Facile synthesis of solid-state fluorescent organosilica nanoparticles with a photoluminescence quantum yield of 73.3% for fingerprint recognition and white-light-emitting diodes

Polymer-like coated OSiNPs with a solid-state PLQY of up to 73.3% for applications in WLEDs and fingerprint recognition are fabricated by a simple hydrothermal method

Analysis on Drivers\u27 Vision-psychology under the Influence of Color Difference after Pavement Pothole Repair

Pavement pothole repair, as one of the commonest methods used in routine road maintenance works, is effective to extend the pavement service life and increase its transport capacity. However, there are significant color differences between the old and new pavements after pothole repair, and there are few studies about the impact of color differences on driving safety. Thus, using multiple typical pavements with the pothole repair, we aim to reveal the impact of the color differences between the old and new materials on the driving safety, and to improve the safety and comfort of drivers. Tobii eye tracker and the ErgoLAB physiological recorder were used to test the driver\u27s visual parameters when the vehicles run under the pothole repair environment, including drivers\u27 gaze frequency, gaze duration, saccade range and heart rate variability. Results show that the color differences of the road after pothole repair have a significant influence on the drivers\u27 vision and psychology, resulting in reduced driving safety and stability. Compared with the unrepaired road section, the gaze frequency and duration of drivers on pothole sections are significantly increased. The speed of saccade is also increased, and the saccade range is reduced. Meanwhile, the average heart rate of drivers on the pothole sections is increased by 10-20%, indicating that drivers\u27 attention and heart rate fluctuate greatly when passing through potholes, which can easily cause emotional tension and misjudgment, and thereby reduce the safety of driving. The obtained conclusions provide a significant reference for subsequent research

Nucleosome Positioning and Its Role in Gene Regulation in Yeast

Nucleosome, composed of a 147-bp segment of DNA helix wrapped around a histone protein octamer, serves as the basic unit of chromatin. Nucleosome positioning refers to the relative position of DNA double helix with respect to the histone octamer. The positioning has an important role in transcription, DNA replication and other DNA transactions since packing DNA into nucleosomes occludes the binding site of proteins. Moreover, the nucleosomes bear histone modifications thus having a profound effect in regulation. Nucleosome positioning and its roles are extensively studied in model organism yeast. In this chapter, nucleosome organization and its roles in gene regulation are reviewed. Typically, nucleosomes are depleted around transcription start sites (TSSs), resulting in a nucleosome-free region (NFR) that is flanked by two well-positioned H2A.Z-containing nucleosomes. The nucleosomes downstream of the TSS are equally spaced in a nucleosome array. DNA sequences, especially 10–11 bp periodicities of some specific dinucleotides, partly determine the nucleosome positioning. Nucleosome occupancy can be determined with high throughput sequencing techniques. Importantly, nucleosome positions are dynamic in different cell types and different environments. Histones depletions, histones mutations, heat shock and changes in carbon source will profoundly change nucleosome organization. In the yeast cells, upon mutating the histones, the nucleosomes change drastically at promoters and the highly expressed genes, such as ribosome genes, undergo more change. The changes of nucleosomes tightly associate the transcription initiation, elongation and termination. H2A.Z is contained in the +1 and −1 nucleosomes and thus in transcription. Chaperon Chz1 and elongation factor Spt16 function in H2A.Z deposition on chromatin. The chapter covers the basic concept of nucleosomes, nucleosome determinant, the techniques of mapping nucleosomes, nucleosome alteration upon stress and mutation, and Htz1 dynamics on chromatin

Impaired function of dendritic cells within the tumor microenvironment

Dendritic cells (DCs), a class of professional antigen-presenting cells, are considered key factors in the initiation and maintenance of anti-tumor immunity due to their powerful ability to present antigen and stimulate T-cell responses. The important role of DCs in controlling tumor growth and mediating potent anti-tumor immunity has been demonstrated in various cancer models. Accordingly, the infiltration of stimulatory DCs positively correlates with the prognosis and response to immunotherapy in a variety of solid tumors. However, accumulating evidence indicates that DCs exhibit a significantly dysfunctional state, ultimately leading to an impaired anti-tumor immune response due to the effects of the immunosuppressive tumor microenvironment (TME). Currently, numerous preclinical and clinical studies are exploring immunotherapeutic strategies to better control tumors by restoring or enhancing the activity of DCs in tumors, such as the popular DC-based vaccines. In this review, an overview of the role of DCs in controlling tumor progression is provided, followed by a summary of the current advances in understanding the mechanisms by which the TME affects the normal function of DCs, and concluding with a brief discussion of current strategies for DC-based tumor immunotherapy

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure

Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. Results: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data

Observation of E8 Particles in an Ising Chain Antiferromagnet

Near the transverse-field induced quantum critical point of the Ising chain, an exotic dynamic spectrum consisting of exactly eight particles was predicted, which is uniquely described by an emergent quantum integrable field theory with the symmetry of the $E_8$ Lie algebra, but rarely explored experimentally. Here we use high-resolution terahertz spectroscopy to resolve quantum spin dynamics of the quasi-one-dimensional Ising antiferromagnet BaCo$_2$V$_2$O$_8$ in an applied transverse field. By comparing to an analytical calculation of the dynamical spin correlations, we identify $E_8$ particles as well as their two-particle excitations.Comment: 6 pages, 3 figures, plus supplementary material

A novel switchgear state assessment framework based on improved fuzzy C-means clustering method with deep belief network

Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use