Gigabyte

Marine biodiversity documentation from the Mumbai Metropolitan Region (MMR) remains neglected despite the region having diversity of marine coastal habitats. The regions intertidal is one such habitat where species documentation remained heavily deficient due to lack of assessments and general apathy towards the habitat. This study addresses the issue of data deficiency of one of the largest taxa, Gastropoda through a decade long citizen science project, Marine Life of Mumbai. There exist large gaps in taxonomic research that have led to inconsistencies in species identification and inadequate ecosystem representation. This study addresses these issues by focusing on one of the largest taxa, the Molluscan class Gastropoda within the MMR. We present the spatial distribution of gastropod assemblages from 28 rocky, sandy and muddy intertidal sites within the Mumbai Metropolitan Region, on west coast of India. A total of 163 species were recorded from 2164 observations of marine gastropods. Among these, 29 species, 34 genera and one family Limapontiidae are new records for the region. Additionally, this study reports rediscoveries of 7 species from their type locality, with 5 species of Heterobranchs recorded after 78 years: one species from Neogastropoda, Lataxiena bombayana, after 131 years and one from Siphonariida, Siphonaria bassiensis after 31 years, from their type locality. These species are herein illustrated with detailed morphological descriptions and their local distribution on 28 sites in the Mumbai Metropolitan Region. Through this study we elucidate that the citizen science efforts and the subsequent taxonomic analysis provide an effective and low-cost method for filling data gaps from large, understudied geographical areas.

As global reptile populations continue to decline, improving reproductive success in managed populations of listed species, such as Phrynosoma cornutum (the Texas horned lizard) has become increasingly critical for species survival. One understudied area of reproductive research in reptile species is gamete collection and storage, a crucial component for maintaining genetic diversity. In Texas, semen was collected from wild P. cornutum (n = 20) in June 2025. Semen collection was performed via electroejaculation (EEJ) under alfaxalone anesthesia. Prior to semen collection, snout-vent-lengths (SVL) and weights were recorded and testes measurements were taken using a portable ultrasound. Average sperm motility and concentration across all lizards was 83.7% and 85.7 x 106 sperm/mL, respectively. While lizards with longer SVLs had higher sperm motility, weight and testis size did not affect sperm parameters. Samples were extended in INRA96 and divided for use in cold-storage longevity or cryopreservation trials. Samples under cold-storage conditions were assessed for motility daily for 10 days. Motility was not significantly reduced until 48 hours post-collection and maintained 19% motility at day 10. For cryopreservation, samples were diluted 1:1 in INRAFreeze cryopreservation media and frozen in liquid nitrogen, then immediately thawed. Average post-thaw sperm motility was 13.9%, with the highest post-thaw motility recorded at 38.2%. This is the first report of semen storage and cryopreservation in Phrynosoma and provides valuable insight into semen storage potential in reptile species.

Malaria is one of the deadliest diseases in sub-Saharan Africa and Southeast Asia. The majority of the fatalities occur mostly in children under 5 years and pregnant women and this is due to infection by Plasmodium spp, of which Plasmodium falciparum is the most virulent and is responsible for most of the morbidity and mortality. Despite various public health interventions such as use of insecticide-treated bed nets, spraying of homes with insecticides and use of WHO recommended artemisinin-based combination therapies (ACT), malaria prevention still faces major setback due to drug and insecticide resistance by P. falciparum and mosquitoes respectively. The study uses molecular docking and immunoinformatics to screen various Plasmodium spp antigens and evaluate their antigenicity and suitability as vaccine candidates. The P. falciparum antigens and T-cell receptor (TCR) structures were obtained from Protein Data Bank (PDB) based on a range of factors related to their role in the lifecycle of the parasite and their status as vaccine targets. Protein structures not available in the PDB were predicted using AlphaFold. The 3D structures of selected P. falciparum antigens and TCR structures were downloaded in PDB format then all water molecules, Hetatm, and bound ligands were deleted from the protein structures using BIOVIA Discovery Studio Visualizer. Subsequently, molecular docking was done using ClusPro v2.0 server and docked complexes were compared. The findings of this study gave valuable insights into the interaction of human immune response with P. falciparum antigens. The best three ranked antigen complexes are PfCyRPA, PfMSP10 and PfCSP and this confirm their use as potential candidates for vaccine development. This study highlights the usefulness of computational docking in identifying P. falciparum antigens of excellent immunogenic potential as vaccine candidates.

