Insights into a Novel blaKPC-2-Encoding IncP-6 Plasmid Reveal Carbapenem-Resistance Circulation in Several Enterobacteriaceae Species from Wastewater and a Hospital Source in Spain

Untreated wastewater, particularly from hospitals and other healthcare facilities, is considered to be a reservoir for multidrug-resistant bacteria. However, its role in the spread of antibiotic resistances in the human population remains poorly investigated. We used whole genome sequencing (WGS) to analyze 25 KPC-2-producing Enterobacteriaceae isolates from sewage water collected during a 3-year period and three clinical Citrobacter freundii isolates from a tertiary hospital in the same collection area in Spain. We detected a common, recently described, IncP-6 plasmid carrying the gene blaKPC-2 in 21 isolates from both sources. The plasmid was present in diverse environmental bacterial species of opportunistic pathogens such as C. freundii, Enterobacter cloacae, Klebsiella oxytoca, and Raoultella ornithinolytica. The 40,186 bp IncP-6 plasmid encoded 52 coding sequences (CDS) and was composed of three uniquely combined regions that were derived from other plasmids recently reported in different countries of South America. The region harboring the carbapenem resistance gene (14 kb) contained a Tn3 transposon disrupted by an ISApu-flanked element and the core sequence composed by ISKpn6 / blaKPC-2 / ?blaTEM-1 / ISKpn27. We document here the presence of a novel promiscuous blaKPC-2 plasmid circulating in environmental bacteria in wastewater and human populations

Neural networks for tea leaf classification

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers

Difference in pulse arrival time at forehead and at finger as a surrogate of pulse transit time

Pulse transit time (PTT) difference (PTTD) to the forehead and finger dynamics are compared to pulse arrival time towards the finger (PATF) dynamics during a tilt table test. Two frequency bands, where different physiological information is expected, are analyzed: low frequency (LF) influenced by both sympathetic and parasympathetic activity, and high frequency (HF) influenced by parasympathetic activity. As PATF, PTTD is influenced by PTT, but in contrast to PATF, PTTD is not influenced by the pre-ejection period (PEP). This is advantaging in certain applications such as arterial stiffness assessment or blood pressure estimation. Results showed higher correlation between PTTD and PATF during rest stages than during tilt stage, when the PEP dynamics have stronger effect in PATF dynamics. This suggests that PTTD variability can potentially be a surrogate of PTT variability that is not influenced by PEP, which is advantaging in the previously mentioned applications. However, further studies must be elaborated in order to evaluate the potential of PTTD in such specific applications

Public health expenditure in Spain: is there partisan behaviour?

This study examines the disparities in the evolution of Spanish regional public health expenditures from 1991 to 2010. We find that the recent development of the Spanish regional public health system have led the regions to reflect a very heterogeneous pattern of behaviour. These differences depend on economic and demographic factors, but also on the ideology of the regional governments. The longer a region is governed by a right-wing party, the lower the public health expenditure. This result suggests the presence of clear partisan behaviour in the Spanish public health system

Public health expenditure in Spain: is there partisan behaviour?

This study examines the disparities in the evolution of Spanish regional public health expenditures from 1991 to 2010. We find that the recent development of the Spanish regional public health system have led the regions to reflect a very heterogeneous pattern of behaviour. These differences depend on economic and demographic factors, but also on the ideology of the regional governments. The longer a region is governed by a right-wing party, the lower the public health expenditure. This result suggests the presence of clear partisan behaviour in the Spanish public health system

Natural Language Explanation Model for Decision Trees

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown

Analysis of the Influence of Power System Diversion on the Optimal Supply Strategy of Renewable Power Plants

This paper presents an analysis of the influence of power system deviation prediction on the bidding strategy of renewable generation units in spot markets. The optimal bid that a renewable generator makes is subject to the best possible forecast at the time it submits the energy bid to the spot market, which is usually between 12–36 hours in advance of the time of delivery. With these lead times, renewable generators have to assume a significant volume risk in relation to the difference that may occur between the energy finally delivered and the energy previously committed for their participation in the market, since deviations from the committed energy will be valued at the deviations price. In this sense, the analysis carried out in this work shows that the prices of deviations are highly influenced by the energy needs to be raised or lowered by the system at the time of delivery. In other words, in the event that the deviation of the renewable generator goes against the system, the generator will generally have a higher penalty, having to assume the cost of the energy deviation at a price higher than the spot market price. On the other hand, if the plant’s deviation benefits the system, the penalty will be significantly lower (and sometimes even zero). The proposed analysis methodology develops the formulation of the expected benefit of the plant obtained through its participation in the spot market and subsequent settlement of the deviations. This formulation includes the modeling of the effect of the system deviation on the plant’s profits, which allows to satisfactorily identify the influence of the prediction of this variable on the optimal offer strategy. This methodology has been tested for the case of a wind farm operating in the Spanish market. For this purpose, real data of forecasts and final production of the wind farm have been used, as well as real data of the spot market, prices of the balancing service and real deviation of the system, which has allowed to verify in totally realistic conditions the importance of the prediction of the direction of deviation of the system in the optimal bidding. In this way, it will be possible to establish new optimal bidding strategies that focus efforts on advanced prediction techniques for this variable, which will result in greater benefits for wind power plants for their participation in the energy markets.

Electrocardiogram Derived Respiration for Tracking Changes in Tidal Volume from a Wearable Armband

A pilot study on tracking changes in tidal volume (TV) using ECG signals acquired by a wearable armband is presented. The wearable armband provides three ECG channels by using three pairs of dry electrodes, resulting in a device that is convenient for long-term daily monitoring. An additional ECG channel was derived by computing the first principal component of the three original channels (by means of principal component analysis). Armband and spirometer signals were simultaneously recorded from five healthy subjects who were instructed to breathe with varying TV. Three electrocardiogram derived respiration (EDR) methods based on QRS complex morphology were studied: the QRS slopes range (SR), the R-wave angle (), and the R-S amplitude (RS). The peak-to-peak amplitudes of these EDR signals were estimated as surrogates for TV, and their correlations with the reference TV (estimated from the spirometer signal) were computed. In addition, a multiple linear regression model was calculated for each subject, using the peak-to-peak amplitudes from the three EDR methods from the four ECG channels. Obtained correlations between TV and EDR peak-to-peak amplitude ranged from 0.0448 up to 0.8491. For every subject, a moderate correlation (>0.5) was obtained for at least one EDR method. Furthermore, the correlations obtained for the subject-specific multiple linear regression model ranged from 0.8234 up to 0.9154, and the goodness of fit was 0.73±0.07 (median ± standard deviation). These results suggest that the peak-to-peak amplitudes of the EDR methods are linearly related to the TV. opening the possibility of estimating TV directly from an armband ECG device.Clinical Relevance - This opens the door to possible continuous monitoring of TV from the armband by using EDR

A robust ECG denoising technique using variable frequency complex demodulation

Background and Objective Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. Methods This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Results Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. Conclusions The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation

Using the redundant convolutional encoder–decoder to denoise QRS complexes in ECG signals recorded with an armband wearable device

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices