Miracle-dz/UniFAR-FTUs huggingface dataset:
The Gene-Enzyme Relationships of Proline Riosynthesis in Escherichia coli

A simple chromatographic procedure has been devised to separate y-glutamyl phosphate reductase and 1-pyrroline-5-carboxylate reductase, allowing the measurement of the former in crude Escherichia coli extracts. Analysis of a number of strains of E. coli has demonstrated that gene proA codes for y-glutamyl phosphate reductase and proB for y-glutamyl kinase. Introduction of a ColE1 hybrid plasmid containing the proA,B region into a strain with a chromosomal deletion of proA,B led to 3-and 17-fold increases in the specific activities of y-glutamyl kinase and y-glutamyl phosphate reductase, respectively. In Escherichia coli, proline is synthesized from glutamate via a series of three enzymic steps with an intervening spontaneous conversion of intermediates : (1) L-Glutamate + ATP (2) y-Glutamyl phosphate + NADPH (3) ~-l-Pyrroline-5-carboxylic acid + NADPH The reduction of glutamate to glutamic acid 5-semialdehyde is via the hypothesized intermediate y-glutamyl phosphate, a compound of extreme lability when in a nucleophilic environment. In reaction (l), the first enzyme in the sequence, y-glutamyl kinase [ATP : L-glutamate 5-phosphotransferase; EC 2.7. 2.1 11 catalyses the ATP-dependent y-phosphorylation of glutamate (Baich, 1969). Regulatory control of the entire pathway by retro-inhibition by proline is exerted at this point (Baich, 1969). Reaction (2), the reduction of the activated intermediate to glutamic acid 5-semialdehyde, is mediated by an NADPH-dependent y-glutamyl phosphate reductase (Baich, 197 1). Spontaneous dehydration and cyclization results in the Schiff base formation of l-pyrroline-5-carboxylate, which is reduced in reaction (3) by 1-pyrroline-5-carboxylate reductase [L-proline : NAD(P)+ 5-oxidoreductase ; It has been suggested that the first and second enzymes of the pathway form a multimolecular complex to afford protection to the labile y-glutamyl phosphate, direct transfer of the intermediate from one enzyme to the other avoiding equilibration with the surrounding medium (Baich, 1969; Gamper & Moses, 1974). Proline biosynthesis is governed by three genetic loci. The loci proA and proB are associated with the conversion of glutamate to glutamic acid 5-semialdehyde. While it is controversial whether they are arranged contiguously (Condamine, 1971 a) or at a distance of about 0.6 min from each other (Broda, 1974), they have been placed at 5.6 min on the linkage map of E. coli (Bachmann et a! ., 1976). TheproC locus is located at 8.7 min on the
The B‐lymphoid Grb2 interaction code

Summary: The growth factor receptor‐bound protein 2 (Grb2) is a ubiquitously expressed and evolutionary conserved adapter protein possessing a plethora of described interaction partners for the regulation of signal transduction. In B lymphocytes, the Grb2‐mediated scaffolding function controls the assembly and subcellular targeting of activating as well as inhibitory signalosomes in response to ligation of the antigen receptor. Also, integration of simultaneous signals from B‐cell coreceptors that amplify or attenuate antigen receptor signal output relies on Grb2. Hence, Grb2 is an essential signal integrator. The key question remains, however, of how pathway specificity can be maintained during signal homeostasis critically required for the balance between immune cell activation and tolerance induction. Here, we summarize the molecular network of Grb2 in B cells and introduce a proteomic approach to elucidate the interactome of Grb2 in vivo.
Efficiency of Local Search

Local search heuristics are an important class of algorithms for obtaining good solutions for hard combinatorial optimization problems. Important issues for these heuristics are solution quality and the time needed to obtain a good solution. Roughly speaking, a local search heuristic starts with an initial solution, e.g. obtained with the help of a constructive heuristic. The local search heuristic then iteratively explores a neighborhood of the current solution and chooses a new solution out of this neighborhood to become the next current solution. Often, only new solutions are considered, which improve on the current one. The local search heuristic halts if some stopping criterion is met, e.g. there exists no better solution in the neighborhood.
Efficient reduction of Gaussian components using MDL criterion for HMM-based speech recognition

A method is proposed to reduce the number of Gaussian components in continuous density hidden Markov models (HMMs). As its initial model, the method employs a well-trained, large-sized HMM in which the components of each state's Gaussian mixture probability density function are clustered into a binary tree. For each state, a subset of Gaussian components is chosen from the Gaussian tree on the basis of the minimum description length (MDL) criterion. By varying the penalty coefficient for large size models in the MDL criterion, it is possible to obtain the total number of Gaussian components desired for smaller models. In our experimental evaluations, the proposed method successfully reduced the number of Gaussian components by 75%, with only 1% degradation in recognition accuracy.
Symmetry in Gardens of Eden

