Miracle-dz/UniFAR-FTUs huggingface dataset:
Conservation of two lineages of peroxisomal (Type I) 3-ketoacyl-CoA thiolases in land plants, specialization of the genes in Brassicaceae, and characterization of their expression in Arabidopsis thaliana

Arabidopsis thaliana has three genes encoding type I 3-ketoacyl-CoA thiolases (KAT1, KAT2, and KAT5), one of which (KAT5) is alternatively transcribed to produce both peroxisomal and cytosolic proteins. To evaluate the potential importance of these four gene products, their evolutionary history in plants and their expression patterns in Arabidopsis were investigated. Land plants as a whole have gene lineages corresponding to KAT2 and KAT5, implying conservation of distinct functions for these two genes. By contrast, analysis of synteny shows that KAT1 arose by duplication of the KAT2 locus. KAT1 is found in the Brassicaceae family, including in the genera Arabidopsis, Capsella, Thellungiella (=Eutrema) and Brassica, but not in the more distantly related Caricaceae (order Brassicales), or other plants. Gene expression analysis using qRT-PCR and β-glucuronidase reporter genes showed strong expression of KAT2 during germination and in many plant tissues throughout the life cycle, consistent with its observed dominant function in fatty acid β-oxidation. KAT1 was expressed very weakly while KAT5 was most strongly expressed during flower development and in seedlings after germination. Isoform-specific qRT-PCR analysis and promoter β-glucuronidase reporters revealed that the two splicing variants of KAT5 have similar expression profiles. Alternative splicing of KAT5 to produce cytosolic and peroxisomal proteins is specific to and ubiquitous in the Brassicaceae, and possibly had an earlier origin in the order Brassicales. This implies that an additional function for KAT5 arose between 43 and 115 mybp. We speculate that this KAT5 mutation was recruited for a cytosolic function in secondary metabolism.
A Rule-Based Acceleration Control Scheme for an Induction Motor

This paper presents a rule-based acceleration control scheme that aims to give an inverter-fed induction motor excellent dynamic performance. In every time interval of the control process, the acceleration increments produced by two different voltage vectors are compared, yielding one optimum stator voltage vector that is selected and retained. The on-line inference control is built using a rule-based system and some heuristic knowledge about the relationship between the motor voltage and acceleration. Because evaluation of integrals is not required and the motor parameters are not involved, the new controller has no accumulation error due to the integrals as in the conventional vector control schemes and the same controller can be used for different motors without modification.
Listeria monocytogenes Traffics from Maternal Organs to the Placenta and Back

Infection with Listeria monocytogenes is a significant health problem during pregnancy. This study evaluates the role of trafficking between maternal organs and placenta in a pregnant guinea pig model of listeriosis. After intravenous inoculation of guinea pigs, the initial ratio of bacteria in maternal organs to placenta was 103–104:1. Rapid increase of bacteria in the placenta changed the ratio to 1:1 after 24 h. Utilizing two wild-type strains, differentially marked by their susceptibility to erythromycin, we found that only a single bacterium was necessary to cause placental infection, and that L. monocytogenes trafficked from maternal organs to the placenta in small numbers. Surprisingly, bacteria trafficked in large numbers from the placenta to maternal organs. Bacterial growth, clearance, and transport between organs were simulated with a mathematical model showing that the rate of bacterial clearance relative to the rate of bacterial replication in the placenta was sufficient to explain the difference in the course of listeriosis in pregnant versus nonpregnant animals. These results provide the basis for a new model where the placenta is relatively protected from infection. Once colonized, the placenta becomes a nidus of infection resulting in massive reseeding of maternal organs, where L. monocytogenes cannot be cleared until trafficking is interrupted by expulsion of the infected placental tissues.
A combined request/response and time delay technique to detect attacks in a PKI-based architecture for m-commerce

PKI-based infrastructure serves as m-commerce platform, especially, for authentication, in order to make the online transactions secure. This paper explains PKI-based request-response based authentication and its related attacks. Further, the merits and de-merits of the same technique have been analyzed and presented. Finally, the request-response approach is combined with the time-delay technique to form a novel authentication protocol which would enhance the validity of authentication process. The exhaustive simulation results have been presented in both scenarios, i.e., in the presence as well as absence of the malicious hackers.
On node density - outage probability tradeoff in wireless networks

