LLukas22/scidocs huggingface dataset:
A framework for automatic heart sound analysis without segmentation

BACKGROUND
A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.


METHOD
Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors.


RESULT
The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration.


CONCLUSION
The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.
Automatic Fault Tree Derivation from Little-JIL Process Definitions

Defects in safety critical processes can lead to accidents that result in harm to people or damage to property. Therefore, it is important to find ways to detect and remove defects from such processes. Earlier work has shown that Fault Tree Analysis (FTA) [3] can be effective in detecting safety critical process defects. Unfortunately, it is difficult to build a comprehensive set of Fault Trees for a complex process, especially if this process is not completely welldefined. The Little-JIL process definition language has been shown to be effective for defining complex processes clearly and precisely at whatever level of granularity is desired [1]. In this work, we present an algorithm for generating Fault Trees from Little-JIL process definitions. We demonstrate the value of this work by showing how FTA can identify safety defects in the process from which the Fault Trees were automatically derived.
[Initial results of robotic esophagectomy for esophageal cancer].

INTRODUCTION
There is scant experience with robot-assisted esophagectomy in cases of esophageal and gastro-esophageal junction cancer. Our aim is to report our current experience.


PATIENTS AND METHODS
Observational cohort study of the first 32 patients who underwent minimally invasive esophagectomy for esophageal cancer from September 2011 to June 2014. The gastric tube was created laparoscopically. In the thoracic field, a robot-assisted thoracoscopic approach was performed in the prone position with intrathoracic robotic hand-sewn anastomosis. Patient and tumour characteristics, surgical technique, short-term outcomes (morbidity and mortality) and oncological results (radicality and number of removed nodes) were evaluated.


RESULTS
Thirty-two patients, with a mean age of 58 years (34-74) were treated by a totally minimally invasive esophagectomy: robotic laparoscopy and thoracoscopy (11 McKeown and 21 Ivor-Lewis). Twenty-nine received neoadjuvant chemoradiotherapy. There were no conversions to open surgery. Console time was 218minutes (190-285). Blood loss was 170ml (40-255). One patient died from cardiac disease. Nine patients had a major complication (Dindo-Clavien grade II or higher). There was no case of respiratory complication or recurrent laryngeal nerve palsy. Five patients had intrathoracic fistula, 4 radiological and one clinical. Three had chylothorax, 2 cervical fistula and one gastric tube necrosis. The median hospital stay was 12 days (8-50). All the resections were R0 and the median of removed lymph nodes was 16 (2-23).


CONCLUSIONS
Our results suggest that minimally invasive esophagectomy with robot-assisted thoracoscopy is safe and achieves oncological standards.
Internet of Things Strategic Research Roadmap Antoine De Saint-exupery

• • • The meaning of things lies not in the things themselves, but in our attitude towards them.
Antidiabetic effect of probiotic dahi containing Lactobacillus acidophilus and Lactobacillus casei in high fructose fed rats.

OBJECTIVE
We investigated the effect of low-fat (2.5%) dahi containing probiotic Lactobacillus acidophilus and Lactobacillus casei on progression of high fructose-induced type 2 diabetes in rats.


METHODS
Diabetes was induced in male albino Wistar rats by feeding 21% fructose in water. The body weight, food and water intakes, fasting blood glucose, glycosylated hemoglobin, oral glucose tolerance test, plasma insulin, liver glycogen content, and blood lipid profile were recorded. The oxidative status in terms of thiobarbituric acid-reactive substances and reduced glutathione contents in liver and pancreatic tissues were also measured.


RESULTS
Values for blood glucose, glycosylated hemoglobin, glucose intolerance, plasma insulin, liver glycogen, plasma total cholesterol, triacylglycerol, low-density lipoprotein cholesterol, very low-density lipoprotein cholesterol, and blood free fatty acids were increased significantly after 8 wk of high fructose feeding; however, the dahi-supplemented diet restricted the elevation of these parameters in comparison with the high fructose-fed control group. In contrast, high-density lipoprotein cholesterol decreased slightly and was retained in the dahi-fed group. The dahi-fed group also exhibited lower values of thiobarbituric acid-reactive substances and higher values of reduced glutathione in liver and pancreatic tissues compared with the high fructose-fed control group.


CONCLUSION
The probiotic dahi-supplemented diet significantly delayed the onset of glucose intolerance, hyperglycemia, hyperinsulinemia, dyslipidemia, and oxidative stress in high fructose-induced diabetic rats, indicating a lower risk of diabetes and its complications.
Animal-assisted activity and emotional status of patients with Alzheimer's disease in day care.

