Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure–function analysis of the kinase-CPD domain of yeast tRNA ligase (Trl1) and requirements for complementation of tRNA splicing by a plant Trl1 homolog

Trl1 is an essential 827 amino acid enzyme that executes the end-healing and end-sealing steps of tRNA splicing in Saccharomyces cerevisiae. Trl1 consists of two domains—an N-terminal ligase component and a C-terminal 5′-kinase/2′,3′-cyclic phosphodiesterase (CPD) component—that can function in tRNA splicing in vivo when expressed as separate polypeptides. To understand the structural requirements for the kinase-CPD domain, we performed an alanine scan of 30 amino acids that are conserved in Trl1 homologs from other fungi. We thereby identified four residues (Arg463, His515, Thr675 and Glu741) as essential for activity in vivo. Structure–function relationships at these positions, and at four essential or conditionally essential residues defined previously (Asp425, Arg511, His673 and His777), were clarified by introducing conservative substitutions. Biochemical analysis showed that lethal mutations of Asp425, Arg463, Arg511 and His515 in the kinase module abolished polynucleotide kinase activity in vitro. We report that a recently cloned 1104 amino acid Arabidopsis RNA ligase functions in lieu of yeast Trl1 in vivo and identify essential side chains in the ligase, kinase and CPD modules of the plant enzyme. The plant ligase, like yeast Trl1 but unlike T4 RNA ligase 1, requires a 2′-PO4 end for tRNA splicing in vivo.
Design of an Actor language for implicit parallel programming

Software tools that support implicit parallel programming hold the key to reducing the complexity of parallel programming and realizing more ubiquitous parallel computation. This paper addresses the topic of implicit concurrent object-oriented programming. It presents a new language, SYMPAL, which unlike most existing COOP languages that provide explicit constructs for concurrency control, is based on a unification of object orientation and pure functional programming with the goals of supporting implicit programming and high efficiency in massively concurrent object-oriented programming. Extensive experience with SYMPAL applications on a parallel machine indicates that it achieves these goals.
Metabolomic Strategy for Studying the Intervention and the Synergistic Effects of the Shexiang Baoxin Pill for Treating Myocardial Infarction in Rats

A metabolomic approach has been developed for evaluating the therapeutic effects of the bioactive components and the synergistic efficacy of the Shexiang Baoxin Pill (SBP) on myocardial infarction (MI) in rats. The MI rats were administered the SBP, muscone, cinnamic acid, bufalin, ginsenoside Re, ginsenoside Rb1, cholic acid, borneol, and a combined version of these bioactive components (SFSBP). Liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS) was used to obtain the mass data from the rats' serum. The number of biomarkers that were reversed by SFSBP was greater than any of the monotherapy groups. The PLS-DA score plots demonstrated that the SFSBP group results were located closer to the sham group than any of the monotherapy groups and that the SBP group was located closer to the sham group than the SFSBP treatment group. The reversing results observed with SFSBP showed synergistic effects when compared with those of the individual bioactive components that were used as monotherapy. Meanwhile, the SBP displayed superior regulation efficacy to SFSBP in MI rats, indicating that there must be other active components in the SBP that were responsible for the treatment of MI that were not included in the SFSBP treatment.
A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods.
Learning or Robust Control

Model mis-specification can cause substantial utility losses in portfolio planning. In this paper, we compare two approaches to cope with this problem, robust control and learning. We derive the optimal portfolio strategies and the utility losses due to model mis-specification. Surprisingly, neither learning nor robust control is uniformly superior to the naive approach where the investor simply ignores model risk. Furthermore, a comparison of the two approaches shows that learning takes some time to have an impact, so that short-term investors are in some (but not all) cases better off with robust control than with learning.
E-biosign: stylus- and finger-input multi-device database for dynamic signature recognition

This paper describes the design, acquisition process and a baseline evaluation of e-BioSign, a new database of dynamic signature and handwriting. e-BioSign is comprised of 5 devices in total, three Wacom devices (DTU-500, DTU-530 and STU 1031) specifically designed to capture dynamic signatures and handwriting, and two Samsung general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV). For these two Samsung tablets data is collected using a pen stylus but also the finger to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 70 subjects, and includes dynamic information of the signature, the full name and number sequences. For signature and the full name skilled forgeries were also performed. A signature baseline evaluation is carried out for a predefined recognition system based on DTW, achieving a benchmark performance for each of the devices. The use of finger for signing achieves good results for the case of random forgeries (less than 1% EER), but the performance is degraded significantly for the case of skilled forgeries compared to the case using the pen stylus.
Partially occluded human detection by boosting SVM

