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Background:. As a systemic disease, pancreatic cancer (PC) can be treated systemically to raise the R0 resection rate and enhance patient prognosis. The best ways to assess the treatment response to systemic treatment of patients with PC are still lacking. Methods:. A total of 122 PC patients were enrolled; 25 of these patients were used as an independent testing set. According to the pathologic response, PC patients were classified into the responder and nonresponder groups. The whole tumor, core, edge, and peritumoral were segmented from the enhanced computed tomography (CT) images. Machine-learning models were created by extracting the variations in radionics features before and after therapy (delta radiomics features). Finally, we compared the performance of models based on radiomics features, changes in tumor markers, and radiologic evaluation. Results:. The model based on the core (area under curve [AUC] = 0.864) and edge features (AUC = 0.853) showed better performance than that based on the whole tumor (AUC = 0.847) or peritumoral area (AUC = 0.846). Moreover, the tumor core_edge combination model (AUC = 0.899) could better increase confidence in treatment response than using either of them alone. The accuracies of models based on changes in tumor markers and radiologic evaluation were relatively poorer than of the radiomics model. Moreover, Patients predicted to respond to therapy using the radiomics model showed a relatively longer overall survival (43 vs 27 months), although there were no significant differences (P = .063). Conclusions:. The tumor core_edge combination delta radiomics model is an effective approach to evaluate pathologic response in PC patients with systemic treatment.

BackgroundThe medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. ObjectiveThis study aims to propose an electronic health record (EHR)–oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. MethodsThe study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. ResultsThe system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. ConclusionsThe proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

BackgroundThe disruption of the circadian clock is associated with inflammatory and immunological disorders. BMAL2, a critical circadian protein, forms a dimer with CLOCK, activating transcription. Extracellular cold-inducible RNA-binding protein (eCIRP), released during sepsis, can induce macrophage endotoxin tolerance. We hypothesized that eCIRP induces BMAL2 expression and promotes macrophage endotoxin tolerance through triggering receptor expressed on myeloid cells-1 (TREM-1).MethodsC57BL/6 wild-type (WT) male mice were subjected to sepsis by cecal ligation and puncture (CLP). Serum levels of eCIRP 20 h post-CLP were assessed by ELISA. Peritoneal macrophages (PerM) were treated with recombinant mouse (rm) CIRP (eCIRP) at various doses for 24 h. The cells were then stimulated with LPS for 5 h. The levels of TNF-α and IL-6 in the culture supernatants were assessed by ELISA. PerM were treated with eCIRP for 24 h, and the expression of PD-L1, IL-10, STAT3, TREM-1 and circadian genes such as BMAL2, CRY1, and PER2 was assessed by qPCR. Effect of TREM-1 on eCIRP-induced PerM endotoxin tolerance and PD-L1, IL-10, and STAT3 expression was determined by qPCR using PerM from TREM-1-/- mice. Circadian gene expression profiles in eCIRP-treated macrophages were determined by PCR array and confirmed by qPCR. Induction of BMAL2 activation in bone marrow-derived macrophages was performed by transfection of BMAL2 CRISPR activation plasmid. The interaction of BMAL2 in the PD-L1 promoter was determined by computational modeling and confirmed by the BIAcore assay.ResultsSerum levels of eCIRP were increased in septic mice compared to sham mice. Macrophages pre-treated with eCIRP exhibited reduced TNFα and IL-6 release upon LPS challenge, indicating macrophage endotoxin tolerance. Additionally, eCIRP increased the expression of PD-L1, IL-10, and STAT3, markers of immune tolerance. Interestingly, TREM-1 deficiency reversed eCIRP-induced macrophage endotoxin tolerance and significantly decreased PD-L1, IL-10, and STAT3 expression. PCR array screening of circadian clock genes in peritoneal macrophages treated with eCIRP revealed the elevated expression of BMAL2, CRY1, and PER2. In eCIRP-treated macrophages, TREM-1 deficiency prevented the upregulation of these circadian genes. In macrophages, inducible BMAL2 expression correlated with increased PD-L1 expression. In septic human patients, blood monocytes exhibited increased expression of BMAL2 and PD-L1 in comparison to healthy subjects. Computational modeling and BIAcore assay identified a putative binding region of BMAL2 in the PD-L1 promoter, suggesting BMAL2 positively regulates PD-L1 expression in macrophages.ConclusioneCIRP upregulates BMAL2 expression via TREM-1, leading to macrophage endotoxin tolerance in sepsis. Targeting eCIRP to maintain circadian rhythm may correct endotoxin tolerance and enhance host resistance to bacterial infection.

Coastal saline soils are an important soil resource that, when restored, can enhance arable land and preserve the natural ecology. With the aim of improving the use of coastal saline soils, we conducted a spot survey at Bohai coastal saline land to investigate the differences in soil properties between different vegetation types. The soil physical and chemical properties of various vegetation types, including Aeluropus sinensis, Imperata cylindrica, Tamarix chinensis, Lycium chinense, Hibiscus moscheutos, Helianthus annuus, Gossypium hirsutum, and Zea mays, were examined at two depth layers: 0–20 cm and 20–40 cm, and in two seasons, spring and autumn. The soil properties were compared with bare land as a control. The results indicated that the electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density of soils with vegetation cover were lower than those with bare land. On the other hand, soil pH, organic matter content, mean weight diameter, and saturated hydraulic conductivity were higher. The redundancy analysis results revealed a substantial positive correlation between soil pH, saturated hydraulic conductivity, water content, mean weight diameter, and organic matter content, as well as a significant positive correlation between soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density. Soil pH, saturated hydraulic conductivity, water content, mean weight diameter, organic matter content, and soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density were negatively correlated. The results of the structural equation model and variance decomposition showed that soil organic matter and bulk density were the key factors affecting the degree of soil salinization, and compared with their independent effects, their combined effect on soil salinization was greater. This study’s conclusions can provide a point of reference for further research on the mechanisms of soil improvement and desalinization in coastal saline land.

