Miracle-dz/UniFAR-FTUs huggingface dataset:
Update on epidemiology of hepatitis B and C in China

A high rate of chronic hepatitis B virus (HBV) infection in China is mainly caused by perinatal or early childhood transmission. Administration of universal HBV vaccination in infants has led to a dramatic decrease in HBV epidemiology, with hepatitis B surface antigen (HBsAg) prevalence declining from 9.75% in 1992 to 7.18% in 2006. The major HBV genotypes are B and C, with B being more prevalent in the southern part and C more prevalent in the northern part of China. A national survey carried out in 1992 showed that the hepatitis C virus (HCV) infection rate was 3.20% in general population in China. After implementation of mandatory HCV screening for blood transfusion and other precautions to prevent blood-borne disease since 1993, the new cases of HCV infection associated with blood or blood product has become very rare. Although the anti-HCV prevalence would be much higher in high-risk groups, a survey carried in 2006 showed that the anti-HCV prevalence rate was only 0.43% in general population. This sharp decline in HCV infection rate was mainly due to stringent administration and monitoring of blood donors and blood products, but may also be related to the remarkably improved specificity of anti-HCV test. The predominant HCV genotype in China is genotype 1b (60-70%), and the host interleukin-28b rs12979860 CC genotype is very frequent in Chinese population (over 80%).
Performance of decision-feedback channel estimation for TDM based control signals in OFDMA downlink

In orthogonal frequency division multiple access (OFDMA), time division multiplexing (TDM) based layer 1 (L1)/layer 2 (L2) control signals provide advantageous features such as a power saving effect using micro-sleep operation and a short round trip delay (RTD) for channel-dependent scheduling and adaptive modulation and coding (AMC) compared to a frequency division multiplexing (FDM) based structure. However, only a portion of the reference signal (RS) symbols is used for channel estimation assuming staggered RS multiplexing in order to obtain the merits of the TDM based control signals. Hence, this paper presents decision-feedback channel estimation (DFCE) methods using soft-symbol estimation for control signals for the TDM based control signals in the OFDMA downlink. In the proposed DFCE methods, channel estimation accuracy is improved by using soft-symbol estimation of the control signals based on the extrinsic log-likelihood ratio (LLR) at the soft output Viterbi algorithm (SOVA) for convolutional coding in addition to the RS symbols. Computer simulation results show that the required average received signal-to-noise power ratio (SNR) satisfying the average block error rate (BLER) of 10-2 using DFCE in the subframe of interest is decreased by approximately 0.6 dB compared to that for RS based channel estimation. Hence, we conclude that DFCE with soft-symbol estimation is very effective in improving the channel estimation accuracy using a limited number of RS symbols over the duration in which TDM based control signals are multiplexed, while maintaining the merits such as an efficient power saving effect using micro-sleep operation and a short processing delay.
Serial non-participation and ecosystem services providers’ preferences towards incentive-based schemes

The incidence of serial non-participation and protest responses has largely been ignored in willingness to accept (WTA) applications. This paper analyses serial non-participation with a focus on choice experiment applications using a WTA format to investigate preferences of ecosystem services providers towards incentive-based schemes. The paper addresses two main objectives. First, a review of the literature on WTA for participation in incentive-based schemes is used to identify and discuss a range of possible motives for protest responses that emerge in a WTA context. Second, drawing on choice experiment data on olive farmers’ preferences for agri-environmental scheme participation in Southern Spain, we analyse the impact on WTA estimates of censoring serial non-participation resulting from protest or high compensation requirements (very high takers) from further analysis. Using a random parameter logit model in WTA space, we find that the inclusion or exclusion of serial non-participants in the analysis can have a significant impact on marginal and total WTA estimates. Based on the findings, the paper makes recommendations on how to minimise the incidence of protest responses through survey design, regarding the identification of protesters as opposed to very high takers, and regarding the treatment of both for WTA estimation.
A general test for rule of thumb behavior

