DEIMS-SDR – A web portal to document research sites and their associated data

Climate change and other drivers are affecting ecosystems around the globe. In order to enable a better understanding of ecosystem functioning and to develop mitigation and adaptation strategies in response to environmental change, a broad range of information, including in-situ observations of both biotic and abiotic parameters, needs to be considered. Access to sufficient and well documented in-situ data from long term observations is therefore one of the key requirements for modelling and assessing the effects of global change on ecosystems. Usually, such data is generated by multiple providers; often not openly available and with improper documentation. In this regard, metadata plays an important role in aiding the findability, accessibility and reusability of data as well as enabling reproducibility of the results leading to management decisions. This metadata needs to include information on the observation location and the research context. For this purpose we developed the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a research and monitoring site registry (https://www.deims.org/) that not only makes it possible to describe in-situ observation or experimentation sites, generating persistent, unique and resolvable identifiers for each site, but also to document associated data linked to each site. This article describes the system architecture and illustrates the linkage of contextual information to observational data. The aim of DEIMS-SDR is to be a globally comprehensive site catalogue describing a wide range of sites, providing a wealth of information, including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available.     

Bacterial dual phototrophy was demystified

Despite solid, growing genomic evidence for bacteria practicing bacteriochlorophyll and rhodopsin-based dual phototrophy, direct physiological proof has been lacking for over a decade until Kopejtka et al. recently solved the puzzle in an Alpine psychrophilic bacterium. Here, I highlight conceptual developments and address an overlooked, ecologically important phototrophic byproduct – heat.     

Viral sponges sequester nucleotide signals to inactivate immunity

Bacteria synthesize specialized nucleotide signals to control anti-phage defense. Two papers – by Huiting et al. and Jenson et al. – now reveal that bacteriophages encode protein ‘sponges’ that sequester cyclic oligonucleotide immune signals and inactivate host antiviral immunity.     

FishSounds Version 1.0: A website for the compilation of fish sound production information and recordings

Many fish species use active sound production for communication in numerous behaviors. Additionally, likely all fish can make passive or incidental sounds that may also serve some signal functions. Despite the ecological importance of fish sounds, their evident passive acoustic monitoring applications, and extensive endeavors to document soniferous fish diversity, the fields of bioacoustics and ichthyology have historically lacked an easily accessible, global inventory of known fish sound production. To alleviate this limitation, we developed http://FishSounds.net, a website that compiles and disseminates fish sound production information and recordings. FishSounds Version 1.0 launched in 2021, cataloging documented examinations for active and passive sound production for 1185 fish species from 837 references as well as 239 exemplary audio recordings. FishSounds allows users to search by taxa (e.g., family or common name), geographical distribution (e.g., region or water body), sound type, or reference. We have also made available the code used to create the website, so that it may be used in other data-sharing efforts—acoustic or otherwise. Subsequent versions of the website will update the data and improve the website functionality. FishSounds will advance research into fish behavior, passive acoustic monitoring, and human impacts on underwater soundscapes; serve as a resource for public outreach; and provide the foundation needed to investigate more of the 96% of fish species that lack published examinations of sound production. We further hope the FishSounds design, implementation, and engagement strategies will serve as a model for future data management and sharing efforts.     

Unleashing the potential of peptides in agriculture and beyond

Peptides display a broad range of regulatory functions. Ormancey et al. recently identified an important new mechanism – complementary peptides (cPEPs) – that provide a versatile means to control cell functions. We draw a parallel between RNA and peptide biology, and discuss new routes of investigation and industrial applications opened by this work.     

