Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential Impact of Biofield Energy Treatment on the Physical and Thermal Properties of Silver Oxide Powder

Silver oxide has gained significant attention due to its antimicrobial activities. The purpose of this study was to evaluate the impact of biofield energy treatment on the physical and thermal properties of silver oxide (Ag2O). The silver oxide powder was divided into two parts, one part was kept as control and another part was received Mr. Trivedi’s biofield energy treatment. The control and treated samples were analyzed using X-ray diffraction (XRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and Fourier transform infrared (FT-IR) spectroscopy. The XRD diffractogram showed that the crystallite size of treated sample was significantly altered on the planes (200), (311), and (220) by 100, 150 and -25% respectively, with respect to control. The DSC result exhibited that the thermal energy required to decompose the silver oxide to silver and oxygen was altered from -12.47 to 71.58% in treated samples as compared to the control. TGA showed that the onset temperature of thermal degradation was reduced from 335°C (control) to 322.4°C. In addition, the rate of weight loss in treated sample was increased by 4.14% as compared to the control. Besides, the FT-IR did not show any alteration in absorption wavenumber of treated sample as compared to the control. Hence, the XRD, DSC and TGA data revealed that the biofield energy treatment has a significant impact on the physical and thermal properties of silver oxide powder. Therefore, the biofield energy treatment might improve the dissolution rate in formulation and bioavailability of treated silver oxide as compared to control.
Networks cardinality estimation using order statistics

We consider a network of collaborative peers that aim at distributedly estimating the network cardinality. We assume nodes to be endowed with unique identification numbers (IDs), and we study the performance of size estimators that are based on exchanging these IDs. Motivated by practical scenarios where the time-to-estimate is critical, we specifically address the case where the convergence time of the algorithm, i.e., the number of communications required to achieve the final estimate, is minimal. We thus construct estimators of the network size by exploiting statistical inference concepts on top of the distributed computation of order statistics of the IDs, i.e., of the M biggest IDs available in the network. We then characterize the statistical performance of these estimators from theoretical perspectives and show their effectiveness in practical estimation situations by means of numerical examples.
Duloxetine: a balanced and selective norepinephrine- and serotonin-reuptake inhibitor.

PURPOSE The pharmacology, pharmacokinetics, efficacy, safety, drug interactions, dosage and administration, cost, and place in therapy of duloxetine for major depression, pain from diabetic peripheral neuropathy, and stress urinary incontinence are reviewed. SUMMARY Duloxetine is a balanced selective serotonin and norepinephrine-reuptake inhibitor available in the United States for the treatment of major depressive disorder (MDD) and diabetic peripheral neuropathic pain (DPNP). Duloxetine has also been used for the treatment of stress urinary incontinence (SUI). Absorption of duloxetine begins two hours after oral administration, reaching a maximum plasma concentration in six hours. Half-life and volume of distribution are 12 hours and 1640 L, respectively. The recommended dosage of duloxetine is 40-80 mg daily, depending on the indication, preferably split into two doses per day. For the treatment of major depression, duloxetine has achieved remission rates similar to that of existing selective serotonin-reuptake inhibitors (SSRIs). For SUI and pain associated with diabetic peripheral neuropathy, duloxetine has not demonstrated equivalence or superiority to existing therapies. The adverse effects of duloxetine are similar to those of traditional SSRIs. Nausea is common and has been cited as the primary reason for discontinuation of duloxetine in trials. Increases in blood pressure have been mild, but caution should be used in patients with hypertension. Patients with a creatinine clearance of <30 mL/min and patients with hepatic impairment should avoid duloxetine. Duloxetine should not be recommended as first-line therapy for SUI or DPNP. For MDD, duloxetine may be a useful alternative for patients who do not benefit from or are unable to tolerate other antidepressant therapy. CONCLUSION Duloxetine has been approved for the treatment of MDD and pain associated with diabetic peripheral neuropathy in adults.
Approximate constraint generation for efficient structured boosting

We propose efficient training methods (SBoost) for totally-corrective boosting based structured learning. The optimization of boosting method for structured learning is more challenging than the structured support vector machine. Basically, we propose smooth and convex formulation for boosting based structured learning, and develop approximate constraint generation together with column generation to solve the optimization with large number of constraints and variables. Because of the convexity and smoothness, the optimization in each generation iteration can be solved efficiently. We demonstrate some structured learning applications in computer vision using SBoost, including invariance learning for digit recognition, object detection and hierarchical image classification.
Signature Extraction for Overlap Detection in Documents

