A risk prediction nomogram for resistant hypertension in patients with obstructive sleep apnea.

Patients with obstructive sleep apnea (OSA) are liable to have resistant hypertension (RH) associated with unfavorable cardiovascular events. It is of necessity to predict OSA patients who are susceptible to resistant hypertension. Hence, we conducted a retrospective study based on the clinical records of OSA patients admitted to Yixing Hospital Affiliated to Jiangsu University from January 2018 to December 2022. According to different time periods, patients diagnosed between January 2018 and December 2021 were included in the training set (n = 539) for modeling, and those diagnosed between January 2022 and December 2022 were enrolled into the validation set (n = 259) for further assessment. The incidence of RH in the training set and external validation set was comparable (P = 0.396). The related clinical data of patients enrolled were collected and analyzed through univariate analysis and least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a nomogram. Finally, five variables were confirmed as independent risk factors for OSA patients with RH, including smoking, heart disease, neck circumference, AHI and T90. The nomogram established on the basis of variables above was shown to have good discrimination and calibration in both the training set and validation set. Decision curve analysis indicated that the nomogram was useful for a majority of OSA patients. Therefore, our nomogram might be useful to identify OSA patients at high risk of developing RH and facilitate the individualized management of OSA patients in clinical practice.

Exosomal Long Noncoding RNAs in NSCLC: Dysfunctions and Clinical Potential.

Exosomes are a typical subset of extracellular vesicles (EVs) that can be transmitted from parent cells to recipient cells via human bodily fluids. Exosomes perform a vital role in mediating intercellular communication by shuttling bioactive cargos, such as nucleic acids, proteins and lipids. Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides without protein translation ability and can be selectively packaged into exosomes. Accumulating evidence indicates that exosomal lncRNAs have a critical role in tumor initiation and progression through regulating tumor proliferation, apoptosis, invasion, metastasis, angiogenesis, treatment resistance and tumor microenvironment. Increasing studies suggest that exosomal lncRNAs have great potential to be served as novel targets and non-invasive biomarkers for diagnosis and prognosis in non-small cell lung cancer (NSCLC). In this review, we provide an overview of current research on the disordered functions of exosomal lncRNAs in NSCLC and summarize their potential clinical applications as diagnostic and prognostic biomarkers and therapeutic targets for NSCLC.

Development of a multispectral fluorescence LiDAR for point cloud segmentation of plants.

The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g., unmanned aerial vehicles (UAV) and poles. Towards the aims above, what we believe to be, a novel multispectral fluorescence LiDAR, featuring compact volume, light weight, and low cost, has been proposed and designed. A 405 nm laser diode was employed to excite the fluorescence of plants, and the point cloud attached with both the elastic and inelastic signal intensities that was obtained through the R-, G-, B-channels of a color image sensor. A new position retrieval method has been developed to evaluate far field echo signals, from which the spectral point cloud can be obtained. Experiments were designed to validate the spectral/spatial accuracy and the segmentation performance. It has been found out that the values obtained through the R-, G-, B-channels are consistent with the emission spectrum measured by a spectrometer, achieving a maximum R2 of 0.97. The theoretical spatial resolution can reach up to 47 mm and 0.7 mm in the x- and y-direction at a distance of around 30 m, respectively. The values of recall, precision, and F score for the segmentation of the fluorescence point cloud were all beyond 0.97. Besides, a field test has been carried out on plants at a distance of about 26 m, which further demonstrated that the multispectral fluorescence data can significantly facilitate the segmentation process in a complex scene. These promising results prove that the proposed multispectral fluorescence LiDAR has great potential in applications of digital forestry inventory and intelligent agriculture.

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks.

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.

Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy.

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

Fluorescence Scheimpflug LiDAR developed for the three-dimension profiling of plants.

This work proposes a novel fluorescence Scheimpflug LiDAR (SLiDAR) technique based on the Scheimpflug principle for three-dimension (3D) plant profile measurements. A 405 nm laser diode was employed as the excitation light source to generate a light sheet. Both the elastic and inelastic/fluorescence signals from a target object (e.g., plants) can be simultaneously measured by the fluorescence SLiDAR system employing a color image sensor with blue, green and red detection channels. The 3D profile can be obtained from the elastic signal recorded by blue pixels through elevation scanning measurements, while the fluorescence intensity of the target object is mainly acquired by red and green pixels. The normalized fluorescence intensity of the red channel, related to the chlorophyll distribution of the plant, can be utilized for the classification of leaves, branches and trunks. The promising results demonstrated in this work have shown a great potential of employing the fluorescence SLiDAR technique for 3D fluorescence profiling of plants in agriculture and forestry applications.

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network.

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

Light-sheet based two-dimensional Scheimpflug lidar system for profile measurements.

This work presents a novel concept for 2D Scheimpflug lidar. A light-sheet based 2D Scheimpflug lidar system is developed and realized for surface profile measurements. The theory of a geometrical relationship underlying the system is developed, and the possibility of 3D profile measurements for a plastic bowl, a rhombic carton box and a manikin are presented. The sizes of reconstructed images are consistent with respective physical objects with small (~mm) errors at close range. Experimental results show that the 2D Scheimpflug lidar system performs well for 3D surface profiling and has great potential for close-range applications in other fields.

Oil pollution discrimination by an inelastic hyperspectral Scheimpflug lidar system.

An inelastic hyperspectral Scheimpflug lidar system is developed for range-resolved oil pollution detection and discrimination. A theory of system parametric design is built for aquatic circumstances, and laser-induced fluorescence spectra with an excitation wavelength of 446 nm are employed to detect oil pollution. Seven kinds of typical oil samples are tested and well distinguished using the principal component analysis (PCA) and linear discriminant analysis (LDA) methods. It has been shown that blue laser diodes (LD) have great potential for oil pollution detection, and our system could be further utilized for more applications in both marine and terrestrial environments.

Measurements of the Imaginary Component of the Refractive Index of Weakly Absorbing Single Aerosol Particles.

The interaction of atmospheric aerosols with radiation remains a significant source of uncertainty in modeling radiative forcing. Laboratory measurements of the microphysical properties of atmospherically relevant particles is one approach to reduce this uncertainty. We report a new method to investigate light absorption by a single aerosol particle, inferring changes in the imaginary part of the refractive index with a change in environmental conditions (e.g., relative humidity) and inferring the size dependence of the optical extinction cross section. More specifically, we present measurements of the response of single aerosol particles to near-infrared (NIR) laser-induced heating at a wavelength of 1520 nm. Particles were composed of aqueous NaCl or (NH4)2SO4 and were studied over ranges in relative humidity (40-85%), particle radius (1-2.2 µm), and NIR laser power. The ensuing size change and real component of the refractive index were extracted from measurements of the angular variation in elastically scattered light. From the heating-induced size change at varying NIR beam intensities, we retrieved the change in the imaginary component of the refractive index. In addition, cavity ring-down spectroscopy measurements monitored the change in extinction cross section with modulation of the heating laser power.

Detection of gaseous elemental mercury using a frequency-doubled green diode laser.

We demonstrate a second-harmonic-generation (SHG) based method for the detection of gaseous elemental mercury by using a newly available green diode laser. Multimode ultraviolet radiation at 253.7 nm is generated through a process of SHG. Correlation spectroscopy is introduced into the scheme to guarantee the measurement accuracy. The limit of detection achieved is 0.6 µg/m3 (0.07 ppb) for 1-m pathlength and 10-s integration time. The measurement accuracy is estimated to be 1.2%. The linear response range is estimated to be 0~60 µg/m2 (6.7 ppb·m), within which the linearity error is less than 1%. Real-time monitoring of mercury volatilization is demonstrated with a time resolution of 1 s. The results of performance characterization show that the proposed method has great potentials for mercury sensing in environmental and industrial fields.

Methane detection using scattering material as the gas cell.

A compact methane (CH4) detection system is presented and developed by using an alumina ceramic scattering material as its gas cell. Due to the material's high scattering performance, the optical path length of the gas cell at 1653.7 nm can reach 15.96 cm although its physical length along the light transmission direction is only 0.50 cm. The wavelength modulation spectroscopy technique is employed to enhance the detection sensitivity, and the second harmonic gas absorption signal with low noise is detected and processed. The long-term stability of the system is investigated by the Allan deviation analysis method. Detection limits of 4.5 and 2.6 ppm are achieved at averaging times of 20 s and 200 s, respectively. The dynamic gas exchange performance is also experimentally studied. The experimental results indicate that our system is a good choice for practical applications owing to its small volume, high sensitivity, and stability.