LLukas22/scidocs huggingface dataset:
Complications associated with endobronchial ultrasound-guided transbronchial needle aspiration: a nationwide survey by the Japan Society for Respiratory Endoscopy

BACKGROUND
With the recent widespread use of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), there have been occasional reports on complications associated with its use. Previous reviews on EBUS-TBNA have been limited to studies by skilled operators, thus the results may not always be applicable to recent clinical practice. To assess the safety of EBUS-TBNA for the staging and diagnosis of lung cancer in Japan, a nationwide survey on its current usage status and complications associated with its use was conducted by the Japan Society for Respiratory Endoscopy (JSRE).


METHODS
A questionnaire about EBUS-TBNA performed between January 2011 and June 2012 was mailed to 520 JSRE-accredited facilities.


RESULTS
Responses were obtained from 455 facilities (87.5%). During the study period, EBUS-TBNA was performed in 7,345 cases in 210 facilities (46.2%) using a convex probe ultrasound bronchoscope, for 6,836 mediastinal and hilar lesions and 275 lung parenchymal lesions. Ninety complications occurred in 32 facilities. The complication rate was 1.23% (95% confidence interval, 0.97%-1.48%), with hemorrhage being the most frequent complication (50 cases, 0.68%). Infectious complications developed in 14 cases (0.19%) (Mediastinitis, 7; pneumonia, 4; pericarditis, 1; cyst infection, 1; and sepsis, 1). Pneumothorax developed in 2 cases (0.03%), one of which required tube drainage. Regarding the outcome of the cases with complications, prolonged hospitalization was observed in 14 cases, life-threatening conditions in 4, and death in 1 (severe cerebral infarction) (mortality rate, 0.01%). Breakage of the ultrasound bronchoscope occurred in 98 cases (1.33%) in 67 facilities (31.9%), and that of the puncture needle in 15 cases (0.20%) in 8 facilities (3.8%).


CONCLUSIONS
Although the complication rate associated with EBUS-TBNA was found to be low, severe complications, including infectious complications, were observed, and the incidence of device breakage was high. Since the use of EBUS-TBNA is rapidly expanding in Japan, an educational program for its safe performance should be immediately established.
Neural Image Compression for Gigapixel Histopathology Image Analysis

We present Neural Image Compression (NIC), a method to reduce the size of gigapixel images by mapping them to a compact latent space using neural networks. We show that this compression allows us to train convolutional neural networks on histopathology whole-slide images end-to-end using weak image-level labels.
Compressed Sensing for Surface Characterization and Metrology

Surface metrology is the science of measuring small-scale features on surfaces. In this paper, a novel compressed sensing (CS) theory is introduced for the surface metrology to reduce data acquisition. We first describe that the CS is naturally fit to surface measurement and analysis. Then, a geometric-wavelet-based recovery algorithm is proposed for scratched and textural surfaces by solving a convex optimal problem with sparse constrained by curvelet transform and wave atom transform. In the framework of compressed measurement, one can stably recover compressible surfaces from incomplete and inaccurate random measurements by using the recovery algorithm. The necessary number of measurements is far fewer than those required by traditional methods that have to obey the Shannon sampling theorem. The compressed metrology essentially shifts online measurement cost to computational cost of offline nonlinear recovery. By combining the idea of sampling, sparsity, and compression, the proposed method indicates a new acquisition protocol and leads to building new measurement instruments. It is very significant for measurements limited by physical constraints, or is extremely expensive. Experiments on engineering and bioengineering surfaces demonstrate good performances of the proposed method.
Large Scale Business Discovery from Street Level Imagery

