(1S,4S,5S,6R)-6-(4-Bromo­phen­yl)-5-nitro­bicyclo­[2.2.2]octan-2-one

The title compound, C14H14BrNO3, contains a bicyclic ring system with four chiral centers. The absolute structure was established by the Flack method

Disruption of the Cr2 Locus Results in a Reduction in B-1a Cells and in an Impaired B Cell Response to T-Dependent Antigen

AbstractCovalent attachment of activated products of the third component of complement to antigen enhances its immunogenicity, but the mechanism is not clear. This effect is mediated by specific receptors, mCR1 (CD35) and mCR2 (CD21), expressed primarily on B cells and follicular dendritic cells in mice. To dissect the role of mCR1 and mCR2 in the humoral response, we have disrupted the Cr2 locus to generate mice deficient in both receptors. The deficient mice (Cr2−/−) were found to have a reduction in the CD5+ population of peritoneal B-1 cells, although their serum IgM levels were within the range of normal mice. Moreover, Cr2−/− mice had a severe defect in their humoral response to T-dependent antigens that was characterized by a reduction in serum antibody titers and in the number and size of germinal centers within splenic follicles. Reconstitution of the deficient mice with bone marrow from MHC-matched Cr2+/+ donors corrected the defect, demonstrating that the defect was due to B cells themselves. These results indicate an obligatory role of B cell complement receptors in responses of the B cells to protein antigens

A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells

Reversing the dysfunctional T cell state that arises in cancer and chronic viral infections is the focus of therapeutic interventions; however, current therapies are effective in only some patients and some tumor types. To gain a deeper molecular understanding of the dysfunctional T cell state, we analyzed population and single-cell RNA profiles of CD8+tumor-infiltrating lymphocytes (TILs) and used genetic perturbations to identify a distinct gene module for T cell dysfunction that can be uncoupled from T cell activation. This distinct dysfunction module is downstream of intracellular metallothioneins that regulate zinc metabolism and can be identified at single-cell resolution. We further identify Gata-3, a zinc-finger transcription factor in the dysfunctional module, as a regulator of dysfunction, and we use CRISPR-Cas9 genome editing to show that it drives a dysfunctional phenotype in CD8+TILs. Our results open novel avenues for targeting dysfunctional T cell states while leaving activation programs intact

Early Second-Trimester Serum MiRNA Profiling Predicts Gestational Diabetes Mellitus

BACKGROUND: Gestational diabetes mellitus (GDM) is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA) have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM. METHODOLOGY/PRINCIPAL FINDINGS: We systematically used the TaqMan Low Density Array followed by individual quantitative reverse transcription polymerase chain reaction assays to screen miRNAs in serum collected at 16-19 gestational weeks. The expression levels of three miRNAs (miR-132, miR-29a and miR-222) were significantly decreased in GDM women with respect to the controls in similar gestational weeks in our discovery evaluation and internal validation, and two miRNAs (miR-29a and miR-222) were also consistently validated in two-centric external validation sample sets. In addition, the knockdown of miR-29a could increase Insulin-induced gene 1 (Insig1) expression level and subsequently the level of Phosphoenolpyruvate Carboxy Kinase2 (PCK2) in HepG2 cell lines. CONCLUSIONS/SIGNIFICANCE: Serum miRNAs are differentially expressed between GDM women and controls and could be candidate biomarkers for predicting GDM. The utility of miR-29a, miR-222 and miR-132 as serum-based non-invasive biomarkers warrants further evaluation and optimization

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.Applied Science, Faculty ofNon UBCEngineering, School of (Okanagan)ReviewedFacultyResearche

Chemical and Light Extinction Characteristics of Atmospheric Aerosols in Suburban Nanjing, China

This work reports the chemical and light extinction characteristics of the atmospheric particles collected from January to November 2014 in suburban Nanjing. Size-segregated measurement results showed that more than 80% of the major aerosol components were concentrated in PM2.5. The concentration of PM2.5 was highest in winter and lowest in autumn. Specifically, K+ concentration peaked in late spring indicating heavy influences from straw burning, while sulfate concentration was highest in summer and its daytime concentration was also higher than its nighttime concentration, both reflecting a significant role of photochemical production. Nevertheless, except for sulfate, all other components had higher concentrations during nighttime, signifying the role of unfavorable meteorological conditions in exacerbating the air pollution. The IMPROVE formula was employed, which can reconstruct the PM2.5 mass and light extinction well. The light extinction was mainly contributed by (NH4)2SO4 and NH4NO3 (together 58.3%). Mass concentrations of all PM2.5 components increased significantly with the increase of pollution levels, but nitrate increased most rapidly; correspondingly, the contribution of nitrate to light extinction also increased quickly when pollution became heavy. Such results were different from those observed in Beijing-Tianjin-Hebei where sulfate increased most quickly. Our results thus highlight that reduction of vehicular NO2 is likely a priority for air quality improvement in Nanjing. Back trajectory analysis showed the dominance of the local air mass and the one from Huanghai, yet the air mass originated from Bohai, and passed though Shandong and north of Jiangsu province could deliver highly-polluted air to Nanjing, as well

Long-Term Prognostic Analysis after Endoscopic Endonasal Surgery for Olfactory Neuroblastoma: A Retrospective Study of 13 Cases.

To summarize the characteristics and long-term outcomes of olfactory neuroblastoma through the analysis of 13 cases in single institution, with the assessment of treatment modality, prognostic factors.A retrospective study of thirteen cases diagnosed as olfactory neuroblastoma and underwent combined treatments during the period 2000-2010. Statistical analysis was performed to search for prognostic factors and compared different treatment modalities.13 patients were enrolled in this study, including 8 male and 5 female, ranging from 15 to 69 (median 43) years old. One patient at stage A was only treated with endoscopic endonasal surgery (EES). Seven patients were treated with preoperative radiotherapy and EES, two with EES and postoperative radiotherapy, and the other three with combined radiotherapy and chemotherapy. The range of follow-up time varied from 23 to 116 months (median 65 months). The 5-year overall survival rate was 46.2% (6/13). To date, these thirteen patients have not suffered local recurrences while two patients had lymph node recurrences and one had distant metastasis in the bone marrow. In 13 patients, 61.5% were diagnosed as late T stage (T3/4), 69.2% late Kadish stage (C/D) and 53.8% were high Hyams grade (I/ II), which indicated poor prognosis. Related prognostic factors were the TNM stage (T stage P = 0.028, N stage P = 0.000, M stage P = 0.007), Kadish stage (P = 0.025) and treatment modality (P = 0.015).Late stage of TNM and Kadish staging system indicated a poor prognosis. Combined treatment modality, including endoscopic endonasal surgery, achieved a better outcome than non-surgical approach