[Experiences of frontline caregivers in the midst of a health crisis : a participatory study in the Liege region]. / Face a la COVID-19. Vécu des soignants de la première ligne de soins au cœur de la crise sanitaire : étude participative en région liégeoise.

The aim of this two-stage research was to document the stressors and resources experienced by front-line professional groups at the heart of the health crisis due to COVID-19, as well as to bring out of a multidisciplinary reflection, a series of priority proposals for strengthening the care system. Our results highlighted great interprofessional similarities in terms of negative and positive experiences (e.g., feeling of powerlessness, support among colleagues), as well as professional specificities (e.g., the painful feeling of being «alone at the front¼ among nurses). Although the stress, fatigue and/or lassitude factors cited by the participants were more numerous than the protective factors, several participants experienced an opportunity for personal and professional development. Overall, this research found that all front-line professionals have a role to play in improving and building resilience in the system, and that this role could be most effective if played in a context of increased multidisciplinary coordination and communication.

Cette recherche en deux étapes avait pour but de documenter les stresseurs et les ressources expérimentés par les groupes professionnels de la première ligne au cœur de la crise sanitaire liée à la COVID-19, ainsi que de faire émerger, d'une réflexion pluridisciplinaire, une série de propositions prioritaires pour le renforcement du système de soins. Nos résultats ont mis en lumière de grandes similitudes interprofessionnelles en termes de vécus négatifs et positifs (par exemple, le sentiment d'impuissance, le soutien entre collègues), ainsi que des spécificités professionnelles (par exemple, le sentiment douloureux d'être «seules au front¼ chez les infirmières). Bien que les facteurs de stress, de fatigue et/ou de lassitude cités par les participants soient plus nombreux que les facteurs de protection, plusieurs participants ont expérimenté une opportunité de développement personnel et professionnel. De manière générale, il est ressorti de cette recherche que tous les professionnels de la 1ère ligne de soins ont un rôle à jouer dans l'amélioration et la résilience du système, et que ce rôle pourrait être d'autant plus efficace s'il se jouait dans un contexte de coordination et de communication pluridisciplinaire intensifié.

Preprocessing NIR Spectra for Aquaphotomics.

Even though NIR spectroscopy is based on the Beer-Lambert law, which clearly relates the concentration of the absorbing elements with the absorbance, the measured spectra are subject to spurious signals, such as additive and multiplicative effects. The use of NIR spectra, therefore, requires a preprocessing step. This article reviews the main preprocessing methods in the light of aquaphotomics. Simple methods for visualizing the spectra are proposed in order to guide the user in the choice of the best preprocessing. The most common chemometrics preprocessing are presented and illustrated by three real datasets. Some preprocessing aims to produce a spectrum as close as possible to the absorbance that would have been measured under ideal conditions and is very useful for the establishment of an aquagram. Others, dedicated to the improvement of the resolution of the spectra, are very useful for the identification of the peaks. Finally, special attention is given to the problem of reducing multiplicative effects and to the potential pitfalls of some very popular methods in chemometrics. Alternatives proposed in recent papers are presented.

Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law.

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

National survey on the management of heart failure in individuals over 80 years of age in French geriatric care units.

