Search

Abstract In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

Hessian fly (HF), Mayetiola destructor, is a major insect pest that causes severe losses in grain yield and quality of wheat (Triticum aestivum). Growing resistant cultivars is the most cost-effective approach to minimize wheat yield losses caused by HF. In this study, 2,496 wheat accessions were screened for resistance to the HF biotype ‘Great Plains’ (GP) in the greenhouse experiments. To purify seeds from heterogeneous resistant accessions, we recovered single resistant plants from 331 accessions that had at least one resistant plant after HF infestation of a global collection of 1,595 accessions and confirmed 27 accessions with high resistance (HR), and 91 accessions with moderate resistance (MR) to the GP biotype using purified seeds. Screening of 203 U.S. winter wheat accessions in three experiments identified 63 HR and 28 MR accessions; and screening of three additional Asian panels identified 4 HR and 25 MR accessions. Together, this study identified 96 HR accessions and 144 MR accessions. Analysis of the geographic distribution of these HR and MR accessions revealed that these countries with HF as a major wheat pest usually showed higher frequencies of resistant accessions, with the highest frequency of HR (81.3%) and MR (30.6%) accessions identified from the U.S. In addition, phenotyping of 39 wheat accessions that carry known HF resistance genes showed that all the accessions except H1H2 remain effective against GP biotype. Some of these newly identified resistant accessions may contain new HF resistance genes and can be valuable sources for developing HF resistant wheat cultivars.

Scrap steel represents a sustainable and recyclable resource, instrumental in diminishing carbon footprints and facilitating the eco-friendly evolution of the steel sector. However, current scrap steel recycling faces a series of challenges, such as high labor intensity and occupational risks for inspectors, complex and diverse sources of scrap steel, varying types of materials, and difficulties in quantifying and standardizing manual visual inspection and rating. Specifically, we propose WaveSegNet, which is based on wavelet transform and a multiscale focusing structure for scrap steel segmentation. Firstly, we utilize wavelet transform to process images and extract features at different frequencies to capture details and structural information in the images. Secondly, we introduce a mechanism of multiscale focusing to further enhance the accuracy of segmentation by extracting and perceiving features at different scales. Through experiments conducted on the public Cityscapes dataset and scrap steel datasets, we have found that WaveSegNet consistently demonstrates superior performance, achieving the highest scores on the mIoU metric. Particularly notable is its performance on the real-world scrap steel dataset, where it outperforms other segmentation algorithms with an average increase of 3.98% in mIoU(SS), reaching 69.8%, and a significant boost of nearly 5.98% in mIoU(MS), achieving 74.8%. These results underscore WaveSegNet’s exceptional capabilities in processing scrap steel images. Additionally, on the publicly available Cityscapes dataset, WaveSegNet shows notable performance enhancements compared with the next best model, Segformer. Moreover, with its modest parameters and computational demands (34.1 M and 322 GFLOPs), WaveSegNet proves to be an ideal choice for resource-constrained environments, demonstrating high computational efficiency and broad applicability. These experimental results attest to the immense potential of WaveSegNet in intelligent scrap steel rating and provide a new solution for the scrap steel recycling industry. These experimental results attest to the immense potential of WaveSegNet in intelligent scrap steel rating and provide a new solution for the scrap steel recycling industry.

Abstract Background Plant height (PH), spike length (SL) and spike compactness (SCN) are important agronomic traits in wheat due to their strong correlations with lodging and yield. Thus, dissection of their genetic basis is essential for the improvement of plant architecture and yield potential in wheat breeding. The objective of this study was to map quantitative trait loci (QTL) for PH, SL and SCN in a recombinant inbred line (RIL) population derived from the cross ‘PuBing3228 × Gao8901’ (PG-RIL) and to evaluate the potential values of these QTL to improve yield. Results In the current study, Five, six and ten stable QTL for PH, SL, and SCN, respectively, were identified in at least two individual environments. Five major QTL QPh.cas-5A.3, QPh.cas-6A, QSl.cas-6B.2, QScn.cas-2B.2 and QScn.cas-6B explained 5.58–25.68% of the phenotypic variation. Notably, two, three and three novel stable QTL for PH, SL and SCN were identified in this study, which could provide further insights into the genetic factors that shape PH and spike morphology in wheat. Conditional QTL analysis revealed that QTL for SCN were mainly affected by SL. Moreover, a Kompetitive Allele Specific PCR (KASP) marker tightly linked to stable major QTL QPh.cas-5A.3 was developed and verified using the PG-RIL population and a natural population. Conclusions Twenty-one stable QTL related to PH, SL, and SCN were identified. These stable QTL and the user-friendly marker KASP8750 will facilitate future studies involving positional cloning and marker-assisted selection in breeding.

Wrist pulse is one kind of biomedical signals, it is affected not only by the heart beatings, but also by the conditions of nerves, organs, muscles, skin, etc. Therefore, wrist pulse signals can reflect a person's physical state and it has been widely used in health status analysis. However, previous works mainly use traditional machine learning methods to analyze wrist pulse signal. Because wrist pulse signal is high-dimensional and complex, it is difficult for traditional machine learning methods to learn effective information from them. This study aims to explore the utilizing of deep learning methods on wrist pulse signal analysis. We propose a novel multi-kernel Convolutional Neural Network for wrist pulse signal classification. Our model can handle multiple kinds of input features and each of them will pass through a convolutional neural network that has three different sizes of convolution kernel to capture multi-scale information in different time steps. We compare our method with traditional machine learning methods on two tasks: Coronary Atherosclerotic Heart Disease Classification and Traditional Chinese Medicine Constitution yin deficiency and yang deficiency Classification. Besides, we also research the influence of different input features and different channels on wrist pulse signal analysis. The results show that our model significantly improves the performance on the two tasks, which proves the deep learning method is more suitable to deal with complex wrist pulse data.

Steam flooding is an important thermal recovery method for heavy oil reservoirs, and convective heating technology is used to fracture oil shale reservoirs with good results. This paper reviews the main prediction methods, optimization approaches for steam flooding performance, and its application in fractured oil shale reservoirs. The prediction methods include experimental, numerical simulation, and statistical models. These provide insights into steam override, heat transfer, and production dynamics. To optimize steam flooding, parameters like quality, temperature, injection rate, and allocation need to be coordinated based on reservoir conditions and monitoring data. Real-time injection control, economic analysis, and sweep efficiency improvements should also be considered in optimization workflows. Although progress has been made, more field studies are needed to establish systematic optimization practices utilizing advanced technologies. This review summarizes the key developments in steam flooding modeling and optimization, providing a reference for further research and field applications.

Anthracnose caused by Colletotrichum gloeosporioides is a destructive disease of Stylosanthes (stylo). Combination treatment of phloretin and pterostilbene (PP) has been previously shown to effectively inhibit the conidial germination and mycelial growth of C. gloeosporioides in vitro. In this study, the effects of PP treatment on the growth of C. gloeosporioides in vivo and the biocontrol mechanisms were investigated. We found that exogenous PP treatment could limit the growth of C. gloeosporioides and alleviate the damage of anthracnose in stylo. Comparative transcriptome analysis revealed that 565 genes were up-regulated and 239 genes were down-regulated upon PP treatment during the infection by C. gloeosporioides. The differentially expressed genes were mainly related to oxidative stress and chloroplast organization. Further physiological analysis revealed that application of PP after C. gloeosporioides inoculation significantly reduced the accumulation of O2•− level and increased the accumulation of antioxidants (glutathione, ascorbic acid and flavonoids) as well as the enzyme activity of total antioxidant capacity, superoxide dismutase, catalase, glutathione reductase, peroxidase and ascorbate peroxidase. PP also reduced the decline of chlorophyll a + b and increased the content of carotenoid in response to C. gloeosporioides infection. These results suggest that PP treatment alleviates anthracnose by improving antioxidant capacity and reducing the damage of chloroplasts, providing insights into the biocontrol mechanisms of PP on the stylo against anthracnose.

In order to reduce the sampling delay and improve bandwidth, sability margin, and the robustness of the active damping in LCL-filtered grid-connected inverters, real-time sampling provides a convenient method. However, aliasing is easily introduced in the control loop because of high-frequency switching harmonics, resulting in a rise in low-order harmonics. To address the challenge of aliasing under real-time sampling, a new method based on multisampling and mean filtering is proposed by combining the proposed harmonic detection and control methods in this paper. This method works by sampling and controlling the fundamental current and selective subharmonic currents separately. The fundamental current control loop without any additional filters maintains real-time sampling, while multisampling and mean filters are applied to the harmonic current control loop. The proposed control method can not only improve the dynamic response performance and control stability of the system but also effectively suppress the selective low-order current harmonics and the aliasing of sampling. Finally, the correctness of the proposed sampling scheme and control strategy is verified by the simulation based on R2018b, MathWorks, Natick, MA, USA and an experimental prototype of a three-phase grid-connected photovoltaic inverter rated at 230 V/50 Hz/40 kW.

Emerging contaminants have been increasingly recognized as critical determinants in global public health outcomes. However, the intricate relationship between these contaminants and glucose metabolism remains to be fully elucidated. The paucity of comprehensive clinical data, coupled with the need for in-depth mechanistic investigations, underscores the urgency to decipher the precise molecular and cellular pathways through which these contaminants potentially mediate the initiation and progression of diabetes mellitus. A profound understanding of the epidemiological impact of these emerging contaminants, as well as the elucidation of the underlying mechanistic pathways, is indispensable for the formulation of evidence-based policy and preventive interventions. This review systematically aggregates contemporary findings from epidemiological investigations and delves into the mechanistic correlates that tether exposure to emerging contaminants, including endocrine disruptors, perfluorinated compounds, microplastics, and antibiotics, to glycemic dysregulation. A nuanced exploration is undertaken focusing on potential dietary sources and the consequential role of the gut microbiome in their toxic effects. This review endeavors to provide a foundational reference for future investigations into the complex interplay between emerging contaminants and diabetes mellitus.

Lactobacillus plantarum is a kind of probiotic that benefits the host by regulating the gut microbiota, but it is easily damaged when passing through the gastrointestinal tract, hindering its ability to reach the destination and reducing its utilization value. Encapsulation is a promising strategy for solving this problem. In this study, transglutaminase (TGase)-crosslinked gelatin (GE)/sodium hexametaphosphate (SHMP) hydrogels were used to encapsulate L. plantarum. The effects of TGase concentration and drying method on the physiochemical properties of the hydrogels were determined. The results showed that at a TGase concentration of 9 U/gGE, the hardness, chewiness, energy storage modulus, and apparent viscosity of the hydrogel encapsulation system were maximized. This concentration produced more high-energy isopeptide bonds, strengthening the interactions between molecules, forming a more stable three-dimensional network structure. The survival rate under the simulated gastrointestinal conditions and storage stability of L. plantarum were improved at this concentration. The thermal stability of the encapsulation system dried via microwave vacuum freeze drying (MFD) was slightly higher than that when dried via freeze drying (FD). The gel structure was more stable, and the activity of L. plantarum decreased more slowly during the storage period when dried using MFD. This research provides a theoretical basis for the development of encapsulation technology of probiotics.

