Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformation of Isopropylamine to l-Alaninol by Pseudomonas sp. Strain KIE171 Involves N-Glutamylated Intermediates

ABSTRACT Pseudomonas sp. strain KIE171 was able to grow with isopropylamine or l-alaninol [S-(+)-2-amino-1-propanol] as the sole carbon source, but not with d-alaninol. To investigate the hypothesis that l-alaninol is an intermediate in the degradation of isopropylamine, two mini-Tn5 mutants unable to utilize both isopropylamine and l-alaninol were isolated. Whereas mutant KIE171-BI transformed isopropylamine to l-alaninol, mutant KIE171-BII failed to do so. The two genes containing a transposon insertion were cloned, and the DNA regions flanking the insertions were sequenced. Two clusters, one comprising eight ipu (isopropylamine utilization) genes (ipuABCDEFGH) and the other encompassing two genes (ipuI and orf259), were identified. Comparisons of sequences of the deduced Ipu proteins and those in the database suggested that isopropylamine is transported into the cytoplasm by a putative permease, IpuG. The next step, the formation of γ-glutamyl-isopropylamide from isopropylamine, ATP, and l-glutamate, was shown to be catalyzed by IpuC, a γ-glutamylamide synthetase. γ-Glutamyl-isopropylamide is then subjected to stereospecific monooxygenation by the hypothetical four-component system IpuABDE, thereby yielding γ-glutamyl-l-alaninol [γ(l-glutamyl)-l-hydroxy-isopropylamide]. Enzymatic hydrolysis by a hydrolase, IpuF, was shown to finally liberate l-alaninol and to regenerate l-glutamate. No gene(s) encoding an enzyme for the next step in the degradation of isopropylamine was found in the ipu clusters. Presumably, l-alaninol is oxidized by an alcohol dehydrogenase to yield l-2-aminopropionaldehyde or it is deaminated by an ammonia lyase to propionaldehyde. Genetic evidence indicated that the aldehyde formed is then further oxidized by the hypothetical aldehyde dehydrogenases IpuI and IpuH to either l-alanine or propionic acid, compounds which can be processed by reactions of the intermediary metabolism.
Testing the stages model in the adaptive radiation of cichlid fishes in East African Lake Tanganyika

Adaptive radiation (AR) is a key process in the origin of organismal diversity. However, the evolution of trait disparity in connection with ecological specialization is still poorly understood. Available models for vertebrate ARs predict that diversification occurs in the form of temporal stages driven by different selective forces. Here, we investigate the AR of cichlid fishes in East African Lake Tanganyika and use macroevolutionary model fitting to evaluate whether diversification happened in temporal stages. Six trait complexes, for which we also provide evidence of their adaptiveness, are analysed with comparative methods: body shape, pharyngeal jaw shape, gill raker traits, gut length, brain weight and body coloration. Overall, we do not find strong evidence for the ‘stages model’ of AR. However, our results suggest that trophic traits diversify earlier than traits implicated in macrohabitat adaptation and that sexual communication traits (i.e. coloration) diversify late in the radiation.
Cyclosparsity: A New Concept for Sparse Deconvolution

Periodic random impulse signals are appropriate tools for several situations of interest and are a natural way for modeling highly localized events occuring randomly at given times. Nevertheless, the impulses are generally hidden and swallowed up in noise because of unwanted convolution. Thus, the resulting signal is not legible and may lead to erroneaous analysis, and hence, the need of deconvolution to restore the random periodic impulses. The main purpose of this study is to introduce the concept of cyclic sparsity or cyclosparsity in deconvolution framework for signals that are jointly sparse and cyclostationary like periodic random impulses. Indeed, all related works in this area exploit only one property, either sparsity or cyclostationarity and never both properties together. Although, the key feature of the cyclosparsity concept is that it gathers both properties to better characterize this kind of signals. We show that deconvolution based on cyclic sparsity hypothesis increases the performances and reduces significantly the computation cost as well. Finally, we use computer simulations to investigate the behavior in deconvolution framework of the algorithms Matching Pursuit (MP) [13], Orthogonal Matching Pursuit (OMP) [14], Orthogonal Least Square (OLS) [15], Single Best Replacement (SBR), [19, 20, 21] and the proposed extensions to cyclic sparsity context: Cyclo-MP, Cyclo-OMP, Cyclo-OLS and Cyclo-SBR.
Compensation methods to support cooperative applications: A case study in automated verification of schema requirements for an advanced transaction model

