Ibrahim Bilau, Stacie Smith, Abdurrahman Baru
et al.
Virtual reality (VR) has emerged as a promising tool for assessing instrumental activities of daily living (IADLs) in older adults. However, the ecological validity of these simulations is often compromised by simplified or low-fidelity environmental design that fails to elicit a genuine sense of presence. This paper documents a reproducible Reality-to-VR pipeline for creating a photorealistic environmental simulation to support a study on cognitive aging in place. The proposed workflow captured the as-built kitchen of the Aware Home building at Georgia Tech using Terrestrial Laser Scanning (TLS) for sub-millimeter geometric accuracy, followed by point cloud processing in Faro SCENE, geometric retopology in SketchUp, and integration into Unreal Engine 5 via Datasmith with Lumen global illumination for high visual fidelity. The pipeline achieved photorealistic rendering while maintaining a stable 90 Hz frame rate, a critical threshold for mitigating cybersickness in older populations. The environment also enables instantaneous manipulation of environmental variables, such as switching between closed cabinetry and open shelving, providing experimental flexibility impossible in physical settings. Participant validation with 17 older adults confirmed minimal cybersickness risk and preserved sensitivity to the experimental manipulation, supporting the pipeline's feasibility for aging-in-place research and establishing a benchmark for future comparative studies.
Background Nitrate (NO3−) can accumulate in closed-circuit ecosystems to a toxic level. Adding heterotrophic denitrification process to the water treatment is a strategy to reduce this level. This type of process usually requires the addition of a carbon source. Carbon-to-nitrogen ratio (C/N) is a key parameter known to influence both the function and the activity of microbial communities in bioprocesses. Few studies have examined the influence of C/N on denitrification systems operated under methylotrophic and marine conditions. Here we assessed the influence of C/N (methanol and NO3−) on the performance of a laboratory-scale, recirculating denitrifying reactor operated under marine conditions. We monitored the evolution of the bacterial community in the biofilm to assess its stability during the operating conditions. Finally, the relative gene expression profiles of Methylophaga nitratireducenticrescens strain GP59, the main denitrifier in the denitrifying biofilm, were determined during the operating conditions and compared with those of GP59 planktonic pure cultures. Methodology A 500-mL methanol-fed recirculating denitrification reactor operated under marine conditions and colonized by a naturally occurring multispecies denitrifying biofilm was subjected to eight different C/N. We monitored several physico-chemical parameters (denitrifying activities, methanol consumption, CO2 production) throughout the operating conditions. The evolution of the bacterial community in the biofilm during these conditions was determined by 16S rRNA gene amplicon sequencing. Metatranscriptomes were derived from the biofilm to determine (1) the relative gene expression profiles of strain GP59, and (2) the functional diversity of the active microorganisms in the biofilm. Results Changes in C/N did not correlate with the denitrification dynamics (NO3− and NO2− reduction rates, NO2− and N2O dynamics), but did correlate with the methanol consumption rates, and the CO2 production rates. Throughout the operating conditions, nitrite and N2O appeared transiently, and ammonium was not observed. The bacterial community in the reactor increased in diversity with biofilm aging, especially among heterotrophic bacteria, at the expense of methylotrophic bacteria. The relative expression profiles of strain GP59 in the biofilm are distinct from those of planktonic pure cultures of strain GP59, and that the expression of several riboswitches and xoxF would be involved in these differences. Conclusions When the biofilm community is well established in the reactor, it can withstand changes in C/N with limited impact on the denitrification performance. The increase in the proportion of heterotrophs would allow the reactor to be more flexible regarding carbon sources. This knowledge can be useful for improving the efficiency of denitrification system treating close circuit systems such as marine recirculating aquaculture wastewater or seawater aquarium.
Anastasia V. Teslya, Artyom A. Stepanov, Darya V. Poshvina
et al.
