Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-hepatitis C virus core IgM antibodies correlate with hepatitis C recurrence and its severity in liver transplant patients

BACKGROUND The significance of immunoglobulin (Ig) M antibody to hepatitis C virus (HCV) core antigen was studied in 60 patients with HCV infection after orthotopic liver transplantation (OLT) diagnosed by polymerase chain reaction. METHODS Patients were followed up for a mean of 28 months after transplantation. Sera collected three months before transplantation, and one and 12 months after transplantation were analysed for anti-HCV core IgM (HCV-IgM EIA 2.0 assay). After OLT protocol biopsies, procedures were performed routinely every six months. Semiquantitative histopathological assessment of allograft hepatitis was performed using Knodell's score. The results were correlated with clinical features, liver histology findings, and virological features, such as genotype and viraemic levels assessed by a branched DNA assay. RESULTS One year after liver transplantation, 29/60 (48%) patients had chronic hepatitis on graft biopsy. The presence of anti-HCV core IgM one month (p=0.004) and 12 months (p=0.003) after OLT was positively correlated with recurrence of chronic hepatitis. The positive predictive value of anti-HCV core IgM detected one month after transplantation was 0.88. A significant relationship was observed between severity of graft disease and presence of anti-HCV core IgM 12 months after transplantation. The mean Knodell score was 8.9 in anti-HCV core IgM positive patients compared with 3.6 in those who were anti-HCV core IgM negative (p=0.001). The presence of IgM anti-HCV did not correlate with serum HCV RNA level or HCV genotype. CONCLUSION We confirm that the presence of anti-HCV core IgM after OLT is a marker of HCV induced graft damage. The recurrence and severity of HCV hepatitis in patients undergoing OLT for HCV cirrhosis is related to the presence of anti-HCV core IgM after liver transplantation. These findings have diagnostic relevance and confirm that measurement of IgM anti-HCV core may help to better monitor the treatment of HCV recurrence after transplantation.
Digital Games for Type 1 and Type 2 Diabetes: Underpinning Theory With Three Illustrative Examples

Digital games are an important class of eHealth interventions in diabetes, made possible by the Internet and a good range of affordable mobile devices (eg, mobile phones and tablets) available to consumers these days. Gamifying disease management can help children, adolescents, and adults with diabetes to better cope with their lifelong condition. Gamification and social in-game components are used to motivate players/patients and positively change their behavior and lifestyle. In this paper, we start by presenting the main challenges facing people with diabetes—children/adolescents and adults—from a clinical perspective, followed by three short illustrative examples of mobile and desktop game apps and platforms designed by Ayogo Health, Inc. (Vancouver, BC, Canada) for type 1 diabetes (one example) and type 2 diabetes (two examples). The games target different age groups with different needs—children with type 1 diabetes versus adults with type 2 diabetes. The paper is not meant to be an exhaustive review of all digital game offerings available for people with type 1 and type 2 diabetes, but rather to serve as a taster of a few of the game genres on offer today for both types of diabetes, with a brief discussion of (1) some of the underpinning psychological mechanisms of gamified digital interventions and platforms as self-management adherence tools, and more, in diabetes, and (2) some of the hypothesized potential benefits that might be gained from their routine use by people with diabetes. More research evidence from full-scale evaluation studies is needed and expected in the near future that will quantify, qualify, and establish the evidence base concerning this gamification potential, such as what works in each age group/patient type, what does not, and under which settings and criteria.
A Middleware for Controlling the Execution of Composite Services

A composite service consists of many Web Services. The execution of a composite service is conducted by an execution engine according to the workflow specification. Since different composite services might share and access some of the Web Services concurrently, ensuring the correctness of the concurrent execution of the composite services becomes an important issue. This paper proposes a concurrency control scheme for executing transactional composite services. The scheme is implemented as a middleware. It uses the information gathered from the workflow specifications of the composite services to guarantee the correctness of the execution of the composite services. The proposed scheme avoids the repeated rollback problem facing most existing concurrency control schemes, and, it is more suitable for the clients who use handheld devices to access the web services.
Visual query operators for temporal databases

Many recent proposals in the literature present easy-to-use query languages, based on visual representations of the database and direct manipulation mechanisms. Such proposals essentially deal with conventional query operations, whereas little effort is devoted to investigate friendly environments for querying temporal databases. Nevertheless, temporal aspects are generally considered extremely relevant in several database applications. Thus, the various users should be provided with powerful and simple query systems also exhibiting temporal features. While attempting to meet this need, we propose a visual query system for temporal databases. It adopts a diagrammatic representation of the database schema (including temporal classes and relationships), on which conventional as well as temporal visual query operators can be applied. In this paper we concentrate on the temporal operators, showing both examples of usage and formalization.
CS224N Project: Let Computers Do Reading Comprehension