The Hessian fly (HF, Mayetiola destructor) is one of the destructive pests of wheat (Triticum aestivum L.) worldwide. Resistant cultivars can effectively minimize wheat damage due to this insect pest. To identify new quantitative trait loci (QTL) for HF resistance, a population of recombinant inbred lines (RILs) was developed from a cross between the HF-resistant wheat cultivar ‘Chokwang’ and susceptible wheat ‘Ning 7840’, and phenotyped for responses to HF attack. A linkage map was constructed using 1147 single nucleotide polymorphism (SNP) markers generated from genotyping-by-sequencing (GBS). One major QTL, QHf.hwwg-6BS, for HF-resistance was identified on chromosome arm 6BS, which explained up to 84.0% of the phenotypic variation and was contributed by Chokwang. Two RILs showed recombination in the candidate interval of QHf.hwwg-6BS, which delimited QHf.hwwg-6BS to a 4.75 Mb physical interval between 6,028,601 bp and 10,779,424 bp on chromosome arm 6BS of IWGSC Chinese Spring reference genome RefSeq v2.0. Seven GBS-SNPs in the candidate interval were converted into Kompetitive allele specific polymerase chain reaction (KASP) markers. Two of them, KASP-6B112698 and KASP-6B7901215, were validated in a U.S. winter wheat panel. KASP-6B112698 was nearly diagnostic, thus can be used to screen QHf.hwwg-6BS and pyramid it with other resistance genes in breeding programs.

Staphylococcus aureus is a common foodborne pathogen which can form biofilms to help them resist to antimicrobials. It brings great harm to human health. Punicalagin has good antimicrobial activities against S. aureus, but its effect on biofilm formation has not been clearly illustrated. The aim of this study was to explore the antibiofilm effects of punicalagin against S. aureus. Results showed that punicalagin did not significantly interfere with the growth of S. aureus at the concentrations of 1/64 MIC to 1/16 MIC. The biomass and metabolic activity of biofilms were significantly reduced when exposed to sub-inhibitory concentrations of punicalagin. The number of viable cells in the biofilms was also decreased after punicalagin treatment. Scanning electron microscopy and confocal laser scanning microscopy images confirmed that punicalagin damaged the structure of biofilms. The antibiofilm mechanism was partly due to the modification of the cell surface which led to the reduction of cell surface hydrophobicity. These findings suggest that punicalagin has the potential to be developed as an alternative to control S. aureus biofilms.

Abstract A comprehensive and accurate estimation of water quality in lakes and reservoirs is vital for the protection of the aquatic biota. Research on the spatiotemporal variations of nitrogen (N) and phosphorus (P) concentrations in lacustrine systems is typically plagued, however, by a lack of long‐term, spatially continuous monitoring data. This paper assembled a 30‐year (1989–2018) data set of water quality in 586 lakes and reservoirs in China, along with basin characteristics and climate conditions, forming the comprehensive data set available. These data were then used in a stacking model (based on random forest, support vector regression, and K‐nearest neighbor models) to identify the relationships between nutrient concentrations and their influencing factors, including net anthropogenic N/P inputs, geographical position, climate, land use pattern, and soil type. The stacking models were developed using data collected over multiple time scales (annual, seasonal, and monthly), which were then applied to reconstruct TN and TP concentrations during the periods of 1980–2018 and 2020s–2050s under the climate scenarios of RCP 4.5 and RCP 8.5. The accuracy of the stacking models was 99.1% and 98.3% for TN and TP concentrations using ensembled data, respectively. The interannual variations in TN and TP contents in the 586 lakes and reservoirs during 1980–2018 exhibited a non‐monotonic pattern with a peak of 1.12 and 0.049 mg/L in 2007, respectively. This study demonstrates that stacking machine learning models represent a new effective approach for estimating nutrient concentrations in unmonitored lakes and reservoirs across broad spatiotemporal scales.

IntroductionIdentification of stable major quantitative trait loci (QTLs) for yield-related traits is important for yield potential improvement in wheat breeding.MethodsIn the present study, we genotyped a recombinant inbred line (RIL) population using the Wheat 660K SNP array and constructed a high-density genetic map. The genetic map showed high collinearity with the wheat genome assembly. Fourteen yield-related traits were evaluated in six environments for QTL analysis.Results and DiscussionA total of 12 environmentally stable QTLs were identified in at least three environments, explaining up to 34.7% of the phenotypic variation. Of these, QTkw-1B.2 for thousand kernel weight (TKW), QPh-2D.1 (QSl-2D.2/QScn-2D.1) for plant height (PH), spike length (SL) and spikelet compactness (SCN), QPh-4B.1 for PH, and QTss-7A.3 for total spikelet number per spike (TSS) were detected in at least five environments. A set of Kompetitive Allele Specific PCR (KASP) markers were converted based on the above QTLs and used to genotype a diversity panel comprising of 190 wheat accessions across four growing seasons. QPh-2D.1 (QSl-2D.2/QScn-2D.1), QPh-4B.1 and QTss-7A.3 were successfully validated. Compared with previous studies, QTkw-1B.2 and QPh-4B.1 should be novel QTLs. These results provided a solid foundation for further positional cloning and marker-assisted selection of the targeted QTLs in wheat breeding programs.

Staphylococcus aureus has posed a huge threat to human health and the economy. Oleuropein has antibacterial activities against various microorganisms but research on its effect on the S. aureus biofilm is limited. This research aimed to estimate the antibiofilm activities of oleuropein against S. aureus. The results suggest that the minimum inhibitory concentration of oleuropein against S. aureus ATCC 25923 was 3 mg/mL. The biomass of biofilms formed on the microplates and coverslips and the viability of bacteria were significantly reduced after the treatment with oleuropein. The scanning electron microscopy observation results indicated that the stacking thickness and density of the biofilm decreased when S. aureus was exposed to oleuropein. It had a bactericidal effect on biofilm bacteria and removed polysaccharides and proteins from mature biofilms. The effects of oleuropein on the biofilm could be explained by a reduction in bacterial secretion of extracellular polymeric substances and a change in bacterial surface hydrophobicity. Based on the above findings, oleuropein has the potential to be used against food pollution caused by S. aureus biofilms.

