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Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

Abstract Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Intellectualization and unmanned manufacturing have been an inevitable trend in industrial development. The landing of intelligent applications is one of the current challenges in the industry. Due to the hierarchical architecture of the industrial automation pyramid, traditional programmable logic controllers (PLCs) that are usually employed in the field cannot cooperate with artificial intelligence (AI) algorithms that require massive data and computing resources. Therefore, it is necessary to research the virtualization of traditional PLCs as dockers, which can be deployed in the cloud, edge, or field. Cloud PLCs can be easily integrated with AI, big data, and cloud computing to achieve intelligent decision-making and control and break down data islands. The visual sorting system has attracted increasing attention for its ability to accurately detect the position of objects. Many deep learning–based methods have achieved remarkable performance in computer vision. Additionally, the requirement of a network is fundamental for guaranteeing data transmission with low latency and high reliability. The combination of 5G and time-sensitive networking (TSN) can achieve the deterministic transmission of several industrial applications. According to the above challenges, joint control between cloud PLCs of low-level devices and visual sorting systems in a reliable network is critical and has industry potential. In this study, we propose a deep learning–based material recognition and location system with a cloud PLC, which is demonstrated in a 5G-TSN network. First, traditional PLC is virtualized to realize flexible PLC function deployment in the field and cloud. Second, we establish a cloud-based AI platform and design a You only look once v5 (YOLOv5)-based object detection algorithm to locate the position and recognize the types of materials to obtain pixel coordinates. Third, the camera calibration method is used to transform pixel and world coordinates, and the material information consists of the world coordinates, types, and timestamps that are sent to cloud PLC. Finally, the commands are transmitted by the 5G-TSN environment from cloud PLC to the low-level devices for real-time control of the multi-crane cooperative. We establish an experimental system to demonstrate the significance and effectiveness of the proposed scheme, which synergistically controls multi-crane operation. The mean average precision (mAP) of material location is up to 99.65%, sorting accuracy reaches 96.67%, and the average consuming time is 25.99 s, which meets the requirements of low latency and high precision in industrial applications.

This study introduces a novel trifluoromethylating reagent, [(bpy)Cu(O2CCF2SO2F)2], notable for not only its practical synthesis from cost-effective starting materials and scalability but also its nonhygroscopic nature. The reagent demonstrates high efficiency in facilitating trifluoromethylation reactions with various halogenated hydrocarbons, yielding products in good yields and exhibiting broad functional group compatibility. The development of [(bpy)Cu(O2CCF2SO2F)2] represents an advancement in the field of organic synthesis, potentially serving as a valuable addition to the arsenal of existing trifluoromethylating agents.

Abstract Black phosphorus quantum dots (BPQDs) are synthesized and combined with graphene sheet. The fabricated BPQDs/graphene devices are capable of detecting visible and near infrared radiation. The adsorption effect of BPQDs in graphene is clarified by the relationship of the photocurrent and the shift of the Dirac point with different substrate. The Dirac point moves toward a neutral point under illumination with both SiO2/Si and Si3N4/Si substrates, indicating an anti-doped feature of photo-excitation. To our knowledge, this provides the first observation of photoresist induced photocurrent in such systems. Without the influence of the photoresist the device can respond to infrared light up to 980 nm wavelength in vacuum in a cryostat, in which the photocurrent is positive and photoconduction effect is believed to dominate the photocurrent. Finally, the adsorption effect is modeled using a first-principle method to give a picture of charge transfer and orbital contribution in the interaction of phosphorus atoms and single-layer graphene. Graphical Abstract

Van der Waals heterostructures of black phosphorous(BP) and Molybdenum Disulfide(MoS2) is designed and evaluated for photodetection application from visible to mid-infrared wavelength up to 4.5 μm. The device possesses a low dark current less than 0.1 μA for short wavelength light. For visible and near infrared radiation, the contribution of heat and photo energy to current enhancement is analyzed. Dark current drift by joule heat and laser heat absorbed is observed in short waveband. The difference in response speed is believed to be a result of the competition of BP and MoS2 photocurrent. Gate tunable response time and on-off ratio makes the device versatile in potential application. For mid infrared radiation, BP-MoS2 heterojunction with a dedicated thickness tailoring of MoS2 exhibits similar negative response as an intrinsic BP. The normalized detectivity is ${D}^*$ of $2.02 \times {10}^8\ Jones$ for the 4.5 μm mid-infrared radiation. Elaborate reduction of the noise is conducted to get the time resolvable photo-response. A low bias is necessary for a time resolved photocurrent, reducing the flicker noise to an acceptable level.

