Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Low-Level Hepatitis B Viremia in Patients with HBV Surface Antigen-Negative Hepatocellular Carcinoma with and without Hepatitis C Virus Infection in Japan: Analysis by COBAS TaqMan Real-Time PCR

Objectives: The effect of circulating low-level hepatitis B virus (HBV), defined as one of the states of ‘occult HBV infection’, on the development of hepatocellular carcinoma (HCC) in HBV surface antigen (HBsAg)-negative patients is controversial. In addition, the prevalence of occult HBV infection strongly depends on the sensitivity of the HBV detection method. We investigated the prevalence of low-level HBV in the serum of HBsAg-negative patients with HCC using a newly developed, sensitive method based on real-time polymerase chain reaction. Methods: Serum was examined for HBV DNA in 132 patients with HBsAg-negative HCC (95 with hepatitis C virus [HCV] infection and 37 without detectable hepatitis virus infection) with the COBAS TaqMan HBV test, of which the 95% hit rate is 35 copies/ml (6.7 IU/ml). Results: Low-level HBV DNA was detected in 2 of 95 (2.1%) patients with HCV-related HCC and 1 of 37 (2.7%) patients with non-viral HCC. Conclusion: The prevalence of the detection of circulating low-level HBV was low in both HBsAg-negative HCC patients with HCV infection and those without detectable hepatitis virus, even with the use of the most sensitive method for the detection of HBV. Circulating low-level HBV does not appear to play an important role in hepatocarcinogenesis in HBsAg-negative HCC.
An Intelligent Quick Prediction Algorithm With Applications in Industrial Control and Loading Problems

The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit.
User-Centered Design of Learn to Quit, a Smoking Cessation Smartphone App for People With Serious Mental Illness

Background Smoking rates in the United States have been reduced in the past decades to 15% of the general population. However, up to 88% of people with psychiatric symptoms still smoke, leading to high rates of disease and mortality. Therefore, there is a great need to develop smoking cessation interventions that have adequate levels of usability and can reach this population. Objective The objective of this study was to report the rationale, ideation, design, user research, and final specifications of a novel smoking cessation app for people with serious mental illness (SMI) that will be tested in a feasibility trial. Methods We used a variety of user-centered design methods and materials to develop the tailored smoking cessation app. This included expert panel guidance, a set of design principles and theory-based smoking cessation content, development of personas and paper prototyping, usability testing of the app prototype, establishment of app’s core vision and design specification, and collaboration with a software development company. Results We developed Learn to Quit, a smoking cessation app designed and tailored to individuals with SMI that incorporates the following: (1) evidence-based smoking cessation content from Acceptance and Commitment Therapy and US Clinical Practice Guidelines for smoking cessation aimed at providing skills for quitting while addressing mental health symptoms, (2) a set of behavioral principles to increase retention and comprehension of smoking cessation content, (3) a gamification component to encourage and sustain app engagement during a 14-day period, (4) an app structure and layout designed to minimize usability errors in people with SMI, and (5) a set of stories and visuals that communicate smoking cessation concepts and skills in simple terms. Conclusions Despite its increasing importance, the design and development of mHealth technology is typically underreported, hampering scientific innovation. This report describes the systematic development of the first smoking cessation app tailored to people with SMI, a population with very high rates of nicotine addiction, and offers new design strategies to engage this population. mHealth developers in smoking cessation and related fields could benefit from a design strategy that capitalizes on the role visual engagement, storytelling, and the systematic application of behavior analytic principles to deliver evidence-based content.
Inkjet printing: technique and applications for organic electronic devices

Since the discovery of the polyacetylene conductivity in 1977 by Shirakawa,MacDiarmid and Heeger, Organic Electronics has been growing and establishing for a new generation of electronic devices. On one hand, the unique properties of polymeric semiconductors and conductors, such as flexibility and transparency, allow the fabrication of low-cost devices over large area: the most common are the Organic Light Emitting Diodes (OLEDs) and the organic photovoltaic cells. On the other hand, much effort has been made to investigate new technologies and processes for the realization of high-performance organic transistors and sensors. Among them, Inkjet Printing is a promising technique which exploits all the advantages of organic materials, such as low-cost and solution processability, and allows the large-scale automated fabrication of large area devices. This thesis describes the work carried outwith a piezoelectric drop-on-demand inkjet printer, theDimatixMaterials Printer 2831. The first chapter gives an overview on Organic and Printed Electronics state-of-art, also describing the physical principles of conductivity in organic molecules. The second chapter is totally focused on the inkjet printing technique: after a brief description of the printer employed, a detailed description of the printing process, from the ink formulation to the drop deposition and drying phenomena, is presented. Moreover the materials employed are described together with the printing parameters set for achieving the best deposition condition. Chapter 3 is devoted to tactile sensing, which is the main application of the Organic Field-Effect Transistors (OFETs) in this work within the ROBOSKIN project. After an overview of inorganic tactile sensors and of the fundamentals of strain sensing in OFETs, the fabrication steps are described followed by the detailed electromechanical characterization of the various strain sensors realized. Finally, the fourth chapter describes other applications of inkjet printing developed in this work: organic diodes and photodiodes within the HYMEC project, patterned electrodes for the detection of cells electrical activity, both in vivo and in vitro, and a RFID tag antenna.
Machine Learning Using Support Vector Machines

