BackgroundCurrent knowledge about elder mistreatment is mainly derived from studies done in Western countries, which indicate that this problem is related to risk factors such as a shared living situation, social isolation, disease burden, and caregiver strain. We know little about prevalence and risk factors for elder mistreatment and mistreatment subtypes in rural China where the elder population is the most vulnerable.MethodsIn 2010, we conducted a cross-sectional survey among older adults aged 60 or older in three rural communities in Macheng, a city in Hubei province, China. Of 2245 people initially identified, 2039 were available for interview and this was completed in 2000. A structured questionnaire was used to collect data regarding mistreatment and covariates. Logistic regression analysis was used to identify factors related to elder mistreatment and subtypes of mistreatment.ResultsElder mistreatment was reported by 36.2% (95% CI: 34.1%–38.3%) of the participants. Prevalence rates of psychological mistreatment, caregiver neglect, physical mistreatment, and financial mistreatment were 27.3% (95% CI: 25.3%–29.2%), 15.8% (95% CI: 14.2%–17.4%), 4.9% (95% CI: 3.9%–5.8%) and 2.0% (95% CI: 1.3%–2.6%), respectively. The multivariate logistic regression analysis revealed that depression, being widowed/divorced/single/separated, having a physical disability, having a labor intensive job, depending solely on self-made income, and living alone were risk factors for elder mistreatment. Different types of elder mistreatment were associated with different risk factors, and depression was the consistent risk factor for the three most common mistreatment subtypes.ConclusionOlder adults in rural China self-report a higher rate of mistreatment than their counterparts in Western countries. Depression is a main risk factor associated with most subtypes of mistreatment. Our findings suggest that prevention and management of elder mistreatment is a challenge facing a rapidly aging Chinese population.

Oscillatory neural activity within the gamma band (25-90 Hz) is generally thought to be able to provide a timing signal for harmonizing neural computations across different brain regions. Using time-frequency analyses of the dynamics of gamma-band activity in the local field potentials recorded from monkey primary visual cortex, we found identical temporal characteristics of gamma activity in both awake and anesthetized brain states, including large variability of peak frequency, brief oscillatory epochs (Ͻ100 ms on average), and stochastic statistics of the incidence and duration of oscillatory events. These findings indicate that gamma-band activity is temporally unstructured and is inherently a stochastic signal generated by neural networks. This idea was corroborated further by our neuralnetwork simulations. Our results suggest that gamma-band activity is too random to serve as a clock signal for synchronizing neuronal responses in awake as in anesthetized monkeys. Instead, gamma-band activity is more likely to be filtered neuronal network noise. Its mean frequency changes with global state and is reduced under anesthesia.

Human Th17 cells may play a major role in rejecting conceptus antigens and therefore may be harmful to the maintenance of pregnancy. The data also suggest that Treg cells are beneficial to pregnancy. There may exist a balance of Th17/Treg in normal pregnancy.

Perceptual grouping of line segments into object contours has been thought to be mediated, in part, by long-range horizontal connectivity intrinsic to the primary visual cortex (V1), with a contribution by top-down feedback projections. To dissect the contributions of intraareal and interareal connections during contour integration, we applied conditional Granger causality analysis to assess directional influences among neural signals simultaneously recorded from visual cortical areas V1 and V4 of monkeys performing a contour detection task. Our results showed that discounting the influences from V4 markedly reduced V1 lateral interactions, indicating dependence on feedback signals of the effective connectivity within V1. On the other hand, the feedback influences were reciprocally dependent on V1 lateral interactions because the modulation strengths from V4 to V1 were greatly reduced after discounting the influences from other V1 neurons. Our findings suggest that feedback and lateral connections closely interact to mediate image grouping and segmentation.perceptual grouping | contour integration | Granger causality | horizontal connection | feedback connection A key step in the visual system's analysis of object shape is to group line segments into global contours and segregate these contours from background features. This process is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination; image segmentation; and, ultimately, object recognition.Contour integration follows the Gestalt principle of good continuation (1). The underlying neural underpinnings have been characterized as an association field (2), which links contour elements that are part of smooth contours. Neurophysiological studies in monkeys have identified that the primary visual cortex (V1) makes a fundamental contribution to contour integration (3-6), and anatomical studies have shown that the topology of horizontal connections in V1 is well suited for mediating interactions between neurons with a similar orientation preference (7-10). Such intracortical circuitry in V1 has been implemented in many computational models to account for the successful process of contour integration (11-13). Although many lines of converging evidence suggest that V1 is intimately involved in contour integration, circuitbased models have to take into account the findings that contour grouping is more than a bottom-up or hard-wired process, but that it is strongly dependent on top-down feedback influences (5, 14-17). Surface segmentation, another important intermediate stage in processing of visual images, is also mediated by interactions between feedforward and feedback connections (18).We have proposed a model whereby cortical feedback contributes to the effective connectivity of horizontal connections within V1 (13,19). A possible role of higher cortical areas in this process is to disambiguate local image components by creating a template that is fed back to V1, which then can select...