeocapsin and scytonemin were also detected in the near-surface areas, suggesting that they play a significant role in the biology of endoliths in this environment. These pigments can be used as biomarkers for basic taxonomic identification, especially in case of cyanobacteria. The findings of this study provide new insights into the diversity and distribution of phototrophic microorganisms and their pigments in gypsum in Southern Sicily. Furthemore, this study highlights the complex nature of endolithic ecosystems and the effects of gypsum varieties on these communities, providing additional information on the general bioreceptivity of these environments.