Construction of a methodology framework to characterize dynamic full-sector land-use carbon emissions embodied in trade.

The globally massive land-use changes associated with unprecedented urbanization rate are leading to prodigious quantities of carbon emissions. Nonetheless, the dynamics of land-use carbon emissions, particularly driven by supply-chain activities across all relevant industrial sectors, remain largely unexplored, especially in non-agricultural sectors. Here, we constructed a novel methodological framework to quantify full-sector land-use carbon emissions in Shenzhen, China, an international megacity grappling with acute land resource scarcity. Then, we integrated this framework with multiregional input-output analysis to uncover the multi-scale embodied land-use emissions propelled by Shenzhen's supply-chain activities. Our results indicate a marked increase in Shenzhen's embodied carbon emissions, approximately two orders of magnitude greater than its physical emissions, tripling during 2005-2018. Remarkably, non-agriculture sectors contributed 81.3-90.5 % of physical and 46.6-58.4 % of embodied land-use emissions. The land-use changes occurred outside Shenzhen accounted for 6.5-13.3 % of Shenzhen's total embodied land-use emissions. The sectoral analysis revealed a transition from traditional manufacturing (e.g., metallurgy, chemical products, textiles, wood products) in 2010-2015 to high-tech sectors (e.g., electronic equipment and other manufacture) in 2015-2018. This shift was primarily attributed to concurrent industry transfer actions, leading to aggressive changes in land-use emission intensity discrepancies within and outside Shenzhen. This study provides a scientific basis for designing effective strategies to mitigate land-use carbon emissions associated with supply-chain activities.

An urban metabolism framework purpose to assess the dynamics of linear alkylbenzenes (LABs) discharge flows and driving forces at the city level in Guangdong province, China.

Linear alkylbenzenes (LABs) are a class of molecular markers derived from anthropogenic activities. A comprehensive understanding of the mechanism that determines their entry into anthroposphere, in terms of magnitude and pathway, is the prerequisite to establish effective mitigation measures. This study develops a methodology framework to analyze the source-sink interactions and driving factors of the direct and indirect LAB discharges from production and living activities in Guangdong Province, China from 2004 to 2017. Results indicated that the total LAB discharges of Guangdong into the environment were averaged at 2.9 kt yr-1, of which 61.9% originated from the Pearl River Delta (PRD) urban agglomeration. An average proportion of 76.0% was discharged into water bodies with the remaining released into land bodied. From 2014 to 2017, the LAB discharges increased by seven times, resulting from the steady increase of urban residential sources, while contribution from industrial sources continuously declined during the studied period. Meanwhile, the discharging hotspots expanded from Guangzhou city to other super-cities around it, including Shenzhen and Dongguan. The other cities exhibited a decreasing trend in discharges as a function of distance from these hotspot cities. The multisectoral sources of LABs differed considerably among cities, and the source contribution of each city changed significantly with progressive urbanization. The factor decomposition analysis indicated that LAB discharges in PRD cities primarily contributed by the pollutant concentration and reflected the treatment structure, while peripheral cities of the PRD mainly contributed by the per capita consumption and pollutant discharge per unit of GDP. Overall, our results provide a scientific database and supports for the regional co-remediation of anthropogenic pollution.

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model's utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663-0.968), and the ROC of the DL model was 0.844 (95% CI:0.717-0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773-0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.

[Effect evaluation of graphical menu labeling in simulated ordering].

OBJECTIVE: To explore the feasibility of applying graphical menu labeling. METHODS: To design a radar chart menu label. From October 2020 to April 2021, convenience sampling was adopted to recruit 1407 research subjects(986 females and 421 males) through the online platform nationwide to complete the questionnaire and simulate ordering. The survey included basic information of the research subjects, their level of nutritional knowledge, and satisfaction with the graphic menu labels. The two simulated orderings were conducted using the regular menu and the menu with graphic nutritional information, respectively. Compare the nutrition scores of the two simulated orders, the selection ratio of each dish in each major category, the energy, fat, cholesterol and sodium content, and the amount of added oil and salt of the selected dishes. RESULTS: Compared with using the normal menu, the nutritional score of the simulated meal ordering increased from 15.57±2.65 to 16.73±3.24(P<0.05) using a menu with graphic nutrition labels, in which people with an income of less than 4000 yuan and a graduate degree or above increased the most. The proportion of dishes with higher nutritional value has increased among pork, fish, vegetables, and soy products. The energy, fat, cholesterol, sodium content, added oil and added salt of the selected dishes are decreased from 8455(7738, 9033) kcal, 658.6(598.1, 709.3) g, 1418(1238, 1665) mg, 17 430(15 695, 19 129)mg, 455(405, 502)g, 41.5(36.5, 47.0)g to 7415(6693, 8191)kcal, 562.54(504.0, 631.2)g, 1274(1076, 1549)mg, 17 185(14 574, 19 576.8)mg, 375(334, 437) g, 38.5(32.4, 43.6) g respectively(P<0.05). The satisfaction score of the graphic nutrition label is relatively high. CONCLUSION: Graphical menu labeling helps consumers to make healthier choices for catering food.

[Foodborne disease outbreaks in China from 2011 to 2020].

OBJECTIVE: To analyze the epidemiological characteristics of foodborne disease outbreaks in China and to provide references for formulating prevention strategies of foodborne diseases in China. METHODS: Collect the monitoring data reported in China's foodborne disease outbreak monitoring system from 2011 to 2020, and calculate relevant indicators. RESULTS: During 2011 and 2020 in 30 provinces(autonomous region, municipality), a total of 35 806 foodborne disease outbreaks were reported, which caused 266 968 illnesses. The western region had the largest number of reported incidents and the largest number of patients, Yunnan Province had the largest percentage of outbreaks(17.7%) and the largest percentage of cases(15.81%). Poisonous mushrooms and microorganisms are the main pathogenic factors. Poisonous mushrooms caused the largest percentage of foodborne disease outbreaks, accounting for 29.09% of the total. Microorganisms caused the largest percentage of cases, accounting for 35.69% of the total. Salmonella and Vibrio parahaemolyticus are the main pathogens. Catering service units were the main places of foodborne disease outbreaks, responsible for the largest percentage of outbreaks(49.31%) and cases(70.59%). CONCLUSION: From 2011 to 2020, the number of reported incidents and the number of patients in foodborne disease outbreaks in China showed an upward trend.

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy.

Objective: We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy. Methods: Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis. Results: The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram. Conclusion: ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.

Noninvasive Evaluation of Lupus Nephritis Activity Using a Radiomics Machine Learning Model Based on Ultrasound.

Introduction: To explore whether ultrasonic radiomics extracted by machine learning method can noninvasively evaluate lupus nephritis (LN) activity. Materials and Methods: This retrospective study included 149 patients with LN diagnosed by renal biopsy. They were divided into a training cohort (n=104) and a test cohort (n=45). Ultrasonic radiomics features were extracted from the ultrasound images, and the radiomics features were constructed. Furthermore, five machine learning algorithms were compared to evaluate LN activity. The performance of the binary classification model was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The average AUC of the five machine learning models was 79.4, of which the MLP model was the best. The AUC of the training group was 89.1, with an accuracy of 81.7%, a sensitivity of 83%, a specificity of 80.7%, a negative predictive value of 85.2%, and a positive predictive value of 78%. The AUC of the test group was 82.2, the accuracy was 73.3%, the sensitivity was 78.9%, the specificity was 69.2%, the negative predictive value was 81.8%, and the positive predictive value was 65.2%. Conclusion: Machine learning classifier based on ultrasonic radiomics has high accuracy for LN activity. The model can be used to noninvasively detect the activity of LN and can be an effective tool to assist the clinical decision-making process.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study.

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

Liquid-Metal-Mediated Electrocatalyst Support Engineering toward Enhanced Water Oxidation Reaction.

Functional and robust catalyst supports are vital in the catalysis field, and the development of universal and efficient catalyst support is essential but challenging. Traditional catalyst fabrication methods include the carbonization of ordered templates and high-temperature dehydration. All these methods involve complicated meso-structural disordering and allow little control over morphology. To this end, a eutectic GaInSn alloy (EGaInSn) was proposed and employed as an intermediate to fabricate low-dimensional ordered catalyst support materials. Owing to the lower Gibbs free energy of Ga2O3 compared to certain types of metals (e.g., Al, Mn, Ce, etc.), we found that a skinny layer of metal oxides could be formed and exfoliated into a two-dimensional nanosheet at the interface of liquid metal (LM) and water. As such, EGaInSn was herein employed as a reaction matrix to synthesize a range of two-dimensional catalyst supports with large specific surface areas and structural stability. As a proof-of-concept, Al2O3 and MnO were fabricated with the assistance of LM and were used as catalyst supports for loading Ru, demonstrating enhanced structural stability and overall electrocatalytic performance in the oxygen evolution reaction. This work opens an avenue for the development of functional support materials mediated by LM, which would play a substantial role in electrocatalytic reactions and beyond.

