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The vacuum preloading method is commonly used to improve soft soils; however, it has rarely been studied from the perspective of energy consumption. The work done by the vacuum pump in the vacuum system can be consumed in various ways, including seepage consolidation of soil, water discharge along prefabricated vertical drains and horizontal drainage system (i.e. sand blanket or horizontal pipes), extraction of the air sealed in the drainage system and air leakage from the atmosphere. Equations governing these aspects of energy consumption were derived in this study. A theoretical model of the energy consumption of the vacuum preloading method was established and then solved to quantify the specific forms of energy consumption. Theoretical predictions and laboratory results demonstrate that the energy consumptions of seepage consolidation and water discharge are negligibly small compared with the total work by the vacuum pump, and most of the work done by the vacuum pump in the vacuum system is consumed during the extraction of leaked-in air.

Objective: To better understand the wetland restoration, the physicochemical properties and microbial community in different compartments (rhizosphere and bulk soil) of the status of living and death T. chinensis covered soil zones were studied in a restored coastal tidal flat located on the Zhoushan island.Results: There were differences between growth conditions in the levels of soil pH, salinity, SOM, and nutrient. The living status of T. chinensis exhibited higher capacity of decreasing saline-alkali soil than the death condition of plants, and the living condition of T. chinensis showed higher uptake of N, P, and K as compared with the death samples. Proteobacteria, Bacteroidota, and Chloroflexi were the predominant bacterial communities as revealed via high-throughput sequencing. This study contributed to our understanding of halophyte growth on coastal phytoremediation, and guide theoretically for management of T. chinensis population.

BackgroundThe COVID-19 pandemic has disrupted the diagnosis, treatment, and care for tuberculosis (TB). Delays in seeking TB care may result in increased community transmission and unfavorable treatment outcomes. We sought to understand the influence of the COVID-19 pandemic on the proportion of patients with TB who delayed seeking the diagnosis and care for TB and explore the reasons for their postponement.MethodsWe surveyed a representative sample of outpatients treated for pulmonary TB from June to November 2020 using an anonymous standardized questionnaire. Multivariable logistic regression was used to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CIs) of factors associated with the postponement of TB care. We used routinely collected surveillance data to assess trends of TB reports before and after the emergence of COVID-19 (2017–2019 vs. 2020–2022) in Tianjin, China.ResultsAmong 358 participants who were diagnosed with pulmonary TB during the COVID-19 response, 61 (17%) postponed seeking TB diagnosis due to COVID-19, with 39 (64%) citing fear as the primary reason. Female sex (aOR:2.0; 95% CI: 1.1–3.7), previous antituberculosis treatment (aOR:3.2; 95%CI: 1.4–7.6), and TB diagnosis during the first-level response (aOR = 3.2, 1.7–6.2) were associated with the postponement. Among all 518 participants receiving antituberculosis treatment, 57 (11%) had postponed their regular healthcare visits due to COVID-19, 175 (34%) received no treatment supervision, and 32 (6%) experienced treatment interruption. Compared to 2017–2019, reported pulmonary TB declined by 36.8% during the first-level response to COVID-19, 23.5% during the second-level response, 14% during the third-level response in 2020, and 4.3% in 2021.ConclusionThe COVID-19 response reduced the number of people who sought and received diagnosis, treatment, and care for TB in Tianjin, China. Integrative programs to ensure access and continuity of TB services should be considered and dual testing for SARS-CoV-2 and M. tuberculosis may facilitate finding cases.

The air-booster vacuum preloading method has been applied to slurry ground improvement. It is based on the conventional vacuum preloading method but with an additional injection of pressurised air into the soil via pre-installed conductors. The drainage effect of air-booster vacuum preloading has been demonstrated by past studies; however, direct observations of the real-time behaviour of slurries subjected to boosted air remain lacking. This study used a combined monitoring technique that included particle image velocimetry, pore water/air pressure gauges, a vortex flowmeter and an electronic balance to conduct a laboratory test of air-booster vacuum consolidation of dredged slurry. The tests allowed analyses of (1) the real-time displacement field of the slurry, (2) the pressure–flux relationship of the pressurised air, and (3) the pore water pressure responses during air boosting. The first aspect allowed direct observation of small-crack initialisation and propagation during pressurisation; while the latter two confirmed the crack initiation based on drops in air and pore water pressures. The measured crack initiation pressure was verified by comparison with theoretical predictions. The results demonstrate that pressurised air induces cracks in soil, which promote the drainage consolidation of dredged slurry.