This paper analyzes whether the usual first order log-linearized version of the Euler Equation is a suitable econometric model to study rule of thumb behavior in consumption. Firstly, we show that ignoring higher-order terms in the Taylor-Expansion leads to inconsistent estimates and tests of rule of thumb behavior. As a result, in attempting to obtain consistent estimates, the Euler Equation in non-linear form was considered. Our results show little evidence of either rule of thumb behavior or habit formation for U.S. consumers Furthermore, both the intertemporal discount factor and risk aversion coefficients are significant, ranging from 0.956 to 0.969 and 0.829 to 1.126, respectively. In summary, our results suggest that: the non-linear Euler Equation is a better econometric model than its log-linearized version there is not significant evidence of neither credit constraints nor habit formation and the simple representative agent model with CRRA utility explains the time series data on consumption and interest rates reasonably well.
The Potential for Gains in Predictive Ability Through Disaggregation: Segmented Annual Earnings

Both the Financial Accounting Standards Board and the Securities and Exchange Commission have issued guidelines for reporting annual earnings on a subentity basis (FASB [1976; 1977] and SEC [1977; 1978]). Presumably, these disclosures can be used by investors for various purposes, including the prediction of total-entity profits. Indeed, researchers have assessed this potential use by examining the relative predictive ability of consolidated (CN) versus segmented (SG) financial disclosures. The results of previous CN-SG comparisons are mixed. While there is evidence that CN forecasts are generally less accurate than SG forecasts (e.g., Kinney [1971] and Collins [1976]), there is also evidence that such differences may be situation-specific rather than general (e.g., Barefield and Comiskey [1975], Fried [1978], Ang [1979], and Barnea and Lakonishok [1980]). In these studies, however, there was no attempt to determine what general conditions are necessary for disaggregation to result in improved forecasts. ' Here, we derive the conditions under which the disaggregation of entity earnings into subentity earnings can lead to gains in predicting annual earnings before extraordinary items and discontinued operations. Simu-
In silico Prediction of Chemical Ames Mutagenicity

Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis of this data set, high predictive models were then built using five machine learning methods, namely support vector machine (SVM), C4.5 decision tree (C4.5 DT), artificial neural network (ANN), k-nearest neighbors (kNN), and naïve Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS), PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP). Performances were measured by cross validation and an external test set containing 831 diverse chemicals. Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold cross validation were from 0.808 to 0.841 for top five models. The range of accuracy for the external validation set was from 0.904 to 0.980, which outperformed that of Toxtree. Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed high and reliable predictive accuracy for the mutagens and nonmutagens and, hence, could be used in prediction of chemical Ames mutagenicity.
Prevalence Studies in Schizophrenia

Over 70 prevalence studies of schizophrenia published since 1948 are reviewed. Despite problems of method, the studies suggest that there may be significant differences in prevalence among different populations and that prevalence may change over time. Further research is needed on how much differences in diagnosis or the course of schizophrenia contribute to the variable prevalence rates. The paper suggests the following as areas for additional enquiry: a possible north-south gradient, special populations (e.g. American Hutterites, northern Swedes, western Irish, castes in India) and ongoing case registers in areas of suspected high and low prevalence.
Fair fracking? Ethics and environmental justice in United Kingdom shale gas policy and planning

ABSTRACT The exploitation of shale gas resources is a significant issue of environmental justice. Uneven distributions of risks and social impacts to local site communities must be balanced against the economic benefits to gas users and developers; and unequal decision-making powers must be negotiated between local and central governments, communities and fracking site developers. These distributive and procedural elements are addressed in relation to UK policy, planning, regulatory and industry development. I adopt an explicitly normative framework of policy evaluation, addressing a research gap on the ethics of shale gas by operationalising Shrader-Frechette’s Principle of Prima Facie Political Equality. I conclude that UK fracking policy reveals inherent contradictions of environmental justice in relation to the Conservative Government’s localist and planning reform agendas. Early fracking policy protected communities from harm in the wake of seismic risk events, but these were quickly replaced with pro-industry economic stimulation and planning legislation that curtailed community empowerment in fracking decision-making, increased environmental risks to communities, transferred powers from local to central government and created the conditions of distributive injustices in the management of community benefit provisions. I argue that only by “re-localising” the scale of fracking governance can political equality be ensured and the distributive and procedural environmental injustices be ameliorated.
Extraction of brain regions for image diagnosis of Alzheimer-type Dementia based on atrophy progress speeds

