Materials fatigue prediction using graph neural networks on microstructure representations.

The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a long-lasting and challenging task. It requires identifying grains tending to accumulate plastic deformation under cyclic loading. We address this task by transcribing ferritic steel microtexture and damage maps from experiments into a microstructure graph. Here, grains constitute graph nodes connected by edges whenever grains share a common boundary. Fatigue loading causes some grains to develop slip markings, which can evolve into microcracks and lead to failure. This data set enables applying graph neural network variants on the task of binary grain-wise damage classification. The objective is to identify suitable data representations and models with an appropriate inductive bias to learn the underlying damage formation causes. Here, graph convolutional networks yielded the best performance with a balanced accuracy of 0.72 and a F1-score of 0.34, outperforming phenomenological crystal plasticity (+ 68%) and conventional machine learning (+ 17%) models by large margins. Further, we present an interpretability analysis that highlights the grains along with features that are considered important by the graph model for the prediction of fatigue damage initiation, thus demonstrating the potential of such techniques to reveal underlying mechanisms and microstructural driving forces in critical grain ensembles.

Advantages and Disadvantages of Using St. John's Wort as a Treatment for Depression.

BACKGROUND AND OBJECTIVES: St. John's wort (SJW) extracts are currently being used to treat depression of various degrees of severity. While many studies have shown it to be superior to placebo, data regarding the effectiveness of using SJW as a stand-alone treatment compared with standard antidepressants has yet to be proven conclusively. This study aims to understand the advantages and disadvantages of SJW as a treatment modality for depression. METHODS:  The authors searched PubMed, JAMA network, Springer Link, Elsevier, Google Scholar, and Scientific Progress databases, from 2011 through August 2021, using the following keywords: St John's wort, Hypericum perforatum, depression, antidepressant, complementary alternative medicine, economic evaluation depression St. wort, St John's wort and depression, antidepressant interactions. This yielded a total of 27 papers following a thorough removal of irrelevant content and dissemination in languages other than English. RESULTS:  In patients with mild and moderate depression, SJW proved superior to placebo. Certain studies comparing the efficacy of SJW versus selective serotonin reuptake inhibitors (SSRIs), especially fluoxetine, reported SJW to be more efficacious, while the majority reported no significant difference. Tricyclic antidepressants were also found to have similar efficacy as SJW. Moreover, treatment with SJW was also found to reduce postmenopausal depression. Regarding the safety profile, although SJW is better tolerated with fewer adverse effects when compared to standardized antidepressants, its predisposition to causing fatal serotonin syndrome, when used in conjunction with other serotonergic agents and drug interactions noted with CYP 450 drugs, raises a question in the safety profile. CONCLUSION:  It is essential to acknowledge that SJW has been used as a treatment measure in Germany. Despite being only listed as a dietary supplement by the FDA and not a drug, SJW has shown to be comparable, if not more efficacious, than most standard treatment options for depression. SJW does prove to be an exciting piece of pharmacotherapy in the realm of mental health and post-menopausal treatment. More prospective studies will help us better understand its efficacy in mild and moderate depression and its ability to serve as a long-term agent. Considering its mechanism of action, its role in relieving patients suffering from an anxiety disorder is also worth considering.

Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration.

Among other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as MigrationsKB (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles.

Optimization and fabrication of the nanosponge carriers of on dansetron using one-factor design.

The current studies were aimed to formulate ethyl cellulose (EC), beta-cyclodextrin (ß-CD facilitated EC based Ondansetron nanosponges (NS) using Response Surface Methodology (RSM) by employing One Factor Design. The NS were fabricated by Emulsion Solvent Diffusion method, followed by characterizations including, drug-polymer compatibility, entrapment efficiency, percentage yield, zeta size, zeta potential and in-vitro release of drug and Scanning Electron Microscopy (SEM) and X-Ray Diffractometry (XRD). The outcomes of Fourier Transformed Infra-Red Spectroscopy (FTIR) have confirmed the compatibility of the drug and excipients. It was found that NS have good entrapment efficiency along with their satisfactory percentage yield. Particle size analysis has confirmed the synthesis of nanosized NS (87.8nm to 108.2nm), having spongy surface, that was described by SEM results. Furthermore, the drug release studies have described a good sustained release of ondansetron for the period of 8 hours. The kinetic modeling has predicted that drug would follow the non fickian type of diffusion mechanism. The application of statistical approach was found helpful in designing and evaluating the NS, avoiding the laborious work, needs to be conducted while using hit and trial method.