Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a Multianalyte Profiling Assay and an Enzyme-Linked Immunosorbent Assay for Serological Examination of Epstein-Barr Virus-Specific Antibody Responses in Diagnosis of Nasopharyngeal Carcinoma

ABSTRACT Assessment of antibody responses to Epstein-Barr virus (EBV) antigens has been used to assist in nasopharyngeal carcinoma (NPC) diagnosis by several methods. In this study, we evaluated an in-house Luminex multianalyte profiling (xMAP) technology and commercial enzyme-linked immunosorbent assay (ELISA) kits for serological examination of EBV-specific antibody responses in 135 NPC patients and 130 healthy controls. Four EBV biomarkers were measured: immunoglobulin A (IgA) against viral capsid antigen (VCA), EBV nuclear antigen 1 (EBNA1), diffused early antigen (EA-D), and IgG against EA-D. The sensitivities and specificities of the four markers ranged between 71.5 and 90% for xMAP assays and 80 and 92% for ELISA. Logistic regression analysis revealed that the combined markers in the xMAP assay had overall sensitivity and specificity values of 82% and 92%, respectively. The correlation coefficient (r) values for the xMAP assay and ELISA were lowest for IgA-VCA (0.468) and highest for IgA-EBNA1 (0.846); for IgA-EA-D and IgG-EA-D, the r values were 0.719 and 0.798, respectively. The concordances of the two methods for NPC discrimination were good (79 to 88%). Our results suggest that both the xMAP assay and ELISA are satisfactory for EBV antibody evaluation when multiple antigens are included.
Local-Search Techniques for Boolean Combinations of Pseudo-Boolean Constraints

Some search problems are most directly specified by boolean combinations of pseudo-boolean constraints. We study a logic PL(PB) whose formulas are of this form, and design local-search methods to compute models of PL(PB)- theories. In our approach we view a PL(PB)-theory T as a data structure -- a concise representation of a certain propositional CNF theory cl(T) logically equivalent to T. We show that parameters needed by local-search algorithms for CNF theories, such as walksat, can be estimated on the basis of T, without the need to compute cl(T) explicitly. Since cl(T) is often much larger than T, running search based on T promises performance gains. Our experimental results confirm this expectation.
NUMERICAL SIMULATION OF FRICTION STIR BUTT WELDING PROCESSES FOR AZ91 MAGNESIUM ALLOY

Friction Stir Welding (FSW) is a solid state welding process. In particular, it can be used to join high-strength aerospace magnesium and other metallic alloys that are hard to weld by conventional fusion welding. It was performed on 4 mm thickness AZ91 Magnesium alloy. Magnesium alloy have more advantage than aluminum such as light weight, softer, tendency to bend easily, cost effective in terms of energy requirements so magnesium alloy has selected in this FSW technique. In friction stir welding (FSW), a momentous residual stress is present in weld due to complex nature of fixturing system compared to fusion welding. These residual stresses can affect properties of welded components during service. Therefore, for estimating magnitude of welding residual stresses and their nature of distribution along with thermal history, a three dimensional non- linear thermo-mechanical finite element (NLTMFE) model using ABAQUS/ CAE package was developed for butt welded magnesium alloy AZ91.
Glypican-3 induces a mesenchymal to epithelial transition in human breast cancer cells