The acidic blackwaters of Southeast Asias peat-swamp forests represent some of the most extreme freshwater environments on Earth. Despite their very low pH values, limited nutrients, and hypoxic conditions, these blackwater habitats harbor a remarkable diversity of freshwater fishes, including multiple lineages that have independently adapted to these extreme conditions and, in some cases, exhibiting extreme body miniaturization. These replicate evolutionary lineages therefore provide a powerful comparative framework to investigate adaptation to extreme environments and the genomic basis of miniaturization. Here, we present high-quality, annotated reference genomes for four cypriniform species endemic to these peat-swamp forest ecosystems: Paedocypris sp., Sundadanio atomus, Boraras brigittae, and Rasbora kalochroma. The first two are progenetic miniatures, including Paedocypris, comprising the smallest known fish, while B. brigittae represents a proportioned dwarf and R. kalochroma a non-miniature taxon. Genome sizes ranged from 401-1,290 Mb and heterozygosity from 0.34-1.7%. All genome assemblies achieved pseudo-chromosome-level contiguity, high k-mer completeness (>99%), and high BUSCO completeness (94.5-98.9%). Repeat analyses revealed lineage-specific differences in transposable element landscapes and abundances, while gene annotation identified notable intron length reduction in progenetic miniatures.

Accurate genome annotation remains challenging as assembly quality often exceeds annotation reliability. Resolving ambiguities of gene presence, absence, and orthology typically requires integrating two complementary lines of evidence: sequence homology between species and the conservation of gene order (i.e., synteny). BLAST remains the standard for homology detection, yet its raw output can be difficult to interpret. Existing tools address this challenge but operate at opposing scales. Alignment viewers provide detailed pairwise statistics without genomic context, while synteny tools offer chromosome-scale perspectives without sequence-level resolution. To fill this intermediate gap, we developed Novabrowse, an interactive BLAST results interpretation framework featuring high-resolution multi-species synteny analysis, chromosomal re-arrangement investigation, ortholog detection, and gene signal discovery. Users define a genomic region of interest in a query species and/or use custom sequences, then select one or more subject species for comparison. The pipeline retrieves query gene sequences via NCBI API integration and performs BLAST searches against each subject transcriptome or genome. Results are presented via an interactive HTML file featuring alignment statistics, chromosomal maps, coverage visualizations, ribbon plots, and distance-based clustering of high-scoring segment pairs into putative gene units. We demonstrate these capabilities by investigating Foxp3, Aire, and Rbl1, three highly conserved vertebrate genes, in the recently assembled genome of the newt Pleurodeles waltl. Foxp3 and Aire have not been described in any salamander species to date, despite availability of multiple assemblies and extensive transcriptomic datasets. Using Novabrowse, we discovered conserved loci and gene signals for both genes in P. waltl, the presence of which was subsequently confirmed via Nanopore long-read RNA sequencing. In contrast, Rbl1 analysis uncovered a chromosomal rearrangement at its expected locus with no gene signal detected, indicating a gene loss specific to P. waltl despite the genes retention in the closely related axolotl (Ambystoma mexicanum). Our findings demonstrate Novabrowses capacity for evidence-based evaluation of annotation artifacts, an essential capability as high-quality assemblies become more available for phylogenetically diverse species. Novabrowse is open source (MIT license) and freely available at: https://github.com/RegenImm-Lab/Novabrowse.

Pollen is a robust and widespread substance that captures a historical snapshot of a specific time and place, and it can be used to track movements through space by examining the pollen deposited on various objects. Palynology, the study of pollen, is used across fields such as conservation, natural history, and forensics, where it is particularly useful for tracing the origin and movement of objects. However, pollen has remained underutilized due to the difficulty of distinguishing many pollen taxa beyond the family level and limited pollen reference material to support location predictions. With recent developments in pollen DNA metabarcoding these issues have been rectified, but much of the available pollen data are primarily from wind-pollinated species, which are widespread and less informative of specific sample locations. Bee-collected pollen presents an untapped resource in training predictive models to geolocate sample origin. Here we compiled bee-collected pollen DNA sequence relative abundance data from three projects in the western U.S. and assessed the accuracy of supervised machine learning models to predict the location of sample origin based solely on pollen assemblage, without the need of incorporating additional data. Random Forest and k-Nearest Neighbors models yielded high accuracy across all projects. We also found that models trained on taxonomically clustered pollen assigned sequence variants (ASVs) performed slightly better than those trained on raw sequence data, but the difference was minor, indicating that models trained on raw sequence data can reliably predict location and avoid the time-consuming taxonomic assignment process. Our results demonstrate the utility of repurposing bee-collected pollen for geolocation and provide a framework for employing supervised machine learning in future geolocation efforts. HighlightsO_LIBee-collected pollen metabarcoding data was used to accurately predict sample origin C_LIO_LIRandom Forest and k-Nearest Neighbors algorithms were most accurate with lowest error C_LIO_LITaxonomically-classified and raw DNA sequence data training sets performed comparably C_LI

