Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of functional modules based on comparative genome analysis and Gene Ontology application

We present a computational method for the prediction of functional modules encoded in microbial genomes. In this work, we have also developed a formal measure to quantify the degree of consistency between the predicted and the known modules, and have carried out statistical significance analysis of consistency measures. We first evaluate the functional relationship between two genes from three different perspectives—phylogenetic profile analysis, gene neighborhood analysis and Gene Ontology assignments. We then combine the three different sources of information in the framework of Bayesian inference, and we use the combined information to measure the strength of gene functional relationship. Finally, we apply a threshold-based method to predict functional modules. By applying this method to Escherichia coli K12, we have predicted 185 functional modules. Our predictions are highly consistent with the previously known functional modules in E.coli. The application results have demonstrated that our approach is highly promising for the prediction of functional modules encoded in a microbial genome.
Author's Personal Copy the Journal of Systems and Software a Software Integration Approach for Designing and Assessing Dependable Embedded Systems

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t Embedded systems increasingly entail complex issues of hardware–software (HW–SW) co-design. As the number and range of SW functional components typically exceed the finite HW resources, a common approach is that of resource sharing (i.e., the deployment of diverse SW functionalities onto the same HW resources). Consequently, to result in a meaningful co-design solution, one needs to factor the issues of processing capability, power, communication bandwidth, precedence relations, real-time deadlines, space, and cost. As SW functions of diverse criticality (e.g. brake control and infotainment functions) get integrated, an explicit integration requirement need is to carefully plan resource sharing such that faults in low-criticality functions do not affect higher-criticality functions. On this background, the main contribution of this paper is a dependability-driven framework that helps to conduct the integration of SW components onto HW resources such that the maintenance of system dependability over integration of diverse criticality components is assured by design. We first develop a clustering strategy for SW components into Fault Containment Modules (FCMs) such that error propagation via interaction is minimized. Subsequently, the rules of composition for FCMs with respect to error propagation are developed. To allocate the resulting FCMs to the existing HW resources we provide several heuristics, each optimizing particular attributes thereof. Further, a framework for assessing the goodness of the achieved HW–SW composition as a dependable embedded system is presented. Two new techniques for quantifying the goodness of the proposed mappings are introduced by examples, both based on a multi-criteria decision theoretic approach.
Making Sense of Spatial Trajectories

Spatial trajectory data is widely available today. Over a sustained period of time, trajectory data has been collected from numerous GPS devices, smartphones, sensors and social media applications. Daily increases of real-time trajectory data have also been phenomenal in recent years. More and more new applications have emerged to derive business values from both trajectory data warehouses and real-time trajectory data. Due to their very large volumes, their nature of streaming, their highly variable levels of data quality, as well as many possible links with other types of data, making sense of spatial trajectory data becomes one of the crucial areas for big data analytics. In this paper we will present a review of the extensive work in spatiotemporal data management and trajectory mining, and discuss new challenges and new opportunities in the context of new applications, focusing on recent advances in trajectory data management and trajectory mining from their foundations to high performance processing with modern computing infrastructure.
Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

Cloud Storage Providers (CSPs) offer geographically data stores providing several storage classes with different prices. An important problem facing by cloud users is how to exploit these storage classes to serve an application with a time-varying workload on its objects at minimum cost. This cost consists of residential cost (i.e., storage, Put and Get costs) and potential migration cost (i.e., network cost). To address this problem, we first propose the optimal offline algorithm that leverages dynamic and linear programming techniques with the assumption of available exact knowledge of workload on objects. Due to the high time complexity of this algorithm and its requirement for a priori knowledge, we propose two online algorithms that make a trade-off between residential and migration costs and dynamically select storage classes across CSPs. The first online algorithm is deterministic with no need of any knowledge of workload and incurs no more than $2\gamma -1$2γ-1 times of the minimum cost obtained by the optimal offline algorithm, where $\gamma$γ is the ratio of the residential cost in the most expensive data store to the cheapest one in either network or storage cost. The second online algorithm is randomized that leverages “Receding Horizon Control” (RHC) technique with the exploitation of available future workload information for $w$w time slots. This algorithm incurs at most $1+\frac{\gamma }{w}$1+γw times the optimal cost. The effectiveness of the proposed algorithms is demonstrated through simulations using a workload synthesized based on characteristics of the Facebook workload.
Testing for Equivalence: A Methodology for Computational Cognitive Modelling