A statistical model of interference in wireless networks is considered, which is based on the traditional propagation channel model and a Poisson model of random spatial distribution of nodes in 1-D, 2-D and 3-D spaces with both uniform and non-uniform densities. The power of nearest interferer is used as a major performance indicator, instead of a traditionally-used total interference power, since at the low outage region, they have the same statistics so that the former is an accurate approximation of the latter. This simplifies the problem significantly and allows one to develop a unified framework for the outage probability analysis, including the impacts of complete/partial interference cancellation, of different types of fading and of linear filtering, either alone or in combination with each other. When a given number of nearest interferers are completely canceled, the outage probability is shown to scale down exponentially in this number. Three different models of partial cancellation are considered and compared via their outage probabilities. The partial cancellation level required to eliminate the impact of an interferer is quantified. The effect of a broad class of fading processes (including all popular fading models) is included in the analysis in a straightforward way, which can be positive or negative depending on a particular model and propagation/system parameters. The positive effect of linear filtering (e.g. by directional antennas) is quantified via a new statistical selectivity parameter. The analysis results in formulation of a tradeoff relationship between the network density and the outage probability, which is a result of the interplay between random geometry of node locations, the propagation path loss and the distortion effects at the victim receiver.
Mixed wavelet-watershed method for nodule detection in high resolution scintigraphy

The recent developments in gamma cameras have lead to significant improvement of intrinsic spatial resolution in radioisotopic imaging. Moreover, the use of smaller photodetectors (PSPMT, photodiodes) enables to build more compact devices which allows to place the detector very near from the organ under study and thus to enhance the global spatial resolution. However, these improvements need the use of smaller image pixel sizes which worsens signal to noise ratio in every pixel. Moreover, a lot of examinations, such as scintimammography will be all the more useful since small nodules can be detected. In this work, we present a method combining a denoising wavelet filtering method with watershed determination, both leading to hot nodule enhancement. This method was applied to Monte Carlo simulations of a 64 mm high volume containing hot nodules of various sizes with a tumor to background ratio equal to 5. Although nodules were not visible in raw images corresponding to 10 min acquisition, the described process allowed to detect 87% of 6 mm nodules and more than 97% of nodules whose diameter was greater or equal to 7 mm. Furthermore, the whole process runs very fast
A Fast Preprocessing Method for Table Boundary Detection: Narrowing Down the Sparse Lines Using Solely Coordinate Information

As the rapid growth of PDF document in digital libraries, recognizing the document structure and detecting specific document components are useful for document storage, classification and retrieval. Tables, as a specific document component, are ubiquitous everywhere. Accurately detecting the table boundary plays a crucial role for the later table structure decomposition and table data collection. In this paper, we propose an easy but effective table boundary detection method. Our method has two unique advantages comparing with other works in this field: 1) Because most tables are text-based, we claim that the text object of PDF itself is good enough for table detection. In addition, we believe that the font information is not so reliable as other works stated. 2) Based on the nature of the table cells, we notice the sparse-line property of table rows. By filtering out the non-sparse lines initially, the table boundary detection problem can be simplified into the sparse line analysis problem easily. The experimental results not only confirm the importance of the coordinate information, but also demonstrate the effectiveness of sparse lines in the table boundary detection. Combining with other keywords, our method is even applicable to detect other document components (e.g., mathematical formula or the references).
Genomic DNA functions as a universal external standard in quantitative real-time PCR

Real-time quantitative PCR (qPCR) is a powerful tool for quantifying specific DNA target sequences. Although determination of relative quantity is widely accepted as a reliable means of measuring differences between samples, there are advantages to being able to determine the absolute copy numbers of a given target. One approach to absolute quantification relies on construction of an accurate standard curve using appropriate external standards of known concentration. We have validated the use of tissue genomic DNA as a universal external standard to facilitate quantification of any target sequence contained in the genome of a given species, addressing several key technical issues regarding its use. This approach was applied to validate mRNA expression of gene candidates identified from microarray data and to determine gene copies in transgenic mice. A simple method that can assist achieving absolute quantification of gene expression would broadly enhance the uses of real-time qPCR and in particular, augment the evaluation of global gene expression studies.
The BioGRID Interaction Database: 2008 update