We constructed an image diagnosis support system for Alzheimer-type Dementia that extracts temporal lobe regions and an intracranial region as regions of interest (ROI) from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the ROI. In this paper, we specifically discuss extraction of brain regions and diagnosis support information based on atrophy speeds of the ROI. The proposed method comprises of three steps. Step 1: A first contour that approximates a shape of the temporal lobe region to a triangle is set and Balloon model is applied. Step 2: A temporal lobe AAM is constructed and it is applied to the result obtained in Step 1. Step 3: Intracranial region is extracted using temporal lobe regions detected in Step 2. We demonstrate the potential of our method using actual diagnosis images. Additionally, we calculate atrophy rates of the ROI to consider a possibility for diagnosis of ATD using atrophy progress speeds of the ROI.
Sleep enhances a spatially mediated generalization of learned values

Sleep is thought to play an important role in memory consolidation. Here we tested whether sleep alters the subjective value associated with objects located in spatial clusters that were navigated to in a large-scale virtual town. We found that sleep enhances a generalization of the value of high-value objects to the value of locally clustered objects, resulting in an impaired memory for the value of high-valued objects. Our results are consistent with (a) spatial context helping to bind items together in long-term memory and serve as a basis for generalizing across memories and (b) sleep mediating memory effects on salient/reward-related items.
Stochastic load flow analysis using artificial neural networks

Stochastic load flow is a method for calculation of the effects of inaccuracies in input data on all output quantities through the load flow calculations. This gives a range of values (confidence limit) for each output quantity, which represent the operative condition of the system, to a high degree of probability or confidence. This paper presents a new method for stochastic load flow analysis using artificial neural networks. It is desirable to know the state of the power system in a range with certain confidence, with consideration of input data uncertainties and inaccuracies, on instant-to-instant basis in the fastest possible way. Present method using artificial neural networks to stochastic load flow problem is an effort in that direction and will be a very useful technique in effectively dealing with demand side uncertainties for power system planning and operation. The proposed artificial neural network model has been tested on a sample power system using two different training algorithms and simulation results are presented
Organization of the association cortical afferent connections of area 5: A retrograde tracer study in the cat

The association (intrahemispheric) cortico‐cortical afferent connections of area 5 were studied in the cat by means of retrograde tracing techniques involving horseradish peroxidase (HRP) free or wheat germ agglutininconjugated (WGA‐HRP) or fluorochrome injections. Single or multiple injections were placed in different parts of areas 5a and 5b, the medial division of area 5 (5m), or in the anterior suprasylvian area (SsA). Labeled cells were plotted on projection drawings of the coronal sections and on two‐dimensional “maps” of the cerebral cortex, which were produced according to an accurate and consistent procedure.
Expression of types I, III and IV collagen genes in fibrotic skin and nerve lesions of toxic oil syndrome patients

We have studied the skin and nerve fibrosis in toxic oil syndrome by in situ hybridization using specific cDNA probes for types I, III, and IV collagens, Fibroblasts with high levels of type I and III collagen mRNA were observed in biopsies from fibrotic skin areas. Similarly, type IV collagen mRNA was abundant in cells within the fibrotic process of the nerves. These results suggest that the excessive accumulation of collagen in toxic oil syndrome results from transcriptional activation of collagen genes in a subpopulation of fibroblasts.
Urinary S100B protein measurements: A tool for the early identification of hypoxic-ischemic encephalopathy in asphyxiated full-term infants

