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.     

Automated distance estimation for wildlife camera trapping

The ongoing biodiversity crisis calls for accurate estimation of animal density and abundance to identify sources of biodiversity decline and effectiveness of conservation interventions. Camera traps together with abundance estimation methods are often employed for this purpose. The necessary distances between camera and observed animals are traditionally derived in a laborious, fully manual or semi-automatic process. Both approaches require reference image material, which is both difficult to acquire and not available for existing datasets. We propose a fully automatic approach we call AUtomated DIstance esTimation (AUDIT) to estimate camera-to-animal distances. We leverage existing state-of-the-art relative monocular depth estimation and combine it with a novel alignment procedure to estimate metric distances. AUDIT is fully automated and requires neither the comparison of observations in camera trap imagery with reference images nor capturing of reference image material at all. AUDIT therefore relieves biologists and ecologists from a significant workload.We evaluate AUDIT on a zoo scenario dataset unseen during training where we achieve a mean absolute distance estimation error over all animal instances of only 0.9864 m and mean relative error (REL) of 0.113. The code and usage instructions are available at https://github.com/PJ-cs/DistanceEstimationTracking     

Variant-to-gene,gene-to-trait: finding new sleep genes through GWAS

Distilling insomnia genome-wide association study (GWAS) variants, Palermo and colleagues identified several genes that participate in sleep regulation in two different model organisms. This workflow sets off an innovative strategy to extract biological relevance from large human genomic databases.     

Plants 'cry' for help through acoustic signals

Plants perceive sounds, while responses to these sounds were already known. A breakthrough is the discovery by Khait et al. that stressed plants emit various informative ultrasonic sound signals, which can be categorized according to plant species, stress type, and stress severity. This discovery may change how plants are cultivated.     

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.     

The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project

There is a need for monitoring biodiversity at multiple spatial and temporal scales to aid conservation efforts. Autonomous recording units (ARUs) can provide cost-effective, long-term and systematic species monitoring data for sound-producing wildlife, including birds, amphibians, insects and mammals over large areas. Modern deep learning can efficiently automate the detection of species occurrences in these sound data with high accuracy. Further, citizen science can be leveraged to scale up the deployment of ARUs and collect reference vocalizations needed for training and validating deep learning models. In this study we develop a convolutional neural network (CNN) acoustic classification pipeline for detecting 54 bird species in Sonoma County, California USA, with sound and reference vocalization data collected by citizen scientists within the Soundscapes to Landscapes project (www.soundscapes2landscapes.org). We trained three ImageNet-based CNN architectures (MobileNetv2, ResNet50v2, ResNet100v2), which function as a Mixture of Experts (MoE), to evaluate the usefulness of several methods to enhance model accuracy. Specifically, we: 1) quantify accuracy with fully-labeled 1-min soundscapes for an assessment of real-world conditions; 2) assess the effect on precision and recall of additional pre-training with an external sound archive (xeno-canto) prior to fine-tuning with vocalization data from our study domain; and, 3) assess how detections and errors are influenced by the presence of coincident biotic and non-biotic sounds (i.e., soundscape components). In evaluating accuracy with soundscape data (n = 37 species) across CNN probability thresholds and models, we found acoustic pre-training followed by fine-tuning improved average precision by 10.3% relative to no pre-training, although there was a small average 0.8% reduction in recall. In selecting an optimal CNN architecture for each species based on maximum F(β = 0.5), we found our MoE approach had total precision of 84.5% and average species precision of 85.1%. Our data exhibit multiple issues arising from applying citizen science and acoustic monitoring at the county scale, including deployment of ARUs with relatively low fidelity and recordings with background noise and overlapping vocalizations. In particular, human noise was significantly associated with more incorrect species detections (false positives, decreased precision), while physical interference (e.g., recorder hit by a branch) and geophony (e.g., wind) was associated with the classifier missing detections (false negatives, decreased recall). Our process surmounted these obstacles, and our final predictions allowed us to demonstrate how deep learning applied to acoustic data from low-cost ARUs paired with citizen science can provide valuable bird diversity data for monitoring and conservation efforts.     

