Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide.Immunotherapy has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual differences.Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival rates.Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately considered.AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy.METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways.Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells.The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related features.Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model.Finally,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene signature.RESULTS The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell communication.The effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltration analysis,survival analysis,and expression analysis.CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment,providing insights for personalized treatment strategies.

Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.

Energy scaling and extended tunability of a ring cavity terahertz parametric oscillator based on KTiOPO_(4) crystal

A wide terahertz tuning range from 0.96 THz to 7.01 THz has been demonstrated based on ring-cavity THz wave parametric oscillator with a KTiOPO_(4)(KTP)crystal.The tuning range was observed intermittently from 0.96 THz to 1.87 THz,from 3.04 THz to 3.33 THz,from 4.17 THz to 4.48 THz,from 4.78 THz to 4.97 THz,from 5.125 THz to 5.168 THz,from5.44 THz to 5.97 THz,and from 6.74 THz to 7.01 THz.The dual-Stokes wavelengths resonance phenomena were observed in some certain tuning angle ranges.Through the theoretical analysis of the dispersion curve of the KTP crystal,the intermittent THz wave tuning range and dual-wavelength Stokes waves operation during angle tuning process were explained.The theoretical analysis was in good agreement with the experiment results.The maximum THz output voltage detected by Golay cell was 1.7 V at 5.7 THz under the pump energy of 210 mJ,corresponding to the THz wave output energy of5.47μJ and conversion efficiency of 2.6×10^(-5).

Microstructure,optical,and photoluminescence properties ofβ-Ga_(2)O_(3)films prepared by pulsed laser deposition under different oxygen partial pressures

Theβ-Ga_(2)O_(3)films are prepared on polished Al_(2)O_(3)(0001)substrates by pulsed laser deposition at different oxygen partial pressures.The influence of oxygen partial pressure on crystal structure,surface morphology,thickness,optical properties,and photoluminescence properties are studied by x-ray diffraction(XRD),atomic force microscope(AFM),scanning electron microscope(SEM),spectrophotometer,and spectrofluorometer.The results of x-ray diffraction and atomic force microscope indicate that with the decrease of oxygen pressure,the full width at half maximum(FWHM)and grain size increase.With the increase of oxygen pressure,the thickness of the films first increases and then decreases.The room-temperature UV-visible(UV-Vis)absorption spectra show that the bandgap of theβ-Ga_(2)O_(3)film increases from4.76 e V to 4.91 e V as oxygen pressure decreasing.Room temperature photoluminescence spectra reveal that the emission band can be divided into four Gaussian bands centered at about 310 nm(~4.0 e V),360 nm(~3.44 e V),445 nm(~2.79 e V),and 467 nm(~2.66 e V),respectively.In addition,the total photoluminescence intensity decreases with oxygen pressure increasing,and it is found that the two UV bands are related to self-trapped holes(STHs)at O1 sites and between two O2-s sites,respectively,and the two blue bands originate from V_(Ga)^(2-)at Ga1 tetrahedral sites.The photoluminescence mechanism of the films is also discussed.These results will lay a foundation for investigating the Ga_(2)O_(3)film-based electronic devices.

Image Information Hiding Method Based on Image Compression and Deep Neural Network

Image steganography is a technique that hides secret information into the cover image to protect information security.The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image,which will cause the size of the secret image to be much smaller than the cover image.Therefore,the problem of small steganographic capacity needs to be solved urgently.This paper proposes a steganography framework that combines image compression.In this framework,the Vector Quantized Variational AutoEncoder(VQ-VAE)is used to achieve the compression of the secret image.The compressed and reconstructed image is visually indistinguishable from the original image and facilitates more embedded data information later.Finally,the compressed image is transmitted to a SegNet deep neural network that contains a set of encoders and decoders to achieve image hiding and extraction.Experimental results show that the steganographic framework guarantees the quality of steganography while its relative steganographic capacity reaches 1.Besides,Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM)values can reach 42 dB and 0.94,respectively.

Application of k-person and k-task maximal efficiency assignment algorithm to water piping repair

Solving the absent assignment problem of the shortest time limit in a weighted bipartite graph with the minimal weighted k-matching algorithm is unsuitable for situations in which large numbers of problems need to be addressed by large numbers of parties. This paper simplifies the algorithm of searching for the even alternating path that contains a maximal element using the minimal weighted k-matching theorem and intercept graph. A program for solving the maximal efficiency assignment problem was compiled. As a case study, the program was used to solve the assignment problem of water piping repair in the case of a large number of companies and broken pipes, and the validity of the program was verified.

