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PurposeDiagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.MethodsFrom August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40 keV was reconstructed from 100 and 150 keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40 keV, 100 keV, 150 keV, and 100 + 150 keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction.ResultsDLR based on 100 + 150 keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85–0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91–0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012).ConclusionsThe DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making.

Objective To investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC). Methods This retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016–October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter. Results Statistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915–0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967–0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant. Conclusion Among all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model. Key Points • In order to ascertain the presence of regional lymph node metastasis in pancreatic ductal adenocarcinoma, a regression diagnostic model was constructed utilizing various dual-energy computed tomography parameters. • The dual-energy CT multi-parameter regression model demonstrates a notable diagnostic efficacy in detecting lymph node metastasis of PDCA, thereby providing valuable assistance in clinical decision-making.

Based on the albumin-bilirubin (ALBI) and platelet-albumin-bilirubin (PALBI) grade to assess the long-term outcomes of patients with large hepatocellular carcinoma (HCC) after transarterial chemoembolization combined with cryoablation (TACE-CRA). We studied 86 patients with HCC nodules (up to 3 HCCs with maximum diameters of 4.1-12.0 cm) who subsequently underwent TACE-CRA from July 2007 to August 2018. The overall survival (OS) was compared between groups classified by ALBI and PALBI grade. Baseline characteristics were collected to identify the risk factors for determination of poor OS after TACE-CRA. The prognostic performances of CTP class, ALBI and PALBI grade were compared. After a median follow-up time of 33.8 months, 41 patients had died. The cumulative1-, 3- and 5-year OS rates were 74.5%, 38.0% and 29.3%, respectively. Stratified according to ALBI grade, the cumulative 3- and 5-year OS rates were 41.2% and 41.2% in grade 1, respectively, and 20.9% and 9.8% in grades 2-3, respectively ( < 0.001). Stratified according to PALBI grade, the cumulative 3- and 5-year OS rates were 41.2% and 37.5% in grade 1, respectively, and 36.3% and 21.2% in grades 2-3, respectively ( = 0.002). Multivariate analysis results showed that older age, and ALBI grade 2-3 were associated with overall mortality. ALBI grade demonstrated significantly greater area under the curve values than CTP class and PALBI in predicting 1-, 3- and 5-year OS. ALBI grade offers accurate prediction of long-term outcome for patients with HCC (diameter > 4 cm) after TACE-CRA.

Many toxic secondary metabolites produced by phytopathogens can subvert host immunity, and some of them are recognized as pathogenicity factors. Fusarium head blight and Verticillium wilt are destructive plant diseases worldwide. Using toxins produced by the causal fungi Fusarium graminearum and Verticillium dahliae as screening agents, here we show that the Arabidopsis P4 ATPases AtALA1 and AtALA7 are responsible for cellular detoxification of mycotoxins. Through AtALA1-/AtALA7-mediated vesicle transport, toxins are sequestered in vacuoles for degradation. Overexpression of AtALA1 and AtALA7 significantly increases the resistance of transgenic plants to F. graminearum and V. dahliae , respectively. Notably, the concentration of deoxynivalenol, a mycotoxin harmful to the health of humans and animals, was decreased in transgenic Arabidopsis siliques and maize seeds. This vesicle-mediated cell detoxification process provides a strategy to increase plant resistance against different toxin-associated diseases and to reduce the mycotoxin contamination in food and feed. Toxic metabolites produced by phytopathogens can subvert host immunity. Here the authors show that the Arabidopsis P4-ATPases, AtALA1 and AtALA7 mediate mycotoxin detoxification by promoting vesicle transport and their subsequent sequestration and degradation in vacuoles.

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet.

To relieve the stress of sewage sludge (SS) disposal and effectively increase the use of renewable energy, a novel renewable-energy microgrid system (REMS) was developed, specifically designed to integrate a wind power plant (WPP) with energy storage and the SS pyrolysis process for heat and power generation. Based on a lab-scale pyrolysis experiment and 7-day numerical analysis, we studied the energy-recycling potential of SS and simulated the operational behaviours of REMS. According to the results, the calorific values of the pyrolytic gaseous and liquid products were better than those of the raw material, at 16.19 and 33.53 MJ/kg, respectively. The proposed REMS performed well in power supply and energy utilization with a design performance index of 99.23 when the WPP capacity was 200 MWe and the initial wind-energy curtailment rate was 30%. It indicates that by converting SS into flammable gas, condensable liquid and carbon-rich solid residue, curtailed wind energy could be saved and the synergy between wind power and the SS pyrolysis process enabled the proposed microgrid system to effectively utilize renewable energy and provide reliable on-demand power service. The REMS installed with a 155-MWe WPP achieved the optimal design in system performance, environmental benefit and construction cost under the initial wind-curtailment rate of 34.12%. The design scheme makes REMS capable of satisfying the 15.10-GWh power demand of end users and the 1700-t/day SS disposal need, and the curtailed wind energy could be reduced to zero.

