Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Frey procedure quality of life in South African patients with painful chronic pancreatitis.

CONTEXT Pre- and post-Frey procedure data assessing quality of life in South African patients with painful chronic pancreatitis were compared using two instruments of measure. OBJECTIVE The objective was to evaluate the post-Frey procedure quality of life and to evaluate which of the two instruments was most appropriate in such patients. METHODS A prospective, observational, longitudinal study using the EORTC QLQ-C30 and a locally developed structured interview was performed. RESULTS Between January 2002, when the QLQ-C30 was introduced, and February 2009, 45 consecutive patients underwent a Frey procedure at the Chris Hani Baragwanath Hospital in Soweto, Johannesburg, South Africa. Thirteen of these patients were lost to follow up. Thirty two participants answered both instruments before and after the procedure. Follow up data were analyzed until June 2009. The mean follow up was 24.8 months ranging from 1 to 83 months. There were clinically relevant improvements in most QLQ-C30 domains and structured interview items at the last post-operative visit. The mean pain levels (VAS 0-10 and QLQ-C30 PA) were significantly reduced post-operatively. Twenty five participants answered both instruments within six months and again later at a minimum of six months after surgery with no significant differences in the overall QLQ-C30 functional (P=0.967) and symptom (P=0.253) scale scores between the two time periods. In general, outcomes measured by the two instruments were similar. CONCLUSIONS Although the follow up period was short, results suggest that benefits were mostly made manifest within six months post-operatively and were sustained during the follow up period. The structured interview included a counseling component and locally pertinent issues not addressed in the QLQ-C30 and it is therefore recommended as the instrument of choice in this setting.
Heterogeneous crystal nucleation on bubbles in silicate melt

Abstract Experiments reported herein document heterogeneous crystal nucleation on bubbles in supercooled lithium disilicate melt. Crystalline lithium disilicate (Li2Si2O5) nucleated and grew on small bubbles (∼1 μm) with a one-to-one correspondence between the number of bubbles and crystals (ranging from <102 to ∼105 bubbles/mm3). Crystals grew on large bubbles (>100 μm) only in samples fused in N2, suggesting a chemical control on nucleating efficiency. Bubbles ∼1 μm in diameter served as nucleation sites for polycrystalline lithium disilicate spherulites; bubbles smaller than ∼1 μm served as nucleation sites for the more common ellipsoidal crystalline form. This difference in behavior might be due to the additional surface area available for crystal nucleation on the 1 μm bubbles. Our findings suggest that superliquidus thermal history can influence crystal nucleation via bubble formation induced by supersaturation, and has implications for both natural samples and experimental studies. Heterogeneous crystal nucleation on bubbles may serve as an efficient nucleation mechanism in natural degassing magmas and may aid in the formation of fine-grained groundmasses common to many volcanic rocks. Furthermore, we have documented a new mechanism of spherulite formation in highly supercooled silicate melt, similar to conditions thought to exist during devitrification of natural glasses. The ability of crystals to nucleate on bubbles can be exploited in the production of commercial glass-ceramic materials.
Character-Based Convolutional Grid Neural Network for Breast Cancer Classification

According to the World Health Organization (WHO)1, an early detection of cancer greatly increases the chances of making the right decision in a successful treatment plan. Over the last decade, the increasing world-wide demand for early detection of breast cancer at the hospitals has resulted in necessity of new research avenues. The traditional domain knowledge based diagnostic method requires hand-crafted features, which are not only time consuming, but also corpus dependent. In this paper, we propose a novel neural net-work architecture for the disease classification that relies only on character level representations. Our model leverages subword information through a convolutional neural net-work (CNN) and a residual network over characters, whose output is given to a Grid long short-term memory (Grid-LSTM) recurrent neural network language model (RNN-LM). It's truly an end-to-end model requiring no task-specific feature engineering or data pre-processing beyond pre-trained character embedding on unlabeled corpora. Thus, it can be easily applied to a wide range of NLP tasks in different domains. We evaluate our module using NCBI disease dataset for classification tasks, finding that it can achieve a state-of-the-art performance with minimal computational cost.
ASPACE(o(log log n)) is Regular