Wheat spike count is one of the critical indicators for assessing the growth and yield of wheat. However, illumination variations, mutual occlusion, and background interference have greatly affected wheat spike detection. A lightweight detection method was proposed based on the YOLOv5s. Initially, the original YOLOv5s was improved by combing the additional small-scale detection layer and integrating the ECA (Efficient Channel Attention) attention mechanism into all C3 modules (YOLOv5s + 4 + ECAC3). After comparing GhostNet, ShuffleNetV2, and MobileNetV3, the GhostNet architecture was finally selected as the optimal lightweight model framework based on its superior performance in various evaluations. Subsequently, the incorporation of five different activation functions into the network led to the identification of the RReLU (Randomized Leaky ReLU) activation function as the most effective in augmenting the network’s performance. Ultimately, the network’s loss function of CIoU (Complete Intersection over Union) was optimized using the EIoU (Efficient Intersection over Union) loss function. Despite a minor reduction of 2.17% in accuracy for the refined YOLOv5s + 4 + ECAC3 + G + RR + E network when compared to the YOLOv5s + 4 + ECAC3, there was a marginal improvement of 0.77% over the original YOLOv5s. Furthermore, the parameter count was diminished by 32% and 28.2% relative to the YOLOv5s + 4 + ECAC3 and YOLOv5s, respectively. The model size was reduced by 28.0% and 20%, and the Giga Floating-point Operations Per Second (GFLOPs) were lowered by 33.2% and 9.5%, respectively, signifying a substantial improvement in the network’s efficiency without significantly compromising accuracy. This study offers a methodological reference for the rapid and accurate detection of agricultural objects through the enhancement of a deep learning network.

BackgroundThe relationship between gestational diabetes (GDM) and the risk of depression has been thoroughly investigated in high-income countries on their financial basis, while it is largely unexplored in low- and middle- income countries. This meta-analysis aims to assess how GDM influences the risk of perinatal depression by searching multiple electronic databases for studies measuring the odds ratios between them in low- and middle-income countries.MethodsTwo independent reviewers searched multiple electronic databases for studies that investigated GDM and perinatal mental disorders on August 31, 2023. Pooled odds ratios (ORs) and confidence intervals (CIs) were calculated using the random effect model. Subgroup analyses were further conducted based on the type of study design and country income level.ResultsIn total, 16 observational studies met the inclusion criteria. Only the number of studies on depression (n=10) satisfied the conditions to conduct a meta-analysis, showing the relationship between mental illness and GDM has been overlooked in low- and middle-income countries. Evidence shows an elevated risk of perinatal depression in women with GDM (pooled OR 1.92; 95% CI 1.24, 2.97; 10 studies). The increased risk of perinatal depression in patients with GDM was not significantly different between cross-sectional and prospective design. Country income level is a significant factor that adversely influences the risk of perinatal depression in GDM patients.ConclusionOur findings suggested that women with GDM are vulnerable to perinatal depressive symptoms, and a deeper understanding of potential risk factors and mechanisms may help inform strategies aimed at prevention of exposure to these complications during pregnancy.

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis.

To explore the effects of different irrigation approaches, mulching, and their interaction on greenhouse melon (Cucumis melo L.) production and water use, a field experiment was conducted in Northern China using four treatments: mulching drip irrigation (MDI), mulching furrow irrigation (MFI), drip irrigation (DI), and furrow irrigation (FI; CK). The plant biomass, yield, water consumption, and water use efficiency (WUE) of melons were measured at different growth stages. The results showed that mulching has significant positive impacts on the growth as well as the fruit yield of melons. However, the water use characteristics of the plant were more greatly determined by the various irrigation approaches, and there was a significant interaction between the irrigation approach and mulching for both the total water consumption and WUE of the greenhouse melon. Of these treatments, MDI resulted in the highest yield of 38.49 t/hm2, which was significantly higher than the yields obtained with DI (32.36 t·hm−2) and FI (CK, 30.34 t·hm−2). In addition, the water consumption under MDI was 45.80% lower than FI (CK), which resulted in the promotion of WUE under MDI. The WUE range of the greenhouse melon is as follows: MDI (334.77 kg·mm−1·hm−2) > DI (244.84 kg·mm−1·hm−2) > MFI (189.78 kg·mm−1·hm−2) > FI (CK; 142.94 kg·mm−1·hm−2). The findings of this study indicate that mulching can boost melon yield, and drip irrigation can limit water consumption. This study provides a reference point for policymakers, indicating that drip irrigation with plastic mulching could be a feasible adaptation strategy for increasing greenhouse melon production in Northern China, as well as other agriculture regions that suffer from water shortages.

As the petroleum industry increasingly exploits unconventional reservoirs with low permeability and porosity, accurate predictions of post-fracture production are becoming critical for investment decisions, energy policy development, and environmental impact assessments. However, despite extensive research, accurately forecasting post-fracture production using well-log data continues to be a complex challenge. This study introduces a new method of data volume expansion, which is to subdivide the gas production of each well on the first day according to the depth of logging data, and to rely on the correlation model between petrophysical parameters and gas production to accurately combine the gas production data while matching the accuracy of the well-log data. Twelve pipelines were constructed utilizing a range of techniques to fit the regression relationship between logging parameters and post-fracture gas production These included data preprocessing methods (StandardScaler and RobustScaler), feature extraction approaches (PCA and PolynomialFeatures), and advanced machine learning models (XGBoost, Random Forest, and neural networks). Hyperparameter optimization was executed via GridSearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), Pearson correlation coefficient (PCC), mean absolute error (MAE), mean squared error (MSE), and root-mean-square error (RMSE) were invoked. Among the several pipelines explored, the PFS-NN exhibited excellent predictive capability in specific reservoir contexts. In essence, integrating machine learning with logging parameters can be used to effectively assess reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties endemic to reservoir exploration but also equips petroleum engineers with the ability to monitor reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides reservoir engineers with an efficient means of reservoir performance oversight.

