Lifeboat Foundation

Finding ways to reduce the invasiveness of brain implants could greatly expand their potential applications. A new device tested in mice that sits on the brain’s surface—but can still read activity deep within—could lead to safer and more effective ways to read neural activity.

There are already a variety of technologies that allow us to peer into the inner workings of the brain, but they all come with limitations. Minimally invasive approaches include functional MRI, where an MRI scanner is used to image changes of blood flow in the brain, and EEG, where electrodes placed on the scalp are used to pick up the brain’s electrical signals.

The former requires the patient to sit in an MRI machine though, and the latter is too imprecise for most applications. The gold standard approach involves inserting electrodes deep into brain tissue to obtain the highest quality readouts. But this requires a risky surgical procedure, and scarring and the inevitable shifting of the electrodes can lead to the signal degrading over time.

Some technologies are so cool they make you do a double take. Case in point: robots being controlled by rat brains. Kevin Warwick, once a cyborg and still a researcher in cybernetics at the University of Reading, has been working on creating neural networks that can control machines. He and his team have taken the brain cells from rats, cultured them, and used them as the guidance control circuit for simple wheeled robots. Electrical impulses from the bot enter the batch of neurons, and responses from the cells are turned into commands for the device. The cells can form new connections, making the system a true learning machine. Warwick hasn’t released any new videos of the rat brain robot for the past few years, but the three older clips we have for you below are still awesome. He and his competitors continue to move this technology forward – animal cyborgs are real.

The skills of these rat-robot hybrids are very basic at this point. Mainly the neuron control helps the robot to avoid walls. Yet that obstacle avoidance often shows clear improvement over time, demonstrating how networks of neurons can grant simple learning to the machines. Whenever I watch the robots in the videos below I have to do a quick reality check – these machines are being controlled by biological cells! It’s simply amazing.

Thirdly, more recent approaches have begun to leverage deep learning (DL) methods. DL models such as U-Net¹² have provided solutions for many image analysis challenges. However, they require ground truth to be generated for training. DL-based methods for SST cell segmentation include GeneSegNet¹³ and SCS¹⁴, though supervision is still required in the form of initial cell labels or based on hard-coded rules. Further limitations of existing methods encountered during our benchmarking, such as lengthy code runtimes, are included in Supplementary Table 1. The self-supervised learning (SSL) paradigm can provide a solution to overcome the requirement of annotations. While SSL-based methods have shown promise for other imaging modalities^15,16, direct application to SST images remains challenging. SST data are considerably different from other cellular imaging modalities and natural images (e.g., regular RGB images), as they typically contain hundreds of channels, and there is a lack of clear visual cues that indicate cell boundaries. This creates new challenges such as (i) accurately delineating cohesive masks for cells in densely-packed regions, (ii) handling high sparsity within gene channels, and (iii) addressing the lack of contrast for cell instances.

While these morphological and DL-based approaches have shown promise, they have not fully exploited the high-dimensional expression information contained within SST data. It has become increasingly clear that relying solely on imaging information may not be sufficient to accurately segment cells. There is growing interest in leveraging large, well-annotated scRNA-seq datasets¹⁷, as exemplified by JSTA¹⁸, which proposed a joint cell segmentation and cell type annotation strategy. While much of the literature has emphasised the importance of accounting for biological information such as transcriptional composition, cell type, and cell morphology, the impact of incorporating such information into segmentation approaches remains to be fully understood.

Here, we present a biologically-informed deep learning-based cell segmentation (BIDCell) framework (Fig. 1 a), that addresses the challenges of cell body segmentation in SST images through key innovations in the framework and learning strategies. We introduce (a) biologically-informed loss functions with multiple synergistic components; and (b) explicitly incorporate prior knowledge from single-cell sequencing data to enable the estimation of different cell shapes. The combination of our losses and use of existing scRNA-seq data in supplement to subcellular imaging data improves performance, and BIDCell is generalisable across different SST platforms. Along with the development of our segmentation method, we created a comprehensive evaluation framework for cell segmentation, CellSPA, that assesses five complementary categories of criteria for identifying the optimal segmentation strategies. This framework aims to promote the adoption of new segmentation methods for novel biotechnological data.

Current technologies of bioinspired and neuromorphic electronics still lack a universal framework for integration into everyday life. This Perspective highlights how bioinspired electronics with soft electrochemical matter based on organic mixed conductors can potentially enable the integration of diverse forms of intelligence everywhere.

Human-tier AI will change the world and jobs “much less than we think,” OpenAI CEO Sam Altman said while attending the World Economic Forum.

Scientists discovered a fossilized grasshopper egg pod filled with eggs that could be unlike anything paleontologists had ever seen before, according to a new study.

Viewers will find out Samsung’s game plan for Galaxy AI and see the new Galaxy S24 lineup.

A new CRISPR-Cas toolkit, dubbed “pAblo·pCasso,” is set to transform the landscape of bacterial genome editing, offering unprecedented precision and flexibility in genetic engineering. The new technology, developed by researchers at The Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain), expands the range of genome sites available for base-editing and dramatically accelerates the development of bacteria for a wide range of bioproduction applications.

PAblo·pCasso sets a new standard in CRISPR-Cas technologies. A key innovation is to enable precise and reversible DNA edits within Gram-negative bacteria, a feat not achievable with previous CRISPR systems. The toolkit utilizes specialized fusion enzymes, modified Cas9 coupled with editor modules CBE or ABE, which act like molecular pencils to alter specific DNA nucleotides, thus accurately controlling gene function.

The development of pAblo·pCasso involved overcoming significant challenges. Traditional CRISPR-Cas systems were limited by their need for specific DNA sequences (PAM sequences) near the target site and were less effective in making precise, single-nucleotide changes. pAblo·pCasso transcends these limitations by incorporating advanced Cas-fusion variants that do not require specific PAM sequences, thereby expanding the range of possible genomic editing sites.

A few months ago, developers with access to an Apple Vision Pro Developer Kit were given access to the App Store to download compatible iPhone and iPad apps. As Vision Pro arrives in stores in February, Apple has made it possible for developers to submit their apps to the App Store. Starting today, these visionOS apps are now rolling out to users.

Developers who submitted their visionOS apps for App Store Review earlier this month are now receiving emails from Apple telling them that the apps have been approved and are now available for download on the visionOS App Store.

As noted by developer Dylan McDonald, the iOS App Store is now showing which apps are compatible with Apple Vision Pro, although screenshots have yet to be made available.