Kenthays1391

The scales of pine cones undergo reversible deformation due to hydration changes in order to optimize seed dispersal. This improves the survivability of the pine. The reversible flexing of the scales is caused by two tissue layers arranged in a sandwich configuration a layer composed of sclereid cells and a sclerenchyma layer. They expand differentially upon hydration (and contract upon dehydration) due to differences in the structure that are analyzed here for Torrey pine (Pinustorreyana) cones. In addition to this well-known mechanism by which the cellulose microfibrils in the scales vary their angle with the wood cell axis, we confirm the presence of a porosity gradient in the sclereid cells and calculate, using a model consisting of three layers, the stresses generated upon dehydration taking into account the effect of hydration on the elastic modulus. Our quantitative analysis reveals that this gradient structure can significantly decrease the stress concentrations due to the mismatch between the two layble, and rehydration of the pine cone recloses the scales. The processes of flexing and straightening are governed by shrinking and swelling which are directed by differences in the arrangement of cellulose microfibrils in a bilayer construct. We demonstrate that the scales are more complex than a simple bilayer structure and that they actually have gradients, which significantly reduce the internal stresses and ensure their integrity. We analyse the process of opening and closing of the scales for a gradient structure in the Torrey pine cone using a simple idealized trilayer model. The results demonstrate a significant decrease in internal stresses produced by the gradient structure. Using the lessons learned from the pine cone, we produce a bilayer junction using hydrogels with different porosities which exhibit the same reversible bending response.Canine non-infectious inflammatory meningoencephalomyelitis is termed meningoencephalomyelitis of unknown origin (MUO) and may affect dogs of every breed at any age. Treatment with immunosuppressive medication, the survival time based on MRI, and cerebrospinal fluid (CSF) findings has been widely reported; however, these studies only included a small number of patients, or they are summaries from the literature. Therefore, the aim of this study was to compare the clinical presentation, diagnostic findings, treatment protocol and long-term survival time in many dogs diagnosed with MUO in one clinic with previously published studies. One hundred eighty-two dogs met the inclusion criteria. Age, sex, duration of clinical signs before diagnosis, presence of neurological signs, MRI and CSF analysis were similar to those in previous reports. Our study revealed that dogs with a brainstem lesion have a 60% lower chance of death before 1 year than dogs with multifocal brain lesions. A total of 55.56% of treated dogs survived for more than 1 year, and 10.55% survived for more than 5 years since diagnosis. The median survival time for all dogs was 540 days. Our findings support glucocorticosteroid monotherapy as a viable treatment option for dogs with MUO.Graph Neural Networks (GNNs), such as GCN, GraphSAGE, GAT, and SGC, have achieved state-of-the-art performance on a wide range of graph-based tasks. These models all use a technique called neighborhood aggregation, in which the embedding of each node is updated by aggregating the embeddings of its neighbors. However, not all information aggregated from neighbors is beneficial. In some cases, a portion of the neighbor information may be harmful to the downstream tasks. For the high-quality aggregation of beneficial information, we propose a flexible method EGAI (Enhancing Graph neural networks by a high-quality Aggregation of beneficial Information). The core concept of this method is to filter out the redundant and harmful information by removing specific edges during each training epoch. The practical and theoretical motivations, considerations, and strategies related to this method are discussed in detail. EGAI is a general method that can be combined with many backbone models (e.g., GCN, GraphSAGE, GAT, and SGC) to enhance their performance in the node classification task. AZD2014 order In addition, EGAI reduces the convergence speed of over-smoothing that occurs when models are deepened. Extensive experiments on three real-world networks demonstrate that EGAI indeed improves the performance for both shallow and deep GNN models, and to some extent, mitigates over-smoothing. The code is available at https//github.com/liucoo/egai.

The Board of the Association of Pediatric Program Directors (APPD) partnered with the APPD Global Health Learning Community (GHLC) to establish the APPD Global Pediatric Educator Scholarship. This award seeks to recognize pediatric educators who demonstrate leadership in improving pediatric education in low- and middle-income countries, and provide them with career development opportunities by attending the APPD Spring meeting. Two educators per year have been awarded the scholarship since 2017.

The authors sent survey questions via email and obtained responses from six (100%) of the scholarship awardees, eight (75%) APPD GHLC leadership individuals, and four (67%) APPD Board members. Three authors analyzed the responses with consensus achieved on themes.

Awardees noted learning about educational strategies, academic opportunities through networking, and context for stronger bilateral exchange with partners. APPD leaders noted an expansion of the organization's mission to include global presence. Challenges included program visibility, sustainable funding, and logistics. Suggestions included better incorporation of awardees into APPD membership, longitudinal mentorship, targeted conference navigation, and visits to local academic institutions.

The APPD Global Educator Scholarship is a replicable model of organizational global outreach that expands the concept of bidirectional exchange to include career sponsorship for global partners.

The APPD Global Educator Scholarship is a replicable model of organizational global outreach that expands the concept of bidirectional exchange to include career sponsorship for global partners.