The measurement of inbreeding has gained significance across diverse fields, including population and conservation genetics, agricultural genetics, breeding programs for animals and plants, and wildlife management. This is due to the fact that inbreeding leads to increased homozygosity and results in lower genetic diversity, rendering populations more vulnerable to environmental changes, diseases, and other stressors. High or mid-coverage whole genome sequencing (WGS) has been widely used for inbreeding estimation, but it is resource-intensive. We aimed to investigate the use of ultra low-coverage whole genome sequencing (ulcWGS) as a cost-effective alternative for inbreeding analysis. Domestic dogs were used for our study as their extensive breeding histories lead to populations with a wide range of inbreeding levels. We constructed a multi-breed reference panel from high-coverage WGS samples. Inbreeding in independent ulcWGS samples was then estimated using runs of homozygosity (RoH) and inbreeding coefficients (F). We modeled the relationship between these measures and sequencing depth using nonlinear regression, to generate inbreeding estimates relative to sequencing depth. Resulting relative RoH and F measurements were significantly correlated, with purebred dogs exhibiting more runs of homozygosity and higher inbreeding coefficients compared to mixed-breed dogs. Our findings demonstrate that ulcWGS can provide reliable and economical estimations of inbreeding, expanding accessibility to genetic monitoring.

BackgroundOviCol has recently been proposed as a disruptive strategy for the surveillance and control of synanthropic Aedes mosquitoes, vectors of dengue, Zika, and chikungunya viruses. The approach integrates monitoring and control through ultra-low-cost ovitraps ([~]0.2 USD), bioattractants, and egg inactivation using hot water. However, large-scale ovitrap surveillance generates thousands of egg substrates that require time-consuming manual counting, creating a major operational bottleneck. To address this limitation, we developed Col-Ovo, an artificial intelligence-based tool for automated counting of Aedes aegypti eggs from real field samples, together with OviLab, a digital platform for annotation, curation, and management of entomological image datasets. Methodology/Principal FindingsThe detection model was trained using YOLOv11m on a dataset of 275 oviposition substrates (20.5 cm strips) collected under routine operational conditions. Images were captured in situ without preprocessing and included substrates heavily stained by bioattractants such as blackstrap molasses and dry yeast (Saccharomyces cerevisiae), as well as sand and particulate debris, reflecting realistic field conditions. The system was designed to operate with standard smartphone images and tolerate compression artifacts produced by messaging platforms such as WhatsApp. Performance was evaluated by comparing automated egg counts with expert manual counts and with virtual-human counts conducted in OviLab using >200% image magnification. Col-Ovo achieved >95% agreement with expert counts and 88% agreement with OviLab while reducing processing time from approximately 15 minutes to <3 seconds per sample. Conclusions/SignificanceCol-Ovo enables rapid, scalable quantification of Ae. aegypti eggs from smartphone images, addressing a critical operational barrier in ovitrap-based surveillance. The system requires no image preprocessing or specialized hardware and is accessible through a lightweight web interface supported by an AI architecture that allows retraining for new ecological contexts or additional Aedes species. Integrated with OviLab, this platform provides a flexible digital infrastructure that can strengthen routine vector surveillance and community-level control programs across regions where Aedes mosquitoes continue to expand. Author SummaryMosquitoes that transmit dengue, Zika, and chikungunya are expanding in many parts of the world. Monitoring their populations is essential for guiding prevention and control actions. A common surveillance method uses small traps where female mosquitoes lay their eggs. By counting the eggs collected in these traps, health programs can estimate mosquito abundance and detect increases in risk. However, the eggs are extremely small: about 0.065 mm{superscript 2}, and are usually counted manually under magnification. This process is slow, requires trained personnel, and limits how many samples can be analyzed in routine surveillance. In this study, we developed a digital tool that automatically counts mosquito eggs from photographs taken with a smartphone. The system was trained using images collected under real field conditions, including samples with stains, dirt, and other materials commonly found in mosquito traps. The tool can analyze images even when they are compressed and shared through WhatsApp. By reducing counting time from 15 minutes to only a 25 seconds, this approach can help strengthen mosquito surveillance and support faster responses to mosquito-borne disease risks.