Artificial Neural Networks (ANN) have been the most widely used machine learning methodology. They draw much of their inspiration from neurosciences. Structurally attempting to mimic the architecture of the human brain during learning, ANN aim to incorporate ‘human-like intelligence’ within computer systems. Recently, a new learning methodology called support vector machine (SVM) have been introduced. SVM is said to perform better than ANN in many cases. Furthermore, SVM can be mathematically derived and simpler to analyze theoretically compared to NN. It also provides a clear intuition of what learning is about. SVM work by mapping training data for learning tasks into a higher dimensional feature space using kernel functions and then find a maximal margin hyper plane, which separates the data. Learning the solution hyper plane involves using quadratic programming (QP) which is computationally intensive. However, many decomposition methods have been proposed that avoids the QP and makes SVM learning practical for many current problems. This paper compares SVM and ANN theoretically and practically in the case of handwriting recognition.
Linear Precoding of Data and Artificial Noise in Secure Massive MIMO Systems

In this paper, we consider secure downlink transmission in a multicell massive multiple-input multiple-output (MIMO) system where the numbers of base station (BS) antennas, mobile terminals, and eavesdropper antennas are asymptotically large. The channel state information of the eavesdropper is assumed to be unavailable at the BS and hence, linear precoding of data and artificial noise (AN) are employed for secrecy enhancement. Four different data precoders (i.e., selfish zero-forcing (ZF)/regularized channel inversion (RCI) and collaborative ZF/RCI precoders) and three different AN precoders (i.e., random, selfish/collaborative null-space-based precoders) are investigated and the corresponding achievable ergodic secrecy rates are analyzed. Our analysis includes the effects of uplink channel estimation, pilot contamination, multicell interference, and path-loss. Furthermore, to strike a balance between complexity and performance, linear precoders that are based on matrix polynomials are proposed for both data and AN precoding. The polynomial coefficients of the data and AN precoders are optimized, respectively, for minimization of the sum-mean-squared-error of and the AN leakage to the mobile terminals in the cell of interest using tools from free probability and random matrix theory. Our analytical and simulation results provide interesting insights for the design of secure multicell massive MIMO systems and reveal that the proposed polynomial data and AN precoders closely approach the performance of selfish RCI data and null-space-based AN precoders, respectively.
Physics-based modelling and simulation of functional cloth for virtual prototyping applications

A CAD-oriented system is proposed for the design of complex-shaped functional cloth, provided with a physics-based modelling core for simulation and virtual prototyping tasks. Textiles are physically modelled as particle grids in 3D space subjected to Newtonian dynamics, with internal spring, bending and shear forces derived from KES-F data measuring material behaviour. Interactions with the environment are expressed as external forces, collisions against obstacles, self-collisions and constraints. Differently from physics-based animation systems, the proposed system is conceived for real design purposes, and includes functionalities emulating the construction process of actual clothing and structural textiles, e.g., mesh sewing/assembly, insertion of small components, multi-layered fabric composition, mechanical shape deformation, and 2D-to-3D mapping methods. As applications, several cases of textile configurations are considered, with geometric models directly provided by industrial companies and presenting different levels of design complexities, such as garment models (e.g., men's jackets) for the clothing sector, or functional textiles used in the automotive industry (e.g., soft car tops).
Meta-analysis of the age-associated decline in maximal aerobic capacity in men: relation to training status.

