Miracle-dz/UniFAR-FTUs huggingface dataset:
Amino Acid Sequence Requirements at Residues 69 and 238 for the SME-1 β-Lactamase To Confer Resistance to β-Lactam Antibiotics

ABSTRACT Carbapenem antibiotics have been used to counteract resistant strains of bacteria harboring β-lactamases and extended-spectrum β-lactamases. Four enzymes from the class A group of β-lactamases, NMC-A, IMI-1, SME-1, and KPC-1, efficiently hydrolyze carbapenem antibiotics. Sequence comparisons and structural information indicate that cysteines at amino acid residues 69 and 238, which are conserved in all four of these enzymes, form a disulfide bond that is unique to these β-lactamases. To test whether this disulfide bond is required for catalytic activity, the codons for residues Cys69 and Cys238 were randomized individually and simultaneously by PCR-based mutagenesis to create random replacement libraries for these positions. Mutants that were able to confer resistance to ampicillin, imipenem, or cefotaxime were selected from these libraries. The results indicate that positions Cys69 and Cys238 are critical for hydrolysis of all of the antibiotics tested, suggesting that the disulfide bond is generally required for this enzyme to catalyze the hydrolysis of β-lactam antibiotics.
N,N’-Substituted 1,2,5 Thiadiazolidine 1,1-Dioxides: Synthesis, Selected Chemical and Spectral Proprieties and Antimicrobial Evaluation†

The sulfamide functional group is increasingly relevant in both medicinal and bioorganic chemistry. We report here practical access to a series of N2,N5-substituted five-membered cyclosulfamides. The five-membered heterocyclic motif was prepared starting from proteogenic amino acids and chlorosulfonyl isocyanate via the Mitsunobu reaction. Selected chemical and spectral proprieties and the antimicrobial evaluation of these compounds are detailed.
Genome-Wide Distribution, Organisation and Functional Characterization of Disease Resistance and Defence Response Genes across Rice Species

The resistance (R) genes and defense response (DR) genes have become very important resources for the development of disease resistant cultivars. In the present investigation, genome-wide identification, expression, phylogenetic and synteny analysis was done for R and DR-genes across three species of rice viz: Oryza sativa ssp indica cv 93-11, Oryza sativa ssp japonica and wild rice species, Oryza brachyantha. We used the in silico approach to identify and map 786 R -genes and 167 DR-genes, 672 R-genes and 142 DR-genes, 251 R-genes and 86 DR-genes in the japonica, indica and O. brachyanth a genomes, respectively. Our analysis showed that 60.5% and 55.6% of the R-genes are tandemly repeated within clusters and distributed over all the rice chromosomes in indica and japonica genomes, respectively. The phylogenetic analysis along with motif distribution shows high degree of conservation of R- and DR-genes in clusters. In silico expression analysis of R-genes and DR-genes showed more than 85% were expressed genes showing corresponding EST matches in the databases. This study gave special emphasis on mechanisms of gene evolution and duplication for R and DR genes across species. Analysis of paralogs across rice species indicated 17% and 4.38% R-genes, 29% and 11.63% DR-genes duplication in indica and Oryza brachyantha, as compared to 20% and 26% duplication of R-genes and DR-genes in japonica respectively. We found that during the course of duplication only 9.5% of R- and DR-genes changed their function and rest of the genes have maintained their identity. Syntenic relationship across three genomes inferred that more orthology is shared between indica and japonica genomes as compared to brachyantha genome. Genome wide identification of R-genes and DR-genes in the rice genome will help in allele mining and functional validation of these genes, and to understand molecular mechanism of disease resistance and their evolution in rice and related species.
A MULTI-OBJECTIVE HYBRID ACO-PSO OPTIMIZATION ALGORITHM FOR VIRTUAL MACHINE PLACEMENT IN CLOUD COMPUTING

