Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation of bistable medium state in nanofluids under the influence of laser radiation

We study the theory of the dynamics of the concentration of nanoparticles in a liquid-phase environment under the influence of a light field. An exact solution for the nonlinear diffusion equation was found in the form of switching waves. It is shown that under the conditions of a stationary and nonlinear temperature coefficient of thermal conductivity in the medium, nanofluids become bistable. A comparative analysis of linear and non-linear approaches to solving the diffusion equation is given.
An official ATS statement: grading the quality of evidence and strength of recommendations in ATS guidelines and recommendations.

Grading the strength of recommendations and the quality of underlying evidence enhances the usefulness of clinical practice guidelines. Professional societies and other organizations, including the American Thoracic Society (ATS), should reach consensus about whether they will use one common grading system and which of the numerous grading systems they would apply across all guidelines. The profusion of guideline grading systems confuses consumers of guidelines, and undermines the value of the grading exercise in conveying a transparent message. In response to this dilemma, the international GRADE working group has developed an approach that is useful for many guideline contexts, and that several national and international organizations have adopted. The GRADE system classifies recommendations as strong or weak, according to the balance of the benefits and downsides (harms, burden, and cost) after considering the quality of evidence. The quality of evidence reflects the confidence in estimates of the true effects of an intervention, and the system classifies quality of evidence as high, moderate, low, or very low according to factors that include the study methodology, the consistency and precision of the results, and the directness of the evidence. On recommendation of the ATS Documents Development and Implementation Committee, the ATS adopted the GRADE approach for its guidelines in line with many other organizations that have recently chosen the GRADE approach. This article informs ATS guideline developers, investigators, and those interpreting future ATS guidelines that follow the GRADE approach about the methodology and applicability of ATS guidelines and recommendations.
Automated Inference System for End-To-End Diagnosis of Network Performance Issues in Client-Terminal Devices

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user’s specific TCP implementation, enabling diagnosis of diverse range of client devices.
Graph partitioning into isolated, high conductance clusters: theory, computation and applications to preconditioning

We consider the problem of decomposing a weighted graph with n vertices into a collection P of vertex disjoint clusters such that, for all clusters C ε P, the graph induced by the vertices in C and the edges leaving C, has conductance bounded below by φ. We show that for planar graphs we can compute a decomposition P such that |P| < n/ρ, where ρ is a constant, in O(log n) parallel time with O(n) work. Slightly worse guarantees can be obtained in nearly linear time for graphs that have fixed size minors or bounded genus. We show how these decompositions can be used in the first known linear work parallel construction of provably good preconditioners for the important class of fixed degree graph Laplacians. On a more theoretical note, we present upper bounds on the Euclidean distance of eigenvectors of the normalized Laplacian from the space of vectors which consists of the cluster-wise constant vectors scaled by the square roots of the total incident weights of the vertices.
Pale-Green Phenotype of atl31 atl6 Double Mutant Leaves Is Caused by Disruption of 5-Aminolevulinic Acid Biosynthesis in Arabidopsis thaliana

Arabidopsis ubiquitin ligases ATL31 and homologue ATL6 control the carbon/nitrogen nutrient and pathogen responses. A mutant with the loss-of-function of both atl31 and atl6 developed light intensity-dependent pale-green true leaves, whereas the single knockout mutants did not. Plastid ultrastructure and Blue Native-PAGE analyses revealed that pale-green leaves contain abnormal plastid structure with highly reduced levels of thylakoid proteins. In contrast, the pale-green leaves of the atl31/atl6 mutant showed normal Fv/Fm. In the pale-green leaves of the atl31/atl6, the expression of HEMA1, which encodes the key enzyme for 5-aminolevulinic acid synthesis, the rate-limiting step in chlorophyll biosynthesis, was markedly down-regulated. The expression of key transcription factor GLK1, which directly promotes HEMA1 transcription, was also significantly decreased in atl31/atl6 mutant. Finally, application of 5-aminolevulinic acid to the atl31/atl6 mutants resulted in recovery to a green phenotype. Taken together, these findings indicate that the 5-aminolevulinic acid biosynthesis step was inhibited through the down-regulation of chlorophyll biosynthesis-related genes in the pale-green leaves of atl31/atl6 mutant.
The Development of Genetic Modification Techniques in Intracellular Parasites and Potential Applications to Microsporidia

