Miracle-dz/UniFAR-FTUs huggingface dataset:
Adenovirus Core Protein pVII Is Translocated into the Nucleus by Multiple Import Receptor Pathways

ABSTRACT Adenoviruses are nonenveloped viruses with an ∼36-kb double-stranded DNA genome that replicate in the nucleus. Protein VII, an abundant structural component of the adenovirus core that is strongly associated with adenovirus DNA, is imported into the nucleus contemporaneously with the adenovirus genome shortly after virus infection and may promote DNA import. In this study, we evaluated whether protein VII uses specific receptor-mediated mechanisms for import into the nucleus. We found that it contains potent nuclear localization signal (NLS) activity by transfection of cultured cells with protein VII fusion constructs and by microinjection of cells with recombinant protein VII fusions. We identified three NLS-containing regions in protein VII by deletion mapping and determined important NLS residues by site-specific mutagenesis. We found that recombinant protein VII and its NLS-containing domains strongly and specifically bind to importin α, importin β, importin 7, and transportin, which are among the most abundant cellular nuclear import receptors. Moreover, these receptors can mediate the nuclear import of protein VII fusions in vitro in permeabilized cells. Considered together, these data support the hypothesis that protein VII is a major NLS-containing adaptor for receptor-mediated import of adenovirus DNA and that multiple import pathways are utilized to promote efficient nuclear entry of the viral genome.
Charging of dielectric barriers in rod-plane gaps

The electric properties of an air gap between a hemisphere-rod covered with 3 mm thick silicone rubber and plane metal electrode have been examined. Observation of discharge activity and measurement of the discharge current have been made under impulse voltage stress. When applying an impulse voltage close to the inception voltage, discharge activity is observed around the hemispheric tip of the rod. Observed light emission seems to be evenly distributed along the hemispheric tip comprising a number of independent discharges similar to discharges in a homogeneous electrode configuration. Applying a conductive layer at the rod tip reduces the discharge activity and measured discharge current considerably. The discharge activity was concentrated close to the tip end of the rod. Relaxation time constant for the deposited charge was measured to be about 4 h. By comparing conductivity of the insulation material, the dominating relaxation mechanism was found to be conduction through the solid insulation.
Synovial Immunophenotype and Anti–Citrullinated Peptide Antibodies in Rheumatoid Arthritis Patients

Serum anti–citrullinated peptide antibodies (ACPAs) may be present before the development of rheumatoid arthritis (RA) and may be predictive of more severe, erosive disease. This study was undertaken to examine the synovial tissue immunophenotype according to ACPA status in patients with RA, as well as the response to treatment and erosion status.
Beverage habits and mortality in Chinese adults.

BACKGROUND There is limited research examining beverage habits, one of the most habitual dietary behaviors, with mortality risk. OBJECTIVE This study examined the association between coffee, black and green tea, sugar-sweetened beverages (soft drinks and juice), and alcohol and all-cause and cause-specific mortality. METHODS A prospective data analysis was conducted with the use of the Singapore Chinese Health Study, including 52,584 Chinese men and women (aged 45-74 y) free of diabetes, cardiovascular disease (CVD), and cancer at baseline (1993-1998) and followed through 2011 with 10,029 deaths. Beverages were examined with all-cause and cause-specific (cancer, CVD, and respiratory disease) mortality risk with the use of Cox proportional hazards regression. RESULTS The associations between coffee, black tea, and alcohol intake and all-cause mortality were modified by smoking status. Among never-smokers there was an inverse dose-response association between higher amounts of coffee and black tea intake and all-cause, respiratory-related, and CVD mortality (black tea only). The fully adjusted HRs for all-cause mortality for coffee for <1/d, 1/d, and ≥2/d relative to no coffee intake were 0.89, 0.86, and 0.83, respectively (P-trend = 0.0003). For the same black tea categories the HRs were 0.95, 0.90, and 0.72, respectively (P-trend = 0.0005). Among ever-smokers there was no association between coffee or black tea and the outcomes. Relative to no alcohol, light to moderate intake was inversely associated with all-cause mortality (HR: 0.87; 95% CI: 0.79, 0.96) in never-smokers with a similar magnitude of association in ever-smokers. There was no association between heavy alcohol intake and all-cause mortality in never-smokers and a strong positive association in ever-smokers (HR: 1.56; 95% CI: 1.40, 1.74). Green tea and sugar-sweetened beverages were not associated with all-cause or cause-specific mortality. CONCLUSIONS Higher coffee and black tea intake was inversely associated with mortality in never-smokers, light to moderate alcohol intake was inversely associated with mortality regardless of smoking status, heavy alcohol intake was positively associated with mortality in ever-smokers, and there was no association between sugar-sweetened beverages and green tea and mortality.
Stability Criterion for Cascaded System With Constant Power Load