Cloud computing is made possible through virtualization technology which makes virtual machines (VMs) to be placed in physical servers. The process of assigning VMs to servers is called virtual machine placement. VM placement in cloud is done by focusing on many objectives like VM allocation time, energy consumption, SLA violation, utilization of resources, etc. In this paper, a multiobjective hybrid ACO-PSO optimization algorithm is proposed for minimizing resource wastage, minimizing power consumption and for load balancing in physical servers. Simulation results show that the proposed algorithm reduces resource wastage and power consumption and also provides load balancing in servers when compared to the existing multi-objective ant colony system algorithm.
Interaction of imipenem with erythromycin and tetracycline assessed by microdilution checkerboard techniques

Microdilution methodology was used to study the interaction of imipenem with erythromycin and tetracycline, a combination therapy that might be used for the treatment of serious pelvic inflammatory disease. The combination of imipenem and erythromycin showed no antagonism for Escherichia coli and Haemophilus influenzae but was antagonistic for Staphylococcus aureus, Enterococcus faecalis, and group B streptococci; the combination of imipenem and tetracycline was antagonistic for all strains except H. influenzae. Correlation between the results of kill curves and the measurement of fractional bactericidal concentration (FBC) indices was good, although FBC indices showed less antagonism than kill curves. Fractional inhibitory concentration indices showed poor correlation, rarely showing antagonism, and indeed showed synergy in three cases. If erythromycin or tetracycline is considered necessary in addition to imipenem in the treatment of pelvic inflammatory disease, it is probably more effective when given after the course of imipenem has been completed.
Legionella pneumophila DotA protein is required for early phagosome trafficking decisions that occur within minutes of bacterial uptake

Numerous intracellular bacterial pathogens modulate the nature of the membrane‐bound compartment in which they reside, although little is known about the molecular basis for this control. Legionella pneumophila is a bacterial pathogen able to grow within human alveolar macrophages and residing in a phagosome that does not fuse with lysosomes. This study demonstrates that the dotA product is required to regulate trafficking of the L. pneumophila phagosome. Phagosomes containing L. pneumophila dotA+ bacteria exhibited differential trafficking profiles when compared with isogenic dotA mutants. Phagosomes containing dotA mutants showed rapid accumulation of the lysosomal glycoprotein LAMP‐1 as early as 5 min after uptake, whereas the majority of wild‐type L. pneumophila phagosomes did not acquire LAMP‐1. The association of LAMP‐1 with phagosomes containing dotA mutant bacteria was concomitant with the appearance of the small GTP‐binding protein Rab7 on the vacuolar membrane. These data demonstrate that phagosomes containing replication‐competent L. pneumophila evade early endocytic fusion events. In contrast, the kinetics of LAMP‐1 and Rab7 association indicate that the dotA mutants are routed along a well‐characterized endocytic pathway leading to fusion with lysosomes. Genetic studies show that L. pneumophila requires DotA expression before macrophage uptake in order to establish an intracellular site for replication. However, the bacteria do not appear to require continuous expression of the DotA protein to maintain a replicative phagosome. These data indicate that DotA is one factor that plays a fundamental role in regulating initial phagosome trafficking decisions either upon or immediately after macrophage uptake.
Maximum Entropy Based Phrase Reordering for Hierarchical Phrase-Based Translation

Hierarchical phrase-based (HPB) translation provides a powerful mechanism to capture both short and long distance phrase reorderings. However, the phrase reorderings lack of contextual information in conventional HPB systems. This paper proposes a context-dependent phrase reordering approach that uses the maximum entropy (MaxEnt) model to help the HPB decoder select appropriate reordering patterns. We classify translation rules into several reordering patterns, and build a MaxEnt model for each pattern based on various contextual features. We integrate the MaxEnt models into the HPB model. Experimental results show that our approach achieves significant improvements over a standard HPB system on large-scale translation tasks. On Chinese-to-English translation, the absolute improvements in BLEU (case-insensitive) range from 1.2 to 2.1.
Using maximum entropy for sentence extraction

