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Chemical Profiles of Essential Oils and Non-Polar Extractables from Sumac (Rhus spp.)
Get PDFSumac is the common name for a genus (Rhus) with >250 individual species of flowering plants in the family Anacardiaceae. These plants are globally distributed in temperate and tropical regions and can grow on marginal lands, making them strong candidates for renewable bioproduct sources. Despite the extensive historical use of some members of Rhus spp. for tannins and other commercial phenolics, little is known about the non-phenolic components of extracts and essentials oils. The current review highlights opportunities available to extend these limited prior studies to other sumac species, and for obtaining value-added compounds to complement already established phenolic extractions in these commercial plant species. To date, a number of individual aldehydes, fatty acids, long chain alcohols, terpenes and terpenoids, and waxes of commercial or bioactive potential in essential oils and non-polar extractables from selected members of the Rhus genera have been identified. Additional studies are needed to broaden the phytochemical database from other sumac species, and to better quantify the potential yields of these valuable compounds from the plants under natural and agriculturally managed conditions
MSTd Neurons Encode Nonlinear Combinations of Retinal and Extra-retinal Signals
Get PDFNeuronal activity in the dorsal Medial Superior Temporal area (MSTd) is assumed to depend on both retinal and extra-retinal input. Most of the neurons show activity when presented with a moving large-field visual stimulus. In addition, many MSTd neurons are activated during smooth pursuit eye movements even in the absence of retinal input. However, the interaction between the retinal and extra-retinal input is not yet fully understood.
Here we present novel insights regarding the tuning of MSTd neurons for combinations of different input variables using an information-theoretic approach. Neuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of input variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as determining the neuronal latencies and finding the combination of input variables which is most related to neuronal activity. Our approach solves these issues by maximizing the mutual information between the probability distributions of spike occurrence and input variables. 
We analyzed the dependence of MSTd neuronal activity in monkeys on various retinal and extra-retinal signals during presentation of a large-field visual stimulus moving randomly with quasi equally distributed frequencies (white noise). Across the population, neuronal activity depended on different combinations of retinal and extra-retinal input. The interrelation between the input variables exhibited in many cases strong non-linear characteristics. These findings support the hypothesis that MSTd uses a basis function representation for encoding various retinal and extra-retinal signals
Parameter Estimation for Hidden Markov models with Intractable Likelihoods
Get PDFIn this talk I consider sequential Monte Carlo (SMC) methods for hidden Markov models. In the scenario for which the conditional density of the observations given the latent state is intractable we give a simple ABC approximation of the model along with some basic SMC algorithms for sampling from the associated filtering distribution. Then, we consider the problem of smoothing, given access to a batch data set. We present a simulation technique which combines forward only smoothing (Del Moral et al, 2011) and particle Markov chain Monte Carlo (Andrieu et al 2010), for an algorithm which scales linearly in the number of particles
ABC SMC for parameter estimation and model selection with applications in systems biology
Get PDFApproximate Bayesian Computation (ABC) methods can be used in situations where the evaluation of the likelihood is computationally prohibitive. They are thus ideally suited for analyzing the complex dynamical models encountered in systems biology, where knowledge of the full (approximate) posterior is often essential.

This talk gives an overview of an ABC algorithm based on Sequential Monte Carlo (ABC SMC). Different uses of the algorithm will be presented, depending on the application question of interest. The first is the general parameter estimation framework, where the interest lies in estimating the posterior parameter distribution from available experimental data. In the second context we ask whether the model can reproduce a desired qualitative or semi-quantitative behavior, and what dynamic behaviors the system can achieve. The third context discussed here for use of the ABC SMC algorithm is that of model selection. Here we ask which of the models (i.e. model topologies) from a pool of proposed candidate models represents the most suitable hypothesis about the biological system of interest.

The focus of the presentation is applications of ABC SMC to questions from systems biology. ABC SMC is applied to a variety of biological models, including stochastic and deterministic descriptions of eukaryotic signaling pathways and prokaryotic stress response pathways. In particular, the algorithm is employed to understand understand the qualitative behavior of the phage shock protein response in bacteria Escherichia coli, and the model selection algorithm is applied to distinguish between differential equation models of MAP kinase phosphorylation dynamics and the JAK-STAT signaling pathway
A new functional role for lateral inhibition in the striatum: Pavlovian conditioning
Get PDFThe striatum has long been implicated in reinforcement learning and has been suggested by several neurophysiological studies as the substrate for encoding the reward value of stimuli. Reward prediction error (RPE) has been used in several basal ganglia models as the underlying learning signal, which leads to Pavlovian conditioning abilities that can be simulated by the Rescorla-Wagner model.

