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Purpose: Colon cancer is the most common malignant tumor in the intestine. Abnormal Carboxylesterases 3 (CES3) expression had been reported to be correlated to multiple tumor progression. However, the association among CES3 expression and prognostic value and immune effects in colonic adenocarcinoma (COAD) were unclear. Patients and methods: The transcription and expression data of CES3 and corresponding clinical information was downloaded from The Cancer Genome Atlas (TCGA). The CES3 protein expression and the prognostic value were verified based on tissue microarray data. The Cancer immune group Atlas (TCIA), Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the GSE78220 immunotherapy cohort were used to forecast immunotherapy efficacy. Finally, a prognostic immune signature was constructed and verified. Results: Compared with normal colon tissues, the expression of mRNA and protein levels of CES3 were downregulated in tumor tissues. CES3 expression was associated with TIICs. Hihg-CES3 COAD patients had better efficacy of concurrent immunotherapy. CES3-related immune genes (CRIs) were identified and were then used to construct prognostic immune signature and had been successfully verified in GES39582. Conclusion: CES3 might be a potential immune-related gene and promising prognostic biomarker in COAD. [ABSTRACT FROM AUTHOR]

Background: BRAF non-V600 mutation occupies a relatively small but critical subset in colorectal cancer (CRC). However, little is known about the biological functions and impacts of BRAF class III mutation in CRC. Here, we aim to explore how D594A mutation impacts on biological behaviors and immune related signatures in murine CRC cells. Methods: BRAF V600E (class I), G469V (class II) and D594A (class III) mutant cell lines were established based on MC38 cells. The biological behaviors of cells were evaluated in respect of cell growth, cell proliferation, cell apoptosis, cell migration and invasion by the methods of colony-forming assay, CCK-8 assay, Annexin V/PI staining and transwell assay. The concentrations of soluble cytokines were detected by ELISA. The membrane expression of immuno-modulatory molecules and the pattern of tumor infiltrating lymphocyte were evaluated by flow cytometry. The molecular mechanism was explored by RNA sequencing. Immunohistochemistry (IHC) staining was used for the detection of CD8α in tumor tissues. qRT-PCR and western blot were performed to assess the mRNA and protein expression. Anti-PD-L1 treatment and cytokines neutralization experiments were conducted in in vivo models. Results: D594A mutant cells displayed lower grade malignancy characteristics than V600E (class I) and G469V (class II) mutant cells. Meanwhile, D594A mutation led to evident immuno-modulatory features including upregulation of MHC Class I and PD-L1. In vivo experiments displayed that the frequency of infiltrated CD8+ T cells was significantly high within D594A mutant tumors, which may provide potential response to anti-PD-L1 therapy. RNA sequencing analysis showed that D594A mutation led to enhanced expression of ATF3 and THBS1, which thus facilitated CXCL9 and CXCL10 production upon IFN-γ treatment. In addition, CXCL9 or CXCL10 neutralization reduced the infiltration of CD8+ T cells into THBS1-overexpressing tumors. Conclusions: D594A mutant CRC exhibited lower aggressiveness and immune-activated phenotype. ATF3-THBS1-CXCL9/CXCL10 axis mediated functional CD8+ T cells infiltration into the microenvironment of D594A mutant CRC. Our present study is helpful to define this mutation in CRC and provide important insights in designing effective immunotherapeutic strategies in clinic. [ABSTRACT FROM AUTHOR]

Optoelectronic synapses are considered to be important cornerstones in the construction of neuromorphic computing systems because of their low power consumption, high operating speeds, and high scalability. In this work, we demonstrate an optoelectronic synaptic device based on a ZnO/HfOx heterojunction in which optical potentiation/electrical depression behaviors and nonvolatile high current state can be implemented. The heterojunction device exhibits conductance evolution with high linearity. The excellent optoelectronic memristive behavior of the device can be attributed to the interface barrier between ZnO and HfOx, which hinders the recombination of photo-excited electron–hole pairs to increase the carrier lifetime, and realizes the nonvolatile high current state. More importantly, the artificial vision system based on optoelectronic synaptic devices can achieved a high recognition accuracy of 96.1%. Our work provides a feasible pathway toward the development of optoelectronic synaptic devices for use in high-performance neuromorphic vision systems. [ABSTRACT FROM AUTHOR]

Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag–TiO2 nanocomposite film. Fully light‐induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low‐level image pre‐processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light‐gated and electrically‐driven synaptic plasticity can be performed in the same device, in which the spike‐timing‐dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high‐level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre‐processing and light‐gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light‐modulated capability that may promote the future development of efficient neuromorphic vision. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proposed ensemble model, which synergistically integrates Convolutional Neural Network, Bagging, and LightGBM algorithms, effectively leverages the unique strengths of each algorithm to achieve accurate mineral prospecting predictions within the study area. • Through utilization of generative adversarial network-based data augmentation and transfer learning techniques, the generalization ability of the ensemble model can be improved while also mitigating the issue of model overfitting. The utilization of machine learning and data-driven approaches for mineral prospectivity modeling has become increasingly important in supporting regional mineral exploration. However, supervised learning methods have encountered several common challenges, such as overfitting and underfitting, in the process of mineral prospectivity modeling, particularly due to imbalanced sample sizes used for model training. This imbalance could limit the generalization capability of models and result in inadequate predictive performance of individual models. The present study introduced a novel hybrid ensemble model (HEM) that integrates various learner methods, including generative adversarial network (GAN) and transfer learning techniques, to effectively address the issues above. These intelligent prediction techniques were applied to analyze the geological, geochemical, and geophysical dataset in order to delineate potential areas for W-Sn mineralization in Malipo County, Yunnan Province, China. Meanwhile, some well-established supervised learning methods such as Convolutional Neural Networks (CNN), Boosting-based model (e.g., LightGBM), and Bagging-based models, which demonstrated exceptional performance in dealing with classification issues were also integrated into the HEM. The weights for integrating these models were determined automatically using the equidistant-sampling technique. The superior performance of the HEM in identifying known deposits made it an efficient tool for mineral prediction mapping. Additionally, this model also offered high flexibility and scalability by allowing adjustments to the types and quantities of base learners based on data characteristics. [ABSTRACT FROM AUTHOR]

Neutron radiography has been widely employed in nondestructive investigations. The noise and blur that are inevitably generated in the process of neutron radiography seriously decrease the image quality and lead to the loss of image information. In particular, white spot noise, which has the characteristics of nonuniform distribution, large size, and high magnitude, is difficult to remove. To solve this issue, we apply a generative adversarial network-based to convert a degraded neutron image into a clean image. The basic idea is to integrate attention mechanisms into our generation and discrimination networks. A fusion block with a visual attention mechanism is proposed, which can extract more potential features from the images and preserve the image texture as much as possible. In addition, due to the lack of neutron image datasets, we propose a neutron image degradation model to simulate the noise and blur in real neutron images. The results of the experiments show that our method can effectively eliminate noise and blur from neutron images while retaining texture information well. • Proposed a deep learning-based method to remove hybrid noise and blur from neutron images. • A fusion block combines a dense residual block and a spatial channel attention mechanism. • Perceptual loss is used to make our network efficiently learn the high-level information of the image. • Visual evaluation and quantitative analysis illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A new structure of the Penning ion gauge (PIG) ion source used for neutron tube is designed, and the experiments on beam current extraction of the PIG negative hydrogen (H −) ion source are performed. The influences of the magnetic field of the ion source, different cathode materials and the aperture size of the extraction cathode on the negative hydrogen ion flow are studied. The neutron experiments are performed with a PIG negative hydrogen (H − PIG) ion source D–D neutron tube, and the experimental data demonstrate that this sort of negative hydrogen ion source can be used to produce neutron tubes with good performance. Highlights • A new structure of PIG ion source used for neutron tube is proposed. • Beam current extraction experiments of PIG H − ion source are performed. • The influence of several factors on negative hydrogen ions flow are studied. • The neutron experiments are performed with a PIG H − ion source D–D neutron tube. • This kind H − ion source can be used to produce neutron tubes with good performance. [ABSTRACT FROM AUTHOR]