Communication and sensing integrated networks (CSINs) refer to the ability of physical digital space perception and ubiquitous intelligent communication at the same time. These networks realize the perception and cooperative communication of multidimensional resources through the cooperative work of communication and sensing resources and have the ability of intelligent interaction and processing of new information flow. First, this study proposes the technical architecture of an enhanced CSIN (E-HFWN), studies its key technologies and performance indicators, and explains the air interface technology, including frame structure design, carrier aggregation, channel detection, physical skyline mapping, beamforming and management, resource allocation and scheduling. In the resource allocation scheme, an actor-critic reinforcement learning (RL) framework is used to divide the wireless resources. The goal is to maximize the amount of mutual information (MI) and minimize the end-to-end delay of the sensing terminal. Then, the performance of the E-HFWN is tested, including numerical simulation of wireless resource management, system peak rate, capacity, end-to-end delay and communication perception waveform sidelobe ratio. Finally, from the results of the E-HFWN index test, the E-HFWN is further enhanced on the basis of 5G mmWave. The enhanced sensing function can provide a priori information for the optimal and rapid scheduling of distributed computing power and provide richer data sources for artificial intelligence (AI) services and applications to enhance the robustness of the training model. The E-HFWN can contribute to the development of technologies related to 6G synaesthesia computing integrated networks, promote the consensus between academia and industry.

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.

This paper demonstrates the capability of sub-nanosecond, high peak power Yb:YAG/Cr4+:YAG/YVO4 passively Q-switched Raman microchip lasers at 1134 nm operated in multiple pulses mode under quasi-continuous-wave (QCW) pumping. Total pulse energy for the Stokes laser was 1.8 mJ with a 4 mm YVO4 crystal and TOC = 16%. The corresponding pulse repetition rate reached 225 kHz within a single pumping pulse. By employing a compact plane-concave cavity and 5 mm YVO4 crystal, the single pulse energy for the Raman laser was further scaled up to 44 μJ. The corresponding peak power was 95 kW. A highest output pulse repetition rate of 87.8 kHz and shortest pulse duration of 464 ps were found for the Raman laser. The results indicate that the Raman microchip laser configuration under QCW LD pumping is a promising approach for developing high peak power, commercial and portable Raman lasers with a pulse duration of several hundred-picoseconds at a pulse repetition rate of hundred kilohertz.

Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

Hand Gesture Recognition (HGR) using Frequency Modulated Continuous Wave (FMCW) radars is difficult because of the inherent variability and ambiguity caused by individual habits and environmental differences. This paper proposes a deformable dual-stream fusion network based on CNN-TCN (DDF-CT) to solve this problem. First, we extract range, Doppler, and angle information from radar signals with the Fast Fourier Transform to produce range-time (RT) and range-angle (RA) maps. Then, we reduce the noise of the feature map. Subsequently, the RAM sequence (RAMS) is generated by temporally organizing the RAMs, which captures a target’s range and velocity characteristics at each time point while preserving the temporal feature information. To improve the accuracy and consistency of gesture recognition, DDF-CT incorporates deformable convolution and inter-frame attention mechanisms, which enhance the extraction of spatial features and the learning of temporal relationships. The experimental results show that our method achieves an accuracy of 98.61%, and even when tested in a novel environment, it still achieves an accuracy of 97.22%. Due to its robust performance, our method is significantly superior to other existing HGR approaches.