BACKGROUND
Preliminary studies suggest beneficial effects of animal-assisted activities (AAA) on behavioral and psychological symptoms of dementia (BPSD), but data are inconsistent. This study aimed to assess the effect of AAA with dogs on cognition, BPSD, emotional status and motor activity in severe Alzheimer's disease (AD).


METHODS
Ten patients attending an Alzheimer Day Care Center (ADCC) participated in a repeated measures study, which included: two weeks' pre-intervention, three weeks' control activity with plush dogs (CA), and three weeks' AAA. Cognitive function (Severe Impairment Battery), mood (Cornell Scale for Depression in Dementia; CSDD), BPSD (Neuropsychiatric Inventory; NPI) and agitation (Cohen-Mansfield Agitation Inventory; CMAI) were assessed at baseline and after each period. Observed Emotion Rating Scale (OERS) for emotional status, Agitated Behavior Mapping Instrument (ABMI) and a checklist for motor activity were completed across the study periods, both during intervention sessions and after three hours.


RESULTS
Cognition and NPI were unchanged across the study. Declines in the CMAI and CSDD scores after AAA were not significant, while the NPI anxiety item score decreased in comparison with CA (CA 3.1±2.3, AAA 1.5±2.7, p = 0.04). OERS "sadness" decreased (p = 0.002), while "pleasure" (p = 0.016) and "general alertness" (p = 0.003) increased during AAA compared with CA sessions, and observed sadness remained lower after three hours (p = 0.002). Motor activity increased significantly during AAA.


CONCLUSION
In this sample of severe AD patients in ADCC, AAA was associated with a decrease in anxiety and sadness and an increase in positive emotions and motor activity in comparison with a control activity.
Fingerprint minutiae extraction from skeletonized binary images

Fingerprint comparison is usually based on minutiae matching. The minutiae considered in automatic identification systems are normally ridge bifurcations and terminations. In this paper we present a set of algorithms for the extraction of fingerprint minutiae from skeletonized binary images. The goal of the present work is the extraction of the real 40—60 minutiae of a fingerprint image from the 2000—3000 contained in typical skeletonized and binarized images. Besides classical methodologies for minutiae filtering, a new approach is proposed for bridge cleaning based on ridge positions instead of classical methods based on directional maps. Finally, two novel criteria and related algorithms are introduced for validating the endpoints and bifurcations. Statistical analysis of the results obtained by the proposed approach shows efficient reduction of spurious minutiae. The use of the fingerprint minutiae extraction algorithms has also been considered in a fingerprint identification system in terms of timing and false reject or acceptance rates. The presented minutiae extraction algorithm performs correctly in dirty areas and on the background as well, making computationally expensive segmentation algorithms unnecessary. The results are confirmed by visual inspections of validated minutiae of the NIST sdb 4 reference fingerprint image database. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
A UHF helical resonator filter

The 6-pole UHF helical resonators filter was designed, simulated, fabricated, and tested. The design factors, simulation results, filter performance characteristics are presented in this paper. The coupling of helical resonators was designed using a mode-matching technique. The design procedures are simple, and measured performance is excellent. The simulated and measured results show the validity of the proposed design method.
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

Training classifiers with datasets which suffer of imbalanced class distributions is an important problem in data mining. This issue occurs when the number of examples representing the class of interest is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. We shortly review the many issues in machine learning and applications of this problem, by introducing the characteristics of the imbalanced dataset scenario in classification, presenting the specific metrics for evaluating performance in class imbalanced learning and enumerating the proposed solutions. In particular, we will describe preprocessing, costsensitive learning and ensemble techniques, carrying out an experimental study to contrast these approaches in an intra and inter-family comparison. We will carry out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem. This will help to improve the current models with respect to: the presence of small disjuncts, the lack of density in the training data, the overlapping between classes, the identification of noisy data, the significance of the borderline instances, and the dataset shift between the training and the test distributions. Finally, we introduce several approaches and recommendations to address these problems in conjunction with imbalanced data, and we will show some experimental examples on the behavior of the learning algorithms on data with such intrinsic characteristics. 2013 Elsevier Inc. All rights reserved.
Improving cooperation in peer-to-peer systems using social networks