The secondary metabolite 2,4-diacetylphloroglucinol (2,4-DAPG), which is produced by <i>Pseudomonas</i> bacteria, is a potent antimicrobial agent with well-documented properties that suppress phytopathogens. However, its broader ecological impact on soil microbial communities is not understood. Through a combination of controlled microcosm and field trials, we have demonstrated that the effects of 2,4-DAPG are highly context-dependent. Laboratory exposure (10 mg kg<sup>−1</sup>) altered the abundance of 8.53% of bacterial and 6.91% of fungal amplicon sequence variants, and simplified the bacterial co-occurrence networks (reduced number of nodes and links). In contrast, field conditions amplified bacterial sensitivity (the Shannon index decreased from 4.77 to 4.17, <i>p</i> < 0.05) but maintained fungal stability (Shannon index varied from 3.93 to 3.97, <i>p</i> > 0.05); these conditions affected a smaller proportion of fungal ASVs (4.23%). Taxonomic analysis revealed consistent suppression of fungi of the Mucoromycota (e.g., <i>Mortierella</i>) and context-dependent shifts in bacteria, with an enrichment of Bacillota (e.g., <i>Bacillus</i>, <i>Paenibacillus</i>) in the laboratory but not in the field. Enzymatic responses revealed a dose-dependent activation of the C-cycle, with up to 7.4-fold increases in the laboratory and up to a 10.5-fold increase in the field. P- and N- cycles showed more complex dynamics, with acid phosphatase activity increasing 3.8-fold in laboratory conditions and recovering from initial suppression to an increase of 144% in field conditions, while N-acetylglucosaminidase activity increased and L-leucine aminopeptidase decreased under laboratory conditions. Our results suggest that the response of microorganisms to 2,4-DAPG in natural soils is reduced, probably due to functional redundancy and pre-adaptation to abiotic stresses. This difference between laboratory and field studies warns against extrapolating data from controlled experiments to predict outcomes in agricultural ecosystems, and emphasizes the need for a context-specific evaluation of biocontrol agents.
Abstract Background Acetaminophen, a widely used analgesic and antipyretic drug, has become a significant aquatic micro-pollutant due to its extensive global production and increased consumption, particularly during the COVID-19 pandemic. Its high-water solubility leads to its pervasive presence in wastewater treatment plants (WWTPs), posing substantial risks to the environment and human health. Biological treatment is one of the promising approaches to remove such pollutants. Although previous studies have isolated acetaminophen-degrading pure cultures and proposed catabolic pathways, the interactions between microbiotas and acetaminophen, the distribution feature of acetaminophen degradation genes, and the gene-driven fate of acetaminophen in the real-world environment remain largely unexplored. Results Among the water samples from 20 WWTPs across China, acetaminophen was detected from 19 samples at concentrations ranging from 0.06 to 29.20 nM. However, p-aminophenol, a more toxic metabolite, was detected in all samples at significantly higher concentrations (23.93 to 108.68 nM), indicating the presence of a catabolic bottleneck in WWTPs. Metagenomic analysis from both the above 20 samples and global datasets revealed a consistently higher abundance of initial acetaminophen amidases compared to downstream enzymes, potentially having explained the reason for the bottleneck. Meanwhile, a close correlation between initial amidases and Actinomycetota revealed by genome-based taxonomy suggests a species-dependent degradation pattern. Additionally, a distinct amidase ApaA was characterized by newly isolated Rhodococcus sp. NyZ502 (Actinomycetota), represents a predominant category of amidase in WWTPs. Significant phylogenetic and structural diversity observed among putative amidases suggest versatile acetaminophen hydrolysis potential in WWTPs. Conclusions This study enhances our understanding of acetaminophen’s environmental fate and highlights the possible occurrence of ecological risks driven by imbalanced genes in the process of acetaminophen degradation in global WWTPs. Video Abstract
Onanong Charoenwai, Pornpawit Tanpichai, Pimwarang Sukkarun
et al.
Background and Aim: Growth retardation syndrome in cultured Penaeus vannamei has been associated with Enterocytozoon hepatopenaei (EHP) and a recently identified decapod hepanhamaparvovirus (DHPV) genotype V. However, data on its prevalence, pathogenicity, and interaction with the shrimp hepatopancreatic microbiome in Thailand remain limited. This study aimed to determine the incidence and co-infection rate of DHPV genotype V with EHP, evaluate its pathogenic potential, and explore microbiome alterations associated with infection.