The Application of Continuous Spatial Scaling Model of NDVI in Validation and Its Sensibility Analysis to Various Image Characteristics

Based on the fractal theory, this study establishes a Continuous Spatial Scaling Model （CSSM） of the Normalized Difference Vegetation Index （NDVI） to address issues arising with spatial up-scaling in quantitative remote sensing. This model is able to quantitatively describe transformation relationships of the NDVI on continuous scales. Then the following experiments are accomplished： （1） the validation of ETM＋ NDVI imagery is implemented based on the GEOEYE-1 image and its NDVI CSSM, and the following conclusion is obtained： because of bad stripes in the ETM＋ image and the limited effect of destriping, the ETM＋ NDVI image had a rather large error, and the error for the entire experimental imagery is about 25%, so the ETM＋ NDVI product is not suitable for direct practical application; （2） Shatian Byland （Beihai City, China） is taken as the experimental area, and four images （two ETM＋ images with wider and smaller coverage, respectively, a GEOEYE-1 image, and an HJ-1B CCD1 image） are studied. The most suitable scale levels are computed and compared for the four images, and a better understanding is obtained of the impact of various image characteristics （area of coverage, spatial resolution, and imaging quality） on determining the scale level for the NDVI CSSM.

Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.

YOLO-CRD:A Lightweight Model for the Detection of Rice Diseases in Natural Environments

Rice diseases can adversely affect both the yield and quality of rice crops,leading to the increased use of pesticides and environmental pollution.Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance.Deep learning-based disease identification technologies have shown promise in automatically discerning disease types.However,effectively extracting early disease features in natural environments remains a challenging problem.To address this issue,this study proposes the YOLO-CRD method.This research selected images of common rice diseases,primarily bakanae disease,bacterial brown spot,leaf rice fever,and dry tip nematode disease,from Tianjin Xiaozhan.The proposed YOLO-CRD model enhanced the YOLOv5s network architecture with a Convolutional Channel Attention Module,Spatial Pyramid Pooling Cross-Stage Partial Channel module,and Ghost module.The former module improves attention across image channels and spatial dimensions,the middle module enhances model generalization,and the latter module reduces model size.To validate the feasibility and robustness of this method,the detection model achieved the following metrics on the test set:mean average precision of 90.2%,accuracy of 90.4%,F1-score of 88.0,and GFLOPS of 18.4.for the specific diseases,the mean average precision scores were 85.8%for bakanae disease,93.5%for bacterial brown spot,94%for leaf rice fever,and 87.4%for dry tip nematode disease.Case studies and comparative analyses verified the effectiveness and superiority of the proposed method.These researchfind-ings can be applied to rice disease detection,laying the groundwork for the development of automated rice disease detection equipment.

Design System of the Two-step Gear Reducer on Case-based Reasoning

The design of the two-step gear reducer is a tedious and time-consuming process. For the purpose of improving the efficiency and intelligence of design process, case-based reasoning（CBR） technology was applied to the design of the two-step gear reducer. Firstly, the current design method for the two-step gear reducer was analyzed and the principle of CBR was described. Secondly, according to the characteristics of the reducer, three key technologies of CBR were studied and the corresponding methods were provided, which are as follows： （a） an object-oriented knowledge representation method, （b） a retrieval method combining the nearest neighbor with the induction indexing, and （c） a case adaptation algorithm combining the revision based on rule with artificial revision. Also, for the purpose of improving the credibility of case retrieval, a new method for determining the weights of characteristics and a similarity formula were presented, which is a combinatorial weighting method with the analytic hierarchy process（AHP） and roughness set theory. Lastly, according to the above analytic results, a design system of the two-step gear reducer on CBR was developed by VC＋＋, UG and Access 2003. A new method for the design of the two-step gear reducer is provided in this study. If the foregoing developed system is applied to design the two-step gear reducer, design efficiency is improved, which enables the designer to release from the tedious design process of the gear reducer so as to put more efforts on innovative design. The study result fully reflects the feasibility and validity of CBR technology in the process of the design of the mechanical parts.