BACKGROUND: To evaluate the prevalence and management of heart failure (HF) in very old patients in geriatric settings. METHODS: Members of the French Society of Geriatrics and Gerontology throughout France were invited to participate in a point prevalence survey and to include all patients ≥80 years old, hospitalized in geriatric settings, with HF (stable or decompensated) on June 18, 2012. General characteristics, presence of comorbidities, blood tests and medications were recorded. RESULTS: Among 7,197 patients in geriatric institution, prevalence of HF was 20.5% (n = 1,478): (27% in acute care, 24.2% in rehabilitation care and 18% in nursing home). Mean age was 88.2 (SD = 5.2) and Charlson co morbidity score was high (8.49 (SD = 2.21)). Left ventricular ejection fraction (LVEF) was available in 770 (52%) patients: 536 (69.6%) had a preserved LVEF (≥ 50%), 120 (15.6%) a reduced LVEF (< 40%), and 114 (14.8%) a midrange LVEF (40-49%). Prescription of recommended HF drugs was low: 42.6% (629) used Angiotensin Converting Enzyme Inhibitors (ACEI) or Angiotensin Receptor Blockers (ARBs), 48.0% (709) ß-blockers, and 21.9% (324) ACEI or ARB with ß-blockers, even in reduced LVEF. In multivariate analysis ACEI or ARBs were more often used in patients with myocardial infarction (1.36 (1.04-1.78)), stroke (1.42 (1.06-1.91)), and diabetes (1.54 (1.14-2.06)). ß blockers were more likely used in patients with myocardial infarction (2.06 (1.54-2.76)) and atrial fibrillation (1.70 (1.28-2.28)). CONCLUSION: In this large very old population, prevalence of HF was high. Recommended HF drugs were underused even in reduced LVEF. These results indicate that management of HF in geriatric settings can still be improved.

Effect of the Architecture of Fiber-Optic Probes Designed for Soluble Solid Content Prediction in Intact Sugar Beet Slices.

Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. One of the main limitations for its competitiveness is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots, especially in breeding programs. In this context, a dedicated near infrared (NIR) fiber-optic probe based approach is proposed. NIR technology is widely used for the estimation of sugar content in vegetable products, while optic fibers allow a wide choice of technical properties and configurations. The objective of this research was to study the best architecture through different technical choices for the estimation of sugar content in intact sugar beet roots. NIR spectral measurements were taken on unprocessed sugar beet samples using two types of geometries, single and multiple fiber-probes. Sugar content estimates were more accurate when using multiple fiber-probes (up to R2 = 0.93) due to a lesser disruption of light specular reflection. In turn, on this configuration, the best estimations were observed for the smallest distances between emitting and collecting fibers, reducing the proportion of multiply scattered light in the spectra. Error of prediction (RPD) values of 3.95, 3.27 and 3.09 were obtained for distances between emitting and collecting fibers of 0.6, 1.2 and 1.8 µm respectively. These high RPD values highlight the good predictions capacities of the multi-fiber probes. Finally, this study contributes to a better understanding of the effects of the technical properties of optical fiber-probes on the quality of spectral models. In addition, and beyond this specificity related to sugar beet, these findings could be extended to other turbid media for quantitative optical spectroscopy and eventually to validate considered fiber-optic probe design obtained in this experimental study.

Chemometrics in analytical chemistry-part II: modeling, validation, and applications.

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial "Chemometrics in analytical chemistry." This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields. This tutorial provides an overview of the popularity of chemometrics in analytical chemistry.

Revisiting short-wave-infrared (SWIR) bands for atmospheric correction in coastal waters.

The shortwave infrared (SWIR) bands on the existing Earth Observing missions like MODIS have been designed to meet land and atmospheric science requirements. The future geostationary and polar-orbiting ocean color missions, however, require highly sensitive SWIR bands (> 1550nm) to allow for a precise removal of aerosol contributions. This will allow for reasonable retrievals of the remote sensing reflectance (Rrs) using standard NASA atmospheric corrections over turbid coastal waters. Design, fabrication, and maintaining high-performance SWIR bands at very low signal levels bear significant costs on dedicated ocean color missions. This study aims at providing a full analysis of the utility of alternative SWIR bands within the 1600nm atmospheric window if the bands within the 2200nm window were to be excluded due to engineering/cost constraints. Following a series of sensitivity analyses for various spectral band configurations as a function of water vapor amount, we chose spectral bands centered at 1565 and 1675nm as suitable alternative bands within the 1600nm window for a future geostationary imager. The sensitivity of this band combination to different aerosol conditions, calibration uncertainties, and extreme water turbidity were studied and compared with that of all band combinations available on existing polar-orbiting missions. The combination of the alternative channels was shown to be as sensitive to test aerosol models as existing near-infrared (NIR) band combinations (e.g., 748 and 869nm) over clear open ocean waters. It was further demonstrated that while in extremely turbid waters the 1565/1675 band pair yields Rrs retrievals as good as those derived from all other existing SWIR band pairs (> 1550nm), their total calibration uncertainties must be < 1% to meet current science requirements for ocean color retrievals (i.e., Δ Rrs (443) < 5%). We further show that the aerosol removal using the NIR and SWIR bands (available on the existing polar-orbiting missions) can be very sensitive to calibration uncertainties. This requires the need for monitoring the calibration of these bands to ensure consistent multi-mission ocean color products in coastal/inland waters.