ObjectiveThis stydy aims to assess the value of monitoring of postoperative neutrophil-to-lymphocyte ratio (NLR), D-dimer, and carbohydrate antigen 153 (CA153) for diagnosis of breast cancer (BC) recurrence and metastasis.Materials/MethodsA cohort of 252 BC patients who underwent surgery at the First Affiliated Hospital of Anhui Medical University between August 2008 and August 2018 were enrolled in this retrospective study. All patients were examined during outpatient follow-ups every 3 months for 5 years postoperation and every 6 months thereafter. Recurrence or metastasis was recorded for 131 patients but not for the remaining 121. Retrospective analysis of hematological parameters and clinicopathological characteristics allowed comparison between the two groups and evaluation of these parameters for the recurrent and metastatic patients.ResultsLymph node metastasis, higher tumor node metastasis (TNM) staging, and higher histological grade correlated with BC recurrence and metastasis (p

Abstract Background Cumulating evidence reveals the key role of aberrant lipogenesis and immunogenomic features in hepatocellular carcinoma (HCC). However, there are still obstacles in our understanding of the complicated interaction between metabolic reprogramming and tumor immune microenvironment. Methods We compared metabolomic, transcriptomic and immunogenomic characteristics of portal vein tumor thrombosis (PVTT) and primary tumor to seek valuable markers. Human HCC samples with PVTT (n = 28) was analyzed through ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). Transcript levels of mRNA in two cohorts from published database GEO (n = 60) and TCGA (n = 411) were downloaded to explore differentially expressed genes and functional enriched gene set. Evaluation of immune infiltration was estimated and validated from transcriptomic data in both cohorts through six immune deconvolution algorithms and in a high-resolution mode (CIBERSORTx). Survival analysis (Kaplan–Meier and multivariable Cox regression model) was performed to examine prognostic value of ACLY, related immune checkpoints and immune infiltration levels from TCGA cohort. LASSO regression was further conducted to determine a gene panel to further predict survival outcomes associated with ACLY. Results We identified a novel signature, ATP citrate lyase, through transcriptomic and metabolomic approaches. We demonstrated that the metabolism adaptations in both fatty acid and cholesterol biosynthesis triggered by ACLY oncogenic activation. We illustrated the crucial function of ACLY in lipogenesis and its potential interaction with immune microenvironment. CD276, a promising target in immune checkpoint blockade, showed correlation to ACLY and differential expression in ACLY risk classification. Combination of ACLY, CD276 and immune infiltration level and a novel ACLY-associated panel from a predictive model retrieved from published database validated the prognostic value to risk stratification in patients with HCC.ACLY blockade to counteract metabolic activation and immunosuppressive status of the tumor microenvironment highlighted attractive prospect for translational application. Conclusions We investigated ACLY and its indispensable role in metabolism, immune function and a prognostic gene panel in HCC. We anticipate that the multifaced role of ACLY may reveal the potential value for mechanistic research and combinational therapy, suggesting that the combination blockade of ACLY and immune checkpoints may work as a promising strategy.

Community structure is an important field in the research of complex networks,and it is also one of the important characteristics of complex networks.It is found that the community structure in the network plays an important role in understanding network functions.Through in-depth research on the literature of heterogeneous network community discovery at home and abroad,a more comprehensive summary of the existing heterogeneous network community discovery algorithm is carried out.Firstly,by summarizing the literature of heterogeneous network community discovery at home and abroad,the basic overview of heterogeneous network community discovery is given,and the basic definition of related issues in the field of heterogeneous network community discovery is defined.Then,it introduces the heterogeneous network community discovery algorithm and the main evaluation index,and classifies the existing methods by using different network structures and algorithms.Finally,the development trend of heterogeneous network community discovery algorithms is summarized and prospected,and the future research focus can be focused on the following aspects:1) Exploring the evaluation criteria of community discovery based on heterogeneous networks to promote the rapid development of this field Development;2) Design a more general algorithm model to solve the problem of the number of unknown communities caused by prior knowledge;3) Carry out more research on dynamic networks.

In this paper, combined with the addition of ethylenediaminetetraacetic acid (EDTA), the electrochemical treatment of waste activated sludge (WAS) was investigated to explore its effect on the release of phosphorus (P) from WAS. The results showed that during the electrochemical treatment, the addition of EDTA could significantly promote the release of P from the WAS to the supernatant, the optimal amount of EDTA was 0.4 g/g total suspended solids (TSS), when the release of total dissolved phosphorus (TDP), organic phosphorus (OP) and molybdate reactive phosphorus (PO43−-P) were 187.30, 173.84 and 13.46 mg/L, respectively. OP was the most likely form of P to be released during this process. Moreover, combined electrochemical-EDTA treatment could promote the release of P and metal ions from extracellular polymeric substances (EPSs) to the supernatant, and increase the solubility and disintegration of sludge. EDTA chelated the metal ions of sludge flocs and phosphate precipitates to cause sludge floc decomposition, thereby promoting the release of P from WAS. HIGHLIGHTS Successfully developed combined electrochemical-EDTA treatment technology to promote the release of P in WAS.; OP was the most likely phosphorus form to be released.; Changes of P and metal elements content in EPSs had been investigated.; EDTA destroys sludge flocs and promotes P release by chelating heavy metals.;