Microsporidia constitute a large phylum of eukaryotic obligate intracellular pathogens that can infect a variety of animal hosts. Understanding the biology of microsporidia is severely limited by our current inability to genetically manipulate these parasites. With a growing number of microsporidian genome sequences available and the revolution of successful CRISPR/Cas9 genome editing in virtually every organism tested, the development of DNA transformation techniques would likely lead to rapid and significant progress in understanding microsporidia biology. Here, we outline the challenges in transformation development and review the exciting recent development of DNA transformation and CRISPR/Cas9 genetic manipulation of Cryptosporidium, as well as summarize the successful genetic manipulation strategies in other intracellular eukaryotic parasites, including Plasmodium and Toxoplasma. By providing a list of possible strategies for transforming microsporidia, we hope to facilitate progress in this important area of microbiology and pathogenesis.
A Modified Recombineering Protocol for the Genetic Manipulation of Gene Clusters in Aspergillus fumigatus

Genomic analyses of fungal genome structure have revealed the presence of physically-linked groups of genes, termed gene clusters, where collective functionality of encoded gene products serves a common biosynthetic purpose. In multiple fungal pathogens of humans and plants gene clusters have been shown to encode pathways for biosynthesis of secondary metabolites including metabolites required for pathogenicity. In the major mould pathogen of humans Aspergillus fumigatus, multiple clusters of co-ordinately upregulated genes were identified as having heightened transcript abundances, relative to laboratory cultured equivalents, during the early stages of murine infection. The aim of this study was to develop and optimise a methodology for manipulation of gene cluster architecture, thereby providing the means to assess their relevance to fungal pathogenicity. To this end we adapted a recombineering methodology which exploits lambda phage-mediated recombination of DNA in bacteria, for the generation of gene cluster deletion cassettes. By exploiting a pre-existing bacterial artificial chromosome (BAC) library of A. fumigatus genomic clones we were able to implement single or multiple intra-cluster gene replacement events at both subtelomeric and telomere distal chromosomal locations, in both wild type and highly recombinogenic A. fumigatus isolates. We then applied the methodology to address the boundaries of a gene cluster producing a nematocidal secondary metabolite, pseurotin A, and to address the role of this secondary metabolite in insect and mammalian responses to A. fumigatus challenge.
A Toolbox for Optophysiological Experiments in Freely Moving Rats

Simultaneous recordings and manipulations of neural circuits that control the behavior of animals is one of the key techniques in modern neuroscience. Rapid advances in optogenetics have led to a variety of probes combining multichannel readout and optogenetic write in. Given the complexity of the brain, it comes as no surprise that the choice of the device is constrained by several factors such as the animal model, the structure and location of the brain area of interest, as well as the behavioral read out. Here we provide an overview of available devices for chronic simultaneous neural recordings and optogenetic manipulation in awake behaving rats. We focus on two fixed arrays and two moveable drives. For both options, we present data from one custom-made (in house) and one commercially available device. Here we provide evidence that simultaneous neural recordings and optogenetic manipulations are feasible with all four tested devices. Further we give detailed information about the recording quality, and also contrast the different features of the probes. As we provide detailed information about equipment and building procedures for combined chronic multichannel readout and optogenetic control with maximum performance at minimized costs, this overview might help especially researchers who want to enter the field of in vivo optophysiology.
T Cells + Response of Memory CD4 Molecular Mechanism for Enhanced Cellular T Cells and Is a + Memory from Naive CD4 Activation-Induced Genes Differentiates Augmentation in Expression of

In an attempt to understand the molecular basis for the immunological memory response, we have used cDNA microarrays to measure gene expression of human memory and naive CD4 1 T cells at rest and after activation. Our analysis of 54,768 cDNA clones provides the ﬁrst glimpse into gene expression patterns of memory and naive CD4 1 T cells at the genome-scale and reveals several novel ﬁndings. First, memory and naive CD4 1 T cells expressed similar numbers of genes at rest and after activation. Second, we have identiﬁed 14 cDNA clones that expressed higher levels of transcripts in memory cells than in naive cells. Third, we have identiﬁed 135 (130 known genes and 5 expressed sequence tags) up-regulated and 68 (42 known genes and 26 expressed sequence tags) down-regulated cDNA clones in memory CD4 1 T after in vitro stimulation with anti-CD3 plus anti-CD28. Inter-estingly, the increase in mRNA levels of up-regulated genes was greater in memory than in naive CD4 1 T cells after in vitro stimulation and was higher with anti-CD3 plus anti-CD28 than with anti-CD3 alone in both memory and naive CD4 1 T cells. Finally, the changes in expression of actin and cytokine genes identiﬁed by cDNA microarrays were conﬁrmed by Northern and protein analyses. Together, we have identiﬁed ; 200 cDNA clones whose expression levels changed after activation and suggest that the level of expression of up-regulated genes is a molecular mechanism that differentiates the response of memory from naive CD4 1 T cells. The Journal of Immunology, 2001, 166: 7335–7344. accel-erated speed strength successive en-counter immunological
Apriori algorithm for association rule mining in high dimensional data

