Miracle-dz/UniFAR-FTUs huggingface dataset:
PCR-Restriction Fragment Length Polymorphism analysis of fljB gene in Salmonella enterica subspecies enterica serovar Typhimurium isolated from avians

Background and Objectives Economic constraint of diseases arising from Salmonella Typhimurium causes the study of this zoonotic organism more important. Most studies on identification and characterization of S. Typhimurium are conducted at DNA level. Flagellin genes (fliC and fljB genes encoding phase-1 and phase-2 flagella, respectively) are useful as a model system for studying genetic differentiation. The objectives of the present study were to identify the polymorphism of fljB among avians in different regions by the PCR-RFLP method. Materials and Methods Fifty-two S. Typhimurium isolates out of 1,870 intestine samples were identified using culture and serotyping as well as multiplex-PCR (broiler (n=13), layer (n=12), duck (n=5), goose (n=5), sparrow (n=8), canary (n=3), pigeon (n=5) and casco parrot (n=1)). Amplification of fljB gene was performed and amplified products subjected to restriction digestion with Hha I enzyme. Results Two RFLP patterns generated DNA fragments between approximately 50 to 800 bps. Pattern A was observed in 33 (63.46%) and pattern B in 19 (36.54%) of isolates. Salmonella Typhimurium recovered from 13 broilers (ten with pattern A and 3 with pattern B) and 8 sparrow (three with pattern A and 5 with pattern B) showed both A and B patterns. Twelve layers, 5 pigeons and 3 canaries showed pattern A and 5 ducks, 5 geese and one casco parrot showed pattern B. None of these patterns was allotted for a special region. Conclusion The results of the present study showed that fljB gene is highly conserved among avians in different geographical regions, suggesting not only the importance of fljB gene in survival of organism in different environmental conditions but also the relation between proteins encoded by fljB gene and serotyping scheme.
Symmetric Graph Regularized Constraint Propagation

This paper presents a novel symmetric graph regularization framework for pairwise constraint propagation. We first decompose the challenging problem of pairwise constraint propagation into a series of two-class label propagation subproblems and then deal with these subproblems by quadratic optimization with symmetric graph regularization. More importantly, we clearly show that pairwise constraint propagation is actually equivalent to solving a Lyapunov matrix equation, which is widely used in Control Theory as a standard continuous-time equation. Different from most previous constraint propagation methods that suffer from severe limitations, our method can directly be applied to multi-class problem and also can effectively exploit both must-link and cannot-link constraints. The propagated constraints are further used to adjust the similarity between data points so that they can be incorporated into subsequent clustering. The proposed method has been tested in clustering tasks on six real-life data sets and then shown to achieve significant improvements with respect to the state of the arts.
LILRA3 Binds Both Classical and Non-Classical HLA Class I Molecules but with Reduced Affinities Compared to LILRB1/LILRB2: Structural Evidence

Structurally, Group 1 LILR (Leukocyte Immunogloblin (Ig)-Like Receptor, also known as Ig-like transcripts, ILT; Leukocyte Ig-like receptor, LIR; and CD85) members are very similar in terms of the HLAIs (human leukocyte antigen class I molecules) binding region and were hypothesized that they all bind to HLAIs. As one of the Group 1 LILRs, LILRA3 is the only secretory LILR and may greatly control the inhibitory immune response induced by LILRB1, LILRB2, and other HLA-binding LILR molecules like LILRA1. Nevertheless, little was known about the binding of LILRA3 to HLAIs. In this report, we present the crystal structure of the LILRA3 domain 1 (D1) and evaluate the D1 and D1D2 (domain 1 and domain 2) binding to classical and non-classical HLAIs using BIAcore® surface plasmon resonance analysis (SPR). We found that LILRA3 binds both classical HLA-A*0201 and non-classical HLA-G1 but with reduced affinities compared to either LILRB1 or LILRB2. The polymorphic amino acids and the LILRA3 D1 structure support this notion.
Random Forest and PCA for Self-Organizing Maps based Automatic Music Genre Discrimination

Digital music distribution industry has seen a tremendous growth in resent years. Tasks such us automatic music genre discrimination address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we base on Self-Organizing Maps to map the high-dimensional audio signals into SOM features. In addition we use Principle Components Analysis (PCA) to reduce feature dimension to improve the classification performance. Regarding the task of genre modeling, we introduce a new method Random Forest. Experiment results show that SOM feature is feasible for music genre classification. Comparisons with traditional classification methods show that the new introduced method can achieve the highest recognition rate. In addition we find that PCA can further improve the discrimination performance of almost all the classifiers we investigate.
ROBUST STABILITY ANALYSIS OF UNCERTAIN INTERCONNECTION IN THE BEHAVIORAL FRAMEWORK