Based on cross-sectional data, we recently reported that, in contrast to the prevailing view, the rate of decline in maximal oxygen consumption (VO(2 max)) with age is greater in physically active compared with sedentary healthy women. We tested this hypothesis in men using a meta-analytic study of VO(2 max) values in the published literature. A total of 242 studies (538 subject groups and 13,828 subjects) met the inclusion criteria and were arbitrarily separated into sedentary (214 groups, 6,231 subjects), active (159 groups, 5,621 subjects), and endurance-trained (165 groups, 1,976 subjects) populations. Body fat percent increased with age in sedentary and active men (P < 0.001), whereas no change was observed in endurance-trained men. VO(2 max) was inversely and strongly related to age within each population (r = -0.80 to -0.88, all P < 0. 001) and was highest in endurance-trained and lowest in sedentary populations at any age. Absolute rates of decline in VO(2 max) with age were not different (P > 0.05) in sedentary (-4.0 ml. kg(-1). min(-1). decade(-1)), active (-4.0), and endurance-trained (-4.6) populations. Similarly, there were no group differences (P > 0.05) in the relative (%) rates of decline in VO(2 max) with advancing age (-8.7, -7.3, and -6.8%/decade, respectively). Maximal heart rate was inversely related to age within each population (r = -0.88 to -0.93, all P < 0.001), but the rate of age-related reduction was not different among the populations. There was a significant decline in running mileage and speed with advancing age in the endurance-trained men. The present cross-sectional meta-analytic findings do not support the hypothesis that the rate of decline in VO(2 max) with age is related to habitual aerobic exercise status in men.
Encoding and Accessing Linguistic Representations in a Dynamically Structured Holographic Memory System

This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference effects during sentence comprehension. Here, we investigate one such case involving the processing of sentences with negative polarity items (NPIs) and consider how a dynamically structured holographic memory system might provide a cognitively plausible and principled explanation of some previously unexplained effects. Specifically, we show that by replacing ACT-R's declarative memory with a dynamically structured memory, we can explain a wider range of behavioral data involving reading times and judgments of grammaticality. We show that our integrated model provides a better fit to human error rates and response latencies than the original ACT-R model. These results provide proof-of-concept for the unification of two independent computational cognitive frameworks.
Analytical and Clinical Evaluation of Troponin I Determination on Dimension RXL-HM

Abstract The evaluation of cardiac troponin I (cTnI) on the Dimension RxL®-HM analyzer is presented. The one-step enzyme immunoassay is based on two cTnI specific monoclonal antibodies. Performed on a separate module of the analyzer, assay-time is 17 minutes. Using as criterion a between-run impression CV <20% the functional limit of detection was set at 0.1 μg/l. Cutoff level for minor myocardial damage of 0.1 μg/l was found. In Duchenne's dystrophy, patients showed increased cardiac Troponin T (cTnT) but no increased cTnI. In patients with a history of coronary heart disease undergoing chronic hemodialysis, cTnT and cTnI were increased. In different patients with submassive pulmonary embolism, increased cTnI was determined. In coronary artery bypass surgery without perioperative myocardial infarction, patients with extracorporeal circulation showed significantly higher cTnI at 24 h after surgery than those with minimal cardiac surgery. In patients with unstable angina, increased cTnI was found more often than on Stratus analyzer. In conclusion, the new assay is a very sensitive cTnI assay, fast and easy to perform in parallel to enzyme and substrate assays.
Irregularly shaped voltage islands generation with hazard and heal strategy

Irregular Shaped Voltage Islands (ISVI) can achieve much better power savings in multiple supply voltage (MSV) technology. This paper proposes a simulated annealing (SA) based floorplanning method with ISVI generation using a Hazard and Heal strategy. In each perturbation of SA, a randomly chosen block is removed from floorplan and then inserted back to a proper position, to join certain voltage island. The solution space for every iteration is O(n2kvi), where n is block number and kvi is voltage island number. Every position with voltage island assignment is evaluated by a linear combination of chip area, wire length, power and voltage island cost. To avoid timing-consuming evaluation of O(n2kvi) possibilities, we propose a fast Hazard method to construct ISVI by skipping lots of distant voltage island candidates while occasionally accepting seldom-emerged illegal (non-contiguous) candidates. To resolve the seldom-emerged non-contiguous voltage islands, a detailed Heal method is applied in each temperature. Experimental results show the proposed method is effective and efficient compared with the latest works.
DESIGN, SYNTHESIS, AND OPTIMIZATION OF NON-FLUOROUS, CO2-PHILIC POLYMERS: A SYSTEMATIC APPROACH

