A 20,000 year old fossil uncovered in a tarantula-filled cave has paleontologists stunned. Breakthroughs, discoveries, and DIY tips sent every weekday. Bees are frequently associated with large queen-serving colonies featuring hundreds if not thousands of insects . They lay their eggs in small cavities, and they leave pollen for the larvae to eat," explained paleontologist Lazasro Viñola López . "Some bee species burrow holes in wood or in the ground, or use empty structures for nests." Viñola López, a researcher at Chicago's Field Museum, added that some European and African species even construct nests inside vacant snail shells. That said, a beehive inside a bone is a new one even for seasoned researchers. Estimated to be around 20,000 years old, this newly discovered specimen is also the first known example of such a home, past or present. The findings are detailed in a study published on December 16 in the journal . Researchers located the unique find while exploring the many limestone caves that dot the southern Dominican Republic. Sinkholes are common across the Caribbean island of Hispaniola, and are often so well sheltered from the elements that they function like underground time capsules. These windows into the past are largely thanks to the work of the island's owls . The predatory birds often make their nests inside these caves, where they regularly cough up owl pellets filled with the undigested bones of their prey. Over thousands of years, these layers of bones fossilize atop one another across carbonate layers created from rainy periods. "The initial descent into the cave isn't too deep-we would tie a rope to the side and then rappel down," Viñola López said. "If you go in at night, you see the eyes of the tarantulas that live inside." After proceeding past the large spiders through about 33 feet of underground tunnel, the paleontologists began finding various fossils. Many belonged to rodents, but there were also bones from birds, reptiles, and even sloths for a total of over 50 different animal species. "We think that this was a cave where owls lived for many generations, maybe for hundreds or thousands of years," said Viñola López. "The owls would go out and hunt, and then come back to the cave and throw up pellets.

Breakthroughs, discoveries, and DIY tips sent every weekday. Oyster larvae that grow in water with traces of common drugs such as cocaine, ketamine, and fentanyl are slower swimmers that appear more stressed. This new research indicates that the common drugs do have an effect on oyster larvae that are found in contaminated water. The results were presented this week at the Society for Risk Analysis' annual conference and published in the journal All sorts of pharmaceuticals, from pain relievers to illegal drugs, can make it into the water supply via human excretion, manufacturing plants, or if they are flushed down the toilet . While that water does go through wastewater treatment, pharmaceuticals can pass right through.

Insect species subject to infection, predation, and anisotropic environmental conditions may exhibit preferential movement patterns. Given the innate stochasticity of exogenous factors driving these patterns over short timescales, individual insect trajectories typically obey overdamped stochastic dynamics. In practice, data-driven modeling approaches designed to learn the underlying Fokker-Planck equations from observed insect distributions serve as ideal tools for understanding and predicting such behavior. Understanding dispersal dynamics of crop and silvicultural pests can lead to a better forecasting of outbreak intensity and location, which can result in better pest management. In this work, we extend weak-form equation learning techniques, coupled with kernel density estimation, to learn effective models for lepidopteran larval population movement from highly sparse experimental data. Galerkin methods such as the Weak form Sparse Identification of Nonlinear Dynamics (WSINDy) algorithm have recently proven useful for learning governing equations in several scientific contexts. We demonstrate the utility of the method on a sparse dataset of position measurements of fall armyworms (Spodoptera frugiperda) obtained in simulated agricultural conditions with varied plant resources and infection status.

Researchers are working on manipulating the digestive systems of wax worms to create a scalable way of disposing of plastic. Plastics that support modern life are inexpensive, strong, and versatile, but are difficult to dispose of and have a serious impact when released into the environment. Polyethylene, in particular, is the most widely produced plastic in the world, with more than 100 million tons distributed annually. Since it can take decades to decompose--and along the way can harm wildlife and degrade into harmful microplastics --its disposal is an urgent issue for mankind. In 2017, European researchers discovered a potential solution.

Breakthroughs, discoveries, and DIY tips sent every weekday. Mosquito bites are much more than just a red and itchy summertime nuisance. The diseases that they carry are notoriously difficult to control and kill over 700,000 people worldwide every year. What's more, many mosquitoes have developed resistance to the synthetic insecticides–the same substances that can also pose environmental and health risks. As a solution, microbiologists are looking into biopesticides derived from living organisms.

Tech expert Kurt Knutsson discusses how robots can milk, feed and clean cows on dairy farms, boosting efficiency and comfort. A threat to American livestock – the New World Screwworm (NWS) fly, which has been considered eradicated from the country since 1966 -- has reemerged as a potential danger following an outbreak in Mexico. The news triggered a shutdown of cattle, horse and bison imports along the southern border, as U.S. Department of Agriculture (USDA) Secretary Brooke Rollins announced in an X post on Sunday. "Due to the threat of New World Screwworm I am announcing the suspension of live cattle, horse, & bison imports through U.S. southern border ports of entry effective immediately," she wrote in the post. "The last time this devastating pest invaded America, it took 30 years for our cattle industry to recover.

Coral reef conservation is a steppingstone to protect marine biodiversity and life in the ocean as we know it. The health of coral also has huge societal implications: reef ecosystems provide sustenance and livelihoods for millions of people around the world. Conserving biodiversity in reef areas is both a social issue and a marine biodiversity priority. In the face of climate change, Annalisa Bracco, professor in the School of Earth and Atmospheric Sciences at Georgia Institute of Technology, and Lyuba Novi, a postdoctoral researcher, offer a new methodology that could revolutionize how conservationists monitor coral. The researchers applied machine learning tools to study how climate impacts connectivity and biodiversity in the Pacific Ocean's Coral Triangle--the most diverse and biologically complex marine ecosystem on the planet.

Zebrafish are a common model organism used to identify new disease therapeutics. High-throughput drug screens can be performed on larval zebrafish in multi-well plates by observing changes in behaviour following a treatment. Analysis of this behaviour can be difficult, however, due to the high dimensionality of the data obtained. Statistical analysis of individual statistics (such as the distance travelled) is generally not powerful enough to detect meaningful differences between treatment groups. Here, we propose a method for classifying zebrafish models of Parkinson's disease by genotype at 5 days old. Using a set of 2D behavioural features, we train a multi-layer perceptron neural network. We further show that the use of integrated gradients can give insight into the impact of each behaviour feature on genotype classifications by the model. In this way, we provide a novel pipeline for classifying zebrafish larvae, beginning with feature preparation and ending with an impact analysis of said features.

Mosquito-borne diseases (MBDs), such as dengue virus, chikungunya virus, and West Nile virus, cause over one million deaths globally every year. Because many such diseases are spread by the Aedes and Culex mosquitoes, tracking these larvae becomes critical in mitigating the spread of MBDs. Even as citizen science grows and obtains larger mosquito image datasets, the manual annotation of mosquito images becomes ever more time-consuming and inefficient. Previous research has used computer vision to identify mosquito species, and the Convolutional Neural Network (CNN) has become the de-facto for image classification. However, these models typically require substantial computational resources. This research introduces the application of the Vision Transformer (ViT) in a comparative study to improve image classification on Aedes and Culex larvae. Two ViT models, ViT-Base and CvT-13, and two CNN models, ResNet-18 and ConvNeXT, were trained on mosquito larvae image data and compared to determine the most effective model to distinguish mosquito larvae as Aedes or Culex. Testing revealed that ConvNeXT obtained the greatest values across all classification metrics, demonstrating its viability for mosquito larvae classification. Based on these results, future research includes creating a model specifically designed for mosquito larvae classification by combining elements of CNN and transformer architecture.

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.