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Cell state recognition and evaluation of its life cycle are the fundamental and critical procedure to identify the pathological mechanism of viruses and analyze pharmacodynamic effectiveness. Modern life science research is based on large-scale biological experiments so that it is inefficiency to estimate and decide the physicochemical results on petri dishes. With the development of target recognition in computer science, especially YOLO (You Only Look Once) approach, we are able to incorporate unified, real-time object detection into cell state recognition in this paper, which realizes automotive process of detection and classification of CPE (cytopathic effect) states. In our work, we build up photomicrograph datasets of Vero cell in CPE state and train our YOLO model with it. Finally, our model learns characteristic pixel structure of cell rounding, syncytium and inclusion bodies, acting as end-to-end detector in cell infected experiments.

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.

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.

This article proposes a method for studying external impacts on the components of a composite material and on the material as a whole. The method is based on the analysis of digital micrographs of composite samples' cross-section at the edge. Results of the analysis show the changes in the parameters of the composite's different sections and of the composite material as a whole, caused by various types of external impacts.

Starting with Gauss and Kelvin, knots in fields were postulated to behave like particles, but experimentally they were found only as transient features or required complex boundary conditions to exist and could not self-assemble into three-dimensional crystals. We introduce energetically stable, micrometer-sized knots in helical fields of chiral liquid crystals. While spatially localized and freely diffusing in all directions, they resemble colloidal particles and atoms, self-assembling into crystalline lattices with open and closed structures. These knots are robust and topologically distinct from the host medium, though they can be morphed and reconfigured by weak stimuli under conditions such as those in displays. A combination of energy-minimizing numerical modeling and optical imaging uncovers the internal structure and topology of individual helical field knots and the various hierarchical crystalline organizations that they form.

The mangrove forest in Indonesia have transformed into conservation area, tourist objects, and fishponds, causing the environmental changing. The purposes of this research are to find out the condition of the environment, the varieties of the species and the anatomy of the leaves. The locations of this research are determined based on the Karimun Java in December 2019. The locations are conservation area in Menjangan Besar Island, fishpond area in Kemujan Island and Mangrove Tracking area. The environment parameters such as temperature, pH, DO, TDS, and salinity. Photomicrograph is used to check stomata. The data are analyzed descriptively. The environmental conditions in those three locations have different condition, except temperature. The species found in Menjangan Island are A.marina and R.stylosa , the species found in Kemujan fishpond are C.tagal, R.apiculata and E.agallocha meanwhile the species found in tracking Kemujan are C.tagal, R.apiculata, R.stylosa and L.racemosa . The result of the observation towards the anatomy of the leaves in those three locations has not showed responses to the environment stress, but the condition of the environment in the fishpond showed the rising in salinity level. Therefore, it is suggested to grow Avicennia because it is more adaptable towards high level of salinity.

In the automotive and aerospace industry, abrasive products lodge the major portion of the machining applications. Among that, the coated abrasive disc is used for a finishing application. Once the disc is fully consumed, the disc is unused and considered waste. The present work focuses on removing phenol-formaldehyde resin coating, and the fiber backing is reused for the same coated abrasive disc production application as flexible fiber backing. A sandblasting technique removes phenol-formaldehyde resin coating and embedded abrasive grains. During the fiber backing recovery process, the experimental parameters such as abrasive pressure, abrasive type, abrasive size, and orientation of the disc are varied to find out the optimal surface roughness value for reusing the produced coated abrasive discs. The results highlight that the recovered backing has an abrasive size of 120 mesh pressure of 0.20 MPa, an abrasive type of garnet, and a standoff distance of 1 mm. Surface features such as surface roughness and micrographs of the eroded surface are analyzed. Finally, the recovered backing was reused in the coated abrasive disc production, and the performance of the recovered disc was compared with the standard discs. The recovered fiber backing disc product was similar to a standard fresh disc.

The additive manufacturing of metals requires optimisation to find the melting conditions that give the desired material properties. A key aspect of the optimisation is minimising the porosity that forms during the melting process. A corresponding analysis of pores of different types (e.g. lack of fusion or keyholes) is therefore desirable. Knowing that pores form under different thermal conditions allows greater insight into the optimisation process. In this work, two pore classification methods were trialled: unsupervised machine learning and defined limits. These methods were applied to 3D pore data from X-ray computed tomography and 2D pore data from micrographs. Data were collected from multiple alloys (Ti-6Al-4V, Inconel 718, Ti-5553 and Haynes 282). Machine learning was found to be the most useful for 3D pore data and defined limits for the 2D pore data; the latter worked by optimising the limits using energy densities.

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.

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.