In this letter, we report a gate engineering method to adjust threshold voltage of carbon nanotube (CNT) based field-effect transistors (FETs) continuously in a wide range, which makes the application of CNT FETs especially in digital integrated circuits (ICs) easier. Top-gated FETs are fabricated using solution-processed CNT network films with stacking Pd and Sc films as gate electrodes. By decreasing the thickness of the lower layer metal (Pd) from 20 nm to zero, the effective work function of the gate decreases, thus tuning the threshold voltage (Vt) of CNT FETs from −1.0 V to 0.2 V. The continuous adjustment of threshold voltage through gate engineering lays a solid foundation for multi-threshold technology in CNT based ICs, which then can simultaneously provide high performance and low power circuit modules on one chip. [ABSTRACT FROM AUTHOR]

• A study on the use of deep learning model in the field of neutron image restoration is performed. • A lightweight, convenient and efficient deep learning network is proposed for neutron image restoration. • The X-ray dataset solves the problem of lack of real neutron image datasets. • The pre-training model achieves good results in real neutron image restoration. Traditional neutron image restoration (NIR) methods lack flexibility and generalization in dealing with multiple image restoration tasks, which indirectly limits the practical applications of neutron images. Deep convolutional neural networks (CNNs) have achieved impressive success in image restoration, so this paper preliminarily explores and applies deep CNNs in NIR. Considering the characteristics and applications of neutron images, we propose a fast and lightweight densely connected attention U-Net (DAUNet) for NIR. We strengthen the network's attention to the image structural information and the processing at different frequencies by adding an attention mechanism to the symmetric skip connection under each scale of U-Net. The proposed DAUNet uses a single model for recovering images with different degradations, such as additive noise and blur. Extensive experiments on synthetic and real neutron-degraded images are conducted, and the results show that DAUNet is effective and efficient in NIR and achieves satisfactory results in terms of both comprehensive performance (PSNR) and intrinsic metrics (parameters, memory cost and running time), which are highly attractive for practical applications. In addition, to address the problem of lacking real NIR datasets, we use X-ray images that the imaging principles are closer to that of neutron imaging as data drivers for the network. [ABSTRACT FROM AUTHOR]

The local binary pattern with H function (H-LBP) technique enables fast and efficient edge extraction in digital radiography. In this paper, we reformulate the model of H-LBP and propose a novel sparsity-based shrinkage approach, in which the threshold can be adapted to the data sparsity. Using this model, we upgrade fast H-LBP framework and apply it to real digital radiography. The experiments show that the method improved using the new shrinkage approach can avoid elaborately artificial modulation of parameters and possess greater robustness in edge extraction compared with the other current methods without increasing processing time. [ABSTRACT FROM AUTHOR]

This memristor enables the combination of sensing and image pre-processing functions in a single device, which may promote the future development of efficient neuromorphic vision. Plasmonic Optoelectronic Memristor Enabling Fully Light-Modulated Synaptic Plasticity for Neuromorphic Vision (Adv. Sci. 6/2022) B Plasmonic Optoelectronic Memristors b In article number 2104632, Zhongqiang Wang, Haiyang Xu, Yichun Liu, and co-workers report a new type of plasmonic optoelectronic memristor for neuromorphic vision, in which the fully light-modulated synaptic plasticity is demonstrated using visible and ultraviolet light stimulations. [Extracted from the article]

As the traditional reactor and accelerator neutron sources are immovable, the research on the compact neutron radiography becomes more and more important. In this study, a portable neutron tube is employed for the compact thermal neutron radiography system. To realize ideal image quality, the optimal material selection and geometry design (viz. the diameter of the aperture, the shape of the channel and the lining material) of the proposed collimator are thereafter carried out by the SuperMC software. Simulation results show that the normalized thermal neutron flux of 1. 42 × 1 0 − 6 ncm − 2 s − 1 can be successfully realized at the exit of the collimator by the proposed collimator with a satisfied nonuniformity of 4.6%. • The neutron tube is used as the source of the compact neutron radiography system. • The proposed collimator is simulated by the SuperMC software. • 45# transformer oil is used for heat dissipation and insulation. • Graphite and beryllium are selected as the reflector materials. • The shape of collimator is cylindrical-conical. [ABSTRACT FROM AUTHOR]