Abstract The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.

IntroductionIn brain function research, each brain region has been investigated independently, and how different parts of the brain work together has been examined using the correlations among them. However, the dynamics of how different brain regions interact with each other during time-varying tasks, such as voluntary motion tasks, are still not well-understood.MethodsTo address this knowledge gap, we conducted functional magnetic resonance imaging (fMRI) using target tracking tasks with and without feedback. We identified the motor cortex, cerebellum, and visual cortex by using a general linear model during the tracking tasks. We then employed a dynamic causal model (DCM) and parametric empirical Bayes to quantitatively elucidate the interactions among the left motor cortex (ML), right cerebellum (CBR) and left visual cortex (VL), and their roles as higher and lower controllers in the hierarchical model.ResultsWe found that the tracking task with visual feedback strongly affected the modulation of connection strength in ML → CBR and ML↔VL. Moreover, we found that the modulation of VL → ML, ML → ML, and ML → CBR by the tracking task with visual feedback could explain individual differences in tracking performance and muscle activity, and we validated these findings by leave-one-out cross-validation.DiscussionWe demonstrated the effectiveness of our approach for understanding the mechanisms underlying human motor control. Our proposed method may have important implications for the development of new technologies in personalized interventions and technologies, as it sheds light on how different brain regions interact and work together during a motor task.

BackgroundSerious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice. ObjectiveThis study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever. MethodsClinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility. ResultsIn total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application. ConclusionsThe clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.

Organic amendment is an effective method to reclaim salt-affected soil. However, in coastal land with shallow saline groundwater, it is limited known about the mechanism of organic amendment on soil desalinization. Thus, to examine the effect of topsoil organic matter content on soil water/salt transport and distribution, two-year field observations in Bohai coastal land, North China, and soil column experiments simulating salt accumulation and salt leaching were conducted, respectively. There were different organic fertilizer amendment rates in 0–20 cm topsoil, 0% (CK), 50% (OA 0.5), and 100% (OA 1.0) (w/w) for soil column experiments. Field observation showed that after organic amendment (OA), the soil’s physical structure was improved, and less of the increase in topsoil salt content was observed, with more salt accumulated in deep soil layers during the dry season. In addition, OA greatly promoted salt leaching during the rainy seasons. The results of the soil column tests further indicated that OA treatments significantly inhibited soil evaporation, with less salt accumulated in the topsoil. Although there was no difference in soil water distribution between the CK and OA 0.5 treatment, the topsoil EC for the OA 0.5 treatment was significantly lower than that for CK. During soil water infiltration, the OA 0.5 and OA 1.0 treatments significantly increased the infiltration rates, enhanced the wetting front, and promoted salt leaching to deeper soil layers, compared with CK. The improvement of soil organic amounts could make the soil more self-resistant to the coastal salinization. The findings of this study provide some insights into soil water/salt regulation in heterogeneous soil masses and on the permanent management of coastal saline farmland.

Abstract Background Tumor micronecrosis is a histopathological feature predicting poor prognosis in patients with hepatocellular carcinoma (HCC) who underwent liver resection. However, the role of tumor micronecrosis in liver transplantation remains unclear. Methods We retrospectively reviewed patients with HCC who underwent liver transplantation between January 2015 and December 2021 at our center. We then classified them into micronecrosis(−) and micronecrosis(+) groups and compared their recurrence-free survival (RFS) and overall survival (OS). We identified independent prognostic factors using Cox regression analysis and calculated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive value of RFS for patients with HCC after liver transplantation. Results A total of 370 cases with evaluable histological sections were included. Patients of the micronecrosis(+) group had a significantly shorter RFS than those of the micronecrosis(−) group (P = 0.037). Shorter RFS and OS were observed in micronecrosis(+) patients without bridging treatments before liver transplantation (P = 0.002 and P = 0.007), while no differences were detected in those with preoperative antitumor therapies that could cause iatrogenic tumor necrosis. Tumor micronecrosis improved the AUC of Milan criteria (0.77–0.79), the model for end-stage liver disease score (0.70–0.76), and serum alpha-fetoprotein (0.63–0.71) for the prediction of prognosis after liver transplantation. Conclusion Patients with HCC with tumor micronecrosis suffer from a worse prognosis than those without this feature. Tumor micronecrosis can help predict RFS after liver transplantation. Therefore, patients with HCC with tumor micronecrosis should be treated with adjuvant therapy and closely followed after liver transplantation. Clinical trials registration Not Applicable.

BackgroundObservational biomedical studies facilitate a new strategy for large-scale electronic health record (EHR) utilization to support precision medicine. However, data label inaccessibility is an increasingly important issue in clinical prediction, despite the use of synthetic and semisupervised learning from data. Little research has aimed to uncover the underlying graphical structure of EHRs. ObjectiveA network-based generative adversarial semisupervised method is proposed. The objective is to train clinical prediction models on label-deficient EHRs to achieve comparable learning performance to supervised methods. MethodsThree public data sets and one colorectal cancer data set gathered from the Second Affiliated Hospital of Zhejiang University were selected as benchmarks. The proposed models were trained on 5% to 25% labeled data and evaluated on classification metrics against conventional semisupervised and supervised methods. The data quality, model security, and memory scalability were also evaluated. ResultsThe proposed method for semisupervised classification outperforms related semisupervised methods under the same setup, with the average area under the receiver operating characteristics curve (AUC) reaching 0.945, 0.673, 0.611, and 0.588 for the four data sets, respectively, followed by graph-based semisupervised learning (0.450, 0.454, 0.425, and 0.5676, respectively) and label propagation (0.475,0.344, 0.440, and 0.477, respectively). The average classification AUCs with 10% labeled data were 0.929, 0.719, 0.652, and 0.650, respectively, comparable to that of the supervised learning methods logistic regression (0.601, 0.670, 0.731, and 0.710, respectively), support vector machines (0.733, 0.720, 0.720, and 0.721, respectively), and random forests (0.982, 0.750, 0.758, and 0.740, respectively). The concerns regarding the secondary use of data and data security are alleviated by realistic data synthesis and robust privacy preservation. ConclusionsTraining clinical prediction models on label-deficient EHRs is indispensable in data-driven research. The proposed method has great potential to exploit the intrinsic structure of EHRs and achieve comparable learning performance to supervised methods.