One of the common results of resource bounded Turing machines is the $\log \log n$ lower bound for the space usage of deterministic and nondeterministic Turing machines that accept nonregular languages. In this paper this result is extended to alternating Turing machines: It is proved that if $f(n) = o(\log \log n)$, then $f(n)$-space-bounded (off-line) alternating Turing machines can accept only regular sets. The problem has been open for a decade.
Scheduling for Clustered Vector Processors Near Memory

The Active Memory Cube is a processing-in-memory device that achieves high power efficiency with a carefully designed microarchitecture eliminating much of the complexity of conventional cores. To deliver high performance it requires a sophisticated compiler, which we present in this work. We propose a novel clustering algorithm for distributed functional units and a scheduling algorithm for temporal vector operations within the cluster. We propose an abstract machine model that precisely captures vector functional units, vector register file sharing at the element level, communication between clusters, and interaction between the core and the memory subsystem. Unlike prior approaches our clustering algorithm operates at the scope of an entire procedure to make globally optimal assignments. We achieve high computational efficiency and linear performance scaling on memoryand compute-bound kernels using standard, portable pragmas and no accelerator-specific program code. Our work is an important step toward building next-generation, power-efficient computing systems.
Genetic Immunization Elicits Antigen-Specific Protective Immune Responses and Decreases Disease Severity in Trypanosoma cruzi Infection

ABSTRACT Immunity to Trypanosoma cruzi requires elicitation of humoral and cell-mediated immune responses to extracellular trypomastigotes and intracellular amastigotes. In this study, the effectiveness of the T. cruzi trans-sialidase family (ts) genes ASP-1, ASP-2, and TSA-1 as genetic vaccines was assessed. Immunization of mice with plasmids encoding ASP-1, ASP-2, or TSA-1 elicited poor antigen-specific cytotoxic-T-lymphocyte (CTL) activity and T. cruzi-specific antibody responses. Codelivery of interleukin-12 and granulocyte-macrophage colony-stimulating factor plasmids with antigen-encoding plasmids resulted in a substantial increase in CTL activity and antibody production and in increased resistance to T. cruzi infection. In pooled results from two to four experiments, 30 to 60% of mice immunized with antigen-encoding plasmids and 60 to 80% of mice immunized with antigen-encoding plasmids plus cytokine adjuvants survived a lethal challenge with T. cruzi. In comparison, 90% of control mice injected with empty plasmid DNA died during the acute phase of infection. However, the pool of three ts genes provided no greater protection than the most effective single gene (ASP-2) either with or without coadministration of cytokine plasmids. Importantly, the extent of tissue parasitism, inflammation, and associated tissue damage in skeletal muscles during the chronic phase of T. cruzi infection in mice immunized with antigen-encoding plasmids plus cytokine adjuvants was remarkably reduced compared to mice immunized with only cytokine adjuvants or empty plasmid DNA. These results identify new vaccine candidates and establish some of the methodologies that might be needed to develop effective vaccine-mediated control of T. cruzi infection. In addition, this work provides the first evidence that prophylactic genetic immunization can prevent the development of Chagas’ disease.
Compression and inference algorithms for Bayesian network modeling of infrastructure systems

The Bayesian network (BN) is an ideal tool for modeling and assessing the reliability of civil infrastructure, particularly when the information about the system and its components is uncertain and evolves in time. One of the major limitations of the BN framework, however, is the size and complexity of the system that can be tractably modeled as a BN. This is due to the size of the conditional probability table (CPT) associated with the system node in the BN model, which grows exponentially with the number of components in the system. In this paper, we present novel compression and inference algorithms that utilize compression techniques to achieve significant savings in memory storage of the system CPT. In addition, heuristics developed to improve the computational efficiency of the algorithms are presented. An application to an example system demonstrates the gains in both memory and computation time requirements achieved by the proposed algorithms.
Prethreshold Voltage as a Low-Component Count Temperature Sensitive Electrical Parameter Without Self-Heating

