Determination of the Pseudoscalar Decay Constant f_{D_{s}^{+}} via D_{s}^{+}→μ^{+}ν_{μ}

Using a 3.19  fb^{-1} data sample collected at an e^{+}e^{-} center-of-mass energy of E_{cm}=4.178  GeV with the BESIII detector, we measure the branching fraction of the leptonic decay D_{s}^{+}→μ^{+}ν_{μ} to be B_{D_{s}^{+}→μ^{+}ν_{μ}}=(5.49±0.16_{stat}±0.15_{syst})×10^{-3}. Combining our branching fraction with the masses of the D_{s}^{+} and μ^{+} and the lifetime of the D_{s}^{+}, we determine f_{D_{s}^{+}}|V_{cs}|=246.2±3.6_{stat}±3.5_{syst}  MeV. Using the c→s quark mixing matrix element |V_{cs}| determined from a global standard model fit, we evaluate the D_{s}^{+} decay constant f_{D_{s}^{+}}=252.9±3.7_{stat}±3.6_{syst}  MeV. Alternatively, using the value of f_{D_{s}^{+}} calculated by lattice quantum chromodynamics, we find |V_{cs}|=0.985±0.014_{stat}±0.014_{syst}. These values of B_{D_{s}^{+}→μ^{+}ν_{μ}}, f_{D_{s}^{+}}|V_{cs}|, f_{D_{s}^{+}} and |V_{cs}| are each the most precise results to date.

First Measurement of the Form Factors in D_{s}^{+}→K^{0}e^{+}ν_{e} and D_{s}^{+}→K^{*0}e^{+}ν_{e} Decays

We report on new measurements of Cabibbo-suppressed semileptonic D_{s}^{+} decays using 3.19  fb^{-1} of e^{+}e^{-} annihilation data sample collected at a center-of-mass energy of 4.178 GeV with the BESIII detector at the BEPCII collider. Our results include branching fractions B(D_{s}^{+}→K^{0}e^{+}ν_{e})=[3.25±0.38(stat)±0.16(syst)]×10^{-3} and B(D_{s}^{+}→K^{*0}e^{+}ν_{e})=[2.37±0.26(stat)±0.20(syst)]×10^{-3}, which are much improved relative to previous measurements, and the first measurements of the hadronic form-factor parameters for these decays. For D_{s}^{+}→K^{0}e^{+}ν_{e}, we obtain f_{+}(0)=0.720±0.084(stat)±0.013(syst), and for D_{s}^{+}→K^{*0}e^{+}ν_{e}, we find form-factor ratios r_{V}=V(0)/A_{1}(0)=1.67±0.34(stat)±0.16(syst) and r_{2}=A_{2}(0)/A_{1}(0)=0.77±0.28(stat)±0.07(syst).

Observation of D^{+}→f_{0}(500)e^{+}ν_{e} and Improved Measurements of D→ρe^{+}ν_{e}

Using a data sample corresponding to an integrated luminosity of 2.93  fb^{-1} recorded by the BESIII detector at a center-of-mass energy of 3.773 GeV, we present an analysis of the decays D^{0}→π^{-}π^{0}e^{+}ν_{e} and D^{+}→π^{-}π^{+}e^{+}ν_{e}. By performing a partial wave analysis, the π^{+}π^{-} S-wave contribution to D^{+}→π^{-}π^{+}e^{+}ν_{e} is observed to be (25.7±1.6±1.1)% with a statistical significance greater than 10σ, besides the dominant P-wave contribution. This is the first observation of the S-wave contribution. We measure the branching fractions B(D^{0}→ρ^{-}e^{+}ν_{e})=(1.445±0.058±0.039)×10^{-3}, B(D^{+}→ρ^{0}e^{+}ν_{e})=(1.860±0.070±0.061)×10^{-3}, and B(D^{+}→f_{0}(500)e^{+}ν_{e},f_{0}(500)→π^{+}π^{-})=(6.30±0.43±0.32)×10^{-4}. An upper limit of B(D^{+}→f_{0}(980)e^{+}ν_{e},f_{0}(980)→π^{+}π^{-})<2.8×10^{-5} is set at the 90% confidence level. We also obtain the hadronic form factor ratios of D→ρe^{+}ν_{e} at q^{2}=0 assuming the single-pole dominance parametrization: r_{V}={[V(0)]/[A_{1}(0)]}=1.695±0.083±0.051, r_{2}={[A_{2}(0)]/[A_{1}(0)]}=0.845±0.056±0.039.