Compensation plays an important role in advanced transaction models, cooperative work and workflow systems. A schema designer is typically required to supply for each transaction $T$ another transaction $T^{-1}$ to semantically undo the effects of $T$. Little attention has been paid to the verification of the desirable properties of such operations, however. This paper demonstrates the use of a higher‐order logic theorem prover for verifying that compensating transactions return a database to its original state. It is shown how an OODB schema is translated to the language of the theorem prover so that proofs can be performed on the compensating transactions. Copyright © 2001 John Wiley & Sons, Ltd.
Macro with Pico Cells (HetNets) System Behavior Using Well-known scheduling Algorithms

This paper demonstrates the concept of using Heterogeneous networks (HetNets) to improve Long Term Evolution (LTE) system by introducing the LTE Advance (LTE-A). The type of HetNets that has been chosen for this study is Macro with Pico cells. Comparing the system performance with and without Pico cells has clearly illustrated using three well-known scheduling algorithms (Proportional Fair PF, Maximum Largest Weighted Delay First MLWDF and Exponential/Proportional Fair EXP/PF). The system is judged based on throughput, Packet Loss Ratio PLR, delay and fairness. A simulation platform called LTE-Sim has been used to collect the data and produce the paper’s outcomes and graphs. The results prove that adding Pico cells enhances the overall system performance. From the simulation outcomes, the overall system performance is as follows: throughput is duplicated or tripled based on the number of users, the PLR is almost quartered, the delay is nearly reduced ten times (PF case) and changed to be a half (MLWDF/EXP cases), and the fairness stays closer to value of 1. It is considered an efficient and cost effective way to increase the throughput, coverage and reduce the latency.
Oall Olic Ropagation I

The paper presents an efficient goal oriented algorithm for symbolic propagation in Bayesian networks. The proposed algorithm performs symbolic propagation using numerical methods. It first takes advantage of the independence relationships among the variables and produce a reduced graph which contains only the relevant nodes and parameters required to compute the desired propagation. Then, the symbolic expression of the solution is obtained by performing numerical propagations associated with specific values of the symbolic parameters. These specific values are called the canonical components. Substantial savings are obtained with this new algorithm. Furthermore, the canonical components allow us to obtain lower and upper bounds for the symbolic expressions resulting from the propagation. An example is used to illustrate the proposed methodology.
Using SVMs with the Command Relation features to identify negated events in biomedical literature

In this paper we explore the identification of negated molecular events (e.g. protein binding, gene expressions, regulation, etc.) in biomedical research abstracts. We construe the problem as a classification task and apply a machine learning (ML) approach that uses lexical, syntactic, and semantic features associated with sentences that represent events. Lexical features include negation cues, whereas syntactic features are engineered from constituency parse trees and the command relation between constituents. Semantic features include event type and participants. We also consider a rule-based approach that uses only the command relation. On a test dataset, the ML approach showed significantly better results (51% F-measure) compared to the command-based rules (35--42% F-measure). Training a separate classifier for each event class proved to be useful, as the micro-averaged F-score improved to 63% (with 88% precision), demonstrating the potential of task-specific ML approaches to negation detection.
Assemblage Structure and Population Dynamics of Phytoplankton in a Brackish Coastal Creek, Badagry, Southwest Nigeria

Aims: The objectives of the study were to determine phytoplankton assemblage structure in Badagry creek; to examine spatial-seasonal variations of abiotic factors and to assess their effects on it. Study Design: Stratified random sampling. Place and Duration of Study: Nine sampling stations were selected using descriptive statistics, ANOVA, cluster and Canonical correspondence analysis (CCA) at α = 0.05. Results: Overall, 242 phytoplankton species of six main classes were identified. Phytoplankton assemblage was dominated by Baccillariophyceae (133 species; 95.02%) specifically Aulacoseira granulata var . angustissima (Ehrenberg) Ralfs and Actinoptychus undulatus (Bailey). Other Algae groups were green algae (59 species), blue-green algae (27 species), euglenoids cell (17 species), dinoflagellates and Chrysophytes had three species each. Few phytoplankton species were reported to be potentially harmful species. Diversity indices values were highest (H = 2.633; E = 0.138; 1-D = 0.896) for st.8 (Iyagbe) and lowest (H = 1.221; E = 0.036; 1-D = 0.589) at Ajido (st.5). Comparatively, phytoplankton species (184) and individuals (541,655) recorded in the rainy season were higher than the dry season of 174 species and 446,516 individuals. Mean values ranged from 1.12 (st.2) - 7.83 psu(st.9), 2259.33 (st.2) - 13642.00 µScm -1 (st.9), 19.67 (st.7) - 36.00 FTU(st.9), 1.38 (st.7) - 6.72 m (st.2) and 3.75 (st.1) - 12.53 µmol/L(st.9) for salinity, conductivity, turbidity, water depth and nitrate, respectively. Seasonal mean variations were pH 7.47; 7.75 and PO 4 6.54; 4.80 µmol/L for dry and rainy seasons, respectively. Correlations between species and variables, suggests the importance of abiotic factors in phytoplankton assemblages. In CCA plot, Phosphate and water temperature have higher correlation ordination axis, closely followed by conductivity (salinity) and water depth. Conclusion: Abiotic factors evaluated showed some influences on abundance and distribution of phytoplankton assemblage in Badagry creek. This study, however, provides baseline information for establishing predictions of phytoplankton population changes in Badagry creek.
Cloning and expression of the major 47-kilodalton surface immunogen of Treponema pallidum in Escherichia coli

