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Multilabel fluorescence imaging is essential for the visualization of complex systems, though a major challenge is the limited width of the usable spectral window. Here, we present a new method, exNEEMO, enabling per-pixel unmixing of spectrally-overlapping fluorophores based on their light-induced dynamics, in a way that is compatible with a very broad range of timescales over which these dynamics may occur. Our approach makes use of non-negative least squares projection to decompose the observed emission given the reference responses of the individual fluorophores that are present in the sample. We apply the approach to the simultaneous of four green photochromic fluorescent proteins, allowing us to visualize the distribution of each fluorophore at the full resolution of the imaging. Our methodology opens new possibilities for the multiplexing of fluorescence imaging.

Background Few studies have evaluated the long-term impact on health-related quality of life (HRQoL) in patients who have been hospitalized for COVID-19 pneumonia. Specific follow-up should be carried out to detect and treat possible pulmonary abnormalities, and the worsening of HRQoL should be estimated to target necessary resources for care of these patients after acute phase. The objective was to know the impact on HRQoL of patients who have been admitted for COVID-19 pneumonia, and to evaluate the clinical-radiological and functional changes of patients who have overcome COVID19 pneumonia at 3 and 10 months of follow-up.Methods Prospective observational study of patients who required hospitalization for COVID-19 pneumonia between April and December 2020. All patients filled out the EuroQol five-dimension (EQ-5D) questionnaire with the EuroQol Visual Analogue Scale (E-VAS) for self-assessment of health status. Respiratory function tests and chest X-ray were carried out at 3 and 10 months of follow-up.Results 61 patients were included in the study. The need for ventilatory support was associated with anxiety / depression on the EQ-5D scale, as well as patients admitted to the intensive care unit (ICU). The mean EQ-5D and E-VAS index scores decreased with hospitalization time, the number of days spent in intermediate respiratory care unit (IRCU) and the level of dyspnoea at the beginning of the hospitalization period. Pulmonary sequelae were observed in 25 patients (41%) at 3 months and 17 (27.9%) at 10 months. Patients improve their forced vital capacity (FVC) by 196 ml (p = 0.001) at 10 months as well as 9% in diffusing capacity of lung for carbon monoxide (DLCO) (p = 0.001) at 10 months. DLCO was found to be correlated to lymphopenia and time spent in IRCU. Low FVC values were detected 10 months after discharge for subjects exhibiting high levels of dyspnoea at 3 months after discharge.Conclusions Hospitalization for COVID-19 pneumonia affects the HRQoL of patients, with greater anxiety / depression in those who were more serious affected and are younger. There is a significant percentage of patients who present fibrotic abnormalities and lung function impairment at the first and second follow-ups after discharge.

In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows or/and columns of a two-way data matrix. These rows/columns are called essential because they are indispensable to reproduce the full data matrix in a convex linear way. The selection of EI is driven by the properties of linear spectral mixtures. In recent works, it has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on principal component analysis (PCA) to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach, denominated EIFD, which consists in extracting EI in the Fourier domain. Spectral information is transformed into Fourier coefficients and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of the first few harmonics. The proposed EIFD approach is broadly applicable and very efficient in terms of spectral compression and denoising. In particular, it has the crucial advantage that the coordinate system of a phasor plot does not depend on the data themselves. This enables dynamic (i.e., performed during data acquisition) and chemometric-driven (i.e., based on spectral relevance for unmixing) selective sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating the possibility of a 50-fold acceleration of Raman acquisition.

Time-resolved fluorescence spectroscopy (TRFS), i.e., measurement of fluorescence decay curves for different excitation and/or emission wavelengths, provides specific and sensitive local information on molecules and on their environment. However, TRFS relies on multiexponential data fitting to derive fluorescence lifetimes from the measured decay curves and the time resolution of the technique is limited by the instrumental response function (IRF). We propose here a multivariate curve resolution (MCR) approach based on data slicing to perform tailored and fit-free analysis of multiexponential fluorescence decay curves. MCR slicing, taking as a basic framework the multivariate curve resolution-alternating least-squares (MCR-ALS) soft-modeling algorithm, relies on a hybrid bilinear/trilinear data decomposition. A key feature of the method is that it enables the recovery of individual components characterized by decay profiles that are only partially describable by monoexponential functions. For TRFS data, not only pure multiexponential tail information but also shorter time delay information can be decomposed, where the signal deviates from the ideal exponential behavior due to the limited time resolution. The accuracy of the proposed approach is validated by analyzing mixtures of three commercial dyes and characterizing the mixture composition, lifetimes, and associated contributions, even in situations where only ternary mixture samples are available. MCR slicing is also applied to the analysis of TRFS data obtained on a photoswitchable fluorescent protein (rsEGFP2). Three fluorescence lifetimes are extracted, along with the profile of the IRF, highlighting that decomposition of complex systems, for which individual isomers are characterized by different exponential decays, can also be achieved.

