Periodic inundation accelerates the release process of organic carbon from plant litter.

The total organic carbon (OC) from plant litter in riparian zones is an important nutrient source for aquatic organisms and plays a crucial role in the nutrient cycling of river ecosystems. Nevertheless, the total amount of OC in dammed rivers gradually decreases, and the restoration methods are rarely researched. A hypothesis was proposed that the periodic inundation altered the process of OC release from plant litter. To explore the impact of periodic inundation on OC release from litter in the riparian zone, litter bags in situ tests were conducted in the Yalong River. Three inundation treatments were conducted for the test samples, which were NS (never submerged by water), PIS (periodic submerged), and PMS (permanent submerged). Results indicated that the amount of OC released from litters in PIS treatment was about 1.1 times that in PMS treatment, and about 2.1 times that in NS treatment. The average release rate coefficient k of PIS treatment (at mean water level) was the highest (12.8 × 10-4 d-1), followed by PMS treatment (11.0 × 10-4 d-1), and NS treatment (5.6 × 10-4 d-1), which demonstrated that the periodic inundation was critical for OC release. The mean water level was a demarcation line where there was a significant difference in the release of OC in the riparian zone (p < 0.05). Flow velocity alone could account for 84% of the variation in OC release rate, while the flow velocity and inundation duration together could achieve an explanatory degree of 86%. This research can provide a valuable scientific basis for the protection and restoration of river ecosystems, especially for the recovery of OC concentration in dammed rivers.

Validation of radiographic quality of simulation software - ImaSim.

BACKGROUND: Virtual radiographic simulation has been found educationally effective for students to practice their clinical examinations remotely or online. A free available virtual simulator-ImaSim has received particular attention for radiographic science education because of its portability, free of charge and no constrain of location and physical facility. However, it lacks evidence to validate this virtual simulation software to faithfully reproduce radiographs comparable to that taken from a real X-ray machine to date. OBJECTIVE: To evaluate image quality of the virtual radiographs produced by the ImaSim. Thus, the deployment of this radiographic simulation software for teaching and experimental studying of radiography can be justified. METHODS: A real medical X-ray examination machine is employed to scan three standard QC phantoms to produce radiographs for comparing to the corresponding virtual radiographs generated by ImaSim software. The high and low range of radiographic contrast and comprehensive contrast-detail performance are considered to characterize the radiographic quality of the virtual simulation software. RESULTS: ImaSim software can generate radiographs with a contrast ranging from 30% to 0.8% and a spatial resolution as low as 0.6mm under the selected exposure setting condition. The characteristics of contrast and spatial resolution of virtual simulation generally agree with that of real medical X-ray examination machine. CONCLUSION: ImaSim software can be used to simulate a radiographic imaging process to generate radiographs with contrast and detail detectability comparable to those produced by a real X-ray imaging machine. Therefore, it can be adopted as a flexible educational tool for proof of concept and experimental design in radiography.

Estimating rotation angle from asymmetric projection of chest.

BACKGROUND: Manual or machine-based analysis of chest radiographs needs the images acquired with technical adequacy. Currently, the equidistance between the medial end of clavicles and the center of spinous processes serves as the only criterion to assess whether a frontal PA chest radiograph is taken with any rotation. However, this measurement is normally difficult to implement because there exists overlapping of anatomies within the region. Moreover, there is no way available to predict exact rotating angles even the distances were correctly measured from PA chest radiographs. OBJECTIVE: To quantitatively assess positioning adequacy of PA chest examination, this study proposes and investigates a new method to estimate rotation angles from asymmetric projection of thoracic cage on radiographs. METHOD: By looking into the process of radiographic projection, generalized expressions have been established to correlate rotating angles of thorax with projection difference of left and right sides of thoracic cage. A trunk phantom with different positioning angles is employed to acquire radiographs as standard reference to verify the theoretical expressions. RESULTS: The angles estimated from asymmetric projections of thoracic cage yield good agreement with those actual rotated angles, and an approximate linear relationship exists between rotation angle and asymmetric projection of thoracic cage. Under the experimental projection settings, every degree of rotation corresponds to the width difference of two sides of thoracic cage around 13-14 pixels. CONCLUSION: The proposed new method may be used to quantify rotating angles of chest and assess image quality for thoracic radiographic examination.

Automated estimation of thoracic rotation in chest X-ray radiographs: a deep learning approach for enhanced technical assessment.