Apriori is one of the best algorithms for learning association rules. Due to the explosion of data, the storage and retrieval mechanisms in various database paradigms have revolutionized the technologies and methodologies used in the architecture. As a result, the database is not only utilized for mere information retrieval but also to infer the analytical aspect of data. Therefore it is essential to find association rules from high dimensional data because the correlation amongst the attributes can help in gaining deeper insight into the data and help in decision making, recommendations as well as reorganizing the data for effective retrieval. The traditional Apriori algorithm is computationally expensive and infeasible with high dimensional datasets. Hence we propose a variant of Apriori algorithm using the concept of QR decomposition for reducing the dimensions thereby reducing the complexity of the traditional Apriori algorithm.
Prediction Promotes Privacy in Dynamic Social Networks

Recent work on anonymizing online social networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. We study the problem of repeatedly publishing OSN data as the network evolves while preserving privacy of users. Publishing multiple instances independently has privacy risks, since stitching the information together may allow an adversary to identify users. We provide methods to anonymize a dynamic network when new nodes and edges are added to the published network. These methods use link prediction algorithms to model the evolution. Using this predicted graph to perform group-based anonymization, the loss in privacy caused by new edges can be eliminated almost entirely. We propose metrics for privacy loss, and evaluate them for publishing multiple OSN instances.
Dealing with Missing Values in a Probabilistic Decision Tree during Classification

This paper deals with the problem of missing values in decision trees during classification. Our approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Our method takes into account the dependence between attributes by using mutual information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on several databases using our approach and Quinlan's method. We also measure the quality of our classification results. Finally, we discuss some perspectives
Fracture stabilization with type II external fixator vs. type I external fixator with IM pin

Summary Bilateral external fixator frames are frequently preferred over unilateral frames due to their superior rigidity. The objective of this study was to compare the biomechanical features of bilateral external fixators with those of unilateral external fixators that are combined with an intra-medullary pin. Three-dimensional, solid models were created of several unilateral and bilateral external fixator frames. The callus in the fracture gap was also modeled. Biomechanical analyses of all constructs were performed by the finite element method. This modeling approach allows the determination of stresses, displacements, and strains in the components of the various constructs, and thus the calculation of their relative stiffness. In addition, local shear strain values in the fracture gap, currently thought to be one of the deciding factors in the process of bone healing, can also be determined. The concept of equivalent stiffness modulus, which represents a weighed average stiffness of a construct to various loads, was defined. Using this concept, it was shown that when the intramedullary pin is well seated in the epiphyseal bone, the various unilateral frames have an equivalent stiffness modulus that is similar or even greater than that of bilateral frames with a similar arrangement of transcortical pins.
System Reliability-Aware Energy Optimization for Network-on-Chip with Voltage-Frequency Islands

In this paper, a novel energy optimization algorithm for Network-on-Chip (NoC) based on voltage-frequency islands (VFIs) is proposed under the system reliability constraints. The proposed algorithm mainly includes the three processes of VFI partitioning, assignment and task mapping to reduce the energy consumption. A system reliability-aware mapping algorithm is proposed which employs a hybrid algorithm based on quantum-behaved particle swarm optimization and multi-neighborhood simulated annealing strategy. The optimum particles upon QPSO are further optimized by annealing operation mentioned above. Besides, the catastrophic operation is executed to enlarge the search scope and avoid getting into the local optimum. It minimizes the communication energy consumption of NoC system on the condition of meeting system reliability constraints. Experimental results show that the proposed algorithm is quite effective in optimizing energy consumption of NoC with voltage-frequency islands under system reliability constraints.
Questimator: Generating Knowledge Assessments for Arbitrary Topics