The carbon emissions related to the land-use changes from 2000 to 2015 in Shenzhen, China: Implication for exploring low-carbon development in megacities.

Megacities exploit enormous amounts of lands from outside of the city boundary. However, there is a large knowledge gap in the impact of socioeconomic activities associated land-use changes on carbon emissions of megacities during the urbanization. In the current work, we combined the material-flow analysis, environmental extended input-output model, and land matrix data to construct a hybrid network framework. Such a framework was used to estimate the carbon emissions driving from trade between sectors and associated land use changes during 2000-2015 in Shenzhen, China. Results indicated that the total carbon emissions of Shenzhen had a growth rate of 262.7% from 2000 to 2010 and a declining rate of 17.6% from 2010 to 2015. This pattern is associated with large declining rates in the overall energy and carbon intensities by 53.8% and 63.2% during the period of 2000-2015. Meanwhile, embodied carbon emissions of Shenzhen kept rising by approximately twofold, accompanied by the increasing trends in the land-use related carbon emissions both inside and outside of city boundary. The land uses per unit GDP showed a dramatical decline by 85.7% and with a large contribution of the transportation and industrial land, and this caused a gradual increase in overall land-use related emissions with average growth rate of 7.1%. In addition, the land-use change related carbon emissions of the transportation and industrial land had a cumulative growth of 85%. As for the embodied land-use related carbon emissions, the dominated contributor was the Agriculture sector which drove an average of 0.13 MtC yr-1 emissions via importing agricultural products from outside of Shenzhen. This study provides a scientific foundation for corporately mitigate carbon emissions between megacities and their surrounding regions.

Exploring Potential Ways to Reduce the Carbon Emission Gap in an Urban Metabolic System: A Network Perspective.

To meet the global need for carbon neutrality, we must first understand the role of urban carbon metabolism. In this study, we developed a land-energy-carbon framework to model the spatial and temporal variation of carbon flows in Beijing from 1990 to 2018. Based on the changes in carbon sequestration and energy consumption, we used ecological network analysis to identify the critical paths for achieving carbon neutrality during land-use changes, thereby revealing possible decarbonization pathways to achieve carbon neutrality. By using GIS software, changes in the center of gravity for carbon flows were visualized in each period, and future urban construction scenarios were explored based on land-use policy. We found that the direct carbon emission peaked in 2010, mostly due to a growing area of transportation and industrial land. Total integrated flows through the network decreased at an average annual rate of 3.8%, and the change from cultivated land to the socioeconomic sectors and the paths between each socioeconomic component accounted for 29.5 and 31.7% of the integrated flows during the study period. The socioeconomic sectors as key nodes in the network should focus both on their scale expansion and on using cleaner energy to reduce carbon emissions. The center of gravity gradually moved southward, indicating that the new emission centers should seek a greener mixture of land use. Reducing carbon emission will strongly relied on transforming Beijing's energy consumption structure and increasing green areas to improve carbon sinks. Our results provide insights into carbon flow paths that must be modified by implementing land-use policies to reduce carbon emission and produce a more sustainable urban metabolism.

Primary intraspinal benign tumors treated surgically: an analysis from China.

PURPOSE: The purpose of this study was to review the clinical features in a cohort of Chinese patients with primary intraspinal benign tumors. METHODS: This retrospective study included a consecutive series of patients with intraspinal benign lesions who received surgery between January 2014 and October 2018 at our hospital. We collected each patient's clinical data, including age, gender, presenting symptoms, the spinal level (cervical, thoracic, lumbar, or sacral), and location (intramedullary or extramedullary) of the tumor. RESULTS: A total of 66 patients were included in this study, of whom 24 were men and 42 (63.6%) were women. The mean age was 52.5 years (range, 21-76 years). The most common symptom was sensory deficits. The most common tumor type, found in 56.1% patients, was schwannoma, followed by meningioma in 33.3% patients. The commonly performed surgery included decompression of spinal canal and excision of spinal lesions. CONCLUSION: Primary intraspinal benign tumors occur in elderly and female population and at the thoracic region. Schwannoma and meningioma are the two with higher incidence. The surgical outcome in terms of tumor excision and functional recovery is good.