Search with local intent is becoming increasingly useful due to the popularity of the mobile device. The creation and maintenance of accurate listings of local businesses world wide is time consuming and expensive. In this paper, we propose an approach to automatically discover businesses that are visible on street level imagery. Precise business store-front detection enables accurate geo-location of bu sinesses, and further provides input for business categoriza tion, listing generation,etc. The large variety of business categories in different countries makes this a very challen ging problem. Moreover, manual annotation is prohibitive due to the scale of this problem. We propose the use of a MultiBox [4] based approach that takes input image pixels and directly outputs store front bounding boxes. This end-to-end learning approach instead preempts the need for hand modelling either the proposal generation phase or the post-processing phase, leveraging large labelled trai ning datasets. We demonstrate our approach outperforms the state of the art detection techniques with a large margin in terms of performance and run-time efficiency. In the evaluation, we show this approach achieves human accuracy in the low-recall settings. We also provide an end-to-end eval uation of business discovery in the real world.
Weld Defect Detection in Industrial Radiography Based Digital Image Processing

Industrial radiography is a famous technique for the identification and evaluation of discontinuities, or defects, such as cracks, porosity and foreign inclusions found in welded joints. Although this technique has been well developed, improving both the inspection process and operating time, it does suffer from several drawbacks. The poor quality of radiographic images is due to the physical nature of radiography as well as small size of the defects and their poor orientation relatively to the size and thickness of the evaluated parts. Digital image processing techniques allow the interpretation of the image to be automated, avoiding the presence of human operators making the inspection system more reliable, reproducible and faster. Perfect knowledge of the geometry of these defects is an important step which is essential to appreciate the quality of the weld. This paper describes our attempt to develop and implement digital image processing algorithms based on global and local approaches for the purpose of automatic defect detection in radiographic images. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of global and local preprocessing and segmentation methods must be appropriated.
Deep Salience Representations for F0 Estimation in Polyphonic Music

Estimating fundamental frequencies in polyphonic music remains a notoriously difficult task in Music Information Retrieval. While other tasks, such as beat tracking and chord recognition have seen improvement with the application of deep learning models, little work has been done to apply deep learning methods to fundamental frequency related tasks including multi-f0 and melody tracking, primarily due to the scarce availability of labeled data. In this work, we describe a fully convolutional neural network for learning salience representations for estimating fundamental frequencies, trained using a large, semi-automatically generated f0 dataset. We demonstrate the effectiveness of our model for learning salience representations for both multi-f0 and melody tracking in polyphonic audio, and show that our models achieve state-of-the-art performance on several multi-f0 and melody datasets. We conclude with directions for future research.
Pretreatment with verapamil in patients with persistent or chronic atrial fibrillation who underwent electrical cardioversion.

OBJECTIVES
To evaluate, in a prospective and randomized fashion, the efficacy of a pretreatment with verapamil (V) in reducing recurrences of atrial fibrillation (AF) after electrical cardioversion (C).


BACKGROUND
The increased vulnerability for AF recurrence is probably due to AF-induced changes in the electrophysiologic properties of the atria. This electrical remodeling seems to be due to intracellular calcium overload.


METHODS
One hundred seven patients with persistent or chronic AF underwent external and/or internal C. All patients received oral propafenone (P) (900 mg/day) three days before and during the entire period of follow-up (three months). In the first group, patients received only the P. In the second group, in adjunct to P, oral V (240 mg/day) was initiated three days before C and continued during the follow-up. Finally, in the third group, oral V was administered three days before and continued only for three days after electrical C.


RESULTS
During the three months of follow-up, 23 patients (23.7%) had AF recurrence. Mantel-Haenszel cumulative chi-square reached a significant level only when comparing AF free survival curves of group I versus group II and group III (chi-square = 5.2 and 4, respectively; p < 0.05). Significantly, 15 (65.2%) AF relapses occurred during the first week after cardioversion with a higher incidence in group I (10/33 patients, 30.3%) than group II (2/34 patients, 5.9%; p = 0.01) and group III (3/30 patients, 10%; p = 0.04).


CONCLUSIONS
Six days of oral V administration centered on the C day, combined with P, significantly reduce the incidence of early recurrences of AF compared with P alone.
A randomized controlled trial of foot orthoses in rheumatoid arthritis.

