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This study presents a groundbreaking approach to modeling the Hall-Héroult cathode used in aluminum production. Our innovative model is grounded in a sophisticated porous electrode methodology coupled with state-of-the-art numerical simulations. This enables us to capture the intricate physicochemical processes within the system precisely, encompassing the migration, diffusion, and convection of ionic species. A key feature of our model is the integration of detailed electrochemical reaction kinetics at the microscale, providing a nuanced understanding of the internal dynamics of the cathode. Furthermore, we have incorporated a unique penalization method that rigorously enforces the principles of electroneutrality and ionic thermodynamic equilibrium, ensuring the model's fidelity to real-world phenomena. Computational simulation using the finite element method (FEM) serves as the backbone of our model, offering unparalleled accuracy and robustness. This has been confirmed through validation against empirical data, underlining the model's potential to significantly enhance both the efficiency and sustainability of aluminum production processes. • Development of an advanced porous electrode model for the Hall-Héroult process, utilizing numerical simulations42 to unravel complex physicochemical dynamics. • Incorporation of a novel penalization method for ensuring electroneutrality and thermodynamic equilibrium, enhancing 44 model accuracy. • Validation of the model against empirical data using FEM, demonstrating potential improvements in aluminum 46 production efficiency and sustainability.

Abstract Source enumeration with large snapshots is feasible for a static uniform circular sonar array (UCSA). However, the dimension of the signal subspace in the array coordinate system exceeds the number of signal sources due to the rotation of the UCSA, which makes standard source enumeration ineffective. To address this issue, a spatial focusing approach for source enumeration with rotating UCSAs is presented. The approach is based on the concept of phase‐mode excitation and makes use of the UCSA heading information to achieve spatial focusing. The proposed algorithm provides reliable source enumeration with a high signal‐to‐noise ratio (SNR) when compared to the previous signal‐subspace transformation (SST) method. Simulations demonstrate that the proposed algorithm outperforms the other three methods in the conditions of a low SNR and a small angular separation.

Abstract Background Self-perceived exposure risk determines the likelihood of COVID-19 preventive measure compliance to a large extent and is among the most important predictors of mental health problems. Therefore, there is a need to systematically identify important predictors of such risks. This study aims to provide insight into forecasting and understanding risk perceptions and help to adjust interventions that target various social groups in different pandemic phases. Methods This study was based on survey data collected from 5001 Norwegians in 2020 and 2021. Interpretable machine learning algorithms were used to predict perceived exposure risks. To detect the most important predictors, the models with best performance were chosen based on predictive errors and explained variances. Shapley additive values were used to examine individual heterogeneities, interpret feature impact and check interactions between the key predictors. Results Gradient boosting machine exhibited the best model performance in this study (2020: RMSE=.93, MAE=.74, RSQ=.22; 2021: RMSE=.99, MAE=.77, RSQ=.12). The most influential predictors of perceived exposure risk were compliance with interventions, work-life conflict, age and gender. In 2020, work and occupation played a dominant role in predicting perceived risks whereas, in 2021, living and behavioural factors were among the most important predictors. Findings show large individual heterogeneities in feature importance based on people’s sociodemographic backgrounds, work and living situations. Conclusion The findings provide insight into forecasting risk groups and contribute to the early detection of vulnerable people during the pandemic. This is useful for policymakers and stakeholders in developing timely interventions targeting different social groups. Future policies and interventions should be adapted to the needs of people with various life situations.

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.

Pesticide stress on plants is receiving increased scrutiny due to its effect on plant secondary metabolism and nutritional quality. Tannic acid (TA) is a natural polyphenolic compound showing excellent antioxidant properties and is involved in alleviating stress. The present study thoroughly investigated the effects and mechanism of exogenous TA on relieving imidacloprid (IMI) stress in tea plants. Our research found that TA(10 mg/L) activated the antioxidant defense system, enhanced the antioxidant ability, reduced the accumulation of ROS and membrane peroxidation, and notably promoted tea plant tolerance to imidacloprid stress. Additionally, TA boosted photosynthetic capacity, strengthened the accumulation of nutrients. regulated detoxification metabolism, and accelerated the digestion and metabolism of imidacloprid in tea plants. Furthermore, TA induced significant changes in 90 important metabolites in tea, targeting 17 metabolic pathways through extensively targeted metabolomics. Specifically, TA activated the flavonoid biosynthetic pathway, resulting in a 1.3- to 3.1-fold increase in the levels of 17 compounds and a 1.5- to 63.8-fold increase in the transcript level of related genes, such as ANR, LAR and CHS in this pathway. As a potential tea health activator, TA alleviates the oxidative damage caused by imidacloprid and improves the yield and quality of tea under pesticide stress.