Alginate Lyase of a Novel Algae Fermentation Strain

A novel algae fermentation strain was obtained in our previous work. This strain can produce alginate lyase and alcohol dehydrogenase used for the ethanol fermentation from algae. This research investigated the fermentation, separation and purification of alginate lyase, and the molecular weight of alginate lyase was determined. The optimum conditions for enzyme fermentation were as follows: fermentation medium with 20 g L–1 alginate, initial pH 6.0, and temperature 35 °C. The flasks were cultured in a shaking incubator at 120 rpm for 96 h. The enzyme was purified using the method of salting out, dialysis, and gel chromatography. After purification, the SDS-PAGE method was used to determine the molecular weight of the protein. The molecular weight of alginate lyase was 30–35 KDa. This research contributes to algae biodegradation and fuels production from algae.

Comprehensive analysis of the mitochondrial DNA diversity in Chinese cattle

Complete mitochondrial DNA D-loop sequences of 1105 individuals were used to assess the diversity of maternal lineages of cattle populations in China. In total, 250 taurine and 88 zebu haplotypes were identified. Five main haplogroups-T1a, T2, T3, T4 and T5-were identified in Bos taurus, whereas Bos indicus harbored two haplogroups-I1 and I2. Our results suggest that the distribution of T1a in Asia was concentrated mainly in the northeast region (northeast China, Korea and Japan); haplogroups T2, T3 and T4 were predominant in Chinese cattle; and T5 was sporadically detected in Mongolian and Pingwu cattle. In contrast to the widespread presence of I1, I2 was distributed only in southwestern China (Yunnan-Guizhou Plateau and the Tibet Autonomous Region) and Xinjiang Uygur Autonomous Region. This is the first time that all five taurine haplogroups and two zebu haplogroups have been found in Mongolian cattle. In addition, eight individuals in Tibetan cattle carried the Bos grunniens mtDNA type. The high mtDNA diversity (H = 0.904 ± 0.008) and the weak genetic structure among the 57 Chinese cattle breeds/populations are consistent with their complex historical background, migration route and ecological environment.

Genetic diversity of Chinese cattle revealed by Y-SNP and Y-STR markers

With its vast territory and complex natural environment, China boasts rich cattle genetic resources. To gain the further insight into the genetic diversity and paternal origins of Chinese cattle, we analyzed the polymorphism of Y-SNPs (UTY19 and ZFY10) and Y-STRs (INRA189 and BM861) in 34 Chinese cattle breeds/populations, including 606 males representative of 24 cattle breeds/populations collected in this study as well as previously published data for 302 bulls. Combined genotypic data identified 14 Y-chromosome haplotypes that represented three haplogroups. Y2-104-158 and Y2-102-158 were the most common taurine haplotypes detected mainly in northern and central China, whereas the indicine haplotype Y3-88-156 predominates in southern China. Haplotypes Y2-108-158, Y2-110-158, Y2-112-158 and Y3-92-156 were private to Chinese cattle. The population structure revealed by multidimensional scaling analysis differentiated Tibetan cattle from the other three groups of cattle. Analysis of molecular variance showed that the majority of the genetic variation was explained by the genetic differences among groups. Overall, our study indicates that Chinese cattle retain high paternal diversity (H = 0.607 ± 0.016) and probably much of the original lineages that derived from the domestication center in the Near East without strong admixture from commercial cattle carrying Y1 haplotypes.

