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Background: Cancer stem cells may be the source of cancer-causing mutant cells and are closely related to the prognosis of cancer. Our study aimed to investigate the potential association between single-nucleotide polymorphisms (SNPs) of cancer stem cell-related genes and the prognosis of lung cancer patients. Methods: The SNP loci were genotyped by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF–MS), and the overall survival of subjects was analyzed by log-rank test after stratifying and adjusting their demographic data, clinical data, and genotypes. The correlation between survival time and quality of life of lung cancer under codominant, dominant, recessive, and additive genetic models was analyzed by the Cox regression model. The association between SNP polymorphism and the prognosis of lung cancer was analyzed by Stata16.0 software, and their heterogeneity was tested. Interaction analysis was performed using R software (version 4.2.0). Results: Stratified analysis unveiled that rs3740535 had recessive AA genotype and additive GG genotype; Rs3130932 dominant GT + GG genotype, additive TT genotype; Rs13409 additive TT genotype; Rs6815391 recessive CC genotype and additional TT genotype were associated with increased risk of lung cancer death. Rs3130932 recessive GG genotype was associated with a reduced risk of lung cancer death. Conclusion: Rs3740535, rs3130932, rs13409, and rs6815391 are associated with the overall survival of lung cancer patients and may be valuable for the prognosis of lung cancer patients. [ABSTRACT FROM AUTHOR]

Bonding is one of the main forms of composite bonding. In order to investigate the effect of low-temperature plasma surface treatment on the bonding properties of carbon fiber-reinforced epoxy resin composites (CF/EP), a single-lap joint of CF/EP was prepared. The surface of the CF/EP was treated with atmospheric pressure "low-temperature plasma spray" equipment, and the tensile shear strength, surface morphology, surface contact angle and surface chemical composition of the CF/EP before and after plasma treatment were characterized. Finally, the samples were treated with traditional sandblasting, compared and analyzed. The results show that the effect of low-temperature plasma surface treatment on CF/EP joints is better than that of traditional sandblasting treatment. After low-temperature plasma surface treatment, the tensile shear strength of the CF/EP single-lap joint increased by 119.59% at most, and the failure form of the joint changed from untreated interface failure to mixed failure dominated by cohesion failure. Plasma can etch the surface of composite materials, the mechanical interlock between the carbon fiber and glue is enhanced and the bonding performance of the composite is improved. In addition, after low-temperature plasma surface treatment, the introduction of a large number of oxygen-containing active groups such as C-O and C=O can increase the surface free energy, reduce the contact angle and improve the surface activity and wettability of the composites. However, too long a treatment time will lead to excessive plasma etching of carbon fibers, thus weakening the active effect of the oxygen-containing active groups on the surface of the composites, and the surface wettability is no longer improved, but the adhesive properties of CF/EP are reduced. This paper plays a guiding role in the bonding technology of composite materials. [ABSTRACT FROM AUTHOR]

A (Ti 2 AlC + Y 2 O 3) synergistically reinforced Ti–48Al–2Cr–2Nb alloy was prepared by directed energy deposition with in situ tailoring of precipitate formation by induction preheating and thermal cycle. The influence of C and Y 2 O 3 coaddition on the microstructure and high-temperature compressive properties was systematically investigated. The results indicated that Ti 2 AlC was formed by adding elemental C, and the Ti 2 AlC phase was mainly distributed in two forms: long rod-shaped Ti 2 AlC with a micron scale that distributed uniformly in the matrix, and lenticular Ti 2 AlC particles with nanometer size that distributed along the (α 2 /γ) lamellar interface. The Y 2 O 3 particles were randomly distributed in the matrix. Columnar grains in vertical cross section along the building direction were completely eliminated by adding C and Y 2 O 3. The high temperature compressive strength increased from 597 MPa to 758 MPa at 850 °C by adding C and Y 2 O 3. Finally, the precipitation mechanism of nano-Ti 2 AlC was discussed in detail. This work provided a valuable reference for the preparation of high-performance TiAl alloys by in situ tailoring of the precipitates during the DED process. [ABSTRACT FROM AUTHOR]

Deoxidation via chemical etch is an important step in the pretreatment of aluminium alloy chemical oxidation. Typically, the chemical deoxidation of aluminium alloy is carried out in triacid solution that contains chromic anhydride. While the use of Cr (VI) in triacid solution can cause severe chemical contamination. In this paper, we have developed a Cr-free solution for deoxidation, iron sulphate and etch inhibitors are selected to replace Cr (VI). The formula of deoxidation solution was optimised through the combination of single-factor method and Box-Behnken method. The results show that the Cr-free deoxidation solution we proposed shows a deoxidation effect comparable to that of a triacid solution. [ABSTRACT FROM AUTHOR]

CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1) is the key photomorphogenic inhibitor that has been extensively studied in higher plants. Nevertheless, its role has not been documented in the economically important soybean. Here we investigated the functions of two COP1 homologous genes, GmCOP1a and GmCOP1b, by analyzing Gmcop1a and Gmcop1b mutants with indels using CRISPR in soybean. We revealed that, although both genes are required for skotomorphogenesis in the dark, the GmCOP1b gene seems to play a more prominent role than GmCOP1a in promoting stem elongation under normal light conditions. Consistently, the bZIP transcriptional factors STF1/2, which repress stem elongation in soybean, accumulated to the highest level in the Gmcop1a1b double mutant, followed by the Gmcop1b and Gmcop1a mutants. Furthermore, the Gmcop1b mutants showed reduced shade response and enhanced performance under high-density conditions in field trials. Taken together, this study provides essential genetic resources for elucidating functional mechanisms of GmCOP1 and breeding of high yield soybean cultivars for future sustainable agriculture. [ABSTRACT FROM AUTHOR]

• We propose a self-supervised learning-based VO framework: SAM-Net. • We present SPFM to detect dynamic objects and to generate refined depth map. • Apply attention mechanism to enhance perception ability in spatial and channel view. • PoseNet with the atrous separable convolution is designed to expand receptive field. • Photometric consistency loss is more robust for the large rotations. 3D scene understanding is an essential research topic in the field of Visual Odometry (VO). VO is usually built under the assumption of a static environment, which does not always hold in real scenarios. Existing works fail to consider the dynamic objects, leading to poor performance. To tackle the aforementioned issues, we propose a self-supervised learning-based VO framework with Semantic probabilistic and Attention Mechanism, SAM-Net, which can jointly learn the single view depth, the ego motion of camera and object detection. For depth estimation, semantic probabilistic fusion mechanism is employed to detect the dynamic objects and generate the semantic probability map as a prior before feeding it to the network to generate a more refined depth map, and attention mechanism is explored to enhance perception ability in spatial and channel view. For pose estimation, we present a novel PoseNet with the atrous separable convolution to expand receptive field. And the photometric consistency loss is employed to alleviate the impact of large rotations. Intensive experiments on the KITTI dataset demonstrate that the proposed approach achieves excellent performance in terms of pose and depth accuracy. [Display omitted] [ABSTRACT FROM AUTHOR]

In the field of collaborative visual simultaneous localization and mapping (CVSLAM), efficient data communication poses a significant challenge, particularly in environments with limited bandwidth. To address this issue, we introduce a method aimed at reducing communication consumption. Our approach starts with a strategic culling of map points, aiming at maximizing pose-visibility and expanding spatial diversity to effectively eliminate redundant data in CVSLAM. We achieve this by formulating the problem of maximizing pose-visibility and spatial diversity as a minimum-cost maximum-flow graph optimization problem. Subsequently, we apply finite state entropy encoding for the compression of visual information, further alleviating bandwidth constraints. To verify the proposed method, we implement it within a centralized collaborative monocular simultaneous localization and mapping (SLAM) system. Our approach has been tested on publicly available datasets and in real-world scene. The results show a prominent reduction in bandwidth usage by 49% while maintaining mapping accuracy and without introducing additional latency, confirming its effectiveness in a multi-agent system setting. [ABSTRACT FROM AUTHOR]

This paper concerns an initial value problem of compressible Navier-Stokes-Poisson equations subject to large and non-flat doping profile in whole space R 3. The global well-posedness of strong solutions with large oscillations and vacuum is established, provided the initial data are of small energy and the steady state is strictly away from vacuum. A weak-strong uniqueness result is also obtained. [ABSTRACT FROM AUTHOR]

The effects of co-fermentation with Torulaspora delbrueckii on the composition of volatile compounds and sensory characteristics of strawberry fruit wines were investigated by fermenting strawberry pulp with T. delbrueckii in pure fermentation, as well as in simultaneous and sequential inoculations with Saccharomyces cerevisiae. E-nose analysis results showed that these strawberry fruit wines with different fermentation patterns exhibited distinct aromatic characteristics. The sensory attributes intensities of 'fruit', 'strawberry', 'sweet' and 'floral' were significantly enhanced in sequential fermentation compared to pure S. cerevisiae fermentation. Altogether, 87 volatile compounds were detected with HS-SPME-GC/MS in strawberry products, with 32 of them identified as key contributors to the overall aroma due to their high odor activity values. The sequential fermentation increased the concentration of desirable aroma-active compounds, including isoamyl alcohol, phenylethanol, isoamyl acetate, and some ethyl esters in strawberry fruit wines. The partial least squares regression analysis revealed that the fruity, floral and sweet characteristics were primarily attributed to specific aroma-active compounds, including esters such as ethyl caproate, ethyl caprylate, ethyl cinnamate, isoamyl acetate, ethyl cinnamate, ethyl 3-phenylpropionate and ethyl butyrate, as well as terpenes such as limonene, α-terpineol and nerolidol. [ABSTRACT FROM AUTHOR]