Construction of a regional inventory to characterize polycyclic aromatic hydrocarbon emissions from coal-fired power plants in Anhui, China from 2010 to 2030.

The infrastructures of coal-fired power plants in China have changed significantly since 2010, but the magnitude and characteristics of polycyclic aromatic hydrocarbon (PAH) emissions remain to be updated. In the present study, a unit-based PAH emission inventory for coal-fired power plants between 2010 and 2017 was constructed for Anhui Province, China. Atmospheric PAH emissions from pulverized coal (PC) and circulating fluidized bed (CFB) units in 2017 were 8600 kg and 7800 kg, respectively. The emission rates and intensities for CFB units (7.2 kg ton-1 and 2.1 kg MW-1) were significantly higher than those for PC units (1.1 kg ton-1 and 0.19 kg MW-1), primarily because CFB boilers were operated at lower combustion temperatures and poor combustion conditions compared to PC boilers. The distribution patterns of PAH emissions across different age groups largely reflected the time periods for constructing coal-fired units in Anhui and for the transition of small units to large ones. The accomplishment of ultralow emission technologies and phase-out of outdated coal-fired units were responsible for the decreasing trend of PAH emissions between 2012 and 2017. The warmer summer in 2013 and 2017 and colder winter in 2011 compared to other years probably caused increased use of air conditioners, resulting in increased electricity consumption and PAH emissions. Future PAH emissions would decrease by 45-57% during 2017-2030, benefitting from power plant fleet optimization, i.e., phasing out outdated coal-fired units and replacing them with large ones. With the best available optimized power plant fleets and end-of-pipe control measures accomplished in Anhui's CFPPs, PAH emissions in 2030 would potentially be reduced by 56-65%.

Sediment records of polybrominated diphenyl ethers (PBDEs) in Yangtze River Delta of Yangtze River in China.

The spatial and temporal distributions of polybrominated diphenyl ethers (PBDEs) were investigated in five sediment cores from the Yangtze River Delta of Yangtze River in China. The surficial concentrations of nine tri- through hepta-BDE congeners (Σ9BDEs) and BDE209 were highest at urban sites S3 and S2, followed by rural site S1 and estuary sites S5 and S4, respectively, based on dry sediment weight. Both BDE209 and ∑9BDE concentrations exponentially increased between 1990 and 2008. Commercial deca-BDE, penta-BDE, and octa-BDE products were likely PBDE sources in the study area. The relative abundances of BDE209 were higher in sediment cores from estuary than those from urban and rural locations, ascribing to the atmospheric transport from the adjacent densely populated northern and eastern coastal regions. This conclusion was further confirmed by the higher ratios of BDE47/BDE99 and BDE100/BDE99 in cores from the estuary than those from other locations.

Global Riverine Plastic Outflows.

Global marine plastic pollution, which is derived mainly from the input of vast amounts of land-based plastic waste, has drawn increasing public attention. Riverine plastic outflows estimated using models based on the concept of mismanaged plastic waste (MPW) are substantially greater than reported field measurements. Herein, we formulate a robust model using the Human Development Index (HDI) as the main predictor, and the modeled riverine plastic outflows are calibrated and validated by available field data. A strong correlation is achieved between model estimates and field measurements, with a regression coefficient of r2 = 0.9. The model estimates that the global plastic outflows from 1518 main rivers were in the range of 57,000-265,000 (median: 134,000) MT year-1 in 2018, which were approximately one-tenth of the estimates by MPW-based models. With increased plastic production and human development, the global riverine plastic outflow is projected to peak in 2028 in a modeled trajectory of 2010-2050. The HDI is a better indicator than MPW to estimate global riverine plastic outflows, and plastic pollution can be effectively assessed and contained during human development processes. The much lower global riverine plastic outflows should substantially ease the public's concern about marine plastic pollution and financial pressure for remediation.

Hydroxylated polycyclic aromatic hydrocarbons in surface soil in an emerging urban conurbation in South China.