3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer

Many ecosystems, particularly wetlands, are significantly degraded or lost as a result of climate change and anthropogenic activities. Simultaneously, developments in machine learning, particularly deep learning methods, have greatly improved wetland mapping, which is a critical step in ecosystem monitoring. Yet, present deep and very deep models necessitate a greater number of training data, which are costly, logistically challenging, and time-consuming to acquire. Thus, we explore and address the potential and possible limitations caused by the availability of limited ground-truth data for large-scale wetland mapping. To overcome this persistent problem for remote sensing data classification using deep learning models, we propose 3D UNet Generative Adversarial Network Swin Transformer (3DUNetGSFormer) to adaptively synthesize wetland training data based on each class's data availability. Both real and synthesized training data are then imported to a novel deep learning architecture consisting of cutting-edge Convolutional Neural Networks and vision transformers for wetland mapping. Results demonstrated that the developed wetland classifier obtained a high level of kappa coefficient, average accuracy, and overall accuracy of 96.99%, 97.13%, and 97.39%, respectively, for the data in three pilot sites in and around Grand Falls-Windsor, Avalon, and Gros Morne National Park located in Canada. The results show that the proposed methodology opens a new window for future high-quality wetland data generation and classification. The developed codes are available at https://github.com/aj1365/3DUNetGSFormer.     

TrypInDB: A searchable online resource of small molecule inhibitors against Trypanosoma sp.

African Trypanosomiasis and American Trypanosomiasis are the diseases affecting more than thousands of people yearly and more than twenty-five million people risk acquiring the disease. The treatment for the disease is generally expensive, and most of the available drugs are of high-toxicity and cause fatal side-effects. Hence, there is a constant need for finding new treatment strategies for Trypanosomiasis. Combination therapy and repurposing or redesigning of existing inhibitors for new drugs are of high importance to address these hurdles, particularly the drug resistance. Hence, here we report TrypInDB, a searchable online resource of small molecule inhibitors having a varying degree of activity towards Trypanosoma sp. Information of about >14,000 small molecules from >700 published research articles was collected and made as an easy-to-search database. Four major sets of information were made available for each collected inhibitors viz., General information (activity values; source of the inhibitors; enzyme targets; etc.,), Structural information, Toxicity information, and Literature information. More than 25 different information about each inhibitor were collected or predicted and made accessible for searching. The database is designed to be queried easily with multiple-field filters with the provisions to perform sub-structure search and similar FDA approved drug searches. The database supports the easy export of queried records and structure in multiple formats. In addition, the TrypInDB is actively integrated into LeishInDB. We believe that the scope of TrypInDB permits the research community to exploit the available data for repurposing the inhibitors as well as for the investigation of new therapeutics.Database URL: http://trypindb.biomedinformri.com/     

Feeding the world sustainably: efficient nitrogen use

Globally, overuse of nitrogen (N) fertilizers in croplands is causing severe environmental pollution. In this context, Gu et al. suggest environmentally friendly and cost-effective N management practices and Hamani et al. highlight the use of microbial inoculants to improve crop yields, while reducing N-associated environmental pollution and N-fertilizer use.     

Wildlife 3D multi-object tracking

The study of wildlife populations and species has gained increased relevance due to significant endangerment, loss of habitats and world climate change. Using camera traps for monitoring is a common method to estimate species diversity, occupancy and relative abundance. We present a 3D multi-object tracking method for wildlife camera trapping using RGB-D cameras. We infer precise three-dimensional pose estimations of individual animals (i.e. locations and orientations) and also three-dimensional estimations of their observed movements, including velocity estimations. More precisely, we achieved a scaled Average Multi-Object Tracking Accuracy (sAMOTA) of 88.43%. The code is available athttps://github.com/m-klasen/3D_wildlife_tracking.     

Improving wildlife tracking using 3D information

The monitoring of wildlife populations is of growing importance due to the worldwide endangerment of many species, global climate change, and land cover change. Wildlife monitoring by camera traps is an established and non-invasive standard approach to quantify species diversity, estimate occupancy and relative abundance and track animal behaviour in 24/7 documentation. We propose a novel wildlife-specific 3D multi-object tracking workflow using inexpensive stereo camera traps. By embedding carefully efficient 2D methods into the overall 3D workflow, we avoid, on the one hand, costly processing of complex 3D data structures, i.e., 3D point clouds but on the other hand outperform significantly typical 2D tracking approaches with our overall 3D workflow in terms of international established multi-object tracking metrics, i.e., with respect to the reliability and accuracy of the tracking results. The code is available at https://github.com/m-klasen/3d_wildlife-tracking     

Sense the moist and feel the heat: mosquito host and oviposition site detection

Laursen et al. identified coreceptor Ir93a that is required for humidity and thermal sensing in the mosquito species Anopheles gambiae and Aedes aegypti. Behavioural studies with mutant mosquitoes with disrupted Ir93a revealed that they were less attracted to a blood meal source and oviposition site at close distance.     

Matching metabolic supply to demand optimizes microbial growth

Recent research has strengthened the notion that microbes allocate their biosynthetic capacity to maximize the growth rate, λ. Yet many microbes can grow substantially faster after laboratory evolution. Chure and Cremer advance a resource-allocation model, which they derive from first principles, that offers resolution to this conundrum.     

ParaSiteDB – A user-friendly database for managing a parasitological collection

ParaSiteDB is an application for arranging and managing a parasitological collection. It has been designed to provide a user-friendly, easily manageable and searchable site and is suitable for small to bigger collections.The source code of the application is available on GitHub: https://github.com/goobar4/aacrg.The demonstration version of the application is available on https://syrota.info/wormbasehttp://syrota.info/wormbase.     

Multi-omic genetic scores advance disease research

Multi-omic analysis is an effective approach for dissecting the mechanisms of diseases; however, collecting multi-omic data in large populations is time-consuming and costly. Recently, Xu et al. developed genetic scores for multi-omic traits and demonstrated their utilization to gain novel insights, advancing the application of multi-omic data in disease research.     

Remote sensing analysis on primary productivity and forest cover dynamics: A Western Ghats India case study

Tropical forest ecosystems are among the most essential habitats on Earth for conserving biological diversity and short-term climate regulation. For this reason, they are key areas of conservation policies in the world. In this paper, we aim to investigate the dynamics of forest cover and their changes in primary productivity by empowering information on historical forest management and fieldwork research with remote sensing vegetation monitoring methods. The study area falls within the central portion of the Indian Western Ghats, a global biodiversity hotspot. In particular, part of the analysis was performed on the Kadamakal Reserve Forest and Pushpagiri Wildlife Sanctuary, which harbours an endemic low elevation dipterocarp evergreen forest. This area was managed by selective logging and became fully protected in 1984. We performed multiple time series macroscale analyses between 1999 and 2020 on the Indian Central Western Ghats region, using satellite products at 1 km spatial resolution from the VITO Copernicus Global Land Service on Dry Matter Productivity, Fraction of Absorbed Photosynthetically Active Radiation, and Normalised Difference Vegetation Index. We also performed a very-high spatial resolution Normalised Difference Vegetation Index differential analysis between 2021 and 2016 with Sentinel 2-L2A products to investigate forest dynamics within the reserve. At the 1 km spatial resolution has been found an increase in all three vegetation indices, by employing the LOESS statistical method for the smoothed transition autoregressive model of raster data medians of our datasets. The boxplot raster distribution analysis also highlighted a significant imbalance in dry matter productivity in the last decade (2010−2020) comparing the previous one (1999–2009). The second part of the analysis, at 10 m spatial resolution within the reserve forest, revealed a growth in the vegetation cover on the top of the Pushpagiri Mountain ridge and in a previously landslide area. The study found new erosion channels down to the upper plateau on the South-West side of the reserve due to an increment of the run-off processes during the monsoon period. This satellite analysis highlighted generalised positive vegetation trends in the Central Western Ghats, India, over the last twenty-two years, enhancing an improvement in the ecosystem functioning and carbon storage ecosystem service. Notably, through this work, we also developed a standardised and open-access framework to monitor the vegetation remotely (SVIT) during periods of forest inaccessibility for fieldwork sampling.     

MAD-YOLO: A quantitative detection algorithm for dense small-scale marine benthos