A maximum entropy classifier can be used to extract sentences from documents. Experiments using technical documents show that such a classifier tends to treat features in a categorical manner. This results in performance that is worse than when extracting sentences using a naive Bayes classifier. Addition of an optimised prior to the maximum entropy classifier improves performance over and above that of naive Bayes (even when naive Bayes is also extended with a similar prior). Further experiments show that, should we have at our disposal extremely informative features, then maximum entropy is able to yield excellent results. Naive Bayes, in contrast, cannot exploit these features and so fundamentally limits sentence extraction performance.
Horizontal vectorization of electron repulsion integrals

We present an efficient implementation of the Obara–Saika algorithm for the computation of electron repulsion integrals that utilizes vector intrinsics to calculate several primitive integrals concurrently in a SIMD vector. Initial benchmarks display a 2–4 times speedup with AVX instructions over comparable scalar code, depending on the basis set. Speedup over scalar code is found to be sensitive to the level of contraction of the basis set, and is best for (lAlB|lClD) quartets when lD = 0 or lB=lD=0 , which makes such a vectorization scheme particularly suitable for density fitting. The basic Obara–Saika algorithm, how it is vectorized, and the performance bottlenecks are analyzed and discussed. © 2016 Wiley Periodicals, Inc.
Machine Learning Techniques for Prediction of Subject Scores: A Comparative Study

In this paper, a novel method is proposed so as to predict the subject wise academic performance of the Engineering students. This study describes the prediction of subject scores in ongoing courses by analyzing subject preludes of previous semester. In this study we try to predict the individual subject scores for ongoing courses while comparing two classification techniques i.e. Naive Bayesian and C4.5 Decision tree classifier. This piece of work adheres to most critical aspect of Quality objectives of Academia i.e. finding students’ academic performances for their ongoing courses well before they face their End semester Examination. Unlike the recent research trends that focused on predicting overall grading of students during their studies, this paper orients itself in identifying students grasping levels subject wise. It was found that from study, that obtained accuracy figure was higher in C4.5 Decision tree classifier than Naïve Bayes.
Do Cash Transfers Improve Birth Outcomes? Evidence from Matched Vital Statistics, Social Security and Program Data

There is limited empirical evidence on whether unrestricted cash social assistance to poor pregnant women improves children's birth outcomes. Using program administrative micro-data matched to longitudinal vital statistics on the universe of births in Uruguay, we estimate that participation in a generous cash transfer program led to a sizeable 15% reduction in the incidence of low birthweight. Improvements in mother nutrition and a fall in labor supply, out-of-wedlock births and mother's smoking all appear to contribute to the effect. We conclude that, by improving child health, unrestricted unconditional cash transfers may help break the cycle of intergenerational poverty.
Spider Behaviour: Flexibility And Versatility

1. Introduction Marie E. Herberstein and Anne Wignall 2. Foraging behaviour Ximena J. Nelson and Robert R. Jackson 3. Web building behaviour Marie E. Herberstein and I-Min Tso 4. Anti-predator behaviour Ximena J. Nelson and Robert R. Jackson 5. Communication Gabriele Uhl and Damian Elias 6. Deception Marie E. Herberstein and Anne Wignall 7. Mating behaviour and sexual selection Jutta Schneider and Maydianne Andrade 8. Group living in spiders: cooperative breeding and coloniality Trine Bilde and Yael Lubin 9. Plasticity, learning and cognition Elizabeth Jakob, Christa Skow and Skye Long 10. Kleptoparasitic spiders - a special case of behavioural plasticity Mary Whitehouse Index.
GOMS, GOMS+, and PDL

Tile e.wressiveness of tile GOMS language for describing "'how-to'" knowledge detepwlines what kinds of human activities cruz be captured by a G01I'IS task analysis. This paper addresses the adequacy of this franwowrk for representb~g humcm behavior in realistically time-pressured, uncertain, and otherwise demanding task enviromnents. Several improvements have been incorporated into a GO)glS-Iike fmTnalism called Procedure Description Language (PDL) and into a simple GOMS extension called GOMS+.
Network Analysis of Search Dynamics: The Case of Stock Habitats