In this paper, a novel method to detect partially occluded humans in still images is proposed. An individual human is modeled as an assembly of natural body parts. Some part based SVM classifiers are trained first by using histogram of orientated gradient feature. Different from other boosting methods, region information is stored in each classifier. When detect human in crowed scene, according to the information of humans that have already been detected, the information of available regions could be obtained, when a new detection window is in process. In classifier sequence, the classifiers whose regions are available are selected for generating the final classifier. This method could achieve good performance on images and video sequences with several occlusions.
A SystemC-TLM Platform for Wireless Sensor Networks Design Exploration

Wireless Sensor Networks (WSN 1 ) have gained worldwide attention in recent years, due to their potential for development of applications in various areas: health, environmental monitoring, control and monitoring processes, etc. Due to the complexity of WSN design, it is necessary to employ design methodologies at a very high level of abstraction which allow the exploration of diverse alternatives of hardware and software architectures. In this research a generic platform using SystemC TLM (Transaction Level Modeling) to support the design of system-level WSN was developed. The platform allows the system’s functional evaluation at a very high level of abstraction. A virtual functional model of a WSN could be built using platform components to estimate system performance and power consumption. A model of a WSN using the MSP430 Microcontroller, the CC2420 radio transceiver and a wireless network model were built to show the effectiveness of this approach. The simulation results were highly satisfactory.
Impact of intracoronary bone marrow cell therapy on left ventricular function in the setting of ST-segment elevation myocardial infarction: a collaborative meta-analysis.

AIMS The objective of the present analysis was to systematically examine the effect of intracoronary bone marrow cell (BMC) therapy on left ventricular (LV) function after ST-segment elevation myocardial infarction in various subgroups of patients by performing a collaborative meta-analysis of randomized controlled trials. METHODS AND RESULTS We identified all randomized controlled trials comparing intracoronary BMC infusion as treatment for ST-segment elevation myocardial infarction. We contacted the principal investigator for each participating trial to provide summary data with regard to different pre-specified subgroups [age, diabetes mellitus, time from symptoms to percutaneous coronary intervention, infarct-related artery, LV end-diastolic volume index (EDVI), LV ejection fraction (EF), infarct size, presence of microvascular obstruction, timing of cell infusion, and injected cell number] and three different endpoints [change in LVEF, LVEDVI, and LV end-systolic volume index (ESVI)]. Data from 16 studies were combined including 1641 patients (984 cell therapy, 657 controls). The absolute improvement in LVEF was greater among BMC-treated patients compared with controls: [2.55% increase, 95% confidence interval (CI) 1.83-3.26, P < 0.001]. Cell therapy significantly reduced LVEDVI and LVESVI (-3.17 mL/m², 95% CI: -4.86 to -1.47, P < 0.001; -2.60 mL/m², 95% CI -3.84 to -1.35, P < 0.001, respectively). Treatment benefit in terms of LVEF improvement was more pronounced in younger patients (age <55, 3.38%, 95% CI: 2.36-4.39) compared with older patients (age ≥ 55 years, 1.77%, 95% CI: 0.80-2.74, P = 0.03). This heterogeneity in treatment effect was also observed with respect to the reduction in LVEDVI and LVESVI. Moreover, patients with baseline LVEF <40% derived more benefit from intracoronary BMC therapy. LVEF improvement was 5.30%, 95% CI: 4.27-6.33 in patients with LVEF <40% compared with 1.45%, 95% CI: 0.60 to 2.31 in LVEF ≥ 40%, P < 0.001. No clear interaction was observed between other subgroups and outcomes. CONCLUSION Intracoronary BMC infusion is associated with improvement of LV function and remodelling in patients after ST-segment elevation myocardial infarction. Younger patients and patients with a more severely depressed LVEF at baseline derived most benefit from this adjunctive therapy.
Literacy Development in Network-Based Classrooms: Innovation and Realizations

