Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptome Analysis of the Model Protozoan, Tetrahymena thermophila, Using Deep RNA Sequencing

Background The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST) database limited the quality of the original genome annotation. Methodology/Principal Findings This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96% of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs) and an updated (larger) size estimate of the T. thermophila transcriptome: 57 Mb, or about 55% of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS) events distributed over 5.2% of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8% of the genes originally predicted by the gene finder, to correct coding sequence boundaries and intron-exon junctions for about a third, and to reassign microarray probes and correct earlier microarray data. Conclusions/Significance RNA-seq data significantly improve the genome annotation and provide a fully comprehensive view of the global transcriptome of T. thermophila. To our knowledge, 5.2% of T. thermophila genes with AS is the highest percentage of genes showing AS reported in a unicellular eukaryote. Tetrahymena thus becomes an excellent unicellular model eukaryote in which to investigate mechanisms of alternative splicing.
SYN flooding Attack countermeasures in Next Generation Network

this paper propose countermeasures against syn flooding attack in the Next Generation Network (NGN), Applying a filtering and PSO algorithm to IP packets flowing from internet in to NGN. IP packets are checked and abnormal packets for syn flooding attack are detected at the internal router, then filter source IP address by security policy that is set in the firewall. In syn flooding attack some sources send a large number of TCP segments, without completing the third handshaking step to quickly exhaust connection resources of the under attack system or victim. After filtering mechanism, number of packets that have not detected, the victim by using queuing mechanism in which attack requests are recognized based on long service time. paper proposes a framework in which the defense issue is formulated as an optimization problem and employ filtering mechanism based on number of SYN/RST segment and particle swarm optimization(PSO) algorithm to optimally solve this problem. Finally by using opnet simulator, simulation results show that the proposed defense strategy improves the performance of the victim in term of efficient consumption of buffer space by attack & regular request. Keywords-component; NGN; Denial of Service (DOS) Attack ;SYN Flooding; PSO, victim
Space transformation search: a new evolutionary technique

In this paper, a new evolutionary technique is proposed, namely space transformation search (STS), which transforms current search space to a new search space. By simultaneously evaluating solutions in current search space and transformed space, we can provide more chances to find solutions more closely to the global optimum and finally accelerate convergence speed. The proposed STS method can be applied to many evolutionary algorithms, and this paper only presents a STS based particle swarm optimization (PSO-STS). Experimental studies on 20 benchmark functions including 10 shifted functions show that the PSO-STS and its variations can not only achieve better results, but also obtain faster convergence speed than the standard PSO.
Uncoupling in services of mobile cloud computing using tuple space model: design and formal specifications

Mobile Cloud Computing widens the service of existing facility of cloud computing paradigm to user applications executing in mobile/portable devices, called mobile applications. It enables existing cloud services to serve these applications, thereby helps to overcome the inherent limitations of mobile$/$portable devices. However, the use of cloud services in existing architecture require tight coupling between mobile applications and cloud services, which is highly undesirable in dynamic and unreliable mobile cloud computing. A new architecture of mobile cloud computing, where interactions between mobile applications and cloud services will be decoupled, is proposed in this paper. Tuple space model is used for uncoupling these interactions in proposed architecture, improving flexibility and efficiency of mobile cloud computing to a great extent. This paper also suggests an approach of formalizing proposed architecture to depict the uncoupling of service access and delivery to mobile applications. Formalization is carried out using Mobile UNITY.
Dynamic Evolution of Web-Based Systems

Software evolution techniques have been traditionally concentrated on the code level. However, recent researches have shifted the focus of evolution from the code level to the architectural level. In this paper, we proposed a formal modeling method of dynamic evolution of web-based systems at the architectural level using hypergraph grammars. We represent architectures of web-based systems with hypergraphes, and give out evolution production rules based on hypergraph grammars, and then modeled dynamic evolution of web-based systems at the architectural level according to those rules. A detailed case study is given to demonstrate our approach.
Direct-coupling analysis of residue coevolution captures native contacts across many protein families

The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.
For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid

Are Power Line Communications (PLC) a good candidate for Smart Grid applications? The objective of this paper is to address this important question. To do so, we provide an overview of what PLC can deliver today by surveying its history and describing the most recent technological advances in the area. We then address Smart Grid applications as instances of sensor networking and network control problems and discuss the main conclusions one can draw from the literature on these subjects. The application scenario of PLC within the Smart Grid is then analyzed in detail. Because a necessary ingredient of network planning is modeling, we also discuss two aspects of engineering modeling that relate to our question. The first aspect is modeling the PLC channel through fading models. The second aspect we review is the Smart Grid control and traffic modeling problem which allows us to achieve a better understanding of the communications requirements. Finally, this paper reports recent studies on the electrical and topological properties of a sample power distribution network. Power grid topological studies are very important for PLC networking as the power grid is not only the information source but also the information delivery system-a unique feature when PLC is used for the Smart Grid.
Multi-Label Text Categorization with a Data Correlated VG-RAM Weightless Neural Network

In multi-label text categorization, one or more la- bels (or categories) can be assigned to a single document. In many such categorization tasks, there can be correlation on the assignment of subsets of the set of categories. This can be exploited to improve machine learning techniques devoted to multi-label text categorization. In this paper, we exam- ine a Virtual Generalizing Random Access Memory Weightless Neural Network (VG-RAM WNN) architecture that takes ad- vantage of the correlation between categories to improve text categorization performance. We compare the performance of this architecture, that we named Data Correlated VG-RAM WNN (VG-RAM WNN-COR), with that of standard VG-RAM WNN and ML-KNN categorizers using ten multi-label text cate- gorization performance metrics. Our experimental results show that VG-RAM WNN-COR has an overall better performance than VG-RAM WNN and ML-KNN for the set of metrics con- sidered.
Arff convertor tool for WEKA data mining software

The paper presents a tool that exports data from different database types into arff format, in order to be used by WEKA data mining and machine learning software. The developed application allows the connection to databases, the visualization of the relationships between tables, the visualization of the structure and of the content of these databases throughout several uniform and dynamic interfaces as well as generating entry files (arff files) for WEKA, that contain data from one or more related tables. In order to use the developed tool, there is no SQL knowledge required. The application determines the numeric, categorical, string and data attributes. For further validation, using the developed application and WEKA, the authors have realized an educational data mining study on a Microsoft Visual Fox Pro database, including data concerning 2100 students. These data have been extracted from the student management application that is working inside Faculty of Automation and Computers of “Politehnica” University from Timisoara.
DROWN: Breaking TLS Using SSLv2

We present DROWN, a novel cross-protocol attack on TLS that uses a server supporting SSLv2 as an oracle to decrypt modern TLS connections. We introduce two versions of the attack. The more general form exploits multiple unnoticed protocol flaws in SSLv2 to develop a new and stronger variant of the Bleichenbacher RSA padding-oracle attack. To decrypt a 2048-bit RSA TLS ciphertext, an attacker must observe 1,000 TLS handshakes, initiate 40,000 SSLv2 connections, and perform 250 offline work. The victim client never initiates SSLv2 connections. We implemented the attack and can decrypt a TLS 1.2 handshake using 2048bit RSA in under 8 hours, at a cost of $440 on Amazon EC2. Using Internet-wide scans, we find that 33% of all HTTPS servers and 22% of those with browser-trusted certificates are vulnerable to this protocol-level attack due to widespread key and certificate reuse. For an even cheaper attack, we apply our new techniques together with a newly discovered vulnerability in OpenSSL that was present in releases from 1998 to early 2015. Given an unpatched SSLv2 server to use as an oracle, we can decrypt a TLS ciphertext in one minute on a single CPU—fast enough to enable man-in-the-middle attacks against modern browsers. We find that 26% of HTTPS servers are vulnerable to this attack. We further observe that the QUIC protocol is vulnerable to a variant of our attack that allows an attacker to impersonate a server indefinitely after performing as few as 217 SSLv2 connections and 258 offline work. We conclude that SSLv2 is not only weak, but actively harmful to the TLS ecosystem.
Independent Component Analysis in Image Denoising Instructor