Most of the real-life networks are heterogeneous networks that contain multiple types of nodes and edges, and heterogeneous networks integrate more information and contain richer semantic information than homogeneous networks. Heterogeneous network representation learning to have powerful modeling capabilities, enables to solve the heterogeneity of heterogeneous networks effectively, and to embed the rich structure information and semantic information of heterogeneous networks into low-dimensional node representations to facilitate downstream task applications. Through sorting out and classifying the current heterogeneous network representation learning methods at home and abroad, reviewed the current research status of heterogeneous network representation learning methods, compared the characteristics of each category model , introduced the related applications of heterogeneous network representation learning, and summarized and prospected the development trend of heterogeneous network representation learning methods. It is proposed that in-depth discussion can be carried out in the following aspects in future: First, avoid predefined meta-paths and fully release the automatic learning capabilities of the model; Second, design heterogeneous network representation learning method suitable for dynamic and large-scale networks.

With the development of deep learning, aspect-based sentiment classification has achieved a lot of results in a single field and a single language, but there is room for improvement in multi-fields. By summarizing up the methods of text aspect-based sentiment classification in recent years, the specific application scenarios of sentiment classification were introduced, and the commonly used data sets of aspect-based sentiment classification were categorized. The development of aspect-based sentiment classification were summarized and prospected, and further research can be carried out in the following areas: exploring methods based on graph neural networks to make up for the limitations of deep learning methods; learning to fuse multi-modal data to enrich the emotional information of a single text; developing more targeted research work on multilingual texts and low-resource languages.

In order to solve the problem of imbalance of data samples in multi-modal data, the resource-rich text modal know-ledge was used to model the resource-poor acoustic mode, and an emotion recognition neural network was constructed by using the similarity between auxiliary modes to supervise training. Firstly, the neural network with bi-GRU as the core was used to learn the initial feature vectors of the text and acoustic modalities. Secondly, the SoftMax function was used for emotion recognition prediction, and simultaneously a fully connected layer was used to generate the target feature vectors corresponding to the two modalities. Finally, the target feature vector assisted the supervised training by calculating the similarity between each other to improve the performance of emotion recognition. The results show that this neural network can perform four emotion classifications on the IEMOCAP data set to achieve a weighted accuracy of 82.6% and an unweighted accuracy of 81.3%. The research result provides a reference and method basis for emotion recognition and auxiliary modeling in the multi-modal field of artificial intelligence.

In order to solve the problem of disambiguation of duplicate authors in large academic databases, a name entity disambiguation model based on meta-path heterogeneous network was proposed. Based on the public data of the large online academic search system DBLP, the author information, title, name of conference journal and other characteristic attributes of academic publications were extracted first. Then the characteristic attribute words generated by the word2vec model tool were embedded into the GRU network for training, so that a PHNet matrix network for random walk operation was constructed to capture the relationship between different types of nodes and finally similar nodes were divided to complete the name disambiguation. The experimental results show that the accuracy of the method is 0.865, the recall rate is 0.792, and the F1 value is 0.815.The meta-path-based heterogeneous network embedding model is superior to the comparison model in terms of accuracy and recall rate. Therefore, the proposed model has a good application prospect in improving the accuracy of disambiguation of large academic databases.

The network representation learning algorithm represents the information network as a low-dimensional dense real vector carrying the characteristic information of network nodes, and is applied to the input of downstream machine learning tasks. With the development of machine learning and deep learning, network representation learning has been widely used due to its powerful modeling capabilities and extensive applications. The network representation learning methods and their application were summarized. Firstly, the current network representation learning methods at home and abroad were categorized into different groups, including traditional methods, network structure-based embedding, embedding with attribute information, and spectral-based Graph Convolutional Networks, spatial-based Graph Convolutional Networks and Graph Attention Networks. Then various models were expounded, and the applicability and characteristics of the models were compared. Secondly, the related applications of network representation learning, including recommender system and biomedical field, were introduced. Commonly used data sets are also given, and open source implementations of representation learning models and powerful graph deep learning libraries for reference were organized. Finally, the developing trend of network representation learning was summarized and forecasted. Deep graph neural network, dynamic and heterogeneous network representation, and generalization ability of network model will need to be further studied.

Ovalbumin (OVA), the main protein in egg white, affects most of the functional properties of egg white protein in food processing. The aim of this study was to investigate the effects of spray drying (SD) and microwave freeze drying (MFD) on the preparation of hydrolyzed/glycosylated ovalbumin (HGOVA) and provide useful information on the applications of egg protein powders in the food industry. Results demonstrated that the structure of HGOVA was considerably changed, and its functional properties were improved compared with those of native OVA. SD and MFD processing did not lead to dissociation of HGOVA subunits. SD-HGOVA exhibited higher protein solubility, emulsifying activity, foaming capacities, and gel hardness than MFD-HGOVA. However, MFD-HGOVA was better than the SD-HGOVA in terms of color, emulsion stability, foam stability, water/oil absorption capacity, and thermal stability. Selection of an appropriate drying method could enhance the potential applications of HGOVA in the food industry. Keywords: Ovalbumin, Glycosylation, Spray drying, Microwave freeze drying