Materials and Methods: Between 2022 and 2023, 1,270 shrimp from 127 grow-out ponds across 46 farms in eastern Thailand and post-larvae 12 from five hatcheries in the south were screened for DHPV and EHP by polymerase chain reaction. Six representative isolates underwent phylogenetic analysis based on non-structural protein 1 (NS1) and NS2 genes. Pathogenicity was evaluated by immersion challenge bioassays in specific pathogen-free P. vannamei. Hepatopancreatic microbiomes of naturally infected and healthy shrimp were compared using 16S ribosomal RNA gene sequencing and Quantitative Insights Into Microbial Ecology 2-based analysis.
Results: DHPV was detected in 54.33% (69/127) of ponds and 4% (1/25) of hatchery tanks. Co-infection with EHP occurred in 40.16% of ponds. Phylogenetic analysis showed 97.99%–98.82% similarity with DHPV genotype V from South Korea, confirming transboundary genetic relatedness. Experimental infection caused low mortality (20%) but resulted in viral replication (101–103 copies/μL) and characteristic intranuclear inclusion bodies in hepatopancreatic cells. DHPV-infected shrimp exhibited distinct microbiome profiles with elevated Firmicutes, Planctomycetota, and Actinobacteriota abundances, supporting a pathobiome shift during infection.
Conclusion: This is the first report of DHPV genotype V in P. vannamei from Thailand and its frequent co-infection with EHP. Despite its low experimental virulence, the widespread occurrence and microbiome dysbiosis suggest that it may have subclinical impacts that could exacerbate growth retardation. Routine molecular screening in hatcheries and farms, coupled with integrated viral–microbiome surveillance, is essential for sustainable aquaculture biosecurity and aligns with the United Nations Sustainable Development Goal 14 (Life Below Water) by promoting resilient aquatic food systems.
Melany Cervantes-Echeverría, Marco Antonio Jimenez-Rico, Rubiceli Manzo
et al.
The gut microbiome, comprising bacteria, viruses, archaea, fungi, and protists, plays a crucial role in regulating host metabolism and health. This study explored the effects of fecal virome transplantation (FVT) from healthy human donors on metabolic syndrome (MetS) in a diet-induced obesity (DIO) mouse model, without diet change. Mice received a single oral dose of human-derived virus-like particles (VLPs) and continued on a high-fat diet (HFD) for 17 weeks. Despite persistent dietary stress, FVT significantly improved glucose tolerance. Longitudinal profiling by virome shotgun metagenomics and bacterial 16S rRNA sequencing revealed marked, durable shifts in both viral and bacterial community composition. Notable bacterial changes included a decrease in Akkermansia muciniphila and Peptococcaceae and increases in Allobaculum and Coprococcus; A. muciniphila positively correlated with glucose levels and negatively correlated with body weight. Together, these results suggests that human-derived virome can durably reshape gut microbial ecology and improve glucose metabolism in mice with obesity, even without dietary modification, offering a novel avenue for developing phage-based therapies. This proof-of-concept study provides foundational observations for using human-derived VLPs for FVT in standard laboratory mouse models, and provides a foundation for elucidating bacteria-phage interactions and their role in host metabolic health.
Empirical analyses on the factors driving vote switching are rare, usually conducted at the national level without considering the parties of origin and destination, and often unreliable due to the severe inaccuracy of recall survey data. To overcome the problem of lack of adequate data and to incorporate the increasingly relevant role of local factors, we propose an ecological inference methodology to estimate the number of vote transitions within small homogeneous areas and to assess the relationships between these counts and local characteristics through multinomial logistic models. This approach allows for a disaggregate analysis of contextual factors behind vote switching, distinguishing between their different origins and destinations. We apply this methodology to the Italian region of Umbria, divided into 19 small areas. To explain the number of transitions toward the right-wing nationalist party that won the elections and towards increasing abstentionism, we focused on measures of geographical, economic, and cultural disadvantages of local communities. Among the main findings, the economic disadvantages mainly pushed previous abstainers and far-right Lega voters to change their choices in favor of the rising right-wing party, while transitions from the opposite political camp were mostly influenced by cultural factors such as a lack of social capital, negative attitude towards the EU, and political tradition.
