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Abstract Based on the annual reports and input–output table data of Chinese listed companies from 2008 to 2020, this paper measures the level of digital transformation of enterprises by using the keyword word frequency index in the annual reports of listed companies and reveals the impact and mechanism of digital transformation of enterprises on industry chain elasticity by using a panel data model. Additionally, it provides a theoretical explanation of how the digital revolution of businesses affects industrial chain resilience. The outcome shows that improving industrial chain resilience can be facilitated by businesses undergoing a digital transition. Moreover, factor mismatch can be reduced by corporate digital transformation, which enhances industrial chain resilience. Furthermore, the beneficial effects of the digital revolution on industrial chain resilience will be enhanced by politically linked businesses. The main conclusions contribute to our understanding of the relationship between industrial chain resilience and enterprises’ digital transformation. It also offers crucial policy recommendations and guidance that the government can use to create sensible digital economy policies and successfully direct businesses’ digital transformations in order to strengthen the industrial chain resilience.

The goal of combination cancer therapy, including chemo-phototherapy, is to achieve highly efficient antitumor effects while minimizing the adverse reactions associated with conventional chemotherapy. Nevertheless, enhancing the contribution of non-chemotherapeutic strategies in combination therapy is often challenging because this requires multiple active ingredients to be encapsulated in a single delivery system. However, most commonly used photothermal reagents are challenging to be loaded in large quantities and have poor biocompatibility. Herein, we developed photothermal co-micelles through a co-assembly strategy using a cholesterol-based liquid crystal block copolymer (LC-BCP) with disulfide bonds in the side chain of the LC blocks and a croconaine-based amphiphile (CBA) containing a cholesterol moiety. This approach allowed the CBA to be effectively embedded within LC-BCPs, serving as the functional component of the drug-loaded carrier. These co-micelles could encapsulate doxorubicin (DOX), showed tunable reduction-responsive drug release, and enabled near-infrared laser-triggered photothermal therapy as well as in vivo fluorescence and photothermal imaging. Following laser irradiation, the photothermal activity of the co-micelles rapidly induced tumor cell death and accelerated drug release. In vitro and in vivo experiments demonstrated that the synergistic photo-chemotherapeutic effects of these drug-loaded co-micelles offer a promising avenue for synergistic precision photothermal-chemotherapy.

The inherent ‘black-box’ characteristic of neural networks renders the physical interpretability of fatigue life prediction challenging, resulting in physically inconsistent prediction results. In response to this challenge, a multi-physics information-integrated neural network framework is proposed for fatigue life prediction. Rooted in the continuous damage mechanics with embedded additive manufacturing process parameters (AM-CDM), coupled with activation functions and loss functions that amalgamate physical information, a multi-tiered and multi-physical information source network framework emerges. The framework incorporates the outcomes of the AM-CDM model as input features for domain knowledge enhancement, embeds the fatigue limit within the activation function, and establishes a loss function integrating the physical model. Through the integration of multiple physical information, the model can be restricted by different physical constraints during training, thereby improving its prediction accuracy and physical consistency. Validation of the proposed framework is conducted using laser powder bed fusion (LPBF)-fabricated Hastelloy X subjected to varying orientations, post-treatments and test temperatures. The findings demonstrate that the prediction accuracy of an integrating-physics-informed neural network (IPINN) is improved by 27.51%, and its life prediction results are capped at the fatigue limit life, yielding a better physical consistency. The IPINN framework provides a novel perspective for fatigue life prediction.

Objective To evaluate the therapeutic effects and explore the mechanisms behind caloric restriction achieved through time-restricted feeding (CR) in inhibiting mouse tumors, providing a theoretical basis and data support for future CR diet-assisted anticancer treatment protocols. Methods C57BL/6 and BALB/c mice were divided into four cell line groups. Each group was further split into normal diet (ND) and a CR diet groups. The ND groups had free access to water and a normal diet, while the CR diet groups had access to water but were only fed from 9 a.m. to 11 a.m., fasting for the remaining 22 h. Food intake was recorded daily starting on day 1 of the experiment. Tumor models were established and assessed every 2 days. Blood biochemical indicators, serum pyruvic acid levels, and cytokine expression were measured. Results The CR diet inhibited tumor growth in mice. Colorimetric assays and ELISAs showed a reduction in pyruvic acid levels and in key upstream and downstream rate-limiting enzymes in the sera of CR mice. Routine blood and blood biochemistry tests suggested minor effects of the CR diet on these parameters. Western blotting revealed that the CR diet suppressed mTOR and AKT protein expression in tumor tissues. ELISA showed that various mTOR-related signaling pathways were downregulated. Immunohistochemistry staining indicated reduced expression of P53, P-AKT, EGFR, and IGF-1 in tumor tissues. TUNEL staining confirmed that the CR diet promoted tumor apoptosis. Conclusion The CR diet inhibited tumor growth by suppressing mTOR and its related upstream and downstream gene signaling pathways, reducing tumor glycolysis, and accelerating tumor cell apoptosis.