Monoclonal antibodies directed against the 47-kilodalton (kDa) major outer membrane surface immunogen of virulent Treponema pallidum subsp. pallidum were used to select Escherichia coli recombinant clones expressing the 47-kDa immunogen. The phenotype of the clones was dependent on the presence of recombinant plasmid in the host cell. Southern hybridization revealed that the cloned T. pallidum subsp. pallidum DNA sequence was an accurate representation of the T. pallidum subsp. pallidum genomic DNA arrangement. Purified immunoglobulin G from rabbits experimentally infected with T. pallidum subsp. pallidum and human secondary syphilitic sera specifically reacted with the clones, while normal human serum or immunoglobulin G from normal rabbit serum did not. Results of Southern hybridization indicated that a homologous 47-kDa immunogen gene was absent in at least four species of nonpathogenic treponemes tested, as well as from total rabbit genomic DNA. Rabbit anti-T. phagedenis biotype Reiter (treponemal nonpathogen) antiserum and a monoclonal antibody directed against a common treponemal determinant were unreactive with the clones. Western blotting and radioimmunoprecipitation experiments with specific monoclonal antibodies revealed that the recombinant (E. coli) and native (T. pallidum subsp. pallidum) forms of the antigen had identical electrophoretic mobilities. The availability of recombinant 47-kDa immunogen provides a new opportunity for biochemical analysis of the protein, structure-function studies, examination of its role in microbial pathogenesis, and assessment of its diagnostic and vaccinogenic potentials.
Local and Global Discriminative Learning for Unsupervised Feature Selection

In this paper, we consider the problem of feature selection in unsupervised learning scenario. Recently, spectral feature selection methods, which leverage both the graph Laplacian and the learning mechanism, have received considerable attention. However, when there are lots of irrelevant or noisy features, such graphs may not be reliable and then mislead the selection of features. In this paper, we propose the Local and Global Discriminative learning for unsupervised Feature Selection (LGDFS), which integrates a global and a set of locally linear regression model with weighted l2-norm regularization into a unified learning framework. By exploring the discriminative and geometrical information in the weighted feature space, which alleviates the effects of the irrelevant features, our approach can find the most representative features to well respect the cluster structure of the data. Experimental results on several benchmark data sets are provided to validate the effectiveness of the proposed approach.
Effects of innervation and drugs upon direct membrane potentials of embryonic chick myocardium.

Direct membrane potentials of embryonic chick atrium and ventricle have been characterized. Membrane potentials of the atrium and the ventricle of both non-innervated and innervated hearts are qualitatively identical and similar to those of the adult frog ventricle. Quantitative differences between membrane potentials of chick hearts of different ages are of minor magnitude and do not indicate that they result from the establishment of innervation. Acetylcholine, epinephrine and digitoxin modify membrane potentials characteristically, the pattern of alteration by each drug being the same in both the atrium and the ventricle and in both the non-innervated and the innervated heart. No marked increase in sensitivity of the innervated myocardium to epinephrine and acetyicholine was observed. These observations support the concept that the non-innervated myocardium possesses marked maturity and do not support the generally accepted concept that establishment of innervation endows the embryonic heart with marked increased sensitivity to chemical mediators.
"Unmixing" tissues: sparse component analysis in multi-contrast MRI

We pose the problem of tissue classification in MRI as a blind source separation (BSS) problem and solve it by means of sparse component analysis (SCA). Assuming that most MR images can be sparsely represented, we consider their optimal sparse representation. Sparse components define a physically-meaningful feature space for classification. We demonstrate our approach on simulated and real multi-contrast MRI data. The proposed framework is general in that it is applicable to other modalities of medical imaging as well, whenever the linear mixing model is applicable.
Balanced Dynamic Planning in Green Heterogeneous Cellular Networks