We record and analyze the movement patterns of the marsupial {\it Didelphis aurita} at different temporal scales. Animals trajectories are collected at a daily scale by using spool-and-line techniques, and with the help of radio-tracking devices animals traveled distances are estimated at intervals of weeks. Small-scale movements are well described by truncated Lévy flight, while large-scale movements produce a distribution of distances which is compatible with a Brownian motion. A model of the movement behavior of these animals, based on a truncated Lévy flight calibrated on the small scale data, converges towards a Brownian behavior after a short time interval of the order of one week. These results show that whether Lévy flight or Brownian motion behaviors apply, will depend on the scale of aggregation of the animals paths. In this specific case, as the effect of the rude truncation present in the daily data generates a fast convergence towards Brownian behaviors, Lévy flights become of scarce interest for describing the local dispersion properties of these animals, which result well approximated by a normal diffusion process and not a fast, anomalous one. Interestingly, we are able to describe two movement phases as the consequence of a statistical effect generated by aggregation, without the necessity of introducing ecological constraints or mechanisms operating at different spatio-temporal scales. This result is of general interest, as it can be a key element for describing movement phenomenology at distinct spatio-temporal scales across different taxa and in a variety of systems.
Oleksandr Cherendichenko, Josephine Solowiej-Wedderburn, Laura M. Carroll
et al.
A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions. With recent advances in genome-scale metabolic models, we are now capable of simulating interactions between thousands of pairs of bacteria in thousands of different environmental settings at a scale infeasible experimentally. These approaches can generate tremendous amounts of data that can be exploited by state-of-the-art machine learning algorithms to uncover the mechanisms driving interactions. Here, we present Friend or Foe, a compendium of 64 tabular environmental datasets, consisting of more than 26M shared environments for more than 10K pairs of bacteria sampled from two of the largest collections of metabolic models. The Friend or Foe datasets are curated for a wide range of machine learning tasks -- supervised, unsupervised, and generative -- to address specific questions underlying bacterial interactions. We benchmarked a selection of the most recent models for each of these tasks and our results indicate that machine learning can be successful in this application to microbial ecology. Going beyond, analyses of the Friend or Foe compendium can shed light on the predictability of bacterial interactions and highlight novel research directions into how bacteria infer and navigate their relationships.
Compound heatwaves increasingly trigger complex cascading failures that propagate through interconnected physical and human systems, yet the fragmentation of disciplinary knowledge hinders the comprehensive mapping of these systemic risk topologies. This study introduces the Heatwave Discovery Agent HeDA as an autonomous scientific synthesis framework designed to bridge cognitive gaps by constructing a high fidelity knowledge graph from 8,111 academic publications. By structuring 70,297 evidence nodes, the system exhibits enhanced inferential fidelity in capturing long tail risk mechanisms and achieves a significant accuracy margin compared to standard foundation models including GPT 5.2 and Claude Sonnet 4.5 in complex reasoning tasks. The resulting topological analysis reveals a critical bio ecological mediation effect where biological systems function as the primary non linear amplifiers of thermal stress that transform physical meteorological hazards into systemic socioeconomic losses. We further identify latent functional couplings between theoretically distinct sectors such as the heat induced synchronization of power grid failures and emergency medical capacity saturation. These findings elucidate the dynamics of compound climate risks and provide an empirical basis for shifting adaptation strategies from static sectoral defense to dynamic cross system resilience.