Electronic communication networks are in wide use for college-level language and writing instruction and are being adopted for use in elementary and secondary school classes. Teachers use network-based approaches to literacy instruction to support authentic reading and writing, collaboration, student-centered learning, writing across the curriculum, and the creation of classroom writing communities. A case study of network-based college classrooms identified great diversity in the ways these goals were realized. Nevertheless, common factors shaped all of the implementations: institutional goals, practices, and gateposts; theories, personalities, and established practices of teachers; student characteristics and expectations; features of the technology; and available resources. These factors suggest that like any innovation, the introduction of computer technology to promote interaction and learning in educational settings is a complex process that cannot be divorced from the users or the setting. This complexity needs to be understood so that perceptions of and expectations regarding the value of the innovation are neither idealized nor superficial.
vHOG, a multispecies vertebrate ontology of homologous organs groups

Motivation: Most anatomical ontologies are species-specific, whereas a framework for comparative studies is needed. We describe the vertebrate Homologous Organs Groups ontology, vHOG, used to compare expression patterns between species. Results: vHOG is a multispecies anatomical ontology for the vertebrate lineage. It is based on the HOGs used in the Bgee database of gene expression evolution. vHOG version 1.4 includes 1184 terms, follows OBO principles and is based on the Common Anatomy Reference Ontology (CARO). vHOG only describes structures with historical homology relations between model vertebrate species. The mapping to species-specific anatomical ontologies is provided as a separate file, so that no homology hypothesis is stated within the ontology itself. Each mapping has been manually reviewed, and we provide support codes and references when available. Availability and implementation: vHOG is available from the Bgee download site (http://bgee.unil.ch/), as well as from the OBO Foundry and the NCBO Bioportal websites. Contact: bgee@isb-sib.ch; frederic.bastian@unil.ch
Entrenchment Kinematics 101

The AGM framework of belief dynamics fails to account for iterated belief change. This paper extends the AGM framework in order to solve this problem. As in the AGM, an epistemic input is represented as a single sentence. Unlike the AGM, however , a belief state is represented as an epistemic entrenchment relation. Three state transition functions { namely, entrenchment expansion, entrenchment contraction and entrenchment revision { are constructed that preserve the basic appeal of the AGM system.
On the design method of the balanced Composite Right-/Left-Handed structures

A design method for the balanced Composite Righ-/Left-Handed (CRLH) is proposed and validated numerically. The method is developed for a CRLH equivalent circuit which takes into account the resonance frequency of the series capacitors, while the parallel resonator is specified by its admittance which may be particularized for any practical implementation. Numerical results are presented for three topologies of the parallel resonator: fully-distributed, semi-lumped and fully-lumped. Free design parameters are defined and their influence upon the other circuit parameters is discussed.
Multi-robot terrain coverage and task allocation for autonomous detection of landmines

Multi-robot systems comprising of heterogeneous autonomous vehicles on land, air, water are being increasingly used to assist or replace humans in different hazardous missions. Two crucial aspects in such multi-robot systems are to: a) explore an initially unknown region of interest to discover tasks, and, b) allocate and share the discovered tasks between the robots in a coordinated manner using a multi-robot task allocation (MRTA) algorithm. In this paper, we describe results from our research on multi-robot terrain coverage and MRTA algorithms within an autonomous landmine detection scenario, done as part of the COMRADES project. Each robot is equipped with a different type of landmine detection sensor and different sensors, even of the same type, can have different degrees of accuracy. The landmine detection-related operations performed by each robot are abstracted as tasks and multiple robots are required to complete a single task. First, we describe a distributed and robust terrain coverage algorithm that employs Voronoi partitions to divide the area of interest among the robots and then uses a single-robot coverage algorithm to explore each partition for potential landmines. Then, we describe MRTA algorithms that use the location information of discovered potential landmines and employ either a greedy strategy, or, an opportunistic strategy to allocate tasks among the robots while attempting to minimize the time (energy) expended by the robots to perform the tasks. We report experimental results of our algorithms using accurately-simulated Corobot robots within the Webots simulator performing a multi-robot, landmine detection operation.
A New Feature Dimensionally Reduction Approach Based on General Tensor Discriminant Analysis in EEG Signal Classification

