Explore the vast range of titles available.

Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source (http://topaz.csail.mit.edu).

In recent ages, green nanotechnology has gained attraction in the synthesis of metallic nanoparticles due to their cost-effectiveness, simple preparation steps, and environmentally-friendly. In the present study, copper oxide nanoparticles (CuO NPs) were prepared using Parthenium hysterophorus whole plant aqueous extract as a reducing, stabilizing, and capping agent. The CuO NPs were characterized via UV–Vis Spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), powder X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Dynamic Light Scattering (DLS). The UV–Vis spectra of CuO NPs showed a surface plasmonic resonance band to occur at 340 nm. FTIR analysis revealed the presence of secondary metabolites on the surface of CuO NPs, with a characteristic Cu–O stretching band being identified at 522 cm −1 . Scanning electron micrographs and transmission electron micrographs showed that CuO NPs were nearly spherical, with an average particle of 59.99 nm obtained from the SEM micrograph. The monoclinic crystalline structure of CuO NPs was confirmed using XRD, and crystallite size calculated using the Scherrer-Debye equation was found to be 31.58 nm. DLS showed the presence of nanoparticle agglomeration, which revealed uniformity of the CuO NPs. Furthermore, the degradation ability of biosynthesized nanoparticles was investigated against rifampicin antibiotic. The results showed that the optimum degradation efficiency of rifampicin at 98.43% was obtained at 65℃ temperature, 50 mg dosage of CuO NPs, 10 mg/L concentration of rifampicin solution, and rifampicin solution at pH 2 in 8 min. From this study, it can be concluded that CuO NPs synthesized from Parthenium hysterophorus aqueous extract are promising in the remediation of environmental pollution from antibiotics. In this light, the study reports that Parthenium hysterophorus -mediated green synthesis of CuO NPs can effectively address environmental pollution in cost-effective, eco-friendly, and sustainable ways.

The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimens must be tightly coupled to data preprocessing to ensure the best data quality and microscope usage. Here we describe Warp, a software that automates all preprocessing steps of cryo-EM data acquisition and enables real-time evaluation. Warp corrects micrographs for global and local motion, estimates the local defocus and monitors key parameters for each recorded micrograph or tomographic tilt series in real time. The software further includes deep-learning-based models for accurate particle picking and image denoising. The output from Warp can be fed into established programs for particle classification and 3D-map refinement. Our benchmarks show improvement in the nominal resolution, which went from 3.9 Å to 3.2 Å, of a published cryo-EM data set for influenza virus hemagglutinin. Warp is easy to install from http://github.com/cramerlab/warp and computationally inexpensive, and has an intuitive, streamlined user interface.

The trimeric SARS coronavirus (SARS-CoV) surface spike (S) glycoprotein consisting of three S1-S2 heterodimers binds the cellular receptor angiotensin-converting enzyme 2 (ACE2) and mediates fusion of the viral and cellular membranes through a pre- to postfusion conformation transition. Here, we report the structure of the SARS-CoV S glycoprotein in complex with its host cell receptor ACE2 revealed by cryo-electron microscopy (cryo-EM). The complex structure shows that only one receptor-binding domain of the trimeric S glycoprotein binds ACE2 and adopts a protruding \"up\" conformation. In addition, we studied the structures of the SARS-CoV S glycoprotein and its complexes with ACE2 in different in vitro conditions, which may mimic different conformational states of the S glycoprotein during virus entry. Disassociation of the S1-ACE2 complex from some of the prefusion spikes was observed and characterized. We also characterized the rosette-like structures of the clustered SARS-CoV S2 trimers in the postfusion state observed on electron micrographs. Structural comparisons suggested that the SARS-CoV S glycoprotein retains a prefusion architecture after trypsin cleavage into the S1 and S2 subunits and acidic pH treatment. However, binding to the receptor opens up the receptor-binding domain of S1, which could promote the release of the S1-ACE2 complex and S1 monomers from the prefusion spike and trigger the pre- to postfusion conformational transition.