In this paper, response surface methodology (RSM) was used to study the optimised process conditions of lotus root starches modified by treatment with sodium hypochlorite (NaOCl). Based on the Box-Behnken design, quadratic models were developed to correlate the reaction variables. Analysis of variance (ANOVA) revealed that the active chlorine content was the most significant variable for the response. Under the experimental conditions in this paper, the calculated carboxyl content (CNCOOH) of obtained samples was approximately 0.98% ± 0.02% (n = 5). The spectra of Fourier transform infrared (FTIR) spectroscopy displayed that the formation of carboxyl groups successfully occurred on the oxidised lotus root starches. Scanning electron microscope (SEM) analysis showed that the oxidised lotus root starch (CNCOOH, 0.98%) granules were mostly spherical in shape and their surfaces were slightly rougher than those of native lotus root starch. This work may contribute to providing technical support and theoretical guidance for the production of oxidised lotus root starches using NaOCl as an oxidising agent.

This paper takes the specific environment covered by vegetation as the research object, carries out modeling and analysis, takes the large-scale fading model of wireless channel as the basis of data processing, researches the transmission law of electromagnetic wave in a typical vegetation environment, which can be divided into four situations. The signal attenuation in each case is theoretically derived and numerically simulated. From the view point of supporting vegetation environment channel, the large-scale channel measurement system is built to meet the actual needs, such as bandwidth, frequency, vegetation coverage, etc. the final vegetation environment channel model under the large-scale fading model is obtained. The results show that the path gain of four scenarios respectively are 81.3 dB, 36.5 dB, 1.6 dB, 1.5 dB, the value of path gain index is within the range of 2~3.5, four scenarios shadow fading standard deviation values are 7.1, 4.8, 10.1, 9.2, reflects the change of received power at the point caused by random factors such as reflection, absorption and scattering. In addition, the proposed channel model improves the gain about 15% compared with the tradition SUI model within vegetation coverage scene. The design process of the proposed model is carried out in the order of "studied the existing foundation → analyzed the existing problems → proposed the optimization scheme → simulation and verification results → actual measurement system". The advantage of paper's method is that, when the signal frequency, transceiver distance, antenna height and vegetation environment characteristic parameters are given, the statistical analysis results of wireless channel data are obtained. The purpose of the proposed work establishes a signal propagation prediction model under the vegetation environment, realizes a theoretical basis for channel simulation, and provides the basis of anti-fading technologies.

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality.

Siamese network based visual tracking has drawn considerable attention recently due to the balanced accuracy and speed. This type of method mostly trains a relatively shallow twin network offline, and measures the similarity online using cross-correlation operation between the feature maps generated by the last convolutional layer of the target and search regions to locate the object. Nevertheless, a single feature map extracted from the last layer of shallow networks is insufficient to describe target appearance, as well as sensitive to the distractors, which could mislead the similarity response map and make the tracker easily drift. To enhance the tracking accuracy and robustness while maintaining the real-time speed, based on the above tracking paradigm, three improvements including reform of backbone network, fusion of hierarchical features and utilization of channel attention mechanism, have been made in this paper. Firstly, we introduce a modified deeper VGG16 backbone network, which could extract more powerful features contributing to distinguishing the target from distractors. Secondly, we fuse diverse features extracted from deep layers and shallow layers to take advantage of both semantic and spatial information of the target. Thirdly, we incorporate a novel lightweight residual channel attention mechanism into the backbone network, which expands the weight gap between different channels and helps the network pay more attention on dominant features. Extensive experimental results on OTB100 and VOT2018 benchmarks demonstrate that our tracker performs better in accuracy and efficiency against several state-of-the-art methods in real-time scenarios.

Intelligent manufacturing is a challenging and compelling topic in Industry 4.0. Many computer vision (CV)-based applications have attracted widespread interest from researchers and industries around the world. However, it is difficult to integrate visual recognition algorithms with industrial control systems. The low-level devices are controlled by traditional programmable logic controllers (PLCs) that cannot realize data communication due to different industrial control protocols. In this article, we develop a multi-crane visual sorting system with cloud PLCs in a 5G environment, in which deep convolutional neural network (CNN)-based character recognition and dynamic scheduling are designed for materials in intelligent manufacturing. First, an YOLOv5-based algorithm is applied to locate the position of objects on the conveyor belt. Then, we propose a Chinese character recognition network (CCRNet) to significantly recognize each object from the original image. The position, type, and timestamp of each object are sent to cloud PLCs that are virtualized in the cloud to replace the function of traditional PLCs in the terminal. After that, we propose a dynamic scheduling method to sort the materials in minimum time. Finally, we establish a real experimental platform of a multi-crane visual sorting system to verify the performance of the proposed methods.