Easy access to the Web has led to increased potential for students cheating on assignments by plagiarising others' work. By the same token, Web-based tools offer the potential for instructors to check submitted assignments for signs of plagiarism. Overlap-detection tools are easy to use and accurate in plagiarism detection, so they can be an excellent deterrent to plagiarism. Documents can overlap for other reasons, too: Old documents are superseded, and authors summarize previous work identically in several papers. Overlap-detection tools can pinpoint interconnections in a corpus of documents and could be used in search engines.We describe a web-accessible text registry based on signature extraction. We extract a small but diagnostic signature from each registered text for permanent storage and comparison against other stored signatures. This comparison allows us to estimate the amount of overlap between pairs of documents, although the total time required is linear in the total size of the documents. We compare our algorithm with several alternatives and present both efficiency and accuracy results.
Iterative clustering of sequence reads for error correction

Exemplary embodiments provide methods and systems for iterative clustering of sequence reads for error correction. Aspects of the exemplary embodiment include receiving a set of sequence reads and associated quality values; grouping the sequence reads into a set of initial clusters based on sequence similarity; generating a cluster consensus for each of the initial clusters; iteratively improving the clustering based on the cluster consensus and the quality values associated with the sequence reads; and generating and outputting a final cluster consensus for each of the clusters.
Robust Common Spatial Patterns for EEG signal preprocessing

The Common Spatial Patterns (CSP) algorithm finds spatial filters that are useful in discriminating different classes of electroencephalogram (EEG) signals such as those corresponding to different types of motor activities. This algorithm is however, sensitive to outliers because it involves the estimation of covariance matrices. Classical sample covariance estimates are easily affected even if a single outlier exists. To improve the CSP algorithm's robustness against outliers, this paper first investigates how multivariate outliers affect the performance of the CSP algorithm. We then propose a modified version of the algorithm whereby the classical covariance estimates are replaced by the robust covariance estimates obtained using Minimum Covariance Determinant (MCD) estimator. Median Absolute Deviation (MAD) is also used to robustly estimate the variance of the projected EEG signals. The results show that the proposed algorithm is able to reduce the influence of the outliers. When an average of 2.5% outliers is introduced, the average drop in the accuracy is 9.21% for the CSP algorithm and 0.72% for the proposed algorithm.
Chemerin Is a Novel Adipocyte-Derived Factor Inducing Insulin Resistance in Primary Human Skeletal Muscle Cells

OBJECTIVE Chemerin is an adipokine that affects adipogenesis and glucose homeostasis in adipocytes and increases with BMI in humans. This study was aimed at investigating the regulation of chemerin release and its effects on glucose metabolism in skeletal muscle cells. RESEARCH DESIGN AND METHODS Human skeletal muscle cells were treated with chemerin to study insulin signaling, glucose uptake, and activation of stress kinases. The release of chemerin was analyzed from in vitro differentiated human adipocytes and adipose tissue explants from 27 lean and 26 obese patients. RESULTS Human adipocytes express chemerin and chemokine-like receptor 1 (CMKLR1) differentiation dependently and secrete chemerin (15 ng/ml from 106 cells). This process is slightly but significantly increased by tumor necrosis factor-α and markedly inhibited by >80% by peroxisome proliferator–activated receptor-γ activation. Adipose tissue explants from obese patients are characterized by significantly higher chemerin secretion compared with lean control subjects (21 and 8 ng from 107 cells, respectively). Chemerin release is correlated with BMI, waist-to-hip ratio, and adipocyte volume. Furthermore, higher chemerin release is associated with insulin resistance at the level of lipogenesis and insulin-induced antilipolysis in adipocytes. Chemerin induces insulin resistance in human skeletal muscle cells at the level of insulin receptor substrate 1, Akt and glycogen synthase kinase 3 phosphorylation, and glucose uptake. Furthermore, chemerin activates p38 mitogen-activated protein kinase, nuclear factor-κB, and extracellular signal–regulated kinase (ERK)-1/2. Inhibition of ERK prevents chemerin-induced insulin resistance, pointing to participation of this pathway in chemerin action. CONCLUSIONS Adipocyte-derived secretion of chemerin may be involved in the negative cross talk between adipose tissue and skeletal muscle contributing to the negative relationship between obesity and insulin sensitivity.
A Low-Complexity Antenna-La-Aware Spatial Covariance Matrix Estimation Method