Alternating least squares, within the multivariate curve resolution framework has seen a lot of practical applications and shows its distinction with its relatively simple and flexible implementation. However, the limitations of least squares should be considered carefully when deviating from the standard assumed data structure. Within this work we highlight the effects of noise in the presence of minor components, and we propose a novel weighting scheme within the weighted multivariate curve-resolution-alternating least squares framework, to resolve it. Two simulated and one Raman imaging case is investigated, by comparing the novel methodology against standard multivariate curve resolution-alternating least squares and essential spectral pixel selection. A trade-off is observed between current methods, while the novel weighting scheme demonstrates a balance, where the benefits of the previous two methods are retained.

Least squares-based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well-known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable have been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR-ALS from the regression point of view. In particular, the following critical aspects will be highlighted: i) in the presence of minor components, if the number of analysed data points is too large, the leverage of those that may be essential for a MCR-ALS resolution might become too low for guaranteeing its correctness and ii) in order to overcome this black hole effect and improve the accuracy of the MCR-ALS output, data pruning can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.

Motivation: ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the ‘holistic’ testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers). Results: We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart., Agencia Andaluza del Conocimiento, Regional Government of Andalucia , in Spain, European Commission B-TIC-136-UGR20, State Research Agency (AEI) of Spain, European Social Fund (ESF) RYC2020-030536-I, AEI PID2020-118139RB-I00

Recently, our group introduced the use of silver nanowires (AgNWs) as novel non-invasive endoscopic probes for detecting intracellular Raman signals. This method, although innovative and promising, relies exclusively on the plasmonic waveguiding effect for signal enhancement. It, therefore, requires sophisticated operational tools and protocols, drastically limiting its applicability. Herein, an advanced strategy is offered to significantly enhance the performance of these endoscopic probes, making this approach widely accessible and versatile for cellular studies. By uniformly forming gold structures on the smooth AgNW surface via a galvanic replacement reaction, the density of the light coupling points along the whole probe surface is drastically increased, enabling high surface-enhanced Raman scattering (SERS) efficiency upon solely focusing the excitation light on the gold-etched AgNW. The applicability of these gold-etched AgNW probes for molecular sensing in cells is demonstrated by detecting site-specific and high-resolved SERS spectra of cell compartment-labeling dyes, namely, 4',6-diamidino-2-phenylindole in the nucleus and 3,3'-dioctadecyloxacarbocyanine on the membrane. The remarkable spectral sensitivity achieved provides essential structural information of the analytes, indicating the overall potential of the proposed approach for cellular studies of drug interactions with biomolecular items.

Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.

Least squares-based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well-known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable have been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR-ALS from the regression point of view. In particular, the following critical aspects will be highlighted: in certain situations, i) if the number of analysed data points is too large, the leverage of those that may be essential for a MCR-ALS resolution might become too low for guaranteeing its correctness and ii) in order to overcome this black hole effect and improve the accuracy of the MCR-ALS output, data pruning - i.e., the reduction of the amount of observations of the investigated datasets - can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.

Advancing age of the global population is one of the main reasons for the uprising trend in atrial fibrillation (AF) prevalence worldwide leading to a proper "AF epidemic". Strictly related to the increasing prevalence of AF in the elderly is the relevant burden of cardiac end extra-cardiac comorbidities that these patients show. Patients with AF are frequently asymptomatic (i.e., asymptomatic or silent AF) and thus the arrhythmia is generally underdiagnosed. Detainment of proper treatment in elderly and comorbid patients may potentially result in significant morbidity and mortality. Therefore, in recent years. several screening strategies (systematic vs opportunistic screening) for asymptomatic AF have been developed and early diagnosis of AF is an important treatment goal that can improve prognosis. This review will focus on the prevalence of asymptomatic AF in the elderly, frequently associated comorbidities, screening strategies, and implications for a correct AF diagnosis.

Testosterone is the most important hormone in male health. Aging is characterized by testosterone deficiency due to decreasing testosterone levels associated with low testicular production, genetic factors, adiposity, and illness. Low testosterone levels in men are associated with sexual dysfunction (low sexual desire, erectile dysfunction), reduced skeletal muscle mass and strength, decreased bone mineral density, increased cardiovascular risk and alterations of the glycometabolic profile. Testosterone replacement therapy (TRT) shows several therapeutic effects while maintaining a good safety profile in hypogonadal men. TRT restores normal levels of serum testosterone in men, increasing libido and energy level and producing beneficial effects on bone density, strength and muscle as well as yielding cardioprotective effects. Nevertheless, TRT could be contraindicated in men with untreated prostate cancer, although poor findings are reported in the literature. In addition, different potential side effects, such as polycythemia, cardiac events and obstructive sleep apnea, should be monitored. The aim of our review is to provide an updated background regarding the pros and cons of TRT, evaluating its role and its clinical applicability in different domains.

The estimation of the postmortem interval (PMI) from skeletal remains represents a challenging task in forensic science. PMI is often influenced by extrinsic factors (humidity, dryness, scavengers, etc.) and intrinsic factors (age, sex, pathology, way of life, medical treatments, etc.). Raman spectroscopy combined with multivariate data analysis represents a promising tool for forensic anthropologists. Despite all the advantages of the technique, Raman spectra of skeletal remains are influenced by these extrinsic and intrinsic factors, which impairs precision and reproducibility. Both parameters have to reach a high level of confidence when such spectroscopy is used as a way to predict PMI. As a consequence, advanced multivariate data analysis is necessary to quantify the effect of all factors to improve the estimation of the PMI. The objective of this work is to evaluate the effect of intrinsic and extrinsic factors on the Raman spectra of skeletal remains. We designed a protocol close to a real-world scenario. We used ANOVA-simultaneous component analysis (ASCA) to unmix and quantify the effect of 1 intrinsic (source body) and 1 extrinsic (burial time) factors on the Raman spectra. In our model, the burial time was found to generate the highest variability after the source body. ASCA showed that the variability due to the burial time has 2 mixed contributions. Seasonal variations are the first contribution. The second contribution is attributed to diagenesis. A decrease in the mineral bands and an increase in the organic bands are observed. The source body was also found to contribute to the variability in Raman spectra. ASCA showed that the source body induces variability related to the composition of bones. This quantification cannot be assessed by basic chemometrics methods such as PCA. The results of this study highlighted the need to use an advanced chemometric data analysis tool (like ASCA) combined with Raman spectroscopy to estimate the postmortem interval.

A novel fast and automatic methodology for the hierarchical classification and similarity matching of mid-infrared spectra of paint samples based on the principles of Soft Independent Modelling of Class Analogy (SIMCA) and on the definition and properties of the Mahalanobis distance is here proposed. This approach was tested in a so-called market study (i.e., targeting products largely accessible to the general public and conceived for a considerably wide range of usages) conducted across the surroundings of the city of Lille, in France, and has permitted not only to successfully achieve the chemical characterisation of most of the analysed samples but also to discover specific commonality patterns among specimens sharing the same chemical features.

Respiratory Syncytial Virus (RSV) is the main cause of lower respiratory tract infections (LRTIs) in newborns in the first two years of life. RSV disease has a traditional seasonal trend, with an onset and offset, duration and peak. Prematurity, male gender, bronchopulmonary dysplasia (BPD), critical congenital cardiovascular disorders (CCHD), neuromuscular diseases, congenital and inherited airways anatomical anomalies are the main risk factors for increased severity of this infection. RSV infection is associated with negative long-term respiratory outcomes, with excess of morbidity, resulting in reduced quality of life of the infected children and representing a burden for the healthcare costs and resources. Despite all the efforts, prevention remains, to date, the most effective strategy to reduce RSV-related morbidity. Among the current prevention strategies, strict hygiene, breastfeeding and passive immunization with the monoclonal antibody Palivizumab are the cornerstone. In the next future, it is likely that new possibilities of prevention will add, including use of more potent and longer-acting monoclonal antibodies, implementation of maternal vaccination in pregnancy, and active immunization in children. The purpose of this review is to provide an overview of the main current and future prevention strategies against RSV.

After death, diagenesis takes place. Numerous processes occur concomitantly, which makes it difficult to identify the diagenetic processes. The diagenetic processes refer to all processes (chemical or physical) that modify the skeletal remains. These processes are highly variable depending on the environmental factors (weather, temperature, age, sex, etc.), especially in the early stages. Numerous studies have evaluated bone diagenetic processes over long timescales (~millions of years), but fewer have been done over short timescales (between days and thousands of years). The objective of the study is to assess the early stages of diagenetic processes by Raman microspectroscopy over 12 months. The mineral and organic matrix modifications are monitored through physicochemical parameters. Ribs from six humans were buried in soil. The modifications of bone composition were followed by Raman spectroscopy each month. The decrease in the mineral/organic ratio and carbonate type-B content and the increase in crystallinity reveal that minerals undergo dissolution–recrystallization. The decrease in collagen cross-linking indicates that collagen hydrolysis induces the fragmentation of collagen fibres over 12 months.