This study was conducted to investigate the effects of long-term low-dose lithium adjunct to antipsychotic agent use on the cognitive performance, whole-brain gray-matter volume (GMV), and interleukin-6 (IL-6) level in drug-naive patients with first-episode schizophrenia, and to examine relationships among these factors. In this double-blind randomized controlled study, 50 drug-naive patients with first-episode schizophrenia each took low-dose (250 mg/day) lithium and placebo (of the same shape and taste) adjunct to antipsychotic agents (mean, 644.70 ± 105.58 and 677.00 ± 143.33 mg/day chlorpromazine equivalent, respectively) for 24 weeks. At baseline and after treatment completion, the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive performance, 3-T magnetic resonance imaging was performed to assess structural brain alterations, and serum IL-6 levels were quantified by immunoassay. Treatment effects were assessed within and between patient groups. Relationships among cognitive performance, whole-brain GMVs, and the IL-6 level were investigated by partial correlation analysis. Relative to baseline, patients in the lithium group showed improved working memory, verbal learning, processing speed, and reasoning/problem solving after 24 weeks of treatment; those in the placebo group showed only improved working memory and verbal learning. The composite MCCB score did not differ significantly between groups. The whole-brain GMV reduction was significantly lesser in the lithium group than in the placebo group (0.46% vs. 1.03%; P < 0.001). The GMV and IL-6 reduction ratios correlated with each other in both groups (r = −0.17, P = 0.025). In the lithium group, the whole-brain GMV reduction ratio correlated with the working memory improvement ratio (r = −0.15, P = 0.030) and processing speed (r = −0.14, P = 0.036); the IL-6 reduction ratio correlated with the working memory (r = −0.21, P = 0.043) and verbal learning (r = −0.30, P = 0.031) improvement ratios. In the placebo group, the whole-brain GMV reduction ratio correlated only with the working memory improvement ratio (r = −0.24, P = 0.019); the IL-6 reduction ratio correlated with the working memory (r = −0.17, P = 0.022) and verbal learning (r = −0.15, P = 0.011) improvement ratios. Both treatments implemented in this study nearly improved the cognitive performance of patients with schizophrenia; relative to placebo, low-dose lithium had slightly greater effects on several aspects of cognition. The patterns of correlation among GMV reduction, IL-6 reduction, and cognitive performance improvement differed between groups.

ADP-ribosylation is a highly dynamic and fully reversible post-translational modification performed by PARP enzymes that modulates protein function, abundance, localization, and turnover. Here we show that PARPs mount an antiviral response to influenza A virus infection causing a rapid and dramatic upregulation of global ADP-ribosylation that inhibits viral replication. Mass spectrometry analyzes define the global ADP-ribosylome during infection, creating an infection-specific profile with almost 4000 modification sites on ~1000 host proteins, as well as over 100 modification sites on viral proteins. Our data suggest that the global increase reflects a change in the form of ADP-ribosylation rather than modification of new targets. Functional assays demonstrate that modification of the viral replication machinery antagonizes its activity. We further show that the influenza A virus protein NS1 counteracts the anti-viral activity of PARPs and ADP-ribosylation, assigning a new activity to the primary viral antagonist of innate immunity. We identify PARP1 as the enzyme producing the majority of poly(ADP-ribose) present during infection. Influenza A virus replicates faster in cells lacking PARP1, linking PARP1 and ADP-ribosylation to the anti-viral phenotype. Together, these data establish ADP-ribosylation as an anti-viral innate immune-like response to viral infection antagonized by a previously unknown activity of NS1. Influenza A virus infection causes a dramatic upregulation of ADP-ribosylation that as part of the cellular antiviral response, a process that is counteracted by the viral NS1 protein.

ADP-ribosylation is a highly dynamic and fully reversible post-translational modification performed by poly(ADP-ribose) polymerases (PARPs) that modulates protein function, abundance, localization and turnover. Here we show that influenza A virus infection causes a rapid and dramatic upregulation of global ADP-ribosylation that inhibits viral replication. Mass spectrometry defined for the first time the global ADP-ribosylome during infection, creating an infection-specific profile with almost 4,300 modification sites on ~1,080 host proteins, as well as over 100 modification sites on viral proteins. Our data indicate that the global increase likely reflects a change in the form of ADP-ribosylation rather than modification of new targets. Functional assays demonstrated that modification of the viral replication machinery antagonizes its activity and further revealed that the anti-viral activity of PARPs and ADP-ribosylation is counteracted by the influenza A virus protein NS1, assigning a new activity to the primary viral antagonist of innate immunity. We identified PARP1 as the enzyme producing the majority of poly(ADP-ribose) present during infection. Influenza A virus replicated faster in cells lacking PARP1, linking PARP1 and ADP-ribosylation to the anti-viral phenotype. Together, these data establish ADP-ribosylation as an anti-viral innate immune-like response to viral infection antagonized by a previously unknown activity of NS1.