Independent component analysis (ICA) is a statistical technique which attempts to nd a representation of observed data such that the components are as independent as possible. This technique has shown great promise in feature extraction, essentially nding the building blocks of any given data. In particular, when applied to image data ICA gives a representation which identiies the contours in the image; these can be considered the primary structure in the data. The term noise refers to any random degradation of a signal. This thesis is concerned with noise in two-dimensional signals, i.e. images. Such noise may be due to disturbances inherent in image acquisition or could result from a noisy transmission channel over which the image is sent. Either way, the denoising task is to use the information we have on the statistical structure of images to remove the eeect of the noise as well as possible. This work applies ICA and related techniques to denoising images. First, a general framework for denoising random vectors is introduced; then it is applied to the speciic case of image data. Finally, extensive tests are performed, comparing the proposed method to traditional denoising methods.
0 30 40 13 v 1 1 0 A pr 2 00 3 Squeezed Statistical Mechanics

We present a formulation of Statistical Mechanics based on the concept of the dimensionless characteristic thermodynamic function, which has the form of a negative Massieu-Planck generalized potential. This function is equivalent to the free energy of the system in kT units. We construct classes of configurations in terms of the constraints of the system and, by applying the condition of minimum for the dimensionless characteristic thermodynamic function, we develop ensemble theory. All expressions derived are formally valid for any system, regardless of its constraints. We present first the results for Boltzmann-Gibbs equilibrium statistical mechanics. The formulation shows the connections between ensembles and also between thermodynamics and statistical mechanics without making explicit use of the concept of probability, which is introduced after developing ensemble theory. A general theory of fluctuations is then also introduced. The treatment followed serves as a starting point to generalize Boltzmann statistical mechanics and a scheme for this generalization is proposed. To achieve that, we define a convenient squeezing function which restricts among the attainable Boltzmann-Gibbs configurations. As an example, Tsallis non-extensive statistics is rebuilt into our formulation and new insights in this theory are provided.
Carbon Nanotube Thin Film Biosensors for Sensitive and Reproducible Whole Virus Detection

Here, we report the label-free, sensitive, and real-time electrical detection of whole viruses using carbon nanotube thin film (CNT-TF) field effect devices. Selective detection of approximately 550 model viruses, M13-bacteriophage, is demonstrated using a simple two-terminal (no gate electrode) configuration. Chemical gating through specific antibody-virus binding on CNT surface is proposed to be the sensing mechanism. Compared to electrical impedance sensors with identical microelectrode dimensions (no CNT), the CNT-TF sensors exhibit sensitivity 5 orders higher. We believe the reported approach could lead to a reproducible and cost-effective solution for rapid viral identification.
Cell transformation by kFGF requires secretion but not glycosylation

The Kfgf gene, which encodes a member of the fibroblast growth factor family, was originally discovered by assaying human tumor DNA for dominantly transforming oncogenes. The 22-kD kFGF product contains a single site for asparagine-linked glycosylation and an amino-terminal signal peptide for vectorial synthesis into the endoplasmic reticulum and eventual secretion. To determine whether these features are necessary for transformation, we have constructed mutants of kFGF that are impaired for glycosylation or secretion. All mutants retained the ability to induce DNA synthesis when added to quiescent cells, and the absence of glycosylation had no appreciable effect on the transformation efficiency on NIH3T3 cells. In contrast, mutants of kFGF that remain in the cytoplasm or are retained in the secretory pathway, through addition of a KDEL motif, score negative in standard transformation assays. Since transformation by either the glycosylated or unglycosylated form of kFGF can be reversed by addition of suramin, the data imply that secretion of kFGF, or surface localization of the ligand/receptor complex, is a prerequisite for transformation.
Can survey item characteristics relevant to mode-specific measurement error be coded reliably?