With the development of renewable energy, dc distribution power system (DPS) becomes more and more attractive. The stability of whole system is still a big concern though every single converter is well designed based on the stand-alone operation with sufficient stability. Since the cascaded connection of power converters is one of the most dominant connection forms in the dc DPS, the stability analysis of the cascaded system is very important to ensure stability of the whole system. Based on the Lyapunov linearization method and Brayton–Moser's mixed potential theory, stability of equilibrium point and an estimation of the region of attraction are investigated for cascaded system in the dc DPS. Based on the analysis, stability prediction criteria for cascaded system under small-signal and large-signal disturbances are obtained. The two criteria are simple and straightforward, which can be unified to get a general stability criterion to predict system's stability under both small-signal and large-signal transient disturbances. The relationship between system parameters and the stability is presented and discussed in the paper. Therefore, instead of trial and error, the proposed criterion can predict and guarantee the stability operation of cascaded system during the design process, and it is also helpful to select matched power converters in system level design. The simulation and experimental results verify the effectiveness of the proposed criterion.
A fibre-optic catheter-tip force sensor with MRI compatibility: A feasibility study

This paper presents the development of a low-cost, Magnetic Resonance Imaging (MRI) compatible fibre-optic sensor for integration with catheters allowing the detection of contact forces between blood vessel walls and the catheter tip. Three plastic optical-fibres are aligned inside a plastic catheter in a circular pattern. A reflector is attached to a separate small part of the catheter tip, which is connected with a small deformable material to the aligned optical-fibres. In this manner a force at the catheter tip leads to a deformation of the elastic material and thus a modulation of the light yields, this is sent and received through the optical-fibres. An electronic circuit amplifies the retrieved light signal and the output voltage is used to classify the forces on the tip. The materials used are of the shelf and have a low magnetic susceptibility making this sensor fully MRI-compatible and inexpensive. Preliminary, experimental results demonstrated good force linearity in static loading and unloading conditions. The sensor was also tested in an artificial blood artery showing good dynamic response.
RELIGION, RELATIONSHIPS AND MENTAL HEALTH IN MIDLIFE WOMEN FOLLOWING ACUTE MYOCARDIAL INFARCTION

Little is known about coping in women following an acute myocardial infarction (AMI). In midlife, women have worse outcomes than men following AMI. Innovative interventions need to be developed that respond to these women's unique recovery needs. In this correlational, descriptive study, 59 women aged 35–64 who had experienced AMI reported low satisfaction with life and decreased mental health; 49% were experiencing depression. However, they also reported that religion, family, and friends provided strength and comfort at the time of their AMI. Greater activation of simple, family-oriented, coping resources during recovery may be key. It is recommended that mental health nurses be essential members of the recovery planning team.
Analysis and prediction of content popularity for online video service: a Youku case study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical application for content publishers, service providers, online advisers and network operators.
Antimicrobial Properties and Release Profile of Ampicillin from Electrospun Poly(εepsilon;-caprolactone) Nanofiber Yarns

Poly(εepsilon;-caprolactone) (PCL) electrospun fibers containing ampicillin sodium salt have been produced and twisted into nanofiber yarns. The fiber diameters and crystallinity, the in vitro antimicrobial properties of the yarns, and the in vitro release of ampicillin from yarns containing various ampicillin concentrations are studied. Decreased fiber diameters and reduced diameter variation are observed with the addition of ampicillin salt into the polymer solution. The results from the zone of inhibition test of the yarns against both gram-positive Staphylococcus aureus and gram-negative Klebsiella pneumoniae indicate that the released ampicillin retains its effectiveness after the production processes, therefore the as-spun yarns are antimicrobial active. A burst release of ampicillin from the yarns has been observed in the first hour, and the release is almost completed in 96 hours. The burst release is believed to be due to the low compatibility of ampicillin with PCL, the accumulation of ampicillin on fiber surface and the small fiber diameters. An empirical release model is developed to describe the release profile. The results indicate that the electrospun nanofibers yarns will have a great potential to be used for biomaterials, such as surgical sutures, to decrease the surgical site infection rate.
A Distraction Can Impair or Enhance Motor Performance