With the rapid development of urbanization and industrialization, the problem of air pollution has become increas-ingly prominent, and air quality prediction is particularly important. Some representative studies currently monitor and forecast air quality in real time. For example, ZHOU Guangqiang et al. Used numerical prediction to analyze air quality in eastern China. However, experimental results show that this method is difficult to predict and is very important. SANKAR et al. Used multiple linear regression to predict air quality, but the experimental results showed that the linear model had low prediction accuracy and slow efficiency;PREZ et al. Used statistical methods to predict air quality, and the experimental results proved the prediction accuracy of the statistical method relatively low; WANG et al. Used an improved BP neural network to establish a prediction model for the air quality index, and their experiments verified that the BP neural network has a slow convergence rate and is prone to fall into the local optimal solution problem; YANG et al. Air quality concentration effect, a PM2.5 concentration prediction model based on random forests was established, and the empirical process proved that the meshing program weakened the quality and efficiency of subsequent air quality analysis; these methods are difficult to model from a time perspective, and the prediction accuracy is low is a more important issue. Because low prediction accuracy may lead to large errors in air quality prediction results. 河北科技大学学报 2020年 第1期 张冬雯，等:基于长短期记忆神经网络模型的空气质量预测 In this paper, a neural network model based on long -term memory (LSTM) is proposed to solve the problem of low prediction accuracy in air quality research.MAPE, RMSE, R, IA and MAE were used to test the predictive performance of LSTM neural network and the comparison model.Since Delhi and Houston are cities with high levels of air pollution, the experimental data sets used in this paper were from the air quality data of Punjabi Bagh monitoring station in Delhi from 2014 to 2016 and the air quality data of Harris County monitoring station in Houston from 2010 to 2016.By comparing LSTM neural network with multiple linear regression and regression model (SVR), the experimental results show that LSTM neural network is suitable for time series prediction with multiple variables or multiple inputs LSTM neural network has the advantages of high prediction accuracy, high speed and strong robustness.

With the rapid development of the internet, how to mine and analyze massive network information has become a recognized hot and difficult problem. Among them, the recommendation system can provide users with accurate and fast business (commodities, projects, services, etc.) information, which is the common interest and research hotspot of industry and academia in recent years. A recommendation system can help users to solve the problem of information overload when there is no clear demand or a large amount of information. However, at present, the types of data are diverse and the application scenarios are extensive. When faced with this situation, the recommendation system also encounters challenges such as cold start and sparse matrix. Deep learning is an important research field and the most important branch of machine learning. In recent years, deep learning has developed rapidly. Researchers have made great breakthroughs and achievements in speech recognition, image processing, natural language processing and other fields by using deep learning. At present, deep learning has also been favored by a large number of researchers in the field of recommendation and has become a new direction. Incorporating deep learning technology into the recommendation method can effectively solve the problems of cold start and sparse matrix in traditional recommendation systems, and improve the performance and recommendation accuracy of the recommendation system. 河北科技大学学报 2020年 第1期 周万珍，等:推荐系统研究综述 This paper mainly summarizes the application of traditional recommendation methods and the application of neural network in current deep learning technology in recommendation methods, among which the traditional recommendation methods can be divided into the following three categories: 1) Content-based recommendation methods is mainly based on the feature information between the user and the project. The connection between users will not affect the recommendation result, so there is no problem of cold start and sparse matrix, but the content-based recommendation results are low in novelty and face the problem of feature extraction. 2) The collaborative filtering recommendation method is the most widely used method that does not require information about users or items, but only makes accurate recommendations based on the user's interactions with items such as clicks, views, and ratings. Although this method is simple and effective, sparse matrix and cold start problems will occur. 3) The hybrid recommendation method combines the characteristics of the first two traditional recommendation methods and can achieve good recommendation effect. However, this method still faces some challenges and difficulties in processing multi-source heterogeneous auxiliary information such as text and images.Recommendation methods based on deep learning are mainly classified according to neural network categories, which are divided into the following four categories: Recommendation methods based on deep neural network (DNN); recommendation methods based on convolutional neural network (CNN); recommended methods based on cyclic neural network (RNN) and long and short term memory neural network (LSTM); and recommended methods based on graph neural network (GNN). Incorporating deep learning technology into the recommendation field, the constructed model has the following five advantages: it has strong representation ability, and can directly extract the characteristics of users and items from the content; with strong anti-noise ability, it can easily process data with noise; in deep learning, cyclic neural network can model dynamic or sequential data; it can learn user or project characteristics more accurately; and deep learning facilitates the unified processing of data and can process large-scale data. Applying deep learning technology to the recommendation field can effectively overcome the challenges faced by traditional recommendation methods and improve the recommendation effect.

Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability.

Barley is an important cereal crop worldwide. Its genetic transformation is now limited to very few cultivars because of the high genotype dependence of embryogenic callus. To reveal the key genes or factors controlling the callus induction and plantlet regeneration in barley, we compared the transcriptomic profiles of immature embryos of Golden Promise and ZU9, which differed dramatically in the efficiency of the genetic transformation. The samples were taken at 0, 5, 10 and 20 days of the culture, respectively. In total, 5386 up-regulated and 6257 down-regulated genes were identified in Golden Promise. Several genes, identified exclusively in GP callus, were selected for further investigation. These genes were mainly involved in protein metabolism, energy metabolism, stress response, detoxification and ubiquitin–proteasome. Four YUCCA flavin monooxygenases, one PIN-FORMED, one tryptophan aminotransferase related, three small auxin up RNA, three indole-3-acetic acid and one adenylate isopentenyl transferase, seven cytokinin oxidase/dehydrogenase, three Arabidopsis histidine kinase, three Arabidopsis histidine phosphotransfer protein, and one Arabidopsis response regulator were differentially expressed in the calli of the two barley genotypes, suggesting that biosynthesis, response and transport of auxin and cytokinin might be associated with cell reprogramming during callus induction. The current results provide insights into molecular mechanisms of callus induction at an early developmental stage and are helpful for optimizing the tissue culture system in barley.