Microbial networks, representing microbes as nodes and their interactions as edges, are crucial for understanding community dynamics in various environments. Analyzing microbiome networks is crucial for identifying keystone taxa that play central roles in maintaining microbial community structure and function, assessing how environmental changes such as pollution, climate shifts, or land use affect microbial dynamics, tracking disease progression by revealing alterations in microbial interactions over time, and predicting microbial community responses to interventions such as antibiotics, probiotics, or changes in diet and habitat. The complexity of microbial interactions necessitates the use of computational tools such as the MiNAA-WebApp, available at https://minaa.wid.wisc.edu, which enhances the accessibility of the Microbiome Network Alignment Algorithm MiNAA. This tool allows researchers to align microbial networks and explore ecological relationships and community dynamics without extensive computational skills. Originally, MiNAA's command-line interface limited its usability for those without programming backgrounds. The web-based MiNAA-WebApp addresses this shortcoming by offering an intuitive interface with visualization tools, allowing easy exploration and analysis of microbial networks. The web app is designed for microbiome networks but also applicable to other biological networks, broadening its use in computational biology and making network-based research accessible to a wider audience.
Lauren C. Martin, Michaela A. O’Hare, Giovanni Ghielmetti
et al.
Abstract Hypervariable region sequencing of the 16S ribosomal RNA (rRNA) gene plays a critical role in microbial ecology by offering insights into bacterial communities within specific niches. While providing valuable genus-level information, its reliance on data from targeted genetic regions limits its overall utility. Recent advances in sequencing technologies have enabled characterisation of the full-length 16S rRNA gene, enhancing species-level classification. Although current short-read platforms are cost-effective and precise, they lack full-length 16S rRNA amplicon sequencing capability. This study aimed to evaluate the feasibility of a modified 150 bp paired-end full-length 16S rRNA amplicon short-read sequencing technique on the Illumina iSeq 100 and 16S rRNA amplicon assembly workflow by utilising a standard mock microbial community and subsequently performing exploratory characterisation of captive (zoo) and free-ranging African elephant (Loxodonta africana) respiratory microbiota. Our findings demonstrate that, despite generating assembled amplicons averaging 869 bp in length, this sequencing technique provides taxonomic assignments consistent with the theoretical composition of the mock community and respiratory microbiota of other mammals. Tentative bacterial signatures, potentially representing distinct respiratory tract compartments (trunk and lower respiratory tract) were visually identified, necessitating further investigation to gain deeper insights into their implication for elephant physiology and health.
Abstract Background Arsenic (As) metabolism pathways and their coupling to nitrogen (N) and carbon (C) cycling contribute to elemental biogeochemical cycling. However, how whole-microbial communities respond to As stress and which taxa are the predominant As-transforming bacteria or archaea in situ remains unclear. Hence, by constructing and applying ROCker profiles to precisely detect and quantify As oxidation (aioA, arxA) and reduction (arrA, arsC1, arsC2) genes in short-read metagenomic and metatranscriptomic datasets, we investigated the dominant microbial communities involved in arsenite (As(III)) oxidation and arsenate (As(V)) reduction and revealed their potential pathways for coupling As with N and C in situ in rice paddies. Results Five ROCker models were constructed to quantify the abundance and transcriptional activity of short-read sequences encoding As oxidation (aioA and arxA) and reduction (arrA, arsC1, arsC2) genes in paddy soils. Our results revealed that the sub-communities carrying the aioA and arsC2 genes were predominantly responsible for As(III) oxidation and As(V) reduction, respectively. Moreover, a newly identified As(III) oxidation gene, arxA, was detected in genomes assigned to various phyla and showed significantly increased transcriptional activity with increasing soil pH, indicating its important role in As(III) oxidation in alkaline soils. The significant correlation of the transcriptional activities of aioA with the narG and nirK denitrification genes, of arxA with the napA and nirS denitrification genes and of arrA/arsC2 with the pmoA and mcrA genes implied the coupling of As(III) oxidation with denitrification and As(V) reduction with methane oxidation. Various microbial taxa including Burkholderiales, Desulfatiglandales, and Hyphomicrobiales (formerly Rhizobiales) are involved in the coupling of As with N and C metabolism processes. Moreover, these correlated As and N/C genes often co-occur in the same genome and exhibit greater transcriptional activity in paddy soils with As contamination than in those without contamination. Conclusions Our results revealed the comprehensive detection and typing of short-read sequences associated with As oxidation and reduction genes via custom-built ROCker models, and shed light on the various microbial taxa involved in the coupling of As and N and C metabolism in situ in paddy soils. The contribution of the arxA sub-communities to the coupling of As(III) oxidation with nitrate reduction and the arsC sub-communities to the coupling of As(V) reduction with methane oxidation expands our knowledge of the interrelationships among As, N, and C cycling in paddy soils. Video Abstract
In this paper, we study the significance of ecological interactions and separation of birth and death dynamics in stochastic heterogeneous populations via general birth-death processes. Interactions can manifest through the birth dynamics, the death dynamics, or some combination of the two. The underlying microscopic mechanisms are important but often implicit in population-level data. We propose an inference method for disambiguating the types of interaction and the birth and death processes from population size time series data of a stochastic $n$-type heterogeneous population. The interspecies interactions considered can be competitive, antagonistic, or mutualistic. We show that different pairs of birth and death rates with the same net growth rate result in different time series statistics. Then, the inference method is validated in the example of a birth-death process inspired by the two-type Lotka-Volterra interaction dynamics. Utilizing stochastic fluctuations enables us to estimate additional parameters in this stochastic Lotka-Volterra model, which are not identifiable in a deterministic model.