In multi - mode questionnaire design, usually some consideration is given to mode - specific measurement error. Despite this consideration, however, these measurement effects can be unexpectedly large. For this reason, there is a strong incentive to better predict mea surement effects. This may be done by constructing profiles of a questionnaire, in which relevant item characteristics are summarized. For all items of a survey, these item characteristics need to be coded and combined. In this paper, we evaluated a list o f item characteristics that literature has reported as relevant to mode - specific measurement error. Most importantly, we evaluated the reliability of the coding of such characteristics. Our results showed that intercoder reliability can be low for the most relevant characteristics. This may be explained by the difficulty of defining the item characteristics and the inherent subjectivity with which these item characteristics are coded. Finally, some sug gestions a re made for coping with low intercoder reliabi lity.
Psychobiological processes linking socio-economic position with health

© Oxford University Press 2006. All rights reserved. This chapter is concerned with how variations in socio-economic position affect physical disease. It begins by describing the pathways that theoretically link socio-economic position with physical disease, and by explaining the potential significance of psychobiological responses. The findings concerning psychobiological processes are organized round the primary methodologies that have been used to explore these pathways, namely animal studies, human laboratory mental stress testing, and naturalistic studies of biological function in everyday life. It is shown that there has been rapid development in this field over recent years, and that although there are still many uncertainties, we are moving into an era in which conclusions about the significance of psychobiological processes can be made with greater confidence.
Biodegradable Nanofiber for Delivery of Immunomodulating Agent in the Treatment of Basal Cell Carcinoma

In this paper we investigate a potential new treatment option for basal cell carcinoma using electrospun polymer nanofibers. Poly(e-caprolactone) fibers incorporated with the anti-cancer drug imiquimod were successfully produced for the first time. These fibers were characterized and their diffusion release profile tested in vitro. A range of different electrospinning parameters were investigated in order to determine the most effective approach in optimizing the fibers for future in vivo testing. Characterization showed stable and homogeneous distribution of imiquimod. Although the drug was released faster than what would be needed to replicate the current treatment model, this system would ideally allow for a treatment option which reduces side effects and is more convenient for the patient than the current topical treatment.
Inferring AS-level Internet topology from router-level path traces

A number of recent studies characterize AS-level topology of the Internet by exploiting connectivity information contained in BGP routing tables. In this paper, we present an alternative method for discovering AS connectivity by inferring individual AS connections from the Internet's router-level topology. This methodology has several advantages over using BGP routing tables. First, it allows us to obtain AS-level connectivity information at a finer granularity (e.g., multiple connections between a pair of ASs); second, we can discover ASs aggregated in BGP routing tables; and third, we can identify AS border routers, which may allow us to further characterize inter-AS connections. Since border routers have, by definition, multiple interfaces, each with an address in a potentially different AS, a major challenge of our approach is to properly map border routers to their corresponding ASs. To this end, we present in this paper several mapping rules and heuristics for inferring the ASs of border routers and report on results showing the effectiveness and validity of these rules and heuristics.
A model-based end-to-end toolchain for the probabilistic analysis of complex systems

We present a model-based environment for the probabilistic analysis of systems operating under uncertain conditions. This uncertainty may result from either the environments in which they operate or the platforms on which they execute. Available probabilistic analysis methods require to capture the system specification using languages that are semantically very close to Markov Chains. However, designers use model-based environments working at much higher abstraction levels. We present an integrated tool, called StoNES (Stochastic analysis of Networked Embedded Systems), that automates the model transformation and probabilistic analysis of systems. We apply our translation and analysis methodology to explore the trade-off between sensor accuracy and computational speed for the vision algorithm of an autonomous helicopter system.
Estimating marginal survival function by adjusting for dependent censoring using many covariates

One goal in survival analysis of right-censored data is to estimate the marginal survival function in the presence of dependent censoring. When many auxiliary covariates are sufficient to explain the dependent censoring, estimation based on either a semiparametric model or a nonparametric model of the conditional survival function can be problematic due to the high dimensionality of the auxiliary information. In this paper, we use two working models to condense these high-dimensional covariates in dimension reduction; then an estimate of the marginal survival function can be derived nonparametrically in a low-dimensional space. We show that such an estimator has the following double robust property: when either working model is correct, the estimator is consistent and asymptotically Gaussian; when both working models are correct, the asymptotic variance attains the efficiency bound.