As the front end of the intake system, the intake dirty pipe is responsible for delivering sufficient and stable air to the air filter. Therefore, in order to meet the requirements of low intake resistance, it is necessary to correspondingly improve the flow resistance performance of the intake dirty pipe. In this study, the main research object was the intake pipe in the intake system of gasoline engine vehicles, and the internal gas flow field was simulated and analyzed. The results show that there are clear discrete velocity regions at the inlet and elbow, which affect the uniformity of the overall fluid flow and cause a certain pressure loss. After structural optimization, the total pressure difference at the inlet and outlet of the pipeline was reduced by 22.67% compared to the original model, and the total pressure loss was significantly reduced. A simplified model was used to make samples of the intake dirty pipes before and after performance improvement, and flow resistance tests were conducted respectively. The difference between test data and simulation data is within a reasonable range, and the simulation results are relatively reliable.

Abstract Background To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural networks (CNNs) have been successfully applied to object detection tasks, however, when applied to mitotic cell detection, most existing methods generate high false-positive rates due to the complex characteristics that differentiate normal cells from mitotic cells. Cell size and orientation variations in each stage make detecting mitotic cells difficult in 2D approaches. Therefore, effective extraction of the spatial and temporal features from mitotic data is an important and challenging task. The computational time required for detection is another major concern for mitotic detection in 4D microscopic images. Results In this paper, we propose a backbone feature extraction network named full scale connected recurrent deep layer aggregation (RDLA++) for anchor-free mitotic detection. We utilize a 2.5D method that includes 3D spatial information extracted from several 2D images from neighbouring slices that form a multi-stream input. Conclusions Our proposed technique addresses the scale variation problem and can efficiently extract spatial and temporal features from 4D microscopic images, resulting in improved detection accuracy and reduced computation time compared with those of other state-of-the-art methods.

The horizontal well is extensively used to exploit oil at the Bohai offshore oilfield, China. It has been proved by practice that formation damage always happens during the production of wells. However, it is a challenging task to evaluate the formation damage for horizontal wells. In this paper, a flow model considering formation damage is established for horizontal wells. The reservoir geometry is divided into two regions: the inner region represents the region of formation damage and the outer region that without damage. The finite element method is used to solve the flow model. The well production rate can be determined by using the material balance method. For different scenarios, such as when the horizontal well is completely in the inner region or when the horizontal well passes through two regions, the effects of some key parameters such as damage radius, damage region permeability, and well position on the oil production rate and pressure distribution characteristics are analyzed. The results show that a smaller damage radius, higher damage zone permeability, and a horizontal well closer to or in greater contact with the outer region result in a higher oil production rate and faster pressure drop propagation. The presented model and obtained results enrich production analysis of horizontal wells in a two-region composite reservoir considering formation damage, which has significance for efficient offshore reservoir development.

Abstract Background With the development of wearable health-monitoring technologies, a variety of textile electrodes have been produced and applied by researchers. However, there are no universal and effective methods even testing platforms for evaluating the skin–electrode electrochemical interface for textile electrodes because different human bodies have different skin characteristics. Methods An electrochemical modeling and evaluation for textile electrodes to skin was proposed, and two electrochemical evaluation platforms (EEP) were set up based on two simulated skin models (SSM). First, skin–electrode electrochemical interface (SEEI) models for traditional wet electrodes and textile electrodes were analyzed. Based on the SEEI models and YY/T 0196-2005 (Chinese YY/T pharmaceutical industry standard for disposable ECG electrode), three skin–electrode electrochemical characteristics (SEEC), including skin–electrode static impedance (SESI), skin–electrode alternating current impedance (SEAI), and skin–electrode polarization voltage (SEPV), were proposed. Then, three electrochemical evaluation methods for textile electrodes to skin were proposed and analyzed, which were the correlation between SEEC and skin–electrode contact pressure (SECP), skin–electrode relative movement (SERM), and conduction loss of active signals (CLAS). Finally, an electrochemical evaluation platform was set up based on an active simulated skin model (ASSM) and passive simulated skin model (PSSM). Results 9 feature parameters based on the passive electrochemical evaluation platform (PEEP) and 11 feature parameters based on the active electrochemical evaluation platform (AEEP) were obtained for evaluating textile electrodes. And four kinds of textile electrode characteristics including SEEC, SECP, SERM, and CLAS were quantitatively measured based on the electrochemical evaluation platform, and the testing accuracy and range for these characteristics were measured separately. Finally, correlation between SEEC and SECP for 10 kinds of textile electrode samples was studied, and 14 electrochemical characteristics and four skin–electrode contact pressure characteristics were extracted. Experimental results showed that significant correlations were found between six SEEC characteristics and SECP characteristics, and the correlation coefficient between ACI_3 and USECP was the highest. And the polarization voltages of most dry electrode samples showed a downward trend with the increase of contact pressure. Conclusions The electrochemical evaluation platform yielded effective experimental data and could provide strong support for the evaluation and application of textile electrodes, which was also effective in evaluating other bioelectric electrodes such as 3M electrode, stainless steel electrode, dry electrode and microneedle electrode.