Conway's Game of Life has inspired enthusiasts to search for a wide range of patterns for this classic cellular automaton. One important challenge in this context is finding the smallest Garden of Eden (GoE), a state without a predecessor. We take up this challenge by applying two techniques. First, we focus on GoEs that contain a symmetry. This significantly reduces the size of the search space for interesting sizes of the grid. Second, we implement the search using incremental satisfiability solving to check thousands of states per second. By combining these techniques, we broke several records regarding GoEs: the fewest defined cells, the smallest bounding box, and the lowest living density. Furthermore, we established a new lower bound for the smallest GoE.
From small scale to large scale user participation: a case study of participatory design in e-government systems

Most experiments with participative design are with small scale, stand alone and not very strategic applications of ICT in organizations. However, modern ICT applications are increasingly based on complex and large scale network technologies. What PD issues arise in this type of projects? What methods can be used for user participation? And, what does this imply for PD strategies?
A Wavelet Representation of Re ectance FunctionsPaul Lalonde and

Analytical models of light reeection are in common use in computer graphics. However, models based on measured reeectance data promise increased realism by making it possible to simulate many more types of surfaces to a greater level of accuracy than with analytical models. They also require less expert knowledge about the illumination models and their parameters. There are a number of hurdles to using measured reeectance functions, however. The data sets are very large. A reeectance distribution function sampled at ve degrees angular resolution, arguably sparse enough to miss highlights and other high frequency eeects, can easily require over a million samples, which in turn amount to over 4 megabytes of data. These data then also require some form of interpolation and ltering to be used eeectively. In this paper we examine issues of representation of measured reeectance distribution functions. In particular we examine a wavelet basis representation of reeectance functions, and the algorithms required for eecient point-wise reconstruction of the BRDF. We show that the non-standard wavelet decomposition leads to considerably more eecient algorithms than the standard wavelet decomposition. We also show that threshholding allows considerable improvement in running times, without unduly sacriicing image quality.
International Standard Serial Number (issn): 2319-8141 International Journal of Universal Pharmacy and Bio Sciences Impact Factor 1.89*** Icv 5.13*** Pharmaceutical Sciences Research Article……!!! Formulation and Evalution of Mouth Dissolving Tablets of Metoclopramide Hydrochloride by Direct Compress

Fast dissolving drug delivery system offers a solution for those patients having difficulty in swallowing tablet. In the present study, an attempt has been made to prepare fast dissolving tablets of the drug Metoclopramide hydrochloride using superdisintegrants Crospovidone, Croscarmellose sodium and Sodium starch glycolate by direct compression technique. The prepared tablets were evaluated for hardness, friability, wetting time, weight variation, in vitro disintegration time and in vitrodissolution study. The hardness of the tablets was in the range of 2.0-4.0 Kg/cm². The percentage fr iability of the tablets was less than one. Weight variation test results showed that the tablets were deviating from the average weight within the permissible limits of ±7.5 %. Drug content uniformity study results showed that uniform dispersion of the drug throughout the formulation i.e. 98.54% to 101.23%. Tablets containing Crospovidone showed better disintegrating character along with the rapid release (97.42% drug within 1 minute).
The WCET Analysis using Counters-A Preliminary Assessment

The Worst-Case Execution Time (WCET) analysis aims to statically and safely bound the longest execution path of a program. It is also desirable for the computed bound to be as close as possible to the actual WCET, thus making the result of a WCET analysis tight. In this paper we propose a methodology for the WCET analysis using an abstract program (with special program variables called counters), in order to better exploit the underlying program semantics (via abstract interpretation) and to produce potentially tighter WCET bounds.
A Theoretical Foundation for the Stakeholder Corporation

This paper presents a model of the stakeholder corporation and analyzes the equilibrium of an economy with stakeholder firms. The analysis is based on a model of a production economy that differs from the standard approach where uncertainty is modeled by an exogenous probability distribution over states of nature. The property that differentiates it from the standard model (which justifies the shareholder approach to the corporation) is that firms’ choices of investment influence the probability distributions of their outputs, and hence exert external effects on consumers and employees: as a result profit maximization and competitive behavior do not lead to Pareto optimality. Using a Coasian approach to resolve the problem of externalities, we show that if firms issue not only equity shares but also marketable property rights for employees and consumers, and if firms’ managers maximize the total values of their firms (shareholder value plus consumer and employee values) then Pareto optimality of equilibrium is restored when agents are identical. In the more realistic case where agents are heterogeneous, reforming capitalism by giving some weight to employee and consumer surpluses in the objective of the firms always increases social welfare.
Person Wide Web for Ubiquitous Computing: Ubiquitous Version of WWW