Dynamic planning has been used by network operators for energy saving by switching ON-OFF base stations (BSs) according to the traffic load condition while providing quality-of-service (QoS) guarantee for mobile users. In the literature, research efforts focus mainly on satisfying the QoS of downlink mobile users. However, the impact of dynamic planning on the QoS of uplink mobile users is overlooked. A dynamic planning approach that relies only on the downlink performance of mobile users to determine the BS switching decision can result in deactivating nearby small cells and associating uplink mobile users to a faraway macro BS. Consequently, uplink mobile users may suffer from QoS degradation due to the longer transmission distance. Hence, while saving energy for the network operators and satisfying the downlink QoS, the uplink QoS could be violated. In this paper, we propose a balanced dynamic planning approach that accounts for QoS requirements both in the uplink and downlink to specify the BS switching decision. The switching decision is formulated as a two-timescale average reward Markov decision process with finite horizon. Due to computational complexity, sub-optimal algorithms are proposed. Simulation results demonstrate the trade-off between energy saving and QoS guarantee.
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)

Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation conditional random fields (SCRFs), is proposed as an effective solution to this problem. In contrast to traditional graphical models, such as the hidden Markov model (HMM), SCRFs follow a discriminative approach. Therefore, it is flexible to include any features in the model, such as overlapping or long-range interaction features over the whole sequence. The model also employs a convex optimization function, which results in globally optimal solutions to the model parameters. On the other hand, the segmentation setting in SCRFs makes their graphical structures intuitively similar to the protein 3-D structures and more importantly provides a framework to model the long-range interactions between secondary structures directly. Our model is applied to predict the parallel beta-helix fold, an important fold in bacterial pathogenesis and carbohydrate binding/cleavage. The cross-family validation shows that SCRFs not only can score all known beta-helices higher than non-beta-helices in the Protein Data Bank (PDB), but also accurately locates rungs in known beta-helix proteins. Our method outperforms BetaWrap, a state-of-the-art algorithm for predicting beta-helix folds, and HMMER, a general motif detection algorithm based on HMM, and has the additional advantage of general application to other protein folds. Applying our prediction model to the Uniprot Database, we identify previously unknown potential beta-helices.
Giant fluctuations of topological charge in a disordered wave guide

We study the fluctuations of the total topological charge of a scalar wave propagating in a hollow conducting waveguide filled with scatterers inside. We investigate the dependence of the screening on the scattering mean free path and on the presence of boundaries. Near the cut-off frequencies of the waveguide, screening is strongly suppressed close to boundaries. The resulting huge fluctuations of the total topological charge are very sensitive to the disorder.
Reasoning about Pragmatics with Neural Listeners and Speakers

We present a model for pragmatically describing scenes, in which contrastive behavior results from a combination of inference-driven pragmatics and learned semantics. Like previous learned approaches to language generation, our model uses a simple feature-driven architecture (here a pair of neural "listener" and "speaker" models) to ground language in the world. Like inference-driven approaches to pragmatics, our model actively reasons about listener behavior when selecting utterances. For training, our approach requires only ordinary captions, annotated _without_ demonstration of the pragmatic behavior the model ultimately exhibits. In human evaluations on a referring expression game, our approach succeeds 81% of the time, compared to a 69% success rate using existing techniques.
A Kalman Filter Implementation for Precision Improvement in Low-Cost GPS Positioning of Tractors

Low-cost GPS receivers provide geodetic positioning information using the NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a quantization grid of some decimeters in size, the dimensions of which vary depending on the point of the terrestrial surface. The aim of this study is to reduce the quantization errors of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model equations were employed to particularize the filter, which was tuned by applying Monte Carlo techniques to eighteen straight trajectories, to select the covariance matrices that produced the lowest Root Mean Square Error in these trajectories. Filter performance was tested by using straight tractor paths, which were either simulated or real trajectories acquired by a GPS receiver. The results show that the filter can reduce the quantization error in distance by around 43%. Moreover, it reduces the standard deviation of the heading by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors. It could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain.
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the \textsc{Common Crawl} project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.
A State Duration Generation Algorithm Considering Global Variance for HMM-based Speech Synthesis

The speech parameter generation algorithm considering global variance (GV) for HMM-based speech synthesis proved to be effective against the over-smoothing problem. In this paper this idea is extended to the generation of state duration. A GV model on syllable duration is proposed and a state duration generation algorithm considering this GV model is presented in details. By improving the GV likelihood on syllable duration, the over-averaging effect on generated state duration is much alleviated. Experimental results are promising which show that the proposed method outperforms the conventional one and the naturalness of synthetic speech is improved.