Abstract Background Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. Methods In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. Results Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. Conclusion In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

The external-cavity quantum cascade laser (ECQCL) is an ideal mid-infrared (MIR) spectral light source for determining large molecular-absorption spectral features with broad transition bands. For this paper, a gas sensor system was developed using a broadband tunable ECQCL and a direct absorption spectroscopy detection scheme with a short path absorption cell of 29.6 cm. For spectral signal detection, a cheap and miniaturized quartz crystal tuning fork- (QCTF) based light detector was used for laser signal detection. The characteristics of the QCTF detector were theoretically simulated and experimentally observed. To demonstrate this sensing technique, sulfur hexafluoride (SF6) was selected as the analyte, which can be used as an effective indicator to identify fault-types of gas-insulated electrical equipment. Preliminary results indicated that a good agreement was obtained between experimentally observed data and reference spectra according to the NIST database and previous publications, and the gas sensor system showed a good linear response to SF6 gas concentration. Finally, Allan–Werle deviation analysis indicated that detection limits of 1.89 ppm for SF6 were obtained with a 1 s integration time, which can be further improved to ~0.38 ppm by averaging up to 131 s.

The characterization and flocculation mechanism of a biopolymer flocculant produced by Sphingomonas sp. HL-1, were investigated. The bio-flocculant HL1 was identified as an acidic polysaccharide, mainly composed of glucose, and also contained a small amount of mannose, galacturonic acid and guluronic acid. The flocculating activity of the purified HL1 polysaccharide could be activated by trivalent cations, and its flocculation mechanism was mainly charge neutralization and bridging. The working concentration of fermentation broth HL1 in a kaolin suspension was only 1/10,000 (v/v), in which the polysaccharide concentration was about 2 mg/L. The bio-flocculant HL1 maintained high efficiency at a wide range of pH (pH 3–10). It also exhibited good flocculating activity at a temperature range of 20–40 °C; it could even tolerate high salinity and kept activity at a mineralization degree of 50,000 mg/L. Therefore, the bio-flocculant HL1 has a good application prospect in the treatment of wastewater over a broad pH range and in high salinity.

Reciprocal toolpaths with four-axis simultaneous motion of five-axis or four-axis machine tools are commonly used in the machining of blades which are widely applied in high-end equipment such as the aero-engine and the marine steam turbine. Due to the complex geometry of the blades, the tool orientation always suffers from frequent swing for this kind of toolpaths, which induces unnecessary acceleration/deceleration of the feed axes, thus degrading processing efficiency and quality. Although there are tool orientation optimization methods aiming at solving the above problem, they are mainly proposed for universal processing of the toolpaths for complex surfaces. Different from them, this paper proposes a piecewise decoupling tool orientation re-scheduling method for this kind of toolpath specifically, which takes full use of the characteristic of the reciprocal toolpaths of the blades, and takes the monotonous variation of rotation axes as an additional constraint. The re-scheduling process is realized based on the construction of a S-θ plane, where the scheduling problem is converted to the adjustment of a S-θ curve inside a feasible channel. Through two procedures, namely linearization scheduling and control-point assigning-based smoothing, the tool orientation path expressed by the S-θ curve can be effectively scheduled in a piecewise manner, and the smoothness between two adjacent pieces of the toolpaths can be ensured directly. The whole algorithm is lightweight and does not involve complex iterative operations or functional optimization solutions. Simulation and experimental tests verify the feasibility and superiority of this method. The results show that the machining efficiency of the blade is improved by 24.5%, due to the reduction of the requirement on highest feed-axis kinematics parameters after rescheduling. In addition, compared with the existing methods, the proposed method not only can improve the dynamics of feed axes in multi-axis machining, but also has advantages in computational complexity and monotonic variation property of the tool orientation.

In this paper, an attractive gas sensing technique based on fiber optical sensing ideal was developed for the detection of multi-gas species simultaneously. Calibration-free laser absorption spectroscopy (LAS) was used and combined with a multi-pass optical absorption cell, two fiber-coupled near-infrared (NIR) distributed feedback (DFB) diode lasers are used and coupled into a single optical path for measuring CO2 and CH4 absorption spectra simultaneously. The optimal sampling pressure, laser tuning characteristics, spectral sampling points, and potential optical interference are theoretically and experimentally investigated in detail. The results indicated that the proposed technique has good reliability and has been successfully demonstrated for ambient CO2 and CH4 detection by using a single sample cell and detector. An Allan–Werle deviation analysis shows that detection limits of 0.12 ppm for CH4 and 35.97 ppm for CO2 can be obtained with an integration time of 181 s and 166 s, respectively. The proposed technique can be expanded to measure more molecules simultaneously by combing laser array and may pave a new way for developing a low-cost and ultra-compact multi-gas laser spectroscopy sensing system.

Ecological restoration of coastal land by planting salt-tolerant plants has been widely used to construct vegetation. The aim of this study was to evaluate the changes induced by tamarisk (Tamarix chinensis) shrub on coastal soil physical quality and as well as the corresponding impact on salt distribution in the soil. A field study was conducted on coastal saline land, North China, where tamarisk plantation was established 5-year-old (T-5yr) and 11-year-old (T-11yr), and compared with barren land as control (CK). Quantitative soil physical properties, soil physical quality index, soil salt distribution, and salt leaching were examined. The results indicated that planting tamarisk improved the coastal soil properties at higher degree in topsoil than in deep soil layers. Tamarisk plantation significantly increased soil organic carbon content and pH. It also enhanced the formation of soil large aggregates and porosity; however, reduction soil bulk density and salt content in topsoil were recorded. Soil physical quality index was positively correlated with root weight density of tamarisk, and soil of T-11 yr plantation exhibited the highest soil physical quality index, with promoted soil physical functions of supporting root growth and the resistance to soil degradation. In addition, tamarisk induced soil physical changes which enhanced the salt-leaching in rainy season and contributed to the homogeneous salt distribution in soil profile. Consequently, the ecological benefits of tamarisk vegetation turned coastal saline land into a fertile land by plant–soil interaction and the soil structure improvement, therefore, it prevented the natural soil accumulation by accelerating the salt leaching after tamarisk was restored. This study provides some insights into the mechanism of tamarisk on coastal soil restoration and its regulation of soil salt distribution.

Recent studies suggested that Chotosan has ameliorative effects on vascular dementia through antioxidative pathways. Nevertheless, no systematic pharmacological research was conducted to evaluate the contribution of nuclear factor-E2-related factor 2 (Nrf2), a crucial regulator of antioxidative system, on Chotosan-induced neuroprotection invascular dementia. The present study aimed to investigate the neuroprotective effect of Chotosan on vascular dementia and reveal the possible molecular mechanism involving Nrf2. We found that Chotosan treatment could ameliorate memory impairment and reduce neuron cell loss induced by common carotid artery occlusion surgery. Furthermore, Chotosan could significantly reverse reactive oxygen species production, neuronal apoptosis and microglia over-activation in hippocampus. In addition, Chotosan enhanced Nrf2 expression and its nuclear translocation as well as its downstream antioxidant protein expression, NAD(P)H/quinone oxidoreductase 1 and heme oxygenase-1. These findings suggest that Chotosan exert neuroprotection in an animal model of vascular dementia via activating Nrf2-mediated antioxidant pathway. Chotosan may serve as a potential candidate and promising Nrf2 activator for treating vascular dementia. Keywords: Chotosan, Vascular dementia, Nrf2, Oxidative stress, Cognitive deficits

Information-Centric Networking (ICN) was born in an era that more and more users are shifting their interests to the content itself rather than the location where contents are stored. This paradigm shift in the usage patterns of the Internet, along with the urgent needs for content naming, pervasive caching, better security, and mobility support, has prompted researchers to consider a radical change to the Internet architecture. However, ICN is still in its infancy and development stage, and many issues still exist and need to be addressed. The common ICN architecture is lacking the host-centric communication ability and difficult to provide seamless mobility in current solutions. To solve this problem, our team had proposed the Named-Node Networking (3N) concept, which not only naming the content but also naming the node and it proved to have better performance of providing seamless mobility in the simulation. However, the previous contributions were limited in the 3N namespace for seamless mobility support in both producer and consumer. In this paper, we have proposed a 3N system which includes 3N naming, data delivery, mobility support, and data security. Moreover, we have created a 3N-based real-time video streaming system to evaluate data delivery performance and mobility handoff performance. The evaluation result proofs that our system performs better than TCP video streaming in a multi-client situation and a WiFi-based handoff was demonstrated.

Abstract Background An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients. Methods Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions. Results Based on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]). Conclusions Based on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

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.

In this paper, a gas sensing technique based on quartz-crystal-tuning-fork-enhanced spectroscopy (QCTFES) and wavelength modulation spectroscopy (WMS) is reported. To explore the capabilities of this technique, a near-infrared (NIR) diode laser emitting at 1,653 nm and a QCTF-based photoelectric detector are developed for measuring trace methane (CH4). For signal processing, a fast and effective signal analysis method based on the fast Fourier transform (FFT) algorithm is proposed for extracting the absorption intensity signal of the QCTFES-WMS, instead of a lock-in amplifier used for harmonic signal demodulation in traditional QCTF-based detection techniques. Primary laboratory results indicate that an excellent linearity response of CH4 concentration and optical power levels are founded, and a detection limit of 64 ppm is achieved with a 1-s averaging time, which can be further improved to 9 ppm at an optimal integral time of 250 s. Improvements in sensitivity and detectivity can be significantly achieved by using laser sources with higher output power. Compared to traditional WMS technique-based semiconductor photodetectors, the room-temperature QCTF-based WMS shows significant advantages of super-broadband wavelength response, much cheap and tiny.

Compared with conventional gas reservoir, percolation mechanism of coalbed methane (CBM) is completely different, it is remarkably affected by adsorption/desorption performance, pressure variation and coalbed characteristics. Then it is difficult to calculate the controlled reserves of CBM wells. Moreover, the connection of edge-bottom water or interbedded water by fractures or faults may largely increase the water yield and drainage period, leading to obvious water invasion in some CBM wells. There are few literature about the predicted production for CBM wells with high water yield. Focusing on the unconventional CBM reservoir, methods of pseudo geological reserves and production index curves are adopted to establish the calculation model of water influx and controlled reserves. It further confirms that the calculation model is successfully applicated on the CBM wells at the middle part of Qinshui Basin in China. Keywords: CBM well, High water yield, Water influx, Controlled reserves, Calculation model

Abstract Background To revise the American Joint Committee on Cancer TNM staging system for colorectal cancer (CRC) based on a nomogram analysis of Surveillance, Epidemiology, and End Results (SEER) database, and to prove the rationality of enhancing T stage’s weighting in our previously proposed T-plus staging system. Methods Total 115,377 non-metastatic CRC patients from SEER were randomly grouped as training and testing set by ratio 1:1. The Nomo-staging system was established via three nomograms based on 1-year, 2-year and 3-year disease specific survival (DSS) Logistic regression analysis of the training set. The predictive value of Nomo-staging system for the testing set was evaluated by concordance index (c-index), likelihood ratio (L.R.) and Akaike information criteria (AIC) for 1-year, 2-year, 3-year overall survival (OS) and DSS. Kaplan–Meier survival curve was used to valuate discrimination and gradient monotonicity. And an external validation was performed on database from the Second Affiliated Hospital of Zhejiang University (SAHZU). Results Patients with T1-2 N1 and T1N2a were classified into stage II while T4 N0 patients were classified into stage III in Nomo-staging system. Kaplan–Meier survival curves of OS and DSS in testing set showed Nomo-staging system performed better in discrimination and gradient monotonicity, and the external validation in SAHZU database also showed distinctly better discrimination. The Nomo-staging system showed higher value in L.R. and c-index, and lower value in AIC when predicting OS and DSS in testing set. Conclusion The Nomo-staging system showed better performance in prognosis prediction and the weight of lymph nodes status in prognosis prediction should be cautiously reconsidered.