This study was aimed at characterizing the physicochemical analysis of stingless bees honey (SBH) in the Wonchi district, Southwest Shewa Zone, Ethiopia. In this study, a total of 30 stingless bees honey samples were collected from Damu Dagele, Fite Wato, and Warabu Messe sites from underground soils through an excavation of natural nests. Physicochemical characterization of properties and proximate analysis of the honey were performed. The result showed a total mean of 20.12{+/-}1.14% moisture content, 8.62{+/-}2.73 meq./kg free acidity, 1.8{+/-}0.52 mS/cm electrical conductivity, 3.39{+/-}0.32 pH, 40.52{+/-}6.61 mg/kg HMF, 0.83{+/-}0.33% ash, 0.56{+/-}0.25% protein, 0.56{+/-}0.24% fat, and 0.59{+/-}0.23% WISC for physicochemical properties of stingless bees honey. Among sugar profiles of SBH, fructose constituted the highest proportion at 18.87 g per 100 g (53.87%), while sucrose exhibited the lowest concentration at 5 g per 100 g (14.33%). The result showed that the highest constituted mean of mineral composition was observed with potassium (K) of 16.64{+/-}0.257 mg/kg, while magnesium (Mg) showed the lowest concentration of 3.48{+/-}0.17 mg/kg. A substantial correlation was observed between K and Mg, with a correlation coefficient of 0.72 and 0.72, and similarly between K and Calcium (Ca); the correlation was highly significant, exhibiting a correlation coefficient of 0.65. Furthermore, the correlation between fatty and other physicochemical and proximate analyses showed very insignificant correlations. In general, this study showed that the SBH produced in the current study area has good physicochemical properties and moisture and contains high-quality honey, which may help its traditional medicinal uses. The findings of the study further suggests the potentiality of the area for quality honey, and to easily locate priority areas for stingless bee conservation, further detailed studies of other stingless species honey medicinal values are recommended.

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

Space-based platforms currently represent the most accurate means to experimentally assess the influence of the space environment on biological systems. However, performing such experiments remains technically challenging and requires highly specialized instrumentation. This study describes the current development and hardware qualification of ExocubeBio, a miniaturized experimental platform for in-situ biological space exposure. This experiment is scheduled for installation on the exterior of the International Space Station in 2027, as part of Exobio, the European Space Agencys new generation exobiology exposure facility. ExocubeBio will expose live microbial samples to the low Earth orbit environment, and combine autonomous in-situ optical density and fluorescence measurements, with the capacity to return preserved samples to Earth. Achieving these experimental goals requires a specialized, robust and reliable hardware system. The ExocubeBio hardware testing described here includes assessment of material biocompatibility and durability, functional validation of the miniaturized fluidic system, and optimization of the integrated optical subsystem for optical density and fluorescence measurements. These results demonstrate that the ExocubeBio experimental hardware components can each execute their core functional and operational requirements; subsystems allow for sample exposure, in-situ measurements of microbial cultures, and the chemical preservation of samples for post-flight analysis. As ExocubeBio transitions from hardware development to mission readiness, the results presented here validate the overall design and engineering approaches utilized. By combining the strengths of in-situ monitoring and sample return, ExocubeBio represents a significant advancement in space-based experimentation, and will provide new insights into microbial responses to the space environment.

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

The common pine sawfly, Diprion pini, is a widespread defoliator of pine forests across Europe and Asia, with outbreaks causing substantial ecological and economic damages. However, genomic resources for this species have been limited, hindering advances in molecular ecology or pest management. Here, we present a near chromosome-level reference genome for D.pini, generated using PacBio HiFi reads, Oxford Nanopore MionION long reads, and 10x Genomics linked reads. The final assembly is organized into mostly chromosome-sized scaffolds. It spans a length of 268 Mb, comprises 81 scaffolds, and has a scaffold N50 of 18.7 Mb. BUSCO analysis (hymenoptera_odb10) indicates a high genome completeness of 97.2%. With 22,7 kb the mitochondrial genome is unusually large due to an extended non-coding control region (6,874 bp). Gene prediction identified 26,335 protein-coding genes, of which 12,769 were functionally annotated. Comparative analyses with other sawflies and Apocrita identified 2,472 proteins unique to D. pini, some of which are putatively associated with the processing of plant secondary metabolites. Notably, our genome assembly highlights that, when a closely related, high-quality reference genome is available, chromosome-scale assemblies can be generated without the need of Hi-C sequencing. The genome provides a valuable foundation for the development of improved monitoring and management strategies for D. pini outbreaks and contributes to advancing fundamental research on Hymenoptera evolution.