Temperature sensitive electrical parameters (TSEPs) are promising for measurement of the insulated gate bipolar transistor (IGBT) junction temperature. Many TSEPs have been proposed in the literature and most require current sensors, and convoluted hardware and data processing, which are associated with increased complexity and cost. In this letter, a new TSEP is proposed called prethreshold TSEP, <inline-formula><tex-math notation="LaTeX">$V_{{\rm GE}({\rm pre}\text{-}{\rm th})}$</tex-math></inline-formula>. <inline-formula><tex-math notation="LaTeX">$V_{{\rm GE}({\rm pre}\text{-}{\rm th})}$</tex-math></inline-formula> requires only a voltage sensor and a counter. Besides less hardware requirement, <inline-formula><tex-math notation="LaTeX">$V_{{\rm GE}({\rm pre}\text{-}{\rm th})}$</tex-math> </inline-formula> does not suffer from self-heating—a problem that most TSEPs encounter. This letter presents the operation of <inline-formula><tex-math notation="LaTeX">$V_{{\rm GE}({\rm pre}\text{-}{\rm th})}$</tex-math> </inline-formula> with the help of simulation. Experimental tests on a 3.3 kV, 800 A IGBT were conducted to characterize <inline-formula><tex-math notation="LaTeX">$V_{{\rm GE}({\rm pre}\text{-}{\rm th})}$</tex-math> </inline-formula>. It is shown that the new TSEP proposed has a sensitivity of −2.2 mV/°C<bold>. </bold>
Video composition by optimized 3D mean‐value coordinates

In this paper, we propose a new video composition method by 3D mean‐value coordinate (MVC). 2D MVCs have been widely used in image composition; however, when 2D MVC is applied to a video sequence directly, because of over‐blending and the lack of temporal consistency, some unnatural effects may appear in the final composite results. Although 3D Poisson editing can maintain spatial and temporal consistency, it also leads to high algorithm complexity. Instead of 3D Poisson editing, we use the 3D MVC to seamlessly blend a given source video patch into a target video sequence; this approach is able to achieve high‐performance blending with less computation. We show that the combination of alpha matte‐based approaches and our method can further refine the produced video when the boundaries of the source object and the target object are very different. Our algorithm can be paralleled and run on a graphics processing unit. The experimental results show that our method is effective and efficient. Copyright © 2012 John Wiley & Sons, Ltd.
Automatic TV Broadcast Structuring

TV broadcast structuring is needed to precisely extract long useful programs. These can be either archived as part of our audio-visual heritage or used to build added-value novel TV services like TVoD or Catch-up-TV. First, the problem of digital TV content structuring is positioned. Related work and existing solutions are deeply and carefully analyzed. This paper presents then DealTV, our fully automatic system. It is based on studying repeated sequences in the TV stream in order to segment it. Segments are then classified using an inductive logic programming-based technique that makes use of the temporal relationships between segments. Metadata are finally used to label and extract programs using simple overlapping-based criteria. Each processing step of DealTV has been separately evaluated in order to carefully analyze its impact on the final results. The system has been proven on a real TV stream to be very effective.
Extraction of phenolic compounds from 'Aglianico' and 'Uva di Troia' grape skins and seeds in model solutions: Influence of ethanol and maceration time

The effect of increasing concentration of ethanol (0, 4, 7.5 and 13 %) and contact time (respectively 1, 4, 7 and 10 days) on the extraction of phenolics from berry skins and seeds of the grape, Vitis vinifera 'Aglianico' and 'Uva di Troia', were examined. Two assays of post-fermentative maceration in two hydroalcoholic solutions at 11 and 13 % ethanol, were also performed. Chromatic properties and phenolics of medium were analyzed by HPLC and spectrophotometric methods. The extraction of total phenolics, anthocyanins, proanthocyanidins, and vanilline reactive flavans (VRF) from berry skins reached the maximum on the 4 th day of maceration. Quercetin and gallic acid were gradually extracted from grape skins. The maximum release of flavan-3-ols from the skins was achieved on the first day of maceration. Total phenolics, tannins and VRF were gradually extracted from seeds. During the postfermentative maceration, higher the content of ethanol, higher the extraction of total polyphenols and tannins from 'Uva di Troia' skins and the extraction of total polyphenols and tannins from 'Aglianico' seeds. These results clearly indicate that the grape cultivar mainly influences the release of phenolic compounds from the solid parts of berry to the must especially during postfermentative maceration.
Psychotherapy of refractory depressions

