Hasil untuk "Microbial ecology"

Miguel Ángel Muñoz-Bañón, Nived Chebrolu, Sruthi M. Krishna Moorthy et al.

Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing methods, which make quantitative evaluation difficult. Terrestrial Laser Scanners (TLS), Mobile Laser Scanners (MLS), or traditional photogrammetry approaches poorly reconstruct thin branches, dense foliage, and lack the scale consistency needed for long-term monitoring. Implicit 3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are promising alternatives, but cannot recover the true scale of a scene and lack any means to be accurately geo-localised. In this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings. Our system proposes a three-level representation: (i) coarse Earth-frame localisation using GNSS, (ii) LiDAR-based SLAM for centimetre-accurate localisation and reconstruction, and (iii) NeRF-derived object-centric dense reconstruction of individual saplings. This approach enables repeatable quantitative evaluation and long-term monitoring of sapling traits. Our experiments in forest plots in Wytham Woods (Oxford, UK) and Evo (Finland) show that stem height, branching patterns, and leaf-to-wood ratios can be captured with increased accuracy as compared to TLS. We demonstrate that accurate stem skeletons and leaf distributions can be measured for saplings with heights between 0.5m and 2m in situ, giving ecologists access to richer structural and quantitative data for analysing forest dynamics.

Ozioma C. Oguine, Elmira Rashidi, Pamela J. Wisniewski et al.

Ecological Momentary Assessment (EMA) is widely used to study adolescents' experiences; yet, how the design of EMA platforms shapes engagement, research practices, and power dynamics in youth studies remains under-examined. We developed a youth-centered EMA platform prioritizing youth engagement and researcher support, and evaluated it through a case study on a longitudinal investigation with adolescent twins focused on mental health and sleep behavior. Interviews with the research team examined how the platform design choices shaped participant onboarding, sustained engagement, risk monitoring, and data interpretation. The app's teen-centered design and gamified features sustained teen engagement, while the web portal streamlined administrative oversight through a centralized dashboard. However, technical instability and rigid data structures created significant hurdles, leading to privacy concerns among parents and complicating the researchers' ability to analyze raw usage metadata. We provide actionable interaction design guidelines for developing EMA platforms that prioritize youth agency, ethical practice, and research goals.

Eunike Tiffany, Kyoung Su Kim, Panida Sittipo et al.

Akkermansia muciniphila, Bacteroides thetaiotaomicron, Mediterraneibacter (formerly Ruminococcus) gnavus, and other mucin-degrading (MD) bacteria play pivotal roles in shaping gut microbial ecosystems, maintaining gut barrier function, and mediating host–microbiota crosstalk. These bacteria influence intestinal homeostasis by modulating epithelial cell differentiation, immune responses, and gut microbiota composition through mucin degradation and the production of bioactive metabolites. Their abundance and functional activities fluctuate dynamically in response to dietary components, host immunity, and environmental factors, resulting in context-dependent effects on gastrointestinal and systemic health. This review summarizes current insights into the ecology and metabolic capabilities of MD bacteria, highlighting their dual roles in metabolic disorders, inflammatory diseases, infection susceptibility, and neuroimmune conditions. Understanding the ecological niches and molecular interactions of MD bacteria offers promising approaches for microbiota-targeted therapies aimed at restoring gut and systemic homeostasis.

Jiuyao Lu, Glen A. Satten, Katie A. Meyer et al.

Abstract Background Microbiome data, like other high-throughput data, suffer from technical heterogeneity stemming from differential experimental designs and processing. In addition to measured artifacts such as batch effects, there is heterogeneity due to unknown or unmeasured factors, which lead to spurious conclusions if unaccounted for. With the advent of large-scale multi-center microbiome studies and the increasing availability of public datasets, this issue becomes more pronounced. Current approaches for addressing unmeasured heterogeneity in high-throughput data were developed for microarray and/or RNA sequencing data. They cannot accommodate the unique characteristics of microbiome data such as sparsity and over-dispersion. Results Here, we introduce quantile thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity tailored to microbiome data. QuanT applies quantile regression across multiple quantile levels to threshold the microbiome abundance data and uncovers latent heterogeneity using thresholded binary residual matrices. We validated QuanT using both synthetic and real microbiome datasets, demonstrating its superiority in capturing and mitigating heterogeneity and improving the accuracy of downstream analyses, such as prediction analysis, differential abundance tests, and community-level diversity evaluations. Conclusions We present QuanT, a novel tool for comprehensive identification of unmeasured heterogeneity in microbiome data. QuanT’s distinct non-parametric method markedly enhances downstream analyses, serving as a valuable tool for data integration and comprehensive analysis in microbiome research. Video Abstract

Maïri Souza Oliveira, Maxime Lenormand, Sandra Luque et al.

Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems and untangling biodiversity complexity. Using national inventories, we identified in situ clusters through hierarchical clustering based on dominant species abundance dissimilarity, determined using the Importance Variable Index. These clusters were characterised by analysing species assemblages and their interactions. We then applied object-oriented Random Forest modelling, segmenting the national forest cover using NDVI to identify the forest ecosystems derived from in situ clusters. Freely available spectral (Sentinel-2) and environmental data were used in the model to delineate and characterise key forest ecosystems. We finished with an assessment of distribution of secondary and old-growth forests within ecosystems. In Costa Rica, 495 dominant tree species defined 10 in situ clusters, with 7 main clusters successfully modelled. The modelling (F1-score: 0.73, macro F1-score: 0.58) and species-based characterisation highlighted the main ecological trends of these ecosystems, which are distinguished by specific species dominance, topography, climate, and vegetation dynamics, aligning with local forest classifications. The analysis of secondary forest distribution provided an initial assessment of ecosystem vulnerability by evaluating their role in forest maintenance and dynamics. This approach also underscored the major challenge of in situ data acquisition.

Sebastian Fajardo, Sina Mohammadi, Jonas Gregorio de Souza et al.

Ancient populations inhabited and transformed neotropical forests, yet the spatial extent of their ecological influence remains underexplored at high resolution. Here we present a deep learning and remote sensing based approach to estimate areas of pre-Columbian forest modification based on modern vegetation. We apply this method to high-resolution satellite imagery from the Sierra Nevada de Santa Marta, Colombia, as a demonstration of a scalable approach, to evaluate palm tree distributions in relation to archaeological infrastructure. Our findings document a non-random spatial association between archaeological infrastructure and contemporary palm concentrations. Palms were significantly more abundant near archaeological sites with large infrastructure investment. The extent of the largest palm cluster indicates that ancient human-managed areas linked to major infrastructure sites may be up to two orders of magnitude bigger than indicated by current archaeological evidence alone. These patterns are consistent with the hypothesis that past human activity may have influenced local palm abundance and potentially reduced the logistical costs of establishing infrastructure-heavy settlements in less accessible locations. More broadly, our results highlight the utility of palm landscape distributions as an interpretable signal within environmental and multispectral datasets for constraining predictive models of archaeological site locations.

Jennifer J. Uehling, Jennifer L. Houtz

Birds show global declines, and understanding the relationship between avian diet and fitness can both answer basic questions in physiological ecology and inform conservation efforts. Diet‐induced changes to the gut microbiome, the collection of microorganisms and their functional genes and metabolites inside the gut, may be of particular importance to avian fitness as the gut microbiome provides a suite of beneficial roles for nutrition and immunity of the host. Furthermore, evidence is growing that the gut microbiome may impact animals' diet choices, which could have cascading impacts on avian fitness. Sequencing technologies allow both diet and gut microbial composition and diversity to be characterized from the same fecal sample, creating ripe opportunities to explore diet–microbiome relationships. In this mini‐review we summarize the existing literature on the effect of diet category, diet shifts, and dietary diversity on the gut microbiome, and the potential for the gut microbiome to serve as a modulator of diet choice in wild birds. We list open questions in the field of avian diet–microbiome interactions and provide methodology considerations for designing studies to sample both diet and gut microbiomes. This mini‐review provides a framework for understanding the reciprocal relationship between diet and gut microbiota in wild birds.

Kaiyan Gong, Shuhui Zhang, Yangjie Pan et al.

Abstract Background Alternate day fasting (ADF) as a healthy dietary pattern has been reported to improve brain functions and behaviors, but the effect of ADF on diabetes-related brain disorders and the potential mechanisms remain unclear. In this study, we investigated the impact of ADF on neuroinflammation and exploratory behavior in type 1 diabetic (T1D) mice and explored the specific molecular mechanisms from the perspective of the gut microbiota and host metabolism. Results ADF can effectively relieve neuroinflammation and exploratory behavioral disorders in T1D mice. According to fecal microbiota transplant and bacterial supplementation, we demonstrated that ADF-driven enrichment of Akkermansia muciniphila (AKK) was necessary for boosting exploratory behavior in T1D mice. The gut microbiota-derived metabolite δ-valerobetaine (VB) reduced hepatic carnitine synthesis by inhibiting BBOX, and caused exploratory behavioral disorders in mice. In vitro and in vivo studies revealed that AKK bacteria had the ability to consume VB, and thereby increased systemic carnitine level. In addition, carnitine was found to deplete lipid droplet accumulation in microglia by enhancing fatty acid oxidation and lipolysis, reduce neuroinflammation and neuron injury, and then increase exploratory behavior in T1D mice. Conclusions Our study sheds light on the gut-liver-brain metabolic axis mechanism on the protective role of ADF in T1D-associated neuroinflammation and exploratory behavioral disorders and AKK bacteria exert as a key mediator. Video Abstract Graphical Abstract

Lauren Kelly Rodriguez, Belén García Ovide, Eleonora Barbaccia et al.

ABSTRACT Monitoring cetaceans is essential for evaluating ecosystem health and informing the establishment of marine protected areas. Conventional cetacean monitoring techniques, such as photo‐identification, acoustic surveys, and satellite tagging, are often resource‐intensive, costly, and sometimes intrusive. Environmental DNA (eDNA)‐based methods have emerged as non‐invasive, cost‐efficient complements based on the analysis of genetic material shed into the environment. However, eDNA research is still evolving, with ongoing efforts to optimize field sampling and laboratory protocols. Building on the challenges of conventional monitoring methods, this study sought to refine eDNA sampling parameters to offer a more efficient and scalable approach for cetacean research, leveraging citizen science platforms. From June to October 2023, eDNA samples were collected across three regions in the Northeast Atlantic Ocean and Mediterranean Sea aboard whale‐watching vessels or monitoring platforms engaging citizen scientists. Samples were analyzed for total DNA concentration using Qubit fluorometry and target DNA concentration with quantitative polymerase chain reactions (qPCR). Key variables tested in the field included water volume (2, 5, and 10 L), sampling timing (immediately after a whale was present and at 5‐, 10‐, and 20‐min intervals), and three filter types (pore sizes of 1.2, 0.8, and 0.45 μm). Our results illustrate that larger water volumes (10 L), sampling immediately after a whale breach or fluking behavior, and Smith‐Root eDNA filters (1.2 μm pore size) significantly increased eDNA detection probability and signal strength. However, the combination of certain filter types with different water volumes had a significant impact on detection probability, with smaller pore sizes more effectively yielding detections with a lower water volume. These findings provide guidance for future cetacean research initiatives and highlight the potential of eDNA methods in enhancing research and conservation efforts through scalable citizen science‐based initiatives.

Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp et al.