Humans have a prodigious capacity to perform multiple tasks simultaneously. Being distracted while, for example, performing a complex motor skill adds complexity to a task and thus leads to a performance impairment. Yet, it may not be just the presence or absence of a distraction that affects motor performance. Instead, the characteristics of the distraction may play a critical role in affecting human motor performance. Here, we show that performance of a motor sequence can be substantially enhanced by simultaneously learning an independent color sequence. In contrast, performance of the same motor sequence was impaired by concurrently counting the number of red cues that were in the color sequence. The color and motor sequences had different lengths (10 vs 12 items), different numbers of elements (five vs four elements), and different temporal patterns (randomly intermittent vs continuous) and thus were independent of one another. These observations show that distracting information does not always impair motor performance, and so is not a sufficient explanation for the impaired performance. Instead, the influence that a distraction exerts upon performance is mediated by the type of processes engaged: when similar core processes are engaged, motor performance is enhanced, whereas when very different processes are engaged (i.e., counting and sequence performance), performance is impaired. Thus, these observations deepen our understanding of how a distraction, depending on its characteristics, can either impair or enhance performance and may offer novel approaches to optimizing human cognition.
Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting

This thesis focuses on two fundamental machine learning problems: unsupervised learning, where no label information is available, and semi-supervised learning, where a small amount of labels are given in addition to unlabeled data. These problems arise in many real word applications, such as Web analysis and bioinformatics, where a large amount of data is available, but no or only a small amount of labeled data exists. Obtaining classification labels in these domains is usually quite difficult because it involves either manual labeling or physical experimentation. This thesis approaches these problems from two perspectives: graph based and distribution based. First, I investigate a series of graph based learning algorithms that are able to exploit information embedded in different types of graph structures. These algorithms allow label information to be shared between nodes in the graph—ultimately communicating information globally to yield effective unsupervised and semi-supervised learning. In particular, I extend existing graph based learning algorithms, currently based on undirected graphs, to more general graph types, including directed graphs, hypergraphs and complex networks. These richer graph representations allow one to more naturally capture the intrinsic data relationships that exist, for example, in Web data, relational data, bioinformatics and social networks. For each of these generalized graph structures I show how information propagation can be characterized by distinct random walk models, and then use this characterization to develop new unsupervised and semi-supervised learning algorithms. Second, I investigate a more statistically oriented approach that explicitly models a learning scenario where the training and test examples come from different distributions. This is a difficult situation for standard statistical learning approaches, since they typically incorporate an assumption that the distributions for training and test sets are similar, if not identical. To achieve good performance in this scenario, I utilize unlabeled data to correct the bias between the training and test distributions. A key idea is to produce resampling weights for bias correction by working directly in a feature space and bypassing the problem of explicit density estimation. The technique can be easily applied to many different supervised learning algorithms, automatically adapting their behavior to cope with distribution shifting between training and test data.
Equalizer for an IR-wireless LAN using RBF neural networks

The application of a RBF (radial basis function) neural network to an adaptive equalizer at the receiver of a wireless IR-LAN is considered. Fixing the decision threshold and classifying the received binary signals are the main functions of the RBF. The general problem of equalization binary signals, passed through a dispersive channel and corrupted with noise, is briefly described. The characterization of the receiver and the effects of both Gaussian and shot noise over the signals are studied. A possible architecture for the equalizer and a comparison with other classical structures (multilayer perceptron and linear transversal equalizer), as well as simulation results are given. Considerations about the way of reducing computational complexity are proposed.
Epidemiology of salmonella infections in pig units and antimicrobial susceptibility profiles of the strains of Salmonella species isolated

