Corticosterone is to rodents as cortisol is to humans. Breakthroughs, discoveries, and DIY tips sent every weekday. It appears that rats will often indulge in a bit of cannabis if given the chance, according to new research. But just like many humans, some rodents are more prone to the recreational drug than others. As neuroscientists explain in a study recently published in the journal, stressed rats are far more likely to take a hit than their calmer relatives.

Environment Animals Wildlife'Attack squirrel' sends two people to the ER A friendly reminder to not feed wildlife. Breakthroughs, discoveries, and DIY tips sent every weekday. The residents of San Rafael, California, have been traumatized by some vicious wildlife . While cougars, coyotes, or great white sharks would be viable guesses for the culprit, this time it was a less formidable predator. The aggressor is a squirrel .

Breakthroughs, discoveries, and DIY tips sent every weekday. Few predators swallow their prey whole. Even fewer can digest their meals with bones and all. Herpetologists have spent years trying to understand how bones are not only safe and healthy for the serpents, but how their biology manages to regulate when and how many bones to digest. Now, researchers believe they have identified an explanation hidden inside the "crypts" of specialized cells.

Breakthroughs, discoveries, and DIY tips sent every weekday. Tiny rodents living in a major American city are unique examples of evolution playing out in real time. Like geologic time itself, the process of evolution itself is generally a very slow process with teeny tiny changes passed down over several generations. All of these small changes eventually result in new adaptations and potentially new species over thousands or millions of years. However, in the face of dramatic shifts in the world around them from climate change to human encroachment, species sometimes must rapidly adapt or die.

Dynamic FDG PET imaging study of n = 52 rats including 26 control Wistar-Kyoto (WKY) rats and 26 experimental spontaneously hypertensive rats (SHR) were performed using a Siemens microPET and Albira trimodal scanner longitudinally at 1, 2, 3, 5, 9, 12 and 18 months of age. A 15-parameter dual output model correcting for spill over contamination and partial volume effects with peak fitting cost functions was developed for simultaneous estimation of model corrected blood input function (MCIF) and kinetic rate constants for dynamic FDG PET images of rat heart in vivo. Major drawbacks of this model are its dependence on manual annotations for the Image Derived Input Function (IDIF) and manual determination of crucial model parameters to compute MCIF. To overcome these limitations, we performed semi-automated segmentation and then formulated a Long-Short-Term Memory (LSTM) cell network to train and predict MCIF in test data using a concatenation of IDIFs and myocardial inputs and compared them with reference-modeled MCIF. Thresholding along 2D plane slices with two thresholds, with T1 representing high-intensity myocardium, and T2 representing lower-intensity rings, was used to segment the area of the LV blood pool. The resultant IDIF and myocardial TACs were used to compute the corresponding reference (model) MCIF for all data sets. The segmented IDIF and the myocardium formed the input for the LSTM network. A k-fold cross validation structure with a 33:8:11 split and 5 folds was utilized to create the model and evaluate the performance of the LSTM network for all datasets. To overcome the sparseness of data as time steps increase, midpoint interpolation was utilized to increase the density of datapoints beyond time = 10 minutes. The model utilizing midpoint interpolation was able to achieve a 56.4% improvement over previous Mean Squared Error (MSE).

Fully optimized automation of behavioral training protocols for lab animals like rodents has long been a coveted goal for researchers. It is an otherwise labor-intensive and time-consuming process that demands close interaction between the animal and the researcher. In this work, we used a data-driven approach to optimize the way rodents are trained in labs. In pursuit of our goal, we looked at data augmentation, a technique that scales well in data-poor environments. Using data augmentation, we built several artificial rodent models, which in turn would be used to build an efficient and automatic trainer. Then we developed a novel similarity metric based on the action probability distribution to measure the behavioral resemblance of our models to that of real rodents.

Navigation is a fundamental cognitive skill extensively studied in neuroscientific experiments and has lately gained substantial interest in artificial intelligence research. Recreating the task solved by rodents in the well-established Morris Water Maze (MWM) experiment, this work applies a transformer-based architecture using deep reinforcement learning -- an approach previously unexplored in this context -- to navigate a 2D version of the maze. Specifically, the agent leverages a decoder-only transformer architecture serving as a deep Q-network performing effective decision making in the partially observable environment. We demonstrate that the proposed architecture enables the agent to efficiently learn spatial navigation strategies, overcoming challenges associated with a limited field of vision, corresponding to the visual information available to a rodent in the MWM. Demonstrating the potential of transformer-based models for enhancing navigation performance in partially observable environments, this work suggests promising avenues for future research in artificial agents whose behavior resembles that of biological agents. Finally, the flexibility of the transformer architecture in supporting varying input sequence lengths opens opportunities for gaining increased understanding of the artificial agent's inner representation of the environment.

For the first time in four centuries, it's good to be a beaver. Long persecuted for their pelts and reviled as pests, the dam-building rodents are today hailed by scientists as ecological saviors. Their ponds and wetlands store water in the face of drought, filter out pollutants, furnish habitat for endangered species, and fight wildfires. In California, Castor canadensis is so prized that the state recently committed millions to its restoration. While beavers' benefits are indisputable, however, our knowledge remains riddled with gaps.

The world's largest pest control group is piloting the use of facial recognition software as a way to exterminate rats in people's homes. Rentokil said it had been developing the technology alongside Vodafone for 18 months. The surveillance technology, which is already being tested in real homes, tracks the rodents' habits and streams real-time analysis using artificial intelligence. A central command centre can then help to decide where and how to kill the rats caught on camera. Rentokil's chief executive, Andy Ransom, told the Financial Times: "With facial recognition technology you can see that rat number one behaved differently from rat number three.

In 2017, a group of scientists were struck by a startling realization – sperm whale vocalizations, that sound like clicks, resemble Morse Code to a great extent. It sowed the seeds for an ambitious project -- the Cetacean Translation Initiative, or Project CETI -- that would use artificial intelligence to translate these whale sounds such that humans would be able to understand them. The introduction of tech into studying animal behavior not only helps us understand them better -- but also, paradoxically, helps reveal our own limits as a species. This could go one of two ways: enable greater conservation efforts, or instil a hubris that could use the newfound knowledge of animal communication against them. It is not just whale communication that has been the subject of translation initiatives.