Abstract This paper considers the robust stability of the interconnection of a linear time-invariant differential nominal system and passive uncertainties in the behavioral framework. A generalized version of the well-known passivity theorem is formulated by using quadratic differential forms. Based on the generalized passivity theorem, it is proved that, if the nominal system is Φ-passive, the interconnection is robustly stable against strictly (-Φ)-passive uncertainty. Moreover, we show that the Φ-passivity of the nominal system is a necessary and sufficient condition for the robust stability of a “regular” interconnection.
An object based image retrieval framework based on automatic image annotation

Automatic image annotation is the process of assigning relevant keywords to the images. It is considered to be potential research area in current scenario. Annotation to an image can be defined as the information which could describe an image by considering three ways i.e. when these images were taken, what are the different objects available in these images and finally the images belongs to whom. For solving the problem of automatic image annotation, many algorithms have been proposed. Efforts are going on to develop more efficient algorithms. In this paper we have proposed an object based image retrieval algorithm for automatic image annotation. The proposed algorithm considers selection of objects with in an image. This object selection helps in dividing the image into different set of groups on the basis of present objects in an image. Thus, we do not need to extract the whole features from the images when a new image comes, rather we extract features from the objects and matches those features against the different groups of images for the feature matching and effective retrieval based on object selection.
Mesothelial cells in tissue repair and fibrosis

Mesothelial cells are fundamental to the maintenance of serosal integrity and homeostasis and play a critical role in normal serosal repair following injury. However, when normal repair mechanisms breakdown, mesothelial cells take on a profibrotic role, secreting inflammatory, and profibrotic mediators, differentiating and migrating into the injured tissues where they contribute to fibrogenesis. The development of new molecular and cell tracking techniques has made it possible to examine the origin of fibrotic cells within damaged tissues and to elucidate the roles they play in inflammation and fibrosis. In addition to secreting proinflammatory mediators and contributing to both coagulation and fibrinolysis, mesothelial cells undergo mesothelial-to-mesenchymal transition, a process analogous to epithelial-to-mesenchymal transition, and become fibrogenic cells. Fibrogenic mesothelial cells have now been identified in tissues where they have not previously been thought to occur, such as within the parenchyma of the fibrotic lung. These findings show a direct role for mesothelial cells in fibrogenesis and open therapeutic strategies to prevent or reverse the fibrotic process.
Conservation of a Gliding Motility and Cell Invasion Machinery in Apicomplexan Parasites

Most Apicomplexan parasites, including the human pathogens Plasmodium, Toxoplasma, and Cryptosporidium, actively invade host cells and display gliding motility, both actions powered by parasite microfilaments. In Plasmodium sporozoites, thrombospondin-related anonymous protein (TRAP), a member of a group of Apicomplexan transmembrane proteins that have common adhesion domains, is necessary for gliding motility and infection of the vertebrate host. Here, we provide genetic evidence that TRAP is directly involved in a capping process that drives both sporozoite gliding and cell invasion. We also demonstrate that TRAP-related proteins in other Apicomplexa fulfill the same function and that their cytoplasmic tails interact with homologous partners in the respective parasite. Therefore, a mechanism of surface redistribution of TRAP-related proteins driving gliding locomotion and cell invasion is conserved among Apicomplexan parasites.
The Effect of an Alcohol-Associated Interference Task on Recall of Words and Drinking Urges In Heavy and Light Drinkers

Results. The data suggest there were no differing effects between heavy and light drinkers in their ability to recall words after exposure to the alcoholrelated stimuli, as well as in the heavy drinkers group that received the alcohol-related or neutral words. Interestingly, a statistically significant difference was found in the number of the words recalled between light drinkers who participated in the alcohol interference task and light drinkers who participated in the neutral interference task. In addition, exposure to alcohol-related words does not seem to affect urges to drink.
Dopamine Transporter (DAT1) and Dopamine Receptor D4 (DRD4) Genotypes Differentially Impact on Electrophysiological Correlates of Error Processing

Recent studies as well as theoretical models of error processing assign fundamental importance to the brain's dopaminergic system. Research about how the electrophysiological correlates of error processing—the error-related negativity (ERN) and the error positivity (Pe)—are influenced by variations of common dopaminergic genes, however, is still relatively scarce. In the present study, we therefore investigated whether polymorphisms in the DAT1 gene and in the DRD4 gene, respectively, lead to interindividual differences in these error processing correlates. One hundred sixty participants completed a version of the Eriksen Flanker Task while a 26-channel EEG was recorded. The task was slightly modified in order to increase error rates. During data analysis, participants were split into two groups depending on their DAT1 and their DRD4 genotypes, respectively. ERN and Pe amplitudes after correct responses and after errors as well as difference amplitudes between errors and correct responses were analyzed. We found a differential effect of DAT1 genotype on the Pe difference amplitude but not on the ERN difference amplitude, while the reverse was true for DRD4 genotype. These findings are in line with predictions from theoretical models of dopaminergic transmission in the brain. They furthermore tie results from clinical investigations of disorders impacting on the dopamine system to genetic variations known to be at-risk genotypes.
A dynamic bayesian network for inference of learners' algebraic knowledge