Testing for Equivalence: A Methodology for Computational Cognitive Modelling The equivalence test (Stewart and West, 2007; Stewart, 2007) is a statistical measure for evaluating the similarity between a model and the system being modelled. It is designed to avoid over-fitting and to generate an easily interpretable summary of the quality of a model. We apply the equivalence test to two tasks: Repeated Binary Choice (Erev et al., 2010) and Dynamic Stocks and Flows (Gonzalez and Dutt, 2007). In the first case, we find a broad range of statistically equivalent models (and win a prediction competition) while identifying particular aspects of the task that are not yet adequately captured. In the second case, we re-evaluate results from the Dynamic Stocks and Flows challenge, demonstrating how our method emphasizes the breadth of coverage of a model and how it can be used for comparing different models. We argue that the explanatory power of models hinges on numerical similarity to empirical data over a broad set of measures.
Using content-aware bitcells to reduce static energy dissipation

Static energy dissipation is an increasing problem in contemporary processor design with shrinking feature sizes. Many schemes are proposed to cope with leakage in the literature ranging from using sleep transistors to lowering supply voltage. In this paper, we introduce a Conscious SRAM (CSRAM) design to lower static energy dissipation in the storage components of a processor. The proposed bitcell design adapts the body bias of its own transistors according to its contents. We show that the use of the proposed CSRAM cells results in significant reduction in the static energy dissipation of on-chip storage components without significant performance degradation. In order to reduce the area overhead introduced by the CSRAM we propose a simplified version of the cell at the circuit level. We also leverage the fact that the contents of adjacent bits of the stored values are highly dependent on each other, especially on the upper order bits of a value, and propose some architectural level solutions that lower the area overhead to as low as 7%.
SAS-Pro: Simultaneous Residue Assignment and Structure Superposition for Protein Structure Alignment

Protein structure alignment is the problem of determining an assignment between the amino-acid residues of two given proteins in a way that maximizes a measure of similarity between the two superimposed protein structures. By identifying geometric similarities, structure alignment algorithms provide critical insights into protein functional similarities. Existing structure alignment tools adopt a two-stage approach to structure alignment by decoupling and iterating between the assignment evaluation and structure superposition problems. We introduce a novel approach, SAS-Pro, which addresses the assignment evaluation and structure superposition simultaneously by formulating the alignment problem as a single bilevel optimization problem. The new formulation does not require the sequentiality constraints, thus generalizing the scope of the alignment methodology to include non-sequential protein alignments. We employ derivative-free optimization methodologies for searching for the global optimum of the highly nonlinear and non-differentiable RMSD function encountered in the proposed model. Alignments obtained with SAS-Pro have better RMSD values and larger lengths than those obtained from other alignment tools. For non-sequential alignment problems, SAS-Pro leads to alignments with high degree of similarity with known reference alignments. The source code of SAS-Pro is available for download at http://eudoxus.cheme. cmu.edu/saspro/SAS-Pro.html.
Knowledge representation and processing with formal concept analysis

During the last three decades, formal concept analysis (FCA) became a well‐known formalism in data analysis and knowledge discovery because of its usefulness in important domains of knowledge discovery in databases (KDD) such as ontology engineering, association rule mining, machine learning, as well as relation to other established theories for representing knowledge processing, like description logics, conceptual graphs, and rough sets. In early days, FCA was sometimes misconceived as a static crisp hardly scalable formalism for binary data tables. In this paper, we will try to show that FCA actually provides support for processing large dynamical complex (may be uncertain) data augmented with additional knowledge. © 2013 Wiley Periodicals, Inc.
Ribosome profiling: a Hi-Def monitor for protein synthesis at the genome-wide scale

Ribosome profiling or ribo‐seq is a new technique that provides genome‐wide information on protein synthesis (GWIPS) in vivo. It is based on the deep sequencing of ribosome protected mRNA fragments allowing the measurement of ribosome density along all RNA molecules present in the cell. At the same time, the high resolution of this technique allows detailed analysis of ribosome density on individual RNAs. Since its invention, the ribosome profiling technique has been utilized in a range of studies in both prokaryotic and eukaryotic organisms. Several studies have adapted and refined the original ribosome profiling protocol for studying specific aspects of translation. Ribosome profiling of initiating ribosomes has been used to map sites of translation initiation. These studies revealed the surprisingly complex organization of translation initiation sites in eukaryotes. Multiple initiation sites are responsible for the generation of N‐terminally extended and truncated isoforms of known proteins as well as for the translation of numerous open reading frames (ORFs), upstream of protein coding ORFs. Ribosome profiling of elongating ribosomes has been used for measuring differential gene expression at the level of translation, the identification of novel protein coding genes and ribosome pausing. It has also provided data for developing quantitative models of translation. Although only a dozen or so ribosome profiling datasets have been published so far, they have already dramatically changed our understanding of translational control and have led to new hypotheses regarding the origin of protein coding genes. WIREs RNA 2013, 4:473–490. doi: 10.1002/wrna.1172
CoLoR: an information-centric internet architecture for innovations