Rational and selfish nodes in P2P systems usually lack effective incentives to cooperate, contributing to the increase of free-riders, and degrading the system performance. Various attacks such as whitewashing, collusion, and software cracking pose great challenges on distributed reputation management. To tackle these problems, we propose to build a social network on P2P system, and use the strength of social connections to facilitate transactions in P2P system. The' small world' character of social networks makes it feasible for nodes to locate resources and conduct transactions while maintain limited local memory history. Such distributed memory combined by relationship between peers constructs a powerful reputation management network, which could have better performance than shared history system and is more robust under various attacks. Our simulation and analysis show that the social network model can greatly incent cooperation in P2P networks and enormously reduce the memory cost.
An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles for the performance improvement of the traditional convolutional neural network (CNN)-based object detection methods. Although recent object detection methods based on CNN can extract features automatically, these methods still separate the feature extraction and detection stages, resulting in high time consumption and low efficiency. As a significant influencing factor, the acquisition of a large quantity of manually annotated samples for HSR remote sensing imagery objects requires expert experience, which is expensive and unreliable. Despite the progress made in natural image object detection fields, the complex object distribution makes it difficult to directly deal with the HSR remote sensing imagery object detection task. To solve the above problems, a highly efficient and robust integrated geospatial object detection framework based on faster region-based convolutional neural network (Faster R-CNN) is proposed in this paper. The proposed method realizes the integrated procedure by sharing features between the region proposal generation stage and the object detection stage. In addition, a pre-training mechanism is utilized to improve the efficiency of the multi-class geospatial object detection by transfer learning from the natural imagery domain to the HSR remote sensing imagery domain. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset were conducted to evaluate the proposed method.
For whom do the ends justify the means? Social class and utilitarian moral judgment.

Though scholars have speculated for centuries on links between individuals' social class standing and approach to moral reasoning, little systematic research exists on how class and morality are associated. Here, we investigate whether the tendency of upper-class individuals to exhibit reduced empathy makes them more likely to resist intuitionist options in moral dilemmas, instead favoring utilitarian choices that maximize the greatest good for the greatest number. In Study 1, upper-class participants were more likely than lower-class participants to choose the utilitarian option in the footbridge dilemma, which evokes relatively strong moral intuitions, but not in the standard trolley dilemma, which evokes relatively weak moral intuitions. In Study 2, upper-class participants were more likely to take resources from one person to benefit several others in an allocation task, and this association was explained by their lower empathy for the person whose resources were taken. Finally, in Study 3, the association between social class and utilitarian judgment was reduced in a condition in which empathy was induced, but not in a control condition, suggesting that reduced empathy helps account for the utilitarianism of upper-class individuals.
Long-term use of peripherally inserted central venous catheters for cancer chemotherapy in children

Peripherally inserted central venous catheters (PICCs) have been increasingly used in pediatric patients. However, little is known about the incidence and risk of complications when using this device in children with cancer. The purposes of this study are to assess the feasibility of PICCs and to determine the risk factors for PICC-related complications in pediatric patients with various types of malignancies. We attempted to place PICCs in 53 patients with a median age of 5 years ranging from 2 months to 20 years. PICCs were used to administer fluid, parenteral nutrition, anticancer agents, antibiotics, and blood products and also for the through-line blood sampling. The duration of catheterization and the incidence of PICC-related complications requiring removal were retrospectively evaluated in association with the diagnosis, sex, age and body weight of the patients, size, insertion site and tip location of the catheters, type of treatment, and duration of leukopenia. PICCs were successfully placed in 109 of 112 attempts (97.3%) in 53 patients, and they were followed for a total of 11,797 catheter days (median placement, 87 days; range, 3 to 512 days). Fifty five PICCs (50.5%) were removed as a result of PICC-related complications with a rate of 4.66 per 1,000 catheter days. The most common reasons for catheter removal were occlusion (n=18), breakage/leakage (15), and infection (10). More than 70% of such complications occurred more than 30 days after placement. The catheter tip location in the superior vena cava or the right atrium might decrease the risk of complications. Other parameters did not influence the incidence of complications. PICCs were found to provide a reliable access for prolonged intravenous administration and blood sampling in children intensively treated for hematologic and solid malignancies, thus leading to a reduction of physical pain and psychological stress in such patients. However, the long-term placement of PICCs may also be related to an increased risk of complications.
Clear air boundary layer spaced antenna wind measurement with the Multiple Antenna Profiler (MAPR)

