Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey propagation as local equilibrium equations

It has been shown experimentally that a decimation algorithm based on survey propagation (SP) equations allows one to solve efficiently some combinatorial problems over random graphs. We show that these equations can be derived as sum–product equations for the computation of marginals in an extended space where the variables are allowed to take an additional value—*—when they are not forced by the combinatorial constraints. An appropriate ‘local equilibrium condition’ cost/energy function is introduced and its entropy is shown to coincide with the expected logarithm of the number of clusters of solutions as computed by SP. These results may help to clarify the geometrical notion of clusters assumed by SP for random K-SAT or random graph colouring (where it is conjectured to be exact) and help to explain which kind of clustering operation or approximation is enforced in general/small sized models in which it is known to be inexact.
Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain–Computer Interface

Although brain-computer interface (BCI) techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classification. In this paper, we propose a hybrid algorithm to improve the classification success rate of MI-based electroencephalogram (EEG) signals in BCIs. The proposed scheme develops a novel cross-correlation based feature extractor, which is aided with a least square support vector machine (LS-SVM) for two-class MI signals recognition. To verify the effectiveness of the proposed classifier, we replace the LS-SVM classifier by a logistic regression classifier and a kernel logistic regression classifier, separately, with the same features extracted from the cross-correlation technique for the classification. The proposed approach is tested on datasets, IVa and IVb of BCI Competition III. The performances of those methods are evaluated with classification accuracy through a 10-fold cross-validation procedure. We also assess the performance of the proposed method by comparing it with eight recently reported algorithms. Experimental results on the two datasets show that the proposed LS-SVM classifier provides an improvement compared to the logistic regression and kernel logistic regression classifiers. The results also indicate that the proposed approach outperforms the most recently reported eight methods and achieves a 7.40% improvement over the best results of the other eight studies.
Analytical characteristics of the Roche highly sensitive troponin T assay and its application to a cardio-healthy population

Background It is desirable that current assays for cardiac troponin (cTn) are able to meet the recommended criterion that the diagnosis and risk assessment of patients present with symptoms of myocardial infarction requires a rise and fall in cTn with at least one point above the 99th percentile of a reference population. We have evaluated the analytical characteristics of the new highly sensitive troponin T (hs-TnT) assay to see if it meets this criterion and applied it to a carefully defined, cardio-healthy Australian reference population. Methods An imprecision profile was determined for the Roche hs-TnT assay (Roche Diagnostics, Sydney, Australia) using multiple samples analysed on nine separate occasions. The distribution of troponin T was determined using 104 samples from a cardio-healthy population. Results The new hs-TnT assay meets the specifications of a coefficient of variation of 10% at the 99th percentile of our cardio-healthy reference population. Of the 104 samples analysed 44 showed troponin T concentrations above the manufacturer's quoted limit of detection. Age and gender differences in the median and 99th percentile troponin T concentration were observed. Conclusions The new hs-TnT assay shows improved precision and sensitivity at very low troponin concentration. We have shown that a significant number of individuals in this cardio-healthy population had detectable circulating troponin concentration. With many apparently healthy people having detectable troponin, clinical judgement will become more important in interpreting troponin results.
Design and analysis of circular MPA using multi-layer substrate sandwich for bandwidth enhancement

In this paper, the design of circular microstrip patch antenna with sandwiching the substrates has been proposed for bandwidth enhancement. The proposed antenna is designed at 2.7GHz (S-band) using High Frequency Structural Simulator. The circular patch antenna is designed on two different substrates, FR4_epoxy and RT Duroid 5880™ and fabricated on the three layer FR4-epoxy substrate. The effect of dielectric constant, substrate stacking on the performance of an antenna system is compared and results from the simulator are analyzed. The result analysis shows that the bandwidth of this antenna has increased 2.5 times with three layer stacking at 10 dB return loss and directive gain of 4.7dBi. The work can be enhanced in the future using metamaterials to reduce the antenna weight and to increase the bandwidth of operation.
Activity‐dependent phosphorylation of Akt/PKB in adult DRG neurons