In order to reveal the propagation law of coal and gas outburst impact airflow, the distribution laws of velocity field, pressure field, impact force field, concentration field of impact airflow in the process of coal and gas outburst are simulated by using the high Mach flow and dense material transfer module in COMSOL software. The simulation results show that the impact airflow in the roadway is jet like, with a velocity of 378.6 m/s, and with the increase of distance, it presents a multi peak vibration attenuation trend. Periodic expansion wave zone and compression wave zone appear in the jet, and the tail of the jet zone develops into turbulence and instability, and moves disorderly in the roadway. The impact force of the protruding blast flow can be as high as 246.3 kPa, and the shock wave moves in the roadway at the local sound speed. The maximum gas diffusion distance is 133.6 m, and the gas volume fraction in the middle of the roadway is close to 100%. Due to the pressure difference in the roadway at the later stage of the outburst, the gas returns, and the high volume fraction gas is further mixed with the air in the roadway.

Aspect-level sentiment analysis can provide a fine-grain sentiment classification for inferring the sentiment polarity of specific aspects. Graph convolutional network (GCN) becomes increasingly popular because its graph structure can characterize the words' correlation for extracting more sentiment information. However, the word distance is often ignored and cause the cross-misclassification of different aspects. To address the problem, we propose a novel dual GCN structure to take advantage of word distance, syntactic information, and sentiment knowledge in a joint way. The word distance is not only used to enhance the syntactic dependency tree, but also to construct a new graph with semantic knowledge. Then, the two kinds of word distance assisted graphs are fed into two GCNs for further classification. The comprehensive results on two self-collected Chinese datasets (MOOC comments and Douban book reviews) as well as five open-source English datasets, demonstrate that our proposed approach achieves higher classification accuracy than the state-of-the-art methods with up to 1.81x training acceleration.

Changes in precipitation storm characteristics especially extreme precipitation events have been frequently reported during recent years, which poses great challenges for flood controls of reservoir basins. In this study, we present a comprehensive examination on the evolution of storm properties during two distinct rainy seasons in Changtan Reservoir Basin located on the southeastern coast of China. We compare the differences in storm duration, inter-storm period, the average storm intensity, and with-in storm pattern between the Meiyu flood season (MFS) and typhoon flood season (TFS). We also explore the future projections of these storm properties based on Coupled Model Inter-comparison Project 6 (CMIP6) precipitation outputs. Our results indicate that precipitation storms in TFS exhibit shorter duration and higher average storm intensity than those of MFS, the flood risk in June is mainly due to accumulative precipitation (longer duration), while in July to September, is mainly due to the storms with high intensity. The projected precipitation shows uncertainties for different emission scenarios, especially during TFS. However, the increasing trend of the average storm intensity is relatively consistent, which is supposed to bring more pressure on flood control in the study area. The results can provide a beneficial reference to water resources management. HIGHLIGHTS The evolution of storm properties was investigated.; Precipitation in typhoon flood season exhibits shorter duration and higher intensity than those of the Meiyu flood season.; Flood risk in June is mainly due to accumulative precipitation in the study area.; Flood risk from July to September is due to storms with high intensity.; Projected precipitation shows uncertainties but a consistent increasing trend in storm intensity.;

This comprehensive review focuses on heterocyclic aromatic amines (HAAs), a class of chemicals that commonly form during the cooking or processing of protein-rich foods. The International Agency for Research on Cancer (IARC) has categorized certain HAAs as probable human carcinogens, highlighting the significance of studying their formation and control in food safety research. The main objective of this review is to address the knowledge gaps regarding HAAs formation and propose approaches to reduce their potential toxicity during thermal processing. By summarizing the mechanisms involved in HAAs formation and inhibition, the review encompasses both conventional and recent detection methods. Furthermore, it explores the distribution of HAAs in thermally processed meats prepared through various cooking techniques and examines their relative toxicity. Additionally, considering that the Maillard reaction, responsible for HAAs formation, also contributes to the unique flavors and aromas of cooked meat products, this review investigates the potential effects of inhibiting HAAs formation on flavor substances. A thorough understanding of these complex interactions provides a foundation for developing targeted interventions to minimize the formation of HAAs and other harmful compounds during food processing.