Current methods for microplastic identification in water samples are costly and require expert analysis. Here, we propose a deep learning segmentation model to automatically identify microplastics in microscopic images. We labeled images of microplastic from the Moore Institute for Plastic Pollution Research and employ a Generative Adversarial Network (GAN) to supplement and generate diverse training data. To verify the validity of the generated data, we conducted a reader study where an expert was able to discern the generated microplastic from real microplastic at a rate of 68 percent. Our segmentation model trained on the combined data achieved an F1-Score of 0.91 on a diverse dataset, compared to the model without generated data's 0.82. With our findings we aim to enhance the ability of both experts and citizens to detect microplastic across diverse ecological contexts, thereby improving the cost and accessibility of microplastic analysis.
Microbially Induced Carbonate Precipitation (MICP) is a biocementation technique that modifies the hydraulic and mechanical properties of porous materials using bacterial solutions. This study evaluates the efficiency of various MICP protocols under different environmental conditions, utilizing two bacterial strains: S. pasteurii and S. aquimarina, to optimize soil strength. Results indicate that bacterial strain and cementation solution concentration significantly affect biochemical outcomes, while temperature is the primary environmental factor. The efficiency of S. pasteurii's chemical conversion ranged from 40% to 80%, compared to only about 20% for S. aquimarina. MICP treatment with S. pasteurii produced CaCO3 content between 5% and 7%, whereas S. aquimarina yielded 0.5% to 1.5%. An optimized cementation solution concentration of 0.5 M was critical for maximum efficiency. The ideal operational temperature is between 20 and 35C, with salinity and oxygen levels having minimal effects. Although salinity influences carbonate crystal characteristics, its impact on Unconfined Compressive Strength (UCS) of treated soil is minor. Samples from a one-phase treatment at pH 6.0 to 7.5 showed UCS strength approximately half that of a two phase treatment. These findings suggest promising applications for MICP in enhancing strength in both terrestrial and marine environments.
Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp
et al.
In the evolving field of psychopathology, the accurate assessment and forecasting of data derived from Ecological Momentary Assessment (EMA) is crucial. EMA offers contextually-rich psychopathological measurements over time, that practically lead to Multivariate Time Series (MTS) data. Thus, many challenges arise in analysis from the temporal complexities inherent in emotional, behavioral, and contextual EMA data as well as their inter-dependencies. To address both of these aspects, this research investigates the performance of Recurrent and Temporal Graph Neural Networks (GNNs). Overall, GNNs, by incorporating additional information from graphs reflecting the inner relationships between the variables, notably enhance the results by decreasing the Mean Squared Error (MSE) to 0.84 compared to the baseline LSTM model at 1.02. Therefore, the effect of constructing graphs with different characteristics on GNN performance is also explored. Additionally, GNN-learned graphs, which are dynamically refined during the training process, were evaluated. Using such graphs showed a similarly good performance. Thus, graph learning proved also promising for other GNN methods, potentially refining the pre-defined graphs.
Do ecosystems primarily reflect evolutionary history or current environment? Predicting land-atmosphere exchange hinges on this unresolved question. Plant traits adapt to particular environments over evolutionary timescales, yet their individual relationships with current climate and soils are often obscured by limited sampling, plant-type effects, and multiple adaptive strategies that can yield similar outcomes. Crucially, it is the coordination of traits, rather than any single trait, that governs vegetation dynamics and ecosystem fluxes, yet such multivariate relationships cannot be directly observed. We present DifferLand, a differentiable hybrid model that integrates process understanding with machine learning to uncover latent trait-environment relationships from global satellite and in-situ observations (2001-2023). DifferLand explains up to 88% of the variance in canopy structure, photosynthesis, and carbon exchange by learning latent ecological axes-leaf economics, plant stature, and cropland distribution-that link long-term adaptation with short-term dynamics. Interpretable machine learning shows that these coordinated axes emerge from nonlinear interactions between plant-type attributes and local environment. Embedding such relationships into terrestrial models establishes a pathway toward adaptive models that better predict ecosystem resilience under climate change.
Ecological theory aids in understanding how disturbances affect ecosystems. However, experimental data are often complex, with multiple post-disturbance theories potentially applying simultaneously to the same ecosystem. This emphasizes the need for tools to experimentally test these theoretical predictions. We introduce MicroEcoTools, an R package designed to test ecological framework predictions using microbial community data. It assesses microbial diversity and evaluates the relative impacts of stochastic and deterministic assembly mechanisms through a taxa-based null model approach for replicated designs. Specifically, the package allows application of Grime's trait-based life-history categories-competitor, stress-tolerant, and ruderal (CSR)-to taxa, functional traits, and ecosystem functions within microbial communities. MicroEcoTools also includes relevant statistical tests, numeric simulations, and publicly available datasets for demonstration. In conclusion, MicroEcoTools facilitates the application of ecological frameworks, including community assembly mechanisms, diversity analysis, and life-history strategies, to microbial ecosystems under disturbance. This R package, along with its source code, can be freely accessed on GitHub at https://www.github.com/Soheil-A-Neshat/MicroEcoTools.
ABSTRACT Cachexia is a lethal muscle-wasting syndrome associated with cancer and chemotherapy use. Mounting evidence suggests a correlation between cachexia and intestinal microbiota, but there is presently no effective treatment for cachexia. Whether the Ganoderma lucidum polysaccharide Liz-H exerts protective effects on cachexia and gut microbiota dysbiosis induced by the combination cisplatin plus docetaxel (cisplatin + docetaxel) was investigated. C57BL/6J mice were intraperitoneally injected with cisplatin + docetaxel, with or without oral administration of Liz-H. Body weight, food consumption, complete blood count, blood biochemistry, and muscle atrophy were measured. Next-generation sequencing was also performed to investigate changes to gut microbial ecology. Liz-H administration alleviated the cisplatin + docetaxel-induced weight loss, muscle atrophy, and neutropenia. Furthermore, upregulation of muscle protein degradation-related genes (MuRF-1 and Atrogin-1) and decline of myogenic factors (MyoD and myogenin) after treatment of cisplatin and docetaxel were prevented by Liz-H. Cisplatin and docetaxel treatment resulted in reducing comparative abundances of Ruminococcaceae and Bacteroides, but Liz-H treatment restored these to normal levels. This study indicates that Liz-H is a good chemoprotective reagent for cisplatin + docetaxel-induced cachexia. IMPORTANCE Cachexia is a multifactorial syndrome driven by metabolic dysregulation, anorexia, systemic inflammation, and insulin resistance. Approximately 80% of patients with advanced cancer have cachexia, and cachexia is the cause of death in 30% of cancer patients. Nutritional supplementation has not been shown to reverse cachexia progression. Thus, developing strategies to prevent and/or reverse cachexia is urgent. Polysaccharide is a major biologically active compound in the fungus Ganoderma lucidum. This study is the first to report that G. lucidum polysaccharides could alleviate chemotherapy-induced cachexia via reducing expression of genes that are known to drive muscle wasting, such as MuRF-1 and Atrogin-1. These results suggest that Liz-H is an effective treatment for cisplatin + docetaxel-induced cachexia.