The serine/threonine kinase Akt/PKB has been implicated in cell survival signalling in many cell types, including the dorsal root ganglion (DRG). However, little is known about its role in physiological and pathophysiological conditions in the adult sensory and nociceptive system. In this study, we show that in naïve animals almost all cells express Akt but only a subset of small‐diameter neurons expresses a high level of phospho‐Akt (p‐Akt Ser 473). Activation of peripheral nociceptors in vivo using intraplantar injections of capsaicin in anaesthetized rats induced a rapid onset and time‐dependent increase in p‐Akt Ser 473 in small‐ and medium‐sized DRG, predominantly TRPV1‐positive neurons. In addition, electrical stimulation of ‘A and C’ fibres in the sciatic nerve induced an increase in the cytoplasmic staining of p‐Akt Ser 473 in small‐ and medium‐size DRG neurons. Blocking neuronal activity in the sciatic nerve using tetrodotoxin reduced the basal level of p‐Akt Ser 473. Cultured DRG neurons confirmed that phosphorylation of Akt in different cellular compartments is triggered by depolarization or receptor activation, and suggested that this effect is mediated in part by phosphatidylinositol 3‐kinase. Our results show that p‐Akt Ser 473 is a marker of nociceptor activation and suggest a novel role for Akt in the transduction of intracellular signals in adult DRG neurons.
Damage-recognition proteins as a potential indicator of DNA-damage-mediated sensitivity or resistance of human cells to ultraviolet radiation.

We have previously identified damage-recognition proteins that bind to cisplatin[cis-diamminedichloroplatinum(II), a DNA cross-linking agent]- or u.v. -modified DNA in HeLa cells [Chao, Huang, Huang & Lin-Chao (1991) Mol. Cell. Biol. 11, 2075-2080; Chao, Huang, Lee & Lin-Chao (1991) Biochem. J. 277, 875-878]. In the present study we compared damage-recognition proteins in cells expressing different sensitivities to DNA damage. An increase in damage-recognition proteins and an enhancement of plasmid re-activation were detected in HeLa cells resistant to cisplatin and u.v. However, repair-defective cells derived from xeroderma-pigmentosum (a rare skin disease) patients did not express less cisplatin damage-recognition proteins than repair-competent cells, suggesting that damage-recognition-protein expression may not be related to DNA repair. By contrast, cells resistant to DNA damage consistently expressed high levels of u.v. -modified-DNA damage-recognition proteins. The results support the notion that u.v. damage-recognition proteins are different from those that bind to cisplatin. These findings also suggest that the damage-recognition proteins identified could be used as potential indicators of the sensitivity or resistance of cells to u.v.
Multiobjective Pareto Optimization of an Industrial Straight Grate Iron Ore Induration Process Using an Evolutionary Algorithm

Multiobjective optimization of an industrial straight grate iron ore induration process is carried out in this study using an evolutionary algorithm. A simultaneous maximization of throughput and pellet quality indices like cold compression strength (CCS) and Tumbler index (TI) is adopted for this purpose, which leads to an improved optimal control of the induration process as compared to the conventional practice of controlling the process based on burn-through point (BTP) temperature. Discretized pressure and temperature profiles, grate speed, and bed height are used as decision variables whereas the bounds on CCS, abrasion index (AI), maximum pellet temperature, and BTP temperature are treated as constraints. The optimization results show that it may be possible to achieve significant improvement in the throughput with similar TI values and without violating any operational constraints. Commonality among decision variables corresponding to various Pareto optimal (PO) solutions obtained as a result of this multiobjective optimization study helps in unveiling the embedded relationship amongst them, which, in turn, can reveal the operating principles of running the process in an optimal fashion. The methodology is quite generic in nature and can be adopted for similar processes. The results of this optimization exercise can be used as a set of operating target points for the underlying model based predictive control algorithms to control and optimize the process.
Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets

Drug development programs have proven successful when performed at a whole cell level, thus incorporating solubility and permeability into the primary screen. However, linking those results to the target within the cell has been a major setback. The Phenotype Microarray system, marketed and sold by Biolog, seeks to address this need by assessing the phenotype in combination with a variety of chemicals with known mechanism of action (MOA). We have evaluated this system for usefulness in deducing the MOA for three test compounds. To achieve this, we constructed a database with 21 known antimicrobials, which served as a comparison for grouping our unknown MOA compounds. Pearson correlation and Ward linkage calculations were used to generate a dendrogram that produced clustering largely by known MOA, although there were exceptions. Of the three unknown compounds, one was definitively placed as an antifolate. The second and third compounds' MOA were not clearly identified, likely because the unique MOA was not represented within the database. The availability of the database generated in this report for Staphylococcus aureus ATCC 29213 will increase the accessibility of this technique to other investigators. From our analysis, the Phenotype Microarray system can group compounds with clear MOA, but the distinction of unique or broadly acting MOA at this time is less clear. Copyright © 2012 John Wiley & Sons, Ltd.
A Retinex theory based points sampling method for mesh simplification