An Intelligent Tutoring System (ITS) is an educational software that provides personal assistance for students, allowing them to learn at their own pace. This is possible because ITSs are able to map the learners' knowledge to create a student model. Most of the tutors use a Bayesian Network (BN) to perform this task, due to their ability to deal with uncertain data. However, classic static BNs are unable to model data, such as the student's knowledge, that changes over time. Dynamic Bayesian Networks (DBN) are an interesting option in this case, because they are a special type of BN that reasons over time. This paper presents an architecture of DBN that aims at inferring student's algebraic knowledge. This network was constructed based on a concept map, which was developed with the goal of structuring the algebraic knowledge, i. e. defining relationships among concepts. The proposed DBN was evaluated with the help of an expert in order to verify the ability of the network to predict the student's knowledge on the application of operations to solve 1st degree equations. This DBN is being integrated into an web-based ITS for algebra learning.
Game Theory Based Optimization of Security Configuration

How to make a trade off between the security of sys- tems and the usability of users is an important issue in network security configuration. To resolve this problem, an optimization method of security configuration based on game theory is proposed. Firstly, a security configuration model based on non-cooperative game is built which in- fers the optimal strategy of systems and users respectively by calculating their strategies and incentives. Secondly, to optimize security configuration further, it cooperatively optimizes the individual optimal strategy by cooperative game, thus eliminating the cases that individual optimal is not the overall optimal. An illustrated experiment shows that this method can coordinate the security of network systems and usability of users so that the security configu- ration of system is optimized magnificently.
Security analysis of WiMAX network: With Misbehavior Node attack

Security has grown to be a major concern in regulate to provide restricted communication in Wireless as well as wired environment. As far as security concern related to WiMAX network i.e for PHY Layer (Jamming and scrambling) and MAC Layer attack protection has been provided by (AES, PKM etc) one separate Layer(Security Layer). In current scenario Network Layer security is more vital and we have analyzed and simulated the effect of misbehaving node (Without MAC Layer). It misbehaves in the sense that the node does not respect its MAC protocols and avariciously sends its packets without any restriction (Flooding of packets) because it doesn't follow any protocol. Using OPNET we investigate the performance comparison analysis with and without misbehavior node. The packets transmitted in a network from Servers to Clients not only indicate the maximum amount of information provided but how much secure information is present in the network is the major point of deliberation. So many unauthorized clients are present in the network which can destroy a packets flowing to authorize node. One of the attacks is Misbehavior Node attack (Network Layer), in which the node does not follow any protocol but continuously transmits Constant Bit Rate (CBR) packets, which introduces noise in the network and upset the performance of the network.
Speech in Schizophrenia Versus Other Psychoses

Abnormalities in language are central to psychosis, particularly the schizophrenic syndrome. This chapter first discusses one clinical manifestation of abnormal language in schizophrenia: ‘thought disorder’. We then give a framework for understanding normal language structure and processing. Within this framework, we review studies of language processing in schizophrenia. Finally, we review some recent neuroimaging and electrophysiologic studies that have attempted to examine the neural correlates of language abnormalities in schizophrenia.
Study of wavelet base and threshold policies impact on the image reconstruction quality based on compressed sensing

The compression sampling scheme is realized, in which sparseness processing is realized in this theme employing wavelet transform; standard pseudo-random number matrix of uniformly distributed sequences and two dimensional central Fourier transform are used for generating random measurement matrix, and algorithm of subsection orthogonal matching pursuit is used for sampling data reconstruction. Influence of wavelet basis and threshold strategy on quality of reconstructed image is researched and elaborate description of the experiment result is provided. The experiment data of this paper provides strong support to optimization of wavelet basis and strategy of threshold selection.
EFFECTIVE LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM

In the current cloud computing environments, VM resource scheduling only considers the current system condition and ignores the previous state of system which causes the system load imbalance. Number of VM migrations is more when most of the load balancing takes place. The entire migration cost becomes a problem when all the VM resources are migrated. This is largely due to granularity of VM resources and the large amount of data transferred in the migration with deferral of VM service. This paper provides a scheduling strategy to enable effective load balancing. The method used in this paper will compute its influence on the system in advance, when current VM resources are allocated to every physical node and will opt for the deployment that will have the least load on the system. This is achieved using genetic algorithm, chronological data and the current state of the system.
A Deficiency Screen for Dominant Suppressors of Telomeric Silencing in Drosophila

Heterochromatin is a specialized chromatin structure in chromosomal regions associated with repeated DNA sequences and low concentrations of genes. Formation of heterochromatin is determined in large part by enzymes that modify histones and structural proteins that bind to these modified histones in a cooperative fashion. In Drosophila, mutations in genes that encode heterochromatic proteins are often dominant and increase expression of genes placed into heterochromatic positions. To find components of telomeric heterochromatin in Drosophila, we screened a collection of autosomal deficiencies for dominant suppressors of silencing of a transgene at the telomere of chromosome 2L. While many deficiency chromosomes are associated with dominant suppressors, in the cases tested on chromosome 2 the suppressor mapped to the 2L telomere, rather than the deficiency. We infer that background effects may hamper the search for genes that play a role in telomeric heterochromatin formation and that either very few genes participate in this pathway or mutations in these genes are not dominant suppressors of telomeric position effect. The data also suggest that the 2L telomere region plays a major role in telomeric silencing.
Supervised and Unsupervised Artificial Neural Networks for Analysis of Diatom Abundance in Tropical Putrajaya Lake , Malaysia ( Rangkaian Neural

Five years of data from 2001 until 2006 of warm unstratified shallow, oligotrophic to mesothropic tropical Putrajaya Lake, Malaysia were used to study pattern discovery and forecasting of the diatom abundance using supervised and unsupervised artificial neural networks. Recurrent artificial neural network (RANN) was used for the supervised artificial neural network and Kohonen Self Organizing Feature Maps (SOM) was used for unsupervised artificial neural network. RANN was applied for forecasting of diatom abundance. The RANN performance was measured in terms of root mean square error (RMSE) and the value reported was 29.12 cell/mL. Classification and clustering by SOM and sensitivity analysis from the RANN were used to reveal the relationship among water temperature, pH, nitrate nitrogen (NO3-N) concentration, chemical oxygen demand (COD) concentration and diatom abundance. The results indicated that the combination of supervised and unsupervised artificial neural network is important not only for forecasting algae abundance but also in reasoning and understanding ecological relationships. This in return will assist in better management of lake water quality.
Object detection based on Gaussian mixture background

The paper analyzes background signal of video. According to characteristic of background signal, a method of choosing every pameter value in algorithm is provided. Using a classical background model detection moving object-Gaussian mixture background model, and a new updating algorithm of background model is provided. Experimental results show that the effectiveness of this method and the robustness in the complex background.
Small-Fiber Neuropathy Nav1.8 Mutation Shifts Activation to Hyperpolarized Potentials and Increases Excitability of Dorsal Root Ganglion Neurons

Idiopathic small-fiber neuropathy (I-SFN), clinically characterized by burning pain in distal extremities and autonomic dysfunction, is a disorder of small-caliber nerve fibers of unknown etiology with limited treatment options. Functional variants of voltage-gated sodium channel Nav1.7, encoded by SCN9A, have been identified in approximately one-third of I-SFN patients. These variants render dorsal root ganglion (DRG) neurons hyperexcitable. Sodium channel Nav1.8, encoded by SCN10A, is preferentially expressed in small-diameter DRG neurons, and produces most of the current underlying the upstroke of action potentials in these neurons. We previously demonstrated two functional variants of Nav1.8 that either enhance ramp current or shift activation in a hyperpolarizing direction, and render DRG neurons hyperexcitable, in I-SFN patients with no mutations of SCN9A. We have now evaluated additional I-SFN patients with no mutations in SCN9A, and report a novel I-SFN-related Nav1.8 mutation I1706V in a patient with painful I-SFN. Whole-cell voltage-clamp recordings in small DRG neurons demonstrate that the mutation hyperpolarizes activation and the response to slow ramp depolarizations. However, it decreases fractional channels resistant to fast inactivation and reduces persistent currents. Current-clamp studies reveal that mutant channels decrease current threshold and increase the firing frequency of evoked action potentials within small DRG neurons. These observations suggest that the effects of this mutation on activation and ramp current are dominant over the reduced persistent current, and show that these pro-excitatory gating changes confer hyperexcitability on peripheral sensory neurons, which may contribute to pain in this individual with I-SFN.
SCSP: An energy efficient network-MAC cross-layer design for wireless sensor networks

This article presents SCSP (for “Sleep Collect and Send Protocol”) for wireless sensor networks, a network-MAC cross layer design that resolves the inherent conflict between energy efficiency and throughput. The protocol uses in its MAC layer a new paradigm that we call “sleep, collect and send”. The idea of SCSP is that a router sleeps for a given amount of time, wakes up and collects data from its children and other routers and then send them into a burst during a period of time that we call transmission period. In its network layer, the protocol uses a hierarchical tree structure as network architecture and a tree routing protocol. SCSP does not require synchronization between routers and dynamically calculates the sleep and collect periods according to the amount of incoming traffic. The protocol is implemented and simulated in OPNET simulator.