An original photograph taken by Felice Frankel (left) and an AI-generated image of the same content. For over 30 years, science photographer Felice Frankel has helped MIT professors, researchers, and students communicate their work visually. Throughout that time, she has seen the development of various tools to support the creation of compelling images: some helpful, and some antithetical to the effort of producing a trustworthy and complete representation of the research. In a recent opinion piece published in Nature magazine, Frankel discusses the burgeoning use of generative artificial intelligence (GenAI) in images and the challenges and implications it has for communicating research. On a more personal note, she questions whether there will still be a place for a science photographer in the research community.

Automation in the life science research laboratory is a paradigm that has gained increasing relevance in recent years. Current robotic solutions often have a limited scope, which reduces their acceptance and prevents the realization of complex workflows. The transport and manipulation of laboratory supplies with a robot is a particular case where this limitation manifests. In this paper, we deduce a taxonomy of biolaboratory liquid containers that clarifies the need for a flexible grasping solution. Using the taxonomy as a guideline, we design fingers for a parallel robotic gripper which are developed with a monolithic dual-extrusion 3D print that integrates rigid and soft materials to optimize gripping properties. We design fine-tuned fingertips that provide stable grasps of the containers in question. A simple actuation system and a low weight are maintained by adopting a passive compliant mechanism. The ability to resist chemicals and high temperatures and the integration with a tool exchange system render the fingers usable for daily laboratory use and complex workflows. We present the task suitability of the fingers in experiments that show the wide range of vessels that can be handled as well as their tolerance against displacements and their grasp stability.

Petri dishes of engineered and native Proteus mirabilis patterns, here stained with colored dyes used for the lab's bacterial art. Researchers in Biomedical Engineering Professor Tal Danino's lab were brainstorming several years ago about how they could engineer and apply naturally-pattern-forming bacteria. There are many bacteria species, such as Proteus mirabilis (P. These bacteria can sense several stimuli in nature and respond to these cues by "swarming"--a highly coordinated and rapid movement of bacteria powered by their flagella, a long, tail-like structure that causes a whip-like motion to help propel them. For inspiration, Danino's team at Columbia Engineering, which has a good deal of experience using synthetic biology methods to manipulate bacteria, discussed where else they might find similar patterns in nature and what their functions might be.

Next time you're getting your ass kicked at Fortnite or online chess, it could be at the metaphorical hands of a petri dish of brain cells. Scientists in Australia taught'sentient' cells to play Pong in just minutes, according to a paper published last week in journal Neuron. "What machines can't do is learn things very quickly," study leader Brett Kagan told AFP. "If you need a machine learning algorithm to learn something, it requires thousands of data samples. But if you ask a human, or train a dog, a dog can learn a trick in two or three tries." A mix of embryonic brain cells from mice and human neurons from adult stem cells were grown on top of electrodes that could deliver electric pulses. Rather than reward successful play with dopamine, which was too slow, the cells instead got regular and predictable electrical signals.

The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U$^2$-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the uncertainties for the DM predictions leads to a deeper understanding of the DM model's deficiencies. Based on our investigation, we propose a self-normalization module in the network. The improved network model, called \textit{Self-Normalized Density Map} (SNDM), can correct its output density map by itself to accurately predict the total number of objects in the image. The SNDM architecture outperforms the original model. Moreover, both statistical frameworks -- bootstrap and MC dropout -- have consistent statistical results for SNDM, which were not observed in the original model. The SNDM efficiency is comparable with the detector-base models, such as Faster and Cascade R-CNN detectors.

A few months ago, during my last year of engineering studies, I was handed the opportunity to start a small AI driven project. It sounded hugely interesting, so I grabbed the opportunity with both hands. At this point in time, the biology department of Vrije Universiteit Brussel was doing tests with different kind of bacteria in petri dishes (please do not ask me which or why, although they tried to explain it multiple times, I seemed to have not exactly the strongest understanding capabilities in biology). The main idea of the test was to contaminate some petri dish with a bacteria, and observe how fast new cell colonies would form, thus counting the amount of cell colonies in a petri dish. This was done by hand, counting each colony and putting a blue dot above it so they would remember which was already counted.

As a lover of tough single player games, I’m quite accustomed to getting my butt handed to me by AI, and usually not even a real one. I also happen to be the owner of a full sized human brain, and though it’s not without its problems, its ability to learn and change is usually why I eventually overcome those difficult in game challenges.So when I read about a few human brain cells in a petri dish that are already performing much better at a videogame than AI can, it’s concerning to me and my gaming future. New Scientist reports that a team in Australia has been growing these small puddles of brain and now one has learnt to play Pong, in fairly impressive time.Cortical labs is a company working on integrating biological neurons with your more traditional silicon based computing hardware. They grow brain cells on microelectronic arrays, so the cells can be stimulated. These hybrid chips are said to be able to learn and restructure themselves to get past problems, like stopping a sneaky ball that wants in your goal.According to Cortical labs, AIs typically take 90 minutes to learn Pong, whereas this ‘DishBrain’ (yes, that’s what it’s called) managed to have it down in five. Though the researchers do note that a good AI would still absolutely demolish the cells, once both properly trained. 

We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion. The developed generator employs traditional computer vision algorithms together with a neural style transfer method for data augmentation. We show that the method is able to synthesize a dataset of realistic looking images that can be used to train a neural network model capable of localising, segmenting, and classifying five different microbial species. Our method requires significantly fewer resources to obtain a useful dataset than collecting and labeling a whole large set of real images with annotations. We show that starting with only 100 real images, we can generate data to train a detector that achieves comparable results (detection mAP 0.416, and counting MAE 4.49) to the same detector but trained on a real, several dozen times bigger dataset (mAP 0.520, MAE 4.31), containing over 7k images. We prove the usefulness of the method in microbe detection and segmentation, but we expect that it is general and flexible and can also be applicable in other domains of science and industry to detect various objects.

DALLAS – August 3, 2021 – Using artificial intelligence (AI), researchers from UT Southwestern have developed a way to accurately predict which skin cancers are highly metastatic. The findings, published as the July cover article of Cell Systems, show the potential for AI-based tools to revolutionize pathology for cancer and a variety of other diseases. "We now have a general framework that allows us to take tissue samples and predict mechanisms inside cells that drive disease, mechanisms that are currently inaccessible in any other way," said study leader Gaudenz Danuser, Ph.D., Professor and Chair of the Lyda Hill Department of Bioinformatics at UTSW. AI technology has significantly advanced over the past several years, Dr. Danuser explained, with deep learning-based methods able to distinguish minute differences in images that are essentially invisible to the human eye. Researchers have proposed using this latent information to look for differences in disease characteristics that could offer insight on prognoses or guide treatments.

Using artificial intelligence (AI), researchers from UT Southwestern have developed a way to accurately predict which skin cancers are highly metastatic. The findings, published as the July cover article of Cell Systems, show the potential for AI-based tools to revolutionize pathology for cancer and a variety of other diseases. "We now have a general framework that allows us to take tissue samples and predict mechanisms inside cells that drive disease, mechanisms that are currently inaccessible in any other way," said study leader Gaudenz Danuser, Ph.D., Professor and Chair of the Lyda Hill Department of Bioinformatics at UTSW. AI technology has significantly advanced over the past several years, Dr. Danuser explained, with deep learning-based methods able to distinguish minute differences in images that are essentially invisible to the human eye. Researchers have proposed using this latent information to look for differences in disease characteristics that could offer insight on prognoses or guide treatments.