Intermediate volatility organic compounds (IVOCs) and semi-volatile organic compounds (SVOCs) have recently been proposed as important precursors of secondary organic aerosol (SOA). In the present work, 97 volatile organic compounds (VOCs) and 80 intermediate volatility and semi-volatile organic compounds (IVOCs and SVOCs) were measured by online gas chromatography-mass spectrometer/flame ionization detection (GC-MS/FID), and offline thermal desorption gas chromatography-mass spectrometer (TD-GC-MS), respectively. The average concentration of speciated VOCs, IVOCs, and SVOCs were 22.36 ± 9.02 μg m−3, 1.01 ± 0.32 μg m−3, and 0.10 ± 0.17 μg m−3. Alkanes and polycyclic aromatic hydrocarbons (PAHs) are the main compounds of total S/IVOCs. With the increase in molecular weight, the concentrations decreased in the gas phase, while increasing in the particle phase. Vehicular emission is the most significant source according to the carbon preference index (CPI) and the carbon of the most abundant alkane (Cmax). The yield method was used to estimate SOA from the oxidation of VOCs and S/IVOCs. The estimated SOA mass from IVOCs and SVOCs (0.70 ± 0.57 μg m−3) was comparable to that of VOCs (0.62 ± 0.61 μg m−3), and the oxidation of PAHs and alkanes took up 28.70 ± 8.26% and 51.97 ± 20.77% of the total SOA estimation, respectively. Compared to previous work, our study provided detailed molecular information of ambient S/IVOC species and elucidated their importance on SOA formation. Despite their low concentration, S/IVOCs species are important SOA precursors which shared comparable contribution compared with VOCs.

With outstanding deep feature learning and nonlinear classification abilities, Convolutional Neural Networks (CNN) have been gradually applied to deal with various fault diagnosis tasks. Affected by variable working conditions and strong noises, the empirical datum always has different probability distributions, and then different data segments may have inconsistent contributions, so more attention should be assigned to the informative data segments. However, most of the CNN-based fault diagnosis methods still retain black-box characteristics, especially the lack of attention mechanisms and ignoring the special contributions of informative data segments. To address these problems, we propose a new intelligent fault diagnosis method comprised of an improved CNN model named Efficient Convolutional Neural Network (ECNN). The extensive view can cover the special characteristic periods, and the small view can locate the essential feature using Pyramidal Dilated Convolution (PDC). Consequently, the receptive field of the model can be greatly enlarged to capture the location information and excavate the remarkable informative data segments. Then, a novel residual network feature calibration and fusion (ResNet-FCF) block was designed, which uses local channel interactions and residual networks based on global channel interactions for weight-redistribution. Therefore, the corresponding channel weight is increased, which puts more attention on the information data segment. The ECNN model has achieved encouraging results in information extraction and feature channel allocation of the feature. Three experiments are used to test different diagnosis methods. The ECNN model achieves the highest average accuracy of fault diagnosis. The comparison results show that ECNN has strong domain adaptation ability, high stability, and superior diagnostic performance.

Recently, heterojunction photodetectors have attracted significant interest due to the multiple degrees of freedom reorganization, integrating advantages of different typed materials. Herein, a high‐performance photodetector based on a 3D Dirac semimetal Cd3As2/tungsten disulfide (WS2) heterojunction is demonstrated, which is constructed by directly transferring exfoliated 2D few layer WS2 on Cd3As2 nano‐belt and following by annealing treatment. The resulting Cd3As2/WS2 heterojunction device presents superior performance with a high on/off ratio (≈5.3 × 104) and a responsivity (Ri) of about 223.5 AW−1 at 520 nm, as well as an outstanding detectivity (D*) of about 2.05 × 1014 Jones at 808 nm near‐IR waveband. However, the optimized noise equivalent power (NEP) is evaluated about 6.17 × 10−14 WHz−1/2 by the noise power density spectrum. The excellent performance can be attributed to a high‐quality heterostructure interface, strong light absorption capacity, and ultralow dark current in a Cd3As2/WS2 heterojunction system. This work provides a promising platform to develop a high‐performance optoelectronic device based on 3D Dirac semimetal and 2D TMDs families.