ObjectiveHypoxic-ischemic encephalopathy (HIE) is one of the major causes of perinatal mortality and morbidity. To date, there are no reliable methods to detect which infants will develop brain damage after asphyxia insult. Design and SettingProspective study conducted in three tertiary departments of neonatology from December 1999 to July 2002. ParticipantsA total of 44 infants with perinatal asphyxia and 68 control infants. InterventionRoutine laboratory variables, neurologic patterns, ultrasound imaging, and urine concentrations of S100B protein were determined at nine time points. Main Outcome MeasuresThe concentrations of S100B protein in urine were measured using an immunoluminometric assay at first urination and 4, 8, 12, 16, 20, 24, 48, and 72 hrs after birth. The results were correlated with the presence or absence of hypoxic-ischemic encephalopathy. Routine laboratory parameters and neurologic patterns were assessed at the same time as urine sampling. ResultsS100B protein levels were significantly higher in samples collected at all monitoring time points from newborns with perinatal asphyxia with or without hypoxic-ischemic encephalopathy than in samples from normal infants (all p < .001). When asphyxiated infants were subdivided according to the presence of mild or absence of hypoxic-ischemic encephalopathy (group A) and of moderate or severe hypoxic-ischemic encephalopathy (group B), S100B levels were significantly higher at all the predetermined monitoring time points in group B infants than group A or control infants (all p < .001). An S100B concentration cutoff of 0.41 &mgr;g/L at first urination had a sensitivity of 91.3% and a specificity of 94.6% for predicting the development of hypoxic-ischemic encephalopathy. The sensitivity and specificity of measurements obtained from 4 to 72 hrs after birth were up to 100% and 98.8%, respectively. ConclusionsLongitudinal S100B protein measurements in urine soon after birth are a useful tool to identify which asphyxiated infants are at risk of hypoxic-ischemic encephalopathy and its possible neurologic sequelae.
A second-order derivative-based edge preserving image interpolation

It is a challenging work to reproduce the high resolution image from the low resolution input while preserving edge structures without causing visual artifacts such as blurring, ringing and jagging. In this paper, we propose an edge guided interpolation method exploiting a data fusion strategy, which interpolates a missing pixel by a weighted sum of directional data estimates for two orthogonal directions. Firstly, each directional data estimate is calculated by 1-D bicubic method along the corresponding direction. Then, we employ the curvature, the second-order directional derivative, to obtain the weights for data fusion. The curvature of the missing pixel decreases as the direction of the curvature is closer to the edge orientation. Experimental results show that the proposed method does not only require the lower computational complexity but also perform better in terms of both subjective and objective visual quality as compared with the state of the art image interpolation algorithms.
The SKS1 protein kinase is a multicopy suppressor of the snf3 mutation of Saccharomyces cerevisiae

Saccharomyces cerevisiae strains carrying snf3 are defective in high affinity glucose transport, and thus are unable to grow fermentatively on media with low concentrations of glucose. A multicopy suppressor of the snf3 growth defect, SKS1 (suppressor kinase of snf3), was found to encode a putative ser/thr protein kinase homologous to Ran1p, a kinase that regulates the switch between meiosis and vegetative growth in Schizosaccharomyces pombe. Overexpression of the SKS1 open reading frame is sufficient for suppression of the growth defects of snf3 mutants. Disruption of the open reading frame eliminates this suppression; as does the mutation of the consensus ATP binding site of Sks1p. A DDSE (DNA dependent snf3 suppressor element) was found to be present in the SKS1 promoter region. The suppression by this DDSE occurs in the absence of SKS1 coding region, that is, the DDSE can suppress a snf3 sks1 double null mutant which fails to grow fermentatively on low glucose as a snf3 mutant does. Both SKS1 and its DDSE can additionally suppress the growth defects of grr1 mutants, which are also impaired in high affinity glucose transport. The snf3 genomic suppressors, rgt1, RGT2 and ssn6, are also capable of suppressing snf3 associated growth defects in a strain lacking sks1.
Proteomic study of amyloid beta (25–35) peptide exposure to neuronal cells: Impact on APE1/Ref‐1's protein–protein interaction

