Miracle-dz/UniFAR-FTUs huggingface dataset:
Corepressor-induced organization and assembly of the biotin repressor: A model for allosteric activation of a transcriptional regulator

The Escherichia coli biotin repressor binds to the biotin operator to repress transcription of the biotin biosynthetic operon. In this work, a structure determined by x-ray crystallography of a complex of the repressor bound to biotin, which also functions as an activator of DNA binding by the biotin repressor (BirA), is described. In contrast to the monomeric aporepressor, the complex is dimeric with an interface composed in part of an extended β-sheet. Model building, coupled with biochemical data, suggests that this is the dimeric form of BirA that binds DNA. Segments of three surface loops that are disordered in the aporepressor structure are located in the interface region of the dimer and exhibit greater order than was observed in the aporepressor structure. The results suggest that the corepressor of BirA causes a disorder-to-order transition that is a prerequisite to repressor dimerization and DNA binding.
Frequency-wise optimal duty-cycle selection in steady state visual evoked potentials: A pilot study

We propose an optimal duty-cycle selection method for steady state visual evoked potentials (SSVEP) based brain computer interface (BCI) system. In SSVEP based BCI, visual stimuli are usually selected in a low frequency range. However, previous studies showed that a higher frequency range is more visually comfortable than the low frequency range for SSVEP based BCI. In addition, frequencies of 15-25Hz ranges are provocative to cause photosensitivity epileptic seizure problem. We designed an experiment to select an optimal duty cycle visual flicker for each frequency in a range of 27.5Hz to 33.5Hz. We utilized canonical correlation analysis (CCA) in order to extract the frequency component features from multi-channel EEG data. The goal of this study was to select optimal visual flicker within minimum visual fatigue. Visual flickers with the largest amplitude among the duty-cycle higher than 50% were selected as optimum. The performance of the proposed system showed an average accuracy of 90%. In conclusion, we proposed a comfortable SSVEP based BCI system by reducing visual fatigue for the user (or patient), with encouraging results.
Online Distributed Interdependency Estimation for critical infrastructures

The paper deals with the problem to provide a global estimation of a scenario composed by several and interdependent infrastructures. Due to the increasing presence of interdependencies, to correctly manage any infrastructure it is more and more relevant to have information about the “state” of the others, especially in critical situations. However, it is unfeasible both any centralized solution and to exchange detailed data about the state of each infrastructure, due to their huge volumes and in order to avoid the disclosure of critical information. To overcome such problems in this paper a fully distributed approach is proposed, attesting a copy of the whole model inside each one of the infrastructures' control room; each copy directly receives information coming from its own field and shares with the others only aggregated and non-sensible data. Moreover, in order to provide consistent outputs, the different copies have to synchronize. To this end a specific procedure has been developed, and some conditions for the convergence have been identified. In this way the proposed system is able to operate as an Online Distributed Interdependency Estimation (ODIE) System. The approach is under experimental tests on a national wide electric and telecommunication networks inside the European Project MICIE.
Noninvasive Optical Measurement of Cerebral Blood Flow in Mice Using Molecular Dynamics Analysis of Indocyanine Green

In preclinical studies of ischemic brain disorders, it is crucial to measure cerebral blood flow (CBF); however, this requires radiological techniques with heavy instrumentation or invasive procedures. Here, we propose a noninvasive and easy-to-use optical imaging technique for measuring CBF in experimental small animals. Mice were injected with indocyanine green (ICG) via tail-vein catheterization. Time-series near-infrared fluorescence signals excited by 760 nm light-emitting diodes were imaged overhead by a charge-coupled device coupled with an 830 nm bandpass-filter. We calculated four CBF parameters including arrival time, rising time and mean transit time of a bolus and blood flow index based on time and intensity information of ICG fluorescence dynamics. CBF maps were generated using the parameters to estimate the status of CBF, and they dominantly represented intracerebral blood flows in mice even in the presence of an intact skull and scalp. We demonstrated that this noninvasive optical imaging technique successfully detected reduced local CBF during middle cerebral artery occlusion. We further showed that the proposed method is sufficiently sensitive to detect the differences between CBF status in mice anesthetized with either isoflurane or ketamine–xylazine, and monitor the dynamic changes in CBF after reperfusion during transient middle cerebral artery occlusion. The near-infrared optical imaging of ICG fluorescence combined with a time-series analysis of the molecular dynamics can be a useful noninvasive tool for preclinical studies of brain ischemia.
Extrinsic Plagiarism Detection in Text Combining Vector Space Model and Fuzzy Semantic Similarity Scheme

