Messenger RNA (mRNA) stability, localization, and translation are largely determined by sequences in the 3' untranslated region (3'UTR). We found a conserved increase in expression of mRNAs terminating at upstream polyadenylation sites after activation of primary murine CD4+ T lymphocytes. This program, resulting in shorter 3'UTRs, is a characteristic of gene expression during immune cell activation and correlates with proliferation across diverse cell types and tissues. Forced expression of full-length 3'UTRs conferred reduced protein expression. In some cases the reduction in protein expression could be reversed by deletion of predicted microRNA target sites in the variably included region. Our data indicate that gene expression is coordinately regulated, such that states of increased proliferation are associated with widespread reductions in the 3'UTR-based regulatory capacity of mRNAs.

This paper presents a Genetic Algorithm (GA) model for finding the optimal operating policy of a multi-purpose reservoir, located on the river Pagladia, a major tributary of the river Brahmaputra. A synthetic monthly streamflow series of 100 years is used for deriving the operating policy. The policies derived by the GA model are compared with that of the stochastic dynamic programming (SDP) model on the basis of their performance in reservoir simulation for 20 years of historic monthly streamflow. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for reservoir operation.

Assessing flood hazard, vulnerability and integrated risk has long been recognised as an important input for the formulation of policies aiming at flood risk management. This investigation is an endeavour to assess hazard, vulnerability and risk due to flooding, using an indicator‐based methodology incorporating stakeholders’ knowledge and multicriteria evaluation in geographic information system (GIS) to achieve community‐based assessment. The framework developed in this work is illustrated for the district of Dhemaji, a chronically flood‐affected area in the Upper Brahmaputra River valley. Results show spatial distribution of hotspots of flood hazard and vulnerability and locations at risk at regional and subregional level. The emerged risk pattern indicates that vulnerability indicators are more significant contributors than hazard indicators while calculating risk for the Upper Brahmaputra River valley. The methodology provides a dynamic platform where the flexibility in uses of hazard and vulnerability indicators, depending on variation in physical and socioeconomic setup, is possible.

Time series of streamflow plays an important role in planning, design and management of water resources system. In the event of non availability of a long series of historical streamflow record, generation of the data series is of utmost importance. Although a number of models exist, they may not always produce satisfactory result in respect of statistics of the historical data. In such event, artificial neural network (ANN) model can be a potential alternative to the conventional models. Streamflow series, which is a stochastic phenomenon, can be suitably modeled by ANN for its strong capability to perform non-linear mapping. An ANN model developed for generating synthetic streamflow series of the Pagladia River, a major north bank tributary of the river Brahmaputra, is presented in this paper along with its comparison with other existing models. The comparison carried out in respect of five different statistics of the historical data and synthetically generated data has shown that among the different models, viz., autoregressive moving average (ARMA) model, Thomas-Fiering model and ANN model, the ANN based model has performed better in generating synthetic streamflow series for the Pagladia River. Notation α momentum factor D dependent stochastic component η learning rate k index for laḡ q m observed average of the historical monthly streamflow series for month m ζ t independent standard normal random variable autoregressive average parameter θ moving average parameter μ v mean of the residual series V t N total number of time series data P periodic component PC k,k partial autocorrelation function at lag k r k autocorrelation at lag k r m observed correlations between month m and m+1 of the historical streamflow series s 2 m observed variance of the historical monthly streamflow series for month m σ v standard deviation of the residual series V t σ 2 v variance of V t series t time index T trend component V t independent stochastic/residual component at time t Z mean value of the time series Z t

In this paper we present preliminary results of a novel unsupervised approach for highprecision detection and correction of errors in the output of automatic speech recognition systems. We model the likely contexts of all words in an ASR system vocabulary by performing a lexical co-occurrence analysis using a large corpus of output from the speech system. We then identify regions in the data that contain likely contexts for a given query word. Finally, we detect words or sequences of words in the contextual regions that are unlikely to appear in the context and that are phonetically similar to the query word. Initial experiments indicate that this technique can produce high-precision targeted detection and correction of misrecognized query words.