The genotoxic, extracellular accumulation of amyloid β (Aβ) protein and subsequent neuronal cell death are associated with Alzheimer's disease (AD). APE1/Ref‐1, the predominant apurinic/apyrimidinic (AP) endonuclease and essential in eukaryotic cells, plays a central role in the base excision repair (BER) pathway for repairing oxidized and alkylated bases and single‐strand breaks (SSBs) in DNA. APE1/Ref‐1 is also involved in the redox activation of several trans‐acting factors (TFs) in various cell types, but little is known about its role in neuronal functions. There is emerging evidence for APE1/Ref‐1's role in neuronal cells vulnerable in AD and other neurodegenerative disorders, as reflected in its nuclear accumulation in AD brains. An increase in APE1/Ref‐1 has been shown to enhance neuronal survival after oxidative stress. To address whether APE1/Ref‐1 level or its association with other proteins is responsible for this protective effect, we used 2‐D proteomic analyses and identified cytoskeleton elements (i.e., tropomodulin 3, tropomyosin alpha‐3 chain), enzymes involved in energy metabolism (i.e., pyruvate kinase M2, N‐acetyl transferase, sulfotransferase 1c), proteins involved in stress response (i.e., leucine‐rich and death domain, anti‐NGF30), and heterogeneous nuclear ribonucleoprotien‐H (hnRNP‐H) as being associated with APE1/Ref‐1 in Aβ(25–35)‐treated rat pheochromocytoma PC12 and human neuroblastoma SH‐SY5Y cell lines, two common neuronal precursor lines used in Aβ neurotoxicity studies. Because the levels of some of these proteins are affected in the brains of AD patients, our study suggests a neuroprotective role for APE1/Ref‐1 via its association with those proteins and modulating their cellular functions during Aβ‐mediated neurotoxicity. © 2012 Wiley Periodicals, Inc.
Synthesis and magnetic characterizations of manganite-based composite nanoparticles for biomedical applications

We report chemically synthesized highly crystalline lanthanum strontium manganite (LaSrMnO3) and Eu-doped Y2O3 and their composites. The synthesis yields nanoparticles of size 30–40nm. Magnetic measurements performed on nanoparticles and composites show magnetic transition at about 370K with a superparamagnetic behavior at room temperature. The ferromagnetic resonance studies of the nanoparticles show large linewidth due to surface strains. The composite nanoparticles also display luminescent behavior when irradiated with ultraviolet light. The manganites as well their composite with the luminescent nanoparticles may be very useful for biomedical applications.
Design and construction of a low cost photovoltaic generator for laboratory investigations

The dependence on weather conditions of photovoltaic (PV) systems coerces a development of an indoor PV generator using sun-rays simulator to study the output characteristics of the solar panel and to evaluate maximum power point tracking (MPPT) algorithms. In this paper, a 40Wp low cost PV generator is constructed using a solar panel illuminated by switched halogen lamps. The output characteristics of the PV generator are investigated under different panel illumination levels. Both constant voltage and perturb and observe (P&O) MPPT control schemes are examined. The different operation modes of standalone PV applications with battery backup are emulated utilizing a dc-dc converter based power conditioning unit with a resistive and battery loads.
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction

Vector-space models, from word embeddings to neural network parsers, have many advantages for NLP. But how to generalise from fixed-length word vectors to a vector space for arbitrary linguistic structures is still unclear. In this paper we propose bag-of-vector embeddings of arbitrary linguistic graphs. A bag-of-vector space is the minimal nonparametric extension of a vector space, allowing the representation to grow with the size of the graph, but not tying the representation to any specific tree or graph structure. We propose efficient training and inference algorithms based on tensor factorisation for embedding arbitrary graphs in a bag-of-vector space. We demonstrate the usefulness of this representation by training bag-of-vector embeddings of dependency graphs and evaluating them on unsupervised semantic induction for the Semantic Textual Similarity and Natural Language Inference tasks.