This paper presents an effective numerical simulation method for production prediction of in situ recovery of oil shale reservoirs with steam injection. In this method, finite volume-based discretization schemes of heat and mass transfer equations of the thermal compositional model are derived and used. The embedded discrete fracture model is used to accurately handle the fractured vertical well. A smooth non-linear solver is proposed to solve the global equations, then cell pressure, temperature, saturation, component mole fractions, and well production rates can be obtained. Compared with the existing commercial software, this new method can have a smoother non-linear solution and handle the complex fracture geometry theoretically. A numerical example is used to test this presented method and can realize accurate calculation results compared with CMG. Another numerical case with a hydraulic fracture and an open thermal boundary condition is implemented to validate the presented method and can effectively handle the actual situation of steam injection-assisted in situ recovery of oil shale, which was difficult to handle using previous methods.

In the natural environments microorganisms coexist in communities as biofilms. Since foodborne pathogens have varying abilities to form biofilms, investigation of bacterial interactions in biofilm formation may enhance our understanding of the persistence of these foodborne pathogens in the environment. Thus the objective of this study was to investigate the interactions between Listeria monocytogenes and Ralstonia insidiosa in dual species biofilms. Biofilm development after 24 h was measured using crystal violet in 96-well microtiter plate. Scanning electron microscopy and cell enumeration were employed after growth on stainless steel coupons. When compared with their single species counterparts, the dual species biofilms exhibited a significant increase in biofilm biomass. The number of L. monocytogenes in co-culture biofilms on stainless steel also increased significantly. However, there was no effect on the biofilm formation of L. monocytogenes when cultured with R. insidiosa separated by a semi-permeable membrane-linked compartment or cultured in R. insidiosa cell-free supernatant, indicating that direct cell-cell contact is critical for this interaction.

CoDA (Communities through Directed Affiliations) algorithm is a kind of community detection algorithm which can successfully detect 2-mode communities based on probability model. The F-measure criterion, for information retrieval, is adapted to the evaluation of CoDA algorithm in directed networks with overlapping communities or non-overlapping communities. The value of F1-measure in F-measure criterion can reflect whether CoDA algorithm performs well or not. The data sets used in the experiment is generated by the LFR Benchmark tool. The minimum number of nodes in data set is 100 and the maximum is 20 000, and evaluated experiment is conducted when every 100 nodes is added. The results show that CoDA algorithm performs well when the number of nodes is bellow 1 600. CoDA algorithm's performance becomes worse with the increase of the number of nodes, which proves the CoDA algorithm based on probability model is applicable to the community detection of small-scale networks.

This study aimed to determine the antimicrobial activity of thymoquinone (TQ) against Listeria monocytogenes, and to examine its inhibitory effects on biofilm formation, motility, hemolysin production, and attachment-invasion of host cells. The minimum inhibitory concentrations (MICs) of TQ against eight different L. monocytogenes strains ranged from 6.25—12.50 μg/mL. Crystal violet staining showed that TQ clearly reduced biofilm biomass at sub-MICs in a dose-dependent manner. Scanning electron microscopy suggested that TQ inhibited biofilm formation on glass slides and induced an apparent collapse of biofilm architecture. At sub-MICs, TQ effectively inhibited the motility of L. monocytogenes ATCC 19115, and significantly impacted adhesion to and invasion of human colon adenocarcinoma cells as well as the secretion of listeriolysin O. Supporting these findings, real-time quantitative polymerase chain reaction analysis revealed that TQ down-regulated the transcription of genes associated with motility, biofilm formation, hemolysin secretion, and attachment-invasion in host cells. Overall, these findings confirm that TQ has the potential to be used to combat L. monocytogenes infection.

Listeria monocytogenes can persist under a wide range of stress conditions, contributing to its ubiquitous distribution and unique pathogenic traits. Light from light-emitting diodes (LEDs) has recently been shown to inactivate various pathogens. Thus, the aim of the present study was to evaluate the effects of light treatment using a 405-nm LED on the subsequent resistance of L. monocytogenes to environmental stresses, including oxidative stress, ultraviolet (UV) irradiation, low temperature, osmotic pressure, simulated gastric fluid (SGF), and bile salts. Following 405-nm LED illumination at 4°C for 150 min, the survival of L. monocytogenes was examined after exposure to oxidative stress (0.04% H2O2), UV irradiation (253.7 nm), low temperature (4°C), osmotic pressure (10, 15, or 20% NaCl), SGF (pH 2.5), or bile salts (2%). The mechanisms responsible for changes in stress tolerance were identified by assessing the transcriptional responses and membrane integrity of L. monocytogenes. The 405-nm LED treatment reduced the resistance of L. monocytogenes to all the stresses tested. Reverse transcription quantitative real-time polymerase chain reaction analysis indicated that the transcription of multiple genes associated with stress resistance, including betL, gbuA, oppA, fri, bsh, and arcA, was reduced by 405-nm LED. Confocal laser scanning microscopy revealed that 405-nm LED treatment disrupted the integrity of the L. monocytogenes cell membrane compared with untreated bacteria. Therefore, 405-nm LED illumination appears to reduce the resistance of L. monocytogenes to various stress conditions. These findings suggest that 405-nm LED treatment could be used to effectively prevent and/or control with L. monocytogenes contamination along the entire food-processing chain, from production to consumption.