[EN] The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers¿ performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful., The authors acknowledge the support provided by the Spanish Ministry of Science and Innovation (PID2020-119262RB-I00), the Generalitat Valenciana (AICO/2021/111), the UPV Research and Development Support Programme PAID-01-17, and the European Social Fund (ACIF/2018/165).

The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

Interactions between epigenetic readers and histone modifications play a pivotal role in gene expression regulation and aberrations can enact etiopathogenic roles in both developmental and acquired disorders like cancer. Typically, epigenetic interactions are studied by mass spectrometry or chromatin immunoprecipitation sequencing. However, in these methods, spatial information is completely lost. Here, we devise an expansion microscopy based method, termed Expansion Microscopy for Epigenetics or ExEpi, to preserve spatial information and improve resolution. We calculated relative co-localization ratios for two epigenetic readers, lens epithelium derived growth factor (LEDGF) and bromodomain containing protein 4 (BRD4), with marks for heterochromatin (H3K9me3 and H3K27me3) and euchromatin (H3K36me2, H3K36me3 and H3K9/14ac). ExEpi confirmed their preferred epigenetic interactions, showing co-localization for LEDGF with H3K36me3/me2 and for BRD4 with H3K9/14ac. Moreover addition of JQ1, a known BET-inhibitor, abolished BRD4 interaction with H3K9/14ac with an IC50 of 137 nM, indicating ExEpi could serve as a platform for epigenetic drug discovery. Since ExEpi retains spatial information, the nuclear localization of marks and readers was determined, which is one of the main advantages of ExEpi. The heterochromatin mark, H3K9me3, is located in the nuclear rim whereas LEDGF co-localization with H3K36me3 and BRD4 co-localization with H3K9/14ac occur further inside the nucleus. ispartof: Nucleic Acids Research vol:50 issue:17 ispartof: location:England status: published

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time ton, off-time toff, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest ton and toff), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of toff can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of ton and toff have been found to be highly dependent from each other and from the measurement duration.

Nowadays, it is clear that there is an increasing importance in spectroscopic imaging in all fields of science. Obviously, one bulk analysis can no longer be satisfactory, as the interest focuses more on the chemical nature and the location of the compounds present within a given complex matrix. This is, evidently, due to the fact that for a more comprehensive exploration of complex samples, one single acquired hyperspectral data cube can provide both spectral and spatial information simultaneously. Although many techniques were proposed by the chemometric community in explorations of these specific datasets, unfortunately, they are almost always focusing on spectral information, even if chemical images were ultimately observed. In other words, spatial information is not well exploited, and therefore lost during the actual chemometric calculation phase. The goal of this short communication is to present a very simple and fast spectral/spatial fusion approach based on 2-D stationary wavelet transform (SWT 2-D) which is able to improve the obtainable information, compared with a classical data analysis, in which the spatial domain would not be considered nor used.

Background Coronary artery disease (CAD) has been recognized as a serious and potentially life‐threatening complication of Hepatitis C Virus (HCV) infection. High on‐treatment platelet reactivity has been associated with high risk of ischemic events in patients with CAD, but data regarding the association with HCV infection are still lacking. This post hoc analysis aims to assess high on‐treatment platelet reactivity, severity of CAD, and long‐term outcomes of patients with acute coronary syndrome (ACS) who were infected with HCV. Methods and Results Patients with ACS who were infected with HCV (n=47) were matched to patients with ACS and without HCV (n=137) for age, sex, diabetes mellitus, hypertension, and renal function. HCV‐infected patients with ACS had higher levels of platelet reactivity (ADP 10 –light transmittance aggregometry, 56±18% versus 44±22% [ P =0.002]; arachidonic acid–light transmittance aggregometry, 25±21% versus 16±15% [ P =0.011]) and higher rates of high on‐treatment platelet reactivity on clopidogrel and aspirin compared with patients without HCV. Moreover, HCV‐infected patients with ACS had higher rates of multivessel disease (53% versus 30%; P =0.004) and 3‐vessel disease (32% versus 7%; P P =0.005) in HCV‐ and non–HCV‐infected patients with ACS, respectively. In addition, thrombolysis In Myocardial Infarction (TIMI) major bleeding rates were higher in HCV‐infected patients (11% versus 3%; P =0.043) compared with noninfected patients. Multivariable analysis demonstrated that HCV infection was an independent predictor of high on‐treatment platelet reactivity, severity of CAD, and long‐term outcome. Conclusions In this hypothesis‐generating study, patients with ACS and HCV infection showed increased on‐treatment platelet reactivity, more severe CAD, and worse prognosis compared with patients without HCV.

