海马脑片抗癫痫药物研究的离体模型

目的：建立离体海马脑片癫痫样放电模型并用于抗癫痫药物研究。方法：在豚鼠海马脑片上灌流青霉素建立颠阗痫样放电的离体模型。并用此模型对抗癫痫药物苯巴比妥钠和苯妥英钠两种药物在不同浓度下对癫痫样放电的对抗作用进行了定量分析,结果：在海马脑片上灌流致痫药物可建立一个较好的离体组织癫痫样放电模型,苯的对抗作用进行了定量分析,结果：在海马脑片上灌流致痫药物可建立一个较好的离体组织癫痫样放电模型。苯巴比妥和苯妥英钠在一定浓度下均有显著对抗癫痫样放电的作用,且与整体实验的结果相一致。结论：本实验建立有离体脑片模型具有实验手段简单,方法灵活,易于建立药物量效关系等优点,可用于抗癫痫药物筛选和研究。     

转基因植物中RNA介导的病毒抗性机制

介绍了近年来转基因植物中RNA介导的抗性特征和解释这种抗性机制的两种模型,一种是阈值模型,一种是质量模型,并对这两种模型的优缺点进行了讨论.     

Monitoring occurrence,extinction, and colonization probabilities for gibbon populations

All gibbon species (Family: Hylobatidae) are considered threatened with extinction and recognized on the International Union for Conservation of Nature Red List of Threatened Species. Because gibbons are one of the most threatened families of primates, monitoring their status is now critically important. Long-term monitoring programs applying occupancy approaches, in addition to assessing occurrence probability, improves understanding of other population parameters such as site extinction or colonization probabilities, which elucidate temporal and spatial changes and are therefore important for guiding conservation efforts. In this study, we used multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities for northern yellow-cheeked crested gibbon Nomascus annamensis in three adjacent protected areas in the Central Annamites mountain range, Vietnam. We collected data at 30 listening posts in 2012, 2014, and 2016 using the auditory point count method. Occurrence probabilities were highest in 2012 (0.74, confidence interval [CI]: 0.56–0.87) but slightly lower in 2014 (0.66, CI: 0.51–0.79) and 2016 (0.67, CI: 0.49–0.81). Extinction probabilities during the 2012–2014 and 2014–2016 intervals were 0.26 (0.14–0.44) and 0.25 (0.12–0.44), respectively. Colonization probabilities during 2012–2014 were 0.44 (0.19–0.73) and between 2014 and 2016 was 0.51 (0.26–0.75). Although local site extinctions have occurred, high recolonization probability helped to replenish the unoccupied sites and kept the occurrence probability stable. Long-term monitoring programs which use occurrence probability alone might not fully reveal the true dynamics of gibbon populations. We strongly recommend including multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities in long-term gibbon monitoring programs.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

Inference of Multisite Phosphorylation Rate Constants and Their Modulation by Pathogenic Mutations

1. Download : Download high-res image (170KB)
2. Download : Download full-size image

     

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse-phase protein arrays–based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

Mechanistic insights into the role of large carnivores for ecosystem structure and functioning

Large carnivores can exert top–down effects in ecosystems, but the size of these effects are largely unknown. Empirical investigation on the importance of large carnivores for ecosystem structure and functioning presents a number of challenges due to the large spatio-temporal scale and the complexity of such dynamics. Here, we applied a mechanistic global ecosystem model to investigate the influence of large-carnivore removal from undisturbed ecosystems. First, we simulated large-carnivore removal on the global scale to inspect the geographic pattern of top–down control and to disentangle the functional role of large carnivores in top–down control in different environmental contexts. Second, we conducted four small-scale ecosystem simulation experiments to understand direct and indirect changes in food-web structure under different environmental conditions. We found that the removal of top–down control exerted by large carnivores (> 21 kg) can trigger large trophic cascades, leading to an overall decrease in autotroph biomass globally. Furthermore, the loss of large carnivores resulted in an increase of mesopredators. The magnitude of these changes was positively related to primary productivity (NPP), in line with the ‘exploitation ecosystem hypothesis’. In addition, we found that seasonality in NPP dampened the magnitude of change following the removal of large carnivores. Our results reinforce the idea that large carnivores play a fundamental role in shaping ecosystems, and further declines and extinctions can trigger substantial ecosystem responses. Our findings also support previous studies suggesting that natural ecosystem dynamics have been severely modified and are still changing as a result of the widespread decline and extinction of large carnivores.     

Disturbance does not have a significant impact on waders in an estuary close to conurbations: importance of overlap between birds and people in time and space

Disturbance of wildlife is a potential cause of conservation concern, not least to overwintering waders Charadrii inhabiting estuaries close to conurbations where human recreational and economic activities are often concentrated. Disturbance from people on and alongside intertidal foraging areas could make it more difficult for birds to survive until spring in good condition by reducing the time available for foraging, increasing energy requirements and displacing birds to poorer foraging areas. We adopted a two-part approach to testing whether such significant impacts occurred in a Special Protection Area where disturbance risk was high because of its small size and close proximity to conurbations. In part one, we recorded over the whole estuary during stages of the tidal cycle when part or all of the intertidal zone was exposed and so accessible to waders (i.e. on receding, low and advancing tides): (1) the numbers and activities of people on the intertidal flats and on the adjacent land in those places where people were visible to waders in the intertidal zone and (2) the numbers of waders present and disturbed into flight, the flight distance and flight duration in the ‘overlap’ areas where people did disturb waders. People occurred on < 25% of the 938 ha of intertidal flats, but most waders foraged on mudflats, whereas most people were on sandflats. People on land were visible to foraging waders along < 35% of the 16.5 km of shoreline. Waders and people were therefore substantially separated in space. Within overlap areas, people and waders were often frequently separated in time: for example, people on land mostly disturbed waders when only the upper shore levels were exposed. The average overwintering wader spent < 0.1% of its foraging time during daylight flying away from people and the additional energy expenditure was equivalent to < 0.02% of its daily requirements. The comparison made in part two between our study area and two comparable estuaries showed that the number of visits each day to the overlap areas would need to be 29 or 43 times greater for disturbance to have lowered the birds’ body condition and winter survival. Both parts of the study therefore suggested strongly that the amount of disturbance was too trivial to have a significant impact on waders. It is concluded that: (1) to properly assess disturbance risk to waders, both extensive and intensive observations must be made on the behaviour of people and birds to quantify the extent to which they overlap in space and time, and (2) it should not be assumed that an estuary's close proximity to conurbations, and the presence of large numbers of people in the vicinity of the SPA, necessarily implies a significant disturbance risk to waders.