Text AbstractIn this study, we characterized the genetic structure and reconstructed the demographic history of cactus wrens (Campylorhynchus brunneicapillus), an endemic species of desert regions of North America, that shows a clear phenotypic and genotypic variation. We evaluated the effects of historical climate change on the structure and population dynamics of desert species using genomic data through genotyping by sequencing (GBS) and applied a population structure analysis (FST and ADMIXTURE), revealing two genetically differentiated groups: one continental and another peninsular in Baja California. Subsequently, we implemented the MSMC2 coalescent model on data divided into autosomal regions and the Z sex chromosome to estimate changes in effective population size (Ne) through evolutionary time. Additionally, we developed ecological niche models (ENMs) projected to the Last Glacial Maximum (LGM), Last Interglacial (LIG), Present times, and Future (2060 - 2080). Results indicate that both populations maintained moderated Nes before the LGM, experienced severe bottlenecks (Ne [~] 102-103), followed by a sustained expansion. However, recovery was limited to the Z chromosome of the peninsular population. These findings reveal how glaciations and interglacials shaped the evolutionary history of desert species and provide genomic evidence of the splitting of C. affinis from C. brunneicapillus. Article summaryThis research examines how climate changes shaped genetic diversity of cactus wrens across North American warm deserts. Using coalescent methods, researchers tracked effective population size changes over 100,000 years, using ecological niche modeling they predicted habitat suitability across climate periods. Results showed that continental and peninsular populations experienced bottlenecks during the Last Glacial Maximum, followed by demographic recovery on warm periods. However, the sex chromosome (Z) revealed male-biased demographic patterns in peninsular populations. Future projections indicated habitat suitability reductions for peninsular populations, highlighting conservation concerns. These findings demonstrate that past climate shaped genetic diversity of cactus wrens.

AimsSponge microbiomes have been extensively studied, in part due to their potential as sources of novel antimicrobials and other biologics, with most research focusing on Demosponges. Here, we investigate the Hexactinellid sponge Pheronema carpenteri, previously identified as a promising source of antibiotic-producing bacteria. MethodsUsing next-generation sequencing of bacterial 16S rRNA genes and a single sponge metagenome, we examined the composition of bacterial communities of P. carpenteri sponges recovered from the Porcupine Seabight, along with local water and sediment samples. ResultsOur results show that P. carpenteri harbours a microbiome abundant in Proteobacteria (47.1-59.4%) and Actinobacteria (11.5-27.5%), with consistent intra-aggregation similarities and structured intra-sponge communities. A metagenomic analysis revealed the presence of several nitrogen cycling genes (nirK, nosZ, nirS homologues of proteobacterial origin), supporting a suggestion that these sponges may play a role in nitrogen cycling, while biosynthetic gene clusters (BGCs) were limited (4 complete clusters). Notably, bacterial community structures within P. carpenteri aggregations resemble those observed in both low and high microbial abundance (LMA/HMA) sponges. ConclusionsHexactinellids are traditionally considered LMA sponges, so identifying species that deviate from this dichotomy provides new insights into sponge microbiome ecology. Integrating Hexactinellids into both culture-dependent and culture-independent studies will advance our broader understanding of sponge-associated microbial diversity and could inform biodiscovery programmes in marine environments. Impact StatementOur findings support the suggestion that a combination of culture-based and molecular analyses is required to generate a comprehensive picture of the biosynthetic potential of P. carpenteri sponges. We also reveal insights into the ecosystem services that sponge microbiomes may contribute towards. These observations could facilitate a deeper understanding of the biotechnological and environmental value of key marine resources.