There is increasing evidence that nonalcoholic steatohepatitis (NASH) is a risk factor for hepatocellular carcinoma (HCC) in the absence of cirrhosis, a phenomenon termed noncirrhotic HCC. Early diagnosis of HCC is critical to a favorable prognosis. We tested the hypothesis that hydroxyproline content of liver biopsy samples is diagnostic for HCC in murine models of NASH induced by diet or by diet and chemicals. The training set comprised mice fed a standard diet or a fast-food diet with or without administration of thioacetamide. At harvest, livers from the modified diet cohort exhibited NASH with a subset of NASH livers exhibiting HCC. Hydroxyproline content was measured in liver biopsy samples with tissue in the NASH+HCC cohort sampled from the remote, nontumor parenchyma. Plotting the receiver operating characteristics (ROC) with hydroxyproline as the continuous variable against the absence or presence of HCC yielded an area under ROC of 0.87, a threshold of >0.18 μg hydroxyproline/mg liver and sensitivity of 91% with a specificity of 83.3%. The use of liver hydroxyproline content as a diagnostic for HCC in a test set comprising healthy, NASH and NASH+HCC livers proved 87% accurate.

Practical core losses in electrical machines differ significantly from those experimental results using the standardized measurement method, i.e. Epstein Frame method. In order to obtain a better approximation of the losses in an electrical machine, a simulation method considering sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) waveforms is proposed. The influence of the pulse width modulation (PWM) parameters on the harmonic components in SPWM and SVPWM is discussed by fast Fourier transform (FFT). Three-level SPWM and SVPWM are analyzed and compared both by simulation and experiment. The core losses of several ring samples magnetized by SPWM, SVPWM and sinusoidal alternating current (AC) are obtained. In addition, the temperature rise of the samples under SPWM, sinusoidal excitation are analyzed and compared.

According to coupling way between the magnetic field and the structural order, structure mode is discussed by engaging finite element (FE) method and both natural frequency and modal shape for a dry-type air-core reactor (DAR) are obtained in this paper. On the basis of harmonic response analysis, electromagnetic force under PWM (Pulse Width Modulation) voltage excitation is mapped with the structure mesh, the vibration spectrum is gained and the consequences represents that the whole structure vibration predominates in the radial direction, with less axial vibration. Referring to the test standard of reactor noise, the rules of emitted noise of the DAR are measured and analyzed at chosen switching frequency matches the sample resonant frequency and the methods of active vibration and noise reduction are put forward. Finally, the low acoustic noise emission of a prototype DAR is verified by measurement.

The extent of scarring or renal interstitial collagen deposition in chronic kidney disease (CKD) can only be ascertained by highly invasive, painful and sometimes risky, tissue biopsy. Interestingly, while CKD-related abnormalities in kidney size can often be visualized using ultrasound, not only does the ellipsoid formula used today underestimate true renal size, but the calculated renal size does not inform tubulointerstitial collagen content. We used coronal kidney sections from healthy mice and mice with kidney disease to develop a new formula for estimating renal parenchymal area. While treating the kidney as an ellipse with the major axis (a) the polar distance, this technique involves extending the minor axis (b) into the renal pelvis to obtain a new minor axis, be. The calculated renal parenchymal area is remarkably similar to the true or measured area. Biochemically determined kidney collagen content revealed a strong and positive correlation with the calculated renal parenchymal area. Picrosirius red staining for tubulointerstitial collagen also correlated with calculated renal parenchymal area. The extent of renal scarring, i.e. kidney interstitial collagen content, can now be computed by making just two axial measurements which can easily be accomplished via noninvasive imaging of this organ.

Although cirrhosis is a key risk factor for the development of hepatocellular carcinoma (HCC), mounting evidence indicates that in a subset of patients presenting with non-alcoholic steatohepatitis (NASH) HCC manifests in the absence of cirrhosis. Given the sheer size of the ongoing non-alcoholic fatty liver disease (NAFLD) epidemic and the dismal prognosis associated with late-stage primary liver cancer there is an urgent need for HCC surveillance in the NASH population. Using serum levels of HCC biomarkers as vectors and biopsy-proven HCC or no HCC as outputs / binary classifier, a supervised learning campaign was undertaken to develop a minimally invasive technique for making a diagnosis of HCC in a clinically relevant model of NASH. Adult mice randomized to control diet or a fast food diet (FFD) were followed for up to 14 mo and serum level of a panel of HCC-relevant biomarkers was compared with liver biopsies at 3 and 14 mo. Both NAFLD Activity Score (NAS) and hepatic hydroxyproline content were elevated at 3 and 14 mo on FFD. Picrosirius red staining of liver sections revealed a filigree pattern of fibrillar collagen deposition with no cirrhosis at 14 mo on FFD. Nevertheless, 46% of animals bore one or more tumors on their livers confirmed as HCC in hematoxylin-eosin-stained liver sections. In this training set, receiver operating characteristic (ROC) curves analysis for serum levels of the HCC biomarkers osteopontin (OPN), alpha-fetoprotein (AFP) and Dickkopf-1 (DKK1) returned concordance-statistic/area under ROC curve of ≥ 0.89. Serum levels of OPN (threshold, 218 ng/mL; sensitivity, 82%; specificity, 86%), AFP (136 ng/mL; 91%; 97%) and DKK1 (2.4 ng/mL; 82%; 81%) diagnostic for HCC were confirmed in a test set comprising mice on control diet or FFD and mice subjected to hepatic ischemia-reperfusion injury. These data suggest that levels of circulating OPN, AFP and DKK1 can be used to make a diagnosis of HCC in a clinically relevant model of NASH.

