Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of biochemical and immunological parameters in nutritional screening and assessment.

OBJECTIVE To evaluate the relationship between serum albumin, total cholesterol and total lymphocyte count with two nutritional assessment methods, to verify if their use is justified in nutritional screening tools. METHODS 101 patients admitted to medical/surgical wards underwent the SGA and the Full Nutritional Assessment (FNA). Blood test which included serum albumin, total cholesterol and total lymphocyte count (TLC), were made. Percentage of weight loss and BMI were calculated. An Anova test was done to measure the differences in the mean levels of the three parameters for the nutritional status evaluated by SGA and FNA. The probability of a patient being malnourished in the four ranges established for each parameter was calculated, as well as the relationship between the ranges and the percentage of weight loss and BMI. Sensitivity and specificity were calculated and the corresponding ROC curves, using SGA as gold standard. RESULTS Prevalence of undernutrition is 43.6% and 44.6% for SGA and FNA respectively. Mean levels of the three parameters decrease as the undernutrition degree increases (p < 0.005 for all cases). The probability of a patient being malnourished gets higher as parameter lowers (p = 0.000 for all cases). Total cholesterol shows a relationship with BMI < 18.5 and presence/absence of weight loss (p = 0.074 and p = 0.002 respectively). The area under ROC curves are albumin (0.823), cholesterol (0.790) and TLC (0.758) respectively. CONCLUSIONS The analytical parameters analyzed show a statistically significant relationship with the nutritional status. Therefore, they are suitable for use in nutritional screening.
Humoral immune response to hepatitis C after liver transplantation: assessment of a new recombinant immunoblot assay

OBJECTIVE:The immune control of infection with hepatitis C virus (HCV) is poorly understood; vigorous antibody responses to viral proteins seem to coexist with the virus and thus whether they are neutralizing remains controversial. HCV-related liver failure is the leading indication for orthotopic liver transplantation (OLT) worldwide. Attenuated antibody responses in immunosuppressed patients and decreased reliability compared to assessment of HCV RNA has hampered the use of antibody testing post-OLT. The goals of this current analysis were twofold: to determine the sensitivity of a prototype strip immunoblot assay (RIBA 3.0, Chiron Diagnostics) for the diagnosis of HCV post-OLT; to determine if there was a correlation between antibody response and severity of histological recurrence.METHODS:The study was comprised of 76 HCV-positive individuals divided into three patient groups: liver allograft recipients with evidence of mild or no histological recurrence (n = 52), liver allograft recipients with evidence of severe HCV recurrence and allograft cirrhosis (n = 12), and nontransplant patients being enrolled in an induction interferon trial (n = 12). All transplant patients had histological follow-up of at least 1 yr.RESULTS:Sixty of the 64 (94%) HCV-positive OLT recipients had 1+ reactivity to two or more recombinant antigens; three of the patients who lacked a detectable response had minimal histological recurrence and one had severe recurrence. All nontransplant patients demonstrated 4+ reactivity to at least two antigens, and 55/64 (86%) OLT recipients demonstrated this same level of reactivity. Seven of the nine patients lacking this high level of reactivity had evidence of minimal recurrence. Furthermore, the mean (± SEM) level of antibody reactivity for c100 (p = 0.04) and NS5 (p = 0.01) were significantly lower for patients with mild recurrence after OLT, compared to the other groups. The level of antibody reactivity was unrelated to HCV genotype or viral load.CONCLUSIONS:The recently developed RIBA 3.0 assay for detection of antibodies to HCV appears to be highly sensitive for the diagnosis of HCV post-OLT. In general, the level of antibody reactivity was comparable in the transplant patients and in nonimmunosuppressed controls. The pathogenic implications of the relatively diminished humoral response in patients with mild recurrence post-OLT are discussed.
The constitutive expression of Arabidopsis plasmodesmal-associated class 1 reversibly glycosylated polypeptide impairs plant development and virus spread

Arabidopsis class 1 reversibly glycosylated polypeptides (C1RGPs) were shown to be plasmodesmal-associated proteins. Transgenic tobacco (Nicotiana tabacum) plants constitutively expressing GFP tagged AtRGP2 under the control of the CaMV 35S promoter are stunted, have a rosette-like growth pattern, and in source leaves exhibit strong chlorosis, increased photoassimilate retention and starch accumulation that results in elevated leaf specific fresh and dry weights. Basal callose levels around plasmodesmata (Pd) of leaf epidermal cells in transgenic plants are higher than in WT. Such a phenotype is characteristic of virus-infected plants and some transgenic plants expressing Pd-associated viral movement proteins (MP). The local spread of Tobacco mosaic virus (TMV) is inhibited in AtRGP2:GFP transgenics compared to WT. Taken together these observations suggest that overexpression of the AtRGP2:GFP leads to a reduction in Pd permeability to photoassimilate, thus lowering the normal rate of translocation from source leaves to sink organs. Such a reduction may also inhibit the local cell-to-cell spread of viruses in transgenic plants. The observed reduction in Pd permeability could be due to a partial Pd occlusion caused either by the accumulation of AtRGP2:GFP fusion in Pd, and/or by constriction of Pd by the excessive callose accumulation.
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach

This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structure thus used as a benchmark to promote further research. With identifiability of the network guaranteed, this paper presents an identification method that infers both the Boolean structure of the network and the internal dynamics between nodes. Identification is performed directly from data without any prior knowledge of the system, including its order. The proposed method solves the identification problem using Maximum a posteriori estimation (MAP) but with inseparable penalties for complexity, both in terms of element (order of nonzero connections) and group sparsity (network topology). Such an approach is widely applied in Compressive Sensing (CS) and known as Sparse Bayesian Learning (SBL). We then propose a novel scheme that combines sparse Bayesian and group sparse Bayesian to efficiently solve the problem. The resulted algorithm has a similar form of the standard Sparse Group Lasso (SGL) while with known noise variance, it simplifies to exact re-weighted SGL. The method and the developed toolbox can be applied to infer networks from a wide range of fields, including systems biology applications such as signaling and genetic regulatory networks.
Plasma NGAL for the diagnosis of AKI in patients admitted from the emergency department setting.

BACKGROUND AND OBJECTIVES The purpose of this study was to determine the accuracy of plasma neutrophil gelatinase-associated lipocalin as a marker of AKI in patients admitted from the emergency department. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS In this prospective cohort study, patients (n=616) admitted from the emergency department from March to November of 2008 were classified according to clinical criteria as AKI, transient azotemia, stable CKD, and normal function. Plasma neutrophil gelatinase-associated lipocalin was measured serially. A logistic regression model using clinical characteristics was fitted to the data, and a second model included discretized plasma neutrophil gelatinase-associated lipocalin. Performance of the models was evaluated by Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve, net reclassification improvement, integrated discrimination improvement, and predictiveness curve. RESULTS Twenty-one percent of patients were classified as AKI; the highest median levels of plasma neutrophil gelatinase-associated lipocalin were in the AKI group (146-174 ng/ml at various time points) and increased with AKI severity (207-244 ng/ml for Acute Kidney Injury Network classification stage>2). The discriminative ability of plasma neutrophil gelatinase-associated lipocalin for AKI diagnosis (area under the curve, 0.77-0.82 at various time points) improved with higher grades of severity (area under the curve, 0.85-0.89 for AKIN>2). Plasma neutrophil gelatinase-associated lipocalin discriminated AKI from normal function and transient azotemia (area under the curve, 0.85 and 0.73, respectively). Patients were classified into three grades of AKI risk according to plasma neutrophil gelatinase-associated lipocalin levels (low, moderate [i.e., the gray zone], and high). Patients with plasma neutrophil gelatinase-associated lipocalin in the high-risk category displayed a 10-fold greater risk of AKI (odds ratio, 9.8; 95% confidence interval, 5.6 to 16.9). The addition of plasma neutrophil gelatinase-associated lipocalin to the clinical model yielded a net reclassification improvement of 94.3% and an integrated discrimination improvement of 0.122. CONCLUSION Plasma neutrophil gelatinase-associated lipocalin is an accurate biomarker for prediction of AKI in patients admitted from the emergency department. This work proposes a three-grade classification of AKI risk based on plasma neutrophil gelatinase-associated lipocalin levels.
Exploiting the critical infrastructure via nontraditional system inputs

Critical infrastructures such as oil and gas pipelines, the electric power grid, and railways, rely on the proper operation of supervisory control and data acquisition (SCADA) systems. The trend to interconnect SCADA system devices using networking technologies, however, has introduced additional entry points for malicious software, often via the Internet. In an attempt to manage the new risks, security personnel are incorporating network isolation and standard information technology practices (e.g., firewalls and access control lists). These boundary protection mechanisms attempt to create gaps between systems in order to logically or physically isolate the SCADA network, making it inaccessible by untrusted actors. However, physical or logical isolation does not necessarily imply a closed system. Untrusted actors can still gain access and control over a SCADA system, despite network security, through exploiting alternate pathways that are not evaluated for proper trust control. These nontraditional, indirect access points are overlooked as ingress paths that can result in system compromise. This paper introduces the concept of extending the security parameter to incorporate the nontraditional access points that are often ignored. Coupled with traditional network security postures, identifying and managing these nontraditional attack entry points can create a more robust, secure environment for critical infrastructures. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CSIIRW ’11, October 12-14, Oak Ridge, Tennessee, USA Copyright c ©2011 ACM 978-1-4503-0945-5 ISBN . $5.00.
Mid-long term power load forecasting based on MG-CACO and SVM method