At present, most of released wheat cultivars or breeding lines in China are susceptible to powdery mildew (Pm) (caused by Blumeria graminis f. sp. tritici, Bgt), so there is an urgent need to rapidly transfer effective and broad-spectrum Pm resistance genes into elite cultivars/lines. Near-isogenic lines (NILs) with short target gene region are very important in molecular breeding and map-based cloning and can be developed by combining marker-assisted selection and conventional phenotypic identification. However, no Pm gene NILs were reported by using this method in the previous studies. A new broad-spectrum dominant resistance gene Pm2b, derived from the Chinese wheat breeding line KM2939, conferred high resistance to Pm at both the seedling and adult stages. In this study, with the aid of forward and background selection (FS and BS) using molecular markers, the Pm2b gene was introgressed into three elite susceptible commercial cultivars Shimai 15, Shixin 828, and Kenong 199 through the back-crossing procedure. With the appropriate backcrossing generations, selected population sizes and marker number for BS, the homozygous resistant BC3F2:3 NILs of Pm2b gene in the three genetic backgrounds with the highest recipient genome composition of about 99%, confirmed by simple sequence repeat markers and 660K single nucleotide polymorphic array, were developed and evaluated for the powdery mildew resistance and agronomic traits. The different resistance and similar or improved agronomic performance between Pm2b NILs and their corresponding recurrent parents indicated their potential value in the marker-assisted breeding of the Pm2b gene. Moreover, the development of four flanked diagnostic markers (CFD81, BWM25, BWM20, and BWM21) of the Pm2 gene can effectively assist the forward selection and accelerate the transfer and use of this resistance gene.

Major depressive disorder (MDD) is a common psychiatric disorder that involves marked disabilities in global functioning, anorexia, and severe medical comorbidities. MDD is associated with not only psychological and sociocultural problems, but also pervasive physical dysfunctions such as metabolic, neurobiological and immunological abnormalities. Nevertheless, the mechanisms underlying the interactions between these factors have yet to be determined in detail. The aim of the present study was to identify the molecular mechanisms responsible for the interactions between MDD and dysregulation of physiological homeostasis, including immunological function as well as lipid metabolism, coagulation, and hormonal activity in the brain. We generated depression-like behavior in mice using chronic mild stress (CMS) as a model of depression. We compared the gene expression profiles in the prefrontal cortex (PFC) of CMS and control mice using microarrays. We subsequently categorized genes using two web-based bioinformatics applications: Ingenuity Pathway Analysis and The Database for Annotation, Visualization, and Integrated Discovery. We then confirmed significant group-differences by analyzing mRNA and protein expression levels not only in the PFC, but also in the thalamus and hippocampus. These web tools revealed that hepatocyte nuclear factor 4 alpha (Hnf4a) may exert direct effects on various genes specifically associated with amine synthesis, such as genes involved in serotonin metabolism and related immunological functions. Moreover, these genes may influence lipid metabolism, coagulation, and hormonal activity. We also confirmed the significant effects of Hnf4a on both mRNA and protein expression levels in the brain. These results suggest that Hnf4a may have a critical influence on physiological homeostasis under depressive states, and may be associated with the mechanisms responsible for the interactions between MDD and the dysregulation of physiological homeostasis in humans.

Rye (Secale cereale L.) possesses many valuable genes that can be used for improving disease resistance, yield and environment adaptation of wheat (Triticum aestivum L.). However, the documented resistance stocks derived from rye is faced severe challenge due to the variation of virulent isolates in the pathogen populations. Therefore, it is necessary to develop desirable germplasm and search for novel resistance gene sources against constantly accumulated variation of the virulent isolates. In the present study, a new wheat-rye line designated as WR49-1 was produced through distant hybridization and chromosome engineering protocols between common wheat cultivar Xiaoyan 6 and rye cultivar German White. Using sequential GISH (genomic in situ hybridization), mc-FISH (multicolor fluorescence in situ hybridization), mc-GISH (multicolor GISH) and EST (expressed sequence tag)-based marker analysis, WR49-1 was proved to be a new wheat-rye 6R disomic addition line. As expected, WR49-1 showed high levels of resistance to wheat powdery mildew (Blumeria graminis f. sp. tritici, Bgt) pathogens prevalent in China at the adult growth stage and 19 of 23 Bgt isolates tested at the seedling stage. According to its reaction pattern to different Bgt isolates, WR49-1 may possess new resistance gene(s) for powdery mildew, which differed from the documented powdery mildew gene, including Pm20 on chromosome arm 6RL of rye. Additionally, WR49-1 was cytologically stable, had improved agronomic characteristics and therefore could serve as an important bridge for wheat breeding and chromosome engineering.

Wheat resistance to Fusarium head blight (FHB) has often been associated with some undesirable agronomic traits. To study the relationship between wheat FHB resistance and agronomic traits, we constructed a linkage map of single nucleotide polymorphisms (SNPs) using an F6:8 population from G97252W × G97380A. The two hard winter wheat parents showed contrasts in FHB resistance, plant height (HT), heading date (HD), spike length (SL), spike compactness (SC), kernel number per spike (KNS), spikelet number per spike (SNS), thousand-grain weight (TGW) and grain size (length and width). Quantitative trait locus (QTL) mapping identified one major QTL (QFhb.hwwg-2DS) on chromosome arm 2DS for the percentage of symptomatic spikelets (PSS) in the spike, deoxynivalenol (DON) content and Fusarium damaged kernel (FDK). This QTL explained up to 71.8% of the phenotypic variation for the three FHB-related traits and overlapped with the major QTL for HT, HD, SL, KNS, SNS, TGW, and grain size. QTL on chromosome arms 2AL, 2DS, 3AL and 4BS were significant for the spike and grain traits measured. G97252W contributed FHB resistance and high SNS alleles at QFhb.hwwg-2DS, high KNS alleles at the QTL on 2AL and 2DS, and high TGW and grain size alleles at QTL on 3AL; whereas G97380A contributed high TGW and grain size alleles at the QTL on 2AL and 2DS, respectively, and the high KNS allele at the 4BS QTL. Combining QFhb.hwwg-2DS with positive alleles for spike and grain traits from other chromosomes may simultaneously improve FHB resistance and grain yield in new cultivars. [ABSTRACT FROM AUTHOR]