In this article, we describe an information-centric Internet architecture called CoLoR that couples service location and inter-domain routing while decoupling them from forwarding. Preliminary results based on implementation and analysis show that CoLoR is promising since it satisfies many requirements of the future Internet, including being information-centric, encouraging innovations, and providing efficient support for mobility, multicast, multi-homing, and middleboxes.
The Importance of Relevance to Student Lives: The Impact of Content and Media in Introduction to Sociology

To increase students’ engagement and achievement in introductory sociology courses, teachers should make them relevant to students’ lives. Students’ relevance perceptions may vary within the classroom, depending on the degree of fit between their sociocultural position and the teaching methods. To test this prospect, an experiment among 1,325 undergraduates distinguished the sociocultural mechanisms underlying content- and medium-related course relevance. The students viewed one of four versions of an introductory video lecture about Durkheim with (1) feminine/masculine content and (2) YouTube/verbal-anecdotal medium manipulated in its examples. The results indicate first, that students’ perceptions of the sociology course as relevant were associated with their course satisfaction and achievement. Second, matching students’ gender with gendered example content stimulated their relevance perceptions, while a mismatch decreased those perceptions. Finally, tentative evidence was found that the use of YouTube examples engaged disadvantaged students in the course without harming advantaged students’ learning.
Drosophila DDX3/Belle Exerts Its Function Outside of the Wnt/Wingless Signaling Pathway

The helicases human DDX3 and Drosophila Belle (Bel) are part of a well-defined subfamily of the DEAD-box helicases. Individual subfamily-members perform a myriad of functions in nuclear and cytosolic RNA metabolism. It has also been reported that DDX3X is involved in cell signaling, including IFN-α and IFN-β inducing pathways upon viral infection as well as in Wnt signaling. Here we used a collection of EMS-induced bel alleles recovered from a Wingless (Wg) suppressor screen to analyze the role of the Drosophila homolog of DDX3 in Wg/Wnt signaling. These EMS alleles, as well as a P-element induced null allele and RNAi-mediated knock down of bel, all suppressed the phenotype of ectopic Wg signaling in the eye. However, they did not affect the expression of known Wg target genes like senseless, Distalless or wingful/Notum. Ectopic Wg signaling in eye imaginal discs induces apoptosis by increasing grim expression. Mutations in bel revert grim expression to wild-type levels. Together, these results indicate that Bel does not function as a core component in the Drosophila Wg pathway, and that mutations affecting its helicase function suppress the effects of ectopic Wg signaling downstream of the canonical pathway.
Noncoherent Radar Pulse Compression Based on Complementary Sequences

Noncoherent pulse compression (NCPC), suggested recently, uses on-off keying (OOK) signals, obtained from Manchester coding a binary sequence with favorable a-periodic autocorrelation. This paper investigates the use of binary complementary pairs as a basis for NCPC. It shows that a pair of Manchester coded, N-element binary complementary sequences will yield a peak sidelobe (PSL) ratio of 1/(2N).
Clinical application of ICF key codes to evaluate patients with dysphagia following stroke

AbstractThis study was aimed to identify and evaluate the International Classification of Functioning (ICF) key codes for dysphagia in stroke patients. Thirty patients with dysphagia after stroke were enrolled in our study. To evaluate the ICF dysphagia scale, 6 scales were used as comparisons, namely the Barthel Index (BI), Repetitive Saliva Swallowing Test (RSST), Kubota Water Swallowing Test (KWST), Frenchay Dysarthria Assessment, Mini-Mental State Examination (MMSE), and the Montreal Cognitive Assessment (MoCA). Multiple regression analysis was performed to quantitate the relationship between the ICF scale and the other 7 scales. In addition, 60 ICF scales were analyzed by the least absolute shrinkage and selection operator (LASSO) method. A total of 21 ICF codes were identified, which were closely related with the other scales. These included 13 codes from Body Function, 1 from Body Structure, 3 from Activities and Participation, and 4 from Environmental Factors. A topographic network map with 30 ICF key codes was also generated to visualize their relationships. The number of ICF codes identified is in line with other well-established evaluation methods. The network topographic map generated here could be used as an instruction tool in future evaluations. We also found that attention functions and biting were critical codes of these scales, and could be used as treatment targets.
Complex motivated behaviors for natural rewards following a binge-like regimen of morphine administration: mixed phenotypes of anhedonia and craving after short-term withdrawal