WWW is composed of web documents and links to connect them in the cyber world. After WWW was developed to be a tool for experts in the beginning, the growth of it was explosive. Now, WWW becomes our daily lives. In this paper, we introduce Person Wide Web(PWW) which is composed of links embedded in the real world and web documents. In PWW system, we can catch the links in the real world to indicate where the information is, and access web documents in their environment through Wireless Personal Area Network(WPAN) technology
Adaptive Transfer Function Estimation of a Notional High-Temperature Superconducting Propulsion Motor

In this paper, the application of a novel time-frequency analysis tool for the online estimation of the transfer functions of a notional high-temperature superconducting (HTS) marine propulsion motor is demonstrated. A detailed simulation model of the notional HTS motor with a realistic load modeling of sea states to produce torque oscillations is utilized for the analysis. A method is presented for the online near real-time estimation of transfer functions between the torque oscillations and the concomitant variations in the field current and mechanical speed of the HTS motor. The transfer functions, obtained by using the novel method, are compared with the results from a traditional offline transfer function estimation technique.
The Stock Market and Cross Country Differences in Relative Prices

This paper studies the impact of stock market development on cross country relative prices (the real exchange rate). A nonlinear relationship is uncovered in the cross section: prices and the stock market increase together in the beginning; then prices fall as the stock market continues to develop. In fact, among rich countries the relationship between prices and the stock market is negative. This result obtains after controlling for per capita income and for endogeneity issues by using legal origins. A small open economy model is presented to explain the connection between stock market development and relative prices: better investment opportunities increase consumption levels and the price of nontradable goods (income effect); but if stock market assets are less labor intensive than previous entrepreneurial technologies, prices can fall as the stock market grows because more labor is available for producing nontradables (substitution effect). This paper illustrates the connection of the stock market with goods and labor markets; it also has potential implications for the political economy of financial development. In a sideline contribution, it provides prices for entrepreneurial assets.
Ieee Css/ras International Workshop on \control Problems in Robotics and Automation: Future

This paper summarizes the major conclusions of twenty years of research in robot force control, points out remaining problems, and introduces issues that need more attention. By looking at force control from a distance, a lot of common features among diierent control approaches are revealed; this allows us to put force control into a broader (e.g., diierential-geometric) context. The paper starts with the basics of force control at an introductory level, by focusing at one or two degrees of freedom. Then the problems associated with the extension to the multidimensional case are described in a diierential-geometric context. Finally, robustness and adaptive control are discussed.
User simulation for the evaluation of bus information systems

In this paper, we describe our contribution to the Spoken Dialog Challenge. We set up a user simulation using the large Let's Go corpus as resource to build our models. Automatic calls were made to all four dialog systems in the SDC, bus information systems that cover the schedule of Pittsburgh, PA. We discuss in detail the architecture and required setup for our system-independent user simulation and report the results and challenges we faced. We also report initial evaluation results.
Binary- and Multi-class Group Sparse Canonical Correlation Analysis for Feature Extraction and Classification

This paper incorporates the group sparse representation into the well-known canonical correlation analysis (CCA) framework and proposes a novel discriminant feature extraction technique named group sparse canonical correlation analysis (GSCCA). GSCCA uses two sets of variables and aims at preserving the group sparse (GS) characteristics of data within each set in addition to maximize the global interset covariance. With GS weights computed prior to feature extraction, the locality, sparsity and discriminant information of data can be adaptively determined. The GS weights are obtained from an NP-hard group-sparsity promoting problem that considers all highly correlated data within a group. By defining one of the two variable sets as the class label matrix, GSCCA is effectively extended to multiclass scenarios. Then GSCCA is theoretically formulated as a least-squares problem as CCA does. Comparative analysis between this work and the related studies demonstrate that our algorithm is more general exhibiting attractive properties. The projection matrix of GSCCA is analytically solved by applying eigen-decomposition and trace ratio (TR) optimization. Extensive benchmark simulations are conducted to examine GSCCA. Results show that our approach delivers promising results, compared with other related algorithms.
Liquidity, Default, Taxes and Yields on Municipal Bonds

We examine the effects of liquidity, default and personal taxes on the relative yields of Treasuries and municipals using a generalized model with liquidity risk. The municipal yield model includes liquidity as a state factor. Using a unique transaction dataset, we estimate the liquidity risk of municipals and its effect on bond yields. Empirical evidence shows that municipal bond yields are strongly affected by all three factors. The effects of default and liquidity risk on municipal yields increase with maturity and credit risk. Liquidity premium accounts for about 9-13% of municipal yields for AAA bonds, 9-15% for AA/A bonds and 8-19% for BBB bonds. A substantial portion of the maturity spread between long- and short-maturity municipal bonds is attributed to the liquidity premium. Ignoring the liquidity risk effect thus results in a severe underestimation of municipal bond yields. Conditional on the effects of default and liquidity risk, we obtain implicit tax rates very close to the statutory tax rates of high-income individuals and institutional investors. Furthermore, these implicit income tax rates are quite stable across bonds of different maturities. Results show that including liquidity risk in the municipal bond pricing model helps explain the muni puzzle.
Impossible Collision Based Side-Channel Attacks on HIGHT with Reduced Masked Rounds