Research done during the last 15 years has proven that one can use CO2 as a solvent in many processes. There have been compounds from surfactants to chelating agents that were shown to be soluble in CO2 to low pressures. By attaching CO2-philic groups to polymer chains, previously insoluble polymers have exhibited miscibility with CO2, allowing for applications such as emulsion polymerization, dispersion polymerization, dissolution of proteins, extraction of heavy metals, and other processes. Unfortunately, the most successful CO2-philes, the fluorinated polymers, have a very unfavorable economic drawback that makes their commercial use impractical. As a result, this work seeks to determine the characteristics that could be built into a carbon based polymer to allow for the polymer to be miscible with CO2 down to low pressures.Several design elements were considered in this work: the cohesive energy density, the free volume, and the Lewis acid/base interactions with CO2 acting as a Lewis acid. Lowering the cohesive energy density and increasing the free volume limited undesirable polymer-polymer interactions, while the addition of Lewis base groups to the polymer in the backbone and as grafted side chains increased the favorable polymer-CO2 interactions. Several side chains containing Lewis base groups were first grafted onto silicon-backbone polymers. The effects of the grafting and degree of substitution were determined, and the best performing side chains were then grafted onto a polyether backbone to investigate their interactions with the carbon/oxygen backbone.This work made clear the importance of adding optimal amounts of a Lewis base group to the polymer, whether in the backbone or as a grafted side chain. While attaining a low cohesive energy density and maintaining a low glass transition temperature are important, polymers with these features alone performed very poorly in CO2. The addition of Lewis base groups in the backbone or as a side chain dramatically improved the solubility of the polymers and showed the importance of favorable polymer-CO2 interactions. A key observation was that an ether functional group may provide the same Lewis acid/base interaction with CO2 that is seen with the acetate group.
Ieee/acm Transactions on Computational Biology and Bioinformatics 1 Fast Exact Algorithms for the Closest String and Substring Problems with Application to the Planted (l, D)-motif Model

We present two parameterized algorithms for the closest string problem. The first runs in O nL + nd · 17.97 d time for DNA strings and in O nL + nd · 61.86 d time for protein strings, where n is the number of input strings, L is the length of each input string, and d is the given upper bound on the number of mismatches between the center string and each input string. The second runs in O nL + nd · 13.92 d time for DNA strings and in O nL + nd · 47.21 d time for protein strings. We then extend the first algorithm to a new parameterized algorithm for the closest substring problem that runs in O (n − 1)m 2 L + d · 17.97 d · m log 2 (d+1) time for DNA strings and in O (n − 1)m 2 L + d · 61.86 d · m log 2 (d+1) time for protein strings, where n is the number of input strings, L is the length of the center substring, L − 1 + m is the maximum length of a single input string, and d is the given upper bound on the number of mismatches between the center substring and at least one substring of each input string. All the algorithms significantly improve the previous bests. To verify experimentally the theoretical improvements in the time complexity, we implement our algorithm in C and apply the resulting program to the planted (L, d)-motif problem proposed by Pevzner and Sze in 2000. We compare our program with the previously best exact program for the problem, namely PMSPrune (designed by Davila, Balla, and Rajasekaran in 2007). Our experimental data shows that our program runs faster for practical cases and also for several challenging cases. Our algorithm uses less memory, too.
Gustation: Intersensory Experience Par Excellence

On the face of it, basic tactile sensation might seem the only essential sensory requirement for the delivery of foods and beverages to the digestive system. In practice, however, the appropriate delivery of raw materials for the maintenance and repair of the body requires complex sensory and cognitive processes, such that flavour sensation arguably constitutes the pre-eminent example of an integrated multicomponent perceptual experience. To raise the profile of the chemical senses amongst researchers in other perceptual domains, I review here the contribution of various sense modalities to the flavour of foods and beverages. Further, in the light of these multisensory inputs, the physiological and psychophysical research summarised in this paper invites optimism that novel ways will be found to intervene when nutritional status is compromised either by specific dietary restraints, or by taste and smell disorders.
Verfahren und Protokolle für sicheren Rechtsverkehr auf dezentralen und spontanen elektronischen Märkten

Die Arbeit thematisiert das Problem der fehlenden Rechtssicherheit aktueller elektronischer Markte. Fur das Szenario eines dezentralen elektronischen Marktplatzes wird eine verteilte Marktplattform vorgestellt, die einen sicheren Rechtsverkehr ermoglicht. Einer der Schwerpunkte der Arbeit stellt dabei der nachweisbare Zugang einer elektronischen Willenserklarung auf dem verteilten Marktplatz dar.