Breast cancer is the disease with the highest impact on global health, being metastasis the main cause of death. To metastasize, carcinoma cells must reactivate a latent program called epithelial-mesenchymal transition (EMT), through which epithelial cancer cells acquire mesenchymal-like traits. Glypican-3 (GPC3), a proteoglycan involved in the regulation of proliferation and survival, has been associated with cancer. In this study we observed that the expression of GPC3 is opposite to the invasive/metastatic ability of Hs578T, MDA-MB231, ZR-75-1 and MCF-7 human breast cancer cell lines. GPC3 silencing activated growth, cell death resistance, migration, and invasive/metastatic capacity of MCF-7 cancer cells, while GPC3 overexpression inhibited these properties in MDA-MB231 tumor cell line. Moreover, silencing of GPC3 deepened the MCF-7 breast cancer cells mesenchymal characteristics, decreasing the expression of the epithelial marker E-Cadherin. On the other side, GPC3 overexpression induced the mesenchymal-epithelial transition (MET) of MDA-MB231 breast cancer cells, which re-expressed E-Cadherin and reduced the expression of vimentin and N-Cadherin. While GPC3 inhibited the canonical Wnt/β-Catenin pathway in the breast cancer cells, this inhibition did not have effect on E-Cadherin expression. We demonstrated that the transcriptional repressor of E-Cadherin - ZEB1 - is upregulated in GPC3 silenced MCF-7 cells, while it is downregulated when GPC3 was overexpressed in MDA-MB231 cells. We presented experimental evidences showing that GPC3 induces the E-Cadherin re-expression in MDA-MB231 cells through the downregulation of ZEB1. Our data indicate that GPC3 is an important regulator of EMT in breast cancer, and a potential target for procedures against breast cancer metastasis.
Response letter of the ﬁrst round of the review

The standard SPARQL query language is currently deﬁned for querying RDF graphs without RDFS semantics. Several extensions to SPARQL have been proposed to query RDF graphs considering RDFS semantics. In this paper, we discuss extensions of SPARQL that uses regular expressions to navigate RDF graphs and may be used to answer queries considering RDFS semantics. In particular, we present and compare nSPARQL and our proposal CPSPARQL. We show that CPSPARQL is expressive enough to answer full SPARQL queries modulo RDFS. Finally, we compare the expressiveness and complexity of both nSPARQL and the corresponding fragment of CPSPARQL, that we call cpSPARQL. We show that both languages have the same complexity through cpSPARQL, being a proper extension of SPARQL graph patterns, is more expressive than nSPARQL.
Memory failures in progressive idiopathic dementia.

Fourteen patients with progressive idiopathic dementia (PID) were compared with 14 normal controls on psychometric and laboratory measures of cognitive processes. The patients had significantly lower scores than controls did on the Wechsler Memory Scale (p < .001) and the Wechsler Adult Intelligence Scale (WAIS) Performance IQ (p < .01) but not on WAIS Verbal or Full Scale IQ nor on Digit Span tests. The patients performed more poorly on learning and memory tasks than controls. Unlike the latter, the patients' recall of categorized word lists was no better than their recall of unrelated words and they did not consistently remember information that had been previously recalled. Patients also were unable to generate as many words that start with a given letter (p < .01) or that belong to a given category (p < .001) as controls did. The data are interpreted as showing that while many intellectual functions are preserved, PID patients are unable to access structures in semantic memory and therefore fail to effectively encode episodic events so that they are memorable. Differences in cognitive dysfunction in PID and in depression are discussed.
Inhibitors in congenital coagulation disorders

The development of inhibitory ‘allo’ antibodies to a deficient coagulation factor is arguably now the most severe and important complication of clotting factor concentrate exposure in haemophilia and other congenital coagulation disorders. Furthermore, development of an inhibitor to the factor VIII or factor IX transgene product remains a significant concern in gene therapy protocols for haemophilia. Although the development of an inhibitor does not usually change the rate, initial severity or pattern of bleeding, it does compromise the ability to manage haemorrhage in affected individuals, resulting in a greater rate of complications, cost and disability. The purpose of this review is to summarize current understanding of the epidemiology, immunobiology, laboratory evaluation and management of inhibitors arising in patients with congenital coagulation disorders. An attempt has been made to focus on recent advances in the immunology of inhibitors, and to speculate on their potential clinical application.
Intractable Problems in Cryptography