In this work, we want to teach the computers to do reading comprehension. More specifically, the task is to let computers read a short passage and then answer some simple questions based on the passage. [1] proposes an end-to-end, RNN-like approach which, according to them, needs significantly less supervision but performs comparatively good to benchmarks for many applications. However, we found that their Q&A result is based on a small synthetic data set defined in [2]. We could see that the dataset is quite small and simple with a vocabulary size of 177. Here is a sample from the dataset.
A note on the optimal scalar Bregman k-means clustering with an application to learning best statistical mixtures

AbstractWe describe a dynamic programming (DP) technique to compute the optimal Bregman k-means clustering of nscalar values. We further show how to incorporate constraints on theminimum sizes of clusters. We then illustrate how to use this DP algorithm for learning univari-ate statistical mixture models of exponential families maximizing the complete data likelihood,and perform model selection from the DP table. 1 Introduction Clustering is a fundamental and key primitive to discover structural groups of homogeneous data,called clusters, in d-dimensional data sets. The most famous clustering technique is the celebratedk-means [1] that seeks to minimize the sum of intra-cluster variances by prescribing beforehandthe number, k, of clusters. On one hand, solving for the k-mean problem is in general NP-hardwhen k>1 and d= 2 [8], or even when k= 2 and dis the parameter of interest [7]. There isa rich and still ourishing literature on k-means that proposes and studies heuristics. In practice,Lloyd’s batched k-means heuristic [1] is often used: Given an initialization of the cluster centers, ititeratively assigns data to their current closest center, and remap those cluster centers to their datacentroids. This heuristic monotonically decreases the sum of intra-cluster variances until it reachesconvergence after a nite number of iterations. It has been noticed that as the dimension increases,some clusters found by Lloyd’s heuristic may become empty [4]. This problem can be bypassed bysetting cluster size constraints [4]. A good initialization for Lloyd’s k-means heuristic is also essentialsince there are many local minima. The k-means++ heuristic [5] is such an initialization procedurethat picks cluster centers iteratively according to a distribution on the distances of the data pointsto the current chosen centers. Although k-means++ (viewed as a discrete k-means) guaranteesto nd a (logk) competitive initialization in expected analysis, it was found very recently thatthere exist bad planar point sets [5] for which the probability to get such a good initialization hasprobability exponentially small in k.
Bilateral video super-resolution using non-local means with adaptive parameters

Super-resolution (SR) algorithms for video sequences with high resolution (HR) guide frames can provide outstanding performances. Non-local means (NLM) algorithm compares the similarity between a pixel and its neighbors. NLM replaces every pixel with a weighted average of its neighbors. The NLM based SR algorithm can super-resolve low resolution (LR) frames using the HR guide frames in the video sequence. However, the fixed decaying factor of NLM cannot satisfy regions of distinct characteristics in a LR frame. The fixed searching window fails to balance the requirements of low computational complexity and good SR images. Thus, we propose novel criteria for selecting the decaying factor and searching window adaptively. Bilateral adjacent HR frames are used to handle the occlusion problem. The experimental results verify the validity of the proposed method.
Aspersymmetide A, a New Centrosymmetric Cyclohexapeptide from the Marine-Derived Fungus Aspergillus versicolor

A new centrosymmetric cyclohexapeptide, aspersymmetide A (1), together with a known peptide, asperphenamate (2), was isolated from the fungus Aspergillus versicolor isolated from a gorgonian coral Carijoa sp. , collected from the South China Sea. The chemical structure of 1 was elucidated by analyzing its NMR spectroscopy and MS spectrometry data, and the absolute configurations of the amino acids of 1 were determined by Marfey’s method and UPLC-MS analysis of the hydrolysate. Aspersymmetide A (1) represents the first example of marine-derived centrosymmetric cyclohexapeptide. Moreover, 1 exhibited weak cytotoxicity against NCI-H292 and A431 cell lines at the concentration of 10 μM.
Measles virus strains circulating in Central and West Africa: Geographical distribution of two B3 genotypes