The Biological General Repository for Interaction Datasets (BioGRID) database (http://www.thebiogrid.org) was developed to house and distribute collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies. Through comprehensive curation efforts, BioGRID now includes a virtually complete set of interactions reported to date in the primary literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. A number of new features have been added to the BioGRID including an improved user interface to display interactions based on different attributes, a mirror site and a dedicated interaction management system to coordinate curation across different locations. The BioGRID provides interaction data with monthly updates to Saccharomyces Genome Database, Flybase and Entrez Gene. Source code for the BioGRID and the linked Osprey network visualization system is now freely available without restriction.
Dynamic Scheduling of Virtual Machines Running HPC Workloads in Scientific Grids

The primary motivation for enterprises to adopt virtualization technologies is to create a more agile and dynamic IT infrastructure -- with server consolidation, high resource utilization, the ability to quickly add and adjust capacity on demand -- while lowering total cost of ownership and responding more effectively to changing business conditions. However, effective management of virtualized IT environments introduces new and unique requirements, such as dynamically resizing and migrating virtual machines (VMs) in response to changing application demands. Such capacity management methods should work in conjunction with the underlying resource management mechanisms. In general, resource multiplexing and scheduling among virtual machines is poorly understood. CPU scheduling for virtual machines, for instance, has largely been borrowed from the process scheduling research in operating systems. However, it is not clear whether a straight-forward port of process schedulers to VM schedulers would perform just as well. We use the open source Xen virtual machine monitor to perform a comparative evaluation of three different CPU schedulers for virtual machines. We analyze the impact of the choice of scheduler and its parameters on application performance, and discuss challenges in estimating the application resource requirements in virtualized environments.
Efficient cross-correlation via sparse representation in sensor networks

Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. We propose cross-correlation via sparse representation: a new framework for ranging based on l1-minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two order of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Furthermore, compared to cross-correlation results, 30-40% measurements are sufficient to obtain precise range estimates with an additional bias of only 2-6cm for high accuracy application requirements, while 5% measurements are adequate to achieve approximately 100cm precision for lower accuracy applications.
2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines

(Circulation. 2014;129[suppl 2]:S1-S45) © 2013 The Expert Panel Members. The Journal of the American College of Cardiology is published on behalf of the American College of Cardiology Foundation by Elsevier Inc.; Circulation is published on behalf of the American Heart Association, Inc., by Wolters Kluwer. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDervis License, which permits use, distribution, and reproduction in any medium, provided that the Contribution is properly cited, the use is non-commercial, and no modifications or adaptations are made.
Negative regulation of phosphatidylinositol 3-phosphate levels in early-to-late endosome conversion

C. elegans SORF-1 and SORF-2 and their mammalian homologs WDR91 and WDR81 maintain appropriate PtdIns3P levels in early-to-late endosome conversion by forming a complex with the Beclin1 subunit of the PI3K complex.
Metric-Based Heuristic Space Diversity Management in a Meta-Hyper-Heuristic Framework

This paper investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm. In contrast to all previously developed heuristic space diversity management strategies, this paper makes use of a heuristic space diversity metric to monitor heuristic space diversity throughout the optimization run and trigger the need for increased or decreased heuristic space diversity. Three different heuristic space diversity management strategies are evaluated. Maintaining a high level of heuristic space diversity throughout the optimization run is shown to be the best performing strategy. Good performance is also demonstrated with respect to a state-of-the-art multi-method algorithm, another successful diversity controlling meta-hyper-heuristic and the best-performing constituent algorithm.
Model stability: a key factor in determining whether an algorithm produces an optimal model from a matching distribution

We investigate the factors leading to producing suboptimal models when training and test class distributions (or misclassification costs) are matched. Our result shows that model stability plays a key role in determining whether the algorithm produces an optimal model from a matching distribution (cost). The performance difference between a model trained from the matching distribution (cost) and the optimal model generally increases as the degree of model stability decreases. The practical implication of our result is that one should only follow the conventional wisdom of using a training class distribution (cost) that matches the test class distribution (cost) to train a classifier if the learning algorithm is known to be stable.
The Time Series Approach to Short Term Load Forecasting

