Developing biomarkers for neurodegenerative diseases using genetically-modified common marmoset models

Mouse and non-human primate models of neurodegenerative disease：The prevalence of age-related neurodegenerative diseases continues to increase with ever increasing aging population over the age of 60.Although the difficulties associated with neurodegenerative diseases present an urgent global issue,there is no effective treatment for these conditions.

Modeling Pain Using fMRI:From Regions to Biomarkers

Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity： multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or ＇signatures＇ of pain, by （1） integrating activity across multiple systems, and （2） using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the ＇Stimulus Intensity-Independent Pain Signature＇, explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, includ- ing medial and lateral prefrontal cortex, nucleus accum- bens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual＇s pain experience.

Blood microRNAs as potential diagnostic markers for hemorrhagic stroke

Proper medical treatment of a stroke victim relies on accurate and rapid differentiation between ischemic and hemorrhagic stroke,which in current practice is performed by computerized tomography（CT） or magnetic resonance imaging（MRI） scans.A panel of micro RNAs could be an extremely useful clinical tool for distinguishing between hemorrhagic and ischemic stroke.This review has shown that blood miRNA profile can distinguish hemorrhagic from ischemic stroke in patients and in experimental animal models.It also seems likely they can differentiate between intracerebral and subarachnoid hemorrhage stroke.The miRNA profile in cerebrospinal fluid could be a useful diagnostic tool for subarachnoid hemorrhagic stroke.Decreased or increased miRNA levels may be needed either as prevention or treatment of stroke.Administration in vivo of miR-130 a inhibitor or miRNA mimic（miR-367,miR-223） in an intracerebral hemorrhage animal model improved neurological outcomes.

Root microbiota shift in rice correlates with resident time in the field and developmental stage

Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.