There is an increasing attention in IS to be able to predict outcomes using search frequency on popular portals. This growing literature focuses on revealing demand patterns of individual assets (e.g., products, stocks, services). However, users typically search many different assets together (e.g., correlated searches) and leave digital footprint, which can help provide insights on the behaviors of a group of assets. Furthermore, such group behavior can be used to predict outcomes (e.g., demand, stock returns) in the future. We analyze the underlying behavior of distinct sub-networks formed by correlated user searches for multiple items in the stock market and use such information for return prediction. Using co-search data for stocks from Yahoo! Finance, we find 50 to 79 search clusters representing 230 to 349 stocks among Russell 3000 stocks at different time periods. These clusters reveal interesting habitats where the returns of stocks within a cluster tend to comove after controlling for known determinants of comovement. When a stock enters (departs) a cluster, the focal stock return comoves (detaches) with the cluster returns. Thus, search cluster based habitats reveal aggregate investment preferences and are more granular than fundamental-based habitats. We find that search based habitats can also improve return predictability of related stocks.
Decision making in uncertain environments: a resting state EEG study

Prior studies have shown that punishment and reward sensitivity influences decision making in a gambling task with a stable and predictable environment. Some unpublished data suggest that this also holds true in unpredictable situations in which the application of reversal learning is needed to optimally perform in a gambling task. The behavioral inhibition system (BIS) regulates and processes punishment, while the behavioral activation system (BAS) does this for rewarding stimuli. It is claimed that the neurobiological substrates of the BIS and BAS can be found in the prefrontal cortex, more specifically in the alpha band of an EEG signal (8-12 Hz). A right-sided asymmetry in the PFC indicates a higher sensitivity to punishment, while left-sided asymmetry indicates a higher sensitivity to reward. This research will try to further investigate the effects on the BIS and BAS as determined by EEG recordings on decision making in a gambling task where reversal learning is needed. The main hypothesis of the present study is that the amount of reversal learning in an uncertain environment can be predicted by measurements of an asymmetry found in the alpha band (8-12 Hz) in baseline EEG. It was expected that a right-sided asymmetry would correlate positively with reversal learning while a left-sided asymmetry would correlate negatively with reversal learning. A novel task was used that consisted of changing reward-punishment contingencies to induce reversal learning. Reversal learning was measured by determining a behavioral adaptation index calculated from task performance. Self-reported BIS and BAS data was obtained through a questionnaire. Resting state EEG data was recorded and a frontal asymmetry index was subtracted from this data. The main hypothesis was not supported by the present research; Results showed that EEG asymmetries calculated from the alpha band (8-12) from baseline EEG recordings did not predict reversal learning. Furthermore, self-reported BIS and BAS scores did not correlate with frontal asymmetry, failing to support the notion that such an asymmetry reflects differences in punishment sensitivity. Finally, self-reported BIS and BAS scores did not correlate significantly with reversal learning, indicating that under uncertain circumstances punishment sensitivity does not affect task performance. Conclusively, none of the hypothesis were confirmed by this study. However, one effect was found that could indicate that reversal learning might be affected by situational factors. The selfreported BIS scores correlated negatively with reversal learning in a certain part of the task. This indicates that self-reported BIS scores can predict reversal learning, but only under certain circumstances such as previous and starting situation. Still, this finding was not sufficient to support the hypothesis in the present study. More research is advised to gain insight in the effect of situational factors on reversal learning as determined by either frontal asymmetry or self-reported BIS and BAS scores.
Performance evaluation of market‐based resource allocation for Grid computing