This work aims at the evaluation of FT-NIR and FT-IR spectroscopy as rapid, easy, and cost-effective tools for the classification of egg white powder (EWP) based on its technological properties. Up to 100 commercial spray-dried EWP samples with known gelling and foaming properties were used to acquire FT-NIR and FT-IR spectra. An appropriate data-splitting algorithm (Duplex) was applied in order to create, for each dataset, a calibration set and a representative validation test set for prediction. Different spectral pre-treatments and their combinations were investigated for the calculation of Partial Least Squares–Discriminant Analysis models in order to classify samples according to gel strength, foam height, and foam instability. A variable selection strategy based on the so-called Variable Importance in Projection scores was also evaluated. Both FT-NIR and FT-IR spectroscopy showed good potential in discriminating EWP samples with different technological properties. Correct classification percentages in prediction ranging from 59% to 89% were obtained with the best models calculated with selected wavenumbers. These results show a promising industrial perspective, demonstrating the possibility of developing cheap and fast instruments spanning a limited spectral range, which can be implemented on the production lines for EWP sorting and quality control.

[EN] This article explores the potential of Kernel-Partial Least Squares (K-PLS) regression for the analysis of data proceeding from mixture designs of experiments. Gower's idea of pseudo-sample trajectories is exploited for interpretation purposes. The results show that, when the datasets under study are affected by severe nonlinearities and comprise few observations, the proposed approach can represent a feasible lternative to classical methodologies (i.e. Scheffe polynomial fitting by means of Ordinary Least Squares - OLS - and Cox polynomial fitting by means of Partial Least Squares - PLS). Furthermore, a way of recovering the parameters of a Scheffe model (provided that it holds and has the same complexity as the K-PLS one) from the trend of the aforementioned pseudo-sample trajectories is illustrated via a simulated case-study., This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R and Shell Global Solutions International B.V. (Amsterdam, The Netherlands).

Background: Multipoint left ventricular (LV) pacing (MultiPoint™ Pacing [MPP], Abbott, Sylmar, CA, USA) improves the response rate to cardiac resynchronization therapy (CRT). We evaluated the feasibility of noninvasive radial artery tonometry (RAT) to characterize arterial pressure morphology changes (pre-ejection period [PEP] and ejection duration [ED]) between conventional CRT and MPP pacing interventions. Methods: Patients with a MPP-enabled CRT device (Quadra Assura MP™, Abbott) underwent noninvasive RAT assessment (SphygmoCor CVMS, AtCor Medical Inc., Itasca, IL, USA) at 3–6 months after implantation. A pacing protocol was performed in a randomized order including one optimized conventional biventricular CRT (CONV) configuration using the distal electrode and five MPP configurations. The PEP, ED, and PEP/ED ratio were determined for each intervention from the RAT pressure waveform and electrocardiogram. Results: Pressure waveforms were successfully recorded in 19 patients (89% male, QRS 147 ± 16ms, 63% ischemic). In 17/19 (89%) patients, at least one MPP intervention resulted in improved PEP, ED, and PEP/ED compared to CONV. The MPP intervention with greatest separation of LV cathodes and minimum intra-LV delay significantly improved PEP (mean PEP –15 ± 33%vs –8 ± 32% [CONV], P=0.04) and ED (mean ED +8 ± 8% [MPP] vs +4 ± 7% [CONV], P=0.02), and PEP/ED (–0.07 ± 0.14 [MPP] vs –0.04 ± 0.13 [CONV], P=0.02) compared with CONV. Conclusions: Noninvasive RAT efficiently characterizes changes in PEP and ED between CONV and MPP interventions. MPP configurations using the widest separation among LV cathodes and minimum intra-LV delay may significantly improve RAT-derived parameters as compared to conventional CRT.

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group.The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them. (c) 2020 Elsevier B.V. All rights reserved.