Knowledge of connectivity in the nervous system is essential to understanding its function. Here we describe connectomes for both adult sexes of the nematode Caenorhabditis elegans , an important model organism for neuroscience research. We present quantitative connectivity matrices that encompass all connections from sensory input to end-organ output across the entire animal, information that is necessary to model behaviour. Serial electron microscopy reconstructions that are based on the analysis of both new and previously published electron micrographs update previous results and include data on the male head. The nervous system differs between sexes at multiple levels. Several sex-shared neurons that function in circuits for sexual behaviour are sexually dimorphic in structure and connectivity. Inputs from sex-specific circuitry to central circuitry reveal points at which sexual and non-sexual pathways converge. In sex-shared central pathways, a substantial number of connections differ in strength between the sexes. Quantitative connectomes that include all connections serve as the basis for understanding how complex, adaptive behavior is generated.  Quantitative connectivity matrices (or connectomes) for both adult sexes of the nematode Caenorhabditis elegans are presented that encompass all connections from sensory input to end-organ output across the entire animal.

We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k – 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org.

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology. Segmentation and classification of microstructures are required by quality control and materials development. The authors apply deep learning for the segmentation of complex phase steel microstructures, providing a bridge between experimental and computational methods for materials analysis.

The aim of this work is to fabricate supercapacitor electrode based on poly (3-hexyl-thiophene-2, 5-diyl) (P3HT) and single-walled carbon nanotubes (SWCNTs) nanocomposites with different ratios onto a graphite sheet as a substrate with a wide voltage window in nonaqueous electrolyte. Structural, morphological and electrochemical properties of the prepared nanocomposites of P3HT/SWCNTs were studied and discussed. The electrochemical properties included cyclic voltammetry (CV), galvanostatic charging-discharging (GCD), and electrochemical impedance spectroscopy (EIS) were investigated. The obtained results indicated that P3HT/SWCNTs nanocomposite possesses higher specific capacitance than that present in its individual component. The high electrochemical performance of the nanocomposite was due to formation of microporous structure which facilitates ions diffusion and electrolyte penetration in these pores. The morphological micrographs of the purified SWCNTs had buckypaper structure while the photomicrographs of P3HT/SWCNTs showed that SWCNTs appear behind and front of the P3HT nanospheres. The specific capacitance of 50% SWCNTs at 0.5 Ag −1 was found to be 245.8 Fg −1 compared with that of pure P3HT of 160.5 Fg −1 .

Microglial morphology is used to measure neuroinflammation and pathology. For reliable inference, it is critical that microglial morphology is accurately quantified and that results can be easily interpreted and compared across studies and laboratories. The process through which microglial morphology is quantified is a key methodological choice and little is known about how this choice may bias conclusions. We applied five of the most commonly used ImageJ-based methods for quantifying the microglial morphological response to a stimulus to identical photomicrographs and individual microglial cells isolated from these photomicrographs, which allowed for direct comparisons of results generated using these approaches. We found a lack of comparability across methods that analyzed full photomicrographs, with significant discrepancies in results among the five methods. Quantitative methods to analyze microglial morphology should be selected based on several criteria, and combinations of these methods may give the most biologically accurate representation of microglial morphology.

Telomerase adds telomeric repeats at chromosome ends to compensate for the telomere loss that is caused by incomplete genome end replication 1 . In humans, telomerase is upregulated during embryogenesis and in cancers, and mutations that compromise the function of telomerase result in disease 2 . A previous structure of human telomerase at a resolution of 8 Å revealed a vertebrate-specific composition and architecture 3 , comprising a catalytic core that is flexibly tethered to an H and ACA (hereafter, H/ACA) box ribonucleoprotein (RNP) lobe by telomerase RNA. High-resolution structural information is necessary to develop treatments that can effectively modulate telomerase activity as a therapeutic approach against cancers and disease. Here we used cryo-electron microscopy to determine the structure of human telomerase holoenzyme bound to telomeric DNA at sub-4 Å resolution, which reveals crucial DNA- and RNA-binding interfaces in the active site of telomerase as well as the locations of mutations that alter telomerase activity. We identified a histone H2A–H2B dimer within the holoenzyme that was bound to an essential telomerase RNA motif, which suggests a role for histones in the folding and function of telomerase RNA. Furthermore, this structure of a eukaryotic H/ACA RNP reveals the molecular recognition of conserved RNA and protein motifs, as well as interactions that are crucial for understanding the molecular pathology of many mutations that cause disease. Our findings provide the structural details of the assembly and active site of human telomerase, which paves the way for the development of therapeutic agents that target this enzyme. A high-resolution structure of human telomerase bound to telomeric DNA reveals details of telomerase assembly and its active site, and sheds light on how mutations alter telomerase function.