Nanoporous Au Behavior in Methyl Orange Solutions.

Nanoporous (NP) gold, the most extensively studied and efficient NP metal, possesses exceptional properties that make it highly attractive for advanced technological applications. Notably, its remarkable catalytic properties in various significant reactions hold enormous potential. However, the exploration of its catalytic activity in the degradation of water pollutants remains limited. Nevertheless, previous research has reported the catalytic activity of NP Au in the degradation of methyl orange (MO), a toxic azo dye commonly found in water. This study aims to investigate the behavior of nanoporous gold in MO solutions using UV-Vis absorption spectroscopy and high-performance liquid chromatography. The NP Au was prepared by chemical removal of silver atoms of an AuAg precursor alloy prepared by ball milling. Immersion tests were conducted on both pellets and powders of NP Au, followed by examination of the residual solutions. Additionally, X-ray photoelectron spectroscopy and electrochemical impedance measurements were employed to analyze NP Au after the tests. The findings reveal that the predominant and faster process involves the partially reversible adsorption of MO onto NP Au, while the catalytic degradation of the dye plays a secondary and slower role in this system.

Synergistic effects of Tb doping in long-persistent luminescence in Ca3Ga4O9: xBi3+, yZn2+ phosphors: Implications for novel phosphorescent materials.

Long afterglow phosphors constitute an emerging class of compounds with wide application in several fields, from photonic to dosimetry, solar energy storage and photocatalysis. In this study, we synthesized and thoroughly characterized a new class of persistent emitting materials, Ca3Ga4O9: xBi3+, yZn2+, zTb3+. Through the utilization of X-ray and Raman spectroscopy, as well as optical measurements including static and time-resolved luminescence, thermoluminescence, and phosphorescence, the effects of the Tb concentration on the optical and structural properties of the material has been deeply studied. A suitable mechanism was proposed to account for the long afterglow emission, wherein Tb3+ and Bi3+ ions occupying the Ca2+ sites serve as recombination centers, facilitating the generation of oxygen defects. Zn2+ in the Ga3+ sites, contribute to the charge balance and generates hole traps in the matrix. The enduring phosphorescence persists for over 3 h following the cessation of UV irradiation, discernible to the naked eye in low-light conditions.

Unveiling Hidden Prints: Optically stimulated luminescence for latent fingerprint detection.

Fluorescent lighting and optical techniques have been widely utilized to enhance the detection of latent fingerprints. However, the development of new techniques is imperative to expand the range of surfaces from which latent fingerprints can be detected. When relying on traditional methods, fingerprint evidence can remain undetected or even disregarded due to insufficient detection and limited detail, especially when dealing with a luminescent background. In this study, we propose the utilization of optically stimulated luminescence (OSL) applied to a Ba2SiO4 matrix, co-doped with Eu2+ and Dy3+, as a powerful method for visualizing latent fingerprints on various surfaces, including thin plastic bags, rigid duct tape, thin aluminum foil, and glass slices. This technique effectively eliminates any luminescent background and significantly enhances optical imaging. This represents the first successful application of OSL in the development of latent fingerprints, thus paving the way for more efficient and effective forensic techniques in the future.

Lung aeration estimated by chest electrical impedance tomography and lung ultrasound during extubation.

BACKGROUND: This study hypothesized that patients with extubation failure exhibit a loss of lung aeration and heterogeneity in air distribution, which could be monitored by chest EIT and lung ultrasound. Patients at risk of extubation failure were included after a successful spontaneous breathing trial. Lung ultrasound [with calculation of lung ultrasound score (LUS)] and chest EIT [with calculation of the global inhomogeneity index, frontback center of ventilation (CoV), regional ventilation delay (RVD) and surface available for ventilation] were performed before extubation during pressure support ventilation (H0) and two hours after extubation during spontaneous breathing (H2). EIT was then repeated 6 h (H6) after extubation. EIT derived indices and LUS were compared between patients successfully extubated and patients with extubation failure. RESULTS: 40 patients were included, of whom 12 (30%) failed extubation. Before extubation, when compared with patients with successful extubation, patients who failed extubation had a higher LUS (19 vs 10, p = 0.003) and a smaller surface available for ventilation (352 vs 406 pixels, p = 0.042). After extubation, GI index and LUS were higher in the extubation failure group, whereas the surface available for ventilation was lower. The RVD and the CoV were not different between groups. CONCLUSION: Before extubation, a loss of lung aeration was observed in patients who developed extubation failure afterwards. After extubation, this loss of lung aeration persisted and was associated with regional lung ventilation heterogeneity. Trial registration Clinical trials, NCT04180410, Registered 27 November 2019-prospectively registered, https://clinicaltrials.gov/ct2/show/NCT04180410 .

Optimizing the Mechanoluminescent Properties of CaZnOS:Tb via Microwave-Assisted Synthesis: A Comparative Study with Conventional Thermal Methods.