Few studies have been conducted on the forest spatial channel matrix, most of which were conducted by experts and scholars in the field of communications. The mature cellular network channel coding matrix was “transplanted” to the vegetation-covered space, and the effects of the various vegetation dielectric constants, the forest canopy density and other objective factors on the traditional cellular channel were ignored. To overcome this problem, this paper built a relay network that is suitable for forest environments. This network was an effective complement to the existing cellular communication network for expanding the coverage of signals in the forest. On this basis, this paper analysed the multi-antenna relay transmission protocol and proposed an additional spatial channel matching (mapping) matrix between the forward and backward filters. This matrix was designed to consider the effect of the complex forest environment on the channel to reduce the relaying noise power. Then, under the forest cellular relay network topology, a spatial channel matrix structure (based on unitary matrix) that is suitable for the forest environment is proposed. This matrix can be incorporated into various relay protocols and considers the source node preprocessing operation and the destination node equalizer. The forest relay cooperative network that is proposed in this paper can effectively expand the coverage of signals in forest areas; however, due to the inherent limitations of the relay network, many problems will be encountered in practical applications. For example, the precoding scheme that is based on phase correction requires the terminal node to feed the phase correction factor back to the forest base station node; however, the system performance is substantially affected by the feedback accuracy and the arrival delay difference. Due to the uncertain rank of the integrated channel in the forest base station precoding scheme, it was necessary to adapt to a variety of antenna ports, which rendered the design of the precoding codebook highly difficult. In addition, when the number of transmission layers was greater than 1, the local precoding scheme also encountered the problem of inter-layer interference (ILI). Based on a detailed analysis of the shortcomings of the previous scheme and the strategy of partial coherence transmission, this paper proposes an improved algorithm of incoherent joint transmission technology and an improved precoding scheme that are suitable for forest relay systems. The signal that is transmitted by the relay node and the feedback signal is corrected via layer exchange between cells and via phase correction. By changing the layer's exchange information and phase information, it can be adapted to various forest environments, such as artificial forests, natural forests, and virgin forests. Finally, the outage probability is considered as a performance indicator in the simulation of the forest environment wireless relay system. The theoretical analysis and simulation results demonstrate the satisfactory performance of the proposed spatial channel mapping matrix and the forest relay precoding scheme.

Glucagon (GCG) plays a stimulatory role in pituitary hormone regulation, although previous studies have not defined the molecular mechanism whereby GCG affects pituitary hormone secretion. To this end, we identified two distinct proglucagons, Gcga and Gcgb, as well as GCG receptors, Gcgra and Gcgrb, in Nile tilapia (Oreochromis niloticus). Using the cAMP response element (CRE)-luciferase reporter system, tilapia GCGa and GCGb could reciprocally activate the two GCG receptors expressed in human embryonic kidney 293 (HEK293) cells. Quantitative real-time PCR analysis revealed that differential expression of the Gcga and Gcgb and their cognate receptors Gcgra and Gcgrb was found in the various tissues of tilapia. In particular, the Gcgrb is abundantly expressed in the neurointermediate lobe (NIL) of the pituitary gland. In primary cultures of tilapia NIL cells, GCGb effectively stimulated SL release, with parallel rises in the mRNA levels, and co-incubation with the GCG antagonist prevented GCGb-stimulated SL release. In parallel experiments, GCGb treatment dose-dependently enhanced intracellular cyclic adenosine monophosphate (cAMP) accumulation with increasing inositol 1,4,5-trisphosphate (IP3) concentration and the resulting in transient increases of Ca2+ signals in the primary NIL cell culture. Using selective pharmacological approaches, the adenylyl cyclase (AC)/cAMP/protein kinase A (PKA) and phospholipase C (PLC)/IP3/Ca2+/calmodulin (CaM)/CaMK-II pathways were shown to be involved in GCGb-induced SL release and mRNA expression. Together, these results provide evidence for the first time that GCGb can act at the pituitary level to stimulate SL release and gene expression via GCGRb through the activation of the AC/cAMP/PKA and PLC/IP3/Ca2+/CaM/CaMK-II cascades.

