This paper presents the classification of a general quadric into an axisymmetric quadric (AQ) and the solution to the problem of the proximity of a given point to an AQ. The problem of proximity in $R^3$ is reduced to the same in $R^2$, which is not found in the literature. A new method to solve the problem in $R^2$ is used based on the geometrical properties of the conics, such as sub-normal, length of the semi-major axis, eccentricity, slope and radius. Furthermore, the problem in $R^2$ is categorised into two and three more sub-cases for parabola and ellipse/hyperbola, respectively, depending on the location of the point, which is a novel approach as per the authors' knowledge. The proposed method is suitable for implementation in a common programming language, such as C and proved to be faster than a commercial library, namely, Bullet.

Closed -loop brain stimulation holds potential as personalized treatment for drug-resistant epilepsy (DRE) but still suffers from limitations that result in highly variable efficacy. First, stimulation is typically delivered upon detection of the seizure to abort rather than prevent it; second, the stimulation parameters are established by trial and error, requiring lengthy rounds of fine -tuning, which delay steady-state therapeutic efficacy. Here, we address these limitations by leveraging the potential of neuromorphic computing. We present a neuromorphic reservoir computing hardware system capable of driving real - time personalized free-run stimulations based on seizure forecasting, wherein each forecast triggers an electrical pulse rather than an arbitrarily predefined fixed -frequency stimulus train. The system achieves 83.33% accuracy in forecasting seizure occurrences during the training phase. We validate the system using hippocampal spheroids coupled to 3D microelectrode array as a simplified testbed, achieving seizure reduction >97% during the real -time processing while primarily using instantaneous stimulation frequencies within 20 Hz, well below what typically used in clinical practice. Our work demonstrates the potential of neuromorphic systems as a next -generation neuromodulation strategy for personalized DRE treatment, leveraging their sparse and event-driven processing for real -time applications. Keywords: Neuromorphic system, drug-resistant epilepsy, seizure forecasting, neuromodulation, closed -loop stimulation, edge-devices.

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.

Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the Simulate, Summarize, Reduce, Cluster, and Analyze (SSRCA) methodology, a machine-learning based pipeline designed to facilitate SA for ABMs. In particular, SSRCA can achieve the following tasks for ABMS: 1) identify sensitive model parameters, 2) reveal common output model patterns, and 3) determine which input parameter values generate these patterns. We use an example ABM of tumor spheroid growth to showcase how SSRCA provides similar SA results to the popular Sobol' Method while also identifying four common patterns from the ABM and the parameter regions that generate these outputs. This analysis could streamline data-driven tasks, such as parameter estimation, for ABMs by reducing parameter space. While we highlight these results with an ABM on tumor spheroid formation, the SSRCA methodology is broadly applicable to biological ABMs.

Microrobots engineered from self-propelling active particles, extend the reach of robotic operations to submillimeter dimensions and are becoming increasingly relevant for various tasks, such as manipulation of micro/nanoscale cargo, particularly targeted drug delivery. However, achieving deep-tissue penetration and drug delivery remain a challenge. This work developed a novel biohybrid microrobot consisting of jellyfish stinging capsules, which act as natural nanoinjectors for efficient penetration and delivery, assembled onto an active Janus particle (JP). While microrobot transport and navigation was externally controlled by magnetic field-induced rolling, capsule loading onto the JP surface was controlled by electric field. Following precise navigation of the biohybrid microrobots to the vicinity of target tissues, the capsules were activated by a specific enzyme introduced to the solution, which then triggered tubule ejection and release of the preloaded molecules. Use of such microrobots for penetration of and delivery of the preloaded drug/toxin to targeted cancer spheroids and live Caenorhabditis elegans was demonstrated in-vitro. The findings offer insights for future development of bio-inspired microrobots capable of deep penetration and drug delivery. Future directions may involve encapsulation of various drugs within different capsule types for enhanced versatility. This study may also inspire in-vivo applications involving deep tissue drug delivery.

NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure of the giant planet. Inferring its interior structure range remains a challenging inverse problem requiring a computationally intensive search of combinations of various planetary properties, such as the cloud-level temperature, composition, and core features, requiring the computation of ~10^9 interior models. We propose an efficient deep neural network (DNN) model to generate high-precision wide-ranged interior models based on the very accurate but computationally demanding concentric MacLaurin spheroid (CMS) method. We trained a sharing-based DNN with a large set of CMS results for a four-layer interior model of Jupiter, including a dilute core, to accurately predict the gravity moments and mass, given a combination of interior features. We evaluated the performance of the trained DNN (NeuralCMS) to inspect its predictive limitations. NeuralCMS shows very good performance in predicting the gravity moments, with errors comparable with the uncertainty due to differential rotation, and a very accurate mass prediction. This allowed us to perform a broad parameter space search by computing only ~10^4 actual CMS interior models, resulting in a large sample of plausible interior structures, and reducing the computation time by a factor of 10^5. Moreover, we used a DNN explainability algorithm to analyze the impact of the parameters setting the interior model on the predicted observables, providing information on their nonlinear relation.

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison to the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

In this paper we present a memristor-inspired computational method for obtaining a type of running spectrogram or fingerprint of epileptiform activity generated by rodent hippocampal spheroids. It can be used to compute on the fly and with low computational cost an alert-level signal for epileptiform events onset. Here, we describe the computational method behind this fingerprint technique and illustrate it using epileptiform events recorded from hippocampal spheroids using a microelectrode array system.

A spheroid is a small, spherical cluster of cells that acts as a 3D cell culture. The term "organoid" is also used interchangeably with "spheroid", although the former specifically refers to a 3D cluster of organ-specific cells. Spheroids and organoids can be artificially assembled through different methods, including the use of centrifugal force or gravity. As the spheroid forms, cells will sort themselves into different regions and layers of the cluster, mimicking natural processes. Cells adhere to each other and exhibit an organic cell shape and architecture.