We examine several variants of the Diffie-Hellman and Discrete Log problems that are connected to the security of cryptographic protocols. We dis- cuss the reductions that are known between them and the challenges in trying to assess the true level of difficulty of these problems, particularly if they are interactive or have complicated input.
A Novel Approach to Artistic Textual Visualization via GAN

While the visualization of statistical data tends to a mature technology, the visualization of textual data is still in its infancy, especially for the artistic text. Due to the fact that visualization of artistic text is valuable and attractive in both art and information science, we attempt to realize this tentative idea in this article. We propose the Generative Adversarial Network based Artistic Textual Visualization (GAN-ATV) which can create paintings after analyzing the semantic content of existing poems. Our GAN-ATV consists of two main sections: natural language analysis section and visual information synthesis section. In natural language analysis section, we use Bag-of-Word (BoW) feature descriptors and a two-layer network to mine and analyze the high-level semantic information from poems. In visual information synthesis section, we design a cross-modal semantic understanding module and integrate it with Generative Adversarial Network (GAN) to create paintings, whose content are corresponding to the original poems. Moreover, in order to train our GAN-ATV and verify its performance, we establish a cross-modal artistic dataset named "Cross-Art". In the Cross-Art dataset, there are six topics and each topic has their corresponding paintings and poems. The experimental results on Cross-Art dataset are shown in this article.
Mean and covariance adaptation based on minimum classification error linear regression for continuous density HMMs

The performance of speech recognition system will be significantly deteriorated because of the mismatches between training and testing conditions. This paper addresses the problem and proposes an algorithm to adapt the mean and covariance of HMM simultaneously within the minimum classification error linear regression (MCELR) framework. Rather than estimating the transformation parameters using maximum likelihood estimation (MLE) or maximum a posteriori, we proposed to use minimum classification error (MCE) as the estimation criterion. The proposed algorithm, called IMCELR (Improved MCELR), has been evaluated on a Chinese digit recognition tasks based on continuous density HMM. The experiments show that the proposed algorithm is more efficient than maximum likelihood linear regression with the same amount of adaptation data.
The hypolipidemic activity of Ayurvedic medicine, Arogyavardhini vati in Triton WR-1339-induced hyperlipidemic rats: A comparison with fenofibrate

Background: Hyperlipidemia is a major risk factor of coronary heart disease. Currently available hypolipidemic drugs have been associated with number of side effects. Arogyavardhini vati, an Ayurvedic polyherbal formulation has been used for liver disorders. Therefore, present study was designed to evaluate the effect of Arogyavardhini vati in Triton WR-1339-induced hyperlipidemia in rats. Objectives: Anti-hyperlipidemic activity evaluation of Arogyavardhini vati against Triton WR-1339-induced hyperlipidemia in rats. Materials and Methods: Overnight fasted male Wistar rats (150-200 g) were randomly divided into normal control group [4% Dimethyl Sulfoxide (DMSO), i.p.], positive control group (Triton WR-1339 in 4% DMSO, 400 mg/kg, i.p.), standard drug treated (fenofibrate 65 mg/kg, p.o. for 7 days after inducing hyperlipidemia) and Arogyavardhini vati treated (50, 100, 200 mg/kg, p.o. for 7 days after inducing hyperlipidemia). Rat doses were calculated by extrapolating the equivalent human dose (therapeutic dose, sub-maximum, and maximum dose). Serum total cholesterol, triglyceride, low-density lipoprotein (LDL), high-density lipoprotein HDL, liver malondialdehyde (MDA), and glutathione (GSH) levels were estimated at end of experiments. Results: Arogyavardhini vati significantly decreased serum cholesterol, triglyceride, LDL, and C-reactive protein (CRP) and significantly increased serum HDL in a dose-dependent manner. Decreased MDA and increased GSH levels in liver were observed at all doses of Arogyavardhini vati (50, 100, 200 mg/kg) and fenofibrate-treated groups when compared with Triton-treated group. Atherogenic Index (AI) level was significantly decreased in fenofibrate and Arogyavardhini vati (200 mg/kg) treated rats when compared with normal control. Conclusion: Arogyavardhini vati, a traditionally used Ayurvedic medicine may be a useful therapy for hypercholesterolemia through reducing oxidative stress (decreasing MDA and increasing GSH) and lipid levels.
LOW TEMPERATURE CRACKING OF ASPHALT CONCRETE PAVEMENTS