Psychotherapy is a potentially valuable intervention for treatment‐resistant depression. This review provides a brief, general overview of the use of psychotherapy to treat depression and more focused consideration of time‐limited interpersonal, behavioral, and cognitive behavioral strategies for patients who are not responsive to antidepressant medication. Effective strategies emphasize individualized assessment, psychoeducation, a high level of structure and therapist activity, operationalized short‐term goals, self‐help and homework activities, and an empirical‐collaborative approach to treatment. Although some treatment‐resistant patients respond to therapy alone, more promising evidence is emerging from studies of combined strategies. Depression and Anxiety 5:190–201, 1997. © 1997 Wiley‐Liss, Inc.
Retinoic Acid Induces Neuroblastoma Cell Death by Inhibiting Proteasomal Degradation of Retinoic Acid Receptor α

To seek a novel therapeutic approach to neuroblastoma (NBL), we used three NBL cell lines (SK-N-DZ, NH12, and SK-N-SH) to examine the underlining molecular mechanisms of cellular reactions and sensitivity to all-trans-retinoic acid (ATRA). SK-N-DZ cells expressed relatively high levels of retinoic acid receptor α (RAR-α) and underwent ATRA-induced cell death that was blocked by an RAR-α antagonist. By contrast, RAR-α expression gradually decreased in NH12 and SK-N-SH cells, which did not experience increased cell death in response to ATRA. We report here the ubiquitin-dependent down-regulation of RAR-α expression during ATRA treatment. Our data suggest that SK-N-DZ cells have a defect in RAR-α down-regulation, resulting in sustained high expression of RAR-α that confers high sensitivity to ATRA. Accordingly, treatment with a proteasome inhibitor dramatically increased ATRA-induced cell death in NH12 and SK-N-SH cell lines. Our results reveal the crucial involvement of the RAR-α signaling pathway in NBL cell death and show that three NBL cell lines are differentially sensitive to ATRA. These data suggest a potential novel therapy for NBL involving retinoic acid treatment combined with the inhibition of RAR-α degradation.
Aero-Sim: An NS-2 Based Simulator for Aeronautical Ad Hoc Networks

Recently, there has been a clear trend towards the application of ad hoc networking technology in civil aviation communication systems, giving birth to a new research field, namely, aeronautical ad hoc networks (AANETs). An AANET is a special type of ad hoc wireless network with a significantly larger scale and distinct characteristics of its mobile nodes. Thus, there is an urgent need to develop a simulator to facilitate the research in these networks. In this paper, we present a network simulator, Aero-Sim, for AANETs. Aero-Sim, which is based on the freely distributed NS-2 simulator, enables detailed packet-level simulations of protocols at the MAC, link, network, transport, and application layers by composing simulations with existing modules and protocols in NS-2. Moreover, Aero-Sim supports three-dimensional network deployment. Through several case studies using realistic China domestic air traffic, we show that the proposed simulator can be used to simulate AANETs and can reproduce the real world with high fidelity.
The use of auxiliary variables in capture-recapture modelling: An overview

