Lifeboat Foundation

The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.

Training deep quantum neural networks.

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The discovery of stars with their outer layers of hydrogen stripped by companions fills a glaring hole in our understanding of supernovas and binary systems with colliding neutron stars.

The advantages of biological learning make OI a tempting notion to embrace, however, there are significant ethical and technological hurdles to overcome before we develop an OI biocomputer.

Optogenetics has revolutionized neuroscience understanding by allowing spatiotemporal control over cell-type specific neurons in neural circuits. However, the sluggish development of noninvasive photon delivery in the brain has limited the clinical application of optogenetics. Focused ultrasound (FUS)-derived mechanoluminescence has emerged as a promising tool for in situ photon emission, but there is not yet a biocompatible liquid-phase mechanoluminescence system for spatiotemporal optogenetics. To achieve noninvasive optogenetics with a high temporal resolution and desirable biocompatibility, we have developed liposome (Lipo@IR780/L012) nanoparticles for FUS-triggered mechanoluminescence in brain photon delivery. Synchronized and stable blue light emission was generated in solution under FUS irradiation due to the cascade reactions in liposomes.

Scientists still debate the origins of Earth’s life-sustaining elements.

Since the technique was first published in 2019, prime editing has grown with lightning speed, alongside hopes for what it can achieve.

The fusion of biological principles with technological innovation has resulted in significant advancements in artificial intelligence (AI) through the development of Brainoware. Developed by researchers at Indiana University, Bloomington, this innovative system leverages clusters of lab-raised brain cells to achieve elementary speech recognition and solve mathematical problems.

The crux of this technological leap lies in the cultivation of specialized stem cells that mature into neurons—the fundamental units of the brain. While a typical human brain comprises a staggering 86 billion neurons interconnected extensively, the team managed to engineer a minute organoid, merely a nanometer wide. This tiny but powerful structure was connected to a circuit board through an array of electrodes, allowing machine-learning algorithms to decode responses from the brain tissue.

Termed Brainoware, this amalgamation of biological neurons and computational circuits exhibited remarkable capabilities after a brief training period. It was discerned between eight subjects based on their diverse pronunciation of vowels with an accuracy rate of 78%. Impressively, Brainoware outperformed artificial networks in predicting the Henon map, a complex mathematical construct within chaotic dynamics.

To advance our overall understanding and discover principles of mechanical intelligence in limbless locomotion and to understand the potential role of bilateral actuation specifically in mechanical control, we took a comparative biological and robophysical approach using two complementary models: a biological model, the nematode C. elegans, and a robophysical model, a limbless robot incorporating a bilateral actuation scheme that permits programmable, dynamic, and quantifiable body compliance (Fig. 1B). This compliance governs the passive body-environment interactions in the horizontal plane that allow mechanical intelligence. Because separating neural and mechanical aspects of control is challenging in a freely locomoting living system, we used the robot as a model (22, 24, 49, 50) that then allowed mechanical intelligence to be isolated from active controls and to be systematically tuned and tested.

Using comparisons between the kinematics and locomotor performance of our biological and robophysical models, we show that mechanical intelligence alone is sufficient for an open-loop limbless robot to reproduce locomotory behavior of nematodes. Mechanical intelligence simplifies controls in terrestrial limbless locomotion by taking advantage of passive body-environment interactions that enable heterogeneity negotiation, thereby stabilizing locomotion. Further, we show that a simple active behavior inspired by nematodes takes advantage of mechanical intelligence to enhance locomotion performance even further. Our method and results not only provide insight into the functional mechanism of mechanical intelligence in organismal limbless locomotion but also provide an alternative paradigm for limbless robot development that simplifies control in complex environments.