Employee tenure in China: comparing foreign joint ventures

The retention of qualified Chinese employees is an increasing concern for managers in China today. Various studies have highlighted the rise of turnover rates, and many have suggested how human resource practices can help promote retention. There is also some indication that employee tenure may vary with the nationality of firms in China. However, little research has compared employee tenure or the use of human resource practices for retention between firms of different nationality in China. Such enquiries would be vital for gaining a better understanding of the phenomenon, and for establishing best practice. We therefore conducted a comparison of average employee tenure and the use of HR practices between Sino-foreign joint ventures (JVs) with Western (EU/US), overseas Chinese (Hong Kong/Taiwan), and other Asian (Japanese/Korean) partners. Primary data from questionnaire surveys were obtained from a total of 316 JVs in Beijing, Tianjin, and Qingdao. We found a significant relationship between nationality and tenure. Tenure was the highest in overseas Chinese, moderate in Japanese/Korean, and lowest in Western firms. The results also suggest that the variation in tenure between nationalities was partly mediated by the firms’ use of a human resource practices. We discuss these results in terms of cultural differences and the long-term orientation of human resource systems

Bridging knowledge gaps: returnees and reverse knowledge spillovers from Chinese local firms to foreign firms

Adopting a knowledge-based perspective and embeddedness theory, this study examines the impact of reverse knowledge spillovers from local Chinese firms to foreign firms using a sample of high-tech firms in Zhongguancun Science Park in China. It also investigates whether returnees in foreign firms help bridge knowledge gaps between local firms and foreign firms. The results show that the presence of returnee CEOs and returnee employees enhances the impact of reverse technological and marketing spillovers on the innovation and financial performance of foreign firms. The findings call for more studies on the social dimensions of knowledge spillovers across international boundaries and have important theoretical and practical implications

Correlating the Chemical Modification of <i>Escherichia coli</i> Ribosomal Proteins with Crystal Structure Data

Various chemical modifications have been applied to study protein structures. In this paper, amidination of E. coli ribosomal proteins was investigated to profile the structure of this large protein/RNA complex. The extent of ribosomal protein amidination was correlated with the solvent accessibility of amine groups in E. coli ribosome crystal structures. The modification of many residues was confirmed by CID of tryptic peptides. The amidination of proteins in the intact ribosome is very consistent with crystal structure data. The extent to which monomethylated amine groups can be amidinated was also examined. This information was used to interpret the amidination of several ribosomal proteins. Interestingly, ribosomal proteins L7 and L12, which share the same sequence and differ only by acetylation of the N-terminus, were found to be methylated to different extents. L12 is largely monomethylated but only a small portion of L7 is so modified

The data mining processes for clinical data (stages as example).

We sort all normal solid tissue stemness data (a: EREG-mRNAsi) and clinical data (b: cancer stage) by patient IDs (TCGAlong.id or id) and pair them with the identical IDs (c). With Table 1, we give each stage a numerical value (d) and sort the data (e) by stemness index (EREG-mRNAsi). By moving the average (f: N = 3 here, N = 21 for actual data), we reduce the noise for the stage trend (g).</p

Pathological section of five tumor tissues with hematoxylin and eosin staining.

Images are downloaded from the TCGA database (https://portal.gdc.cancer.gov/). IDs are taken from the lower left and upper right corners of Fig 6. The complete ID of the sample, tumor type, stage or T stage, and Normal stemness value are marked on the top of the picture. a1–a2: BRCA (Breast invasive carcinoma); b1–b2: LUAD (Lung adenocarcinoma); c1–c2: PRAD (Prostate adenocarcinoma); d1–d2: LUSC (Lung squamous cell carcinoma); e1–e2: STAD (Stomach adenocarcinoma). The translucent scales are at the bottom left of each image (100 μm or 200 μm).</p

TCGA Sample IDs of five tumors sampled in Normal stemness (0–1) and cancer stage (stage I–stage IV, or T1–T4) grids.

Red: BRCA (Breast invasive carcinoma); Purple: LUAD (Lung adenocarcinoma); Green: PRAD(Prostate adenocarcinoma); Blue: LUSC(Lung squamous cell carcinoma); Black: STAD(Stomach adenocarcinoma); ‘/’: There is no eligible TCGA Sample ID in this area.</p

The sample discrete value table of TCGA clinical-stage data.

The sample discrete value table of TCGA clinical-stage data.</p

Paired data.

Paired data by patient ID. 639 groups contain both normal tissue stemness and clinical cancer metastasis staging data. (https://doi.org/10.6084/m9.figshare.20407044). (XLSX)</p

The sample discrete value table of TCGA clinical-stage data.

The sample discrete value table of TCGA clinical-stage data.</p

Four clinical data as a function of normal stemness.

The linear regression results of the denoised clinical data (a: cancer stage; b: tumor size and invasion; c: distant metastasis; d: lymph node involvement) and Normal stemness in all intervals (0, 1) are shown as solid black lines, and black numbers represent the linear regression results on the interval (0.5, 1) with solid red lines and red numbers. Orange error bars show the calculated SEM while denoising. The error of the slope and intercept is directly expressed in the regression equation (± behind the value represents error; N is the total number of data; R2 is the square of the correlation coefficient; p is the p-value for which the slope is not zero; * means significant).</p