Harnessing dislocation motion using an electric field

Dislocations, line defects in crystalline materials, play an essential role in the mechanical[1,2], electrical[3], optical[4], thermal[5], and phase transition[6] properties of these materials. Dislocation motion, an important mechanism underlying crystal plasticity, is critical for the hardening, processing, and application of a wide range of structural and functional materials[1,7,8]. For decades, the movement of dislocations has been widely observed in crystalline solids under mechanical loading[9-11]. However, the goal of manipulating dislocation motion via a non-mechanical field alone remains elusive. Here, we present real-time observations of dislocation motion controlled solely by an external electric field in single-crystalline zinc sulfide (ZnS). We find that 30{\deg} partial dislocations can move back and forth depending on the direction of the electric field, while 90{\deg} partial dislocations are motionless. We reveal the nonstoichiometric nature of dislocation cores using atomistic imaging and determine their charge characteristics by density functional theory calculations. The glide barriers of charged 30{\deg} partial dislocations, which are lower than those of 90{\deg} partial dislocations, further decrease under an electric field, explaining the experimental observations. This study provides direct evidence of dislocation dynamics under a non-mechanical stimulus and opens up the possibility of modulating dislocation-related properties

Non-invasive and accurate risk evaluation of cerebrovascular disease using retinal fundus photo based on deep learning

BackgroundCerebrovascular disease (CeVD) is a prominent contributor to global mortality and profound disability. Extensive research has unveiled a connection between CeVD and retinal microvascular abnormalities. Nonetheless, manual analysis of fundus images remains a laborious and time-consuming task. Consequently, our objective is to develop a risk prediction model that utilizes retinal fundus photo to noninvasively and accurately assess cerebrovascular risks.Materials and methodsTo leverage retinal fundus photo for CeVD risk evaluation, we proposed a novel model called Efficient Attention which combines the convolutional neural network with attention mechanism. This combination aims to reinforce the salient features present in fundus photos, consequently improving the accuracy and effectiveness of cerebrovascular risk assessment.ResultOur proposed model demonstrates notable advancements compared to the conventional ResNet and Efficient-Net architectures. The accuracy (ACC) of our model is 0.834 ± 0.03, surpassing Efficient-Net by a margin of 3.6%. Additionally, our model exhibits an improved area under the receiver operating characteristic curve (AUC) of 0.904 ± 0.02, surpassing other methods by a margin of 2.2%.ConclusionThis paper provides compelling evidence that Efficient-Attention methods can serve as effective and accurate tool for cerebrovascular risk. The results of the study strongly support the notion that retinal fundus photo holds great potential as a reliable predictor of CeVD, which offers a noninvasive, convenient and low-cost solution for large scale screening of CeVD

Automatic and fast classification of barley grains from images: A deep learning approach

Australia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use, as the intrinsic quality of the variety determines its market value. Manual identification of barley varieties by the naked eye is challenging and time-consuming for all stakeholders, including growers, grain handlers and traders. Current industrial methods for identifying barley varieties include molecular protein weights or DNA based technology, which are not only time-consuming and costly but need specific laboratory equipment. On grain receival, there is a need for efficient and low-cost solutions for barley classification to ensure accurate and effective variety segregation. This paper proposes an efficient deep learning-based technique that can classify barley varieties from RGB images. Our proposed technique takes only four milliseconds to classify an RGB image. The proposed technique outperforms the baseline method and achieves a barley classification accuracy of 94% across 14 commercial barley varieties (some highly genetically related)

A chromosome conformation capture ordered sequence of the barley genome

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Direct Synthesis of Thioesters from Feedstock Chemicals and Elemental Sulfur

The development of a mild, atom- and step-economical catalytic strategy that effectively generates value-added molecules directly from readily available commodity chemicals is a central goal of organic synthesis. In this context, the thiol-ene click chemistry for carbon-sulfur (C-S) bond construction has found widespread applications in the synthesis of pharmaceuticals and functional materials. In contrast, the selective carbonyl thiyl radical addition to carbon-carbon multiple bonds remains underdeveloped. Herein, we report a carbonyl thiyl radical-based thioester synthesis through three-component coupling from feedstock aldehydes, alkenes, or alkynes and elemental sulfur by direct photocatalyzed hydrogen atom transfer. This method represents an orthogonal strategy to the conventional thiol-based nucleophilic substitution and exhibits a remarkably broad substrate scope ranging from simple commodity chemicals such as ethylene and acetylene to complex pharmaceutical molecules. This protocol can be easily extended to the synthesis of thiolactones, oligomer/polymers, and thioacids. Its synthetic utility has been demonstrated by a two-step synthesis of the drug esonarimod. Mechanistic studies indicate that the use of elemental sulfur to trap acyl radicals is both thermodynamically and kinetically favored, illustrating its great potential for the synthesis of sulfur-containing molecules

Modular and Practical 1,2-Aryl(alkenyl) Heteroatom Functionalization of Alkenes through Iron/Photoredox Dual Catalysis

Efficient methods for synthesizing 1,2-aryl(alkenyl) heteroatomic cores, encompassing heteroatoms such as nitrogen, oxygen, sulfur, and halogens, are of significant importance in medicinal chemistry and pharmaceutical research. In this study, we present a mild, versatile and practical photoredox/iron dual catalytic system that enables access to highly privileged 1,2-aryl(alkenyl) heteroatomic pharmacophores with exceptional efficiency and site selectivity. Our approach exhibits an extensive scope, allowing for the direct utilization of a wide range of commodity or commercially available (hetero)arenes as well as activated and unactivated alkenes with diverse functional groups, drug scaffolds, and natural product motifs as substrates. By merging iron catalysis with the photoredox cycle, a vast array of alkene 1,2-aryl(alkenyl) functionalization products that incorporate a neighboring azido, amino, halo, thiocyano and nitrooxy group were secured. The scalability and ability to rapid synthesize numerous bioactive small molecules from readily available starting materials highlight the utility of this protocol

Molecular Characterization of Mycobiota and Aspergillus Species from Eupolyphaga sinensis Walker Based on High-Throughput Sequencing of ITS1 and CaM

Eupolyphaga sinensis Walker is a valuable traditional Chinese animal medicine first recorded in Shennong Bencao. Previous research has shown that E. sinensis is easily contaminated by aflatoxins (AFs), which are highly toxic mycotoxins, during harvest, storage, and transport, thereby posing a considerable threat to consumer health. Most often, these AFs are produced by Aspergillus species. In this study, we contrast the traditional culture-based dilution plating method to the high-throughput sequencing (HTS) technology for fungal identification in TCM E. sinensis. Both of the methods used internal transcribed spacer 1 (ITS1) and calmodulin (CaM) sequencing for fungal molecular identification. The new CaM primer we designed in the study is suitable for MiSeq PE300 sequencing used for identification of Aspergillus species in community DNA samples. More fungal species were found in the E. sinensis samples based on HTS than those found using the culture-based dilution plating method. Overall, combining the sequencing power of ITS1 and CaM is an effective method for the detection and monitoring of potential toxigenic Aspergillus species in E. sinensis. In conclusion, HTS can be used to obtain a large amount of sequencing data about fungi contaminating animal medicine, allowing earlier detection of potential toxigenic fungi and ensuring the efficient production and safety of E. sinensis

An Efficient Emulsion-Induced Interface Assembly Approach for Rational Synthesis of Mesoporous Carbon Spheres with Versatile Architectures

Mesoporous carbon matrix with open pore structure, short diffusion length, and large pore size can favor the in-pore immobilization of active species and facilitate mass diffusion during catalytic reactions. However, a great difficulty still remains on controllable synthesis of uniform mesoporous carbon spheres with these structural characteristics. Herein, using amphiphilic Pluronic F127 as the surfactant, 1,3,5-trimethyl benzene (TMB) as the pore swelling and interface-adjusting agent, and dopamine as the carbon source, a robust emulsion-induced interface assembly approach for rational synthesis of mesoporous carbon spheres is demonstrated. The interface assembly process, including dopamine polymerization and fusion of TMB/F127/dopamine emulsions, can be regulated by tuning the dosage of dopamine and ammonia water, resulting in mesoporous carbon spheres with tunable pore sizes and versatile architectures, such as vesicles, walnut shapes, spheres with dendritic-like 3D radially aligned mesochannels (RA-MC), and isolated spherical mesopores. Moreover, the derived RA-MC is used as a promising matrix to immobilize ultra-small Au nanoparticles (≈3 nm). The Au/RA-MC exhibits no mass diffusion limitations in reduction of 4-nitrophenol, showing high conversion efficiency and good recyclability. This work paves a new avenue for controllable synthesis of mesoporous carbon spheres with well-developed mesoporosity and architectures and their application as novel heterogeneous catalysts.Scopu