OBJECTIVE
To investigate the clinical effectiveness of early foot orthosis intervention for painful correctable valgus deformity of the rearfoot in rheumatoid arthritis (RA).


METHODS
Patients with RA were randomized to receive custom manufactured rigid foot orthoses under podiatry supervision (n = 50) or enter a control group (n = 48). The control group received foot orthoses only when prescribed under normal medical care. Foot pain and disability, using the Foot Function Index (FFI), along with disease activity, tolerance, and adverse reactions, were serially measured over 30 mo continuous treatment.


RESULTS
The group assigned foot orthoses demonstrated an immediate clinical improvement, the effect peaking at 12 mo. At 30 mo the FFI total score was reduced by 23.1% from baseline in the intervention group. Area under the curve analysis showed a statistically significant reduction in FFI scores for total score (p = 0.026), foot pain (p = 0.014), and foot disability (p = 0.016) when intervention was compared to control scores. There were no confounding effects from differences between groups for disease activity or pharmacological or other management strategies. Most patients (96%) used their orthoses and most found them comfortable (97%), although minor adverse reactions, such as tender spots, blisters, and callus, were reported in 30% of patients in the early stages of treatment and persisted in 12% for 30 mo.


CONCLUSION
Custom designed foot orthoses used continuously over a 30 mo treatment period resulted in a reduction in foot pain by 19.1%, foot disability by 30.8%, and functional limitation by 13.5%. Clinical effectiveness might be enhanced by their use in the early stages of rearfoot pain and deformity.
Energy bounds for codes and designs in Hamming spaces

We obtain universal bounds on the energy of codes and for designs in Hamming spaces. Our bounds hold for a large class of potential functions, allow unified treatment, and can be viewed as a generalization of the Levenshtein bounds for maximal codes.
Technical Analysis on Financial Forecasting

Financial forecasting is an estimation of future financial outcomes for a company, industry, country using historical internal accounting and sales data. We may predict the future outcome of BSE_SENSEX practically by some soft computing techniques and can also optimized using PSO (Particle Swarm Optimization), EA (Evolutionary Algorithm) or DEA (Differential Evolutionary Algorithm) etc. PSO is a biologically inspired computational search & optimization method developed in 1995 by Dr. Eberhart and Dr. Kennedy based on the social behaviors of fish schooling or birds flocking. PSO is a promising method to train Artificial Neural Network (ANN). It is easy to implement then Genetic Algorithm except few parameters are adjusted. PSO is a random & pattern search technique based on populating of particle. In PSO, the particles are having some position and velocity in the search space. Two terms are used in PSO one is Local Best and another one is Global Best. To optimize problems that are like Irregular, Noisy, Change over time, Static etc. PSO uses a classic optimization method such as Gradient Decent & Quasi-Newton Methods. The observation and review of few related studies in the last few years, focusing on function of PSO, modification of PSO and operation that have implemented using PSO like function optimization, ANN Training & Fuzzy Control etc. Differential Evolution is an efficient EA technique for optimization of numerical problems, financial problems etc. PSO technique is introduced due to the swarming behavior of animals which is the collective behavior of similar size that aggregates together. Keywords— Financial Forecasting, Neural Network, Particle Swarm Optimization, Global Best, Particle Best
The Application of Integrated GPS and Dead Reckoning Positioning in Automotive Intelligent Navigation System