Resource allocation is an important aspect of Grid computing. Over the past few years, various systems have been developed which use market mechanisms to allocate resources. However, the performance of such policies has not been sufficiently studied. In this paper, we investigate under which circumstances market‐based resource allocation by continuous double auctions and by the proportional share protocol, respectively, outperforms a conventional round‐robin approach. We develop a model for clients, servers and the market, and present simulation results. Factors which are studied include the amount of load in the system, the number of resources, different degrees of resource heterogeneity, and communication delays. Copyright © 2004 John Wiley & Sons, Ltd.
A cultural confluence: Approaches to embedding cultural insights and inclusion throughout the marketing process

This study explored the culturally driven variables in the marketing process that contribute to a brand’s success or minimize cultural missteps. The primary research question was: How are consumer brand organizations building capabilities and organizing to embed cultural inclusion and insights throughout the marketing communications process? Twenty senior-level marketing executives contributed to the mixed-methods study. Five critical variables were found to be consistent across industries: effective data collection, reporting, and integration are critical; leadership must be courageous and culturally skilled; strategic alignment around cultural inclusion throughout the enterprise and across internal and external stakeholders is necessary; culturally adaptive talent management is required; and an end-to-end focus and recalibration of the processes requires resource investments and a commitment to hardwire a refined approach. Recommendations for leveraging the variables included a greater focus on data integration, inclusive leadership development, process recalibration and development of cultural agility competencies, and knowledge sharing across industries.
Integer-Forcing Architectures for Uplink Cloud Radio Access Networks

Consider an uplink cloud radio access network where users are observed simultaneously by several base stations, each with a rate-limited link to a central processor, which wishes to decode all transmitted messages. Recent efforts have demonstrated the advantages of compression-based strategies that send quantized channel observations to the central processor, rather than attempt local decoding. We study the setting where channel state information is not available at the transmitters, but known fully or partially at the base stations. We propose an end-to-end integer-forcing framework for compression-based uplink cloud radio access, and show that it operates within a constant gap from the optimal outage probability if channel state information is fully available at the base stations. We demonstrate via simulations that our framework is competitive with state-of-the-art Wyner-Ziv-based strategies.
Some selected and functionalised organometallic molecules for NLO applications

Organometallics nano complexes are important targets for the construction of advanced materials, the investigation of electron-transfer processes and of novel devices for non-linear optical (NLO) applications. In this work the diagnostic and characterisation of novel molecular organics nano complexes thin films functionalized for NLO applications will be discussed. On other hand a rich study of the formation of surface-relief gratings (SRGs), using a picosecond pulsed laser at 532 nm, resulted in instantaneous inscription will be presented.
Effects of measurement error in piecewise regression models

A study is made of the join point estimate in a piecewise linear regression model when measurement error is present in the independent variable. Initially, the effects of measurement error on a standard estimation technique for piecewise regression are investigated. Later, a new estimation technique is proposed which is Dot affected by aeasure-ment error. The effects of measurement error on the ordinary least squares (OLS) estimate of the join point, as proposed by Hudson (1966), are examined by obtaining the asymptotic expected value and mean squared error (MSE) using a Taylor series approximation. Furthermore, the empirical distribution of the join point estimate is found from a simulation study. Comparisons are then made between the asymptotic and e~ pirieal results for various values of measurement error variance. It is found that the join point estimate based on OLS estimation is biased and the MSF. increases with increasing measurement error. Following Wald (1940), an estimation procedure is proposed which is based on a grouping technique known to be asymptotically unbiased for the regression coefficients. In addition to a simulation study parallel to that done for the OLS estimate, a numerical technique is introduced which enables one to obtain an estimate of the variance of the join point estimate from the sample data. Results from this variance estimate are compared with the variance from the empirical distribution obtained through the simulation study. The grouping approach is found to yield consistent join point estimates and the numerical estimate of the variance is close to the variance from the empirical distribution. A less comprehensive study is made of estimation procedurel as proposed by Tultey (1951) which can be used when the data are replicated. In this cale it is possible to obtain estimates of the error variances and use this additional information in estimating the relreasion coefficients and join point of the piecewise mdel. The techniques are applied to the problem of estimatinl day of ovu-lation based on a woman '. ba.al body temperature taken throughout a aen-strual cycle.