Potential of vis-NIR spectroscopy to monitor the silica precipitation reaction.

Controlling production online is an important issue for chemical companies. Visible and near-infrared (NIR) spectroscopy offers a number of important advantages for process monitoring, and has been used since the 1980s. For complex media such as silica precipitation samples, it is interesting to be able to study independently the scattering and absorption effects. From the scattering coefficient it is possible to extract information on the physical structure of the medium. In this work, the physical changes were monitored during a silica precipitation reaction by simple measurement of collimated transmittance NIR spectra. It is shown that it is possible to differentiate samples before and after the gel point, which is a key parameter for monitoring the process. From these NIR spectra the scattering coefficients were simply extracted, allowing a global vision of the physical changes in the medium. Then principal component analysis of the spectra allowed refinement of the understanding of the scattering effects, in combination with particle size monitoring.

Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools.

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

Calibration transfer via filter learning.

BACKGROUND: Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible. RESULTS: To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method. SIGNIFICANCE: Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer.

Centrorhynchus spp. (Acanthocephala) in South America: new anuran record and checklist of vertebrate hosts.

The aim of this study was to record Centrorhynchus sp. associated with the exotic species Aquarana catesbeiana (bullfrog) in southern Brazil and to present a checklist of vertebrate hosts in South America. Twenty-nine adults and juveniles of A. catesbeiana were collected in Capão do Leão, state of Rio Grande do Sul, Brazil, between October 2019 and December 2020. We found 275 specimens of Centrorhynchus sp. cystacanths in the stomach musculature and coelomic cavity of 55.1% of hosts (16). There was no significant differences in the prevalence and mean intensity of infection with cystacanths when compared males and females of A. catesbeiana. The prevalence was significantly higher in adults than in juveniles. The checklist presents 106 species of vertebrate hosts and 14 taxa of Centrorhynchus recorded in nine South American countries. Avian were the main definitive hosts of Centrorhynchus spp. and snakes Dipsadidae, anurans Hylidae and Leptodactylidae the main paratenic hosts in South America. This is the first record of Centrorhynchus cystacanths in A. catesbeiana in the South America. The study provides tools to help understand the parasitic relationships between species of Centrorhynchus and A. catesbeiana and other hosts in areas where bullfrog have been introduced.

PROSAC as a selection tool for SO-PLS regression: A strategy for multi-block data fusion.

BACKGROUND: Spectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the selection of suitable pre-processing methods and identification of informative data blocks becomes increasingly complex and time-consuming when dealing with a large number of blocks. The problem addressed in this work is the efficient pre-processing, selection, and ordering of data blocks for targeted applications in SO-PLS. RESULTS: We introduce the PROSAC-SO-PLS methodology, which employs pre-processing ensembles with response-oriented sequential alternation calibration (PROSAC). This approach identifies the best pre-processed data blocks and their sequential order for specific SO-PLS applications. The method uses a stepwise forward selection strategy, facilitated by the rapid Gram-Schmidt process, to prioritize blocks based on their effectiveness in minimizing prediction error, as indicated by the lowest prediction residuals. To validate the efficacy of our approach, we showcase the outcomes of three empirical near-infrared (NIR) datasets. Comparative analyses were performed against partial-least-squares (PLS) regressions on single-block pre-processed datasets and a methodology relying solely on PROSAC. The PROSAC-SO-PLS approach consistently outperformed these methods, yielding significantly lower prediction errors. This has been evidenced by a reduction in the root-mean-squared error of prediction (RMSEP) ranging from 5 to 25 % across seven out of the eight response variables analyzed. SIGNIFICANCE: The PROSAC-SO-PLS methodology offers a versatile and efficient technique for ensemble pre-processing in NIR data modeling. It enables the use of SO-PLS minimizing concerns about pre-processing sequence or block order and effectively manages a large number of data blocks. This innovation significantly streamlines the data pre-processing and model-building processes, enhancing the accuracy and efficiency of chemometric models.