Spaced antenna (SA) wind measurement techniques are applied to Multiple Antenna Profiler (MAPR) data to evaluate its performance in clear air conditions. MAPR is a multiple antenna 915 MHz wind profiler developed at the National Center for Atmospheric Research (NCAR) and described in Cohn et al. (1997), designed to make high resolution wind measurements. Previous reported measurements with MAPR were restricted to precipitation because of low signal to noise (SNR) and signal to ground-clutter (SCR) ratios. By using a standard pulse-coding technique and upgrading the profiler control software, increases in average power and SNR were achieved, making routine measurements in clear air possible. Comparison of winds measured by MAPR and by a sonic anemometer on a nearby 300 m tower show correlation coefficients in the range of R2 = 0.75 – 0.80, and an average absolute error of ∼1.4 m s−1. This compares favorably with the agreement typically found in wind profiler comparisons. We also consider the use of the parameter ah, which is related to the value of the cross-correlation function at its zero crossing. This parameter is a data quality indicator and possibly a key component in a ground clutter removal technique.
Increased sagittal plane segmental motion in the lower cervical spine in women with chronic whiplash-associated disorders, grades I-II: a case-control study using a new measurement protocol.

STUDY DESIGN
Case-control study comparing sagittal plane segmental motion in women (n = 34) with chronic whiplash-associated disorders, Grades I-II, with women (n = 35) with chronic insidious onset neck pain and with a normal database of sagittal plane rotational and translational motion.


OBJECTIVE
To reveal whether women with chronic whiplash-associated disorders, Grades I-II, demonstrate evidence of abnormal segmental motions in the cervical spine.


SUMMARY OF BACKGROUND DATA
It is hypothesized that unphysiological spinal motion experienced during an automobile accident may result in a persistent disturbance of segmental motion. It is not known whether patients with chronic whiplash-associated disorders differ from patients with chronic insidious onset neck pain with respect to segmental mobility.


METHODS
Lateral radiographic views were taken in assisted maximal flexion and extension. A new measurement protocol determined rotational and translational motions of segments C3-C4 and C5-C6 with high precision. Segmental motion was compared with normal data as well as among groups.


RESULTS
In the whiplash-associated disorders group, the C3-C4 and C4-C5 segments showed significantly increased rotational motions. Translational motions within each segment revealed a significant deviation from normal at the C3-C4 segment in the whiplash-associated disorders and insidious onset neck pain groups and at the C5-C6 segment in the whiplash-associated disorders group. Significantly more women in the whiplash-associated disorders group (35.3%) had abnormal increased segmental motions compared to the insidious onset neck pain group (8.6%) when both the rotational and the translational parameters were analyzed. When the translational parameter was analyzed separately, no significant difference was found between groups, or 17.6% (whiplash-associated disorders group) and 8.6% (insidious onset neck pain group), respectively.


CONCLUSION
Hypermobility in the lower cervical spine segments in 12 out of 34 patients with chronic whiplash-associated disorders in this study point to injury caused by the accident. This subgroup, identified by the new radiographic protocol, might need a specific therapeutic intervention.
Medical bioremediation: a concept moving toward reality.

Abstract A major driver of aging is catabolic insufficiency, the inability of our bodies to break down certain substances that accumulate slowly throughout the life span. Even though substance buildup is harmless while we are young, by old age the accumulations can reach a toxic threshold and cause disease. This includes some of the most prevalent diseases in old age-atherosclerosis and macular degeneration. Atherosclerosis is associated with the buildup of cholesterol and its oxidized derivatives (particularly 7-ketocholesterol) in the artery wall. Age-related macular degeneration is associated with carotenoid lipofuscin, primarily the pyridinium bisretinoid A2E. Medical bioremediation is the concept of reversing the substance accumulations by using enzymes from foreign species to break down the substances into forms that relieve the disease-related effect. We report on an enzyme discovery project to survey the availability of microorganisms and enzymes with these abilities. We found that such microorganisms and enzymes exist. We identified numerous bacteria having the ability to transform cholesterol and 7-ketocholesterol. Most of these species initiate the breakdown by same reaction mechanism as cholesterol oxidase, and we have used this enzyme directly to reduce the toxicity of 7-ketocholesterol, the major toxic oxysterol, to cultured human cells. We also discovered that soil fungi, plants, and some bacteria possess peroxidase and carotenoid cleavage oxygenase enzymes that effectively destroy with varied degrees of efficiency and selectivity the carotenoid lipofuscin found in macular degeneration.