Explore the vast range of titles available.

BackgroundAlthough robotic surgery is popular around the world, its safety and efficacy over classical open surgery is still controversial. The purpose of this article is to compare the safety and efficacy of robotic pancreaticoduodenectomy (RPD) and open pancreaticoduodenectomy (OPD).MethodsA literature search of PubMed, Web of Science, and the Cochrane Library database up to July 29, 2018 was performed and the meta-analysis was performed using RevMan 5.2 software with Fixed and random effects models applied. The IRB approval and written consent were not needed for this paper.ResultsTwelve non-randomized retrospective studies and 1 non-randomized prospective study consisting of 2403 patients were included in this meta-analysis. There were 788 (33%) patients in the RPD group and 1615 (67%) patients in the OPD group. Although RPD was associated with a longer operative time (weighted mean difference [WMD]: 71.74 min; 95% CI 23.37–120.12; p = 0.004), patient might benefit from less blood loss (WMD: − 374.03 ml; 95% CI − 506.84 to − 241.21; p < 0.00001), shorter length of stay (WMD: − 5.19 day; 95% CI − 8.42 to − 1.97; p = 0.002), and lower wound infection rate (odds ratio: 0.17; 95% CI 0.04–0.80; p = 0.02). No statistically significant difference was observed in positive margin rate, lymph nodes harvested, postoperative complications, reoperation or readmission rate, and mortality rate.ConclusionsRobotic pancreaticoduodenectomy is a safe and feasible alternative to open pancreaticoduodenectomy with regard to short-term outcomes. Further studies on the long-term outcomes of these surgical techniques are needed.

Chronic nonhealing diabetic wound therapy and complete skin regeneration remains a critical clinical challenge. The controlled release of bioactive factors from a multifunctional hydrogel was a promising strategy to repair chronic wounds. Herein, for the first time, we developed an injectable, self-healing and antibacterial polypeptide-based FHE hydrogel (F127/OHA-EPL) with stimuli-responsive adipose-derived mesenchymal stem cells exosomes (AMSCs-exo) release for synergistically enhancing chronic wound healing and complete skin regeneration. The materials characterization, antibacterial activity, stimulated cellular behavior and full-thickness diabetic wound healing ability of the hydrogels were performed and analyzed. The FHE hydrogel possessed multifunctional properties including fast self-healing process, shear-thinning injectable ability, efficient antibacterial activity, and long term pH-responsive bioactive exosomes release behavior. , the FHE@exosomes (FHE@exo) hydrogel significantly promoted the proliferation, migration and tube formation ability of human umbilical vein endothelial cells (HUVECs). , the FHE@exo hydrogel significantly enhanced the healing efficiency of diabetic full-thickness cutaneous wounds, characterized with enhanced wound closure rates, fast angiogenesis, re-epithelization and collagen deposition within the wound site. Moreover, the FHE@exo hydrogel displayed better healing outcomes than those of exosomes or FHE hydrogel alone, suggesting that the sustained release of exosomes and FHE hydrogel can synergistically facilitate diabetic wound healing. Skin appendages and less scar tissue also appeared in FHE@exo hydrogel treated wounds, indicating its potent ability to achieve complete skin regeneration. This work offers a new approach for repairing chronic wounds completely through a multifunctional hydrogel with controlled exosomes release.

Optoelectronic memory plays a vital role in modern semiconductor industry. The fast emerging requirements for device miniaturization and structural flexibility have diverted research interest to two-dimensional thin layered materials. Here, we report a multibit nonvolatile optoelectronic memory based on a heterostructure of monolayer tungsten diselenide and few-layer hexagonal boron nitride. The tungsten diselenide/boron nitride memory exhibits a memory switching ratio approximately 1.1 × 10 6 , which ensures over 128 (7 bit) distinct storage states. The memory demonstrates robustness with retention time over 4.5 × 10 4  s. Moreover, the ability of broadband spectrum distinction enables its application in filter-free color image sensor. This concept is further validated through the realization of integrated tungsten diselenide/boron nitride pixel matrix which captured a specific image recording the three primary colors (red, green, and blue). The heterostructure architecture is also applicable to other two-dimensional materials, which is confirmed by the realization of black phosphorus/boron nitride optoelectronic memory. Continued device miniaturization and feasibility of integrating two-dimensional materials into circuits have enabled flexible and transparent optoelectronic memories. Here, the authors show a WSe 2 –hBN-based heterostructure memory with switching ratio of ~1.1 × 10 6 , ensuring over 128 distinct storage states and retention time of ~4.5 × 10 4  s.

DNA extraction, a vital pre-requisite for most biological studies, continues to be studied extensively. According to some studies, DNA shows a certain degree of absorbability on filter paper made of plant fiber-based adsorbent material. However, the principle underlying such specific adsorption as well as plant species associated with plant fiber-based adsorbents and optimized extraction conditions have not yet been studied. This study demonstrates the tight correlation between crystallinity and hygroscopicity in plant fiber-based adsorbents used for DNA extraction and proposes the concept of DNA adsorption on plant fiber-based adsorbents, for the first time. We also explored optimal extracting and eluting conditions and developed a novel plant fiber-based DNA extraction method that was quadruple times more powerful than current approaches. Starting with the screening of various types of earthed plant fiber-based adsorbents, we went on to mine new plant fiber-based adsorbents, bamboo paper and degreased cotton, and succeeded in increasing their efficiency of DNA extraction to 4.2 times than that of current approaches. We found a very strong correlation between the crystallinity and hygroscopicity of plant fiber-based adsorbents which showed efficiency for DNA extraction, and thus propose a principle that potentially governs such specific adsorption processes, in the hope that this information may guide related multidisciplinary research studies in the future. Nanodrop, electrophoresis and PCR were selected to demonstrate the quantity, quality, integrity and utility of the extracted DNA. Furthermore, crystallinity, hygroscopicity, pore size distribution and composition of plant fiber-based adsorbents were studied to explore their correlation in an attempt to understand the principle underlying this particular type of adsorption. The findings of this study may be further extended to the extraction of other types of nucleic acids with similar biochemical properties.

Aiming at the problem of the inefficiency of coal mine water reuse, a multi-level scheduling method for mine water reuse based on an improved whale optimization algorithm is proposed. Firstly, the optimization objects of mine water reuse time and reuse cost are used to establish the optimal scheduling model of mine water. Secondly, in order to overcome the defect that the whale optimization algorithm (WOA) is prone to local convergence, the opposition-based learning strategy is introduced to speed up the convergence speed, the Levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimization, the nonlinear convergence factor is used to balance the global and local search ability, and the adaptive inertia weight is used to improve the optimization accuracy of the algorithm. Finally, the improved whale optimization algorithm (IWOA) is applied to the mine water optimization scheduling model with multiple objects and constraints. The results show that the reuse efficiency of the multi-level scheduling method of mine water reuse is increased by 30.2% and 31.9%, respectively, in the heating and nonheating seasons, which can significantly improve the reuse efficiency of mine water and realize the efficient utilization of mine water reuse deployment. At the same time, experiments show that the improved whale optimization algorithm has higher convergence accuracy and speed, which proves the feasibility and superiority of its improvement strategies.

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.

The deformation and failure mechanisms of layered rocks under the combined effects of bedding-plane orientation and confining pressure are crucial for maintaining the stability of both surface and underground geotechnical structures. In this study, rock specimens with different bedding orientations were fabricated using a custom-designed compaction device, and uniaxial compression tests combined with acoustic emission (AE) monitoring were conducted. Furthermore, the microscopic failure processes of layered rock under varying confining pressures and bedding orientations were simulated using three-dimensional particle flow code (PFC3D). The results reveal that, near the peak stress, the slopes of the cumulative AE ring-down counts and cumulative energy curves rose sharply, indicating that the surface cracks are expanding rapidly. The dominant AE frequencies were concentrated in two low-frequency bands and one high-frequency band, with the low-frequency components prevailing. Under uniaxial compression, the peak strength of the specimens exhibited a ‘U’-shaped dependence on bedding angle, whereas the peak AE ring-down count followed an inverted ‘n’-shaped pattern. The peak AE ring-down count displayed an “m”-shaped pattern under confining pressure. Moreover, increasing confining pressure reduced the peak AE activity and attenuated the effect of bedding orientation.

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.

Background Increasing evidence indicates that epithelial-mesenchymal transition (EMT) can be regulated by microRNAs (miRNAs). miR-615-3p was shown to be involved in tumor development. However, the role of miR-615-3p in the metastasis of breast cancer remains largely unknown. Methods The expression of miR-615-3p in breast cancer cells and tissues was assessed by qRT-PCR and situ hybridization assays. Effects of miR-615-3p on tumor metastasis were evaluated with experiments in vitro and mouse model. EMT markers were detected by western blot and immunofluorescence assays. Molecular mechanism of miR-615-3p in the regulation of breast cancer cell metastasis was analyzed by Western Blot, Co-immunoprecipitation, and Luciferase assay. Results In the present study, we found that miR-615-3p was significantly elevated in breast cancer cells and tissues, especially in those with metastasis. In breast cancer cell lines, stable overexpression of miR-615-3p was sufficient to promote cell motility in vitro, and pulmonary metastasis in vivo, accompanied by the reduced expression of epithelial markers and the increased levels of mesenchymal markers. Further studies revealed that the reintroduction of miR-615-3p increased the downstream signaling of TGF-β, the type I receptor (TGFBRI) by targeting the 3′-untranslated regions (3′-UTR) of PICK1. PICK1 inhibits the binding of DICER1 to Smad2/3 and the processing of pre-miR-615-3p to mature miR-615-3p in breast cancer cells, thus exerting a negative feedback loop. Conclusions Our data highlight an important role of miR-615-3p in the molecular etiology of breast cancer, and implicate the potential application of miR-615-3p in cancer therapy.

Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.