Recent developments in lighting and display technologies have led to an increased focus on materials and phosphors with high efficiency, chemical stability, and eco-friendliness. Mechanoluminescence (ML) is a promising technology for new lighting devices, specifically in pressure sensors and displays. CaZnOS has been identified as an efficient ML material, with potential applications as a stress sensor. This study focuses on optimizing the mechanoluminescent properties of CaZnOS:Tb through microwave-assisted synthesis. We successfully synthesized CaZnOS doped with Tb3+ using this method and compared it with samples obtained through conventional solid-state methods. We analyzed the material's characteristics using various techniques to investigate their structural, morphological, and optical properties. We then studied the material's mechanoluminescent properties through single impacts with varying energies. Our results show that materials synthesized through microwave methods exhibit similar optical and, primarily, mechanoluminescent properties, making them suitable for use in photonics applications. The comparison of the microwave and conventional solid-state synthesis methods highlights the potential of microwave-assisted methods to optimize the properties of mechanoluminescent materials for practical applications.

What is missing to advance foliar fertilization using nanotechnology?

An urgent challenge within agriculture is to improve fertilizer efficiency in order to reduce the environmental footprint associated with an increased production of crops on existing farmland. Standard soil fertilization strategies are often not very efficient due to immobilization in the soil and losses of nutrients by leaching or volatilization. Foliar fertilization offers an attractive supplementary strategy as it bypasses the adverse soil processes, but implementation is often hampered by a poor penetration through leaf barriers, leaf damage, and a limited ability of nutrients to translocate. Recent advances within bionanotechnology offer a range of emerging possibilities to overcome these challenges. Here we review how nanoparticles can be tailored with smart properties to interact with plant tissue for a more efficient delivery of nutrients.

Effects of the Parent Alloy Microstructure on the Thermal Stability of Nanoporous Au.

Nanoporous (NP) metals represent a unique class of materials with promising properties for a wide set of applications in advanced technology, from catalysis and sensing to lightweight structural materials. However, they typically suffer from low thermal stability, which results in a coarsening behavior not yet fully understood. In this work, we focused precisely on the coarsening process undergone by NP Au, starting from the analysis of data available in the literature and addressing specific issues with suitably designed experiments. We observe that annealing more easily induces densification in systems with short characteristic lengths. The NP Au structures obtained by dealloying of mechanically alloyed AuAg precursors exhibit lower thermal stability than several NP Au samples discussed in the literature. Similarly, NP Au samples prepared by annealing the precursor alloy before dealloying display enhanced resistance to coarsening. We suggest that the microstructure of the precursor alloy, and, in particular, the grain size of the metal phases, can significantly affect the thermal stability of the NP metal. Specifically, the smaller the grain size of the parent alloy, the lower the thermal stability.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches.

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

Correction to: Effect of PEEP decremental on respiratory mechanics, gas exchange, pulmonary regional ventilation, and hemodynamics in patients with SARS-Cov-2-associated Acute Respiratory Distress Syndrome.

An amendment to this paper has been published and can be accessed via the original article.

A Low Power and Real-Time Architecture for Hough Transform Processing Integration in a Full HD-Wireless Capsule Endoscopy.

We propose a new paradigm of a smart wireless endoscopic capsule (WCE) that has the ability to select suspicious images containing a polyp before sending them outside the body. To do so, we have designed an image processing system to select images with Regions Of Interest (ROI) containing a polyp. The criterion used to select an ROI is based on the polyp's shape. We use the Hough Transform (HT), a widely used shape-based algorithm for object detection and localization, to make this selection. In this paper, we present a new algorithm to compute in real-time the Hough Transform of high definition images (1920 x 1080 pixels). This algorithm has been designed to be integrated inside a WCE where there are specific constraints: a limited area and a limited amount of energy. To validate our algorithm, we have realized tests using a dataset containing synthetic images, real images, and endoscopic images with polyps. Results have shown that our algorithm is capable to detect circular shapes in synthetic and real images, but also can detect circles with an irregular contour, like that of polyps. We have implemented our architecture and validated it in a Xilinx Spartan 7 FPGA device, with an area of [Formula: see text], which is compatible with integration inside a WCE. This architecture runs at 132 MHz with an estimated power consumption of 76 mW and can work close to 10 hours. To improve the capacity of our architecture, we have also made an ASIC estimation, that let our architecture work at 125 MHz, with a power consumption of only 17.2 mW and a duration of approximately 50 hours.

Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System.

Interest in sleep has been growing in the last decades, considering its benefits for well-being, but also to diagnose sleep troubles. The gold standard to monitor sleep consists of recording the course of many physiological parameters during a whole night. The human interpretation of resulting curves is time consuming. We propose an automatic knowledge-based decision system to support sleep staging. This system handles temporal data, such as events, to combine and aggregate atomic data, so as to obtain high-abstraction-levels contextual decisions. The proposed system relies on a semantic reprentation of observations, and on contextual knowledge base obtained by formalizing clinical practice guidelines. Evaluated on a dataset composed of 131 full night polysomnographies, results are encouraging, but point out that further knowledge need to be integrated.