Single-molecule DNA mapping has the potential to serve as a powerful complement to high-throughput sequencing in metagenomic analysis. Offering longer read lengths and forgoing the need for complex library preparation and amplification, mapping stands to provide an unbiased view into the composition of complex viromes and/or microbiomes. To fully enable mapping-based metagenomics, sensitivity and specificity of DNA map analysis and identification need to be improved. Using detailed simulations and experimental data, we first demonstrate how fluorescence imaging of surface stretched, sequence specifically labeled DNA fragments can yield highly sensitive identification of targets. Second, a new analysis technique is introduced to increase specificity of the analysis, allowing even closely related species to be resolved. Third, we show how an increase in resolution improves sensitivity. Finally, we demonstrate that these methods are capable of identifying species with long genomes such as bacteria with high sensitivity. ispartof: NAR GENOMICS AND BIOINFORMATICS vol:2 issue:1 ispartof: location:England status: published

Aims Continuous rhythm monitoring is valuable for adequate atrial fibrillation (AF) management in the clinical setting. Subcutaneous leadless implantable cardiac monitors (ICMs) yield an improved AF detection, overcoming the intrinsic limitations of the currently available external recording systems, thus resulting in a more accurate patient treatment. The study purpose was to assess the detection performance of a novel three-vector ICM device equipped with a dedicated AF algorithm. Methods and results Sixty-six patients (86.4% males; mean age 60.4 ± 9.4 years) at risk to present AF episodes, having undergone the novel ICM implant (BioMonitor, Biotronik SE&Co. KG, Berlin, Germany), were enrolled. External 48-h ECG Holter was performed 4 weeks after the device implantation. The automatic ICM AF classification was compared with the manual Holter arrhythmia recordings. Of the overall study population, 63/66 (95.5%) had analysable Holter data, 39/63 (62%) showed at least one true AF episode. All these patients had at least one AF episode stored in the ICM. On Holter monitoring, 24/63 (38%) patients did not show AF episodes, in 16 of them (16/24, 67%), the ICM confirmed the absence of AF. The AF detection sensitivity and positive predictive value for episodes' analysis were 95.4 and 76.3%, respectively. Conclusion Continuous monitoring using this novel device, equipped with a dedicated detection algorithm, yields an accurate and reliable detection of AF episodes. The ICM is a promising tool for tailoring individual AF patient management. Further long-term prospective studies are necessary to confirm these encouraging results.

We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that approximate the convex hull of a data cloud and correspond to the most linearly dissimilar observations. Identifying archetypes is a way to select essential samples (ESs) and essential variables (EVs) of a data matrix before MCR decomposition. Working with ESs and EVs, we then identify three main implications. The first is data reduction, which brings simplicity and computational speed. The second is prioritization, with the ESs and EVs profiles being the most dominant features to solve the MCR problem. The third is interpretability: the reduced data sets provide more direct insights and better understanding of final MCR models. Overall, the selection of ESs and EVs offers new opportunities that are worth being explored.

An approach exploiting the principles of Receiver Operating Characteristic (ROC) curves for the simultaneous optimization of both the complexity and the decision threshold in Soft Independent Modeling of Class Analogy (SIMCA) classification models is here proposed. The outcomes resulting from the analysis of two simulated and four real case-studies highlight that, in the presence of strong overlap among various categories of samples, the implemented method can lead to better classification efficiency in external validation, compared to fixing such a threshold a priori. This guarantees a higher robustness toward class dispersion. On the other hand, in cases of clearer and more definite separation among the different groups of observations, their classification performance is equally satisfactory for test samples. ispartof: ANALYTICAL CHEMISTRY vol:90 issue:18 pages:10738-10747 ispartof: location:United States status: published

Background Cardiac resynchronization therapy (CRT) with multipoint left ventricular (LV) pacing (MultiPoint™ Pacing [MPP], St. Jude Medical) improves acute LV function and LV reverse remodeling at 3 months. Objective The purpose of this study was to test the hypothesis that MPP can also improve LV function at 12 months. Methods Consecutive patients receiving a CRT implant (Unify Quadra MP™ or Quadra Assura MP™ CRT-D and Quartet™ LV lead, St. Jude Medical) were randomized to receive pressure–volume (PV) loop optimized biventricular pacing with either conventional cardiac resynchronization therapy (CONV) or MPP. CRT response was defined by a reduction in end-systolic volume (ESV) ≥15% relative to BASELINE as determined by a blinded observer and alive status. Results Forty-four patients (New York Heart Association class III, ejection fraction [EF] 29% ± 6%, QRS 152 ± 17 ms) were enrolled and randomized to either CONV (N = 22) or MPP (N = 22). During the observation period, 2 patients died of noncardiac causes and 2 patients were lost to follow-up. After 12 months, 12 of 21 patients (57%) in the CONV group and 16 of 21 patients (76%) in the MPP group were classified as CRT responders ( P = .33). ESV reduction and EF increase relative to BASELINE were significantly greater with MPP than with CONV (ESV: median –25%, interquartile range [IQR] [–39% to –20%] vs median –18%, IQR [–25% to –2%], P = .03; EF: median +15%, IQR [8% to 20%] vs median +5%, IQR [–1% to 8%], P Conclusion Sustaining the trend observed 3 months postimplant, PV loop-guided multipoint LV pacing resulted in greater LV reverse remodeling and increased LV function at 12 months compared to PV loop-guided conventional CRT.