Marine biological resources are abundant, and the reasonable development, research and protection of marine biological resources are of great significance to marine ecological health and economic development. At present, underwater object quantitative detection plays a very important role in marine biological science research, marine species richness survey, and rare species conservation. However, the problems of a large amount of noise in the underwater environment, small object scale, dense biological distribution, and occlusion all increase the detection difficulty. In this paper, a detection algorithm MAD-YOLO (Multiscale Feature Extraction and Attention Feature Fusion Reinforced YOLO for Marine Benthos Detection) is proposed, which is based on improved YOLOv5 is proposed to solve the above problems. To improve the adaptability of the network to the underwater environment, VOVDarkNet is designed as the feature extraction backbone. It uses the intermediate features with different receptive fields to reinforce the ability to extract feature. AFC-PAN is proposed as the feature fusion network so that the network can learn correct feature information and location information of objects at various scales, improving the network's ability to perceive small objects. Label assignment strategy SimOTA and decoupled head are introduced to help the model better handles occlusion and dense distribution problems. Experiments show the MAD-YOLO algorithm increases mAP_0.5:0.95 on the URPC2020 dataset from 49.8% to 53.4% compared to the original YOLOv5. Moreover, the advantages of the model are visualized and analyzed by the method of controlling variables in the experimental part. The experiments show that MAD-YOLO is suitable for detecting blurred, dense, and small-scale objects. The model performs well in marine benthos detection tasks and can effectively promote marine life science research and marine engineering implementation. The source code is publicly available at https://github.com/JoeNan1/MAD-YOLO.     

PyMEGABASE: Predicting Cell-Type-Specific Structural Annotations of Chromosomes Using the Epigenome

The folding patterns of interphase genomes in higher eukaryotes, as obtained from DNA-proximity-ligation or Hi-C experiments, are used to classify loci into structural classes called compartments and subcompartments. These structurally annotated (sub) compartments are known to exhibit specific epigenomic characteristics and cell-type-specific variations. To explore the relationship between genome structure and the epigenome, we present PyMEGABASE (PYMB), a maximum-entropy-based neural network model that predicts (sub) compartment annotations of a locus based solely on the local epigenome, such as ChIP-Seq of histone post-translational modifications. PYMB builds upon our previous model while improving robustness, capability to handle diverse inputs and user-friendly implementation. We employed PYMB to predict subcompartments for over a hundred human cell types available in ENCODE, shedding light on the links between subcompartments, cell identity, and epigenomic signals. The fact that PYMB, trained on data for human cells, can accurately predict compartments in mice suggests that the model is learning underlying physicochemical principles transferable across cell types and species. Reliable at higher resolutions (up to 5 kbp), PYMB is used to investigate compartment-specific gene expression. Not only can PYMB generate (sub) compartment information without Hi-C experiments, but its predictions are also interpretable. Analyzing PYMB’s trained parameters, we explore the importance of various epigenomic marks in each subcompartment prediction. Furthermore, the predictions of the model can be used as input for OpenMiChroM software, which has been calibrated to generate three-dimensional structures of the genome. Detailed documentation of PYMB is available at https://pymegabase.readthedocs.io, including an installation guide using pip or conda, and Jupyter/Colab notebook tutorials.     

The DendroEcological Network: A cyberinfrastructure for the storage,discovery and sharing of tree-ring and associated ecological data

The DendroEcological Network (DEN; https://www.uvm.edu/femc/dendro) is an opensource repository of high quality dendrochronological and associated ecological data. Launched in 2018, the mission of the DEN is to provide a centralized, standards-driven cyberinfrastructure for data storage, exploration and sharing. Specifically, the objectives of the DEN are to, 1) act as an integrator of dendrochronological and ecological data, 2) facilitate synthetic investigation and analyses of these data, 3) uphold the scientific community’s goals of data transparency and reproducibility of results, 4) serve as a long-term data archiving platform for use by individuals, laboratories and the greater scientific, management and conservation communities and, 5) leverage and extend previous and future research. The DEN facilitates the gathering of individual studies into a larger network, expanding the scale of inquiry to address pressing ecological questions that no single study can answer alone.     

Molecular information technology with DNA

Qian and Winfree constructed complex biochemical circuits with computation capability from scratch, demonstrating the programmability of biomolecules. One day, programming molecular information processing may be just like how electronic machines are programmed today, with exciting applications in nanoscale science and biotechnology.     

Malaria high-content imaging,where to next?

High-content imaging has produced greater insights into the complexities of cell biology. The ability to characterise specific phenotypes, as demonstrated by Rosenthal and Ng, provides a powerful tool for elucidating mechanisms of action and resistance, illustrating that high-content imaging in malaria research is only limited by our creativity.