In the field of psychopathology, Ecological Momentary Assessment (EMA) studies offer rich individual data on psychopathology-relevant variables (e.g., affect, behavior, etc) in real-time. EMA data is collected dynamically, represented as complex multivariate time series (MTS). Such information is crucial for a better understanding of mental disorders at the individual- and group-level. More specifically, clustering individuals in EMA data facilitates uncovering and studying the commonalities as well as variations of groups in the population. Nevertheless, since clustering is an unsupervised task and true EMA grouping is not commonly available, the evaluation of clustering is quite challenging. An important aspect of evaluation is clustering explainability. Thus, this paper proposes an attention-based interpretable framework to identify the important time-points and variables that play primary roles in distinguishing between clusters. A key part of this study is to examine ways to analyze, summarize, and interpret the attention weights as well as evaluate the patterns underlying the important segments of the data that differentiate across clusters. To evaluate the proposed approach, an EMA dataset of 187 individuals grouped in 3 clusters is used for analyzing the derived attention-based importance attributes. More specifically, this analysis provides the distinct characteristics at the cluster-, feature- and individual level. Such clustering explanations could be beneficial for generalizing existing concepts of mental disorders, discovering new insights, and even enhancing our knowledge at an individual level.

Chen Chen, Khalil Mrini, Kemeberly Charles et al.

Ecological momentary assessment (EMA) is used to evaluate subjects' behaviors and moods in their natural environments, yet collecting real-time and self-report data with EMA is challenging due to user burden. Integrating voice into EMA data collection platforms through today's intelligent virtual assistants (IVAs) is promising due to hands-free and eye-free nature. However, efficiently managing conversations and EMAs is non-trivial and time consuming due to the ambiguity of the voice input. We approach this problem by rethinking the data modeling of EMA questions and what is needed to deploy them on voice-first user interfaces. We propose a unified metadata schema that models EMA questions and the necessary attributes to effectively and efficiently integrate voice as a new EMA modality. Our schema allows user experience researchers to write simple rules that can be rendered at run-time, instead of having to edit the source code. We showcase an example EMA survey implemented with our schema, which can run on multiple voice-only and voice-first devices. We believe that our work will accelerate the iterative prototyping and design process of real-world voice-based EMA data collection platforms.

Sarah A. Vollert, Christopher Drovandi, Matthew P. Adams

Quantitative population modelling is an invaluable tool for identifying the cascading effects of ecosystem management and interventions. Ecosystem models are often constructed by assuming stability and coexistence in ecological communities as a proxy for abundance data when monitoring programs are not available. However, a growing body of literature suggests that these assumptions are inappropriate for modelling conservation outcomes. In this work, we develop an alternative for dataless population modelling that instead relies on expert-elicited knowledge of species abundances. While time series abundance data is often not available for ecosystems of interest, these systems may still be highly studied or observed in an informal capacity. In particular, limits on population sizes and their capacity to rapidly change during an observation period can be reasonably elicited for many species. We propose a robust framework for generating an ensemble of ecosystem models whose population predictions match the expected population dynamics, as defined by experts. Our new Bayesian algorithm systematically removes model parameters that lead to unreasonable population predictions without incurring excessive computational costs. Our results demonstrate that models constructed using expert-elicited information, rather than stability and coexistence assumptions, can dramatically impact population predictions, expected responses to management, conservation decision-making, and long-term ecosystem behaviour. In the absence of data, we argue that field observations and expert knowledge are preferred for representing ecosystems observed in nature instead of theoretical assumptions of coexistence and stability.

Li Bi, Zi‐Yang He, Bao Anh Thi Nguyen et al.

Abstract Viruses play a crucial role in regulating microbial communities and ecosystem functioning. However, the biogeographic patterns of viruses and their responses to climate factors remain underexplored. In this study, we performed viral size fraction metagenomes on 108 samples collected along a 2600 km transect across Australia, encompassing distinct climate conditions. A total of 14,531 viral operational taxonomic units were identified. Climate factors had a greater influence than edaphic and biotic factors on driving the alpha diversity of viral communities. The strongest relationship was observed between mean annual temperature and the diversity of viral communities. Moreover, climate factors, particularly aridity index, were the primary drivers of viral community structure. Overall, these findings underscore the pivotal role of climate factors in shaping viral communities and have implications for understanding how climate change influences soil viral ecology.