To investigate the effects of human activity on contaminants in regional soil, hydroxylated polycyclic aromatic hydrocarbons (OH-PAHs) were measured in 187 surface soil samples of different land-use types collected from the Pearl River Delta (PRD), South China. The concentrations of Σ9OH-PAH (sum of nine target analytes) ranged from 0.36 to 252ng/g (median: 5.98ng/g), with phenanthrene derivatives as the dominant components, accounting for ~70%. Among different land-use types, residency soil contained the highest levels of Σ9OH-PAH (median: 11.3ng/g), followed by landfill soil (9.28ng/g), industry soil (7.51ng/g), agriculture soil (6.04ng/g), forestry soil (4.28ng/g) and drinking water source soil (4.20ng/g). A higher value was also observed in soil from the central PRD (6.94ng/g) than the surrounding areas (5.94ng/g), which indicated a significant impact of human activity on OH-PAH contamination in soil. Correlation and principal component analysis indicated that OH-PAHs in PRD soil are likely derived from the degradation of their parent PAHs in the atmosphere and/or soil and not directly from the same source as the parent PAHs. The ratios of OH-PAHs to their parent PAHs also varied among different land-use types, which may be partly attributed to the different populations of microorganisms in different soil types or the different chemical properties of PAHs and their metabolites.

The effects of prosperity indices and land use indicators of an urban conurbation on the occurrence of hexabromocyclododecanes and tetrabromobisphenol A in surface soil in South China.

Hexabromocyclododecane (HBCD) and tetrabromobisphenol A (TBBPA) are legacy brominated flame retardants which are still produced and used in China. In this study, 187 surface soils from the Pearl River Delta (PRD) urban conurbation in China were collected, and the effects of urban conurbation development on the concentrations, distributions and human exposure risk of HBCDs and TBBPA were investigated. The concentration ranges of Σ3HBCD (sum of α-, ß-, and γ-HBCD) and TBBPA in soil were below the limit of quantification (

An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation.

The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

TWEAK/Fn14 Signals Mediate Burn Wound Repair.

TWEAK acts by engaging with Fn14 to regulate inflammatory responses, fibrosis, and tissue remodeling, which are central in the repair processes of wounds. This study aims to explore the potential role of the TWEAK/Fn14 pathway in the healing of cutaneous burn wounds. Third-degree burns were introduced in wild-type and Fn14-deficient BALB/c mice, followed by evaluation of wound areas and histological changes. The downstream cytokines including growth factors were also examined in lesional skin. Moreover, human dermal microvascular endothelial cells and dermal fibroblasts were analyzed in vitro upon TWEAK stimulation. The healing of burn wounds was delayed in Fn14-deficient mice and was accompanied by the suppression of inflammatory responses, growth factor production, and extracellular matrix synthesis. Moreover, TWEAK/Fn14 activation enhanced the migration and cytokine production of both dermal microvascular endothelial cells and dermal fibroblasts. TWEAK also facilitates the expression of α-SMA and palladin in dermal fibroblasts. Furthermore, transfection of Fn14 small interfering RNA abrogated such promotion effect of TWEAK on these cells. In conclusion, TWEAK/Fn14 signals mediate the healing of burn wounds, possibly involving TWEAK regulation of the function of resident cells.

An Optimized Two-Step Magnetic Correction Strategy by Means of a Lagrange Multiplier Estimator with an Ellipsoid Constraint.

The geomagnetic field is as fundamental a constituent of passive navigation as Earth's gravity. In cases where no other external attitude reference is available, for the direct heading angle estimation by a typical magnetic compass, a two-step optimized correction algorithm is proposed to correct the model coefficients caused by hard and soft iron nearby. Specifically, in Step 1, a Levenberg-Marquardt (L-M) fitting estimator with an ellipsoid constraint is applied to solve the hard magnetic coefficients. In Step 2, a Lagrange multiplier estimator is used to deal with the soft magnetic iron circumstance. The essential attribute of "the two-step" lies in its eliminating the coupling effects of hard and soft magnetic fields, and their mutual interferences on the pure geomagnetic field. Under the conditions of non-deterministic magnetic interference sources with noise, the numerical simulation by referring to International Geomagnetic Reference Field (IGRF), and the laboratory tests based upon the turntable experiments with Honeywell HMR3000 compass (Honeywell, Morristown, NJ, USA) conducted, the experimental results indicate that, in the presence of the variation of multi-magnetic interferences, the RMSE (Root Mean Square Error) value of the estimated total magnetic flux density by the proposed two-step estimator falls to 0.125 µT from its initial 2.503 µT, and the mean values of the heading angle error estimates are less than 1°. The proposed solution therefore, exhibits ideal convergent properties, fairly meeting the accuracy requirements of non-tactical level navigation applications.