To improve the accuracy of power load forecasting, a new model for load forecasting based on support vector machine and continuous ant colony optimization algorithm is established in this paper. A new continuous ant colony optimization algorithm called MG-CACO is used to optimize the parameters of SVM in this model. Then the case study of SVM base on continuous ant colony optimization algorithm to a mid-long term load prediction of an actual power system of Tianjin is proposed. Forecasting result shows that this method can improve the accuracy and speed in forecasting, and that the feasibility and effectiveness in the mid-long term forecasting.
Cell-matrix and cell-cell contacts of myofibroblasts: role in connective tissue remodeling

Summary It is presently accepted that fibroblast/myofibroblast modulation represents a crucial step in granulation tissue contraction and in the production of the connective tissue deformations typical of fibrocontractive diseases. In addition to synthesizing extracellular matrix (ECM) components, myofibroblasts can develop tensile force through the neoformation of α-smooth muscle actin (α-SMA) containing cytoplasmic stress fibers. Tension has been shown to be a crucial regulator of connective tissue remodeling. In order to coordinate tension distribution within connective tissue, cell-matrix and cell-cell contacts appear essential. This review addresses the formation, molecular structure and function of such structures that are characterized by their association with intracytoplasmic actin filaments. Actin associated cell-matrix adhesions appear to provide the interface between ECM components and intracellular stress fibers, thus contributing to the transmission of force to the substrate and to the detection of stress level in the matrix. Cell-cell adherens junctions appear to synchronize myofibro-blast contractile activity. Further studies investigating the functions of these structures will be important for the understanding of the mechanisms of granulation tissue evolution and for the planification of strategies in view of influencing connective tissue deformations.
Learning conflict resolution strategies for cross-language Wikipedia data fusion

In order to efficiently use the ever growing amounts of structured data on the web, methods and tools for quality-aware data integration should be devised. In this paper we propose an approach to automatically learn the conflict resolution strategies, which is a crucial step in large-scale data integration. The approach is implemented as an extension of the Sieve data quality assessment and fusion framework. We apply and evaluate our approach on the use case of fusing data from 10 language editions of DBpedia, a large-scale structured knowledge base extracted from Wikipedia. We also propose a method for extracting rich provenance metadata for each DBpedia fact, which is later used in data fusion.
Random Walker Based Estimation and Spatial Analysis of Probabilistic fMRI Activation Maps

Conventional univariate fMRI analysis typically examines each voxel in isolation despite the fact that voxel interactions may be another indication of brain activation. Here, we propose using a graph-theoretical algorithm called “Random Walker” (RW), to estimate probabilistic activation maps that encompass both activation effects and functional connectivity. The RW algorithm has the distinct advantage of providing a unique, globallyoptimal closed-form solution for computing the posterior probabilities. To explore the implications of incorporating functional connectivity, we applied our previously proposed invariant spatial features to the RW-based probabilistic activation maps, which detected activation changes in multiple brain regions that conform well to prior neuroscience knowledge. In contrast, similar analysis on traditional activation statistics maps, which ignores functional connectivity, resulted in reduced detection, thus demonstrating benefits for integrating additional functional attributes into the activation detection procedures.
Interrater reliability of quantitative ultrasound using force feedback among examiners with varied levels of experience

Background. Quantitative ultrasound measures are influenced by multiple external factors including examiner scanning force. Force feedback may foster the acquisition of reliable morphometry measures under a variety of scanning conditions. The purpose of this study was to determine the reliability of force-feedback image acquisition and morphometry over a range of examiner-generated forces using a muscle tissue-mimicking ultrasound phantom. Methods. Sixty material thickness measures were acquired from a muscle tissue mimicking phantom using B-mode ultrasound scanning by six examiners with varied experience levels (i.e., experienced, intermediate, and novice). Estimates of interrater reliability and measurement error with force feedback scanning were determined for the examiners. In addition, criterion-based reliability was determined using material deformation values across a range of examiner scanning forces (1–10 Newtons) via automated and manually acquired image capture methods using force feedback. Results. All examiners demonstrated acceptable interrater reliability (intraclass correlation coefficient, ICC = .98, p < .001) for material thickness measures obtained using force feedback. Individual examiners exhibited acceptable reliability with the criterion-based reference measures (ICC > .90, p < .001), independent of their level of experience. The measurement error among all examiners was 1.5%–2.9% across all applied stress conditions. Conclusion. Manual image capture with force feedback may aid the reliability of morphometry measures across a range of examiner scanning forces, and allow for consistent performance among examiners with differing levels of experience.
Asymmetric Dimethylarginine Responses During Interferon-&agr;–Induced Depression in Patients With Chronic Hepatitis C Infection