To accelerate the processing for integration, registration, representation and recognition of point clouds, it is of growing necessity to simplify the surface of 3-D models. Simplification is an approach to vary the levels of visual details as appropriate, thereby improving on the overall performance of the applications. This paper proposes a Retinex theory based points sampling method for mesh simplification. The sampling method considers both the local details and the overall shape. The local details are captured by a graph-based segmentation, while for the overall shape, the approach voxelizes the model and samples points in terms of the entropy of the shape index of vertices in voxels. Like many mesh simplification methods, this approach reduces the number of vertices in a model. We present a number of results to show that the method significantly simplifies the surface without losing local details and global shape.
A Cortical Area Selective for Visual Processing of the Human Body

Despite extensive evidence for regions of human visual cortex that respond selectively to faces, few studies have considered the cortical representation of the appearance of the rest of the human body. We present a series of functional magnetic resonance imaging (fMRI) studies revealing substantial evidence for a distinct cortical region in humans that responds selectively to images of the human body, as compared with a wide range of control stimuli. This region was found in the lateral occipitotemporal cortex in all subjects tested and apparently reflects a specialized neural system for the visual perception of the human body.
Systemic rheumatoid vasculitis in the era of modern immunosuppressive therapy.

OBJECTIVES Systemic rheumatoid vasculitis (SRV) is a rare but potentially serious systemic disease manifestation of rheumatoid arthritis (RA) characterized by the development of necrotizing vasculitis. The incidence of SRV appears to be decreasing possibly reflecting progress in RA treatment. The aims of this study were to review the clinical manifestations of SRV in a stable well-defined population during 2001-10 and to compare with our previous cohort (1988-2000) and also a cohort from 1975 to 1981. METHODS Using Norfolk Vasculitis Register, a prospective register of patients with systemic vasculitis since 1988, all patients with a diagnosis of SRV from 1 January 2001 until 31 December 2010 were identified. SRV was defined according to the Scott and Bacon criteria (1984). Clinical features were obtained by retrospective case note review. RESULTS Eighteen patients with SRV were identified (10 male), median age at diagnosis was 72 years and average disease duration 15.6 years. The average annual incidence for 2001-10 was 3.9 per million. One-year mortality was 12% and 5-year mortality 60%. The clinical manifestations were similar apart from systemic and cutaneous features which were more common in the earlier cohorts. CONCLUSION The incidence of SRV has declined significantly in the last 40 years; but the clinical manifestations remain similar. Systemic symptoms, and cutaneous manifestations such as infarcts and nodules, are slightly less common in the recent cohort. Despite modern immunosuppressive therapy the prognosis remains poor.
Acute Kidney Injury Biomarkers for Patients in a Coronary Care Unit: A Prospective Cohort Study

Background Renal dysfunction is an established predictor of all-cause mortality in intensive care units. This study analyzed the outcomes of coronary care unit (CCU) patients and evaluated several biomarkers of acute kidney injury (AKI), including neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18) and cystatin C (CysC) on the first day of CCU admission. Methodology/Principal Findings Serum and urinary samples collected from 150 patients in the coronary care unit of a tertiary care university hospital between September 2009 and August 2010 were tested for NGAL, IL-18 and CysC. Prospective demographic, clinical and laboratory data were evaluated as predictors of survival in this patient group. The most common cause of CCU admission was acute myocardial infarction (80%). According to Acute Kidney Injury Network criteria, 28.7% (43/150) of CCU patients had AKI of varying severity. Cumulative survival rates at 6-month follow-up following hospital discharge differed significantly (p<0.05) between patients with AKI versus those without AKI. For predicting AKI, serum CysC displayed an excellent areas under the receiver operating characteristic curve (AUROC) (0.895±0.031, p<0.001). The overall 180-day survival rate was 88.7% (133/150). Multiple Cox logistic regression hazard analysis revealed that urinary NGAL, serum IL-18, Acute Physiology, Age and Chronic Health Evaluation II (APACHE II) and sodium on CCU admission day one were independent risk factors for 6-month mortality. In terms of 6-month mortality, urinary NGAL had the best discriminatory power, the best Youden index, and the highest overall correctness of prediction. Conclusions Our data showed that serum CysC has the best discriminative power for predicting AKI in CCU patients. However, urinary NGAL and serum IL-18 are associated with short-term mortality in these critically ill patients.
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization

The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries instead of sentences and use a simple greedy algorithm to find the best summary. Furthermore, we show possibilities to scale up to larger input document collections by selecting a small number of sentences from each document prior to constructing the summary. Experiments were done on the DUC2004 dataset for multi-document summarization. We observe a higher performance over the original model, on par with more complex state-of-the-art methods.
Trust Model Based on Feedback Evaluation in Cloud Manufacturing Environment

Aiming at the existing trust issues under manufacturing environment. This paper proposes a trust model based on feedback evaluation, TMBFCM, from the characteristics of human of trust relationship of human society. The model proposed a set of evaluation indicators of cloud manufacturing services properties, introduced the dynamic trust mechanism for attenuation by time,established the service which cloud manufacturing services providers provided and the feedback evaluation and incentive mechanism given by the user of cloud manufacturing service, improved the dynamic adaptability of the model. The results show that, compared with the existing trust model, the evaluation results are closer to the true service behavior of cloud manufacturing services provider, it can resist all kinds of malicious attacks acts effectively, demonstrated good robustness and recognition.
User centered and context dependent personalization through experiential transcoding

The pervasive presence of devices exploited to use and deliver entertainment text-based content can make reading easier to get but more difficult to enjoy, in particular for people with reading-related disabilities. The main solutions that allow overcoming some of the difficulties experienced by users with specific and special needs are based on content adaptation and user profiling. This paper presents a system that aims to improve content legibility by exploiting experiential transcoding techniques. The system we propose tracks users' behaviors so as to provide a tailored adaptation of textual content, in order to fit the needs of a specific user on the several different devices she/he actually uses.
The effect of recombinant and plasma‐derived prothrombin on prothrombin time in human plasma

When investigating coagulation assays to measure the effect of infused prothrombin (FII) in in vivo coagulopathy models, we found that addition of FII, plasma‐derived human FII (pd‐hFII) or recombinant human FII (r‐hFII), to normal plasma resulted in a concentration‐dependent increase in prothrombin time (PT) initiated with Innovin®.
EEG source localization: A neural network approach

Abstract Functional activity in the brain is associated with the generation of currents and resultant voltages which may be observed on the scalp as the electroencephelogram. The current sources may be modeled as dipoles. The properties of the current dipole sources may be studied by solving either the forward or inverse problems. The forward problem utilizes a volume conductor model for the head, in which the potentials on the conductor surface are computed based on an assumed current dipole at an arbitrary location, orientation, and strength. In the inverse problem, on the other hand, a current dipole, or a group of dipoles, is identified based on the observed EEG. Both the forward and inverse problems are typically solved by numerical procedures, such as a boundary element method and an optimization algorithm. These approaches are highly time-consuming and unsuitable for the rapid evaluation of brain function. In this paper we present a different approach to these problems based on machine learning. We solve both problems using artificial neural networks which are trained off-line using back-propagation techniques to learn the complex source-potential relationships of head volume conduction. Once trained, these networks are able to generalize their knowledge to localize functional activity within the brain in a computationally efficient manner. [Neurol Res 2001; 23: 457-464]
What does Big Data tell? Sampling the social network by communication channels

Big Data has become the primary source of understanding the structure and dynamics of the society at large scale. The network of social interactions can be considered as a multiplex, where each layer corresponds to one communication channel and the aggregate of all of them constitutes the entire social network. However, usually one has information only about one of the channels or even a part of it, which should be considered as a subset or sample of the whole. Here we introduce a model based on a natural bilateral communication channel selection mechanism, which for one channel leads to consistent changes in the network properties. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get a monotonically decreasing distribution as observed in empirical studies of single-channel data. We also find that assortativity may occur or get strengthened due to the sampling method. We analyze the far-reaching consequences of our findings.
Security Provision for Data Stored in Cloud using Decentralized Access Control with Anonymous Authentication

As we store more sensitive data on cloud and also share it with third party. Though cloud storage provides on-demand services to multiple users but there are several issues related security. So in this paper we are trying to address security related issues of cloud, so that users can store any confidential data on cloud without any fear and enjoy the services provided by cloud. In this paper we have proposed a new decentralized access control mechanism to secure data stored on cloud. This proposed scheme will support anonymous authentication scheme. In that system will initially verifies the identity of the user . Here Authentication and access control schemes are decentralized unlike other systems which are centralized. Also here we are storing access policies on cloud in hash codes so that we can hide it from third parties. The proposed scheme also addresses the problem of replay attack and user revocation. So the proposed scheme is more robust than the existing scheme as it doesn‟t store the authentication information on cloud itself.