• A simple white spots removal method is proposed for degraded neutron images. • A'detection-location-removal' iterative strategy is introduced for the white spots detection and removal. • The qualitative and quantitative analysis are used for evaluating the performance of the mentioned methods. • The proposed method has a better performance in spots removal and details preservation than the comparison methods. Neutron radiography (NR) technology has been widely used in non-destructive investigations. However, the neutron images are always polluted by random white spots due to the interactions between the gamma rays, scattered neutron and other radiation and chip of the charge-coupled device (CCD) camera. In this paper, we propose a novel denoising method based on a 'detection-location-removal' iterative strategy, which adopts an improved robust principal component analysis (RPCA) in combination with the suitable threshold selection strategy and median filters for white spots noise removal of neutron images. Experimental results based on both simulation testing and real neutron images show that the proposed method is effective in improving neutron image quality in terms of numerical criteria and visual results. The comparison with the other denoising methods shows that the proposed method possesses higher robustness and effectiveness in practical denoising applications. [ABSTRACT FROM AUTHOR]

Neutron radiography (NR) has been successfully implemented on reactors and accelerators in the past few decades. However, the promotion of NR is severely hindered by the immovability of the reactor neutron source. In order to solve this problem, the advanced neutron tube is employed as the neutron source of the compact NR system. To maintain the thermal neutron flux and reduce the gamma dose rate at the image plane, we give a novel design of moderator and collimator for the compact NR systems by using SuperMC software. Simulation results show that, under the neutron source yield of 1 × 1 0 9 n ∕ s , we can obtain a uniform thermal neutron flux of 1. 03 × 1 0 − 5 cm − 2 per source particle with n/ γ ratio of 2. 47 × 1 0 11 n cm − 2 Sv − 1 at the sample position by using the proposed moderator and collimator device, thus demonstrating its application potential in the compact NR systems. [ABSTRACT FROM AUTHOR]

• A collaborative filtering method is adopted for gamma white spots detection. • The spots are removed through a"detection-corrosion" step by step strategy. • The proposed method can effectively remove the gamma white spots andsimultaneouslypreservethe details in the neutron images. In CCD-based neutron radiography and tomography, resulting digital neutron images are usually degraded by gamma white spots caused by the interaction between high energy gamma radiation and the charge-coupled device (CCD) detector. The gamma white spots have a major impact on the accuracy of quantitative data analysis and are difficult to remove due to their high magnitudes and non-uniform distribution. In this paper, we put forward a novel iterative filtering method based on a "detection-corrosion" strategy for degraded neutron image denoising. A collaborative filtering method is adopted via integrating a spatially adaptive filter and a median filter for gamma white spots detection and removal. The experimental results show that the proposed method can achieve satisfactory denoising effect with preferable preservation of image details. [ABSTRACT FROM AUTHOR]

The no‐reference image quality assessment (NR‐IQA) method can evaluate the distortions in an image without the reference image. However, due to the diversity of the image contents and distortion types, it is hard for the existing NR‐IQA algorithms to obtain competitive performance on both synthetically and authentically distorted images. To address the problem, a multiscale feature representation‐based NR‐IQA method that performs well for both synthetic and authentic distortions is proposed. This model consists of two parts: The feature extraction part and the feature fusion part. First, part of the Res2Net‐50 network is chosen as the feature extraction part due to its high ability in increasing the range of receptive fields. Then, the feature fusion part consisting of a novel residual block and two fully connected layers is designed to fuse the extracted features and realize the quality score mapping. After a series of stepwise optimization experiments, the most competitive network architecture consisting of the feature extraction part and the feature fusion part is obtained. Comprehensive experiments on the LIVE, TID2013, CSIQ, KADID‐10k, KonIQ‐10K, and LIVE challenge databases demonstrate that the proposed method can work powerfully on both the synthetic and authentic distortions and also has a strong generalization ability. [ABSTRACT FROM AUTHOR]

