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Recent reports that antibodies to SARS-CoV-2 are not maintained in the serum following recovery from the virus have caused alarm. However, the absence of specific antibodies in the serum does not necessarily mean an absence of immune memory. Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.Here, Cox and Brokstad briefly discuss T cell- and B cell-mediated immunity to SARS-CoV-2, stressing that a lack of serum antibodies does not necessarily equate with a lack of immunity to the virus.

Gram-negative bacteria are intrinsically resistant to many antibiotics, due in large part to the permeability barrier formed by their cell envelope. The complex and synergistic interplay of the two Gram-negative membranes and active efflux prevents the accumulation of a diverse range of compounds that are effective against Gram-positive bacteria. A lack of detailed information on how components of the cell envelope contribute to this has been identified as a key barrier to the rational development of new antibiotics with efficacy against Gram-negative species. This review describes the current understanding of the role of the different components of the Gram-negative cell envelope in preventing compound accumulation and the state of efforts to describe properties that allow compounds to overcome this barrier and apply them to the development of new broad-spectrum antibiotics.

Nitrogen-containing heterocyclic rings are common structural components of marketed drugs. Among these heterocycles, imidazole/fused imidazole rings are present in a wide range of bioactive compounds. The unique properties of such structures, including high polarity and the ability to participate in hydrogen bonding and coordination chemistry, allow them to interact with a wide range of biomolecules, and imidazole-/fused imidazole-containing compounds are reported to have a broad spectrum of biological activities. This review summarizes recent reports of imidazole/fused imidazole derivatives as anticancer agents appearing in the peer-reviewed literature from 2018 through 2020. Such molecules have been shown to modulate various targets, including microtubules, tyrosine and serine-threonine kinases, histone deacetylases, p53-Murine Double Minute 2 (MDM2) protein, poly (ADP-ribose) polymerase (PARP), G-quadraplexes, and other targets. Imidazole-containing compounds that display anticancer activity by unknown/undefined mechanisms are also described, as well as key features of structure-activity relationships. This review is intended to provide an overview of recent advances in imidazole-based anticancer drug discovery and development, as well as inspire the design and synthesis of new anticancer molecules.

Artificial photosystems are advanced by the development of conformal catalytic materials that promote desired chemical transformations, while also maintaining stability and minimizing parasitic light absorption for integration on surfaces of semiconductor light absorbers. Here, we demonstrate that multifunctional, nanoscale catalysts that enable high-performance photoelectrochemical energy conversion can be engineered by plasma-enhanced atomic layer deposition. The collective properties of tailored Co 3 O 4 /Co(OH) 2 thin films simultaneously provide high activity for water splitting, permit efficient interfacial charge transport from semiconductor substrates, and enhance durability of chemically sensitive interfaces. These films comprise compact and continuous nanocrystalline Co 3 O 4 spinel that is impervious to phase transformation and impermeable to ions, thereby providing effective protection of the underlying substrate. Moreover, a secondary phase of structurally disordered and chemically labile Co(OH) 2 is introduced to ensure a high concentration of catalytically active sites. Application of this coating to photovoltaic p + n-Si junctions yields best reported performance characteristics for crystalline Si photoanodes. In photosynthesis the oxidation of water is a requirement for providing sufficient protons and electrons for fuel formation. A biphasic water splitting catalyst tailored for integration with high-performance semiconductor photoanodes is now reported.

Herein, we present a new data-driven multiscale framework called FE [Formula omitted] which is based on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) as macroscopic surrogate models and an autonomous data mining process. Our approach allows the efficient simulation of materials with complex underlying microstructures which reveal an overall anisotropic and nonlinear behavior on the macroscale. Thereby, we restrict ourselves to finite strain hyperelasticity problems for now. By using a set of problem specific invariants as the input of the ANN and the Helmholtz free energy density as the output, several physical principles, e. g., objectivity, material symmetry, compatibility with the balance of angular momentum and thermodynamic consistency are fulfilled a priori. The necessary data for the training of the ANN-based surrogate model, i. e., macroscopic deformations and corresponding stresses, are collected via computational homogenization of representative volume elements (RVEs). Thereby, the core feature of the approach is given by a completely autonomous mining of the required data set within an overall loop. In each iteration of the loop, new data are generated by gathering the macroscopic deformation states from the macroscopic finite element simulation and a subsequently sorting by using the anisotropy class of the considered material. Finally, all unknown deformations are prescribed in the RVE simulation to get the corresponding stresses and thus to extend the data set. The proposed framework consequently allows to reduce the number of time-consuming microscale simulations to a minimum. It is exemplarily applied to several descriptive examples, where a fiber reinforced composite with a highly nonlinear Ogden-type behavior of the individual components is considered. Thereby, a rather high accuracy could be proved by a validation of the approach.

Because older victims of abuse tend to be isolated, their interactions with physicians are important opportunities to recognize abuse and intervene. This review explores the manifestations of elder abuse and the role of multidisciplinary teams in its assessment and management. Although it has probably existed since antiquity, elder abuse was first described in the medical literature in the 1970s. 1 Many initial attempts to define the clinical spectrum of the phenomenon and to formulate effective intervention strategies were limited by their anecdotal nature or were epidemiologically flawed. The past decade, however, has seen improvements in the quality of research on elder abuse that should be of interest to clinicians who care for older adults and their families. Financial exploitation of older adults, which was explored only minimally in the initial studies, has recently been identified as a virtual epidemic and as . . .