The applications of Global Positioning System (GPS) are increasingly widespread in China. GPS positioning is more and more popular. Especially, the automotive navigation system which relies on GPS and Dead Reckoning technology is developing quickly because of the anticipated huge future market in China. In this paper a practical combined positioning model of GPS/DR is put forward. This model designed for automotive navigation system makes use of a Kalman filter to improve position and map matching veracity by means of filtering the raw GPS and DR signals. In practical examples, the validity of the model is illustrated. Several experiments and their results of integrated GPS/DR positioning in automotive navigation system will show that a Kalman Filter based on integrated GPS/DR position is necessary, feasible and efficient for automotive navigation application. Certainly, this combined positioning model, similar to other models, can not resolve all situation issues. In the paper, the applicable principles of the model are given and the advantages and disadvantages of this model are compared with other positioning models. Finally, suggestions are given for further improving integrated GPS/DR system performance, and the application aspects of integrated GPS/DR technology in the automotive navigation system are summarized.
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet [1], while its categorywise accuracy is only 8% less. We evaluated ESPNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset. Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet [16], ShuffleNet [17], and ENet [20] on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices. Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.
Wideband low-profile circular polarization slot antenna based on metasurface

A wideband low-profile circular polarization (CP) slot antenna is proposed based on the corner-cut square metasurface structure. Both simulations and measurements are performed to demonstrate the CP antenna and good agreements are obtained. The proposed antenna exhibits a measured impedance matching bandwidth of 32% and an AR bandwidth of 20% centered at 5.6 GHz.
Targeted gene expression in dopamine and serotonin neurons of the mouse brain

We used a knock-in strategy to generate two lines of mice expressing Cre recombinase under the transcriptional control of the dopamine transporter promoter (DAT-cre mice) or the serotonin transporter promoter (SERT-cre mice). In DAT-cre mice, immunocytochemical staining of adult brains for the dopamine-synthetic enzyme tyrosine hydroxylase and for Cre recombinase revealed that virtually all dopaminergic neurons in the ventral midbrain expressed Cre. Crossing DAT-cre mice with ROSA26-stop-lacZ or ROSA26-stop-YFP reporter mice revealed a near perfect correlation between staining for tyrosine hydroxylase and beta-galactosidase or YFP. YFP-labeled fluorescent dopaminergic neurons could be readily identified in live slices. Crossing SERT-cre mice with the ROSA26-stop-lacZ or ROSA26-stop-YFP reporter mice similarly revealed a near perfect correlation between staining for serotonin-synthetic enzyme tryptophan hydroxylase and beta-galactosidase or YFP. Additional Cre expression in the thalamus and cortex was observed, reflecting the known pattern of transient SERT expression during early postnatal development. These findings suggest a general strategy of using neurotransmitter transporter promoters to drive selective Cre expression and thus control mutations in specific neurotransmitter systems. Crossed with fluorescent-gene reporters, this strategy tags neurons by neurotransmitter status, providing new tools for electrophysiology and imaging.
Onset of bronchodilation with fluticasone/formoterol combination versus fluticasone/salmeterol in an open-label, randomized study.

INTRODUCTION
The inhaled corticosteroid, fluticasone propionate (fluticasone), and the long-acting beta(2)-agonist, formoterol fumarate (formoterol), have been combined in a single aerosol inhaler (fluticasone/formoterol). In a randomized, open-label study, fluticasone/formoterol showed similar efficacy to fluticasone/salmeterol after 12 weeks of treatment. This post-hoc analysis compared the onset of bronchodilation with the two treatments.


METHODS
Adults with mild-to-moderate-severe persistent asthma were randomized to fluticasone/formoterol (100/10 or 250/10 μg twice daily [b.i.d.]) or fluticasone/salmeterol (100/50 or 250/50 μg b.i.d.) for 12 weeks. The onset of bronchodilation (the first post-dose time point at which the forced expiratory volume in 1 second [FEV(1)] was ≥12% greater than the pre-dose value), responder rates (the proportion of patients achieving bronchodilation), and changes in FEV(1) were assessed at days 0 (baseline) and 84.


RESULTS
Fluticasone/formoterol (n = 101) provided more rapid onset of bronchodilation than fluticasone/salmeterol (n = 101) over the first 120 min post-dose on days 0 (hazard ratio [HR] = 1.47 [95% CI 1.05-2.05]) and 84 (HR = 1.77 [95% CI 1.14-2.73]). The odds of a patient achieving bronchodilation within 5 min of dosing were almost four-times higher with fluticasone/formoterol than with fluticasone/salmeterol on day 0 (odds ratio [OR] = 3.97 [95% CI 1.96-8.03]) and almost 10-times higher on day 84 (OR = 9.58 [95% CI 2.14-42.90]); the odds of achieving bronchodilation within 120 min post-dose were approximately twofold higher with fluticasone/formoterol on both days. The overall percentage increase in least-squares (LS) mean FEV1 during the 120-min post-dose period was significantly greater with fluticasone/formoterol than fluticasone/salmeterol on days 0 (LS mean treatment difference: 4.70% [95% CI 1.57-7.83]; P = 0.003) and 84 (2.79% [95% CI 0.65-4.93]; P = 0.011).


CONCLUSION
These analyses showed that fluticasone/formoterol provided a faster onset of bronchodilation than fluticasone/salmeterol, which was maintained over 12 weeks of treatment. This benefit may facilitate treatment adherence among patients with asthma.
Primary Interventions to Support Breastfeeding.

A.R., a 26-year-old woman, presents for a routine prenatal visit at 28 weeks' gestation. Her medical history is significant for poorly controlled asthma, and her family history is significant for breast cancer diagnosed in her mother. Her physical examination is unremarkable.
Renal failure in Nigerian children: factors limiting access to dialysis

A 5-year clinical and laboratory study of Nigerian children with renal failure (RF) was performed to determine the factors that limited their access to dialysis treatment and what could be done to improve access. There were 48 boys and 33 girls (aged 20 days to 15 years). Of 81 RF patients, 55 were eligible for dialysis; 33 indicated ability to afford dialysis, but only 6 were dialyzed, thus giving a dialysis access rate of 10.90% (6/55). Ability to bear dialysis cost/dialysis accessibility ratio was 5.5:1 (33/6). Factors that limited access to dialysis treatment in our patients included financial restrictions from parents (33%), no parental consent for dialysis (6%), lack or failure of dialysis equipment (45%), shortage of dialysis personnel (6%), reluctance of renal staff to dialyze (6%), and late presentation in hospital (4%). More deaths were recorded among undialyzed than dialyzed patients (P<0.01); similarly, undialyzed patients had more deaths compared with RF patients who required no dialysis (P<0.025). Since most of our patients could not be dialyzed owing to a range of factors, preventive nephrology is advocated to reduce the morbidity and mortality from RF due to preventable diseases.
Asymmetric Conditional Volatility Models: Empirical Estimation and Comparison of Forecasting Accuracy

This paper compares several statistical models for daily stock return volatility in terms of sample fit and out-of-sample forecast ability. The focus is on U.S. and Romanian daily stock return data corresponding to the 2002-2010 time interval. We investigate the presence of leverage effects in empirical time series and estimate different asymmetric GARCH-family models (EGACH, PGARCH and TGARCH) specifying successively a Normal, Student's t and GED error distribution. We find that GARCH family models with normal errors are not capable to capture fully the leptokurtosis in empirical time series, while GED and Student’s t errors provide a better description for the conditional volatility. In addition, we outline some stylized facts about volatility that are not captured by conventional ARCH or GARCH models, but are considered by the asymmetric models and document their presence in empirical time series. Finally, we report that volatility estimates given by the EGARCH model exhibit generally lower forecast errors and are therefore more accurate than the estimates given by the other asymmetric GARCH models.
Ad Hoc Cloud Computing

This paper presents the first complete, integrated and end-to-end solution for ad hoc cloud computing environments. Ad hoc clouds harvest resources from existing sporadically available, non-exclusive (i.e. Primarily used for some other purpose) and unreliable infrastructures. In this paper we discuss the problems ad hoc cloud computing solves and outline our architecture which is based on BOINC.