Bisphenol A (BPA) has been widely used in the manufacture of polymeric materials. BPA is regarded as an endocrine disrupting chemical, posing a great threat to the public health. In this study, a bacterial strain LM-1, capable of utilizing BPA as the sole carbon and energy source under aerobic conditions, was originally isolated from an activated sludge sample. The isolate was identified as Pseudomonas sp. based on 16S rRNA gene sequence analysis. Strain LM-1 was able to completely degrade 25-100 mg/L BPA within 14-24 h, and it also exhibited high capacity for BPA degradation at a range of pH (6.0-8.0). (NH

Under long-term traffic loading, the soil elements in subgrade are subjected to continuous principal stress rotation. In order to study the deformation properties of soft clays under traffic loading with principal stress rotation, a series of cyclic torsional shear tests were conducted on Wenzhou soft clays under different torsional cyclic stress ratios and degrees of principal stress rotation. The test results showed the stiffness softening of soil under long-term traffic loading. In addition, the principal stress rotation induced by traffic loading aggravated the deformation of clay samples and pore pressure accumulation. A modified dynamic pore pressure model was applied to consider the effect of principal stress rotation on undrained cumulative pore pressure, predicting the growth of cumulative pore pressure at different cycles. Considering loading cycles and the principal stress rotation, a modified Hardin–Drnevich (H-D) backbone curve model under traffic loading with principal stress rotation was proposed, and the predictive values of this model agreed well with the experimental values. Compared with the traditional H–D model, this model better reflects the cyclic deformation of soft clays under long-term traffic loading with principal stress rotation.

Classifying multi-label instances using incompletely labeled instances is one of the fundamental tasks in multi-label learning. Most existing methods regard this task as supervised weak-label learning problem and assume sufficient partially labeled instances are available. However, collecting or annotating such instances is expensive and time-consuming. In contrast, abundant unlabeled instances are easy to accumulate. Recently, some methods move toward exploiting unlabeled instances and performing transductive multi-label classification. However, these methods can not directly apply to new instances, which are not available during training process. In this paper, we proposed an approach called Semi-supervised multi-label classification using incomplete label information (SMILE for short). SMILE first estimates label correlation from partially labeled instances and replenishes missing labels of these instances. Then, it takes advantage of labeled and unlabeled instances to construct a neighborhood graph. Next, the known labels and replenished ones of labeled instances, along with unlabeled instances are exploited to train a graph based semi-supervised linear classifier. SMILE can further replenish the missing labels of training instances based on the adopted neighborhood graph. In addition, it can directly predict the labels of completely unlabeled new instances. The empirical study on multi-label datasets shows that SMILE performs significantly better than other related methods across various evaluation criteria and it is important to leverage unlabeled data with label correlation for multi-label classification.

// Yanming Yu 1, * , Junde Zhou 2, * , Chen Gong 1 , Zhiping Long 1 , Jingshen Tian 1 , Lin Zhu 1 , Jing Li 1 , Hongyuan Yu 1 , Fan Wang 1 and Yashuang Zhao 1 1 Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, P. R. China 2 Department of Colorectal Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, P. R. China * These authors have contributed equally to this work Correspondence to: Fan Wang, email: yifan.701@163.com Yashuang Zhao, email: zhao_yashuang@263.net Keywords: dietary factors, miRNA-binding site, polymorphisms, IL13, colorectal cancer Received: December 20, 2016 Accepted: April 21, 2017 Published: May 07, 2017 ABSTRACT Long-term dietary intake influences the structure and activity of microorganisms residing in the human gut. The immune response and gut microbiota have a mutual influence on the risk of colorectal cancer (CRC). This study examines the association of gut microbiota–related dietary factors and polymorphisms in the microRNA-binding site of the interleukin 13 gene ( IL13 ) with the risk and prognosis of CRC. Three polymorphisms (rs847, rs848, and rs1295685) were selected for genotyping in a case–control study (513 cases, 572 controls), and 386 CRC patients were followed up. Two dietary factors closely related with gut microbiota (allium vegetables, overnight meal) were significantly associated with CRC development. Although the three SNPs showed no statistically significant associations with the risk and prognosis of CRC, a significant antagonistic interaction was found between rs848 (G–T) and allium vegetable intake (ORi (odds ratio of interaction), 0.92; 95% CI (confidence interval): 0.86, 0.99; P = 0.03); moreover, significant combined and synergistic interactions were observed for all three SNPs and overnight meal intake. This is the first report of significant combined and interactive effects between dietary factors and polymorphisms in the microRNA binding site of IL13 in CRC and may provide direct guidance on intake of allium vegetable and overnight meals for individuals with specific genetic variants of IL13 to modify their susceptibility to CRC.

Background: Metabolomics has been widely applied and has great potentialities in biomarkers identification for colorectal cancer (CRC). However, it turns out to be a hindrance by far for effective clinical translation owing to huge number and high inconsistency of biomarkers. Methods: We identified and verified differential plasma metabolites of CRC via a two-stage case-control metabolomics study in a population from Northeast China. Using receiver operating characteristic curve and pathway enrichment analyses, we selected biomarkers with favorable diagnostic efficiency and biological significance. We confirmed the tumor-specificity of these biomarkers by a metabolomics study of tumor-adjacent non-malignant paired tissue. Moreover, we conducted a systemic review of previous metabolomics studies in CRC to find biomarkers with external reproducibility in multiple populations. Results: We proposed three sets of candidate biomarkers of CRC with a particular focus on diagnostic efficiency, biological significance, tumor specificity, and external reproducibility. We showed that a five-biomarker panel of L-Tryptophan, 13-OxoODE, IDP, Glycochenodeoxycholate, and LysoPC(16:0) exhibited excellent diagnostic performance, favorable biological significance, and tumor specificity in our population. A six-biomarker panel of L−Phenylalanine, Linoleic acid, Citric acid, Inosine, Glycocholic acid, and LysoPC(14:0) showed the best extrapolation in diverse populations. Four metabolites (L-Tryptophan, Linoleic acid, Glycocholic acid, and LysoPC(16:0)) were ultimately recommended as the best combination with multiple advantages. Conclusion: We screened out the optimal panels of plasma biomarkers for CRC diagnosis and elucidated the potential usefulness of CRC metabolites under different requirements. Our proposed method may provide a powerful approach for metabolic biomarkers screening in other diseases. Funding Statement: This work was supported by grants from National Nature Science Foundation of China (81773503, 81573147, and 81473055), Scientific Research Foundation for the Returned Overseas Scholars of Heilongjiang Province (LC2018033), and Natural Science Foundation of Heilongjiang Province of China (for Youths, QC2018111). Declaration of Interests: All authors have declared that no conflict of interest exists. Ethics Approval Statement: We carried out this study after obtaining written informed consent from all the study subjects and approval from the Human Research and Ethics Committee of Harbin Medical University. All experiments including relevant details were performed in accordance with relevant guidelines and regulations.

Fungal peritonitis is a catastrophic complication of peritoneal dialysis (PD) and often requires termination of PD. It is usually caused by Candida species. Here we report a rare case of Exserohilum peritonitis. The patient was successfully treated with catheter removal and anti-fungal therapy.

As the lightweight robotic arm has been applied widely, it is significant to make lightweight robotic arms much safer and lighter. In this study, the axial flux permanent magnet (AFPM) brushless DC motor with high torque density is proposed and designed to apply for the robotic joint based on a prototype. A major design objective is to achieve a lightweight AFPM motor embedded in a robotic joint. This is accomplished by the complex method, where the structural dimensions and electromagnetic parameters are parameterized as design variables. Constraints are formulated on the basis of kinematic performance, dynamic performance and electromagnetic specifications. Finally, through the comparison between the original and optimal, the optimal design has lighter weight and smaller size.

Multi-label dimensionality reduction methods often ask for sufficient labeled samples and ignore abundant unlabeled ones. To leverage abundant unlabeled samples and scarce labeled ones, we introduce a method called Semi-supervised Multi-label Linear Discriminant Analysis (SMLDA). SMLDA measures the dependence between pairwise samples in the original space and in the projected subspace to utilize unlabeled samples. After that, it optimizes the target projective matrix by minimizing the distance of within-class samples, whilst maximizing the distance of between-class samples and the dependence term. Extensive empirical study on multi-label datasets shows that SMLDA outperforms other related methods across various evaluation metrics, and the dependence term is an effective alternative to the widely-used smoothness term.

Background Sepsis is an inflammatory response undergoing the complicate pathophysiological changes for host defense against pathogens. Previous studies suggested that dexmedetomidine (DEX) was served to controlling the over-reactive inflammatory effects to protect from the sepsis-induced organ failure via modulating histone methylation. However, the genome-wide changes of histone methylations upon DEX for sepsis treatment were poorly explored. Materials and methods The acute kidney injury (AKI) mouse model were induced by lipopolysaccharide (LPS). DEX and KDM5 (H3K4 demethylases) inhibitors were used to add additionally. H3K4me3 antibody was used to conduct the ChIP-seq assay in renal cortex tissues. Results We observed that the overall H3K4me3 levels were obviously declined in AKI group compared to the normal control. We further observed that the therapeutic effect of DEX was basically equal with CPI-455 and KDM5A–IN–1 but better than PBIT. The overall H3K4me3 level was reduced in AKI group compared to DEX (p = 0.008), and KDM5A–IN–1 groups (p = 0.022). The H3K4me3 enrichment of the multiple genes associated with inflammatory cytokines such as TNF-α, NOS2 and CCL2 increased in AKI model, but decreased upon DEX or KDM5A–IN–1 treatment. Consistently, transcription and protein levels of genes such as TLR4, MYD88, MTA1, PTGS2, CASP3 associated with NF-κB signaling pathway were all compromising after treated with DEX or KDM5A–IN–1 groups compared to AKI group. Conclusion Taken together, our data determined that DEX could attenuate AKI through KDM5A inhibition in sepsis.

To develop a method that combines a fixed-T1, fuzzy c-means (FCM) technique with a reference region (RR) model (T1-FCM method) to estimate pharmacokinetic parameters without measuring the arterial input function or baseline T1, or T1(0), and to demonstrate its feasibility in the assessment of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer by using data from dynamic contrast material-enhanced magnetic resonance (MR) imaging.This study was approved by the human investigation committees of the two participating institutions. All patients gave written informed consent. A conventional dual-flip-angle gradient-echo method was used to evaluate the effects of noise and the T1 in the tissue itself on the accuracy of T1 estimation. Both conventional RR and fixed-T1 methods were used to evaluate the effects of noise and preselected T1(0) on the estimation of pharmacokinetic parameters by means of a simulation study. Thirty-three women (age range, 32-66 years; mean age, 45 years) with pathologically proved breast tumors were examined to evaluate the feasibility of using the T1-FCM method as a means of assessing treatment response to NAC. A nonparametric Mann-Whitney U test was used to assess the difference in each of the MR imaging parameters between patients with a major histologic response to treatment and those with a nonmajor histologic response.With use of the dual-flip-angle method, the accuracy and distribution of T1 estimation are dependent on the T1 in the tissue itself. The T1-FCM method is more accurate than other methods and is relatively insensitive to the effects of noise and incorrect T1(0) selection. Preliminary clinical data revealed a significant difference (P.01) in the change of the volume transfer constant after two cycles of NAC between the major and nonmajor histologic response groups.Results of the simulation study demonstrate that the T1-FCM method appears to be relatively insensitive to noisy dynamic contrast-enhanced MR imaging data. This method could prove useful in the evaluation of breast cancer therapy.

Purpose: To investigate the feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. Methods: Two experiments were designed to investigate the feasibility of using reference image based compressed sensing (RICS) technique in DCE-MRI of the breast. The first experiment examined the capability of RICS to faithfully reconstruct uptake curves using undersampled data sets extracted from fully sampled clinical breast DCE-MRI data. An average approach and an approach using motion estimation and motion compensation (ME/MC) were implemented to obtain reference images and to evaluate their efficacy in reducing motion related effects. The second experiment, anin vitro phantom study, tested the feasibility of RICS for improving temporal resolution without degrading the spatial resolution. Results: For the uptake-curve reconstruction experiment, there was a high correlation between uptake curves reconstructed from fully sampled data by Fourier transform and from undersampled data by RICS, indicating high similarity between them. The mean Pearson correlation coefficients for RICS with the ME/MC approach and RICS with the average approach were 0.977 ± 0.023 and 0.953 ± 0.031 , respectively. The comparisons of final reconstruction results between RICS with the average approach and RICS with the ME/MC approach suggested that the latter was superior to the former in reducing motion related effects. For the in vitro experiment, compared to the fully sampled method, RICS improved the temporal resolution by an acceleration factor of 10 without degrading the spatial resolution. Conclusions: The preliminary study demonstrates the feasibility of RICS for faithfully reconstructing uptake curves and improving temporal resolution of breast DCE-MRI without degrading the spatial resolution.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the MRI technique of choice for detecting breast cancer, which can be roughly classified as either quantitative or semiquantitative. The major advantage of quantitative DCE-MRI is its ability to provide pharmacokinetic parameters such as volume transfer constant (Ktrans) and extravascular extracellular volume fraction (ve). However, semiquantitative DCE-MRI is still the clinical MRI technique of choice for breast cancer diagnosis due to several major practical difficulties in the implementation of quantitative DCE-MRI in a clinical setting, including (1) long acquisition necessary to acquire 3D T1(0) map, (2) challenges in obtaining accurate artery input function (AIF), (3) long computation time required by conventional nonlinear least square (NLS) fitting, and (4) many illogical values often generated by conventional NLS method. The authors developed a new analysis method to estimate pharmacokinetic parameters Ktrans and ve from clinical DCE-MRI data, including fixed T1(0) to eliminate the long acquisition for T1(0) map and "reference region" model to remove the requirement of measuring AIF. Other techniques used in our analysis method are (1) an improved formula to calculate contrast agent (CA) concentration based on signal intensity of SPGR data, (2) FCM clustering-based techniques for automatic segmentation and generation of a clustered concentration data set (3) an empirical formula for CA time course to fit the clustered data sets, and (4) linear regression for the estimation of pharmacokinetic parameters. Preliminary results from computer simulation and clinical study of 39 patients have demonstrated (1) the feasibility of their analysis method for estimating Ktrans and ve from clinical DCE-MRI data, (2) significantly less illogical values compared to NLS method (typically less than 1% versus more than 7%), (3) relative insensitivity to the noise in DCE-MRI data; (4) reduction in computation time by a factor of more than 30 times compared to NLS method on average, (5) high statistic correlation between the method used and NLS method (correlation coefficients: 0.941 for Ktrans and 0.881 for ve), and (6) the potential clinical usefulness of the new method.

Driven equilibrium single pulse observation of T1 (DESPOT1) is a rapid spin–lattice relaxation constant (T1) mapping technique in magnetic resonance imaging (MRI). However, DESPOT1 is very sensitive to flip angle (FA) inhomogeneity, resulting in T1 inaccuracy. Here, a five-point DESPOT1 method is proposed to reduce the sensitivity to FA inhomogeneity through FA measurement and calibration. Phantom and in vivo experiments are performed to validate the technique. As a result, a rapid and accurate T1 mapping is acquired by using the proposed five-point DESPOT1 method.