Rotational core loss of the silicon steel laminations are measured under elliptical rotating excitation. The core loss decomposition model is very important in magnetic core design, in which the decomposition coefficients are calculated through the measurement data. By using the transformation of trigonometric function, the elliptical rotational magnetic flux can be decomposed into two parts along two directions. It is assumed that the rotating core loss is the sum of alternating core losses along rolling and transverse directions. The magnetic strength vector H of non-grain oriented (NGO) silicon steel 35WW270 along rolling and transverse directions is measured by a novel designed 3-D magnetic properties tester. Alternating core loss along the rolling, transverse directions and rotating core loss in the xoy-plane of this specimen in different frequencies such as 50 Hz, 100 Hz, and 200 Hz. Experimental results show that the core loss model is more accurate and useful to predict the total core loss.

(1) Background: As a halophytic species, Tamarix (Tamarix chinensis) can be used for saline soil rehabilitation in China. The reclamation and rehabilitation of saline soil depend on the water consumption of plants. However, whether water resources in saline soil can support the construction of Tamarix vegetation is still unknown. (2) Methods: In this study, we measured the transpiration (T) of Tamarix for 3 years using sap flow and the evaporation (E) for 1 year using a micro-lysimeter in Tamarix land. The evaporation values in 2016 and 2017 were estimated with the soil crop coefficients obtained in 2018. (3) Results: The evapotranspiration (ET) ranged from 514.2 to 573.8 mm and was greatly affected by the wind speed, VPD and groundwater table. Transpiration was the main form of water consumption in this region, accounting for 60.2% of the total evapotranspiration. Compared with bare land, vegetation construction increased soil moisture dissipation by 377.6 mm in 2018. According to on-site measurements and estimates, the water shortage in the dry year was 107.2 mm, and the residual water values in the normal year and wet year were 77.8 mm and 187.5 mm, respectively. May and September were months of widespread water shortages in different precipitation years. Although the cultivation of this plant increased water consumption, the groundwater table remained at approximately 0.5 m during the study year. (4) Conclusions: These results indicated that planting Tamarix in coastal saline soil was feasible for the reclamation and rehabilitation of saline soil. In the dry year (2017), the consumption of evapotranspiration exceeded the precipitation. The inverse occurred in the normal year (2016) and wet year (2018). Taken together, our findings showed that the water resources in the coastal saline soil of China could tolerate vegetation construction and laid a strong foundation for saline soil rehabilitation.

Changes in renal dimensions, including total kidney volume, not only inform ongoing renal disease but also disease progression. Determination of renal dimensions can inform drug efficacy, is important for matching recipients with potential donors, and to inform debulking of renal tumors. Imaging of kidney and application of the ellipse-based formula has become standard for estimating renal dimensions. Nevertheless, the existing ellipse-based formula underestimates renal dimensions including total kidney volume, regardless of the imaging modality used. Based on a model of murine kidney disease, this laboratory has previously proposed a modification to this formula which exhibits better estimation of renal dimensions. The present study sought to determine whether this modified formula is applicable to additional models of kidney disease. Kidneys were sourced from etiologically distinct murine and rat models of renal scarring. In each case, renal dimensions calculated using the existing ellipse-based formula was significantly lesser than the measured dimensions. By contrast, there was no difference between the measured dimensions and those calculated using the modified formula. In a model of polycystic kidney disease, total kidney volume calculated using the existing formula significantly underestimated measured kidney volume whereas use of the modified formula yielded a calculated kidney volume in excellent agreement with the measured volume. Use of this modified formula provides a better estimate of renal dimensions across a number of disease models.

Caroli syndrome, characterized by saccular dilatation of intrahepatic ducts and congenital hepatic fibrosis, is without therapy in part due to its ultra-rare prevalence and the apparent lack of availability of a suitable experimental model. While the PCK rat has long been used as a model of fibropolycystic kidney disease, hepatobiliary biophysics in this animal model is incompletely characterized. Compared to age-matched, wild-type controls, the PCK rat demonstrated severe hepatomegaly and large saccular dilated intrahepatic ducts. Nevertheless, hepatic density was greater in the PCK rat, likely due to severe duct wall sclerosis accompanied by scarring across the hepatic parenchyma. Extracellular matrix accumulation appeared proportional to duct cross-sectional area and liver volume and appeared compensatory in nature. The PCK rat livers exhibited both cholangiocarcinoma and hepatocellular carcinoma coincident with areas of increased extracellular matrix deposition. Together, these data suggest that the PCK rat model mimics at least in part the spectrum of hepatobiliary pathology observed in Caroli syndrome and highlights the attendant risk associated with this disease.

Boundary effects are caused by incomplete data in the boundary regions when the analysis window gets closer to the edge of a signal. Various extension schemes have been developed to handle the boundaries of finite length signals to reduce the boundary effects. Zero padding, periodic extension, and symmetric extension are some basic extension methods. However, these solutions have drawbacks. In this paper, we consider the problem of handling the boundary effects due to improper extension methods in the wavelet transform for the application of fault diagnosis of rotating machine. An extension algorithm based on curve fitting with properties that make it more suitable for boundary effects reduction is presented. This extension algorithm could preserve the time-varying characteristics of the signals and be effective to reduce distortions appearing at the boundary. Then, an interpolation approach is used in the boundary effects region to further alleviate the distortions. Procedures for realization of these two algorithms and relative issues are presented. Several experimental tests show that the proposed algorithms are efficient to alleviate the boundary effects in comparison to the existing extension methods.

With the development of virtual reality technology, portal slewing crane monitoring system is also gradually moving towards virtualization; virtual simulation laboratory software appeared in large numbers. After a comprehensive analysis of the domestic and foreign construction of virtual reality technology development status, this paper proposes a virtual reality method based on WPF technology. The specific content of this method is summarized as follows. Firstly, the portal slewing crane model was built by 3ds Max, which includes the undercarriage, cab, and main hook. Secondly, the scenes of the port are added, which includes the sea, the sky, and the port. Finally, we use WPF to create the virtual reality module of the portal slewing crane monitoring system. According to a group of performance tests, we can get the performance tests show that the WPF system could improve the CPU utilization and reduce the memory usage during simulating the 3D model of the portal slewing crane.