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Rural landscape conservation is one important issue of Chinese Rural Revitalization strategy, but fast urbanization has affected rural landscapes in the last four decades. This paper, based on the field works in 480 villages of 13 provinces and many local villages in the last few years in China and abroad , analyzes the landscape conservation challenges of regional characteristics, spatial landscape, agricultural context and social cultural landscapes, and advances that rural landscape is facing forces of diversities disappearing, architecture-urbanization, built-up landscape-disordering and artificializing, agricultural landscape-deecologing and de-countrysiding and rural community destructuring. Some genetic factors are explored including urban and industry culture invading, improper government intervening, shortage of rural value cognition, lack of public participation and lagging of rural theory. Finally, some conservation agendas, mainly based on Korean, Japanese and European experience, are put forward including reconstructing cultural confidence, fusion of the time and the past, emphasizing villagers dominant role, playing social organizations' role, reuniting scattered resources, supporting systematically, being based on national conditions, legislating, strongthen theoretical studies and propping up rural landscape maintaining.

Resting state functional MRI (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable, and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both Network-based Statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison to the control group. Evaluation of mean connectivity between all ROIs within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual, and striatal networks, and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and internetwork synchrony may support cognitive function in the face of PD-related network disruptions.

Background: Heart transplant (HT) has a high in-hospital mortality of around 5%. Risk prediction in-hospital mortality can be informative for transplant candidacy and post-HT prognosis. Elixhauser Comorbidity Index (ECI) is an ICD diagnostic code-based comorbidity measurement tool that can predict in-hospital mortality. While it has been validated in the large in-patient population, the accuracy of the mortality prediction has not been assessed in HT. Methods: This study assessed the in-hospital mortality risk prediction by ECI as well as demographic variables in HT patients in the National Inpatient Sample (NIS) database. Demographic information was included in the multivariable ECI with demographics (ECID) model to assess in-hospital mortality. Moreover, ECI and age were used to develop a single index adjusted ECI (aECI) for mortality prediction. Results: Age best predicts (c-statistic = 0.673, 95% CI = 0.638-0.709) in-hospital mortality, followed by ECI (c-statistic = 0.638, 95% CI = 0.598-0.678), race (c-statistic = 0.571, 95% CI = 0.533-0.609). Sex did not have predictive power (c-statistic = 0.501, 95% CI = 0.467-0.535) for in-hospital mortality. The predictive power of ECI was improved (c-statistic = 0.753, 95% CI = 0.720-0.785) in the ECID model. The single measure aECI had comparable discriminative power (c-statistic = 0.763, 95% CI = 0.731-0.794) to ECID in predicting in-hospital mortality. Conclusion: This study showed that ECI was an effective measure to predict post-HT in-hospital mortality. The improved measure aECI can be easily derived from ECI as a quick reference to assess post-HT in-hospital mortality in both the clinic and health administration.