This paper proposed a low-complexity antenna-la-aware (ALA) covariance matrix estimation method. In the estimation process, antenna layout is assumed known at the estimator. Using this information, the estimator finds antenna pairs with statistically equivalent covariance values and sets their covariance values to the average of covariance values of all these antenna pairs. ALA for both uniform linear array (ULA) and uniform planar array (UPA) is discussed. This method takes the benefit that covariance matrices do not have full degrees of freedom. Then, the proposed ALA covariance matrix method is applied to a multi-cell network. Simulations have demonstrated that the proposed method can provide better performance than the widely used viaQ method, with respect to mean square errors and downlink spectral efficiencies.
Performance Modeling and Analysis of AF Relaying with Hidden Nodes

In this paper, we investigate and analyse the performance of amplify and forward (AF) relay system connecting a group of independent sources to their destinations employing a random carrier sense multiple access (CSMA) protocol. The sources are isolated from their destination and the only means for them to communicate with their destination is through the common relay. Sources are arbitrarily distributed around the relay and experience Rayleigh fading channels. This paper develops a new and more accurate model for multiple access interference at both the relay and destination which takes into account random sources location and the effects of fading and thermal noise at both the relay and destination. Moreover, the effect of the hidden nodes on the system performance is considered as a randomly occurring factor. This leads to the derivation of new analytical expressions for the overall spectral efficiency (SE) which can be used to estimate the throughput of the CSMA-based relay in Rayleigh fading channels and to study the impact of the different system parameters on their efficiency. The accuracy of the new mathematical results is confirmed by Monte Carlo simulation.
Shannon and Non-Shannon Measures of Entropy for Statistical Texture Feature Extraction in Digitized Mammograms

This paper deals with the problem of texture feature extraction in digital mammogram. For texture feature extraction, gray level histogram moments statistical texture analysis method is normally used. Entropy is an important texture feature, which is computed based on this method, to build a robust descriptor towards correctly classifying abnormal and normal regions of mammograms. Entropy measures the randomness of intensity distribution. In most feature descriptors, Shannon's measure is used to measure entropy. In this paper non-Shannon measures are used to measure entropy. Non-Shannon entropies have a higher dynamic range than Shannon entropy over a range of scattering conditions, and are therefore useful in estimating scatter density and regularity. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). The Results of this study are quite promising. This work is a part of developing a computer aided decision (CAD) system for early detection of breast cancer.
People like us: mining scholarly data for comparable researchers

There are many situations that one needs to find comparable others for a given researcher. Examples include finding peer reviewers, programming committees for conferences, and comparable individual asked in recommendation letters for tenure evaluation. The task is often done on an ad hoc and informal basis. In this paper, we address an interesting problem that has not been adequately studied so far: mining cumulated large scale scholarly data to find comparable researchers. We propose a standard to quantify the quality of individual's research output, through the quality of publishing venues. We represent a researcher as a sequence of her publication records, and develop methods to compute the distance between two researchers through sequence matching. Multiple variations of distances are considered to target different scenarios. We define comparable relation by the distance, and conduct experiments on a large corpus and demonstrate the effectiveness our methods through examples. In the end of the paper, we identify several promising directions for further work.
Effects of Mixed Herbal Extracts from Parched Puerariae Radix, Gingered Magnoliae Cortex, Glycyrrhizae Radix and Euphorbiae Radix (KIOM-79) on Cardiac Ion Channels and Action Potentials

KIOM-79, a mixture of ethanol extracts from four herbs (parched Puerariae radix, gingered Magnoliae cortex, Glycyrrhizae radix and Euphorbiae radix), has been developed for the potential therapeutic application to diabetic symptoms. Because screening of unexpected cardiac arrhythmia is compulsory for the new drug development, we investigated the effects of KIOM-79 on the action potential (AP) and various ion channel currents in cardiac myocytes. KIOM-79 decreased the upstroke velocity (Vmax) and plateau potential while slightly increased the duration of action potential (APD). Consistent with the decreased Vmax and plateau potential, the peak amplitude of Na+ current (INa) and Ca2+ current (ICa,L) were decreased by KIOM-79. KIOM-79 showed dual effects on hERG K+ current; increase of depolarization phase current (Idepol) and decreased tail current at repolarization phase (Itail). The increase of APD was suspected due to the decreased Itail. In computer simulation, the change of cardiac action potential could be well simulated based on the effects of KIOM-79 on various membrane currents. As a whole, the influence of KIOM-79 on cardiac ion channels are minor at concentrations effective for the diabetic models (0.1-10 µg/mL). The results suggest safety in terms of the risk of cardiac arrhythmia. Also, our study demonstrates the usefulness of the cardiac computer simulation in screening drug-induced long-QT syndrome.
Scaling RDF with Time

