Baldwinmassey0770

However, the serotype-specific nature of these vaccines have driven the bacteria to adapt by mechanisms that affect the capsule antigens through either capsule switching or capsule replacement in addition to the possibility of unmasking of strains or serotypes not covered by the vaccines. OT82 The post-vaccine molecular epidemiology of vaccine-preventable bacterial meningitis is discussed based on findings obtained with newer genomic laboratory surveillance methods.Nowadays, the self-healing approach in materials science mainly relies on functionalized polymers used as matrices in nanocomposites. Through different physicochemical pathways and stimuli, these materials can undergo self-repairing mechanisms that represent a great advantage to prolonging materials service-life, thus avoiding early disposal. Particularly, the use of the Joule effect as an external stimulus for self-healing in conductive nanocomposites is under-reported in the literature. However, it is of particular importance because it incorporates nanofillers with tunable features thus producing multifunctional materials. The aim of this review is the comprehensive analysis of conductive polymer nanocomposites presenting reversible dynamic bonds and their energetical activation to perform self-healing through the Joule effect.

To provide a human-Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches.

A scoping literature review is performed on Scopus and Google Scholar using the terms "human in the loop", "human in the loop machine learning", and "interactive machine learning". Peer-reviewed papers published from 2015 to 2020 are included in our review.

We design four questions to investigate and describe human-AI interaction in ML applications. These questions are "Why should humans be in the loop?", "Where does human-AI interaction occur in the ML processes?", "Who are the humans in the loop?", and "How do humans interact with ML in Human-In-the-Loop ML (HILML)?". To answer the first question, we describe three main reasons regarding tddress the second question, human-AI interaction is investigated in three main algorithmic stages 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human-AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.We propose a flexible anti-metal radio frequency identification (RFID) tag antenna based on a high-conductivity graphene assembly film (HCGAF). The HCGAF has a conductivity of 1.82 × 106 S m-1, a sheet resistance of 25 mΩ and a thickness of 22 μm. The HCGAF is endowed with high conductivity comparable to metal materials and superb flexibility, which is suitable for making antennas for microwave frequencies. Through proper structural design, parameter optimization, semiautomatic manufacturing and experimental measurements, an HCGAF antenna could realize a realized gain of -7.3 dBi and a radiation efficiency of 80%, and the tag could achieve a 6.4 m read range at 915 MHz on a 20 × 20 cm2 flat copper plate. In the meantime, by utilizing flexible polyethylene (PE) foam, good conformality was obtained. The read ranges of the tags attached to curved copper plates with different bending radii were measured, as well as those of those attached to several daily objects. All the results demonstrate the excellent performance of the design, which is highly favorable for practical RFID anti-metal applications.Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13-15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13-15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.Gastric cancer is one of the most aggressive cancers, with a median survival of 12 months. This illustrates its complexity and the lack of therapeutic options, such as personalized therapy, because predictive markers do not exist. Thus, gastric cancer remains mostly treated with cytotoxic chemotherapies. In addition, less than 20% of patients respond to immunotherapy. TP53 mutations are particularly frequent in gastric cancer (±50% and up to 70% in metastatic) and are considered an early event in the tumorigenic process. Alterations in the expression of other members of the p53 family, i.e., p63 and p73, have also been described. In this context, the role of the members of the p53 family and their isoforms have been investigated over the years, resulting in conflicting data. For instance, whether mutations of TP53 or the dysregulation of its homologs may represent biomarkers for aggressivity or response to therapy still remains a matter of debate. This uncertainty illustrates the lack of information on the molecular pathways involving the p53 family in gastric cancer.