Abstract We aimed to establish a computerized diagnostic model to predict placenta accrete spectrum (PAS) disorders based on T2-weighted MR imaging. We recruited pregnant women with clinically suspected PAS disorders between January 2015 and December 2018 in our institution. All preoperative T2-weighted imaging (T2WI) MR images were manually outlined on the picture archive communication system terminal server. A nnU-Net network for automatic segmentation and the corresponding radiomics features extracted from the segmented region were applied to build a radiomics-clinical model for PAS disorders identification. Taking the surgical or pathological findings as the reference standard, we compared this computerized model’s diagnostic performance in detecting PAS disorders. In the training cohort, our model combining both radiomics and clinical characteristics yielded an accuracy of 0.771, a sensitivity of 0.854, and a specificity of 0.750 in identifying PAS disorders. In the testing cohort, this model achieved a segmentation mean Dice coefficient of 0.890 and yielded an accuracy of 0.825, a sensitivity of 0.830 and a specificity of 0.822. In the external validation cohort, this computer-aided diagnostic model yielded an accuracy of 0.690, a sensitivity of 0.929 and a specificity of 0.467 in identifying placenta increta. In the present study, a machine learning model based on preoperative T2WI-based imaging had high accuracy in identifying PAS disorders in respect of surgical and histological findings.

Abstract Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

The efficiently renewable bioethanol can help to alleviate energy crisis and environmental pollution. Genetically modified strains for efficient use of xylose and developing lignocellulosic hydrolysates play an essential role in facilitating cellulosic ethanol production. Here we present a promising strain GRE3OE via GRE3 overexpressed in a previously reported Saccharomyces cerevisiae strain WXY70. A comprehensive evaluation of the fermentation level of GRE3OE in alkaline-distilled sweet sorghum bagasse, sorghum straw and xylose mother liquor hydrolysate. Under simulated corn stover hydrolysate, GRE3OE produced 53.39 g/L ethanol within 48 h. GRE3OE produced about 0.498 g/g total sugar in sorghum straw hydrolysate solution. Moreover, GRE3OE consumed more xylose than WXY70 in the high-concentration xylose mother liquor. Taken together, GRE3OE could be a candidate strain for industrial ethanol development, which is due to its remarkable fermentation efficiency during different lignocellulosic hydrolysates.

This research investigates and robustly verifies the impact of expansionary monetary policy actions on green innovation, conducted on a panel covering 133 countries from 1960 to 2018. Overall, we find that such actions have a significantly positive effect on green innovation performance, no matter in the static or dynamic model. A lower degree of central bank independence and poorer property rights protection in developing countries may hinder monetary policies’ effect to be transmitted to green innovation activities smoothly. Moreover, stringent environmental regulation contributes to magnifying the expansionary monetary policy’s positive effect on green innovation, but such a positive moderating effect should be supported by good national governance quality (including better control over corruption, higher efficiency of governments and a complete law system). Accordingly, several policy suggestions are provided.

ObjectiveTo investigate whether radiomics can help radiologists and thoracic surgeons accurately predict invasive adenocarcinoma (IAC) manifesting as part-solid nodules (PSNs) with solid components

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ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p

A gene integrates the effects of all SNPs in its sequence span, which benefits the genome-wide association study. To explore gene-level variations affecting economic traits in maize, we extended the SNP-based GWAS analysis software Single-RunKing developed by our team to gene-based GWAS, which used the FaST-LMM algorithm to convert the linear mixed model into simple linear model association analysis. An F-test statistic was formulated to test and identify candidate genes. We compared the statistical efficiency of using 80% principal components (EPC), the first principal component (FPC), and all SNP markers (ALLSNP) as independent variables, which predecessors commonly used to integrate SNPs and represent genes. With a Huazhong Agricultural University (HAU) genomic dataset of 2.65M SNPs from 540 maize plants, 34,774 genes were annotated across the whole genome. Genome-wide association studies with 20 agronomic traits were performed using the software developed here. Another maize dataset from the Ames panel (AP) was also analyzed. The EPC method fits the model well and has good statistical efficiency. It not only overcomes the false negative problem when using all SNP markers for analysis (ALLSNP) but also solves the false positive problem of its corresponding simple linear model method EPCLM. Compared with FPC, the EPC method has higher statistical efficiency. A total of 132 quantitative trait genes (QTG) were identified for the 20 traits from HAU maize dataset and one trait of AP maize.