Epileptic seizure detection based on expected activity measurement and Neural Network classification.

Epilepsy is known as the second reason to visit a neurophysiologist after migraine. In this paper, we propose a new approach to automatically detect crises of epilepsy in an Electroencephalogram (EEG). Our algorithm is based on image transformation, Wavelet Decomposition (DWT) and taking advantage of the correlation between wavelet coefficients in each sub-band. Therefore, an Expected Activity Measurement (EAM) is calculated for each coefficient as a feature extraction method. These features are fed into back propagation Neural Network (ANN) and the periods with epileptic seizures and non-seizures are classified. Our approach is validated using a public dataset and the results are very promising, reaching accuracy up to 99.44% for detection epileptic seizures.

Decision System Integrating Preferences to Support Sleep Staging.

Scoring sleep stages can be considered as a classification problem. Once the whole recording segmented into 30-seconds epochs, features, extracted from raw signals, are typically injected into machine learning algorithms in order to build a model able to assign a sleep stage, trying to mimic what experts have done on the training set. Such approaches ignore the advances in sleep medicine, in which guidelines have been published by the AASM, providing definitions and rules that should be followed to score sleep stages. In addition, these approaches are not able to solve conflict situations, in which criteria of different sleep stages are met. This work proposes a novel approach based on AASM guidelines. Rules are formalized integrating, for some of them, preferences allowing to support decision in conflict situations. Applied to a doubtful epoch, our approach has taken the appropriate decision.

Towards a Wireless Smart Polysomnograph Using Symbolic Fusion.

Polysomnography is the gold standard test for sleep disorders among which the Sleep Apnea Syndrome (SAS) is considered a public health issue because of the increase of the cardio-and cerebro-vascular risk it is associated with. However, the reliability of this test is questioned since sleep scoring is a time-consuming task performed by medical experts with a high inter- and intra-scorers variability, and because data are collected from 15 sensors distributed over a patient's body surface area, using a wired connection which may be a source of artefacts for the patient's sleep. We have used symbolic fusion to support the automated diagnosis of SAS on the basis of the international guidelines of the AASM for the scoring of sleep events. On a sample of 70 patients, and for the Apnea-Hypopnea Index, symbolic fusion performed at the level of sleep experts (97.1% of agreement). The next step is to confirm these preliminary results and move forward to a smart wireless polysomnograph.

Personalized sleep staging system using evolutionary algorithm and symbolic fusion.

This paper presents a novel system for automatic sleep staging based on evolutionary technique and symbolic intelligence. Proposed system mimics decision making process of clinical sleep staging using Symbolic Fusion and considers personal singularity with an adaptive thresholds setting up system using Evolutionary Algorithm. It proved to be an effective and promising system in personalizing sleep staging. This system can also be integrated with other medical systems to realize remote sleep monitoring or home-care.

High frame rate medical quality video compression for tele-EEG.

More and more, exams require medical images as a tool to diagnose pathologies. Thus, the transfer and storage of the exam data becomes a critical issue. To address this issue, an image compression algorithm called Waaves has been developed and certified for medical imaging. Our work in this paper deals with a scenario of EEG exams where video of the patient is also recorded in order to correctly diagnose myoclonus pathologies. To achieve this goal, the video needs to be of high quality and at frame rate of at least 100 frames per second. This high data rate cannot be compressed on the fly by Waaves codec. In this paper, we present a novel codec based on the Waaves compression algorithm that fits the requirements of tele-video-EEG. We have used the characteristics of the input sequence and the analysis of the original codec, to improve the compression speed. The proposed video codec has shown a speed-up of around 3.4 times compared to the original algorithm. In addition, we have been able to improve the compression ratio while retaining necessary quality to identify myoclonus.

Synchronizing physiological data and video in a telemedicine application: A multimedia approach.

Several medical examinations require that multiple modalities of exams are stored in a synchronized manner. For instance, an EEG exam needs that several physiological signals along with video of the exam are acquired synchronously to aid the neurophysiologists to perform their diagnostics. Furthermore support for telemedicine for such exams have become important in recent years. The existing EDF standard that is used for physiological signals makes it difficult to provide integrated support of adding video and compressed component data, however due to widespread use of EDF standard in the domain, cross compatibility with EDF standard for physiological data is also essential. We present in this work a novel idea to solve these issues. Our approach uses standard multimedia containers in which physiological data are embedded alongside video and audio. This paper provides our analyses of the state of the art of multimedia containers EDF, AVI, ASF, MPEG and MKV and their potentials for a telemedicine application and outlines how MKV stands out as an interesting option in this regard, allowing also capability of compression of physiological data if needed.

Orione, a web-based framework for NGS analysis in microbiology.

UNLABELLED: End-to-end next-generation sequencing microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult owing to a lack of interoperability, reproducibility and transparency. To overcome these limitations we present Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics. AVAILABILITY AND IMPLEMENTATION: Orione is available online at http://orione.crs4.it.