High Mobility Group Box 1 Contributes to the Acute Rejection of Liver Allografts by Activating Dendritic Cells

Acute rejection induced by the recognition of donor alloantigens by recipient T cells leads to graft failure in liver transplant recipients. The role of high mobility group box 1 (HMGB1), an inflammatory mediator, in the acute allograft rejection of liver transplants is unknown. Here, rat orthotopic liver transplantation was successfully established to analyze the expression pattern of HMGB1 in liver allografts and its potential role in promoting the maturation of dendritic cells (DCs) to promote T cell proliferation and differentiation. Five and 10 days after transplantation, allografts showed a marked upregulation of HMGB1 expression accompanied by elevated levels of serum transaminase and CD3+ and CD86+ inflammatory cell infiltration. Furthermore, in vitro experiments showed HMGB1 increased the expressions of co-stimulatory molecules (CD80, CD83, and MHC class II) on bone marrow-derived DCs. HMGB1-pulsed DCs induced naive CD4+ T cells to differentiate to Th1 and Th17 subsets secreting IFN-γ and IL-17, respectively. Further in vivo experiments confirmed the administration of glycyrrhizic acid, a natural HMGB1 inhibitor, during donor liver preservation had therapeutic effects by reducing inflammation and hepatocyte damage reflected by a decline in serum transaminase and prolonged allograft survival time. These results suggest the involvement of HMBG1 in acute liver allograft rejection and that it might be a candidate therapeutic target to avoid acute rejection after liver transplantation

Land use classification in mine-agriculture compound area based on multi-feature random forest: a case study of Peixian

IntroductionLand use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized.MethodsA quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted.ResultsThe quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm.ConclusionThe findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area

A Wideband Receiver with Adaptive Strong Interference Suppression

In this paper, a wideband receiver with high dynamic range is proposed. At the front end of the proposed receiver, a sensing waveform is used to sense the input signal. And by adjusting the sensing waveform so as to project the interference to zero, the receiver can eliminate the strong interference signal adaptively before sampling. Both the theoretic analysis and simulation show that this method can suppress the interference signal effectively and improve the sampling accuracy of the weak desired signal when the instantaneous dynamic range of the input signal is larger than the dynamic range of the ADC's quantizer

Potential Influences of Volcanic Eruptions on Future Global Land Monsoon Precipitation Changes

The global monsoon system is of exceptional socioeconomic importance owing to its impacts on two-thirds of the globe’s population. Major volcanic eruptions strongly influence global land monsoon (GLM) precipitation change. By using 60 plausible eruption scenarios sampled from reconstructed volcanic proxies over the past 2,500 years, 21st century volcanic influences on GLM precipitation projections are examined with an Earth system model under a moderate emission scenario. The decadal-scale ensemble spread with realistic eruptions (VOLC) increases by 17.5% and 20.1% compared to no-volcanic (NO-VOLC) and constant background-volcanic (VOLC-CONST) scenarios, respectively. Compared with NO-VOLC, the centennial mean VOLC GLM precipitation shows a 10% overall reduction and regionally, Asia is the most impacted. Changes in atmospheric circulation in the aftermath of large volcanic eruptions match the global warming response patterns well with opposite sign, with the North American monsoon precipitation enhanced following large volcanic eruptions, which is in sharp contrast to the robust decrease in Asian monsoon rainfall. Volcanic activity could delay the time of emergence of anthropogenic influence by five years on average over about 60% of the GLM area. Our results demonstrate the importance of statistical representation of potential volcanism for the projections of future monsoon variability. Quantifying volcanic impacts on regional climate projections and their socioeconomic influences on infrastructure planning, food security, and disaster management should be a priority of future work.publishedVersio

Reconstructed springtime (March–June) precipitation tracked by tree rings dating back to 1760 CE in the Qinling-Bashan mountainous area

In recent decades, considerable advances have been made in dendroclimatic reconstruction in the eastern monsoon region of China. However, understanding of long-term hydroclimatic changes has not been comprehensive due to the complexity of the regional geography in China's north-south transitional zone. Growth-climate response analysis indicated that springtime precipitation is the main factor limiting the radial growth of pine trees in the Qinling-Bashan mountainous area. Based on the three tree ring chronologies distributed in the southeast of Shaanxi Province, we developed a March–June precipitation reconstruction spanning 1760–2020 CE for the Qinling-Bashan mountainous area. Precipitation reconstruction accounts for 40.6% of the total precipitation variance during the instrumental period 1955–2016. Spatial correlation analysis indicated that the precipitation reconstruction recorded similar common precipitation signals for the eastern Qinling Mountains and the Yangtze-Huai River Basin. The results of the superposed epoch analysis (SEA) revealed that low precipitation was one of the main causes of severe drought and locust plague events. The preliminary synoptic climatology analysis showed that our reconstructed precipitation is closely linked to the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) variability.Fil: Wang, Shijie. Yunnan University; ChinaFil: Man, Wenmin. Chinese Academy of Sciences; República de ChinaFil: Chen, Feng. Yunnan University; China. China Meteorological Administration; ChinaFil: Chen, Youping. Yunnan University; ChinaFil: Yu, Shulong. China Meteorological Administration; ChinaFil: Cao, Honghua. Yunnan University; ChinaFil: Hu, Mao. Yunnan University; ChinaFil: Hou, Tiyuan. Yunnan University; ChinaFil: Hadad, Martín Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina. Universidad Mayor; Chil

Synthesis, Structure–Activity Relationship Studies, and ADMET Properties of 3‐Aminocyclohex‐2‐en‐1‐ones as Chemokine Receptor 2 (CXCR2) Antagonists

Herein we describe the synthesis and structure–activity relationships of 3‐aminocyclohex‐2‐en‐1‐one derivatives as novel chemokine receptor 2 (CXCR2) antagonists. Thirteen out of 44 derivatives were found to inhibit CXCR2 downstream signaling in a Tango assay specific for CXCR2, with IC50 values less than 10 μm. In silico ADMET prediction suggests that all active compounds possess drug‐like properties. None of these compounds show significant cytotoxicity, suggesting their potential application in inflammatory mediated diseases. A structure–activity relationship (SAR) map has been generated to gain better understanding of their binding mechanism to guide further optimization of these new CXCR2 antagonists.Combating inflammatory disease: New derivatives of 3‐aminocyclohex‐2‐en‐1‐ones were synthesized and evaluated for their CXCR2 inhibition. Structure– activity relationship studies of these compounds were performed. Several compounds display CXCR2 IC50 values less than 10 μm, and also show selectivity against CXCR2 and low cytotoxicity. In silico ADMET prediction suggests most active compounds possess good drug‐like properties.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143675/1/cmdc201800027.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143675/2/cmdc201800027_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143675/3/cmdc201800027-sup-0001-misc_information.pd