The aim of this article is to understand the natural history of WPW syndrome to prevent sudden death is important to clinicians in establishing accurate prognosis and appropriate treatment. We report our experience on untreated WPW patients purely looking at the natural history of the disease. In a 15-year period (1995-2010), among 11 237 WPW patients referred to our Arrhythmology Department, a total of 1847 selected patients (820 symptomatic) underwent electrophysiological testing without ablation and were followed for a median (25th-75th) follow-up of 8 (5-8) years. During follow-up, malignant arrhythmias (MA) occurred in 16 patients (0.9%) of whom 14 (1.4%) were initially asymptomatic and two (0.2%) symptomatic (P = 0.01). Potentially MA developed in 143 patients (7.7%) without difference between asymptomatic and symptomatic population (P = 0.663). Benign recurrences developed in 295 patients (16%) while ventricular pre-excitation disappeared in 356 patients (19.3%) of whom 155 were initially asymptomatic. All patients were successfully ablated after arrhythmia occurrence. Patients with MA had similar accessory pathways antegrade refractory periods (AP-AERP) (P = 0.064) and more frequently inducible atrioventricular reciprocating tachycardia triggering atrial fibrillation (AVRT-AF) than those with potentially MA (P < 0.001). Symptoms did not predict MA, which were predicted by AP-AERP (HR 0.912, 95% CI 0.887-0.939, P < 0.001) and AVRT-AF (HR 8.306, 95% CI 2.269-30.405, P = 0.001). The natural history of WPW syndrome and the risk of sudden death essentially depend on intrinsic electrophysiological accessory pathway properties rather than on symptoms and electrophysiologic testing is the gold standard to identify patients at risk. The Authors encourage more intensive screening programs to identify asymptomatic patients at risk for prophylactic ablation.

Multipoint LV Pacing Improves Mid-Term CRT Response Introduction Cardiac resynchronization therapy (CRT) with multipoint left ventricular (LV) pacing in a single coronary sinus branch improves acute LV function. We hypothesized that multipoint pacing (MPP) can improve midterm echocardiographic and clinical response compared with conventional CRT. Methods and Results Consecutive patients receiving a CRT implant (Unify Quadra MP™ or Quadra Assura MP™ CRT-D and Quartet™ LV lead, St. Jude Medical, Sylmar, CA, USA) were randomized to receive biventricular (BiV) pacing with either conventional LV pacing (CONV group) or MPP (MPP group). For each patient, an optimal pacing configuration for the assigned pacing mode was programmed based on intraoperative pressure-volume (PV) loop measurements. A clinical evaluation and transthoracic echocardiogram were performed before implant (BASELINE) and at 3 months postimplant and analyzed by a blinded observer. A reduction in end-systolic volume (ESV) of ≥15% relative to BASELINE was prospectively defined as response to CRT. Forty-four patients (NYHA Class III, EF 29 ± 6%, QRS duration 152 ± 17 milliseconds) were enrolled and randomized. One patient in the MPP group was lost to follow-up and excluded from further analysis. After 3 months, 11 of 22 (50%) CONV patients and 16 of 21 (76%) MPP patients were classified as responders. ESV reduction, EF increase, and NYHA class reduction relative to BASELINE were significantly greater in the MPP group than in the CONV group (ESV: −21.0 ± 13.9 vs. −12.6 ± 11.1%, P = 0.03; EF: +9.8 ± 5.1 vs. +2.0 ± 7.8 percentage points, P < 0.001; ΔNYHA: −1.05 ± 0.22 vs. −0.72 ± 0.46 functional classes, P = 0.006). Conclusion PV loop optimized BiV pacing with MPP resulted in an improved rate of response to CRT.

Aims Supraventricular arrhythmias are a major cause of morbidity and mortality in adult patients with congenital heart disease (CHD). Intraoperative ablation offers an alternative for patients who failed ablation procedures or are requiring concomitant surgical intervention. We present our long-term results with the surgical treatment of arrhythmias in adults with CHD (ACHD) undergoing elective cardiac surgery and the clinical predictors for arrhythmia recurrence. Methods and results Between 2002 and 2013, 80 consecutive patients with CHD, mean age of 39 years, underwent intraoperative ablation with monopolar irrigated radiofrequency during cardiac surgery procedures. Significant clinical predictors of arrhythmia recurrence were determined by univariate analysis. We performed 47 right-sided Maze procedures, and 33 Cox-Maze III procedures. In 75 survivors, the ablation was effective immediately. Over an average follow-up period of 72 months (12–155 months), arrhythmias recurred in nine (20%) patients after right-sided Maze, and in six (19%) patients after Cox-Maze III. Eleven patients were controlled with medical therapy, three underwent catheter ablation of the arrhythmia, and one required a permanent pacemaker. Preoperative arrhythmia length ≥3 years ( P ≤ 0.001), tetralogy of Fallot ( P ≤ 0.006), and preoperative atrial fibrillation ( P ≤ 0.016) were associated with recurrence arrhythmia. Conversely, NYHA class