In this paper, a mathematical model based on spherical differential unit cell is proposed as a model for studying seasonal freeze-thaw soil space infinitesimal differential unit cell. From this model, the basic equations of permafrost moisture and heat flow motion are directly derived, then the linked equations form the permafrost water-heat coupled transport model. On this basis, the one-dimensional seasonal permafrost water-heat transport equation is derived. The model reduces the original spatial three-variable coordinate system (parallel hexahedron) into a coupled equation with a single spherical radius (R) as the independent variable, so the iterations of the numerical simulation algorithm is greatly reduced and the complexity is decreased. Finally, the model is used to simulate the seasonal freeze-thaw soil in the ShiHeZi region of Xinjiang, China. The principle of the simulation is to collect the soil temperature and humidity values of the region in layers and fixed-points using a homemade freeze-thaw soil sensor, after that we solve it by numerical calculation using MATLAB. The analysis results show that the maximum relative error of the model we proposed is 4.36, the minimum error is 0.98, and the average error is 2.515. The numerical simulation results are basically consistent with the measured data, then the proposed model is consistent with the matching states of permafrost moisture content and soil temperature in the region at different times. In addition, the experiments also demonstrate the reliability and accuracy of the model.

In recent years, lead selenide (PbSe) has gained considerable attention for its potential applications in optoelectronic devices. However, there are still some challenges in realizing mid-infrared detection applications with single PbSe film at room temperature. In this paper, we use a chemical bath deposition method to deposit PbSe thin films by varying deposition time. The effects of the deposition time on the structure, morphology, and optical absorption of the deposited PbSe films were investigated by x-ray diffraction, scanning electron microscopy, and infrared spectrometer. In addition, in order to activate the mid-infrared detection capability of PbSe, we explored its application in infrared photodetection by improving its crystalline quality and photoconductivity and reducing tge noise and high dark current of PbSe thin films through subsequent iodine treatment. The iodine sensitization PbSe film showed superior photoelectric properties compared to the untreated sample, which exhibited the maximum of responsiveness, which is 30.27 A/W at 808 nm, and activated its detection ability in the mid-infrared (5000 nm) by introducing PbI2, increasing the barrier height of the crystallite boundary and carrier lifetimes. This facile synthesis strategy and the sensitization treatment process provide a potential experimental scheme for the simple, rapid, low-cost, and efficient fabrication of large-area infrared PbSe devices.

Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly under-sampled k-space data, which significantly reduced the scan time. Using full k-space 3D dMRI data of post-mortem mouse brains, we retrospectively compared the performance of the so-called kernel low rank (KLR) method with a conventional compressed sensing (CS) method in terms of image quality and ability to resolve complex fiber orientations and connectivity. The results demonstrated that the KLR-CS method outperformed the conventional CS method for acceleration factors up to 8 and was likely to enhance our ability to investigate brain microstructure and connectivity using high-resolution 3D dMRI.

According to the existing multi-antenna relay forwarding protocol, the relay matrix can be split into two parts-relay nodes forward and backward filters direct concatenation, then in actual channel matrix design, relay's forward and backward channel conditions are not ideal, which make system bit error rate (BER) performance is poor. Based on this problem, this paper proposes a modular algebraic precoding relay system model based on lattice reduction. At first, this method re-builds the relay's forward and backward channel matrices, which can make the channel satisfy the orthogonality characteristics. Then, a modified equivalent relay channel matrix is precoding, which solves tradition algorithms' poor performance problem when channel orthogonality is not good, and based on this idea, two improved algorithms are proposed. These two new algorithms can achieve better BER performance under poor relay channel conditions, make each user's performance average, and satisfy system fairness requirements. Simulation results show that comparing with tradition algorithms, this paper proposes a relay channel model which is based on the lattice reduction precoding method, it gains a certain bit error performance. Finally, the improved algorithms are compared and analyzed with the sorting-based algorithm. The results show that the proposed algorithms have better performance. Therefore, in order to ensure users fairness, the optimization algorithm can be used instead of the high complexity sorting-based algorithm in the actual system.

Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters.