Breakthroughs, discoveries, and DIY tips sent every weekday. Biologists estimate that four out of five animals on Earth are nematodes (AKA roundworms).The tiny, wriggling, transparent invertebrates are the most abundant creatures on the planet and are found nearly everywhere–from permafrost to the deep ocean. More than one million species make up this ubiquitous group, which includes parasites, decomposers, predators, and more. "They're not about to take over the world, because they already did," says Serena Ding, a biologist at the Max Planck Institute of Animal Behavior in Konstanz, Germany tells Popular Science. "Global worming has already happened."

Caenorhabditis elegans (C. elegans) is an excellent model organism because of its short lifespan and high degree of homology with human genes, and it has been widely used in a variety of human health and disease models. However, the segmentation of C. elegans remains challenging due to the following reasons: 1) the activity trajectory of C. elegans is uncontrollable, and multiple nematodes often overlap, resulting in blurred boundaries of C. elegans. This makes it impossible to clearly study the life trajectory of a certain nematode; and 2) in the microscope images of overlapping C. elegans, the translucent tissues at the edges obscure each other, leading to inaccurate boundary segmentation. To solve these problems, a Bilayer Segmentation-Recombination Network (BR-Net) for the segmentation of C. elegans instances is proposed. The network consists of three parts: A Coarse Mask Segmentation Module (CMSM), a Bilayer Segmentation Module (BSM), and a Semantic Consistency Recombination Module (SCRM). The CMSM is used to extract the coarse mask, and we introduce a Unified Attention Module (UAM) in CMSM to make CMSM better aware of nematode instances. The Bilayer Segmentation Module (BSM) segments the aggregated C. elegans into overlapping and non-overlapping regions. This is followed by integration by the SCRM, where semantic consistency regularization is introduced to segment nematode instances more accurately. Finally, the effectiveness of the method is verified on the C. elegans dataset. The experimental results show that BR-Net exhibits good competitiveness and outperforms other recently proposed instance segmentation methods in processing C. elegans occlusion images.

We aim to learn the functional co-response group: a group of taxa whose co-response effect (the representative characteristic of the group showing the total topological abundance of taxa) co-responds (associates well statistically) to a functional variable. Different from the state-of-the-art method, we model the soil microbial community as an ecological co-occurrence network with the taxa as nodes (weighted by their abundance) and their relationships (a combination from both spatial and functional ecological aspects) as edges (weighted by the strength of the relationships). Then, we design a method called gFlora which notably uses graph convolution over this co-occurrence network to get the co-response effect of the group, such that the network topology is also considered in the discovery process. We evaluate gFlora on two real-world soil microbiome datasets (bacteria and nematodes) and compare it with the state-of-the-art method. gFlora outperforms this on all evaluation metrics, and discovers new functional evidence for taxa which were so far under-studied. We show that the graph convolution step is crucial to taxa with relatively low abundance (thus removing the bias towards taxa with higher abundance), and the discovered bacteria of different genera are distributed in the co-occurrence network but still tightly connected among themselves, demonstrating that topologically they fill different but collaborative functional roles in the ecological community.

Every year, plant parasitic nematodes, one of the major groups of plant pathogens, cause a significant loss of crops worldwide. To mitigate crop yield losses caused by nematodes, an efficient nematode monitoring method is essential for plant and crop disease management. In other respects, efficient nematode detection contributes to medical research and drug discovery, as nematodes are model organisms. With the rapid development of computer technology, computer vision techniques provide a feasible solution for quantifying nematodes or nematode infections. In this paper, we survey and categorise the studies and available datasets on nematode detection through deep-learning models. To stimulate progress in related research, this survey presents the potential state-of-the-art object detection models, training techniques, optimisation techniques, and evaluation metrics for deep learning beginners. Moreover, seven state-of-the-art object detection models are validated on three public datasets and the AgriNema dataset for plant parasitic nematodes to construct a baseline for nematode detection.

It might sound like something out of a Hollywood sci-fi movie -- bringing a 46,000-year-old frozen worm'back to life' after digging it up in Siberia. But that is exactly what scientists revealed they had done in a landmark study published yesterday. The experts managed to'resurrect' a long-extinct roundworm from a hibernation-like state known as cryptobiosis, which allowed it to survive the harsh frozen temperatures. Scientists previously thought that roundworms could only remain in this state for less than 40 years, so the development was an eye-opening moment for the scientific world. So, how exactly did they do it?

Limbless locomotors, from microscopic worms to macroscopic snakes, traverse complex, heterogeneous natural environments typically using undulatory body wave propagation. Theoretical and robophysical models typically emphasize body kinematics and active neural/electronic control. However, we contend that because such approaches often neglect the role of passive, mechanically controlled processes (i.e., those involving mechanical intelligence), they fail to reproduce the performance of even the simplest organisms. To discover principles of how mechanical intelligence aids limbless locomotion in heterogeneous terradynamic regimes, here we conduct a comparative study of locomotion in a model of heterogeneous terrain (lattices of rigid posts). We use a model biological system, the highly studied nematode worm C. elegans, and a novel robophysical device whose bilateral actuator morphology models that of limbless organisms across scales. The robot's kinematics quantitatively reproduce the performance of the nematodes with purely open-loop control; mechanical intelligence simplifies control of obstacle navigation and exploitation by reducing the need for active sensing and feedback. An active behavior observed in C. elegans, undulatory wave reversal upon head collisions, robustifies locomotion via exploitation of the systems' mechanical intelligence. Our study provides insights into how neurally simple limbless organisms like nematodes can leverage mechanical intelligence via appropriately tuned bilateral actuation to locomote in complex environments. These principles likely apply to neurally more sophisticated organisms and also provide a new design and control paradigm for limbless robots for applications like search and rescue and planetary exploration.

Traditional approaches to classify the political bias of news articles have failed to generate accurate, generalizable results. Existing networks premised on CNNs and DNNs lack a model to identify and extrapolate subtle indicators of bias like word choice, context, and presentation. In this paper, we propose a network architecture that achieves human-level accuracy in assigning bias classifications to articles. The underlying model is based on a novel Mesh Neural Network (MNN),this structure enables feedback and feedforward synaptic connections between any two neurons in the mesh. The MNN ontains six network configurations that utilize Bernoulli based random sampling, pre-trained DNNs, and a network modelled after the C. Elegans nematode. The model is trained on over ten-thousand articles scraped from AllSides.com which are labelled to indicate political bias. The parameters of the network are then evolved using a genetic algorithm suited to the feedback neural structure. Finally, the best performing model is applied to five popular news sources in the United States over a fifty-day trial to quantify political biases in the articles they display. We hope our project can spur research into biological solutions for NLP tasks and provide accurate tools for citizens to understand subtle biases in the articles they consume.

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of world production. Besides, identifying these species through microscopic analysis by an expert with taxonomy knowledge is often laborious, time-consuming, and susceptible to failure. In this perspective, robust and automatic approaches are necessary for identifying phytonematodes capable of providing correct diagnoses for the classification of species and subsidizing the taking of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and a comparative assessment with thirteen popular models of CNNs, all of them representing the state of the art classification and recognition. The general average calculated for each model, on a from-scratch training, the NemaNet model reached 96.99% accuracy, while the best evaluation fold reached 98.03%. In training with transfer learning, the average accuracy reached 98.88\%. The best evaluation fold reached 99.34% and achieve an overall accuracy improvement over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, when compared to other popular models.

Researchers from the Vienna University of Technology (VUT) have put a brain on a circuit board -- specifically, the brain of the nematode C. elegans. They are now training it to perform tasks without a single line of human-written code. Image credits PROZEISS Microscopy / Flickr. But in one respect, this little nematode is unique and uniquely valuable for science -- it's the only living being whose neural system has been fully analyzed and mapped. In other words, its brain can be recreated as a circuit -- either onto a circuit board or one simulated with software -- without losing any of its function.

It's a process as old as time, but there's a twist: This worm is a bit of open-source software that encodes biological data gleaned from decades of scientific study into the nematode C. elegans. The parameters are programmed, but the worm acted on its own. Well, the widely studied nematode was the first multicellular organism to have its entire genome mapped. With just 1,031 cells and 302 neurons, the 1 millimeter-long transparent worm is a manageable animal to recreate as a software-based artificial life form. The simple life form nevertheless moves, mates, eats and even socializes, and replicating it using computer code may yield some biological insights into the biological bases for those behaviors.