The anhedonia-like behaviors following about 1-week withdrawal from morphine were examined in the present study. Male rats were pretreated with either a binge-like morphine paradigm or daily saline injection for 5 days. Three types of natural reward were used, food reward (2.5, 4, 15, 30, 40, and 60% sucrose solutions), social reward (male rat) and sexual reward (estrous female rat). For each type of natural stimulus, consummatory behavior and motivational behaviors under varied testing conditions were investigated. The results showed that the morphine-treated rats significantly reduced their consumption of 2.5% sucrose solution during the 1-h consumption testing and their operant responding for 15, 30, and 40% sucrose solutions under a fixed ratio 1 (FR1) schedule. However, performance under a progressive ratio (PR) schedule increased in morphine-treated rats reinforced with 60% sucrose solution, but not in those reinforced with sucrose concentrations lower than 60%. Pretreatment with morphine significantly decreased the male rats' ejaculation frequency (EF) during the 1-h copulation testing, and impaired the maintenance of appetitive motivations to sexual and social stimuli under a free-approach condition. Moreover, the morphine-treated rats demonstrated a diminished motivation to approach social stimulus in the effort-based appetitive behavior test but showed a remarkable increase in motivation to approach sexual stimulus in the risky appetitive behavior test. These results demonstrated some complex motivated behaviors following about 1 week of morphine withdrawal: (1) The anhedonia-like behavior was consistently found in animals withdrawn from morphine. However, for a given reward, there was often a dissociation of the consummatory behaviors from the motivational behaviors, and whether the consummatory or the motivational anhedonia-like behaviors could be discovered heavily depended on the type and magnitude of the reward and the type of testing task; (2) These anhedonia-like behaviors coexisted with a craving for the high-incentive reward which was evidenced by the increased PR performance for the 60% sucrose solution and the heightened risky appetitive behavior for the sexual stimulus. The craving for the high-incentive reward alongside with the impaired inhibitory control in drug-withdrawn subjects might form one of psychological mechanisms underlying drug relapse after withdrawal.
Permeability Estimation Using a Hybrid Genetic Programming and Fuzzy/Neural Inference Approach

We have developed a methodology that provides permeability estimates for all rock-types or lithologies, for a wide range of permeability. This is a hybrid Genetic Programming and Fuzzy/Neural Net inference system and which utilizes lithologic and permeability facies as indicators. This work was motivated by a need to have a volumetric estimate of permeability for reservoir modeling purposes. To this end, for our purposes,the inputs to this process are limited to properties that can be estimated from seismic data. The permeability transform is first estimated at the well locations using core permeability, elastic parameter logs and porosity. The output from the process can then be used, in conjunction with estimates of these properties from 3D seismic data, to provide an estimate of permeability on a volume basis. The inputs are then, the volume of shale (Vsh) or any other log type used to determine lithology, the sonic and density logs, the porosity log and core permeability measurments. The transform system is composed of three distinct modules. The first module serves to classify lithology and separates the reservoir interval into user-defined lithology types. The second module, based on Genetic Programming, is designed to predict permeability facies within lithology type. A permeability facies is defined as as a low, medium or high permeability set associated with each lithology type. A Fuzzy/Neural Net inference algorithm makes up the third module of the system, in which a TSK fuzzy logic relationship is formed, for each permeability facies and lithology. The system has been applied in two oil fields, both offshore West Africa. In comparison with current estimation approaches, this system yields more consistent estimated permeability. The results from conducting cross-validation suggest this methodology is robust in estimating permeability in complex heterogeneous reservoirs. This system is designed to use elastic log properties inverted from seismic data, such as acoustic velocity and density as input so permeability volume can be obtained.
Clustering with Proximity Graphs: Exact and Efficient Algorithms

Graph Proximity Cleansing GPC is a string clustering algorithm that automatically detects cluster borders and has been successfully used for string cleansing. For each potential cluster a so-called proximity graph is computed, and the cluster border is detected based on the proximity graph. However, the computation of the proximity graph is expensive and the state-of-the-art GPC algorithms only approximate the proximity graph using a sampling technique. Further, the quality of GPC clusters has never been compared to standard clustering techniques like k-means, density-based, or hierarchical clustering. In this article the authors propose two efficient algorithms, PG-DS and PG-SM, for the exact computation of proximity graphs. The authors experimentally show that our solutions are faster even if the sampling-based algorithms use very small sample sizes. The authors provide a thorough experimental evaluation of GPC and conclude that it is very efficient and shows good clustering quality in comparison to the standard techniques. These results open a new perspective on string clustering in settings, where no knowledge about the input data is available.
LRP: Line routing protocol of robot-sensor networks for mine rescue

In this paper, sensor nodes are carried and deployed by a robot in mine accident scene. These sensor nodes form a line network which makes the performance of many traditional routing protocols terrible. Line routing protocol (LRP), which is simple and efficient, is proposed for such line networks. LRP is implemented in TinyOS and simulations are performed in TOSSIM. Simulation results show that LRP always chooses the shortest-hop routing path and keeps high packet delivery ratio over a long routing path. On the other hand, the routing overhead of LRP is very low.