I review the use of auxiliary variables in capture-recapture models for estimation of demographic parameters (e.g. capture probability, population size, survival probability, and recruitment, emigration and immigration numbers). I focus on what has been done in current research and what still needs to be done. Typically in the literature, covariate modelling has made capture and survival probabilities functions of covariates, but there are good reasons also to make other parameters functions of covariates as well. The types of covariates considered include environmental covariates that may vary by occasion but are constant over animals, and individual animal covariates that are usually assumed constant over time. I also discuss the difficulties of using time-dependent individual animal covariates and some possible solutions. Covariates are usually assumed to be measured without error, and that may not be realistic. For closed populations, one approach to modelling heterogeneity in capture probabilities uses observable individual covariates and is thus related to the primary purpose of this paper. The now standard Huggins-Alho approach conditions on the captured animals and then uses a generalized Horvitz-Thompson estimator to estimate population size. This approach has the advantage of simplicity in that one does not have to specify a distribution for the covariates, and the disadvantage is that it does not use the full likelihood to estimate population size. Alternately one could specify a distribution for the covariates and implement a full likelihood approach to inference to estimate the capture function, the covariate probability distribution, and the population size. The general Jolly-Seber open model enables one to estimate capture probability, population sizes, survival rates, and birth numbers. Much of the focus on modelling covariates in program MARK has been for survival and capture probability in the Cormack-Jolly-Seber model and its generalizations (including tag-return models). These models condition on the number of animals marked and released. A related, but distinct, topic is radio telemetry survival modelling that typically uses a modified Kaplan-Meier method and Cox proportional hazards model for auxiliary variables. Recently there has been an emphasis on integration of recruitment in the likelihood, and research on how to implement covariate modelling for recruitment and perhaps population size is needed. The combined open and closed 'robust' design model can also benefit from covariate modelling and some important options have already been implemented into MARK. Many models are usually fitted to one data set. This has necessitated development of model selection criteria based on the AIC (Akaike Information Criteria) and the alternative of averaging over reasonable models. The special problems of estimating over-dispersion when covariates are included in the model and then adjusting for over-dispersion in model selection could benefit from further research.
Imaging Docking and Fusion of Insulin Granules Induced by Antidiabetes Agents

Sulfonylurea and glinide drugs, commonly used for antidiabetes therapies, are known to stimulate insulin release from pancreatic β-cells by closing ATP-sensitive K+ channels. However, the specific actions of these drugs on insulin granule motion are largely unknown. Here, we used total internal reflection fluorescence (TIRF) microscopy to analyze the docking and fusion of single insulin granules in live β-cells exposed to either the sulfonylurea drug glibenclamide or the glinide drug mitiglinide. TIRF images showed that both agents caused rapid fusion of newcomer insulin granules with the cell membrane in both control and diabetic Goto-Kakizaki (GK) rat pancreatic β-cells. However, in the context of β-cells from sulfonylurea receptor 1 (SUR1) knockout mice, TIRF images showed that only mitiglinide, but not glibenclamide, caused fusion of newcomer insulin granules. Compositely, our data indicate that 1) the mechanism by which both sulfonylurea and glinide drugs promote insulin release entails the preferential fusion of newcomer, rather than previously docked, insulin granules, and that 2) mitiglinide can induce insulin release by a mechanism independent of mitiglinide binding to SUR1.
Nuclear receptor PPARγ-regulated monoacylglycerol O-acyltransferase 1 (MGAT1) expression is responsible for the lipid accumulation in diet-induced hepatic steatosis

Recently, hepatic peroxisome proliferator-activated receptor (PPAR)γ has been implicated in hepatic lipid accumulation. We found that the C3H mouse strain does not express PPARγ in the liver and, when subject to a high-fat diet, is resistant to hepatic steatosis, compared with C57BL/6 (B6) mice. Adenoviral PPARγ2 injection into B6 and C3H mice caused hepatic steatosis, and microarray analysis demonstrated that hepatic PPARγ2 expression is associated with genes involved in fatty acid transport and the triglyceride synthesis pathway. In particular, hepatic PPARγ2 expression significantly increased the expression of monoacylglycerol O-acyltransferase 1 (MGAT1). Promoter analysis by luciferase assay and electrophoretic mobility shift assay as well as chromatin immunoprecipitation assay revealed that PPARγ2 directly regulates the MGAT1 promoter activity. The MGAT1 overexpression in cultured hepatocytes enhanced triglyceride synthesis without an increase of PPARγ expression. Importantly, knockdown of MGAT1 in the liver significantly reduced hepatic steatosis in 12-wk-old high-fat–fed mice as well as ob/ob mice, accompanied by weight loss and improved glucose tolerance. These results suggest that the MGAT1 pathway induced by hepatic PPARγ is critically important in the development of hepatic steatosis during diet-induced obesity.