Fatigue life prediction is an important part of the reliability and durability analysis of automobile components. Based on Wang and Brown’s framework, multiaxial random fatigue damage was adopted to predict the fatigue life of half-shaft. The stress analysis of half-shaft was resolved analytically to determine the local stress tensor in the potential area of fracture. The maximum shear strain fatigue damage parameter and the normal stress fatigue damage parameter were evaluated to predict the fatigue life of half-shaft. The results show that the prediction method is reliable and meets the service life and safety requirements.

The detection of genetically modified (GM) maize events is an inevitable necessity under the strict regulatory systems of many countries. To screen for GM maize events, we developed a multiplex PCR system to specifically detect 29 GM maize events as well as the cauliflower mosaic virus 35S promoter, the Agrobacterium tumefaciens nos terminator, the Streptomyces viridochromogenes pat gene, and the endogenous zSSIIb maize reference gene. These targets were divided into five panels for screening and event-specific detection by multiplex (10-plex, 7-plex, 7-plex, 4-plex, and 5-plex) PCR. All amplification products were separated and visualized by fluorescence capillary electrophoresis (CE). By taking advantage of the high resolution, multiple fluorescence detection, and high sensitivity of CE, our system was able to identify all targets simultaneously with a limit of detection of 0.1%. The accurate identification of specific amplification peaks from different GM maize materials by CE confirmed the specificity of the system. To verify the practical applicability of this system, we analyzed 20 blind samples. We successfully identified five MON810, four TC1507, and three MIR162 samples. The detection of concomitant elements also verified the accuracy of this approach. Our system can, therefore, be used for the screening and detection of GM maize events. The system, which is easy to use, facilitates high-throughput detection with the help of a high-throughput platform and automated identification software. Multiplex PCR coupled with CE is, thus, very suitable for the detection of genetically modified organisms (GMOs) with a large number of detection targets. Additional multiplexed electrophoretic targets can be easily incorporated as well, thereby increasing the usefulness of this system as the number of GMO events continues to increase.

Coxsackievirus B3 (CVB3) is the most common cause of acute and chronic viral myocarditis, primarily in children, while human adenovirus infections represent a significant cause of morbidity and mortality worldwide, in people of all ages. A series of novel 2-benzoxyl-phenylpyridine derivatives were evaluated for their potential antiviral activities against CVB3 and adenovirus type 7 (ADV7). Preliminary assays indicated that some of these compounds exhibited excellent antiviral effects on both CVB3 and ADV7 viruses; they could effectively inhibit virus-induced cytopathic effects, reduce viral progeny yields, and had similar or superior antiviral activities compared with the control drug, ribavirin. Further, these compounds targeted the early stages of CVB3 replication in cells, including viral RNA replication and protein synthesis, rather than inactivating the virus directly, inhibiting virus adsorption/entry, or affecting viral release from cells. Our data demonstrate that the tested 2-benzoxyl-phenylpyridine derivatives are effective inhibitors of CVB3 and ADV7, raising the possibility that these compounds might be feasible candidates for anti-viral agents.

To improve the service quality of complaint handling service in a manufacturing company, it is key to analyze the business processes. Process mining is quite a useful approach to diagnose complaint handling service process problems, such as bottlenecks and deviations. However, the current business process analysis methodologies based on process mining mainly focus on operational process analysis and neglect other system level analysis. In this study, we introduce the method of Accimap from the discipline of accident analysis to analyze the diagnosis results of process mining. By creating a complaint handling service process management Accimap model, the process mining results analysis can be carried out across different system levels. A case study in a big manufacturing company in China is implemented to verify our approach. In the case study, 42 complaint handling process management factors are identified and the complaint handling process management Accimap model is created. The testing results by Rasmussen’s seven predictions in his risk management framework show that Accimap method presents a systematic approach to analyze the process diagnosis results based on process mining.