The proposed work combines Vector Space Model with Fuzzy similarity measure to detect plagiarism cases in documents. For a given suspicious document the aim is to identify the set of source documents from which the suspicious document is copied. In the first step, all the documents need to be processed to perform tokenization, stop word removal, stemming, etc. In the next step, a subset of documents that may possibly be the sources of plagiarism need to be selected. Vector Space Model (VSM) can be used for this candidate selection. Similarity between a suspicious document and a source document can be computed using cosine similarity measure between the document vectors weighted by tf-idf scoring. Thirdly, a sentence-wise in-depth analysis using fuzzy semantic based approach to find the plagiarized parts in the suspicious documents. This can detect similar, yet not necessarily the same, statements based on the similarity degree between words in the statements and the fuzzy set. Adjacent sentences regarded as plagiarism are joined together, and the final plagiarism cases are reported. The similarity Index (SI) and overall similarity index (OSI) can be used to report the similarity between source document and suspicious document. The system that combines vector space model and fuzzy semantic similarity was evaluated in terms of precision, recall and F-measure and showed improved performance over pure vector space model for extrinsic plagiarism detection.
Correction: Estimates of Excess Medically Attended Acute Respiratory Infections in Periods of Seasonal and Pandemic Influenza in Germany from 2001/02 to 2010/11

The AGI sentinel system contacts practices whereas our analysis relies on physi3 cians. Therefore, as a rst step we convert the practice information into infor4 mation about physicians. This is easy for practices with only one physician. 5 For practices with more than one physician we proceed in the following way: if 6 all physicians have the same specialty assume that each of these physicians have 7 served the an equal proportion of the patients for the whole practice. In case that 8 a pediatrician runs a practice together with a GP we assume that all patients 9 younger than 15 year were served by the pediatrician, while all older patient were 10 served by the GP. In case that an internist in primary care runs a practice to11 gether with a GP we assume that all patients younger than 15 year were served 12 by the GP, while the internist and the GP serve an equal proportion of all patient 13 older than 14. 14
Editor's Preface a Brief Overview of Network Algorithms

In the rapidly growing world of Internet infrastructures, we face many challenging new algorithmic problems. These arise in part because the usual assumptions made in problems of this general type may no longer hold. For example, many typical questions deal with massive data sets and huge networks of prohibitively large sizes so that the (exact) number of nodes or edges is no longer a useful parameter. Instead, only partial information can be obtained. In addition, the network and the data are evolving dynamically. For example, the number of Internet hosts as of July 2005 topped 353 million and the number of Web pages indexed by large search engines now exceeds 8 billion [24]. Driven by the need of current technology and guided by the myriad of existing examples of large scale networks, the area of network algorithms has been rapidly expanding and evolving into a new and rich field. This field straddles both theoretical computer science and networking through the strong interplay between theory and practice. Furthermore, this field is closely connected to a variety of areas including graph theory, game theory, probability, statistics, physics, bioinformatics and the social sciences. Through this connection, a wealth of knowledge and methodology from diverse areas have been brought into play. As a result, new tools and insights have been developing and enriching all these relevant areas as well. To help foster research in this area, we include here a list of topics in network algorithms, together with brief discussions. 1. Network structures What are the 'laws' that govern discrete structures such as the Internet? For a massive network, is it possible to extract relatively few parameters to predict its shape and structure ? How are the structural properties related to various behaviors of the network? Why are most existing networks so 'similar' to each other? Are there simple local growth rules that dictate the global behavior of the network? In spite of the complexity of these problems, a great deal of progress has been made. It has been observed that numerous networks, including the Web graph, the telephone graph, the collaboration graph, among others [1, 2, 3, 6, 17], all satisfy a power law, which asserts that the number of nodes of degree k is proportional to k −β for some positive β. To model power law graphs, various versions of random graphs come into play — including the on-line and the off-line …
Special issue on epileptic seizure prediction