One hundred and thirteen finishing pig units and 74 sow units in Catalonia, Spain, were examined to determine the prevalence of salmonella infections and the factors that could be associated with them. Pooled faecal samples were taken from the finishing units, and samples of faeces were collected from individual sows. The Salmonella isolates were serotyped, phage typed and examined for their antimicrobial susceptibility to 18 common antimicrobial drugs. In addition, blood samples from pigs on 141 farms were analysed by elisa. In both the bacteriological and serological surveys, a questionnaire with 84 questions was completed for each farm. Salmonella species were isolated from 20 per cent of the finishing units and 24 per cent of the sow units; 14 serotypes were detected in the finishing pigs and 11 in the sows. More than 30 per cent of the strains were resistant to tetracycline, sulphonamides, ampicillin or streptomycin, and 69 per cent of the strains were resistant to three or more agents up to 10 compounds. Seventy-seven per cent of the farms had at least one seropositive animal, and 26 per cent of these farms had an individual seroprevalence of 50 per cent or more. The factors associated (P<0·05) with the excretion of Salmonella species in the finishing units were the practice of raising livestock other than pigs (odds ratio [or]=6·18), the herd size (or=5·87), and a past history of clinical salmonellosis (or=4·97). For the sows, the factors associated (P<0·05) with the excretion of Salmonella species were having open-flushed drainage of sewage (or=34·48), a lack of rodent control measures (or=0·05) and the number of sows in the unit (or=9·26). Factors associated with seropositivity in the finishing units were a lack of bird-proof nets (or=0·30) and the use of water from private wells (or=3·64).
Light Field Convergency: Implicit Photometric Consistency on Transparent Surface

This paper proposes a method for estimating the surface of transparent objects based on light field convergency. The light field convergency represents the degree of convergence of the light field at each point. The proposed method utilizes local photo consistency, which is one of characteristics of the light field convergency. Around a boundary contour, a point that is visible from viewpoints with a small conver- gence angle is locally consistent. In other words, the local photo consistency is implicitly maintained near the bound- ary contour. We use a light field camera as a camera array with small convergence angles to estimate the surface of the transparent object. Experimental results have demonstrated that the depth of the transparent object is estimated from the captured image by the light field camera.
ECG classification and prognostic approach towards personalized healthcare

A very important aspect of personalized healthcare is to continuously monitor an individual’s health using wearable biomedical devices and essentially to analyse and if possible to predict potential health hazards that may prove fatal if not treated in time. The prediction aspect embedded in the system helps in avoiding delays in providing timely medical treatment, even before an individual reaches a critical condition. Despite of the availability of modern wearable health monitoring devices, the real-time analyses and prediction component seems to be missing with these devices. The research work illustrated in this paper, at an outset, focussed on constantly monitoring an individual's ECG readings using a wearable 3-lead ECG kit and more importantly focussed on performing real-time analyses to detect arrhythmia to be able to identify and predict heart risk. Also, current research shows extensive use of heart rate variability (HRV) analysis and machine learning for arrhythmia classification, which however depends on the morphology of the ECG waveforms and the sensitivity of the ECG equipment. Since a wearable 3-lead ECG kit was used, the accuracy of classification had to be dealt with at the machine learning phase, so a unique feature extraction method was developed to increase the accuracy of classification. As a case study a very widely used Arrhythmia database (MIT-BIH, Physionet) was used to develop learning, classification and prediction models. Neuralnet fitting models on the extracted features showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. Current experiments show 99.4% accuracy using k-NN Classification models and show values of Cross-Entropy Error of 7.6 and misclassification error value of 1.2 on test data using scaled conjugate gradient pattern matching algorithms. Software components were developed for wearable devices that took ECG readings from a 3-Lead ECG data acquisition kit in real time, de-noised, filtered and relayed the sample readings to the tele health analytical server. The analytical server performed the classification and prediction tasks based on the trained classification models and could raise appropriate alarms if ECG abnormalities of V (Premature Ventricular Contraction: PVC), A (Atrial Premature Beat: APB), L (Left bundle branch block beat), R (Right bundle branch block beat) type annotations in MITDB were detected. The instruments were networked using IoT (Internet of Things) devices and abnormal ECG events related to arrhythmia, from analytical server could be logged using an FHIR web service implementation, according to a SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to take appropriate and timely decisions. The system focused on ‘preventive care rather than remedial cure’ which has become a major focus of all the health care and cure institutions across the globe.