Africa remains one of the major reservoirs of measles infection. Molecular epidemiological studies have permitted different measles virus isolates to be grouped into clades and genotypes; the major group, which has been identified as indigenous to Africa, is clade B. The viruses from epidemics in the Gambia (1993) and in the Cameroon (2001) were examined. In both studies, the homogeneity of the virus isolates within the epidemic as shown by sequence analysis revealed less than 0.2% variation of nucleotides between isolates. The measles viruses isolated in 1983 in Yaoundé, Cameroon, were designated as the B1 genotype. However, in 2001 only viruses belonging to the B3 genotype were found in this city. The viruses in the Gambia (1993) were also of the B3 genotype. However, these viruses could be distinguished from each other at the antigenic level and by comparative sequence analysis. The B3 Cameroon (2001) viruses were related to the proposed B3.1 subgroup, whereas the Gambian (1993) isolates corresponded to the B3.2 subgroup. The geographical distribution for the period 1993–2001 of these two viruses shows that B3.1 is found from the Sudan to Nigeria and Ghana extending south to the Cameroon, whereas the B3.2 genotype is found in West Africa. In Nigeria and Ghana, the viruses co‐circulate. The identification of these viruses will permit more meaningful epidemiological studies after the proposed increase in measles vaccination coverage. J. Med. Virol. 68:433–440, 2002. © 2002 Wiley‐Liss, Inc.
A Literature Overview of Fuzzy Database Modeling

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.
Nanobubbles for enhanced ultrasound imaging of tumors

The fabrication and initial applications of nanobubbles (NBs) have shown promising results in recent years. A small particle size is a basic requirement for ultrasound contrast-enhanced agents that penetrate tumor blood vessel pores to allow for targeted imaging and therapy. However, the nanoscale size of the particles used has the disadvantage of weakening the imaging ability of clinical diagnostic ultrasound. In this work, we fabricated a lipid NBs contrast-enhanced ultrasound agent and evaluated its passive targeting ability in vivo. The results showed that the NBs were small (436.8 ± 5.7 nm), and in vitro ultrasound imaging suggested that the ultrasonic imaging ability is comparable to that of microbubbles (MBs). In vivo experiments confirmed the ability of NBs to passively target tumor tissues. The NBs remained in the tumor area for a longer period because they exhibited enhanced permeability and retention. Direct evidence was obtained by direct observation of red fluorescence-dyed NBs in tumor tissue using confocal laser scanning microscopy. We have demonstrated the ability to fabricate NBs that can be used for the in vivo contrast-enhanced imaging of tumor tissue and that have potential for drug/gene delivery.
Task Complexity Differentially Affects Executed and Imagined Movement Preparation: Evidence from Movement-Related Potentials

Background The neural simulation theory predicts similarity for the neural mechanisms subserving overt (motor execution) and covert (movement imagination) actions. Here we tested this prediction for movement preparation, a key characteristic of motor cognition. Methodology/Principal Findings High-density electroencephalogram (EEG) was recorded during covert and overt actions. Movement preparation was studied with a motor priming paradigm, which varied task complexity and amount of advance information. Participants performed simple or complex sequential finger movements either overtly or covertly. Advance information was either fully predictive or partially predictive. Stimulus-locked event-related potential (ERP) data showed the typical pattern of foreperiod activation for overt and covert movements. The foreperiod contingent negative variation (CNV) differed between simple and complex movements only in the execution task. ERP topographies differed between execution and imagination only when advance information was fully predictive. Conclusions/Significance Results suggest a differential contribution of the movement preparation network to action imagination and execution. Overt and covert actions seem to involve similar though not identical mechanisms, where overt actions engage a more fine-grained modulation of covert preparatory states.
Distinct SUMO Ligases Cooperate with Esc2 and Slx5 to Suppress Duplication-Mediated Genome Rearrangements

Suppression of duplication-mediated gross chromosomal rearrangements (GCRs) is essential to maintain genome integrity in eukaryotes. Here we report that SUMO ligase Mms21 has a strong role in suppressing GCRs in Saccharomyces cerevisiae, while Siz1 and Siz2 have weaker and partially redundant roles. Understanding the functions of these enzymes has been hampered by a paucity of knowledge of their substrate specificity in vivo. Using a new quantitative SUMO-proteomics technology, we found that Siz1 and Siz2 redundantly control the abundances of most sumoylated substrates, while Mms21 more specifically regulates sumoylation of RNA polymerase-I and the SMC-family proteins. Interestingly, Esc2, a SUMO-like domain-containing protein, specifically promotes the accumulation of sumoylated Mms21-specific substrates and functions with Mms21 to suppress GCRs. On the other hand, the Slx5-Slx8 complex, a SUMO-targeted ubiquitin ligase, suppresses the accumulation of sumoylated Mms21-specific substrates. Thus, distinct SUMO ligases work in concert with Esc2 and Slx5-Slx8 to control substrate specificity and sumoylation homeostasis to prevent GCRs.
Apoc2 loss-of-function zebrafish mutant as a genetic model of hyperlipidemia