E PILEPSY is the most common neurological disorder after stroke, and affects almost 60 million people worldwide. Medications control seizures in only 2/3 of those affected, and another 7%–8% are potentially curable by surgery. This leaves fully 25%, or 15 million people whose seizures cannot be controlled by any available therapy. Over the past ten years engineers and quantitative scientists have amassed evidence that seizures do not begin abruptly, as was previously thought, but develop over time, even hours before they cause clinical symptoms. This discovery leads to the exciting notion that there might be time to understand the physiological events that give rise to seizures and perhaps alter them before they can disrupt normal function. Collaborations between engineers, physicists, mathematicians , clinicians, neuroscientists, and industry are focusing on how to translate this work into therapeutic devices that can predict and modulate epileptic seizures, as well as how to explore these findings in the hope of understanding how seizures are generated in the brain and the underlying cellular mechanisms that drive this process. These collaborative efforts may eventually lead to triggering therapy, such as local electrical or magnetic stimulation, drug infusion, or cooling, by preventing the onset or limiting the spread of seizures. Work on these interventions is still early in its development, but a wave of gathering interest is propelling the field in a variety of directions, efforts that are mostly fueled by bioengineering technology. This special issue provides a snapshot of the engineering science behind seizure prediction at a crucial stage. The excitement of initial " proof of principle " studies has already led to more carefully planned prospective and hypothesis-driven work. These efforts are arranged vertically, spanning methods designed to interpret complex human recordings in real-time all the way to theoretical studies on simple cellular systems. The papers included in this issue reflect this diverse body of work, and include a wide range of topics based upon two main areas: 1) techniques to understand, measure, and track precursor events leading up to seizures; and 2) applying these techniques to data from humans and animal models of epilepsy to develop practical, reliable systems for implementation in implantable warning and therapeutic devices. Studies of seizure detection have purposely not been included, despite some very worthy submissions. These papers will be published in subsequent issues of the IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. The real challenge of seizure prediction, and the …
Imas The Dynamics of Discrimination : Theory and Evidence

We model the dynamics of discrimination and show how its evolution can identify the underlying cause. We test these theoretical predictions in a field experiment on a large online platform where users post content that is evaluated by other users on the platform. We assign posts to accounts that exogenously vary by gender and history of evaluations. With no prior evaluations, women face significant discrimination, while following a sequence of positive evaluations, the direction of discrimination reverses: posts by women are favored over those by men. According to our theoretical predictions, this dynamic reversal implies discrimination driven by biased beliefs.
Way out/way in: How the relationship between WRN and CDK1 may change the fate of collapsed replication forks

ABSTRACT Replication-dependent double-strand breaks (DSBs) are the main source of genomic instability as their inaccurate repair stimulates chromosomal rearrangements. In a recent work, we uncover a novel regulatory circuit that involves the Werner's syndrome helicase and CDK1, and that is essential for repair pathway choice at replication-dependent DSBs.
A Survey Paper on the Gender Recognition Techniques

Gender is one of the important demographic features of human being. Gender recognition is very important perceptible task as most of the human interaction is based on such gender perception. Investigation of human gender with the help of computer based perception is an interesting topic to be studied. This paper presents the study to perceive human gender on the basis on movements of the body part including the still position of human body. Human movements, for example, running, walking or climbing has been formerly proposed to identify the gender of the person. This paper surveys the approaches based on this technique. It also points out the hurdles to implement this method. We also study how different body parts help as to implement this method. This paper also surveys a brief about different approaches and obstacles that come in implementing different methods in recognizing the gender with help of computerized vision.
The Graft-to-Capacity Ratio: Volumetric Planning in Large-Volume Fat Transplantation

Background: Variability in large-volume fat transplantation has been linked to technique. Meanwhile, the recipient-site volume and its relation to the volume of grafted fat have been relatively overlooked. Graft-to-capacity concepts are evidenced in other soft-tissue transplantation procedures such as skin and hair transplantation. The authors define the graft-to-capacity ratio as the volume of grafted fat in relation to the volume of the recipient site. The authors postulate its theoretical limits, and empirically analyze its potential clinical importance in large-volume fat transplantation. Methods: Thirty cases of large-volume fat transplantation to the breast were reviewed. All patients underwent (1) preoperative quantitative volumetric analysis using three-dimensional breast imaging and (2) large-volume fat transplantation using the large-syringe technique. The volume of fat transplanted into each breast at the time of surgery was noted. Quantitative volumetric breast imaging was repeated 12 months postoperatively. Results: The average graft-to-capacity ratio was 117 percent, with a standard deviation of 22 percent, consistent with deduced theoretical limits. Cases where the graft-to-capacity ratio exceeded 1 SD demonstrated lower percentage volume maintenance. Cases where the graft-to-capacity ratio was lower than 1 SD appeared to demonstrate higher percentage volume maintenance. Univariate linear regression of percentage volume maintenance as a function of the graft-to-capacity ratio demonstrated a significant inverse relationship. Conclusion: The graft-to-capacity ratio appears to be a relevant variable in percentage volume maintenance outcomes and may be useful in establishing consistency in large-volume fat transplantation. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
SHILL: A Secure Shell Scripting Language