Objective: The objective of this study was to assess whether socioeconomic status still remain a barrier to COVID-19 vaccination in eastern Oslo, Norway. Study design: A cross-section study. Methods: We conducted a web-based survey among the residents of six eastern parishes in Oslo, Norway. Text (SMS) messages were sent to 59978 potential participants. 5447 surveys were completed for a response rate of 9.1%. After removing participants who had not been offered the COVID-19 vaccine, we ended up with a valid sample of 4000. Results: We find a significant association between education and the likelihood of taking the COVID-19 vaccine in bivariate logistic regression. Further, we find a significant higher likelihood of taking the vaccine in the above-low-income group compared to the low-income group. However, when we add control variables to the regression, the significant results concerning both income and education are eliminated. In further analysis, we found that age worked as a moderator between socioeconomic status and vaccine uptake: In the youngest age group (18–29), we found a significant higher likelihood of taking the vaccine in the above-low-income group compared to the low-income group, and in the higher education group compared to the primary education group. Conclusion: Socioeconomic status remains a barrier to COVID-19 vaccination in the eastern parishes of Oslo, Norway. Indicating that Norwegians of lower socioeconomic status still disproportionately face barriers such as transportation, language, flexible work hours, and paid sick time. However, our analysis shows that this association is only found in the age group 18–29.

The study aimed to investigate the effect of isoliquiritigenin (ISL) on model of alcoholic liver fibrosis (ALF). C57BL/6 mice were used to establish animal model of ALF, HSC-T6 cells were used to establish alcohol-activated cell model, and tandem mass tag (TMT) assays were used to analyze the proteome. The results showed that ISL obviously alleviated hepatic fibrosis in model mice. ISL visually improved the area of liver pathological stasis and deposition of fibrillar collagen (Sirius Red staining, Masson staining), inhibited the mRNA expression levels of interleukin 6 (IL-6), tumor necrosis factor α (TNF-α) and interleukin 1β (IL-1β) in liver tissues. ISL down-regulated the mRNA expression levels of IL-6 and transforming growth factor-β1(TGF-β1) in activated hepatic stellate cells (HSCs). And ISL significantly reduced annexin A2 (ANXA2) in vitro detected by TMT proteomics technology. Interestingly, it was found for the first time that ISL could inhibit ANXA2 expression both in vivo and in vitro, block the sphingosine kinases (SPHKs)/sphingosine-1-phosphate (S1P)/interleukin 17 (IL-17) signaling pathway and regulate the expression of α-smooth muscle actin (α-SMA) by inhibiting the phosphorylation of signal transducer and activator of transcription 3 (STAT3) at the downstream signal to finally reverse HSCs activation and hepatic fibrosis. Thus, we demonstrated that ISL is a drug monomer with notable anti-hepatic fibrosis activity.

Photosynthetic characteristics of dominant plant species are widely used as indicators of ecosystem responses to global environmental changes such as precipitation change. How different plant species respond physiologically to seasonal precipitation change in coastal marshes is largely unclear. Spring is a critical stage for plant colonization and growth, and a field experiment was conducted to study the response in leaf carbon fixation of dominant plant species to 5 seasonal precipitation distribution treatments [+73%, +56%, control (CK), −56%, and −73%] in coastal marshes. Results showed a dual asymmetric response of the maximum photosynthetic rate (Pn max) of dominant species (Phragmites australis) to spring precipitation distribution (SPD), showing that Pn max was more sensitive to increased than decreased SPD, and the effect size of increased SPD on it was approximately 2.4 times greater than decreased SPD under the −56% to +56% precipitation ranges. On the contrary, when the precipitation distribution range was further extended to ±73%, Pn max more sensitive to decreased than increased SPD, and the effect size of decreased SPD on it was approximately 2 times greater than increased SPD. The structural equation modeling revealed that the effect of SPD on Pn max is primarily mediated by the direct effect of soil salinity and gs and the indirect effect of soil water content, which determined the magnitude direction of the effect of SPD on Pn max. Our findings demonstrate that soil salinity is the main controlling factor for the carbon sequestration of leaves under SPD in a coastal marsh. This leads to adaptive strategies for functional traits of dominant species, which, in turn, influence leaf-level CO2 fixation and the carbon sink function of the entire ecosystem.

Lycium barbarum L., goji berry, is a precious traditional Chinese medicine and it is homology of medicine and food. Its growth is heavily dependent on nitrogen. The use of chemical fertilizers has significantly promoted the yield of goji berry and the development of the L. barbarum L. industry. However, crop plants are inefficient in the acquisition and utilization of applied nitrogen, it often leads to excessive application of nitrogen fertilizers by producers, which cause negatively impact to the environment ultimately. The exploration of an interaction model which deals with crops, chemical fertilizers, and rhizosphere microbes to improve nitrogen use efficiency, is, therefore, an important research objective to achieve sustainable development of agriculture greatly. In our study, we explored the effects of nitrogen input on soil microbial community structure, soil nitrogen cycling, and the contents of nutrients in L. barbarum fruits. The structure and composition of the bacterial community in the rhizosphere soil of L. barbarum were significantly different under different nitrogen supply conditions, and high nitrogen addition inhibited the diversity and stability of bacterial communities. Low nitrogen input stimulated the relative abundance of ammonia-oxidizing bacteria (AOB), such as Nitrosospira, catalyzing the first step of the ammonia oxidation process. The results of the GLMM model showed that the level of nitrogen fertilizer (urea) input, the relative abundance of AOB, the relative abundance of Bradyrhizobium, and their combinations had significant effects on the soil nitrogen cycling and contents of nutrients in L. barbarum fruits. Therefore, we believe that moderately reducing the use of urea and other nitrogen fertilizers is more conducive to improving soil nitrogen use efficiency and Goji berry fruit quality by increasing the nitrogen cycling potential of soil microorganisms.

Abstract Background Although regional cerebral oxygen saturation (rScO2) monitoring has been widely used in clinical practice, the relationship between hemoglobin (dHB) content and rScO2 is incompletely understood. The aim of this study was to analyze the effect of hemoglobin content on rScO2 in pediatric patients undergoing general anesthesia for correction of scoliosis. Methods Ninety-two pediatric patients aged 3 to 14 years undergoing scoliosis correction surgery were enrolled. Continuous monitoring of bilateral regional cerebral oxygen saturation by near-infrared spectroscopy (NIRS, CASMED, USA) was performed after entering the operation room. rScO2 was recorded when the patients entered the operating room (T0, baseline), after anesthesia induced intubation (T1), and after radial artery puncture (T2). The lowest value of rScO2 during surgery was also recorded. The arterial blood pressure (ABP), heart rate (HR), pulse oxygen saturation (SpO2), end tidal carbon dioxide partial pressure (PetCO2) were continuously recorded. Patients were classified as low rScO2 or high rScO2 group according to whether the lowest intraoperative rScO2 was 15% lower than the baseline value. An analysis and comparison of differences in hemoglobin content in these two groups was carried out. Results The preoperative hemoglobin-postoperative hemoglobin of patients in the high rScO2 group was significantly lower than that in the low rScO2 group (t = − 7.86, p

A survey was designed to investigate the pesticide residues in agricultural produce and to estimate their potential intake risks to inhabitants. A total of 314 samples of nine types of fruits and vegetables were collected from the supermarkets and vegetable markets of Shandong Province (China) from October 2020 to February 2022. An accurate and reliable multi-residue method, based on GC-MS/MS detection, as well as the multiplug filtration cleanup method, based on SBA-15-C18, was prepared by a solution chemical reaction. Additionally, an in situ co-condensation method was established for the quantification of 139 pesticide residues. Residues that contained no pesticides were detected in 66.5% of the 314 samples. Moreover, of the samples, 30.6% were at or below the MRLs, and 2.9% were above the MRLs. Residues of procymidone were found to be the one that most often exceeded the MRLs (1.3% of the samples). Tebuconazole was found most frequently in 22.0% of the samples analyzed. Consumer exposure to the 139 pesticides did not exceed 100% ADI and ARfD. This led to a consideration that these pesticide residues in the nine commodities may not raise the health risk of the consumers in the long and short term. The highest value of chronic dietary intake was obtained from spirodiclofen, which resulted in a 24.1% of ADI. Furthermore, the highest exposure levels in the short term were obtained from the consumption of leeks with procymidone (58.3% ARfD).

PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson’s disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson’s Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson’s correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong’s test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Background: The COVID-19 outbreak has posed considerable challenges for people's health, work situations and life satisfaction. This article reports on a study of the relationship between self-reported health and life satisfaction before and during the COVID-19 pandemic in Norway, and examines the role of work in explaining the health–life satisfaction relationship. Method: The study was based on survey data collected from 3185 Norwegian employees in 2019 and 3002 employees in 2020. Propensity score matching techniques were used to assess the mediating effects of work situations and income loss on the health–life satisfaction relationship. Skew-t regression models were further applied to estimate changes in life satisfaction before and during the pandemic, as well as to explore different underlying mechanisms for the health–life satisfaction association. Results: The study found a negative association between ill health and life satisfaction. Compared to the healthy population, people with poor health were more likely to experience worsened work situations. A negative work situation is further associated with lower life satisfaction, and the pandemic aggravated life satisfaction for those who had worsened work situations. When exploring central contributing factors for life satisfaction, we found that health-related risks and work-life balance played predominant roles in predicting life satisfaction before the pandemic, while different types of household structure were among the most important predictors of life satisfaction during the pandemic. Conclusion: A reduction in life satisfaction is explained by ill health, but different underlying mechanisms facilitated people's life satisfaction before and during the pandemic. While work situation and health risks were important predictors for life satisfaction in 2019, worries about more unstable work situations and less access to family support accentuated worsened life satisfaction in 2020. The findings suggest the necessity of labour market interventions that address the security and maintenance of proper and predictable work situations, especially in these more uncertain times.

Abstract Background The clinical features and outcomes of subacute sclerosing panencephalitis (SSPE) in younger children are different from those of adults, leading easily to misdiagnosis during the early stage. So far, there are limited data related to SSPE in preschool children. Methods In order to summarize the clinical data and evolution of SSPE in preschool children and to expand the phenotypes of SSPE, the medical charts of preschool patients diagnosed with SSPE were retrospectively reviewed and analyzed; the clinical outcomes of the enrolled cases were evaluated and followed up. Results Overall, we included three cases in the study. Their onset age was 5 years and 2 months, 4 years and 3 months, and 4 years and 2 months, respectively. All patients presented drop attacks or jerks as the onset symptom, and one patient had concurrent gait disturbance. Atypical periodic complexes on electroencephalography (EEG) were recorded in all patients. The brain magnetic resonance imaging (MRI) findings of two cases showed demyelinating lesions predominantly on the white matter. The neurological conditions of all cases deteriorated rapidly. Two children died at 21 months and 6 months after onset, respectively. The other case progressively developed vegetative status and akinetic mutism within 4 months. Conclusions In younger children, the characteristic features of SSPE may be seizures and gait instability as onset manifestations, atypical periodic complexes on EEG, and rapid worsening of neurological conditions.

The development of liver fibrosis is closely related to the gut microbiota, and the “gut-liver axis” is the most important connection between the two. ethyl acetate extract of Cichorium pumilum Jacq (CGEA) is an herbal extract consisting mainly of sesquiterpenoids. The anti-inflammatory and hepatoprotective effects of CGEA have been reported, but the anti-fibrotic effects of CGEA via intestinal microbes and the “gut-liver axis” cycle have rarely been reported. In this study, we observed that CGEA not only directly attenuated inflammatory factor levels in inflamed mice, but also attenuated liver inflammation as well as liver fibrosis degeneration in rats with liver fibrosis caused by colitis. We observed in vitro that CGEA significantly promoted the growth of Bifidobacterium adolescentis. Similarly, fecal 16S rDNA sequencing of liver fibrosis rats showed that CGEA intervention significantly altered the composition of the intestinal microbiota of liver fibrosis rats. CGEA increased the abundance of intestinal microbiota, specifically, CGEA increased the ratio of Firmicutes to Bacteroidetes, CGEA could significantly increase the levels of Ruminococcus. In addition, CGEA intervention significantly protected intestinal mucosal tissues and improved intestinal barrier function in rats. Lactucin is the main sesquiterpenoid in CGEA, and HPLC results showed its content in CGEA was up to 6%. Lactucin has been reported to have significant anti-inflammatory activity, and in this study, we found that Lactucin decreased p38 kinases (p38), phosphorylation of the extracellular signal-regulated kinase (ERK) and protein kinase B (AKT) protein phosphorylation in lipopolysaccharide (LPS)-activated RAW264.7 cells, thereby reducing mRNA expression and protein expression of pro-inflammatory factors inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), and inhibiting the release of inflammatory factors interleukin (IL)-6 and nitric oxide (NO), exerting anti-inflammatory effects. In summary, the prevention of liver fibrosis caused by intestinal inflammation by CGEA may be achieved by regulating the intestinal microbiota and restoring the intestinal barrier thereby improving the “gut-liver axis” circulation, reducing liver inflammation, and ultimately alleviating liver fibrosis. Notably, the direct anti-inflammatory effect of CGEA may be due to its content of Lactucin, which can exert anti-inflammatory effects by inhibiting the phosphorylation of Mitogen-activated protein kinase (MAPK) and Akt signaling pathways.

Semantic segmentation has always been a fundamental and critical task to scene understanding. Current deep convolutional neural networks (DCNN) are able to successfully learn context from very large receptive fields due to convolutions with deep layers. However, current convolutions in DCNNs does not consider local object boundaries that are the borders among different semantic regions. Convolution with equal contribution on the pixels across the boundary may lead to inferior segmentation results. In this paper, a novel boundary-aware convolution is proposed. It is able to effectively fuse features by adaptively assigning contributions from pixels within receptive fields according to the boundary similarity map. A new semantic segmentation network based on classical FCN8S is then designed by employing multi-scale boundary-aware convolution. The whole network is implemented end-to-end and evaluated with heterogeneous RGB and depth input. Experiments conducted on multiple datasets show that our boundary-aware CNN can effectively improve the semantic segmentation performance.

The frequent and massive use of chlorfenapyr has led to pesticide residues in crops, threatening food safety and human health. However, there is limited research on the detection of tralopyril, which is the major metabolite of chlorfenapyr with high toxicity. This study aimed to develop a novel, sensitive, and highly efficient method for the determination of chlorfenapyr and tralopyril residues in 16 crops. The optimized purification procedure provided satisfactory recovery of 76.6–110%, with relative standard deviations of 1.3–11.1%. The quantification values of pesticides in crop matrixes were all 0.01 μg kg−1. The optimal method was adopted to determine the chlorfenapyr and tralopyril residues in field trials in 12 regions in China and monitor their residues in 16 agricultural products. The results of the dissipation and terminal residue experiments show that the final residue of chlorfenapyr was less than MRL (maximum residue limit) and no tralopyril was detected in the field samples. Moreover, the qualification proportion of these residues in market samples were up to 99.5%. The RQ (risk quotient) values of chlorfenapyr and chlorfenapyr with consideration of tralopyril were both apparently lower than an RQ of 100%, indicating an acceptable level. This research provides a thorough long-term dietary risk evaluation on chlorfenapyr and tralopyril and would provide reference for their scientific and safe utilization.

Salicylic acid (SA) is an important signal molecule, regulating oxidative stress response in plants. In this study, we evaluated the influences of SA (1 mg L−1, 10 mg L−1 and 50 mg L−1) on the accumulation of clothianidin (CLO), dinotefuran (DFN) and difenoconazole (DFZ) (5 mg L−1) and pesticide-induced (CLO-10 mg L−1, DFN-20 mg L−1, and DFZ-10 mg L−1) oxidative stress in cucumber plants. Exogenous SA at 10 mg L−1 significantly reduced the half-lives of three pesticides in nutrient solution and prevented the accumulation of pesticides in roots and leaves. And the role of SA in reducing residues was related to the major accumulation sites of pesticides. By calculating the root concentration factor (RCF) and translocation factor (TF), we found that SA at 10 mg L−1 reduced the ability of roots to absorb pesticides and enhanced the translocation ability from roots to leaves. Roots exposed to high concentrations of three pesticides could reduce biomass, low chlorophyll content, increase the accumulation of reactive oxygen species (ROS) and proline, promote lipid peroxidation, and alter the activities of a range of antioxidant enzymes, respectively. Exogenous SA at low concentrations (1 mg L−1 and 10 mg L−1) significantly mitigated these negative effects. Hence, application of exogenous SA at 10 mg L−1 could effectively alleviate the accumulation of pesticides and induce stress tolerance in cucumber planting systems.

It is widely recognised that income alone may not accurately reflect people's economic circumstances. In recent years, there has been increasing focus on multidimensional measures of economic scarcity. This study employs the newest survey data from Consumption Research Norway to explore the relationship between economic scarcity and self-reported health (SRH) in Norway. It defines economic scarcity by identifying disadvantaged social groups in terms of consumption, income and wealth/homeownership. Using propensity score matching, we compare health outcomes for economically disadvantaged and advantaged social groups – finding that consumption measures of scarcity are significantly associated with health, while there is no significant relationship between health and homeownership. When using matching estimators, health scores differ significantly between people with higher and lower incomes, but the associations are weakened when other socioeconomic variables are controlled for. This study applies empirical evidence from Norway to the existing health literature and contributes to a relatively new analytical approach by incorporating consumption into the prediction of health outcomes.

A convenient and fast multi-residue method for the efficient identification and quantification of 72 pesticides belonging to different chemical classes in red and white grape wines has been developed. The analysis was based on gas chromatography tandem quadrupole mass spectrometric determination (GC–MS/MS). The optimization strategy involved the selection of the amount of multi-walled carbon nanotubes (MWCNTs) and the number of cleanup procedure cycles for multi-plug filtration cleanup (m-PFC) to achieve ideal recoveries and reduce the sample matrix compounds in the final extracts. The optimized procedure obtained consistent recoveries between 70.2 and 108.8% (70.2 and 108.8% for white wine, and 72.3 and 108.4% for red wine), with relative standard deviations (RSDs) that were generally lower than 9.2% at the three spiking levels of 0.01, 0.05 and 0.1 mg/kg. The linearity was studied in the range between 0.002 and 0.1 mg/kg using pesticide standards prepared both in pure solvent and in the presence of the matrix, showing coefficients of determination (R2) higher than 0.9495 for all the pesticides. To improve accuracy, matrix-matched calibration curves were used for calculating the quantification results. Finally, the method was used successfully for detecting pesticide residues in commercial grape wines.

Abstract Background Compound Muniziqi granule (MNZQ) is a multi-component herbal preparation and a popular traditional Uighur medicine used in China for treating endocrine disorder-induced acne, chloasma, dysmenorrhea, menopausal syndrome, and melanoma. Harmine presented in MNZQ has been confirmed potential anticancer effect on the B16 cells among others. The purpose of this study is to explore the inhibitory effects of MNZQ against B16 cells and mechanism of autophagy and apoptosis induced by harmine in B16 cells. Methods The cell viability was calculated by CCK8 assay. The in vitro tyrosinase activity was determined by spectrophotometry. The harmine-induced autophagy was demonstrated by electron microscopy and MDC staining. Flow cytometry was used to measure cell death and cell cycle distribution. All proteins expression was assessed by western blot. Results MNZQ and some herb extracts contained in preparation displayed inhibitory effects on B16 cells but without inhibition on mushroom tyrosinase compared with kojic acid. The formation of autophagosome was markedly induced by harmine with the accretion of LC3-II and the degeneration of p62 in B16 cells, which indicated that harmine was an autophagy inducer. Cell death and sub-G2 population suggested that harmine could induce cell death. Particularly, 3-MA, an autophagy inhibitor, was discovered to prevent harmine-induced decrease of the cell viability and cell cycle arrest on G2 phase, indicating that autophagy was vital to the cell death. In addition, the results indicated that harmine could inhibit the phosphorylation of Akt and mTOR, which might mediate autophagy. Conclusion Harmine could induce autophagy and apoptosis by inhibiting Akt/mTOR pathway in B16 cells. Harmine might be a promising therapeutic agent for treatment of melanoma in MNZQ.

Abstract In 2000, the Chinese government initiated a campaign to develop Western China and reduce local and regional inequality. Privatisation has been a central component of the campaign. This article studies the relationship between privatisation and income inequality in Western China, based on survey data from 11 Western provinces collected between 2004 and 2005. This study first explores the link between privatisation and income inequality at the prefecture level, investigating whether a higher degree of privatisation can be linked to increased income inequality. Using multilevel random coefficient modelling, it then studies whether the impact of an individual’s socioeconomic position on their income varies across regions with different degrees of privatisation. The final part presents simulations of economic inequality based on the random slope model. The study shows that income inequality is higher in regions with higher degrees of privatisation, and larger privatisation correlates with lower individual income. Furthermore, the study confirms that individual income is strongly affected by education, occupation, employment sector and household registration status. Income differences based on these factors do not vary with the level of privatisation; however, a finding that contradicts market transition theory in the context of Western China.