Objective The aim of this study was to examine the association of depressive symptoms with asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA). Patients with chronic hepatitis C infection were examined during interferon-&agr; (IFN-&agr;) treatment, which is often associated with treatment-induced depression. The associations between IFN-&agr;–induced depressive symptoms with ADMA and SDMA levels were prospectively investigated until 3 months after treatment. Methods Psychiatric and biological assessments were obtained at six different time points: before, during (at 1, 3, 6, and 9 months), and after the end of IFN-&agr; treatment. Results During IFN-&agr; treatment, 22 (53.7%) patients fulfilled the criteria for a treatment-related depressive disorder at least once during treatment. The increase in ADMA levels from baseline (depression group: 0.63 [0.08] &mgr;M, no depression group: 0.69 [0.08] &mgr;M) in response to IFN-&agr; treatment was considerably higher in patients with IFN-&agr; treatment–induced depressive episodes compared with patients without treatment-induced depressive episodes (3 months after the start of treatment: depression group: 0.72 [0.08] &mgr;M, no depression group: 0.72 [0.11] &mgr;M; ADMA: repeated-measure design analysis of variance [time × depression]: F(5,151) = 2.446, p = . 036). The increase in SDMA was not associated with treatment-induced depression. Conclusions Depression in response to IFN-&agr; treatment is associated with elevated ADMA levels. These findings are relevant to nitric oxide–related biological pathways linking depression to increased cardiovascular disease risk. Future studies are needed to clarify the role of serotonin in these pathways and may lead to preventative treatment strategies.
Towards a Formal Semantics for the Process Model of the Taverna Workbench. Part I

Workflow development and enactment workbenches are becoming a standard tool for conducting in silico experiments. Their main advantages are easy to operate user interfaces, specialized and expressive graphical workflow specification languages and integration with a huge number of bioinformatic services. A popular example of such a workbench is Taverna, which has many additional useful features like service discovery, storing intermediate results and tracking data provenance. We discuss a detailed formal semantics for Scufl - the workflow definition language of the Taverna workbench. It has several interesting features that are notmet in other models including dynamic and transparent type coercion and implicit iteration, control edges, failure mechanisms, and incominglinks strategies. We study these features and investigate their usefulness separately as well as in combination, and discuss alternatives. The formal definition of such a detailed semantics not only allows to exactly understand what is being done in a given experiment, but is also the first step toward automatic correctness verification and allows the creation of auxiliary tools that would detect potential errors and suggest possible solutions to workflow creators, the same way as Integrated Development Environments aid modern programmers. A formal semantics is also essential for work on enactment optimization and in designing the means to effectively query workflow repositories. This paper is the second of two. In the first one [13] we have defined, explained and discussed fundamental notions for describing Scufl graphs and their semantics. Here, in the second part, we use these notions to define the semantics and show that our definition can be used to prove properties of Scufl graphs.
LIP features for speech and speaker recognition

This paper implicitly differentiates between the quality of visual representation necessary for speech and speaker recognition and assesses the performance of visual lip features with respect to well established audio features. Blue lip highlighted data is used to show how variations in lip measurements can influence speech and speaker recognition. From these experiments and other researchers results [1] it is postulated that the fine detail of the lips is critical for speaker recognition, but conversely, the same amount of detail does not noticeably improve visual speech recognition. Visual error rates of 26.3% and 70% are achieved for cross-digit speaker and cross-speaker speech recognition respectively.
Binary graphs and message passing strategies for Compressed Sensing in the noiseless setting

We propose a scheme for Compressed Sensing in the noiseless setting that reconstructs the original signal operating on a binary graph where the samples are obtained sequentially. The proposed scheme has an affordable computational complexity and a large performance enhancement with respect to similar schemes in the literature, thanks to the proposed measurement matrix structure and enhanced decoding based on a message passing algorithm.
2 The model 2 . 1 Fields and symmetries

We construct a model for tri-bimaximal lepton mixing which employs only family symmetries and their soft breaking; neither vacuum alignment nor supersymmetry, extra dimensions, or non-renormalizable terms are used in our model. It is an extension of the Standard Model making use of the seesaw mechanism with five right-handed neutrino singlets. The scalar sector comprises four Higgs doublets and one complex gauge singlet. The horizontal symmetry of our model is based on the permutation group S3 of the lepton families together with the three family lepton numbers—united this constitutes a symmetry group ∆(6∞2). The model makes no predictions for the neutrino masses. E-mail: walter.grimus@univie.ac. at E-mail: balio@cftp.ist. utl.pt 1