Limited by the low-frequency data acquisition, vehicle global positioning system (GPS) data are difficult to implement in the area of microtraffic simulation. Based on the functional design of mobile phone positioning technology, mobile phones can be used to acquire bus GPS data every second. In this paper, an analytical model is proposed to determine the parameters of signal coordination for bus priority along an arterial based on GPS data of mobile phones. First, bus priority evaluation indicators are established using bus GPS data, which are acquired by mobile phones. Second, the signal timing parameters of the arterial road are optimized, and a preliminary timing plan is developed by evaluating small changes in the plan. Finally, the corresponding final plan is developed using VISSIM micro simulation software. The feasibility of the analytical model is verified by simulating an actual arterial in Fuzhou city, China. [ABSTRACT FROM AUTHOR]

This study focuses on the different impacts of platform training and learning by doing on gig workers' platform income. Based on survey data of China's delivery riders on the platform in 2020, via quantitative methods combined with the case study, it is found that the platform training is negatively correlated with riders' incomes, while learning by doing is positively correlated with their incomes. Workers with a high level of platform-income dependence earn more than those with an average level of dependence under the same platform training, or learning by doing. Overall, the incomes of the former are significantly lower than those of the latter, where the difference is mainly due to unobservable factors. Both platform training and learning by doing significantly reduce the income gap. In addition, the instrumental variable and the propensity score matching approaches are introduced to handle the endogeneity problem, and robust results are obtained. Plain language summary: Influence of learning by doing and platform training on gig workers' incomes This study looks at how two different ways of learning affect gig workers' earnings on platforms. We used data from a survey of delivery riders in China in 2020. We found that training provided by the platform tends to lower riders' earnings, while learning from actual work experience tends to increase earnings. Riders who rely more on their platform income earn more than those who don't, even after training or learning by doing. Both types of learning help reduce the income gap. We used special methods to make sure our results are accurate. [ABSTRACT FROM AUTHOR]

We perform a numerical evaluation on improving the thermal transfer at the graphene/polypropylene (PP) interface with non-covalent functionalization techniques. The results indicate that it can significantly improve the interfacial thermal conductivity (ITC) of the graphene/polypropylene (PP) and this enhancement depends on the surface functionalization degree of graphene. We detected some fantastic phenomena that the chain length of straight alkane chains of functionalized molecules significantly affects the enhancement of ITC. The effect of the number of graphene layers was further studied. ITC decreases with the increase in the number of layers. Non-covalent functionalization still has a positive effect on multilayer graphene. The vibrational density of states (VDOS) analysis shows that non-covalent functionalization treatment would cause the phonon distribution of graphene to be broadened, and the phonon peak height would decrease as the layer number grows. It implies a detailed understanding of manipulating the ITC of graphene/polypropylene in practical thermal management applications. • We perform a numerical evaluation on improving the thermal transfer at the graphene/polypropylene (PP) interface. • We detected that the enhancement for ITC depends on the surface functionalization rate of graphene. • We detected some fantastic phenomena that graphene layer number on ITC. • It implies a detailed understanding for manipulating the ITR of graphene/polypropylene in thermal management. [ABSTRACT FROM AUTHOR]

This paper is concerned with the free boundary value problem (FBVP) for the spherically symmetric barotropic compressible Navier-Stokes equations (CNS) with density-dependent viscosity coefficients in the case that across the free surface stress tensor is balanced by a nonconstant exterior pressure. Under certain assumptions imposed on the initial data, we show that there exists a unique global strong solution which is strictly positive for any finite time and decays pointwise to zero time-asymptotically. [ABSTRACT FROM AUTHOR]

This paper is concerned with the Cauchy problem of the three-dimensional nematic liquid crystals. We establish the global well-posedness and time decay rates of the classical solutions with smooth initial data which are of small energy by using Fourier splitting technique and pure energy method. [ABSTRACT FROM AUTHOR]

This paper deals with an initial-boundary value problem to the two-dimensional equations of incompressible micropolar fluids. We first prove that as the angular and micro-rotational viscosities go to zero (i.e., $${\gamma, \zeta \to 0}$$ ), the solution converges to a global weak solution of the original equations with zero angular and micro-rotational viscosities. Convergence rates are also obtained. Then, we study the boundary effects and prove that a boundary-layer thickness is of the value $${\delta(\gamma) = \gamma^\alpha}$$ with $${\alpha \in (0, 1/2)}$$ , provided $${\lim_{\gamma \to 0} \zeta \gamma^{1/2} < \infty}$$ . [ABSTRACT FROM AUTHOR]

ABSTRACT Hyperlipidemia is one kind of common and frequently occurring syndromes and closely related to cardiovascular and cerebrovascular disease occurrences in the world. It is significant to explore the marine foods and potential drug resources for the application in treating the hyperlipidemia. We conducted this study to investigate the reducing effect and mechanism of kelp ( Saccharina japonica) on serum lipid of rat models with hyperlipidemia induced by a fat-rich diet. The levels of serum lipid including the triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) were detected by biochemical assay. Enzyme-linked immunosorbent assay was applied to determine the level of oxidized LDL (ox-LDL). The activity of lipoprotein lipase (LPL) and hepatic lipase (HL) was determined by chemical colorimetry. The concentrations of malondialdehyde (MDA) and nitric oxide (NO) were respectively measured by thiobarbituric acid assay and nitrate reductase assay. The activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) were respectively determined by xanthinoxidase assay and chemical colorimetry. The results showed that the serum levels of TG, TC and LDL decreased, while HDL increased significantly in treated group compared with those of model groups ( P < 0.05). The value of ox-LDL of treated groups was much lower than that of model groups ( P < 0.05). The activities of LPL and HL in serum and hepatic tissue in treated groups were significantly higher than those of model groups ( P < 0.05). The levels of MDA and NO in serum and hepatic tissue were lower than those of model groups ( P < 0.05), while the activities of SOD and GSH-PX were significantly higher than those of model groups ( P < 0.05). Our study indicated that kelp may correlate with the metabolism of TG, TC, LDL and HDL by enhancing the activities of LPL and HL, and regulate the levels of serum lipids by increasing the activities of SOD and GSH-PX to reduce the levels of MDA and NO. PRACTICAL APPLICATIONS The objective of this work is to explore the potential application of kelp for treating hyperlipidemia. It is showed that the kelp could regulate to the normal level of triglyceride, total cholesterol, low-density lipoprotein and high-density lipoprotein in the models. It also could enhance the activity of the lipoprotein lipase and hepatic lipase to change the oxidative stress. [ABSTRACT FROM AUTHOR]

This paper is concerned with a blow-up criterion of strong solutions for three-dimensional compressible isentropic magnetohydrodynamic equations with vacuum. It is shown that if the density and velocity satisfy $\|\rho\|_{L^\infty(0,T;L^\infty)}\,{+}\,\|{\bf u}\|_{L^{s}(0,T;L^{r}_w)} \lt \infty$, where $\frac{2}{s}+\frac{3}{r}\leq 1$, 3 < r ≤ ∞ and $L^r_w\,{\scriptstyle\supsetneqq}\,L^r$ denotes the weak Lr-space, then the strong solutions to the Cauchy problem of the compressible magnetohydrodynamic equations can exist globally over [0, T]. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we prove a blow-up criterion of strong solutions to the 3D viscous and non-resistive isentropic compressible magnetohydrodynamic equations with initial vacuum. This blow-up criterion depends only on the gradient of velocity, which is analogous to the one for the compressible Navier–Stokes equations (cf. Huang et al. (2010) ). [Copyright &y& Elsevier]

This paper is concerned with an initial value problem of the compressible full Hall-MHD equations in three-dimensional whole space. Both the global existence and the optimal decay rates of solutions are obtained, when the smooth initial data are sufficiently close to the non-vacuum equilibrium in H 1 . As a by-product of the uniform estimates, the vanishing limit of Hall coefficient is also justified. [ABSTRACT FROM AUTHOR]

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-driven single neuron predictive control strategy is proposed for non-Gaussian networked control systems with metrology delays in the information theory framework. Firstly, survival information potential (SIP), instead of minimum entropy, is used to formulate the performance index to characterize the randomness of the considered systems, which is calculated by oversampling method. Then the minimum values can be computed by optimizing the SIP-based performance index. Finally, the proposed strategy, minimum entropy method and mean square error (MSE) are applied to a networked motor control system, and results demonstrated the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Learning rich representations efficiently plays an important role in the multi-modal recognition task, which is crucial to achieving high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-LRF-ELM) structure, while maintaining ELM’s advantages of training efficiency. In this structure, LRF-ELM is first conducted for feature extraction for each modality separately. And then, the shared layer is developed by combining these features from each modality. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for the final decision. Experimental validation on Washington RGB-D Object Dataset illustrates that the proposed multiple modality fusion method achieves better recognition performance. [ABSTRACT FROM AUTHOR]

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO 2 content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions. [Display omitted] • Methanation reaction conditions were predictively modeled by machine learning. • ELM-STASA optimization improved the stability and accuracy of model prediction. • Very small error was obtained between predicted and experimental results. • Machine learning showed advantages with saving time, labor and material resources. [ABSTRACT FROM AUTHOR]

With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Sm-Nd analyses of seven scheelite samples from scheelite-quartz veins in the Honghuaerji scheelite deposit produce a well-defined linear array on an isochron diagram with a mean square weighted deviation (MSWD) of 0.87 corresponding to an age of 178.4 ± 2.9 Ma with εNd(t) = + 1.50. This age is interpreted to represent the age of scheelite mineralization. The scheelite Sm-Nd age is in good agreement with U–Pb ages obtained from a mineralization-related granite (179.4 ~ 178.6 Ma), indicating that there is no apparent age gap between granite crystallization and ore formation. Rare earth element (REE) abundances in the scheelite were determined by in situ laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and the scheelite samples contain elevated REE concentrations with total ΣREE + Y contents in the range of 3339 to 6321 ppm. The chondrite-normalized REE distribution patterns of all scheelites are middle REE (MREE)-enriched, with strong negative Eu-anomalies (Eu/Eu* = 0.09 ~ 0.23). The REE characteristics of the Honghuaerji scheelite suggest that REE 3 + substituted into the Ca site along with Na and Nb (dominated by Na), whereas Eu is predominantly present as Eu 2 + in the scheelite and may have crystallized from relatively reduced fluids. All sulfur isotope data (δ 34 S) for sulfide separates range from + 2.0 to + 3.8‰, with an average of 3.2‰. Measured 206 Pb/ 204 Pb, 207 Pb/ 204 Pb, and 208 Pb/ 204 Pb ratios vary from 18.243 to 18.451, 15.494 to 15.574, and 37.933 to 38.340, respectively. On the basis of common Sr-Nd-Pb-Hf isotopic characteristics between the scheelite and the host granite and the positive initial Nd isotope ratios (+ 1.46 − + 1.52), low 87 Sr/ 86 Sr ratios (0.704983–0.705297) in the scheelites, it is inferred that the hydrothermal fluids responsible for tungsten mineralization at Honghuaerji were of magmatic origin with a mantle-derived signature. Based on the regional geology, tectonic evolution, and geochemical evidence, the Honghuaerji granite is thought to have been derived from the partial melting of thickened lower crust produced during post-collisional orogenic events associated with the closure of the Mongol-Okhotsk Ocean. [ABSTRACT FROM AUTHOR]

Background: Although immunotherapy has shown clinical activity in lung adenocarcinoma (LUAD), LUAD prognosis has been a perplexing problem. We aimed to construct an immune-related lncRNA pairs (IRLPs) score for LUAD and identify what immunosuppressor are appropriate for which group of people with LUAD.Methods: Based on The Cancer Genome Atlas (TCGA)-LUAD cohort, IRLPs were identified to construct an IRLPs scoring system by Cox regression and validated in the Gene Expression Omnibus (GEO) dataset using log-rank test and the receiver operating characteristic curve (ROC). Next, we used spearman's correlation analysis, t-test, signaling pathways analysis and gene mutation analysis to explore immune and molecular characteristics in different IRLP subgroups. The "pRRophetic" package was used to predict the sensitivity of immunosuppressant.Results: The IRLPs score was constructed based on eight IRLPs calculated as 2.12 × (MIR31HG|RRN3P2) + 0.43 × (NKX2-1-AS1|AC083949.1) + 1.79 × (TMPO-AS1|LPP-AS2) + 1.60 × (TMPO-AS1|MGC32805) + 1.79 × (TMPO-AS1|PINK1-AS) + 0.65 × (SH3BP5-AS1|LINC01137) + 0.51 × (LINC01004|SH3PXD2A-AS1) + 0.62 × (LINC00339|AGAP2-AS1). Patients with a lower IRLPs risk score had a better overall survival (OS) (Log-rank test P TCGA train dataset < 0.001, P TCGA test dataset = 0.017, P GEO dataset = 0.027) and similar results were observed in the AUCs of TCGA dataset and GEO dataset (AUC TCGA train dataset = 0.777, AUC TCGA test dataset = 0.685, AUC TCGA total dataset = 0.733, AUC GEO dataset = 0.680). Immune score (Cor = -0.18893, P < 0.001), stoma score (Cor = -0.24804, P < 0.001), and microenvironment score (Cor = -0.22338, P < 0.001) were significantly decreased in the patients with the higher IRLP risk score. The gene set enrichment analysis found that high-risk group enriched in molecular changes in DNA and chromosomes signaling pathways, and in this group the tumor mutation burden (TMB) was higher than in the low-risk group (P = 0.0015). Immunosuppressor methotrexate sensitivity was higher in the high-risk group (P = 0.0052), whereas parthenolide (P < 0.001) and rapamycin (P = 0.013) sensitivity were lower in the high-risk group.Conclusions: Our study established an IRLPs scoring system as a biomarker to help in the prognosis, the identification of molecular and immune characteristics, and the patient-tailored selection of the most suitable immunosuppressor for LUAD therapy. [ABSTRACT FROM AUTHOR]

In this study, we consider the multiple traveling salesman problem (MTSP) with multiple depots, closed paths, and a constraint on the number of cities visited by each traveling salesman. In our previous study, we proposed a state transition simulated annealing algorithm (STASA) that employs the Metropolitan criterion for simulated annealing to solve the traveling salesman problem (TSP). The results obtained were significantly improved according to our previous experiments. In the present study, we propose a new intelligent operator to solve the MTSP called an elementary breakpoint operator, which can generate the solution space for MTSP through elementary matrix operations. A neighborhood search structure called 2opt, which can improve the quality of the solution by connecting two shorter non-adjacent arcs, is then introduced into STASA to enhance the ability to capture the global optimal solution. Finally, comparative experiments were conducted based on a wide range of TSPLIB benchmark instances using our proposed algorithm and other algorithms, and the results demonstrated the superior performance of our proposed approach compared with all other state-of-the-art approaches for MTSP. [ABSTRACT FROM AUTHOR]

Point cloud registration precision degrades when the scene contains symmetric structures or repeated patches. Also, the common operation of continuous downsampling in existing registration methods causes the loss of detailed information and leads to further registration precision degradation. In this paper, we propose a high precision point cloud registration network (HPNet) to learn discriminative features by considering both multi-scale information and position information. We first propose a multi-scale feature extraction module similar to the feature pyramid that allows the extracted features to contain multi-scale information. Then, an information interaction is performed on the features to learn global contextual information by using Transformer with a hybrid position encoding, which takes into account both absolute and relative positions of points. Finally, the features obtained from the information interaction module are directly used to predict the point correspondences. Comprehensive experiments on 3DMatch, 3DLoMatch, ModelNet, and ModelLoNet datasets demonstrate the state-of-the-art performance of the proposed method and the implementation of HPNet in SLAM shows its effectiveness in real application. [Display omitted] • Point cloud registration loses detail information during continuous downsampling. • Feature fusion of low-level detail information and high-level semantic information. • Hybrid position encoding consider both absolute and relative positions. positions. • Point cloud registration algorithms applied to SLAM. [ABSTRACT FROM AUTHOR]

[Display omitted] • A series of hydrostyryl pyridinium derivatives with dual-state emission behaviors were employed for synthesis of fluorescent polymers resulting unique fluorescent behaviors. • Significant solvent- and excitation-dependent fluorescent emissions were observed and their multichromatic behaviors caused by enol-to-keto transition of phenol group and aggregation were addressed by using quantum computation. To synthesize fluorescent polymers, hydrostyryl pyridinium derivatives were methacrylated and afforded monomers of MA-SP-OH-X (X = NO 2 , F, substituted group on phenyl ring) followed by free-radically homo- and copolymerized with methyl methacrylate (MMA), butyl acrylate (BA) and styrene (St), respectively. The resulting copolymers showed green, orange and red emissions with significant multichromatic behaviors and solvent- and excitation-dependence. Moreover, P(BA- co -MA-SP-OH-NO 2) behaved as a thermal elastomer and showed high stretchability and self-heal performance. In addition, with the copolymers as macro-additives, fluorescent poly(vinylidene difluoride) (PVDF) films were fabricated by using electrospinning technique. The unexpected multichromatic fluorescent behaviors showed by the copolymers were attributed to the enol-to-keto transition of phenol group in SP-OH moiety and significant aggregation of fluorophore moieties. [ABSTRACT FROM AUTHOR]

Soybean is an important legume crop that displays the classic shade avoidance syndrome (SAS), including exaggerated stem elongation, which leads to lodging and yield reduction under density farming conditions. Here, we compared the effects of two shade signals, low red light to far-red light ratio (R:FR) and low blue light (LBL), on soybean status and revealed that LBL predominantly induces excessive stem elongation. We used CRISPR-Cas9-engineered Gmcry mutants to investigate the functions of seven cryptochromes (GmCRYs) in soybean and found that the four GmCRY1s overlap in mediating LBL-induced SAS. Light-activated GmCRY1s increase the abundance of the bZIP transcription factors STF1 and STF2, which directly upregulate the expression of genes encoding GA2 oxidases to deactivate GA 1 and repress stem elongation. Notably, GmCRY1b overexpression lines displayed multiple agronomic advantages over the wild-type control under both dense planting and intercropping conditions. Our study demonstrates the integration of GmCRY1-mediated signals with the GA metabolic pathway in the regulation of LBL-induced SAS in soybean. It also provides a promising option for breeding lodging-resistant, high-yield soybean cultivars in the future. Low blue light induces exaggerated stem elongation in soybean, which leads to lodging and yield loss. This study reveals a GmCRY1s-STFs-GA module that mediates blue light inhibition of stem elongation in soybean. Increasing GmCRY1 signaling activity significantly improves the lodging-resistance and yield potential of soybean under density farming conditions. [ABSTRACT FROM AUTHOR]

The moth search algorithm (MS) is a novel intelligent optimization algorithm based on moth population behavior, which can solve many problems in different fields. However, the algorithm is easy to fall into local optimization when solving complex optimization problems. This study develops a new hybrid moth search-fireworks algorithm (MSFWA) to solve numerical and constrained engineering optimization problems. The explosion and mutation operators from the fireworks algorithm are introduced into the MS, which not only preserves the advantages of fast convergence and strong exploitation capability of the algorithm, but also significantly enhances the exploration capability. The performance of the MSFWA is tested using 23 benchmark functions. The hybrid algorithm is superior to other highly advanced metaheuristic algorithms for most benchmark functions, demonstrating the characteristics of fast convergence and high stability. Finally, the ability of the MSFWA to solve practical constrained problems is evaluated on six well-known engineering application problems. Compared with other optimization algorithms, the MSFWA is very competitive in its solution of these complex and constrained practical problems. [ABSTRACT FROM AUTHOR]

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology. [ABSTRACT FROM AUTHOR]

Machine vision-based surface defect detection and classification have always been the hot research topics in Artificial Intelligence. However, existing work focuses mainly on the detection rather than the classification. In this article, we propose GSPSO-LRF-ELM that is the grid search (GS) and the particle swarm optimization- (PSO-) based local receptive field-enabled extreme learning machine (ELM-LRF) for the detection and classification of the surface defects on the magnetic tiles. In the ELM-LRF classifier, the balance parameter C and the number of feature maps K via the GS algorithm and the initial weight Ainit via the PSO algorithm are optimized to improve the performance of the classifier. The images used in the experiments are from the dataset collected by Institute of Automation, Chinese Academy of Sciences. The experiment results show that the proposed algorithm can achieve 96.36% accuracy of the classification, which has significantly outperformed several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of similarity measurement. CMSM obtains the internal information of time series from the general perspective and local trend using the cloud model, which reduces the uncertainty of measurement. The neighbor set is selected from time series by CMSM and used to construct a prediction model based on least squares support vector machine. The proposed technique reduces the potential for overfitting and uncertainty and improves model prediction quality and generalization. Experiments were performed with four datasets selected from Time Series Data Library. The experimental results show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A cognitive modelling based new inversion method, the successive differential evolution (DE-S) algorithm, is proposed to estimate the Q factor and velocity from the zero-offset vertical seismic profile (VSP) record for oil-gas reservoir exploration. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. This algorithm is suitable for the high-dimensional nonseparable model space where the inversion leads to recognition and prediction of hydrocarbon reservoirs. The viscoelastic medium is split into layers whose thicknesses equal to the space between two successive VSP geophones, and the estimated parameters of each layer span the related subspace. All estimated parameters span to a high dimensional nonseparable model space. We develop bottom-up workflow, in which the Q factor and the velocity are estimated using the DE algorithm layer by layer. In order to improve the inversion precision, the crossover strategy is discarded and we derive the weighted mutation strategy. Additionally, two kinds of stopping criteria for effective iteration are proposed to speed up the computation. The new method has fast speed, good convergence and is no longer dependent on the initial values of model parameters. Experimental results on both synthetic and real zero-offset VSP data indicate that this method is noise robust and has great potential to derive reliable seismic attenuation and velocity, which is an important diagnostic tool for reservoir characterization. Highlights • We propose a heuristic optimization method to estimate Q and velocity in an up-bottom and bottom-up workflow. • We use both direct downgoing and primarily reflected upgoing wave of zero-offset VSP data to inverse. • A weighting factor as mutation strategy to improve inversion precision & 2 stopping criteria for efficiency/flexibility. • Independent on initial values and good feasibility for nonseparable model space, fast computation and good convergence. • The proposed method can eliminate the influence of upgoing wave at the interfaces even under certain degree of noise. [ABSTRACT FROM AUTHOR]

Abstract Convolutional neural networks (CNNs) have outperformed most state-of-the-art methods in object detection. However, CNNs suffer the difficulty of detecting objects with rotation, because the dataset used to train the CCNs often does not contain sufficient samples with various angles of orientation. In this paper, we propose a novel data-augmentation approach to handle samples with rotation, which utilizes the distribution of the object's orientation without the time-consuming process of rotating the sample images. Firstly, we present an orientation descriptor, named as "principal-axis orientation", to describe the orientation of the object's principal axis in an image and estimate the distribution of objects' principal-axis orientations (PODs) of the whole dataset. Secondly, we define a similarity metric to calculate the POD similarity between the training set and an additional dataset, which is built by randomly selecting images from the benchmark ImageNet ILSVRC2012 dataset. Finally, we optimize a cost function to obtain an optimal rotation angle, which indicates the highest POD similarity between the two aforementioned data sets. In order to evaluate our data augmentation method for object detection, experiments, conducted on the benchmark PASCAL VOC2007 dataset, show that with the training set augmented using our method, the average precision (AP) of the Faster RCNN in the TV-monitor is improved by 7.5%. In addition, our experimental results also demonstrate that new samples generated by random rotation are more likely to result in poor performance of object detection. [ABSTRACT FROM AUTHOR]

The Kukaazi Pb-Zn-Cu-W polymetallic deposit, located in the Western Kunlun orogenic belt, is a newly discovered skarn-type deposit. Ore bodies mainly occur in the forms of lenses and veins along beddings of the Mesoproterozoic metamorphic rocks. Three ore blocks, KI, KII, and KIII, have been outlined in different parts of the Kukaazi deposit in terms of mineral assemblages. The KI ore block is mainly composed of chalcopyrite, scheelite, pyrrhotite, sphalerite, galena and minor pyrite, arsenopyrite, and molybdenite, whereas the other two ore blocks are made up of galena, sphalerite, magnetite and minor arsenopyrite and pyrite. In this study, we obtained a molybdenite isochron Re-Os age of 450.5 ± 6.4 Ma (2σ, MSWD = 0.057) and a scheelite Sm-Nd isochron age of 426 ± 59 Ma (2σ, MSWD = 0.49) for the KI ore block. They are broadly comparable to the ages of granitoid in the region. Scheelite grains from the KI ore block contain high abundances of rare earth elements (REE, 42.0-95.7 ppm) and are enriched in light REE compared to heavy REE, with negative Eu anomalies (δEu = 0.13-0.55). They display similar REE patterns and Sm/Nd ratios to those of the coeval granitoids in the region. Moreover, they also have similar Sr and Nd isotopes [Sr/Sr = 0.7107-0.7118; ε(t) = −4.1 to −4.0] to those of such granitoids, implying that the tungsten-bearing fluids in the Kukaazi deposit probably originate from the granitic magmas. Our results first defined that the Early Paleozoic granitoids could lead to economic Mo-W-(Cu) mineralization at some favorable districts in the Western Kunlun orogenic belt and could be prospecting exploration targets. [ABSTRACT FROM AUTHOR]

Canola (Brassica napus) is one of several important oil-producing crops, and the physiological processes, enzymes, and genes involved in oil synthesis in canola seeds have been well characterized. However, relatively little is known about the dynamic metabolic changes that occur during oil accumulation in seeds, as well as the mechanistic origins of metabolic changes. To explore the metabolic changes that occur during oil accumulation, we isolated metabolites from both seed and silique wall and identified and characterized them by using gas chromatography coupled with mass spectrometry (GC-MS). The results showed that a total of 443 metabolites were identified from four developmental stages. Dozens of these metabolites were differentially expressed during seed ripening, including 20 known to be involved in seed development. To investigate the contribution of tissue-specific carbon sources to the biosynthesis of these metabolites, we examined the metabolic changes of silique walls and seeds under three treatments: leaf-detachment (Ld), phloem-peeling (Pe), and selective silique darkening (Sd). Our study demonstrated that the oil content was independent of leaf photosynthesis and phloem transport during oil accumulation, but required the metabolic influx from the silique wall. Notably, Sd treatment resulted in seed senescence, which eventually led to a severe reduction of the oil content. Sd treatment also caused a significant accumulation of fatty acids (FA), organic acids and amino acids. Furthermore, an unexpected accumulation of sugar derivatives and organic acid was observed in the Pe- and Sd-treated seeds. Consistent with this, the expression of a subset of genes involved in FA metabolism, sugar and oil storage was significantly altered in Pe and Sd treated seeds. Taken together, our studies suggest the metabolite profiles of canola seeds dynamically varied during the course of oil accumulation, which may provide a new insight into the mechanisms of the oil accumulation at the metabolite level. [ABSTRACT FROM AUTHOR]

Background: Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. Results: We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. Conclusions: On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and Sn values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, Sn value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria. [ABSTRACT FROM AUTHOR]