Heterogeneity of the Impact of the Social Old-Age Insurance and the Medical Insurance on the Mortality Risk of the Elderly.

Based on the frailty Cox model, this paper analyzes CLHLS data from 2008 to 2017/2018 to examine the impact of the social old-age insurance and the medical insurance on the mortality risk of the elderly based on the age structures, urban/rural areas and regions. The results reveal the heterogeneous impact as follows. In terms of the age structures, the social old-age insurance significantly reduces the mortality risk of the elderly aged below 80, but has no significant impact on the elderly aged 80 and above, whereas the medical insurance significantly reduces the mortality risk of the elderly aged 80 and above, but has no significant impact on the elderly aged below 80. In urban/rural areas and different regions, the social old-age insurance has no significant impact on the mortality risk of the elderly, whereas the social medical insurance significantly increases the mortality risk of the elderly in urban areas and the East, and reduces that of the elderly in rural areas and the Middle and the West. When implementing the insurances, China should pay attention to the different attributes of the elderly to guarantee the service quality, including the age structures, urban/rural areas and regions. A full consideration should be given to the allocation of investment and social security resources, so as to address the issue of the mismatch between the supply and demand of medical resources, and finally achieve the success of healthy aging and health equity.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Anatomy Ontology Matching Using Markov Logic Networks.

The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relationships between ontologies describing different species. Ontology matching is a kind of solutions to find semantic correspondences between entities of different ontologies. Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. We combine several different matching strategies through first-order logic formulas according to the structure of anatomy ontologies. Experiments on the adult mouse anatomy and the human anatomy have demonstrated the effectiveness of proposed approach in terms of the quality of result alignment.

[Assessment of risk factors for patients with anatomical left ventricular aneurysm post acute ST-elevation myocardial infarction by use of multiple-risk-factor assessment models].

OBJECTIVE: To set up the multiple risk factors model of patients with anatomical left ventricular aneurysm (LVA) post acute ST-elevation myocardial infarction (STEMI) and quantitatively assess the pathopoiesis of all the factors. METHODS: A total of 518 consecutive inpatients with acute STEMI hospitalized from June 2010 to December 2013 in our hospital were enrolled in this study, patients were divided into two groups: LVA group (n = 106, 20.5%) and non-LVA group (n = 412, 79.5%). All demographic and clinical data were collected by cardiologists. Finally, all of the risk factors for anatomical LVA in the acute STEMI patients were quantitatively analyzed by a binary logistic regression model. RESULTS: The multiple risk factors logistic regression model was set up for the anatomical LVA in patients with acute STEMI. Anterior wall myocardial infarction, occlusion of the left anterior descending branch, two or three vessels stenosis, high systolic blood pressure, sinus tachycardia and white blood cell count over 10 000 per microliter were all independent risk factors of the LVA in acute STEMI, with the odds ratio (OR) 18.21, 21.56, 4.22, 7.16, 1.98 and 1.57, respectively (all P < 0.05) . However, first medical contact less than 12 hours (OR = 0.60), collateral circulation of the coronary arteries(OR = 0.53), primary percutanous coronary intervention(OR = 0.23) and venous thrombolysis(OR = 0.12) were all protecting factors of the LVA in acute STEMI patients (all P < 0.05). CONCLUSION: Anterior wall STEMI, occlusion of the left anterior descending branch, two or three vessels stenosis, high systolic blood pressure, sinus tachycardia and white blood cell count over 10 000 per microlitre are independent risk factors of the LVA in acute STEMI patients. However, first medical contact less than twelve hours, collateral circulation of the coronary arteries, together with the primary percutanous coronary intervention and venous thrombolysis are protective factors of the LVA in patients with acute STEMI. It is important for cardiologists to assess the risks of LVA and make emergent and suitable efforts to reduce the risk of developing LVA in STEMI patients.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology.

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.