In situ, real time monitoring of surface transformation: ellipsometric microscopy imaging of electrografting at microstructured gold surfaces.

Surface chemical reactivity is imaged by combining electrochemical activation of a surface transformation process with spatiotemporal ellipsometric microscopy. An imaging ellipsometric microscope is built, allowing ellipsometric images of surfaces with a lateral resolution of ∼1 µm and a thickness sensitivity of ∼0.1 nm in air and 0.4 nm in a liquid. These performances are particularly adapted for using such optical setup as an in situ, real time chemical microscope to observe a chemical surface transformation. This microscope is tested for the monitoring of the electrochemically actuated diazonium grafting of a gold surface. Such reaction is a model system of organic material deposition on a gold surface induced by an electrochemical actuation. Using either plain or physically or chemically structured electrodes, it allows for the characterization of local phenomena associated with the electrografting process. This illustrates its potential to reveal the local (electro)chemical reactivity of surfaces.

A method for evaluating population and infrastructure exposed to natural hazards: tests and results for two recent Tonga tsunamis.

Background: Coastal communities are highly exposed to ocean- and -related hazards but often lack an accurate population and infrastructure database. On January 15, 2022 and for many days thereafter, the Kingdom of Tonga was cut off from the rest of the world by a destructive tsunami associated with the Hunga Tonga Hunga Ha'apai volcanic eruption. This situation was made worse by COVID-19-related lockdowns and no precise idea of the magnitude and pattern of destruction incurred, confirming Tonga's position as second out of 172 countries ranked by the World Risk Index 2018. The occurrence of such events in remote island communities highlights the need for (1) precisely knowing the distribution of buildings, and (2) evaluating what proportion of those would be vulnerable to a tsunami. Methods and Results: A GIS-based dasymetric mapping method, previously tested in New Caledonia for assessing and calibrating population distribution at high resolution, is improved and implemented in less than a day to jointly map population clusters and critical elevation contours based on runup scenarios, and is tested against destruction patterns independently recorded in Tonga after the two recent tsunamis of 2009 and 2022. Results show that ~ 62% of the population of Tonga lives in well-defined clusters between sea level and the 15 m elevation contour. The patterns of vulnerability thus obtained for each island of the archipelago allow exposure and potential for cumulative damage to be ranked as a function of tsunami magnitude and source area. Conclusions: By relying on low-cost tools and incomplete datasets for rapid implementation in the context of natural disasters, this approach works for all types of natural hazards, is easily transferable to other insular settings, can assist in guiding emergency rescue targets, and can help to elaborate future land-use planning priorities for disaster risk reduction purposes. Supplementary Information: The online version contains supplementary material available at 10.1186/s40677-023-00235-8.

Monitoring of water sorption and swelling of potato starch-glycerol extruded blend by magnetic resonance imaging and multivariate curve resolution.

Magnetic resonance microimaging (MRµI) is an outstanding technique for studying water transfers in millimetric bio-based materials in a non-destructive and non-invasive manner. However, depending on the composition of the material, monitoring and quantification of these transfers can be very complex, and hence reliable image processing and analysis tools are necessary. In this study, a combination of MRµI and multivariate curve resolution-alternating least squares (MCR-ALS) is proposed to monitor the water ingress into a potato starch extruded blend containing 20% glycerol that was shown to have interesting properties for biomedical, textile, and food applications. In this work, the main purpose of MCR is to provide spectral signatures and distribution maps of the components involved in the water uptake process that occurs over time with various kinetics. This approach allowed the description of the system evolution at a global (image) and a local (pixel) level, hence, permitted the resolution of two waterfronts, at two different times into the blend that could not be resolved by any other mathematical processing method usually used in magnetic resonance imaging (MRI). The results were supplemented by scanning electron microscopy (SEM) observations in order to interpret these two waterfronts in a biological and physico-chemical point of view.

