Collagen fibril-like injectable hydrogels from self-assembled nanoparticles for promoting wound healing.

Soft hydrogels are excellent candidate materials for repairing various tissue defects, yet the mechanical strength, anti-swelling properties, and biocompatibility of many soft hydrogels need to be improved. Herein, inspired by the nanostructure of collagen fibrils, we developed a strategy toward achieving a soft but tough, anti-swelling nanofibrillar hydrogel by combining the self-assembly and chemical crosslinking of nanoparticles. Specifically, the collagen fibril-like injectable hydrogel was subtly designed and fabricated by self-assembling methylacrylyl hydroxypropyl chitosan (HM) with laponite (LAP) to form nanoparticles, followed by the inter-nanoparticle bonding through photo-crosslinking.

Polysaccharides synergistic boosting drug loading for reduction pesticide dosage and improve its efficiency.

Overuse of pesticides is an urgent issue to be solved in sustainable agriculture. Based on the synergistic drug loading effect of β-cyclodextrin (β-CD) encapsulation and alginate (Alg) cross-linking, a new environment-responsive drug delivery system (TMX-loaded Alg/β-CD) was constructed. The relationship between carrier structure and solubility of thiamethoxam (TMX) was researched by molecular simulation.

On Sketch-Based Selections From Scatterplots Using KDE, Compared to Mahalanobis and CNN Brushing.

Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this article, we detail a solution, based on kernel density estimation, which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain how this technique relates to two alternative approaches, i.

Sketch-Based Fast and Accurate Querying of Time Series Using Parameter-Sharing LSTM Networks.

Sketching is one common approach to query time series data for patterns of interest. Most existing solutions for matching the data with the interaction are based on an empirically modeled similarity function between the user's sketch and the time series data with limited efficiency and accuracy. In this article, we introduce a machine learning based solution for fast and accurate querying of time series data based on a swift sketching interaction.

Personalized Sketch-Based Brushing in Scatterplots.

Brushing is at the heart of most modern visual analytics solutions and effective and efficient brushing is crucial for successful interactive data exploration and analysis. As the user plays a central role in brushing, several data-driven brushing tools have been designed that are based on predicting the user's brushing goal. All of these general brushing models learn the users' average brushing preference, which is not optimal for every single user.