Acinetobacter baumannii and Klebsiella pneumoniae are opportunistic pathogens frequently co-isolated from polymicrobial infections. The infections where these pathogens co-exist can be more severe and recalcitrant to therapy than infections caused by either species alone, however there is a lack of knowledge on their potential synergistic interactions. In this study we characterise the genomes of A. baumannii and K. pneumoniae strains co-isolated from a single human lung infection. We examine various aspects of their interactions through transcriptomic, phenomic and phenotypic assays that form a basis for understanding their effects on antimicrobial resistance and virulence during co-infection. Using co-culturing and analyses of secreted metabolites, we discover the ability of K. pneumoniae to cross-feed A. baumannii by-products of sugar fermentation. Minimum inhibitory concentration testing of mono- and co-cultures reveals the ability for A. baumannii to cross-protect K. pneumoniae against the cephalosporin, cefotaxime. Our study demonstrates distinct syntrophic interactions occur between A. baumannii and K. pneumoniae , helping to elucidate the basis for their co-existence in polymicrobial infections. Here, the authors characterise Acinetobacter baumanii and Klebsiella pneumoniae isolated from a single human lung infection and proceed to define their interactions to shed light on how this impacts their evolution, growth parameters, metabolism and antimicrobial responses.

Pseudomonas aeruginosa is a major opportunistic pathogen, causing a wide range of acute and chronic infections. β-lactam antibiotics including penicillins, carbapenems, monobactams, and cephalosporins play a key role in the treatment of P. aeruginosa infections. However, a significant number of isolates of these bacteria are resistant to β-lactams, complicating treatment of infections and leading to worse outcomes for patients. In this review, we summarize studies demonstrating the health and economic impacts associated with β-lactam-resistant P. aeruginosa. We then describe how β-lactams bind to and inhibit P. aeruginosa penicillin-binding proteins that are required for synthesis and remodelling of peptidoglycan. Resistance to β-lactams is multifactorial and can involve changes to a key target protein, penicillin-binding protein 3, that is essential for cell division; reduced uptake or increased efflux of β-lactams; degradation of β-lactam antibiotics by increased expression or altered substrate specificity of an AmpC β-lactamase, or by the acquisition of β-lactamases through horizontal gene transfer; and changes to biofilm formation and metabolism. The current understanding of these mechanisms is discussed. Lastly, important knowledge gaps are identified, and possible strategies for enhancing the effectiveness of β-lactam antibiotics in treating P. aeruginosa infections are considered.

Herein, we present a new data-driven multiscale framework called FE ANN which is based on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) as macroscopic surrogate models and an autonomous data mining process. Our approach allows the efficient simulation of materials with complex underlying microstructures which reveal an overall anisotropic and nonlinear behavior on the macroscale. Thereby, we restrict ourselves to finite strain hyperelasticity problems for now. By using a set of problem specific invariants as the input of the ANN and the Helmholtz free energy density as the output, several physical principles, e. g., objectivity, material symmetry, compatibility with the balance of angular momentum and thermodynamic consistency are fulfilled a priori. The necessary data for the training of the ANN-based surrogate model, i. e., macroscopic deformations and corresponding stresses, are collected via computational homogenization of representative volume elements (RVEs). Thereby, the core feature of the approach is given by a completely autonomous mining of the required data set within an overall loop. In each iteration of the loop, new data are generated by gathering the macroscopic deformation states from the macroscopic finite element simulation and a subsequently sorting by using the anisotropy class of the considered material. Finally, all unknown deformations are prescribed in the RVE simulation to get the corresponding stresses and thus to extend the data set. The proposed framework consequently allows to reduce the number of time-consuming microscale simulations to a minimum. It is exemplarily applied to several descriptive examples, where a fiber reinforced composite with a highly nonlinear Ogden-type behavior of the individual components is considered. Thereby, a rather high accuracy could be proved by a validation of the approach.

A multi-tissue gene expression resource representative of the genotypic and phenotypic diversity of modern inbred maize reveals the effect of rare alleles and evolutionary history on the regulation of gene expression. Gene expression and fitness in maize Karl Kremling, Edward Buckler and colleagues report a multi-tissue gene expression resource for maize that includes RNA-sequencing data on seven tissues from across 299 maize lines, which are representative of the genotypic and phenotypic diversity of modern inbred maize. The authors examined how gene expression is regulated, mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. They also show a correlation between cumulative deviation in the expression of the most highly expressed genes and seed-weight fitness. Here we report a multi-tissue gene expression resource that represents the genotypic and phenotypic diversity of modern inbred maize, and includes transcriptomes in an average of 255 lines in seven tissues. We mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. Some of the new mutations that arise in the maize genome can be deleterious; although selection acts to keep deleterious variants rare, their complete removal is impeded by genetic linkage to favourable loci and by finite population size 1 , 2 , 3 , 4 . Modern maize breeders have systematically reduced the effects of this constant mutational pressure through artificial selection and self-fertilization, which have exposed rare recessive variants in elite inbred lines 5 . However, the ongoing effect of these rare alleles on modern inbred maize is unknown. By analysing this gene expression resource and exploiting the extreme diversity and rapid linkage disequilibrium decay of maize 6 , we characterize the effect of rare alleles and evolutionary history on the regulation of expression. Rare alleles are associated with the dysregulation of expression, and we correlate this dysregulation to seed-weight fitness. We find enrichment of ancestral rare variants among expression quantitative trait loci mapped in modern inbred lines, which suggests that historic bottlenecks have shaped regulation. Our results suggest that one path for further genetic improvement in agricultural species lies in purging the rare deleterious variants that have been associated with crop fitness.