Visible - Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the monitoring of apple fire blight.

This dataset consists of three groups of hyperspectral images of apple tree plants. The first group of images consists of a temporal monitoring of seven apple tree plants, infected with fire blight (Erwinia amylovora), and six control plants over a period of 15 days. The second group of images includes a temporal monitoring of three infected plants, seven plants subjected to water stress, and seven control plants. The third group of images corresponds to acquisitions made in the orchard on nine trees showing symptoms of fire blight and six control trees. The pixel locations of infected areas have been provided for all images featuring symptomatic plants.

N-CovSel, a new strategy for feature selection in N-way data.

In data analysis, how to select meaningful variables is a hot and wide-debated topic, and several variable selection (or feature reduction) approaches have been proposed in the literature. Although feature selection methods are numerous, most of them are suitable for data matrices, but not for higher order structures. This is mainly due to the fact the assessment of the relevancy of variables in a multi-way context has not been extensively discussed. To the best of our knowledge, among variable selection approaches developed for standard 2-way data arrays, only VIP analysis and selectivity ratio have been extended to higher-order structures. This aspect is not given by an irrelevance of the topic; on the contrary, the possibility of selecting information in a complex data set such as a multi-way structure is crucial. In the light of these considerations, the present paper discusses a feature selection strategy for N-way data based on the Covariance Selection (CovSel) approach, thus called N-CovSel. This method allows the selection of features of different dimensionality (from 1- up to (N-1)-way), depending on the nature of the original data array. The novel method has been applied on a simulated data set, in order to inspect its ability in selecting features compatible with the ground truth of the system, and on a real data set. In both cases, N-CovSel has demonstrated to be able to select meaningful features. Eventually, different strategies for the further analysis of the selected features have been proposed; some, based on sequential multi-block methods, providing a further data reduction, and some, N-PLS-based, respecting the multi-way nature of the data.

Unsupervised dynamic orthogonal projection. An efficient approach to calibration transfer without standard samples.

Calibration transfer has been traditionally performed in the context of transferring models between instruments using standard samples. Recently, new methodologies and applications have shown that transfer techniques can be adopted to achieve calibration transfer between other types of domains, such as product form, variant or seasonality. In addition, to achieving a higher efficiency for calibration transfer, it is desirable to perform the transfer without the need for standard samples or new reference analyses. Therefore, we propose a method for unsupervised calibration transfer based on the orthogonalization for structural differences between domains. The method has been successfully applied to one simulated dataset and two real datasets. In the studied cases, the proposed methodology allowed to achieve a successful transfer of calibration models and enabled the interpretation of the interferences responsible for the degradation of the original calibration models when transferred to the new domain.

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects.

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O2).gTS-1 and 92 mL(CH4).gTS-1. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.

Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques.

Near-infrared (NIR) spectra of pharmaceutical tablets get affected by light scattering phenomena, which mask the underlying peaks related to chemical components. Often the best performing scatter correction technique is selected from a pool of pre-selected techniques. However, the data corrected with different techniques may carry complementary information, hence, use of a single scatter correction technique is sub-optimal. In this study, the aim is to prove that NIR models related to pharmaceuticals can directly benefit from the fusion of complementary information extracted from multiple scatter correction techniques. To perform the fusion, sequential and parallel pre-processing fusion approaches were used. Two different open source NIR data sets were used for the demonstration where the assay uniformity and active ingredient (AI) content prediction was the aim. As a baseline, the fusion approach was compared to partial least-squares regression (PLSR) performed on standard normal variate (SNV) corrected data, which is a commonly used scatter correction technique. The results suggest that multiple scatter correction techniques extract complementary information and their complementary fusion is essential to obtain high-performance predictive models. In this study, the prediction error and bias were reduced by up to 15 % and 57 % respectively, compared to PLSR performed on SNV corrected data.