Provenance becomes a critical requirement for healthcare IT infrastructures, especially when pervasive biomedical sensors act as a source of raw medical streams for large-scale, automated clinical decision support systems. Medical and legal requirements will make it obligatory for such systems to answer queries regarding the underlying data samples from which output alerts are derived, the IDs of the processing components used and the privileges of the individuals and software components accessing the medical data. Unfortunately, existing models of either annotation or process based provenance are designed for transaction-oriented systems and do not satisfy the unique requirements for systems processing high-volume, continuous medical streams. This paper proposes a simple, but useful, hybrid provenance model called Time-Value Centric (TVC) provenance. In this model, each entry in the output data stream (e.g., an output alert) is linked to some specific time windows of incoming data samples that contribute to the generation of the particular output entry, with the time-dependence potentially varying with the data values. An initial design of the provenance storage and querying architecture for this TVC model is also presented.

In the study, three benzimidazolate-based Cu²⁺ complexes were identified as SOD1 mimics to explore their effects on the levels of reactive oxygen species (ROS) and activities of antioxidant enzymes in drought-stressed rice organs. Superoxide dismutase (SOD) activity of the mimics was found to be controlled by unsaturated coordination, auxiliary ligands, and counter-anions. In comparison to the control, SOD1 mimic treatment for rice seeds significantly reduced ROS (O₂•⁻, H₂O₂, and •OH) levels in the rice leaf and root while notably increased activities of antioxidant enzymes, including SOD1 and catalase. It can enhance the tolerance of plant organs to drought stress and, thus, has a practical potency of application in rice production on arid land.

To analyze the characteristics, influencing factors, and microscopic mechanisms of county-level city shrinkage, this paper uses a quantitative push-pull model to explore the shrinking counties of Shandong Province between 2000 and 2018. The measurement method formulates three research objectives. First, the shrinking intensity and characteristics are analyzed according to statistics about the average annual rate of population growth, the primary production proportion, and public expenditure. Second, the influence factors are explored. Living standards, industrial development, social input, and public resource indicators are selected to quantitatively identify the push factors and pull factors and the correlated relationship of how the factors influence the population decline using ridge regression. Finally, the circular feedback mechanism and push-pull effect of multiple factors are explained. How do the factors affect each other and which is the decisive factor shaping county shrinkage? The push-pull mechanism is analyzed using dynamic relationship testing and the Granger causality test. The results show that the shrinkage of county-level cities faces common problems, including lack of resources, slowing down of the economy, and declining cityscape quality of life, which are the push factors for the population decline. There are differentiated characteristics of shrinkage. There has not yet been a full-scale recession in Shandong Province in terms of the degree of shrinkage. The towns with population loss accounted for only 15.4%, and the loss of population was less than 10% in ten years. In terms of impact mechanisms, county economic strength has a nonlinear correlation to population migration. Some counties tend to shrink in population and society. The degradation of the cultural environment, quality of life, and social welfare highlight social shrinkage signs in counties. A healthy living environment, equal public services, and a slowing down of relative deprivation have become essential pull factors for migration. County governments should shift from economic growth to people’s well-being, balancing government governance, economic growth, cultural development, environmental protection, and improving the livability level, as they are important directions for improving shrinking counties’ resilience.

The linkage mechanisms and optimization strategies between land use transition and eco-environmental effects that occur in the production-living-ecological space of karst mountain areas remain under-explored in the current literature. Based on county data collected in Longlin Multinational Autonomous County of Guangxi, which is located in the rocky desertification area of Yunnan, Guangxi, and Guizhou, this study contributes a county-level analysis on land use transition and eco-environmental effects by addressing two research questions: (1) Which factors of land use transition are related to the eco-environmental effects of production-living-ecological space? (2) What are the key land allocation mechanisms behind the interventions of local rocky desertification regulation policies? We conducted two sets of analyses to answer these two questions: quantitative analyses of the spatial and temporal evolution between land use transition, rocky desertification, and its eco-environmental effects, and qualitative analyses of policy interventions on production-living-ecological land development and rocky desertification management. The findings show that the occurrence of rocky desertification accompanied by unreasonable land use structure transition and its important factor is caused by ecological land being restricted by production-living land. Specifically, urbanization strategies coordinating ecological and socio-economic effects is significant to karst mountain areas. Moreover, the orderly increase of woodland slows down rocky desertification. Policies of “returning farmland to forest” and “afforestation of wasteland” have significantly reduced rocky desertification that can be applied to other geographical situations.

Multiple meta-analyses in Europeans showed that ENPP1 K121Q polymorphism was associated with type 2 diabetes. However, no association in Japanese, Korean, and Chinese in Taiwan, and inconsistent results in mainland Chinese were reported. In this study, the single nucleotide polymorphism K121Q of the ENPP1gene was genotyped in 539 type 2 diabetes patients and 404 healthy controls. No difference was observed in the genotypic and alle-lic frequencies of ENPP1 K121Q between the cases and the controls. Logistic regression analysis with adjustment of sex, age, and BMI suggested that the XQ genotype was significantly associated with the risk of type 2 diabetes (OR=1.5, 95%CI: 1.39-1.62, P<0.001). Sub-group analysis by gender revealed that the association between ENPP1 K121Q and type 2 diabetes was observed only in women (Q: 12.4% vs. 6.1%, P=0.001; XQ: 23.7% vs. 11.7%, P=0.001). Our results suggest that the association of ENPP1 K121Q with type 2 diabetes in Hubei Han Chinese population is more evident in women. The first meta-analysis of 10 Chinese studies indicated that the Q allele increased the risk of type 2 diabetes (OR=1.42, P=0.042).