The Principle of Least Privilege suggests that software should be executed with no more authority than it requires to accomplish its task. Current security tools make it difficult to apply this principle: they either require significant modifications to applications or do not facilitate reasoning about combining untrustworthy components. We propose SHILL, a secure shell scripting language. SHILL scripts enable compositional reasoning about security through contracts that limit the effects of script execution, including the effects of programs invoked by the script. SHILL contracts are declarative security policies that act as documentation for consumers of SHILL scripts, and are enforced through a combination of language design and sandboxing. We have implemented a prototype of SHILL for FreeBSD and used it for several case studies including a grading script and a script to download, compile, and install software. Our experience indicates that SHILL is a practical and useful system security tool, and can provide fine-grained security guarantees.
High-Throughput Interpolator Architecture for Low-Complexity Chase Decoding of RS Codes

In this paper, a high-throughput interpolator architecture for soft-decision decoding of Reed-Solomon (RS) codes based on low-complexity chase (LCC) decoding is presented. We have formulated a modified form of the Nielson's interpolation algorithm, using some typical features of LCC decoding. The proposed algorithm works with a different scheduling, takes care of the limited growth of the polynomials, and shares the common interpolation points, for reducing the latency of interpolation. Based on the proposed modified Nielson's algorithm we have derived a low-latency architecture to reduce the overall latency of the whole LCC decoder. An efficiency of at least 39%, in terms of area-delay product, has been achieved by an LCC decoder, by using the proposed interpolator architecture, over the best of the previously reported architectures for an RS(255,239) code with eight test vectors. We have implemented the proposed interpolator in a Virtex-II FPGA device, which provides 914 Mb/s of throughput using 806 slices.
Gallbladder Cancer Predisposition: A Multigenic Approach to DNA-Repair, Apoptotic and Inflammatory Pathway Genes

Gallbladder cancer (GBC) is a multifactorial disease with complex interplay between multiple genetic variants. We performed Classification and Regression Tree Analysis (CART) and Grade of Membership (GoM) analysis to identify combinations of alleles among the DNA repair, inflammatory and apoptotic pathway genetic variants in modifying the risk for GBC. We analyzed 16 polymorphisms in 8 genes involved in DNA repair, apoptotic and inflammatory pathways to find out combinations of genetic variants contributing to GBC risk. The genes included in the study were XRCC1, OGG1, ERCC2, MSH2, CASP8, TLR2, TLR4 and PTGS2. Single locus analysis by logistic regression showed association of MSH2 IVS1+9G>C (rs2303426), ERCC2 Asp312Asn (rs1799793), OGG1 Ser326Cys (rs1052133), OGG1 IVS4-15C>G (rs2072668), CASP8 -652 6N ins/del (rs3834129), PTGS2 -1195G>A (rs689466), PTGS2 -765G>C (rs20417), TLR4 Ex4+936C>T (rs4986791) and TLR2 –196 to –174del polymorphisms with GBC risk. The CART analysis revealed OGG1 Ser326Cys, and OGG1 IVS4-15C>G polymorphisms as the best polymorphic signature for discriminating between cases and controls. In the GoM analysis, the data was categorized into six sets representing risk for GBC with respect to the investigated polymorphisms. Sets I, II and III described low intrinsic risk (controls) characterized by multiple protective alleles while sets IV, V and VI represented high intrinsic risk groups (GBC cases) characterized by the presence of multiple risk alleles. The CART and GoM analyses also showed the importance of PTGS2 -1195G>A polymorphism in susceptibility to GBC risk. In conclusion, the present multigenic approach can be used to define individual risk profiles for gallbladder cancer in North Indian population.
Antecedents and Effect of IT Usage on Performance : A Research Framework and Empirical Study

Information Technology (IT) Usage is an important construct in Information Systems research. While the relationship between IT Usage and Performance is well studied along with the antecedents of IT Usage, extant research has not yet fully explored an integrated model of IT Usage, its antecedents and impact on performance. In this paper, we propose an integrated theoretical framework for such an effort. We detail our preliminary results for a section of the theoretical model to demonstrate the viability of the research model. We find that for our research context, the preliminary results align well with theoretical predictions. We show strong statistical relationships between actual IT Usage and performance at the business unit level in both cross sectional and panel data analysis. We conclude by discussing the proposed data collection and analysis approach for testing the integrated theoretical framework for the relationship between IT Usage, its antecedents and impact on performance.