Formative assessments allow learners to quickly identify knowledge gaps. In traditional educational settings, expert instructors can create assessments, but in informal learning environment, it is difficult for novice learners to self assess because they don't know what they don't know. This paper introduces Questimator, an automated system that generates multiple-choice assessment questions for any topic contained within Wikipedia. Given a topic, Questimator traverses the Wikipedia graph to find and rank related topics, and uses article text to form questions, answers and distractor options. In a study with 833 participants from Mechanical Turk, we found that participants' scores on Questimator-generated quizzes correlated well with their scores on existing online quizzes on topics ranging from philosophy to economics. Also Questimator generates questions with comparable discriminatory power as existing online quizzes. Our results suggest Questimator may be useful for assessing learning in topics for which there is not an existing quiz.
Effects of Elsholtzia splendens and Cirsium japonicum on premenstrual syndrome

Premenstrual syndrome is a common chronic disorder in most women of reproductive age. The main symptoms are depression, anxiety, tension, feeling out of control, and mastalgia. In premenstrual syndrome, the effects of aromatic edible Elsholtzia splendens and Cirsium japonicum were investigated for over 3 months in 30 women participants in their twenties. In the Elsholtzia splendens capsule treated group, scores of depression and anxiety were significantly lower than those in the Cirsium japonicum capsule treated group. Moreover, instability of the premenstrual assessment form was significantly decreased in the Elsholtzia splendens capsule treated group. Our results suggest that Elsholtzia splendens could be an effective plant material in relieving symptoms of premenstrual syndrome.
Error-Prone Candidates Vie for Somatic Mutation

Antibodies play a major role in the resistance of higher organisms to disease. This is made possible because mice, humans, and other species have evolved three unusual molecular mechanisms that allow the generation of an enormously diverse repertoire of antibodies from a relatively small amount of genetic material. First, each antibody-forming B cell assembles different combinations of variable (V), diversity (D), and joining (J) minigenes to create an antibody with a unique antigen-binding site. Second, to generate the high-affinity antibodies that are required for survival, B cells target many point mutations to the V(D)J regions of the H and VJ region of the L chain immunoglobulin (Ig) genes. Those B cells that are making higher affinity antibodies are selected for further growth and differentiation, resulting in the affinity maturation of the antibody response. Third, B cells rearrange the H chain V region to various downstream C regions that encode the different isotypes. This makes it possible for each of the many antigen-binding sites to mediate the effector functions that are encoded in the different C region genes and to be distributed throughout the body 1.
Two-stage Color Texture Synthesis using the Structure Tensor Field

Since reproducing the realism of the physical word is a major goal for computer graphics, color texture synthesis is important for rendering synthetic images and animations. Most of the existing synthesis techniques provide impressive results in many cases, but fail in difficult situations with large patterns, or with long range directional variations. Based on a previously developed two-stage structure/texture synthesis algorithm where the structure tensor is used to represent the structure layer, an extension to color texture synthesis is proposed. Two different methods are used for the computation of the color structure tensor field. An acceleration method for the proposed algorithm is also presented. Results show that the proposed approach successfully synthesizes the output texture in many situations where traditional algorithms fail to reproduce the exemplarâs patterns and dynamics. These promising results pave the way towards 3D color textures synthesis showing multi-scale patterns.
Distributed parallel computing technique for EM modeling

This paper proposes a novel distributed parallel EM modeling technique to speed up the process of neural network modeling for EM structures. Existing techniques for EM modeling usually need to repeatedly change the parameters of microwave devices and drive the EM simulator to obtain sufficient training and testing samples. As the complexity in EM modeling problem increases, traditional techniques are computationally expensive on data generation and training due to the limited performance of a single computer. Our technique incorporates distributed parallel computing technique to neural network modeling. It generates data and trains neural network models in parallel using message passing interface (MPI). An example shows that our technique is much faster than traditional technique while maintaining good model accuracy.
A modified colonial competitive algorithm for optimizing convex functions

Purpose – This article is going to introduce a modified variant of the imperialist competitive algorithm (ICA). The paper aims to discuss these issues. Design/methodology/approach – ICA is a meta-heuristic algorithm that is introduced based on a socio-politically motivated global search strategy. It is a population-based stochastic algorithm to control more countries. The most powerful countries are imperialists and the weakest countries are colonies. Colonies movement toward their relevant imperialist, and making a competition among all empires to posses the weakest colonies of the weakest empires, form the basis of the ICA. This fact that the imperialists also need to model and they move towards top imperialist state is the most common type of political rules from around the world. This paper exploits these new ideas. The modification is the empire movement toward the superior empire for balancing the exploration and exploitation abilities of the ICA. Findings – The algorithms are used for optimization .