Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy.

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay.

This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 "Reefs" habitat, Dendrophyllia cornigera and Phakellia ventilabrum, in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline based on deep learning models to extract species density data from raw images obtained by a remotely operated towed vehicle (ROTV). Different object detection models were evaluated and compared in various shelf zones at the head of submarine canyon systems using metrics such as precision, recall, and F1 score. The best-performing model, YOLOv8, was selected for generating density maps of the two species at a high spatial resolution. The study also generated synthetic images to augment the training data and assess the generalization capacity of the models. The proposed approach provides a cost-effective and non-invasive method for monitoring and assessing the status of these important reef-building species and their habitats. The results have important implications for the management and protection of the 1170 habitat in Spain and other marine ecosystems worldwide. These results highlight the potential of deep learning to improve efficiency and accuracy in monitoring vulnerable marine ecosystems, allowing informed decisions to be made that can have a positive impact on marine conservation.

Advanced Light Source Technologies for Photodynamic Therapy of Skin Cancer Lesions.

Photodynamic therapy (PDT) is an increasingly popular dermatological treatment not only used for life-threatening skin conditions and other tumors but also for cosmetic purposes. PDT has negligible effects on underlying functional structures, enabling tissue regeneration feasibility. PDT uses a photosensitizer (PS) and visible light to create cytotoxic reactive oxygen species, which can damage cellular organelles and trigger cell death. The foundations of modern photodynamic therapy began in the late 19th and early 20th centuries, and in recent times, it has gained more attention due to the development of new sources and PSs. This review focuses on the latest advancements in light technology for PDT in treating skin cancer lesions. It discusses recent research and developments in light-emitting technologies, their potential benefits and drawbacks, and their implications for clinical practice. Finally, this review summarizes key findings and discusses their implications for the use of PDT in skin cancer treatment, highlighting the limitations of current approaches and providing insights into future research directions to improve both the efficacy and safety of PDT. This review aims to provide a comprehensive understanding of PDT for skin cancer treatment, covering various aspects ranging from the underlying mechanisms to the latest technological advancements in the field.

Automatic classification of Candida species using Raman spectroscopy and machine learning.

One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.

Comparison of Mg/Ca concentration series from Patella depressa limpet shells using CF-LIBS and LA-ICP-MS.

The elemental composition of marine mollusk shells can offer valuable information about environmental conditions experienced by a mollusk during its lifespan. Previous studies have shown significant correlations between Mg/Ca concentration ratios measured on biogenic carbonate of mollusk shells and sea surface temperature (SST). Here we propose the use of Laser-Induced Breakdown Spectroscopy (LIBS) and the validation of the Calibration-Free LIBS (CF-LIBS) approach for the rapid measurement and estimation of Mg/Ca molar concentration profiles within Patella depressa Pennant, 1777 limpet shells. To achieve these objectives, results derived from CF-LIBS methodology are compared with those obtained from an established analytical technique for this purpose, such as Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). Concentration series obtained with both methodologies show defined temporal patterns and reflect the season-of-capture in each specimen. The results evidence a significant correlation (R2 = 0.63-0.81) between CF-LIBS and LA-ICP-MS Mg/Ca molar concentration profiles within four live-collected P. depressa shells. Averaged error for the molar concentration estimated with CF-LIBS was lower than 10% in every specimen. The comparison between the results obtained from two techniques used in this study has allowed us to demonstrate for the first time that Mg/Ca molar concentration measured in biogenic carbonates were accurately inferred using CF-LIBS technique. The CF-LIBS approach validation represents great potential for the rapid and large-scale paleoenvironmental and archaeological analysis of this mollusk species, which is frequently found in archaeological sites.

Design and Verification of Integrated Circuitry for Real-Time Frailty Monitoring.

In this study, a new wireless electronic circuitry to analyze weight distribution was designed and incorporated into a chair to gather data related to common human postures (sitting and standing up). These common actions have a significant impact on various motor capabilities, including gait parameters, fall risk, and information on sarcopenia. The quality of these actions lacks an absolute measurement, and currently, there is no qualitative and objective metric for it. To address this, the designed analyzer introduces variables like Smoothness and Percussion to provide more information and objectify measurements in the assessment of stand-up/sit-down actions. Both the analyzer and the proposed variables offer additional information that can objectify assessments depending on the clinical eye of the physicians.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Automated Measurement of Magnesium/Calcium Ratios in Gastropod Shells Using Laser-Induced Breakdown Spectroscopy for Paleoclimatic Applications.

