tRNA ligase structure reveals kinetic competition between non-conventional mRNA splicing and mRNA decay.

Yeast tRNA ligase (Trl1) is an essential trifunctional enzyme that catalyzes exon-exon ligation during tRNA biogenesis and the non-conventional splicing of HAC1 mRNA during the unfolded protein response (UPR). The UPR regulates the protein folding capacity of the endoplasmic reticulum (ER). ER stress activates Ire1, an ER-resident kinase/RNase, which excises an intron from HAC1 mRNA followed by exon-exon ligation by Trl1. The spliced product encodes for a potent transcription factor that drives the UPR. Here we report the crystal structure of Trl1 RNA ligase domain from Chaetomium thermophilum at 1.9 Å resolution. Structure-based mutational analyses uncovered kinetic competition between RNA ligation and degradation during HAC1 mRNA splicing. Incompletely processed HAC1 mRNA is degraded by Xrn1 and the Ski/exosome complex. We establish cleaved HAC1 mRNA as endogenous substrate for ribosome-associated quality control. We conclude that mRNA decay and surveillance mechanisms collaborate in achieving fidelity of non-conventional mRNA splicing during the UPR

International evidence on sticky consumption growth

We estimate the degree of 'stickiness' in aggregate consumption growth (sometimes interpreted as reflecting consumption habits) for thirteen advanced economies. We find that, after controlling for measurement error, consumption growth has a high degree of autocorrelation, with a stickiness parameter of about 0.7 on average across countries. The sticky-consumption-growth model outperforms the random walk model of Hall (1978), and typically fits the data better than the popular Campbell and Mankiw (1989) model. In several countries, the sticky-consumption-growth and Campbell-Mankiw models work about equally well

How large is the housing wealth effect? : a new approach

This paper presents a simple new method for estimating the size of ‘wealth effects’ on aggregate consumption. The method exploits the well-documented sluggishness of consumption growth (often interpreted as ‘habits’ in the asset pricing literature) to distinguish between short-run and long-run wealth effects. In U.S. data, we estimate that the immediate (next-quarter) marginal propensity to consume from a $1 change in housing wealth is about 2 cents, with a final long-run effect around 9 cents. Consistent with several recent studies, we find a housing wealth effect that is substantially larger than the stock wealth effect. We believe that our approach is preferable to the currently popular cointegrationbased estimation methods, because neither theory nor evidence justifies faith in the existence of a stable cointegrating vector. JEL Classification: E21, E32, C2

Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

Knowledge- and Labor-Light Morphological Analysis

We describe a knowledge and labor-light system for morphological analysis of fusional languages, exemplified by analysis of Czech. Our approach takes the middle road between completely unsupervised systems on the one hand and systems with extensive manually-created resources on the other. For the majority of languages and applications neither of these extreme approaches seems warranted. The knowledge-free approach lacks precision and the knowledge- intensive approach is usually too costly. We show that a system using a little knowledge can be effective. This is done by creating an open, flexible, fast, portable system for morphological analysis. Time needed for adjusting the system to a new language constitutes a fraction of the time needed for systems with extensive manually created resources: days instead of years. We tested this for Russian, Portuguese and Catalan.The work described in this paper was partially supported by NSF CAREER Award 0347799

JPKWIC - General key word in context and subject index report generator

JPKWIC computer program is a general key word in context and subject index report generator specifically developed to help nonprogrammers and nontechnical personnel to use the computer to access files, libraries and mass documentation. This program is designed to produce a KWIC index, a subject index, an edit report, a summary report, and an exclusion list

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system

Abstract Accurate estimation of the soil water balance of the soil-plant-atmosphere system is key to determining the availability of water resources and their optimal management. Evapotranspiration and leaching are the main sinks of water from the system affecting soil water status and hence crop yield. The accuracy of soil water content and evapotranspiration simulations affects crop yield simulations as well. DSSAT is a suite of field-scale, process-based crop models to simulate crop growth and development. A “tipping bucket” water balance approach is currently used in DSSAT for soil hydrologic and water redistribution processes. By comparison, HYDRUS-1D is a hydrological model to simulate water flow in soils using numerical solutions of the Richards equation, but its approach to crop-related process modeling is rather limited. Both DSSAT and HYDRUS-1D have been widely used and tested in their separate areas of use. The objectives of our study were: (1) to couple HYDRUS-1D with DSSAT to simulate soil water dynamics, crop growth and yield, (2) to evaluate the coupled model using field experimental datasets distributed with DSSAT for different environments, and (3) to compare HYDRUS-1D simulations with those of the tipping bucket approach using the same datasets. Modularity in the software design of both DSSAT and HYDRUS-1D made it easy to couple the two models. The pairing provided the DSSAT interface an ability to use both the tipping bucket and HYDRUS-1D simulation approaches. The two approaches were evaluated in terms of their ability to estimate the soil water balance, especially soil water contents and evapotranspiration rates. Values of the d index for volumetric water contents were 0.9 and 0.8 for the original and coupled models, respectively. Comparisons of simulations for the pod mass for four soybean and four peanut treatments showed relatively high d index values for both models (0.94–0.99)

Optimization of test programs diesel injection pumps

Bakalářská práce „Optimalizace zkušebních programů vstřikovacích čerpadel vznětových motorů“ je zaměřena na proces testování vysokotlakých vstřikovacích čerpadel CP3 z hlediska jejich správné funkce. Samotné čerpadlo, které je nedílnou součástí systému Common Rail je jedním z nosných produktů firmy Bosch Diesel Jihlava. Práce se v první části zabývá představením vlastního čerpadla, především však podrobným popisem jeho funkčního testování na zkušební stanici montážní linky. Druhá část je úzce propojena s rozborem a analýzou dat získaných během samotné funkční zkoušky jednotlivých typů čerpadel CP3. Následně jsou navrženy možnosti zkrácení délky testovacích programů, díky čemuž je možné příznivě snížit vytíženost zkušební stanice vzhledem k taktu montážní linky.Bachelor thesis "Optimization of test programs injection pumps of diesel engines" is focused on the process of testing of proper function the high-pressure injection pumps CP3. This pump is an integral part of the system Common Rail and is one of the main products of the Bosch Diesel Jihlava company. The thesis in the first part deals with the presentation of the pump itself, and presents detailed description of the functional testing on the test station of assembly line. The second part is closely connected with the analysis of data which were collected during functional testing of each type of CP3 pumps. Following options are designed to reduce the length of test programs, making it possible to reduce the workload of test stations of assembly line.

How Large Is the Housing Wealth Effect? A New Approach

This paper presents a simple new method for estimating the size of ‘wealth effects?on aggregate consumption. The method exploits the well-documented sluggishness of consumption growth (often interpreted as ‘habits?in the asset pricing literature) to distinguish between short-run and long-run wealth effects. In U.S. data, we estimate that the immediate (next-quarter) marginal propensity to consume from a $1 change in housing wealth is about 2 cents, with a final longrun effect around 9 cents. Consistent with most recent studies, we find a housing wealth effect that is substantially larger than the stock wealth effect. We believe that our approach has sounder theoretical foundations than the currently popular cointegration-based estimation methods, because neither theory nor evidence provides any reason for faith in the existence of a stable cointegrating vector.