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Spike sorting, an indispensable process in the analysis of neural biosignals, aims to segregate individual action potentials from mixed recordings. This study delves into a comprehensive investigation of diverse unsupervised classification algorithms, some of which, to the best of our knowledge, have not previously been used for spike sorting. The methods encompass Principal Component Analysis (PCA), K-means, Self-Organizing Maps (SOMs), and hierarchical clustering. The research draws insights from both macaque monkey and human pancreatic signals, providing a holistic evaluation across species. Our research has focused on the utilization of the aforementioned methods for the sorting of 327 detected spikes within an in vivo signal of a macaque monkey, as well as 386 detected spikes within an in vitro signal of a human pancreas. This classification process was carried out by extracting statistical features from these spikes. We initiated our analysis with K-means, employing both unmodified and normalized versions of the features. To enhance the performance of this algorithm, we also employed Principal Component Analysis (PCA) to reduce the dimensionality of the data, thereby leading to more distinct groupings as identified by the K-means algorithm. Furthermore, two additional techniques, namely hierarchical clustering and Self-Organizing Maps, have also undergone exploration and have demonstrated favorable outcomes for both signal types. Across all scenarios, a consistent observation emerged: the identification of six distinctive groups of spikes, each characterized by distinct shapes, within both signal sets. In this regard, we meticulously present and thoroughly analyze the experimental outcomes yielded by each of the employed algorithms. This comprehensive presentation and discussion encapsulate the nuances, patterns, and insights uncovered by these algorithms across our data. By delving into the specifics of these results, we aim to provide a nuanced understanding of the efficacy and performance of each algorithm in the context of spike sorting.

Impacts due to drops or crashes between moving vehicles necessitate the search for energy absorption elements to prevent damage to the transported goods or individuals. To ensure safety, a given level of acceptable deceleration is provided. The optimization of deformable parts to absorb impact energy is typically conducted through explicit simulations, where kinetic energy is converted into plastic deformation energy. The introduction of additive manufacturing techniques enables this optimization to be conducted with more efficient shapes, previously unachievable with conventional manufacturing methods. This paper presents an initial approach to validating explicit simulations of impacts against solid cubes of varying sizes and fabrication directions. Such cubes were fabricated using PLA, the most used material, and a desktop printer. All simulations could be conducted using a single material law description, employing solid elements with a controlled time step suitable for industrial applications. With this approach, the simulations were capable of predicting deceleration levels across a broad range of impact configurations for solid cubes.

This study reports a route to spatial control of neuronal adhesion onto Diamond-Like Carbon (DLC) by surface functionalisation by poly (oligo-ethyleneglycol methacrylate) (pOEGMA) and consequent laser ablation to produce cell adhesive tracks. DLC can be deposited as a tough and low friction coating on implantable devices and surgical instruments and has favourable properties for use as a biomaterial. The pOEGMA surface coating renders the DLC surface antifouling and the laser ablation creates graphitised tracks on the surface. The surfaces were coated with laminin, which adhered preferentially to the ablation tracks. The patterned surfaces were investigated for neuronal cell growth with NG108-15 cells for short term culture and rat neural stem cells for longer term culture. The cells initially adhered highly selectively to the ablation tracks while longer term cell culture revealed a more uniform cell coverage of the surface.

Microfluidic-based tissue-on-a-chip devices have generated significant research interest for biomedical applications, such as pharmaceutical development, as they can be used for small volume, high throughput studies on the effects of therapeutics on tissue-mimics. Tissue-on-a-chip devices are evolving from basic 2D cell cultures incorporated into microfluidic devices to complex 3D approaches, with modern designs aimed at recapitulating the dynamic and mechanical environment of the native tissue. Thus far, most tissue-on-a-chip research has concentrated on organs involved with drug uptake, metabolism and removal (e.g., lung, skin, liver, and kidney); however, models of the drug metabolite target organs will be essential to provide information on therapeutic efficacy. Here, we develop an osteogenesis-on-a-chip device that comprises a 3D environment and fluid shear stresses, both important features of bone. This inexpensive, easy-to-fabricate system based on a polymerized High Internal Phase Emulsion (polyHIPE) supports proliferation, differentiation and extracellular matrix production of human embryonic stem cell-derived mesenchymal progenitor cells (hES-MPs) over extended time periods (up to 21 days). Cells respond positively to both chemical and mechanical stimulation of osteogenesis, with an intermittent flow profile containing rest periods strongly promoting differentiation and matrix formation in comparison to static and continuous flow. Flow and shear stresses were modeled using computational fluid dynamics. Primary cilia were detectable on cells within the device channels demonstrating that this mechanosensory organelle is present in the complex 3D culture environment. In summary, this device aids the development of ‘next-generation’ tools for investigating novel therapeutics for bone in comparison with standard laboratory and animal testing.