This report investigates the low temperature behavior of asphalt pavements based on experimental results obtained for three mixtures used in the construction of cells 33, 34, and 35 at Mn/ROAD facility as well as field information acquired over the years at Mn/ROAD. As a result of this research a new test method was developed to determine the low temperature fracture properties of asphalt mixtures based on tests performed on semi circular specimens tested in bending. This method can be used to select materials with better fracture resistance and therefore better performance in the field. Two models were developed to predict the low temperature cracking performance of asphalt pavements. The first model predicts the crack spacing in asphalt pavements exposed to low temperatures based on continuum mechanics and the frictional restraint provided by the aggregate base. The second model predicts the accumulation of damage and the propagation of the crack through the asphalt layer as temperature drops based on cohesive zone model.
Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps

A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a piecewise-continuous function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system.
Evaluating a Focus-Based Approach to Anaphora Resolution

We present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing MUC (Message Understanding Conference) Information Extraction system, allowing quantitative evaluation against a substantial corpus of annotated real-world texts. Extensions to the basic focusing mechanism can be easily tested, resulting in refinements to the mechanism and resolution rules. Results show that the focusing algorithm is highly sensitive to the quality of syntactic-semantic analyses, when compared to a simpler heuristic-based approach.
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability

We give a robust version of the celebrated result of Kruskal on the uniqueness of tensor decompositions: we prove that given a tensor whose decomposition satisfies a robust form of Kruskal's rank condition, it is possible to approximately recover the decomposition if the tensor is known up to a sufficiently small (inverse polynomial) error. Kruskal's theorem has found many applications in proving the identifiability of parameters for various latent variable models and mixture models such as Hidden Markov models, topic models etc. Our robust version immediately implies identifiability using only polynomially many samples in many of these settings. This polynomial identifiability is an essential first step towards efficient learning algorithms for these models. Recently, algorithms based on tensor decompositions have been used to estimate the parameters of various hidden variable models efficiently in special cases as long as they satisfy certain "non-degeneracy" properties. Our methods give a way to go beyond this non-degeneracy barrier, and establish polynomial identifiability of the parameters under much milder conditions. Given the importance of Kruskal's theorem in the tensor literature, we expect that this robust version will have several applications beyond the settings we explore in this work.
Stable force-myographic control of a prosthetic hand using incremental learning

Force myography has been proposed as an appealing alternative to electromyography for control of upper limb prosthesis. A limitation of this technique is the non-stationary nature of the recorded force data. Force patterns vary under influence of various factors such as change in orientation and position of the prosthesis. We hereby propose an incremental learning method to overcome this limitation. We use an online sequential extreme learning machine where occasional updates allow continual adaptation to signal changes. The applicability and effectiveness of this approach is demonstrated for predicting the hand status from forearm muscle forces at various arm positions. The results show that incremental updates are indeed effective to maintain a stable level of performance, achieving an average classification accuracy of 98.75% for two subjects.
Dependence of threshold current and linearity on Bragg wavelength for grating-surface-emitting lasers

We report on a systematic experimental study of the dependence of the threshold current and power-current linearity on the detuning of the Bragg wavelength from the gain peak for grating-surface-emitting lasers. Minimum threshold currents were obtained for lasers emitting at wavelengths 2 to 9 nm on the long wavelength side of the gain peak, depending on the cavity length. Threshold currents as low as 98 A/cm/sup 2/ were obtained at the optimum Bragg wavelength. A strong dependence of the linearity on the detuning was found and the lasers showed good linearity at the wavelengths yielding optimum low threshold currents. <<ETX>>
Action Research Against Violence: An Experience from Southern Mexico