The chemical composition of mollusk shells offers information about environmental conditions present during the lifespan of the organism. Shells found in geological deposits and in many archeological sites can help to reconstruct past climatic conditions. For example, a correlation has been found between seawater temperature and the amount of some substituent elements (e.g., magnesium, strontium) in the biogenerated calcium carbonate matrix of the shell, although it is very species-specific. Here we propose the use laser-induced breakdown spectroscopy (LIBS) to estimate Mg/Ca ratios in modern specimens of the common limpet Patella vulgata. An automated setup was used to obtain a sequence of Mg/Ca ratios across a sampling path that could be compared with the seawater temperatures recorded during the organism's lifespan. Results using four shells collected in different months of the year showed a direct relationship between the Mg/Ca ratios and the seawater temperature, although the sequences also revealed small-scale (short-term) variability and an irregular growth rate. Nevertheless, it was possible to infer the season of capture and the minimum and maximum seawater temperatures from the LIBS sequences. This fact, along with the reduction in sampling and measurement time compared with other spectrometric techniques (such as inductively coupled plasma mass spectrometry [ICP-MS]), makes LIBS useful in paleoclimatic studies.

Normalization of laser-induced breakdown spectroscopy spectra using a plastic optical fiber light collector and acoustic sensor device.

To estimate the acoustic plasma energy in laser-induced breakdown spectroscopy (LIBS) experiments, a light collecting and acoustic sensing device based on a coil of plastic optical fiber (POF) is proposed. The speckle perturbation induced by the plasma acoustic energy was monitored using a CCD camera placed at the end of a coil of multimode POF and processed with an intraimage contrast ratio method. The results were successfully verified with the acoustic energy measured by a reference microphone. The proposed device is useful for normalizing LIBS spectra, enabling a better estimation of the sample's chemical composition.

Feasibility study of imaging spectroscopy to monitor the quality of online welding.

An online welding quality system based on the use of imaging spectroscopy is proposed and discussed. Plasma optical spectroscopy has already been successfully applied in this context by establishing a direct correlation between some spectroscopic parameters, e.g., the plasma electronic temperature and the resulting seam quality. Given that the use of the so-called hyperspectral devices provides both spatial and spectral information, we propose their use for the particular case of arc welding quality monitoring in an attempt to determine whether this technique would be suitable for this industrial situation. Experimental welding tests are presented, and the ability of the proposed solution to identify simulated defects is proved. Detailed spatial analyses suggest that this additional dimension can be used to improve the performance of the entire system.

Use of the Plasma Spectrum RMS Signal for Arc-Welding Diagnostics.

A new spectroscopic parameter is used in this paper for on-line arc-welding quality monitoring. Plasma spectroscopy applied to welding diagnostics has typically relied on the estimation of the plasma electronic temperature, as there is a known correlation between this parameter and the quality of the seams. However, the practical use of this parameter gives rise to some uncertainties that could provoke ambiguous results. For an efficient on-line welding monitoring system, it is essential to prevent the appearance of false alarms, as well as to detect all the possible defects. In this regard, we propose the use of the root mean square signal of the welding plasma spectra, as this parameter will be proven to exhibit a good correlation with the quality of the resulting seams. Results corresponding to several arc-welding field tests performed on Inconel and titanium specimens will be discussed and compared to non-destructive evaluation techniques.

Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection.

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution.

Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks.

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.

Embedded spectroscopic fiber sensor for on-line arc-welding analysis.

A new fiber sensor system designed for spectroscopic analysis and on-line quality assurance of arc-welding processes is presented here. Although several different approaches have been considered for the optical capture of plasma emission in arc-welding processes, they tend to be invasive and make use of optical devices such as collimators or photodiodes. The solution proposed here is based on the arrangement of an optical fiber, which is used at the same time as the optical capturing device and also to deliver the optical information to a spectrometer, embedded within an arc-welding torch. It will be demonstrated that, by using the shielding gas as a protection for the fiber end, the plasma light emission is efficiently collected, forming a sensor system completely transparent and noninvasive for the welding operator. The feasibility of the proposed sensor designed to be used as the input optics of a welding quality-assurance system based on plasma spectroscopy will be demonstrated by means of several welding tests.

Optical techniques for real-time penetration monitoring for laser welding.

Optical techniques for real-time full-penetration monitoring for Nd:YAG laser welding have been investigated. Coaxial light emission from the keyhole is imaged onto three photodiodes and a camera. We describe the spectral and statistical analyses from photodiode signals, which indicate the presence of a full penetration. Two image processing techniques based on the keyhole shape recognition and the keyhole image intensity profile along the welding path are presented. An intensity ratio parameter is used to determine the extent of opening at the rear of a fully opened keyhole. We show that this parameter clearly interprets a hole in formation or a lack of penetration when welding is performed on workpieces with variable thicknesses at constant laser power.

Closed-loop power and focus control of laser welding for full-penetration monitoring.

We describe a closed-loop control system ensuring full penetration in welding by controlling the focus position and power of a 4-kW Nd:YAG laser. A focus position monitoring system was developed based on the chromatic aberration of the focusing optics. With the laser power control system we can determine the degree of penetration by analyzing the keyhole image intensity profile. We demonstrate performance in bead-on-plate welding of Inconel 718 and titanium. The focus control system maintained a focal position on tilted and nonflat workpieces, and the penetration monitoring technique successfully controlled the laser power to maintain the full-penetration regime in the presence of linear and step changes of thickness. Finally we discuss the performances and the limits of the systems when applied to a realistic complex aerospace component.