feature selection from electroencephalogram (EEG) signals is important steps in BCI and medicine application. In this paper, a feature dimensionally reduction approach based on general tensor discriminant analysis (GTDA) is proposed. In this approach, EEG signal epochs are decomposed as spectral, spatial and temporal domain by Gabor functions as third order tensors. Then, projection vectors are extracted from tensor-represented EEG signals by GTDA. In this approach, the discriminative information in the training tensors is preserved that is a benefit in comparison with common feature space reduction approaches such as linear discriminant analysis (LDA) and principal component analysis (PCA). The proposed approach is evaluated to classify three mental tasks. The results indicate the improvement of classification performance in comparison with current methods.
INFLATION IN IRELAND

The purpose of this paper is to address some important questions surrounding the determinants of Irish inflation. As a backdrop, the international theoretical literature on inflation is selectively reviewed, with a view towards assessing its relevance to Ireland. Subsequently, the relevant Irish empirical literature is assessed in order to highlight the main outstanding areas of disagreement surrounding the determination of Irish inflation. This is contrasted with the widespread agreement which prevailed prior to our entry into the EMS. Following on from this, an empirical model of aggregate Irish inflation is formulated with a view towards specifically addressing the roles of (i) the exchange rate, (ii) wages and (iii) foreign factors in the determination of Irish inflation. Our overall results, obtained using the Johansen multivariate cointegration technique, highlight the long-run interrelationships between Irish prices, wages and the nominal effective exchange rate. In addition, it is shown that the purchasing power parity model is an acceptable long-run framework within which to consider price determination in Ireland. However, the results also support the view that excessive wage pressure, or domestic inflationary impulses in general, can exert upward pressure on Irish prices as long as the exchange rate is free to move. * The authors would like to thank John Frain, Tom O'Connell and other colleagues in the Central Bank for helpful suggestions on an earlier draft. We would also like to thank participants at a Dublin Economics Workshop seminar for their helpful suggestions. Comments from Professor Dermot McAleese and two anonymous referees were also gratefully received. The views expressed in the paper are not necessarily those held by the Bank and are the personal responsibility of the authors.
The Two Dimensions of Motivation and a Reciprocal Model of the Creative Process

Past research investigating the role of motivation in creativity has closely examined the role of intrinsic (i.e., process-focused) and extrinsic (i.e., outcome-focused) motivation. Results from this literature have shown that the effects of social factors on creativity (e.g., social inhibition or facilitation effects) are at least partially explained by their effect on intrinsic and extrinsic motivation. The present review seeks to broaden the scientific understanding of the role of motivation in creativity by proposing that a second dimension of motivation needs to be taken into account. In addition to creators’ locus of motivation (i.e., whether they focus on the intrinsic process and/or extrinsic outcomes), creative behavior also appears to be driven by its intended beneficiaries (i.e., the self and/or others). Supporting the importance of this second dimension, recent empirical research has begun to investigate the relationship between prosocial motivation (i.e., the desire to contribute to other people's lives) and creativity. Thus, a combined look at research on these two dimensions of motivation highlights the bidirectional nature of the social processes at play in creativity: While studies investigating creators’ locus of motivation have examined the influence of others on creators, recent studies considering creators’ intended beneficiaries have stressed the importance of also understanding how creators wish to affect others in return. To integrate these two perspectives, we propose a reciprocal model of the creative process in which creators’ general motivational orientations (falling along the dimensions outlined above) are translated into specific motivational goals, and we review possible psychological mechanisms explaining how motivation may guide creative cognition.
Optimal Colored Noise for Estimating Phase Response Curves

The phase response curve (PRC) is an important measure representing the interaction between oscillatory elements. To understand synchrony in biological systems, many research groups have sought to measure PRCs directly from biological cells including neurons. Ermentrout et al. and Ota et al. showed that PRCs can be identified through measurement of white-noise spike-triggered averages. The disadvantage of this method is that one has to collect more than ten-thousand spikes to ensure the accuracy of the estimate. In this paper, to achieve a more accurate estimation of PRCs with a limited sample size, we use colored noise, which has recently drawn attention because of its unique effect on dynamical systems. We numerically show that there is an optimal colored noise to estimate PRCs in the most rigorous fashion.