This article reflects on an experience of action research aimed at improving the understanding of the problem of chronic violence, raising awareness of its causes and implications, and promoting initiatives that seek to combat it through the assertion of rights. The research was developed as part of a university diploma taught in two Mexican states where chronic violence is an issue. The diploma aimed to build and support networks that promote cooperation between social actors. The action research was designed to offer a shared space in which diploma participants could develop joint initiatives aimed at raising awareness of the nature and dimensions of violence, and to confront the problem. Through building trust, the experience resulted in the creation of new approaches to the complex problem of violence, and new ways to understand it.
Pre-synthesis Optimization of Multiplications to Improve Circuit Performance

Conventional high-level synthesis uses the worst case delay to relate all inputs to all outputs of an operation. This is a very conservative approximation of reality, especially in arithmetic operations (where some bits are required later than others and some bits are produced earlier than others). This paper proposes a pre-synthesis optimization algorithm that takes advantage of this feature for more efficient high-level synthesis of data-flow graphs formed by additions and multiplications. The presented pre-processor analyzes the critical path at bit-granularity and splits the arithmetic operations into sub-words fragments. In particular, some of the specification multiplications are broken up into several smaller multiplications, additions, and other operations of three new types specially defined to reduce the clock cycle duration. These fragments become the input to any regular high-level synthesis tool to speed up circuit execution times. The experimental results carried out show that implementations obtained from the optimized specification are on average 70% faster and in most cases substantial area reductions are also achieved
Fair overload handling using proof-of-work functions

Overload can reduce the overall performance of a computer system up to a point where the whole service might collapse. Thus, for example the load on a server must be controlled to achieve optimal system performance. Doing so some requests from clients must be rejected. We show that we can distinguish four different types of user profiles, why micropayments are no suitable solution and how a proof-of-work based system can be used to allocate the rare resources slots to those clients that generate the maximal use out of these requests. We then present the new HashCashLin function we developed in order to overcome the poor adjustment possibilities of the classic HashCash function and how such a proof-of-work can be augmented. We describe our proof-of-work auction system and finally we show the results obtained by running a simulated model of our system
Spatio-Temporal View Interpolation

We propose a fully automatic algorithm for view interpolation of a completely non-rigid dynamic event across both space and time. The algorithm operates by combining images captured across space to compute voxel models of the scene shape at each time instant, and images captured across time to compute the "scene flow" between the voxel models. The scene-flow is the non-rigid 3D motion of every point in the scene. To interpolate in time, the voxel models are "flowed" using an appropriate multiple of the scene flow and a smooth surface fit to the result. The novel image is then computed by ray-casting to the surface at the intermediate time instant, following the scene flow to the neighboring time instants, projecting into the input images at those times, and finally blending the results. We use our algorithm to create re-timed slow-motion fly-by movies of dynamic real-world events.
Design of a novel dual mode filter based on HMSIW structure using mode shifting technique

In this paper a new SIW cavity for dual mode operation is presented. With introduction of a perturbation in a square SIW cavity, the resonant frequency of the first mode (TE101) is shifted to near the second mode (TE102). By cutting the perturbed structure along the even symmetry axis of these modes, a dual mode filter based on HMSIW structure is realized. The proposed filter is simulated by HFSS and its circuit modeling is studied. FBW of this filter at 6.07GHz is 5% and a transmission zero locates at 7GHz.
Handling Inconsistency for Multi-Source Integration

The overwhelming amount of information sources now available through the internet has increased the need to combine or integrate the data retrieved from these sources in an intelligent and efficient manner. A desirable approach for information integration would be to have a single interface, like the SIMS information broker [1], which allows access to multiple information sources. An example application is to retrieve all the menus of restaurants from Joe's Favorite Restaurants site which have been rated highly by the Department of Health.