The World Wide Web Consortium's RDF standard primarily consists of (subject, property, object) triples that specify the value that a given subject has for a given property. However, it is frequently the case that even for a fixed subject and property, the value varies with time. As a consequence, efforts have been made to annotate RDF triples with "valid time" intervals. However, to date, no proposals exist for efficient indexing of such temporal RDF databases. It is clearly beneficial to store RDF data in a relational DB - however, standard relational indexes are inadequately equipped to handle RDF's graph structure. In this paper, we propose the tGRIN index structure that builds a specialized index for temporal RDF that is physically stored in an RDBMS. Past efforts to store RDF in relational stores include Jena2 from HP, Sesame from OpenRDF.org, and 3store from the University of Southampton. We show that even when these efforts are augmented with well known temporal indexes like R+ trees, SR-trees, ST-index, and MAP21, the tGRIN index exhibits superior performance. In terms of index build time, tGRIN takes two thirds or less of the time used by any other system, and it uses a comparable amount of memory and less disk space than Jena, Sesame and 3store. More importantly, tGRIN can answer queries three to six times faster for average query graph patterns and five to ten times faster for complex queries than these systems.
Scaptics and Highlight-Planes: Immersive Interaction Techniques for Finding Occluded Features in 3D Scatterplots

Three-dimensional scatterplots suffer from well-known perception and usability problems. In particular, overplotting and occlusion, mainly due to density and noise, prevent users from properly perceiving the data. Thanks to accurate head and hand tracking, immersive Virtual Reality (VR) setups provide new ways to interact and navigate with 3D scatterplots. VR also supports additional sensory modalities such as haptic feedback. Inspired by methods commonly used in Scientific Visualisation to visually explore volumes, we propose two techniques that leverage the immersive aspects of VR: first, a density-based haptic vibration technique (Scaptics) which provides feedback through the controller; and second, an adaptation of a cutting plane for 3D scatterplots (Highlight-Plane). We evaluated both techniques in a controlled study with two tasks involving density (finding high- and low-density areas). Overall, Scaptics was the most time-efficient and accurate technique, however, in some conditions, it was outperformed by Highlight-Plane.
A novel cross layer design approach for video transmission over wireless ad hoc networks

Video transmission over wireless ad hoc networks is very sensitive to temporal dynamics of the wireless channel. In order to achieve good end to end performance, it is necessary for upper layers to quickly adapt to the variations of the wireless links. Based on this motivation, we propose a novel cross layer design (CLD) which employs an adaptive link estimator to ensure reliable transmissions. The link-quality estimation scheme can provide accurate link quality information for routing decisions and predicts the link characteristics for the next time interval by using a Kalman filter. Through simulations and experiments we demonstrate that the proposed cross layer approach can quickly adapt to the temporal dynamics of the links, accurately predict the link characteristics and improve the robustness of video transmission over mobile ad hoc networks.
A collaborative intelligent tutoring system for medical problem-based learning

This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773).
Polarity mechanisms such as contact inhibition of locomotion regulate persistent rotational motion of mammalian cells on micropatterns

Significance During the growth of an embryo or the spreading of a tumor, cells may travel collectively. We study a computational model of a simple example of collective migration: two cells confined to a square adhesive pattern. In this confinement, some cell types rotate, whereas others do not. We model these crawling cells, the forces between them, and several possible ways that the cells could choose what direction they will crawl—their “polarity mechanism. ” We show that the cell polarity mechanism can control whether the pairs of cells rotate or remain fixed. This suggests that we can learn about how large groups of cells choose their direction by studying the rotation of pairs. Pairs of endothelial cells on adhesive micropatterns rotate persistently, but pairs of fibroblasts do not; coherent rotation is present in normal mammary acini and kidney cells but absent in cancerous cells. Why? To answer this question, we develop a computational model of pairs of mammalian cells on adhesive micropatterns using a phase field method and study the conditions under which persistent rotational motion (PRM) emerges. Our model couples the shape of the cell, the cell’s internal chemical polarity, and interactions between cells such as volume exclusion and adhesion. We show that PRM can emerge from this minimal model and that the cell-cell interface may be influenced by the nucleus. We study the effect of various cell polarity mechanisms on rotational motion, including contact inhibition of locomotion, neighbor alignment, and velocity alignment, where cells align their polarity to their velocity. These polarity mechanisms strongly regulate PRM: Small differences in polarity mechanisms can create significant differences in collective rotation. We argue that the existence or absence of rotation under confinement may lead to insight into the cell’s methods for coordinating collective cell motility.