Gastrointestinal disorder caused by the ingestion of (oo)cysts of Cryptosporidium and Giardia is one of the major health problems in developing countries. Fruits and vegetables that are usually consumed unpeeled, poorly washed and or cooked and are the major modes of transmission. Frequent large-scale screening of the food samples is necessary to prevent outbreaks but screening of vegetables for such microbes is limited in Nepal. In this study, we used a smartphone microscopy system to study prevalence and quantification of (oo)cysts of Cryptosporidium and Giardia in 651 vegetable samples collected from nine major vegetable collection sites across Nepal. The overall prevalence rate of vegetable samples was 37.5% with at least with one of the parasites. We found that 23.2% samples were contaminated with Giardia and 33.3% samples were contaminated with Cryptosporidium. Among eight vegetable types, the prevalence rate was lowest in carrot (20%) and highest in spinach (48%). The prevalence rate of vegetable samples at different sites ranged from 13% in Dhading to 61% in Dhangadi. The contamination rate was 28% for winter, 43% for summer and 33% for monsoon seasons in samples collected from Kathmandu. These vegetables should be considered as a potential source of parasitic contamination in people. These vegetables can cause infection if consumed poorly washed and or cooked, posing a potential source of parasitic contamination in people.

The global expansion of highly pathogenic avian influenza (HPAI) virus A(H5N1) underscores the need for rapid surveillance at high-risk wildlife interfaces. Taiaroa Head (45.7828{degrees} S, 170.7333{degrees} E) in the South Island of Aotearoa New Zealand hosts a plethora of aquatic wildlife including a large red-billed gull (Chroicocephalus novaehollandiae scopulinus) colony as well as the only mainland breeding colony of northern royal albatross (Diomedea sanfordi). The Royal Albatross Centre is also a major nature tourism destination, attracting tens of thousands of visitors annually, thereby creating a dense ecological and human-wildlife interface vulnerable to viral incursion. We evaluated the GeneXpert II platform using the Xpert(R) Xpress Flu/RSV cartridge as a field-deployable tool for avian influenza virus detection in environmental and wildlife-associated samples. The assay detected synthetic influenza A viral RNA and multiple endemic low pathogenic avian influenza virus subtypes (A(H3N8), A(H1N9), A(H5N2) and A(H7N7)) circulating in New Zealand birds. Influenza A virus was reliably identified in spiked environmental water samples with no consistent PCR inhibition as well as naturally occurring avian influenza virus in duck pond water. Field deployment demonstrated that the system could be operated by non-laboratory personnel with minimal training in a non-clinical setting. This study establishes the feasibility of near-real-time environmental monitoring. Repurposing clinical cartridge-based point-of-care diagnostics offers a practical early warning approach for avian influenza virus surveillance at ecologically and economically significant locations.

Common buckwheat (Fagopyrum esculentum Moench) is a globally cultivated pseudocereal with a high nutritional quality and economic value. Due to its self-incompatibility, common buckwheat exhibits a high level of heterozygosity, making genome assembly challenging. Consequently, reference-level haplotype-resolved assemblies of common buckwheat are scarce, hindering research and genomics-assisted breeding. Here, we present a near-complete, chromosome-level, haplotype-resolved assembly of a common buckwheat F1 genotype (named Tuka), generated using a trio-binning approach that integrated parental Illumina short-read data with PacBio HiFi and Hi-C data from Tuka. The Tuka assembly comprises two haplomes, Tuka_h1 and Tuka_h2, both showing high contiguity (contig N50 of 76.68 Mb and 84.57 Mb, respectively), high completeness (assembly sizes of 1.28 Gb and 1.23 Gb with BUSCO scores of 96.9% and 96.8%, respectively), high base-level accuracy (QV of 59.08 and 63.03, respectively), and few gaps (35 and 30, respectively). This near-complete assembly of Tuka serves as a valuable genomic resource for common buckwheat, enabling advanced genomic analyses and accelerating research and breeding using state-of-the-art genomic tools.

Recent advances in long-read sequencing have enabled near telomere-to-telomere (T2T) assemblies across diverse taxa. However, avian genomes remain challenging due to numerous microchromosomes, small, typically < 20Mb, elements that are gene-, GC-, and repeat-rich. As a consequence, microchromosomes are often missing from genome assemblies. Here, we present a chromosome-level, haplotype-resolved genome assembly for the Western barn owl (Tyto alba). Using a trio-binning strategy with Illumina parental reads combined with PacBio HiFi and Oxford Nanopore Technologies data, we generated two phased contig sets. These were scaffolded into 40 linkage groups using a linkage map. Comparative analyses identified unplaced HiFi scaffolds corresponding to microchromosomes, which we integrated into six additional microchromosomes using long reads information. The two assemblies present 46 chromosomes, matching the karyotype of the species. They exhibit strong synteny between parental haplotypes, except for a [~]38 Mb complex region on chromosome 7 containing nested inversions. This high-quality reference provides the first haplotype-resolved and chromosome-level genome for Strigiformes, enabling fine-scale studies of structural variation and avian genome evolution.