To investigate the feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.Two experiments were designed to investigate the feasibility of using reference image based compressed sensing (RICS) technique in DCE-MRI of the breast. The first experiment examined the capability of RICS to faithfully reconstruct uptake curves using undersampled data sets extracted from fully sampled clinical breast DCE-MRI data. An average approach and an approach using motion estimation and motion compensation (ME/MC) were implemented to obtain reference images and to evaluate their efficacy in reducing motion related effects. The second experiment, an in vitro phantom study, tested the feasibility of RICS for improving temporal resolution without degrading the spatial resolution.For the uptake-curve reconstruction experiment, there was a high correlation between uptake curves reconstructed from fully sampled data by Fourier transform and from undersampled data by RICS, indicating high similarity between them. The mean Pearson correlation coefficients for RICS with the ME/MC approach and RICS with the average approach were 0.977 +/- 0.023 and 0.953 +/- 0.031, respectively. The comparisons of final reconstruction results between RICS with the average approach and RICS with the ME/MC approach suggested that the latter was superior to the former in reducing motion related effects. For the in vitro experiment, compared to the fully sampled method, RICS improved the temporal resolution by an acceleration factor of 10 without degrading the spatial resolution.The preliminary study demonstrates the feasibility of RICS for faithfully reconstructing uptake curves and improving temporal resolution of breast DCE-MRI without degrading the spatial resolution.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the MRI technique of choice for detecting breast cancer, which can be roughly classified as either quantitative or semiquantitative. The major advantage of quantitative DCE-MRI is its ability to provide pharmacokinetic parameters such as volume transfer constant (Ktrans) and extravascular extracellular volume fraction (ve). However, semiquantitative DCE-MRI is still the clinical MRI technique of choice for breast cancer diagnosis due to several major practical difficulties in the implementation of quantitative DCE-MRI in a clinical setting, including (1) long acquisition necessary to acquire 3D T1(0) map, (2) challenges in obtaining accurate artery input function (AIF), (3) long computation time required by conventional nonlinear least square (NLS) fitting, and (4) many illogical values often generated by conventional NLS method. The authors developed a new analysis method to estimate pharmacokinetic parameters Ktrans and ve from clinical DCE-MRI data, including fixed T1(0) to eliminate the long acquisition for T1(0) map and “reference region” model to remove the requirement of measuring AIF. Other techniques used in our analysis method are (1) an improved formula to calculate contrast agent (CA) concentration based on signal intensity of SPGR data, (2) FCM clustering-based techniques for automatic segmentation and generation of a clustered concentration data set (3) an empirical formula for CA time course to fit the clustered data sets, and (4) linear regression for the estimation of pharmacokinetic parameters. Preliminary results from computer simulation and clinical study of 39 patients have demonstrated (1) the feasibility of their analysis method for estimating Ktrans and ve from clinical DCE-MRI data, (2) significantly less illogical values compared to NLS method (typically less than 1% versus more than 7%), (3) relative insensitivity to the noise in DCE-MRI data; (4) reduction in computation time by a factor of more than 30 times compared to NLS method on average, (5) high statistic correlation between the method used and NLS method (correlation coefficients: 0.941 for Ktrans and 0.881 for ve), and (6) the potential clinical usefulness of the new method.

Purpose: The purpose of this work is to implement the CS‐based MRI in DCE‐MRI to develop a new method that can exhibit both high temporal and high spatial resolution results which are significant for DCE‐MRI and related diagnosis. Method and Materials: By exploring the DCE specific characteristic that administration of contrast agent (CA) induces change only in image signal intensity in certain areas such as vessels and lesions no significant change in anatomical structure we have developed a novel approach Reference imAge based Compressed sEnsing (RACE) to capitalize the sparsity and compressibility of DCE‐MRI. Phantom experiments have been performed on an MAGNETOM ESSANZA 1.5T MRI scanner (Siemens Erlangen Germany)focusing on the study of temporal and spatial resolution respectively. Spatial finite difference (SPD) is chosen to be the sparse transformation. 3D radial sampling is implemented as under sampling scheme. The reconstruction is based on solving a constrained total variation (TV)‐norm minimization problem. Results: According to the phantom experiments during a same time course much more (up to 10 times) time frames can be obtained with RACE which means higher temporal resolution. The comparisons demonstrate that the details of the dynamic curves can be detected using RACE especially when the intensity varying rate is high (Fig.3 c). Meanwhile the spatial resolution is not degraded. Conclusion: This work proved that RACE which properly explored the features of DCE‐MRI can significantly improve the temporal resolution of DCE‐MRI without degrading its spatial resolution.Conflict of Interest (only if applicable): Phantom experiments are supported by Siemens Mindit Magnetic Resonance Ltd. Co.