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.     

Measuring tree-ring widths using the CooRecorder software application

We present a brief overview of how to measure tree-ring widths in the software application CooRecorder (Cybis Elektronik & Data AB) for tree-ring analysis complementing two video tutorials. The first tutorial covers the basics of opening files, measuring ring widths, preliminary crossdating with a reference chronology, and setting dates. The second tutorial covers setting earlywood-latewood boundaries, measuring across cracks, inserting locally absent or missing rings, manual adjustments, and metadata. The video tutorials can be found here https://www.youtube.com/watch?v=c-GNKHVUj9I and here https://www.youtube.com/watch?v=xO7Phc93xyM&t=3s. Videos have been closed-captioned in English. Video is also accessible via Mendeley Data, https://doi.org/10.17632/r3v7236kkz.1.     

Dual function of the CHS3-CSA1 immune receptor pair

Plant nucleotide-binding leucine-rich repeat (NLR) receptors mediate specific recognition of pathogen effectors to initiate effector-triggered immunity. Recently, studies by Schulze et al., Yang et al., and Gu et al. collectively show that the Arabidopsis (Arabidopsis thaliana) NLR pair CHS3-CSA1 acts through two distinct activation modes to recognize different pathogen effectors, thus revealing the dual function of the CHS3-CSA1 pair in plant disease resistance.     

How to build and install your own CaptuRING

CaptuRING is a reliable and affordable tool to transform tree-ring samples into digital images combining open source software and do-it-yourself philosophies. A Raspberry Pi runs the system through an Arduino board that controls the wood sample movement across a linear screw at the time that a digital camera takes sequential high resolution (>4500 dpi) images from a wood sample. Here, we present three video tutorials, with English and Spanish subtitles, to construct and install CaptuRING (github.com/CambiumRG/CaptuRING) from scratch. First video tutorial explains the necessary components and how to assemble them to construct the CaptuRING platform, second tutorial covers Arduino board and Raspberry Pi connections, and the third tutorial is devoted to hardware configuration, software installation and CaptuRING use.     

A comprehensive review on the phytochemistry,pharmacokinetics, and antidiabetic effect of Ginseng

BackgroundRadix Ginseng, one of the well-known medicinal herbs, has been used in the management of diabetes and its complications for more than 1000 years.PurposeThe aim of this review is devoted to summarize the phytochemistry and pharmacokinetics of Ginseng, and provide evidence for the antidiabetic effects of Ginseng and its ingredients as well as the underlying mechanisms involved.MethodsFor the purpose of this review, the following databases were consulted: the PubMed Database (https://pubmed.ncbi.nlm.nih.gov), Chinese National Knowledge Infrastructure (http://www.cnki.net), National Science and Technology Library (http://www.nstl.gov.cn/), Wanfang Data (http://www.wanfangdata.com.cn/) and the Web of Science Database (http://apps.webofknowledge.com/).ResultsGinseng exhibits glucose-lowering effects in different diabetic animal models. In addition, Ginseng may prevent the development of diabetic complications, including liver, pancreas, adipose tissue, skeletal muscle, nephropathy, cardiomyopathy, retinopathy, atherosclerosis and others. The main ingredients of Ginseng include ginsenosides and polysaccharides. The underlying mechanisms whereby this herb exerts antidiabetic activities may be attributed to the regulation of multiple signaling pathways, including IRS1/PI3K/AKT, LKB1/AMPK/FoxO1, AGEs/RAGE, MAPK/ERK, NF-κB, PPARδ/STAT3, cAMP/PKA/CERB and HIF-1α/VEGF, etc. The pharmacokinetic profiles of ginsenosides provide valuable information on therapeutic efficacy of Ginseng in diabetes. Although Ginseng is well-tolerated, dietary consumption of this herb should follow the doctors’ advice.ConclusionGinseng may offer an alternative strategy in protection against diabetes and its complications through the regulations of the multi-targets via various signaling pathways. Efforts to understand the underlying mechanisms with strictly-controlled animal models, combined with well-designed clinical trials and pharmacokinetic evaluation, will be important subjects of the further investigations and weigh in translational value of this herb in diabetes management.     