The application of time series analysis methods to load forecasting is reviewed. It is shown than Box and Jenkins time series models, in particular, are well suited to this application. The logical and organized procedures for model development using the autocorrelation function and the partial autocorrelation function make these models particularly attractive. One of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature. A simple procedure for overcoming this difficulty is introduced, and several Box and Jenkins models are compared with a forecasting procedure currently used by a utility company.
The fat thumb: using the thumb's contact size for single-handed mobile interaction

Modern mobile devices allow a rich set of multi-finger interactions that combine modes into a single fluid act, for example, one finger for panning blending into a two-finger pinch gesture for zooming. Such gestures require the use of both hands: one holding the device while the other is interacting. While on the go, however, only one hand may be available to both hold the device and interact with it. This mostly limits interaction to a single-touch (i.e., the thumb), forcing users to switch between input modes explicitly. In this paper, we contribute the Fat Thumb interaction technique, which uses the thumb's contact size as a form of simulated pressure. This adds a degree of freedom, which can be used, for example, to integrate panning and zooming into a single interaction. Contact size determines the mode (i.e., panning with a small size, zooming with a large one), while thumb movement performs the selected mode. We discuss nuances of the Fat Thumb based on the thumb's limited operational range and motor skills when that hand holds the device. We compared Fat Thumb to three alternative techniques, where people had to precisely pan and zoom to a predefined region on a map and found that the Fat Thumb technique compared well to existing techniques.
Determining Mood for a Blog by Combining Multiple Sources of Evidence

Mood classification for blogs is useful in helping user-to-agent interaction for a variety of applications involving the web, such as user modeling, recommendation systems, and user interface fields. It is challenging at the same time because of the diversity of the characteristics of bloggers, their experiences, and the way moods are expressed. As an attempt to handle the diversity, we combine multiple sources of evidence for a mood type. Support vector machine based mood classifier (SVMMC) is integrated with mood flow analyzer (MFA) that incorporates commonsense knowledge obtained from the general public (i.e. ConceptNet), the affective norms english words (ANEW) list, and mood transitions. In combining the two different approaches, we employ a statistically weighted voting scheme based on the support vector machine (SVM). For evaluation, we have built a mood corpus consisting of manually annotated blogs, which amounts to over 4000 blogs. Our proposed method outperforms SVMMC by 5.68% in precision. The improvement is attributed to the strategy of choosing more trustable classification results in an interleaving fashion between the SVMMC and our MFA.
Interphase Nucleo-Cytoplasmic Shuttling and Localization of SIRT2 during Mitosis

The human NAD+-dependent protein deacetylase SIRT2 resides predominantly in the cytoplasm where it functions as a tubulin deacetylase. Here we report that SIRT2 maintains a largely cytoplasmic localization during interphase by active nuclear export in a Crm1-dependent manner. We identified a functional, leptomycin B-sensitive, nuclear export signal sequence within SIRT2. During the cell cycle, SIRT2 becomes enriched in the nucleus and is associated with mitotic structures, beginning with the centrosome during prophase, the mitotic spindle during metaphase, and the midbody during cytokinesis. Cells overexpressing wild-type or a catalytically inactive SIRT2 exhibit an increase in multinucleated cells. The findings suggest a novel mechanism of regulating SIRT2 function by nucleo-cytoplasmic shuttling, as well as a role for SIRT2 in the nucleus during interphase and throughout mitosis.
PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations

Real-world observable physical and chemical characteristics are increasingly being calculated from the 3D structures of biomolecules. Methods for calculating pKa values, binding constants of ligands, and changes in protein stability are readily available, but often the limiting step in computational biology is the conversion of PDB structures into formats ready for use with biomolecular simulation software. The continued sophistication and integration of biomolecular simulation methods for systems- and genome-wide studies requires a fast, robust, physically realistic and standardized protocol for preparing macromolecular structures for biophysical algorithms. As described previously, the PDB2PQR web server addresses this need for electrostatic field calculations (Dolinsky et al., Nucleic Acids Research, 32, W665–W667, 2004). Here we report the significantly expanded PDB2PQR that includes the following features: robust standalone command line support, improved pKa estimation via the PROPKA framework, ligand parameterization via PEOE_PB charge methodology, expanded set of force fields and easily incorporated user-defined parameters via XML input files, and improvement of atom addition and optimization code. These features are available through a new web interface (http://pdb2pqr.sourceforge.net/), which offers users a wide range of options for PDB file conversion, modification and parameterization.