Protein Classification of Diffuse-Type Gastric Cancer Using Formalin-Fixed Paraffin-Embedded Samples

Background: Gastric cancer (GC) is the third leading cause of cancer deaths in the world. It is highly heterogeneous. Many molecular therapies for GC have entered clinical trials but, apart from trastuzumab, apatinib and ramucirumab, all have failed. One important reason is that insufficient attention is paid to the underlying subtypes and characteristics of GC, especially the diffuse-type gastric cancer (DGC) according to the Lauren classification with worst clinical outcomes. Aim: Here we firstly investigated formalin-fixed paraffin-embedded (FFPE) samples of DGC to establish clinically relevant molecular classification based on proteomics analysis. Also, we tried to generate a suitable classifier of DGC that can guide patient therapy. Methods: We screened a total of 2548 cases retrospectively, who underwent GC resection at Beijing Cancer Hospital from October 2006 to December 2011. We used a fast mass spectrometry workflow for proteome profiling. Finally we carried out proteome profiling of 99 DGC paired tumor-nearby tissues from FFPE sections. Median overall survival of the whole population was 55.0 months. Proteome profiling data from these samples were used to develop a subtype prediction model. We used consensus clustering to identify molecular subtypes based on differentially expressed proteins. The pathway enrichment was performed by GSEA, and the prediction classifier was generated by elastic-net machine learning. Kaplan-Meier survival analysis and Cox regression multivariate analysis were used. Results: A total of 8201 gene products were identified in this study, and 1249 differential expressed proteins between tumor and nearby-normal tissue was detected (FDR q-value < 0.01 by SAM). Tumor upregulated proteins mostly enriched into pathways including RNA processing, epithelial-mesenchymal transition (EMT), immune response and inflammation related pathways. Tumor downregulated proteins mostly enriched into metabolic pathways such as oxidative phosphorylation pathway. Based on proteome profiling alone, DGC can be subtyped into 3 major classes (PX1-3) that exhibit distinct proteome features and correlate with distinct clinical outcomes. PX1 (31 patients) exhibits RNA processing proteins and associates with the best prognosis; PX2 (26 patients) exhibits highly expressed cell cycle features, and the patients have poorer prognosis than those with cluster1 but better prognosis than those with cluster3; PX3 (42 patients) features EMT and the worst prognosis. We built a classifier of 12 marker proteins that can stratify DGC patients into these 3 subtypes, opening a door for protein classification in clinical application and intervention. Conclusion: Our study demonstrated that proteome profiling alone from FFPE samples was able to subtype DGC into 3 protein subtypes that were linked to distinct patterns of molecular alterations and prognosis. The prediction model need to be further verified in more clinical cohorts.

The multiple arrhythmia dataset evaluation database (M.A.D.A.E.)

The convergence between wearable and medical device technologies is a natural progression. Miniaturization has allowed the design of small, compact monitoring systems that can record physiological signals over longer periods of time. Thus, the potential for these devices to expand the understanding of disease progression and patients' clinical status is very high. The accuracy of these devices, however, is dependent upon the computer algorithms utilized in the analysis of the large volume of physiological data monitored and/or recorded by the devices. Automated interpretation of the data by these new technologies, therefore, necessitates closer examination by regulatory organizations. The current requirements for the validation of novel Ambulatory ECG (A-ECG) annotation algorithms are based on the AAMI/ANSI-EC57 and IEC60601-2-47 Standard. These standards are being updated, but they rely on a very limited set of digitized ECG recordings from a couple of ECG databases built in the first half of the 70's. These reference signals are obsolete. We are developing a validation tool for computerized methods designed to detect and monitor cardiac activities based on body-surface ECGs. We will rely on a set of existing digital high-resolution 12‑lead A-ECG recordings acquired in cardiac patients and healthy individuals. These ECG signals include a large and unique set of electrocardiographic events. This tool is being qualified by the Center for Devices and Radiological Health of the United States Food and Drug Administration (FDA) as a Medical Device Development Tool (MDDT). This document provides insights into the design of the M.A.D.A.E. database, its functionalities, and its ultimate role in enabling the next generations of automatic interpretation of ECG signals.