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

Considering the requirement of the near-field calibration under strong underwater multipath condition, a high-precision geometric calibration method based on maximum likelihood estimation is proposed. It can be used as both auxiliary-calibration and self-calibration. According to the near-field geometry error model, the objective function of nonlinear optimization problem is constructed by using the unconditional maximum likelihood estimator. The influence of multipath on geometric calibration is studied. The strong reflections are considered as the coherent sources, and the compensation strategy for auxiliary-calibration is realized. The optimization method (differential evolution, DE) is used to solve the geometry errors and sources’ position. The method in this paper is compared with the eigenvector method. The simulation results show that the method in this paper is more accurate than the eigenvector method especially under high signal-to-noise ratio (SNR) and multipath environment. Experiment results further verify the effectiveness.

As the core task of scene understanding, semantic segmentation and depth completion play a vital role in lots of applications such as robot navigation, AR/VR and autonomous driving. They are responsible for parsing scenes from the angle of semantics and geometry, respectively. While great progress has been made in both tasks through deep learning technologies, few works have been done on building a joint model by deeply exploring the inner relationship of the above tasks. In this paper, semantic segmentation and depth completion are jointly considered under a multi-task learning framework. By sharing a common encoder part and introducing boundary features as inner constraints in the decoder part, the two tasks can properly share the required information from each other. An extra boundary detection sub-task is responsible for providing the boundary features and constructing cross-task joint loss functions for network training. The entire network is implemented end-to-end and evaluated with both RGB and sparse depth input. Experiments conducted on synthesized and real scene datasets show that our proposed multi-task CNN model can effectively improve the performance of every single task.

Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections.

Objective. Angiotensin II, one component of renin-angiotensin system (RAS), is formed from Ang I by the catalysing of angiotensin converting enzyme (ACE). Angiotensin II plays an important role in the development of insulin resistance. ACE2, a homologue of ACE, couterregulate the actions of angiotensin II by facilitating its breakdown to angiotensin-(1–7). RAS has been implicated in the pathogenesis of non-alcoholic steatohepatitis (NASH). Earlier demonstration that thiazolidinediones (TZDs) improve steatohepatitis promoted us to evaluate the change of hepatic ACE2 expression in rats with high fat diet (HFD)-induced NASH and the effects of TZDs on the hepatic ACE2 expression.Material and methods. Rats were divided into normal control group, high fat diet (HFD) group, and pioglitazone group. After 24 weeks of treatment with pioglitazone, a TZD, we evaluated changes in liver histology, insulin sensitivity, lipid metabolism, circulating RAS levels and hepatic ACE2 expression.Results. Compared with normal controls, the concentrations of serum lipid, aminotransaminase, glucose, insulin, ACE, angiotensin II, ACE2, angiotensin-(1–7) and the degree of hepatic ACE2 expression were significantly higher in rats with HFD-induced NASH. Pioglitazone significantly reduced the concentrations of serum lipid, aminotransaminase, glucose, insulin, ACE, angiotensin II while markedly raised the concentrations of serum ACE2, angiotensin-(1–7) and the degree of hepatic ACE2 expression.Conclusion. Hepatic ACE2 expression markedly increased in rats with HFD-induced NASH and was further upregulated by pioglitazone. Hepatic ACE2 may be a new target of pioglitazone treatment for NASH.

In this paper, the issue that the underwater unmanned vehicle (UUV) with a sonar array achieves the passive detection of vessel targets by detecting the tones radiated from the targets is considered. The multi-beam low-frequency analysis and recording method is widely applied in a manned sonar system. The sonar operator provides an auxiliary decision to extract the target tones from the multiple beams output. However, the complexity of the multi-dimensional information fusion makes it difficult to apply the multi-beam processing in the unmanned sonar system. Aiming at this problem, we introduce the self-adjusting characteristics of adaptive line enhancer to a time domain broadband beamformer and then propose a self-steering broadband beamformer. The proposed beamformer can adaptively steer the main beam to the direction-of-arrival (DOA) of the tonal target. There is no need to pre-form the multiple beams. The complexity of the UUV-based tone detection is reduced. Theoretical derivation and simulation experiments verify that the main beam of the proposed beamformer can track the DOA of tonal target which is rapidly changing. Meanwhile, the tonal interferences as well as the wide-band noise are well suppressed.