Enhancing the wettability of polyetheretherketone (PEEK) membrane with ozone for improving fuel cell performance

Ozone was reacted with the aromatic membrane polyetheretherketone (PEEK) to form oxidized functional groups on the surface to enhance the attraction and transport of protons in fuel cells. Ozonation of unsaturated C-C sp2 bonds in PEEK formed a primary ozonide which dissociated to primarily produce O=C-O/O=C-OH moieties, and the root mean squared roughness factor (Rq) decreased from 7.4 nm, for the untreated sample, down to 3.1 nm. The oxidation of the surface and decrease in surface roughness made the surface increase in hydrophilicity as observed by the decrease in the water contact angle (CA) from 80.3° for untreated PEEK down to 21.7°. Washing the treated surface with solvent decreased the O at % on the surface indicating the formation of a weak boundary layer because of bond breakage during the decomposition of the ozonide

Catheter manipulation analysis for objective performance and technical skills assessment in transcatheter aortic valve implantation

Purpose Transcatheter aortic valve implantation (TAVI) demands precise and efficient handling of surgical instruments within the confines of the aortic anatomy. Operational performance and dexterous skills are critical for patient safety, and objective methods are assessed with a number of manipulation features, derived from the kinematic analysis of the catheter/guidewire in fluoroscopy video sequences. Methods A silicon phantom model of a type I aortic arch was used for this study. Twelve endovascular surgeons, divided into two experience groups, experts (n=6) and novices (n=6), performed cannulation of the aorta, representative of valve placement in TAVI. Each participant completed two TAVI experiments, one with conventional catheters and one with the Magellan robotic platform. Video sequences of the fluoroscopic monitor were recorded for procedural processing. A semi-automated tracking software provided the 2D coordinates of the catheter/guidewire tip. In addition, the aorta phantom was segmented in the videos and the shape of the entire catheter was manually annotated in a subset of the available video frames using crowdsourcing. The TAVI procedure was divided into two stages, and various metrics, representative of the catheter’s overall navigation as well as its relative movement to the vessel wall, were developed. Results Experts consistently exhibited lower values of procedure time and dimensionless jerk, and higher average speed and acceleration than novices. Robotic navigation resulted in increased average distance to the vessel wall in both groups, a surrogate measure of safety and reduced risk of embolisation. Discrimination of experience level and types of equipment was achieved with the generated motion features and established clustering algorithms. Conclusions Evaluation of surgical skills is possible through the analysis of the catheter/guidewire motion pattern. The use of robotic endovascular platforms seems to enable more precise and controlled catheter navigation

Extração do óleo da semente da ameixa amarela (Eriobotrya japonica)

A ameixa amarela (Eriobotrya japonica) é uma fruta cítrica de origem asiática, cultivada no sul e sudeste do Brasil. As frutas contêm de 3 a 5 sementes que são consideradas resíduos industriais e o óleo presente nestas sementes apresenta elevados níveis de polifenóis, que ajudam a combater radicais livres, podendo se tornar uma possível aplicação na área alimentícia e química. Neste sentido, este trabalho teve como objetivo avaliar diferentes condições de extração do óleo presente na semente da ameixa amarela. A fruta madura foi colhida no município de Chapecó/Santa Catarina e as sementes foram retiradas manualmente e a extração foi realizada pelo método de Soxhlet. Através de um planejamento fatorial completo 2², em duplicata, variou-se o tipo de solvente (hexano e etanol) e o tempo de extração (60 e 120 min). Os resultados mostraram que apenas o tipo de solvente foi significativo, sendo que, o solvente mais eficaz foi o etanol extraindo 15% de óleo. O modelo matemático obtido foi preditivo e explica 99,16% das respostas obtidas por ele. O solvente etanol, por ser oriundo de recursos renováveis, abre portas para futuras pesquisas na área de extração, em comparação com o solvente hexano, que pode deixar resíduos tóxicos nos óleos

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts