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1.
Microorganisms ; 12(7)2024 Jul 20.
Artículo en Inglés | MEDLINE | ID: mdl-39065257

RESUMEN

Microcystis-dominated cyanobacterial blooms (MCBs) frequently occur in freshwaters worldwide due to massive Microcystis colony formation and severely threaten human and ecosystem health. Quorum sensing (QS) is a direct cause of Microcystis colony formation that drives MCBs outbreak by regulating Microcystis population characteristics and behaviors. Many novel findings regarding the fundamental knowledge of the Microcystis QS phenomenon and the signaling molecules have been documented. However, little effort has been devoted to comprehensively summarizing and discussing the research progress and exploration directions of QS signaling molecules-mediated QS system in Microcystis. This review summarizes the action process of N-acyl homoserine lactones (AHLs) as major signaling molecules in Microcystis and discusses the detailed roles of AHL-mediated QS system in cellular morphology, physiological adaptability, and cell aggregation for colony formation to strengthen ecological adaptability and competitive advantage of Microcystis. The research progress on QS mechanisms in Microcystis are also summarized. Compared to other QS systems, the LuxI/LuxR-type QS system is more likely to be found in Microcystis. Also, we introduce quorum quenching (QQ), a QS-blocking process in Microcystis, to emphasize its potential as QS inhibitors in MCBs control. Finally, in response to the research deficiencies and gaps in Microcystis QS, we propose several future research directions in this field. This review deepens the understanding on Microcystis QS knowledge and provide theoretical guidance in developing strategies to monitor, control, and harness MCBs.

2.
Polymers (Basel) ; 16(14)2024 Jul 13.
Artículo en Inglés | MEDLINE | ID: mdl-39065328

RESUMEN

In this paper, a comparative study of the mode-I fracture behaviors of two types of specimens with a V-notch defect under plane stress conditions was performed using the digital gradient sensing (DGS) method. First, two types of specimens (namely one-piece specimen and bonded specimen) with the same V-notch defect were both made of polymethyl methacrylate (PMMA), and three different V-notch angles' defect were considered for each type of specimen. Then, three-point bending tests were performed on both types of specimens. The angular deflection field of light near the V-notch region was recorded using a CCD during the experiments. Finally, by utilizing the relationship between the stress gradient and angular deflection as established by the elasto-optic effect, in conjunction with the principles of linear elastic fracture mechanics theory, the stress intensity factors (SIFs) of two types of specimens under different stress conditions were calculated using the least square method. According to the experimental results, the influence of V-notch angle on fracture load and fracture toughness of two kinds of specimens was discussed. Meanwhile, the experimental results show the significant differences in the fracture behaviors of the two types of specimens under mode-I loading conditions.

3.
Sensors (Basel) ; 24(14)2024 Jul 10.
Artículo en Inglés | MEDLINE | ID: mdl-39065857

RESUMEN

Dehydration is a common problem in the aging population. Medical professionals can detect dehydration using either blood or urine tests. This requires experimental tests in the lab as well as urine and blood samples to be obtained from the patients. This paper proposed 100 GHz millimeter wave radiometry for early detection of dehydration. Reflectance measurements were performed on healthy and dehydrated patients of both genders (120 males and 80 females) in the aging population. Based on the cause of dehydration, the patient groups were divided into three categories: (1) patients dehydrated due to less thirst sensation, (2) patients dehydrated due to illnesses (vomiting and diarrhea), and (3) patients dehydrated due to diabetes. Reflectance measurements were performed on eight locations: (1) the palm, (2) the back of the hand, (3) the fingers, (4) the inner wrist, (5) the outer wrist, (6) the volar side of the arm, (7) the dorsal surface of the arm, and (8) the elbow. Skin dehydrated due to vomiting and diarrhea was found to have lower reflectance at all the measurement locations compared with healthy and other types of dehydrated skin. The elbow region showed the highest difference in reflectance between healthy and dehydrated skin. This indicates that radiometric sensitivity is sufficient to detect dehydration in a few seconds. This will reduce the patient's waiting time and the healthcare professional's intervention time as well as allow early treatment of dehydration, thus avoiding admission to hospitals.


Asunto(s)
Deshidratación , Radiometría , Humanos , Deshidratación/diagnóstico , Masculino , Femenino , Radiometría/métodos , Persona de Mediana Edad , Adulto , Anciano
4.
Sensors (Basel) ; 24(14)2024 Jul 10.
Artículo en Inglés | MEDLINE | ID: mdl-39065868

RESUMEN

An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.

5.
Sensors (Basel) ; 24(14)2024 Jul 11.
Artículo en Inglés | MEDLINE | ID: mdl-39065875

RESUMEN

Hematite (α-Fe2O3) is widely used in sensor sensitization due to its excellent optical properties. In this study, we present a sensitivity-enhanced surface plasmon resonance alcohol sensor based on Fe2O3/Au. We describe the fabrication process of the sensor and characterize its structure. We conduct performance testing on sensors coated multiple times and use solutions with the same gradient of refractive indices as the sensing medium. Within the refractive index range of 1.3335-1.3635, the sensor that was coated twice achieved the highest sensitivity, reaching 2933.2 nm/RIU. This represents a 30.26% enhancement in sensitivity compared to a sensor with a pure gold monolayer film structure. Additionally, we demonstrated the application of this sensor in alcohol concentration detection by testing the alcohol content of common beverages, showing excellent agreement with theoretical values and highlighting the sensor's potential in food testing.

6.
Sensors (Basel) ; 24(14)2024 Jul 11.
Artículo en Inglés | MEDLINE | ID: mdl-39065883

RESUMEN

Spores from the fungus Pithomyces chartarum are commonly found on Azorean pastures. When consumed by cattle along with the grass, these spores cause health issues in the cattle, resulting in animal suffering and financial losses. For approximately two years, we monitored meteorological parameters using weather stations and collected and analyzed grass samples in a laboratory to control for the presence of spores. The data confirmed a connection between meteorology and sporulation, enabling the prediction of sporulation risk. To detect the presence of spores in pastures rather than predict it, we employed field spectrometry and Sentinel-2 reflectance data to measure the spectral signatures of grass while controlling for spores. Our findings indicate that meteorological variables from the past 90 days can be used to predict sporulation, which can enhance the accuracy of a web-based alert system used by farmers to manage the risk. We did not detect significant differences in spectral signatures between grass with and without spores. These studies contribute to a deeper understanding of P. chartarum sporulation and provide actionable information for managing cattle, ultimately improving animal welfare and reducing financial losses.


Asunto(s)
Tecnología de Sensores Remotos , Esporas Fúngicas , Animales , Bovinos , Tecnología de Sensores Remotos/métodos , Esporas Fúngicas/aislamiento & purificación , Poaceae/microbiología , Azores , Internet de las Cosas
7.
Sensors (Basel) ; 24(14)2024 Jul 11.
Artículo en Inglés | MEDLINE | ID: mdl-39065889

RESUMEN

Remote sensing images are characterized by high complexity, significant scale variations, and abundant details, which present challenges for existing deep learning-based super-resolution reconstruction methods. These algorithms often exhibit limited convolutional receptive fields and thus struggle to establish global contextual information, which can lead to an inadequate utilization of both global and local details and limited generalization capabilities. To address these issues, this study introduces a novel multi-branch residual hybrid attention block (MBRHAB). This innovative approach is part of a proposed super-resolution reconstruction model for remote sensing data, which incorporates various attention mechanisms to enhance performance. First, the model employs window-based multi-head self-attention to model long-range dependencies in images. A multi-branch convolution module (MBCM) is then constructed to enhance the convolutional receptive field for improved representation of global information. Convolutional attention is subsequently combined across channels and spatial dimensions to strengthen associations between different features and areas containing crucial details, thereby augmenting local semantic information. Finally, the model adopts a parallel design to enhance computational efficiency. Generalization performance was assessed using a cross-dataset approach involving two training datasets (NWPU-RESISC45 and PatternNet) and a third test dataset (UCMerced-LandUse). Experimental results confirmed that the proposed method surpassed the existing super-resolution algorithms, including Bicubic interpolation, SRCNN, ESRGAN, Real-ESRGAN, IRN, and DSSR in the metrics of PSNR and SSIM across various magnifications scales.

8.
Sensors (Basel) ; 24(14)2024 Jul 12.
Artículo en Inglés | MEDLINE | ID: mdl-39065913

RESUMEN

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.


Asunto(s)
Microondas , Humanos , Algoritmos , Accidente Cerebrovascular/diagnóstico por imagen , Accidente Cerebrovascular/diagnóstico
9.
Sensors (Basel) ; 24(14)2024 Jul 12.
Artículo en Inglés | MEDLINE | ID: mdl-39065926

RESUMEN

Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious and expensive. Gamma-ray spectrometry (GS) is a suitable tool for predicting clay content in precision agriculture when locally calibrated, but it has scarcely been tested site-independently and in vineyards. This study evaluated GS to predict clay content with a site-independent calibration and four machine learning algorithms (Support Vector Machines, Random Forest, k-Nearest Neighbors, and Bayesian regulated neuronal networks) in eight vineyards from four German vine-growing regions. Clay content in the studied soils ranged from 62 to 647 g kg-1. The Random Forest calibration was most suitable. Test set evaluation revealed good model performance for the entire dataset with RPIQ = 4.64, RMSEP = 56.7 g kg-1, and R2 = 0.87; however, prediction quality varied between the sites. Overall, GS with the Random Forest model calibration was appropriate to predict the clay content and its spatial distribution, even for heterogeneous geopedological settings and in individual plots. Therefore, GS is considered a valuable tool for soil mapping in vineyards, where clay content and product quality are closely linked.

10.
Sensors (Basel) ; 24(14)2024 Jul 13.
Artículo en Inglés | MEDLINE | ID: mdl-39065933

RESUMEN

Technologies that capture and analyze movement patterns for diagnostic or therapeutic purposes are a major locus of innovation in the United States. Several studies have evaluated their measurement properties in different conditions with variable findings. To date, the authors are not aware of any systematic review of studies conducted to assess the concurrent validity of pressure-sensing walkway technologies. The results of such an analysis could establish the body of evidence needed to confidently use these systems as reference or gold-standard systems when validating novel tools or measures. A comprehensive search of electronic databases including MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was performed. The initial search yielded 7670 papers. After removing duplicates and applying study inclusion/exclusion criteria, 11 papers were included in the systematic review with 10 included in a meta-analysis. There were 25 spatial and temporal gait parameters extracted from the included studies. The results showed there was not a significant bias for nearly all spatiotemporal gait parameters when the walkway system was compared to the reference systems. The findings from this analysis should provide confidence in using the walkway systems as reference systems in future studies to support the evaluation and validation of novel technologies deriving gait parameters.


Asunto(s)
Marcha , Humanos , Marcha/fisiología , Caminata/fisiología , Presión
11.
Sensors (Basel) ; 24(14)2024 Jul 15.
Artículo en Inglés | MEDLINE | ID: mdl-39065972

RESUMEN

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results.

12.
Sensors (Basel) ; 24(14)2024 Jul 16.
Artículo en Inglés | MEDLINE | ID: mdl-39065996

RESUMEN

Ultrasonic guided waves, which are often generated and detected by piezoelectric transducers, are well established to monitor engineering structures. Wireless solutions are sought to eliminate cumbersome wire installation. This work proposes a method for remote ultrasonic-based structural health monitoring (SHM) using mechanoluminescence (ML). Propagating guided waves transmitted by a piezoelectric transducer attached to a structure induce elastic deformation that can be captured by elastico-ML. An ML coating composed of copper-doped zinc sulfide (ZnS:Cu) particles embedded in PVDF on a thin aluminium plate can be used to achieve the elastico-ML for the remote sensing of propagating guided waves. The simulation and experimental results indicated that a very high voltage would be required to reach the threshold pressure applied to the ML particles, which is about 1.5 MPa for ZnS particles. The high voltage was estimated to be 214 Vpp for surface waves and 750 Vpp for Lamb waves for the studied configuration. Several possible technical solutions are suggested for achieving ultrasonic-induced ML for future remote SHM systems.

13.
Sensors (Basel) ; 24(14)2024 Jul 16.
Artículo en Inglés | MEDLINE | ID: mdl-39066010

RESUMEN

Non-invasive monitoring of pulmonary health may be useful for tracking several conditions such as COVID-19 recovery and the progression of pulmonary edema. Some proposed methods use impedance-based technologies to non-invasively measure the thorax impedance as a function of respiration but face challenges that limit the feasibility, accuracy, and practicality of tracking daily changes. In our prior work, we demonstrated a novel approach to monitor respiration by measuring changes in impedance from the back of the thigh. We reported the concept of using thigh-thigh bioimpedance measurements for measuring the respiration rate and demonstrated a linear relationship between the thigh-thigh bioimpedance and lung tidal volume. Here, we investigate the variability in thigh-thigh impedance measurements to further understand the feasibility of the technique for detecting a change in the respiratory status due to disease onset or recovery if used for long-term in-home monitoring. Multiple within-session and day-to-day impedance measurements were collected at 80 kHz using dry electrodes (thigh) and wet electrodes (thorax) across the five healthy subjects, along with simultaneous gold standard spirometer measurements for three consecutive days. The peak-peak bioimpedance measurements were found to be highly correlated (0.94 ± 0.03 for dry electrodes across thigh; 0.92 ± 0.07 for wet electrodes across thorax) with the peak-peak spirometer tidal volume. The data across five subjects indicate that the day-to-day variability in the relationship between impedance and volume for thigh-thigh measurements is smaller (average of 14%) than for the thorax (40%). However, it is affected by food and water and might limit the accuracy of the respiratory tidal volume.


Asunto(s)
COVID-19 , Impedancia Eléctrica , Humanos , COVID-19/diagnóstico , Masculino , Adulto , Respiración , Monitoreo Fisiológico/métodos , Monitoreo Fisiológico/instrumentación , Volumen de Ventilación Pulmonar/fisiología , Femenino , SARS-CoV-2 , Electrodos , Muslo/fisiología
14.
Sensors (Basel) ; 24(14)2024 Jul 17.
Artículo en Inglés | MEDLINE | ID: mdl-39066018

RESUMEN

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.


Asunto(s)
Aprendizaje Profundo , Radar , Humanos , Enfermedad de Alzheimer/diagnóstico , Marcha/fisiología , Algoritmos , Hemodinámica/fisiología , Signos Vitales/fisiología
15.
Sensors (Basel) ; 24(14)2024 Jul 18.
Artículo en Inglés | MEDLINE | ID: mdl-39066056

RESUMEN

The application of distributed fiber optic strain and temperature measurement can be utilized to address a multitude of measurement tasks across a diverse range of fields, particularly in the context of structural health monitoring in the domains of building construction, civil engineering, and special foundation engineering. However, a comprehensive understanding of the influences on the measurement method and the sensors is essential to prevent misinterpretations or measurement deviations. In this context, this study investigated the effects of moisture exposure, including various salt solutions and a high pH value, on a distributed strain measurement using Rayleigh backscattering. Three fiber optic sensors with different coating materials and one uncoated fiber were exposed to five different solutions for 24 h. The study revealed significant discrepancies (∼38%) in deformation between the three coating types depending on the surrounding solution. Furthermore, in contrast to the prevailing literature, which predominantly describes swelling effects, a negative deformation (∼-47 µÎµ) was observed in a magnesium chloride solution. The findings of this study indicate that corresponding effects can impact the precision of measurement, potentially leading to misinterpretations. Conversely, these effects could be used to conduct large-scale monitoring of chemical components using distributed fiber optic sensing.

16.
Sensors (Basel) ; 24(14)2024 Jul 18.
Artículo en Inglés | MEDLINE | ID: mdl-39066065

RESUMEN

The advancement of medical imaging has profoundly impacted our understanding of the human body and various diseases. It has led to the continuous refinement of related technologies over many years. Despite these advancements, several challenges persist in the development of medical imaging, including data shortages characterized by low contrast, high noise levels, and limited image resolution. The U-Net architecture has significantly evolved to address these challenges, becoming a staple in medical imaging due to its effective performance and numerous updated versions. However, the emergence of Transformer-based models marks a new era in deep learning for medical imaging. These models and their variants promise substantial progress, necessitating a comparative analysis to comprehend recent advancements. This review begins by exploring the fundamental U-Net architecture and its variants, then examines the limitations encountered during its evolution. It then introduces the Transformer-based self-attention mechanism and investigates how modern models incorporate positional information. The review emphasizes the revolutionary potential of Transformer-based techniques, discusses their limitations, and outlines potential avenues for future research.


Asunto(s)
Diagnóstico por Imagen , Humanos , Diagnóstico por Imagen/métodos , Diagnóstico por Imagen/tendencias , Aprendizaje Profundo , Procesamiento de Imagen Asistido por Computador/métodos , Redes Neurales de la Computación
17.
Sensors (Basel) ; 24(14)2024 Jul 19.
Artículo en Inglés | MEDLINE | ID: mdl-39066085

RESUMEN

Satellite remote sensing is currently an established, effective, and constantly used tool and methodology for monitoring agriculture and fertilisation. At the same time, in recent years, the need for the detection of livestock manure and digestate spreading on the soil is emerging, and the development of spectral indices and classification processes based on satellite multispectral data acquisitions is growing. However, the application of such indicators is still underutilised and, given the polluting impact of livestock manure and digestate on soil, groundwater, and air, an in-depth study is needed to improve the monitoring of this practice. Additionally, this paper aims at exposing a new spectral index capable of detecting the land affected by livestock manure and digestate spreading. This indicator was created by studying the spectral response of bare soil and livestock manure and digestate, using Copernicus Sentinel-2 MSI satellite acquisitions and ancillary datasets (e.g., soil moisture, precipitation, regional thematic maps). In particular, time series of multispectral satellite acquisitions and ancillary data were analysed, covering a survey period of 13 months between February 2022 and February 2023. As no previous indications on fertilisation practices are available, the proposed approach consists of investigating a broad-spectrum area, without investigations of specific test sites. A large area of approximately 236,344 hectares covering three provinces of the Emilia-Romagna Region (Italy) was therefore examined. A series of ground truth points were also collected for assessing accuracy by filling in the confusion matrix. Based on the definition of the spectral index, a value of the latter greater than three provides the most conservative threshold for detecting livestock manure and digestate spreading with an accuracy of 62.53%. Such results are robust to variations in the spectral response of the soil. On the basis of these very encouraging results, it is considered plausible that the proposed index could improve the techniques for detecting the spreading of livestock manure and digestate on bare ground, classifying the areas themselves with a notable saving of energy compared to the current investigation methodologies directly on the ground.

18.
Sensors (Basel) ; 24(14)2024 Jul 19.
Artículo en Inglés | MEDLINE | ID: mdl-39066094

RESUMEN

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-µm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth's surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-µm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-µm band image and images of any other OLI bands located in the 0.4-2.5 µm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-µm band image and the 11-µm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-µm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes.

19.
Anal Chim Acta ; 1318: 342933, 2024 Aug 22.
Artículo en Inglés | MEDLINE | ID: mdl-39067936

RESUMEN

BACKGROUND: The aggregation of isotropic particles through interparticle reactions poses a challenge in control due to the ability of all surfaces to bind to each other, rendering the quantitative detection of such interparticle reactions based on particle size difficult. Here, we proposed a novel detection scheme for DNA utilizing an assembly of Janus particles (JPs) employing dynamic light scattering (DLS). DNA molecules are tethered on one hemisphere of the JP, while the other hemisphere retains its hydrophobic properties. RESULTS: Aggregation of JPs was induced by the sandwich hybridization of target DNA between them. The assembly of JPs was effectively monitored by the changes in hydrodynamic diameter detected by DLS, revealing that aggregation peaks at 2-3 particles and further reaction was hindered due to the inability of one hemisphere of the JP to interact with another JP. The target DNA demonstrated detectability at concentrations as low as several tens of pM to several nM using a digital sensing method. The two types of target DNA, such as simple (14 base pairs) and HIV-2 specific sequences (20 base pairs) were detectable at nM and pM levels, respectively. Moreover, we substantiated the robustness of our detection scheme through stoichiometric calculations based on an equilibrium model. The present detection mechanism was well explained based on the binding affinity of DNA hybridization. SIGNIFICANCE: This detection method harnesses the anisotropic nature of JPs and represents the first detection approach based on aggregation. By altering the modification molecules on JPs to match target molecules, such as proteins and organic compounds, a wide range of versatile molecules can be detected using this scheme with high sensitivity. This underscores the broad applicability of the present method.


Asunto(s)
ADN , Dispersión Dinámica de Luz , ADN/química , Tamaño de la Partícula , Hibridación de Ácido Nucleico , Técnicas Biosensibles/métodos
20.
JGH Open ; 8(8): e70006, 2024 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-39081578

RESUMEN

Despite the huge pool of ideas on how diet can be manipulated to ameliorate or prevent illnesses, our understanding of how specific changes in diet influence the gastrointestinal tract is limited. This review aims to describe two innovative investigative techniques that are helping lift the veil of mystery about the workings of the gut. First, the gas-sensing capsule is a telemetric swallowable device that provides unique information on gastric physiology, small intestinal microbial activity, and fermentative patterns in the colon. Its ability to accurately measure regional and whole-gut transit times in ambulant humans has been confirmed. Luminal concentrations of hydrogen and carbon dioxide are measured by sampling through the gastrointestinal tract, and such application has enabled mapping of the relative amounts of fermentation of carbohydrates in proximal-versus-distal colon after manipulation of the types and amounts of dietary fiber. Second, changes in the smell of feces, via analysis of volatile organic compounds, occur in response to the diet, and by the presence and therapy of irritable bowel syndrome and inflammatory bowel disease. Such information is likely to aid our understanding of what dietary change can do to the colonic luminal microenvironment, and may value-add to diagnosis and therapeutic design. In conclusion, such methodologies enable a more complete physiological profile of the gastrointestinal tract to be created. Systematic description in various cohorts and effects of dietary interventions, particularly when co-ordinated with the analysis of microbiome, are needed.

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