A lightweight keypoint matching framework for insect wing morphometric landmark detection

Geometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. Shape could be digitized as a set of landmarks from specimen images. However, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. Motivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. While still archiving comparable accuracy to that of the state-of-the-art method, our framework requires less initial annotated data to build prediction model and runs faster. It is lightweight also in terms of implementation, in which a four-step workflow is provided with user-friendly graphical interfaces to produce correct landmark coordinates both by model prediction and manual correction. The utility iMorph is freely available at https://github.com/ha-usth/InsectWingLandmark, currently supporting Windows, MacOS, and Linux.     

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.     

Effector protein structures: a tale of evolutionary relationship

Effector proteins are highly diverse, often lacking similarity in their protein sequences, making it challenging to determine their biological function. Using AlphaFold2 (AF2), Seong and Krasileva recently found that effector structures, but not sequences, share commonality. This helps further understanding of effector evolution across fungal species and reveals unique sequence-unrelated, structurally similar, effector families.     

A novel module regulating ROS in NLR-mediated immunity

The regulatory mechanisms of apoplastic reactive oxygen species (ROS) production during pattern-triggered immunity (PTI) are well known. However, how ROS levels are regulated during effector-triggered immunity (ETI) remains largely unknown. Recently, Zhang et al. discovered that MAPK-Alfin-like 7 module enhances nucleotide-binding, leucine-rich repeat receptor (NLR)-mediated immunity by negatively regulating genes encoding ROS scavenging enzymes, deepening our understanding of ROS control during ETI in plants.     

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.     

The most abundant mammals on Earth

New estimates of global mammal abundance that use relationships between traits, estimates of range size, and International Union for Conservation of Nature’s (IUCN’s) Red List categories to predict the biomass of thousands of species have been developed by Greenspoon et al. This approach and some of the challenges that contribute to these estimates are summarized here.     

Newly evolved genes in the human lineage are functional

Genes restricted to a given species or lineage are mysterious. Many emerged de novo from ancestral noncoding genomic regions rather than from pre-existing genes. A new study by Vakirlis and colleagues shows that, in humans, many of these are associated with phenotypic effects, accelerating our understanding of their functional importance.     

The bioprocess TEA calculator: An online technoeconomic analysis tool to evaluate the commercial competitiveness of potential bioprocesses

Techno-economic analysis connects R&D, engineering, and business. By linking process parameters to financial metrics, it allows researchers to understand the factors controlling the potential success of their technologies. In particular, metabolic and bioprocess engineering, as disciplines, are aimed at engineering cells to synthesize products with an ultimate goal of commercial deployment. As a result it is critical to be able to understand the potential impact of strain engineering strategies and lab scale results on commercial potential. To date, while numerous techno-economic models have been developed for a wide variety of bioprocesses, they have either required process engineering expertise to adapt and/or use or do not directly connect financial outcomes to potential strain engineering results. Despite the clear value of techno-economic analysis, these challenges have made it inaccessible to many researchers. I have developed this online calculator (https://bioprocesstea.com OR http://bioprocess-tea-calculator.herokuapp.com/) to make the basic capabilities of early-stage techno-economic analysis of bioprocesses readily accessible. The tool, currently focused on aerobic fermentation processes, can be used to understand the impact of fermentation level metrics on the commercial potential of a bioprocess for the production of a wide variety of organic molecules. Using the calculator, I review the commercially relevant targets for an aerobic bioprocess for the production of diethyl malonate.