Carbon nanotube (CNT) network thin film field‐effect transistors (TFTs), which used to be considered as low cost and low performance transistors for display driving or flexible electronics, have recently been used to construct digital integrated circuits (ICs). However, few studies have focused on exploring how optimal CNT TFTs can be achieved according to transistor standards in digital applications. In this work, sub‐micrometer TFTs based on high‐quality and high‐purity solution‐derived CNT films are fabricated and the potential performance restriction due to the switching‐off property of these transistors is explored. Specifically, subthreshold swing (SS) severely degrades upon scaling down the channel length or increasing the CNT density in TFTs, and a tradeoff between peak transconductance (gm) and SS in CNT TFTs due to the random orientation distribution of CNTs has been observed in experiments and proven by theoretical simulations. A well‐designed balance between gm and SS is necessary to build CNT TFTs with SS of 120 mV dec−1 and gm of 150 µS µm−1 to meet device requirements in digital ICs powered by a supplied voltage, VDD, lower than 2.0 V. [ABSTRACT FROM AUTHOR]

In this letter, we explore the vertical scaling-down behavior of carbon nanotube (CNT) network film field-effect transistors (FETs) and show that by using a high-efficiency gate insulator, we can substantially improve the subthreshold swing (SS) and its uniformity. By using an HfO2 layer with a thickness of 7.3 nm as the gate insulator, we fabricated CNT network film FETs with a long channel (>2 μm) that exhibit an SS of approximately 60 mV/dec. The preferred thickness of HfO2 as the gate insulator in a CNT network FET is between 7 nm and 10 nm, simultaneously yielding an excellent SS (<80 mV/decade) and low gate leakage. However, because of the statistical fluctuations of the network CNT channel, the lateral scaling of CNT network film-based FETs is more difficult than that of conventional FETs. Experiments suggest that excellent SS is difficult to achieve statistically in CNT network film FETs with a small channel length (smaller than the mean length of the CNTs), which eventually limits the further scaling down of this kind of CNT FET to the sub-micrometer regime. [ABSTRACT FROM AUTHOR]

Carbon nanotube field-effect transistors (CNT FETs) with gate length down to 10 nm are fabricated and analyzed by fitting the experimental data through the virtual-source (VS) model. Due to the quasi-ballistic carrier transport property, it is shown that carriers in CNTs exhibit the injection velocity of ${3}\times 10^{{7}}$ cm/s, approximately two times of the target velocity in the 10-nm gate length transistors required by International Roadmap for Devices and Systems (IRDS 2017). The transport parameters for the first time, namely, critical length, unidirectional thermal velocity, and backscattering mean free path (MFP), are extracted by the Lundstrom model from the measured data of quasi-ballistic short-channel CNT FETs to diffusive long-channel CNT FETs, which help arriving at a conclusion that, down to 10-nm gate length, the CNT FETs operate close to 73% of the ballistic regime. [ABSTRACT FROM AUTHOR]

Neutron radiography (NR) has been widely used in non-destructive investigations. Since the industrial neutron radiographic images (NRIs) usually suffer from unclear defect features, insufficient data volume, and low efficiency of manual detection, we propose a proof-of-concept defect-detection method based on the modified YOLO network for the degraded NRIs in this paper. Firstly, 6336 radiographic images including crack, inclusion, blow hole, and residual core are built as the defect-detection dataset. Secondly, the types and the relative coordinates of defects are labeled by a graphical image annotation tool (i.e., Labelimg). Finally, the adaptive spatial feature fusion (ASFF) and convolutional block attention module (CBAM) are introduced to the modified YOLO network to enhance its ability of small-size defect detection. Experimental results demonstrate that the proposed method can achieve the average accuracy of 98.1% and detection rate of 85.985 frames per second (FPS) with a single NVIDIA GeForce RTX 4090 GPU on the built radiographic image dataset. In addition, the proposed method shows a good potential in detecting the above defects contained in the real NRIs. • A YOLO network-based automatic defect detection method with higher accuracy and lower detection time is proposed. • Data augmentation is employed to build a large-scale radiographic-image dataset with 6336 defect images. • ASFF and CBAM are used to solve the inconsistency of different-scale features and enhance the small-size detection ability. [ABSTRACT FROM AUTHOR]

