Gottliebmcclure7426

Zinc is one of the essential trace elements in eukaryotes and it is a critical structural component of a large number of proteins. Zinc finger proteins (ZNFs) are zinc-finger domain-containing proteins stabilized by bound zinc ions and they form the most abundant proteins, serving extraordinarily diverse biological functions. In recent years, many ZNFs have been identified and characterized in the human fungal pathogen Cryptococcus neoformans, a fungal pathogen causing fatal meningitis mainly in immunocompromised individuals. It has been shown that ZNFs play important roles in the morphological development, differentiation, and virulence of C. neoformans. In this review, we, first, briefly introduce the ZNFs and their classification. Then, we explain the identification and classification of the ZNFs in C. neoformans. Next, we focus on the biological role of the ZNFs functionally characterized so far in the sexual reproduction, virulence factor production, ion homeostasis, pathogenesis, and stress resistance in C. neoformans. We also discuss the perspectives on future function studies of ZNFs in C. neoformans.In roses (Rosa sp.), peduncle morphology is an important ornamental feature. The common physiological abnormality known as the bent peduncle phenomenon (BPP) seriously decreases the quality of rose flowers and thus the commercial value. Because the molecular mechanisms underlying this condition are poorly understood, we analysed the transcriptional profiles and cellular structures of bent rose peduncles. Numerous differentially expressed genes involved in the auxin, cytokinin, and gibberellin signaling pathways were shown to be associated with bent peduncle. Paraffin sections showed that the cell number on the upper sides of bent peduncles was increased, while the cells on the lower sides were larger than those in normal peduncles. We also investigated the large, deformed sepals that usually accompany BPP and found increased expression level of some auxin-responsive genes and decreased expression level of genes that are involved in cytokinin and gibberellin synthesis in these sepals. Furthermore, removal of the deformed sepals partially relieved BPP. In summary, our findings suggest that auxin, cytokinin, and gibberellin all influence the development of BPP by regulating cell division and expansion. To effectively reduce BPP in roses, more efforts need to be devoted to the molecular regulation of gibberellins and cytokinins in addition to that of auxin.Anionic polymerization techniques were employed for the synthesis of linear polystyrene (PS) and block copolymer of PS and polyisoprene (PI) PS-b-PI bearing end hydroxyl groups. Following suitable organic chemistry transformation, the -OH end groups were converted to moieties able to form complementary hydrogen bonds including 2,6-diaminopurine, Dap, thymine, Thy, and the so-called Hamilton receptor, Ham. selleck The formation of hydrogen bonds was examined between the polymers PS-Dap and PS-b-PI-Thy, along with the polymers PS-Ham and PS-b-PI-Thy. The conditions under which supramolecular triblock copolymers are formed and the possibility to form aggregates were examined both in solution and in the solid state using a variety of techniques such as 1H-NMR spectroscopy, size exclusion chromatography (SEC), dilute solution viscometry, dynamic light scattering (DLS), thermogravimetric analysis (TGA), differential thermogravimetry (DTG), and differential scanning calorimetry (DSC).In this era of precision medicine, insights into the resistance mechanism of drugs are integral for the development of potent therapeutics. Here, we sought to understand the contribution of four point mutations (N51I, C59R, S108N, and I164L) within the active site of the malaria parasite enzyme dihydrofolate reductase (DHFR) towards the resistance of the antimalarial drug pyrimethamine. Homology modeling was used to obtain full-length models of wild type (WT) and mutant DHFR. Molecular docking was employed to dock pyrimethamine onto the generated structures. Subsequent all-atom molecular dynamics (MD) simulations and binding free-energy computations highlighted that pyrimethamine's stability and affinity inversely relates to the number of mutations within its binding site and, hence, resistance severity. Generally, mutations led to reduced binding affinity to pyrimethamine and increased conformational plasticity of DHFR. Next, dynamic residue network analysis (DRN) was applied to determine the impact of mutations and pyrimethamine binding on communication dispositions of DHFR residues. DRN revealed residues with distinctive communication profiles, distinguishing WT from drug-resistant mutants as well as pyrimethamine-bound from pyrimethamine-free models. Our results provide a new perspective on the understanding of mutation-induced drug resistance.During image segmentation tasks in computer vision, achieving high accuracy performance while requiring fewer computations and faster inference is a big challenge. This is especially important in medical imaging tasks but one metric is usually compromised for the other. To address this problem, this paper presents an extremely fast, small and computationally effective deep neural network called Stripped-Down UNet (SD-UNet), designed for the segmentation of biomedical data on devices with limited computational resources. By making use of depthwise separable convolutions in the entire network, we design a lightweight deep convolutional neural network architecture inspired by the widely adapted U-Net model. In order to recover the expected performance degradation in the process, we introduce a weight standardization algorithm with the group normalization method. We demonstrate that SD-UNet has three major advantages including (i) smaller model size (23x smaller than U-Net); (ii) 8x fewer parameters; and (iii) faster inference time with a computational complexity lower than 8M floating point operations (FLOPs). Experiments on the benchmark dataset of the Internatioanl Symposium on Biomedical Imaging (ISBI) challenge for segmentation of neuronal structures in electron microscopic (EM) stacks and the Medical Segmentation Decathlon (MSD) challenge brain tumor segmentation (BRATs) dataset show that the proposed model achieves comparable and sometimes better results compared to the current state-of-the-art.