ABSTRACT Apolipoprotein C-II (APOC2) is an obligatory activator of lipoprotein lipase. Human patients with APOC2 deficiency display severe hypertriglyceridemia while consuming a normal diet, often manifesting xanthomas, lipemia retinalis and pancreatitis. Hypertriglyceridemia is also an important risk factor for development of cardiovascular disease. Animal models to study hypertriglyceridemia are limited, with no Apoc2-knockout mouse reported. To develop a genetic model of hypertriglyceridemia, we generated an apoc2 mutant zebrafish characterized by the loss of Apoc2 function. apoc2 mutants show decreased plasma lipase activity and display chylomicronemia and severe hypertriglyceridemia, which closely resemble the phenotype observed in human patients with APOC2 deficiency. The hypertriglyceridemia in apoc2 mutants is rescued by injection of plasma from wild-type zebrafish or by injection of a human APOC2 mimetic peptide. Consistent with a previous report of a transient apoc2 knockdown, apoc2 mutant larvae have a minor delay in yolk consumption and angiogenesis. Furthermore, apoc2 mutants fed a normal diet accumulate lipid and lipid-laden macrophages in the vasculature, which resemble early events in the development of human atherosclerotic lesions. In addition, apoc2 mutant embryos show ectopic overgrowth of pancreas. Taken together, our data suggest that the apoc2 mutant zebrafish is a robust and versatile animal model to study hypertriglyceridemia and the mechanisms involved in the pathogenesis of associated human diseases. Highlighted Article: Apoc2 loss-of-function zebrafish display severe hypertriglyceridemia, which is characteristic of human patients with defective lipoprotein lipase activity.
Autonomous, Low Voltage, High Efficiency, CMOS Rectifier for Three-Phase Micro Generators

The paper reports on the design, fabrication and testing of a fully integrated, low voltage rectifier for three-phase micro-generators. Focus is applied to the design of a stand-alone rectifier for three-phase AC inputs in the range of 1-3.3 V, 5-500 mA at 10-100 kHz. Specific low powering issues such as low-voltage rectification, self-powering, automatic start-up and anti-parasitic protection are clearly presented. These issues have been solved thanks to original designs and concepts realized using CMOS 0.35 mum technology. The measurements on the fabricated mu-rectifier coupled to a three-phase micro-generator indicate its full functionality and high conversion efficiency, up to 90 %.
ContagAlert: Using Contagion Theory for Adaptive, Distributed Alert Propagation

Large-scale distributed systems, e.g., grid or P2P networks, are targets for large-scale attacks. Unfortunately, few existing systems support propagation of alerts during the attack itself while also suppressing disruptive alerts from faulty or malicious sources. This paper proposes the "ContagAlert" protocol, which uses contagion spreading behavior to spread alerts. ContagAlert rapidly propagates alerts during attacks while also suppressing disruptive alerts. The core contagion protocols in the system are completely localized, but result in desired behavior at the network scale. We analyze and evaluate our protocol with synthetic simulations and in both Internet worm and DoS attack scenarios
Quantitative determination of pairing interactions for high-temperature superconductivity in cuprates

The extracted pairing interactions responsible for high-temperature superconductivity lead to a clear discrimination among theories. A profound problem in modern condensed matter physics is discovering and understanding the nature of fluctuations and their coupling to fermions in cuprates, which lead to high-temperature superconductivity and the invariably associated strange metal state. We report the quantitative determination of normal and pairing self-energies, made possible by laser-based angle-resolved photoemission measurements of unprecedented accuracy and stability. Through a precise inversion procedure, both the effective interactions in the attractive d-wave symmetry and the repulsive part in the full symmetry are determined. The latter is nearly angle-independent. Near Tc, both interactions are nearly independent of frequency and have almost the same magnitude over the complete energy range of up to about 0.4 eV, except for a low-energy feature at around 50 meV that is present only in the repulsive part, which has less than 10% of the total spectral weight. Well below Tc, they both change similarly, with superconductivity-induced features at low energies. Besides finding the pairing self-energy and the attractive interactions for the first time, these results expose the central paradox of the problem of high Tc: how the same frequency-independent fluctuations can dominantly scatter at angles ±π/2 in the attractive channel to give d-wave pairing and lead to angle-independent repulsive scattering. The experimental results are compared with available theoretical calculations based on antiferromagnetic fluctuations, the Hubbard model, and quantum-critical fluctuations of the loop-current order.
AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.