Selecting the correct number of factors in principal component analysis (PCA) is a critical step to achieve a reasonable data modelling, where the optimal strategy strictly depends on the objective PCA is applied for. In the last decades, much work has been devoted to methods like Kaiser's eigenvalue greater than 1 rule, Velicer's minimum average partial rule, Cattell's scree test, Bartlett's chi-square test, Horn's parallel analysis, and cross-validation. However, limited attention has been paid to the possibility of assessing the significance of the calculated components via permutation testing. That may represent a feasible approach in case the focus of the study is discriminating relevant from nonsystematic sources of variation and/or the aforementioned methodologies cannot be resorted to (eg, when the analysed matrices do not fulfill specific properties or statistical assumptions). The main aim of this article is to provide practical insights for an improved understanding of permutation testing, highlighting its pros and cons, mathematically formalising the numerical procedure to be abided by when applying it for PCA factor selection by the description of a novel algorithm developed to this end, and proposing ad hoc solutions for optimising computational time and efficiency.

Background— The management of Wolff-Parkinson-White is based on the distinction between asymptomatic and symptomatic presentations, but evidence is limited in the asymptomatic population. Methods and Results— The Wolff-Parkinson-White registry was an 8-year prospective study of either symptomatic or asymptomatic Wolff-Parkinson-White patients referred to our Arrhythmology Department for evaluation or ablation. Inclusion criteria were a baseline electrophysiological testing with or without radiofrequency catheter ablation (RFA). Primary end points were the percentage of patients who experienced ventricular fibrillation (VF) or potentially malignant arrhythmias and risk factors. Among 2169 enrolled patients, 1001 (550 asymptomatic) did not undergo RFA (no-RFA group) and 1168 (206 asymptomatic) underwent ablation (RFA group). There were no differences in clinical and electrophysiological characteristics between the 2 groups except for symptoms. In the no-RFA group, VF occurred in 1.5% of patients, virtually exclusively (13 of 15) in children (median age, 11 years), and was associated with a short accessory pathway antegrade refractory period ( P P P Conclusions— The prognosis of the Wolff-Parkinson-White syndrome essentially depends on intrinsic electrophysiological properties of AP rather than on symptoms. RFA performed during the same procedure after electrophysiological testing is of benefit in improving the long-term outcomes.

Cardiac resynchronization therapy (CRT) with multipoint left ventricular (LV) pacing in a single coronary sinus branch improves acute LV function. We hypothesized that multipoint pacing (MPP) can improve midterm echocardiographic and clinical response compared with conventional CRT.Consecutive patients receiving a CRT implant (Unify Quadra MP™ or Quadra Assura MP™ CRT-D and Quartet™ LV lead, St. Jude Medical, Sylmar, CA, USA) were randomized to receive biventricular (BiV) pacing with either conventional LV pacing (CONV group) or MPP (MPP group). For each patient, an optimal pacing configuration for the assigned pacing mode was programmed based on intraoperative pressure-volume (PV) loop measurements. A clinical evaluation and transthoracic echocardiogram were performed before implant (BASELINE) and at 3 months postimplant and analyzed by a blinded observer. A reduction in end-systolic volume (ESV) of ≥15% relative to BASELINE was prospectively defined as response to CRT. Forty-four patients (NYHA Class III, EF 29 ± 6%, QRS duration 152 ± 17 milliseconds) were enrolled and randomized. One patient in the MPP group was lost to follow-up and excluded from further analysis. After 3 months, 11 of 22 (50%) CONV patients and 16 of 21 (76%) MPP patients were classified as responders. ESV reduction, EF increase, and NYHA class reduction relative to BASELINE were significantly greater in the MPP group than in the CONV group (ESV: -21.0 ± 13.9 vs. -12.6 ± 11.1%, P = 0.03; EF: +9.8 ± 5.1 vs. +2.0 ± 7.8 percentage points, P0.001; ΔNYHA: -1.05 ± 0.22 vs. -0.72 ± 0.46 functional classes, P = 0.006).PV loop optimized BiV pacing with MPP resulted in an improved rate of response to CRT.