At CHES 2007, Biryukov and Knovratovich intro- duced a concept of side-channel attacks based on impossible collisions, and applied it to AES with reduced masked rounds. In this paper, we propose impossible collision based side-channel attacks on HIGHT with 11 reduced masked rounds. Our attack needs 2 17 chosen plaintexts and 2 23.6 curve comparisons. It is known to be the first side-channel attack on HIGHT with reduced masked rounds. Index Terms—Side-Channel Attacks, HIGHT, Impossible- Collision
Optimization via gradient oriented polar random search

Search algorithms are often used for optimization problems where its mathematical formulation is difficult to be analyzed, e.g., simulation optimization. In literature, search algorithms are either driven by gradient or based on random sampling within specified neighborhood, but both methods have limitation as gradient search can be easily trapped at a local optimum and random sampling loses efficiency by not utilizing local information such as gradient direction that might be available. A combination of the two is believed to overcome both disadvantages. However, the main difficulty is how to incorporate and control randomness in a direction instead of a point. Thus, this paper makes use of a polar coordinate representation in any high dimension to randomly generate directions where the concentration can be explicitly controlled, based on which a brand new Gradient Oriented Polar Random Search (GO-POLARS) is designed and proved to satisfy the conditions for strong local convergence.
Multi Stage Filter Using Enhanced Adaboost for Network Intrusion Detection

Based on the analysis and distribution of network attacks in KDDCup99 dataset and real time traffic, this paper proposes a design of multi stage filter which is an efficient and effective approach in dealing with various categories of attacks in networks. The first stage of the filter is designed using Enhanced Adaboost with Decision tree algorithm to detect the frequent attacks occurs in the network and the second stage of the filter is designed using enhanced Adaboost with Naive Byes algorithm to detect the moderate attacks occurs in the network. The final stage of the filter is used to detect the infrequent attack which is designed using the enhanced Adaboost algorithm with Naive Bayes as a base learner. Performance of this design is tested with the KDDCup99 dataset and is shown to have high detection rate with low false alarm rates.
Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models

Ozellikle son on yilda yapay sinir aglari modelleri portfoy olusturma ve hisse senedi piyasasi tahminleri gibi finansal problemleri cozmede uygulanmaktadir. Cesitli yapay sinir aglari modelleri arasinda, cok-katmanli porseptron modelleri finansal tahmin calismalari icin yaygin ve etkili bir sekilde kullanilmaktadir. Bu calisma, cok-katmanli porseptron modellerinin IMKB-100 endeksinin gunluk ve seanslik getirilerinin tahmin edilmesindeki etkinligini incelemektedir. Calismanin bulgularindan yola cikilarak, cok-katmanli porseptron modellerinin IMKB-100 endeks getirisini tahmin etmede umut vaat eden bir performans gosterdigi sonucuna varilabilir. Fakat, yapay sinir aglari modellerinin tahmin gucleri farkli degiskenler ve farkli model yapilari kullanilarak daha da arttirilabilir.
Reviewing the Impact of Increased Atmospheric CO2 on Oceanic pH and the Marine Ecosystem

The world's oceans contain an enormous reservoir of carbon, greater than either the terrestrial or atmospheric systems. The fluxes between these reservoirs are relatively rapid such that the oceans have taken up around 50% of the total carbon dioxide (CO2) released to the atmosphere via fossil fuel emissions and other human activities in the last 200 years. Whilst this has slowed the progress of climate change, CO2 ultimately results in acid- ification of the marine environment. Ocean pH has already fallen and will continue to do so with certainty as the oceans take up more anthropogenic CO2. Acidification has only recently emerged as a serious issue and it has the potential to affect a wide range of marine biogeochemical and ecological processes. Based on theory and an emerging body of research, many of these effects may be non-linear and some potentially complex. Both positive and negative feedback mechanisms exist, making prediction of the consequences of changing CO2 levels difficult. Integrating the net effect of acidification on marine processes at regional and basin scales is an outstanding challenge that must be addressed via integrated programs of experimentation and modelling. Ocean acidification is another argument, alongside that of cli- mate change, for the mitigation of anthropogenic CO2 emissions.
An Embedded Two-Layer Feature Selection Approach for Microarray Data Analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.