Lateral inhibition between striatal projection neurons was once thought to have a winner-take-all function, useful in selecting between possible actions. However, it has been noted that the necessary reciprocal connections for this interpretation are too few, and the relative strength of these synaptic connections is weak. Still, modeling studies show that lateral inhibition does have an overall suppression effect on striatal activity and may play an important role in striatal processing. 

Neurophysiological recordings show task-relevant ensembles of responsive neurons at specific points in a behavioral paradigm (Barnes et al., 2005), which appear to be induced by lateral inhibition (see Ponzi and Wickens, 2010). We have developed a similarly responding, RPE-based model of the striatum by incorporating lateral inhibition. Model neurons are assigned to either the direct or the indirect pathway but lateral connections occur within and between these groups, leading to competition between both the individual neurons and their pathways. We successfully applied this model to the simulation of Pavlovian phenomena beyond those of the Rescorla-Wagner model, including negative patterning, unovershadowing, and external inhibition
Increasing incidence of thyroid cancer in shanghai, China, 1983-2007
Get PDFIncreasing incidences of thyroid cancer were observed in some countries, like USA, UK, France, etc. Jointpoint regression was used to analyze the incidence of thyroid cancer in Shanghai, China, from 1983 to 2007. The results showed there were both two distinct slopes, in males representing a significant APC of 2.6% from 1983 to 2000 (P<0.001), followed by a sharply increased APC of 14.4% (P <0 .001), and in females representing a significant APC of 4.9% from 1983 to 2003 (P<0.001), followed by a sharply increased APC of 19.9% (P =0 .001). Incidence of thyroid cancer increased 5 to 8 years after the supplement of iodine, for males and females respectively, suggesting that either the developed screening techniques or supplement of iodine might contribute to this accelerated increase in incidence of thyroid cancer. The predicated future burdens indicated that thyroid cancer is never an unusual cancer, either in the present or in the future
Wellcome Trust Policy on Data Management and Sharing
Get PDFThe Wellcome Trust's policy statement on data management and sharing, which was originally published in January 2007 and revised in August 2010
Efficient Replication of Over 180 Genetic Associations with Self‐Reported Medical Data
No full textWhile the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web‐based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We
found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in‐depth questions to refine self‐reported diagnoses. Our data suggests that online collection of self‐reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations
Antimicrobial and Phytochemical Analysis of Centella asiatica (L.)
Get PDFIn vitro antibacterial studies were carried out using hexane, dichloromethane and methanol extract of leaves of Centella asiatica by disc-diffusion method against gram-positive and gram-negative bacteria. The methanol and dichloromethane extracts of leaf showed a broad spectrum antibacterial activity. Thus the results substantiate the traditional usage of this plant as a medicine
Quantification of miRNAs and Their Networks in the light of Integral Value Transformations
Get PDFMicroRNAs (miRNAs) which are on average only 21-25 nucleotides long are key post-transcriptional regulators of gene expression in metazoans and plants. A proper quantitative understanding of miRNAs is required to comprehend their structures, functions, evolutions etc. In this paper, the nucleotide strings of miRNAs of three organisms namely Homo sapiens (hsa), Macaca mulatta (mml) and Pan troglodytes (ptr) have been quantified and classified based on some characterizing features. A network has been built up among the miRNAs for these three organisms through a class of discrete transformations namely Integral Value Transformations (IVTs), proposed by Sk. S. Hassan et al [1, 2]. Through this study we have been able to nullify or justify one given nucleotide string as a miRNA. This study will help us to recognize a given nucleotide string as a probable miRNA, without the requirement of any conventional biological experiment. This method can be amalgamated with the existing analysis pipelines, for small RNA sequencing data (designed for finding novel miRNA). This method would provide more confidence and would make the current analysis pipeline more efficient in predicting the probable candidates of miRNA for biological validation and filter out the improbable candidates