Walid Hachem

This paper is divided into two parts. The first part is devoted to the study of a class of Approximate Message Passing (AMP) algorithms which are widely used in the fields of statistical physics, machine learning, or communication theory. The AMP algorithms studied in this part are those where the measurement matrix has independent elements, up to the symmetry constraint when this matrix is symmetric, with a variance profile that can be sparse. The AMP problem is solved by adapting the approach of Bayati, Lelarge, and Montanari (2015) to this matrix model. \\ The Lotka-Volterra (LV) model is the standard model for studying the dynamical behavior of large dimensional ecological food chains. The second part of this paper is focused on the study of the statistical distribution of the globally stable equilibrium vector of a LV system in the situation where the random symmetric interaction matrix among the living species is sparse, and in the regime of large dimensions. This equilibrium vector is the solution of a Linear Complementarity Problem, which distribution is shown to be characterized through the AMP approach developed in the first part. In the large dimensional regime, this distribution is close to a mixture of a large number of truncated Gaussians.

Lindley R. Slipetz, Ami Falk, Teague R. Henry

When using ecological momentary assessment data (EMA), missing data is pervasive as participant attrition is a common issue. Thus, any EMA study must have a missing data plan. In this paper, we discuss missingness in time series analysis and the appropriate way to handle missing data when the data is modeled as an idiographic discrete time continuous measure state-space model. We found that Missing Completely At Random, Missing At Random, and Time-dependent Missing At Random data have less bias and variability than Autoregressive Time-dependent Missing At Random and Missing Not At Random. The Kalman filter excelled at handling missing data under most conditions. Contrary to the literature, we found that, using a variety of methods, multiple imputation struggled to recover the parameters.

Isabell Klawonn, Silke Van den Wyngaert, Morten H. Iversen et al.

Fungal parasites are found to effectively control the fate of phytoplankton-derived organic matter, potentially enhancing remineralization and reducing sedimentation in freshwater and coastal systems.

Christian Diener, Sean M. Gibbons

ABSTRACT Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.

Steve Tsham Mpinda Ataky, Alessandro Lameiras Koerich

Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In this regard, computational tools have been proposed to assist the specialist in interpreting the breast digital image exam, providing features for detecting and diagnosing tumors and cancerous cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing the false positives rate is still challenging. Texture descriptors have been quite popular in medical image analysis, particularly in histopathologic images (HI), due to the variability of both the texture found in such images and the tissue appearance due to irregularity in the staining process. Such variability may exist depending on differences in staining protocol such as fixation, inconsistency in the staining condition, and reagents, either between laboratories or in the same laboratory. Textural feature extraction for quantifying HI information in a discriminant way is challenging given the distribution of intrinsic properties of such images forms a non-deterministic complex system. This paper proposes a method for characterizing texture across HIs with a considerable success rate. By employing ecological diversity measures and discrete wavelet transform, it is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets compared with state-of-the-art methods.

Zhengzhi Lin, Yueyao Wang, Yili Hong

The Poisson multinomial distribution (PMD) describes the distribution of the sum of $n$ independent but non-identically distributed random vectors, in which each random vector is of length $m$ with 0/1 valued elements and only one of its elements can take value 1 with a certain probability. Those probabilities are different for the $m$ elements across the $n$ random vectors, and form an $n \times m$ matrix with row sum equals to 1. We call this $n\times m$ matrix the success probability matrix (SPM). Each SPM uniquely defines a PMD. The PMD is useful in many areas such as, voting theory, ecological inference, and machine learning. The distribution functions of PMD, however, are usually difficult to compute. In this paper, we develop efficient methods to compute the probability mass function (pmf) for the PMD using multivariate Fourier transform, normal approximation, and simulations. We study the accuracy and efficiency of those methods and give recommendations for which methods to use under various scenarios. We also illustrate the use of the PMD via three applications, namely, in voting probability calculation, aggregated data inference, and uncertainty quantification in classification. We build an R package that implements the proposed methods, and illustrate the package with examples.