A Combining Call Admission Control and Power Control Scheme for D2D Communications Underlaying Cellular Networks

As device-to-device(D2D) communications usually reuses the resource of cellular networks, call admission control(CAC) and power control are crucial problems. However in most power control schemes, total data rates or throughput are regarded as optimization criterion. In this paper, a combining call admission control(CAC) and power control scheme under guaranteeing QoS of every user equipment(UE) is proposed. First, a simple CAC scheme is introduced. Then based on the CAC scheme, a combining call admission control and power control scheme is proposed. Next, the performance of the proposed scheme is evaluated. Finally, maximum DUE pair number and average transmitting power is calculated. Simulation results show that D2 D communications with the proposed combining call admission control and power control scheme can effectively improve the maximum DUE pair number under the premise of meeting necessary QoS.

Single Image Enhancement in Sandstorm Weather via Tensor Least Square

In this paper,we present a tensor least square based model for sand/sandstorm removal in images.The main contributions of this paper are as follows.First,an important intrinsic natural feature of outdoor scenes free of sand/sandstorm is found that the outlines in RGB channels are somewise similar,which discloses the physical validation using the tensor instead of the matrix.Second,a tensor least square optimization model is presented for the decomposition of edge-preserving base layers and details.This model not only decomposes the color image(taken as an inseparable indivisibility)in X,Y directions,but also in Z direction,which meets the statistical feature of natural scenes and can physically disclose the intrinsic color information.The model’s advantages are twofold:one is the decomposition of edgepreserving base layers and details that can be employed for contrast enhancement without artificial halos,and the other one is the color driving ability that makes the enhanced images as close to natural images as possible via the inherent color structure.Thirdly,the tensor least square optimization model based image enhancement scheme is discussed for the sandstorm weather images.Finally,the experiments and comparisons with the stateof-the-art methods on real degraded images under sandstorm weather are shown to verify our method’s efficiency.

Evolutionary Dynamics Modeling of Symbolic Social Network Structure Equilibrium

The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.

THE RESEARCH OF GRADATION FUSION ALGORITHM BASED ON MULTISENSOR ASYNCHRONOUS SAMPLING SYSTEM

This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.

The progress of operational forest fire monitoring with infrared remote sensing

Forest wildfires pose significant and growing threats to human safety, wildlife habitat, regional economies and global climate change. It is crucial that forest fires be subject to timely and accurate monitoring by forest fire managers and other stake-holders. Measurement by spaceborne equipment has become a practical and appealing method to monitor the occurrence and development of forest wildfires. Here we present an overview of the principles and case studies of forest fire monitoring（FFM） with satelliteand drone-mounted infrared remote sensing（IRRS）. This review includes four types of FFM-relevant IRRS algorithms： bi-spectral methods, fixed threshold methods, spatial contextual methods, and multi-temporal methods. The spatial contextual methods are presented in detail since they can be applied easily with commonly available satellite IRRS data, including MODIS, VIIRS, and Landsat 8 OLI. This review also evaluates typical cases of FFM using NOAAAVHRR, EOS-MODIS, S-NPP VIIRS, Landsat 8 OLI,MSG-SEVIRI, and drone infrared data. To better implement IRRS applications in FFM, it is important to develop accurate forest masks, carry out systematic comparative studies of various forest fire detection systems（known as forest fire products）, and improve methods for assessing the accuracy of forest fire detection. Medium-resolution IRRS data are effective for landscape-scale FFM, and the VIIRS 375 m contextual algorithm and RST-FIRES algorithm are helpful for closely tracking forest fires（including small and shortlived fires） and forest-fire early warning.

Accurate detection method of pig's temperature based on non-point source thermal infrared image

Body temperature measurement is a very important task in the sow breeding process.The authors used an infrared camera to detect the temperature of the body surface of the sows,relying on calculating the average of the infrared image temperature in the ear root region.Based on the grayscale value of the target image of the infrared image and the corresponding temperature value of 180 infrared images,a G-T(Gray-Temperature)model was established by linear least squares method,which achieved temperature inversion of each pixel of the target pig.For the different growth stages and different breeds of sows,the R-square of the all established models is greater than 0.95.The average relative error of the model inversion of the body temperature was only 0.076977%.This means that the body temperature of the sows could be detected without relying on the software.Based on the G-T model,the authors design a kind of sow's ear root recognition and body surface temperature detection algorithm for different sow population scenarios.

Vision Enhancement Technology of Drivers Based on Image Fusion

The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.

Novel Retinex algorithm by interpolation and adaptive noise suppression

In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.