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]

There has been evidence of warming rate varying largely over space and between seasons. However, little has been done to evaluate the spatial and temporal variability of air temperature in the Tarim Basin, northwest China. In this study, we collected daily air temperature from 19 meteorological stations for the period of 1960–2012, and analyzed annual mean temperature (AMT), the annual minimum (Tmin) and maximum temperature (Tmax), and mean temperatures of all twelve months and four seasons and their anomalies. Trend analyses, standard deviation of the detrended anomaly (SDDA) and correlations were carried out to characterize the spatial and temporal variability of various mean air temperatures. Our data showed that increasing trend was much greater in the Tmin (0.55°C/10a) than in the AMT (0.25°C/10a) and Tmax (0.12°C/10a), and the fluctuation followed the same order. There were large spatial variations in the increasing trends of both AMT (from −0.09 to 0.43 °C/10a) and Tmin (from 0.15 to 1.12°C/10a). Correlation analyses indicated that AMT had a significantly linear relationship with Tmin and the mean temperatures of four seasons. There were also pronounced changes in the monthly air temperature from November to March at decadal time scale. The seasonality (i.e., summer and winter difference) of air temperature was stronger during the period of 1960–1979 than over the recent three decades. Our preliminary analyses indicated that local environmental conditions (such as elevation) might be partly responsible for the spatial variability, and large scale climate phenomena might have influences on the temporal variability of air temperature in the Tarim Basin. In particular, there was a significant correlation between index of El Niño-Southern Oscillation (ENSO) and air temperature of May (P = 0.004), and between the index of Pacific Decadal Oscillation (PDO) and air temperature of July (P = 0.026) over the interannual to decadal time scales. [ABSTRACT FROM AUTHOR]

The existing semantic segmentation methods have some shortcomings in feature extraction of remote sensing images. Therefore, an image semantic segmentation method based on deep learning in UAV aerial remote sensing images is proposed. First, original remote sensing images obtained by S185 multirotor UAV are divided into smaller image blocks through sliding window and normalized to provide high-quality image set for subsequent operations. Then, the symmetric encoding-decoding network structure is improved. Bottleneck layer with 1 × 1 convolution is introduced to build ISegNet network model, and pooling index and convolution are used to fuse semantic information and image features. The improved encoding-decoding network gradually strengthens the extraction of details and reduces the number of parameters. Finally, based on ISegNet network, five-classification problem is transformed into five binary classification problems for network training, so as to obtain high-precision image semantic segmentation results. The experimental analysis of the proposed method based on TensorFlow framework shows that the accuracy value reaches 0.901, and the F1 value is not less than 0.83. The overall segmentation effect is better than those of other comparison methods. [ABSTRACT FROM AUTHOR]

Objective image quality metric is a challenging problem for neutron radiography due to the lack of reference images. The existing no-reference image quality metric (NR-IQM) method designed for neutron radiographic images (NRIs) still exists two main shortcomings. Firstly, the limited L/D of the collimator would inevitably lead to geometric blur in the NRIs. However, the special distortion type of mixed Poisson–Gaussian noise is merely considered but ignores the blur distortion. Secondly, the distortion levels of Poisson noise added to an image cannot be adjusted. In order to address these two issues, a more practical objective NR-IQM method is proposed to predict the perceptual quality of NRIs in this paper. Experimental results demonstrate that the proposed method shows good consistency with the quality of authentic NRIs and also performs well in reflecting the tiny quality difference of enhanced NRIs, thus demonstrating its application potential in neutron radiography. [ABSTRACT FROM AUTHOR]

In this paper, a neutron source system for compact thermal neutron radiography (TNR) was designed and optimized to improve its moderation efficiency via SuperMC simulations. The DSNH long-life D–T neutron tube with self-target technology independently developed by Northeast Normal University is used as the neutron source. The main optimizations in material and structure include the use of tungsten and lead primary moderators, polyethylene secondary moderators, sapphire filter, and 4 cm magnesium thermal neutron aggregation layer. The improved system provides thermal neutron flux of 3. 77 × 1 0 4 n cm − 2 s − 1 with thermal neutron content of 85.20% at the exit of the neutron channel, which is suitable for thermal neutron radiography. [ABSTRACT FROM AUTHOR]

Neutron imaging (NI) has been widely employed in non-destructive investigations. Since the image quality assessment (IQA) method can be beneficial in reflecting the performance of imaging systems and image processing algorithms, we propose a proof-of-concept IQA method for the NI system based on a deep bilinear convolutional neural network (CNN) framework with two designed datasets. Due to the lack of neutron IQA database, different levels of authentic distortion induced by NI are first simulated on the natural and neutron radiographic images to generate the pre-training and fine-tuning datasets, respectively. Then, the gradient magnitude similarity deviation (GMSD) algorithm and transfer learning method are respectively employed to label the above datasets and optimize the prediction performance. Experimental results demonstrate that the proposed method can maintain good consistency with human perception in predicting the quality scores of both the authentic and enhanced neutron radiographic images. [ABSTRACT FROM AUTHOR]

[Display omitted] • LncRNA HULC is upregulated in the spinal cord tissue of sciatic nerve injury rats. • LncRNA HULC promotes neuropathic pain progression and aggravated H 2 O 2 -induced Schwann cell injury. • SIX1 transcriptionally regulates lncRNA HULC expression. • SIX1 promotes neuropathic pain progression and aggravated H 2 O 2 -induced Schwann cell injury via lncRNA HULC. Neuropathic pain is a severe and debilitating condition caused by damage to the peripheral nerve or central nervous system. Although several mechanisms have been identified, the underlying pathophysiology of neuropathic pain is still not fully understood. Unfortunately, few effective therapies are available for this condition. Therefore, there is an urgent need to investigate the underlying mechanisms of neuropathic pain to develop more effective treatments. Long non-coding RNAs (lncRNAs) have recently gained attention due to their potential to modulate protein expression through various mechanisms. LncRNAs have been implicated in many diseases, including neuropathic pain. This study aimed to identify a novel lncRNA involved in neuropathic pain progression. The lncRNA microarray analysis showed that lncRNA Upregulated in Liver Cancer (HULC) was significantly upregulated in spinal cord tissue of sciatic nerve injury (SNI) rats. Further experiments confirmed that HULC promoted neuropathic pain progression and aggravated H 2 O 2 -induced Schwann cell injury. Mechanistically, Sine Oculis Homeobox 1 (SIX1) regulated the transcriptional expression of HULC, and both SIX1 and HULC were involved in neuropathic pain and Schwann cell injury. The results of our research indicate the existence of a previously unknown SIX1/HULC axis that plays a significant role in the development and progression of neuropathic pain, shedding light on the complex mechanisms that underlie this debilitating condition. These findings offer novel insights into the molecular pathways involved in neuropathic pain. This study underscores the potential of targeting lncRNAs as a viable approach to alleviate the suffering of patients with neuropathic pain. [ABSTRACT FROM AUTHOR]

In image denoising, the recovery of high-frequency regions, such as image edges, directly affects the quality of the denoised images. However, previous deep learning-based denoising methods fail to effectively allocate the transmission of different frequency information and have difficulty giving network attention to high-frequency regions. In this paper, we rethink the fusion of image gradients in a neural network and deeply mine the intrinsic structure of the input image to propose a novel layered input gradient network (LIGN) for image denoising. The core of our network focuses on the features of different frequencies through two networks, which contain several key elements: (a) The input noise image is layered to widen the shallow layer of the network and to promote the hierarchical learning of different types of frequencies. (b) A multiscale feature extraction (MFE) block and information shunting (IS) block are proposed to integrate and separate various frequency features. (c) A gradient network (GradiNet) is designed to extract high-frequency information by network training, and the information is adaptively added to the input of the parallel main network (MainNet) through normalization to obtain high-quality images. Furthermore, we propose a sharpening loss function to enhance the texture details of the denoised image and improve visual quality. Extensive experiments on synthetic and real-world datasets show that the proposed method greatly enhances perceptual visual quality and achieves state-of-the-art performance on both PSNR and SSIM. The source code and pretrained models are available at https://github.com/JerryYann/LIGN. • A layered input gradient network (LIGN) based on a dual U-Net for high-quality image denoising is proposed. • Layered input and sharpening loss greatly improve the perceptual quality of the denoised image. • Multi-scale feature extraction block can capture more semantic information. • LIGN achieves the SoTA performance compared with the latest methods on synthetic and real noise datasets. [ABSTRACT FROM AUTHOR]

RelB confers the aggressiveness to prostate cancer (PC) cells. Exosomes modulate the oncogenesis and progression of PC. We aimed to identify the downstream molecule in the exosomes, by which RelB increases the aggressiveness of DU145. Totally, 137 upregulated and 55 downregulated exosomal proteins were identified from RelB-knockdown DU145 cells by Liquid Chromatography-Mass Spectrometry. UALCAN, GeneMANIA and tissue microarray analysis revealed that intercellular adhesion molecule-1 (ICAM1) was positively related to and co-expressed with RelB in PC. Luciferase reporter assay revealed that RelB bound directly to the promoter of ICAM1. ICAM1 overexpression enhanced the migration and invasion abilities of DU145 cells. Exposure to exosomes derived from ICAM1 overexpressing cells (hICAM1-exo) strengthened the aggressiveness of RelB-knockdown cells, especially the migration and invasion capabilities. Mechanistically, the expression of ICAM1, Integrin β1, MMP9 and uPA were upregulated in RelB-knockdown cells upon hICAM1-exo treatment. Exosomal ICAM1 is the key molecule regulated by RelB, which increased the aggressiveness of DU145. The study suggests that cell-cell communication via exosomal ICAM1 is a novel mechanism by which RelB promotes PC progression. • RelB regulates the proteins expression in exosomes derived from DU145. • ICAM1 overexpression enhances the migration and invasion abilities of DU145. • Exosomal ICAM1 rescues the aggressiveness of DU145 due to RelB-knockdown. • RelB- exo -ICAM1 axis maybe a mechanism of metastasis of DU145. [ABSTRACT FROM AUTHOR]

Abstract: Projections of changes in climate extreme are important in assessing the potential impacts of climate change on social and natural systems. This article presents future projections of climate extremes in the Tibetan Plateau constructed from ensembles of coupled general circulation models (CGCMs) contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), under a range of emission scenarios. The extremes of temperature and precipitation are described by seven indices, namely, the frost day (FD), percentage of nights when Tmin >90th percentile (TN90), consecutive dry days (CDD), annual count of days when precipitation >=10mm (R10), maximum 5-day precipitation total (R5D), simple daily intensity index (SDII), and annual total precipitation when precipitation >95th percentile (R95T). Results indicate that frost days decrease over the Tibetan Plateau in the 21st century. More frequent warm nights are also projected in the plateau. The increases of these temperature extremes under A2 and A1B scenarios are more pronounced than under B1. Heavy precipitation events for single days and pentads are projected to increase in their intensity over most parts of Tibetan Plateau. CDD, R10, R5D, R95T and SDII collectively suggest more extreme precipitation in the region (2011–2020). In addition, impacts of climate extremes changes on local water resources and fragile ecosystem are discussed as an extension of this article. The findings will be beneficial to project regional responses in this unique region to global climate change, and then to formulate regional strategies against the potential menaces of climate change. [Copyright &y& Elsevier]