OBJECTIVES: This study aims to develop an automated approach for estimating the vertical rotation of the thorax, which can be used to assess the technical adequacy of chest X-ray radiographs (CXRs). METHODS: Total 800 chest radiographs were used to train and establish segmentation networks for outlining the lungs and spine regions in chest X-ray images. By measuring the widths of the left and right lungs between the central line of segmented spine and the lateral sides of the segmented lungs, the quantification of thoracic vertical rotation was achieved. Additionally, a life-size, full body anthropomorphic phantom was employed to collect chest radiographic images under various specified rotation angles for assessing the accuracy of the proposed approach. RESULTS: The deep learning networks effectively segmented the anatomical structures of the lungs and spine. The proposed approach demonstrated a mean estimation error of less than 2° for thoracic rotation, surpassing existing techniques and indicating its superiority. CONCLUSIONS: The proposed approach offers a robust assessment of thoracic rotation and presents new possibilities for automated image quality control in chest X-ray examinations. ADVANCES IN KNOWLEDGE: This study presents a novel deep-learning-based approach for the automated estimation of vertical thoracic rotation in chest X-ray radiographs. The proposed method enables a quantitative assessment of the technical adequacy of CXR examinations and opens up new possibilities for automated screening and quality control of radiographs.

Prediction model and application of machine learning for supersaturated total dissolved gas generation in high dam discharge.

Supersaturation of total dissolved gas (TDG) caused by high dam discharge is an ecological risk that cannot be ignored in the operation of hydropower stations. The establishment of an efficient and concise TDG generation prediction model is of great significance to the water ecology and water environment protection of hydropower development reaches. The flow conditions and the process of water-gas mass transfer in discharge and energy dissipation are very complicated and difficult to observe in the field, bringing difficulties to the establishment of prediction model and parameter calibration. Increasingly abundant observations make it possible to establish an efficient machine learning prediction model for supersaturated TDG. In this study, extreme learning machine (ELM) and support vector regression (SVR) were used to establish the prediction model. The main influencing factors of supersaturated TDG, obtained by the analysis of the physical process of the generation of supersaturated TDG, were used as the input of the machine learning model. Then, this research took Dagangshan hydropower station and Xiluodu hydropower station as objects, and established machine learning prediction model for supersaturated TDG with several years of observation data in different discharge scenarios. Four models, including ELM, SVR, GA-ELM and GA-SVR, were obtained through genetic algorithm optimization. The relative errors of the simulation results of each model are mostly less than 5%, mean absolute error (MAE) values less than 1.6%, and root mean square error (RMSE) values less than 2.5%. The results showed that these models are highly accurate and time-saving. Based on this, TDG saturation in downstream of Dagangshan hydropower station with different discharge scenarios was simulated by machine learning model, on which the discharge optimization scheme was put forward. The proposed models, as an important supplement to the prediction of supersaturated TDG, enjoy practical significance and engineering value.

Adaptive denoising hyperspectral data for visualization enhancement of intraoperative tissue.

The vast amount of reflectance information obtained from the hyperspectral imaging devices offers great opportunities for investigating the function and structure of human tissue. However, the captured hyperspectral data often contain various noises due to the intrinsic imperfection of associated electrical and optical imaging components. This work proposed an automatic total variation algorithm to suppress the noises while preserving the details of the spectral and spatial information. The variation of spectral images at neighboring bands was calculated for regulating the total variation of hyperspectral data so that the spectral-dependent noises can be treated differentially across all bands. Experimental results demonstrated that the proposed method could effectively remove the spectral noises, especially near the ends of those extreme bands. The noise suppressed hyperspectral data could then be used for the visualization enhancement on pathophysiological conditions of intraoperative observed anatomies such as the vessels of brain tissues.

Band selection for mapping chromophores of skin tissue.

A numerical approach has been proposed to identify bands for optimally estimating the concentration of three types of viable chromophores within biological tissue. The bands are determined according to the condition number of absorption matrix associated with the attenuation coefficients of chromophores. The effectiveness of different sets of selected band combination was verified by using the spectral reflectance images of skin tissue acquired from standard forearm vascular occlusion tests via a spectroradiometer. Experimental results demonstrated that the concentration of chromophores within skin tissue could be estimated correctly and robustly only when the bands were deliberately selected.

Anticancer Mechanisms of Salinomycin in Breast Cancer and Its Clinical Applications.

Breast cancer (BC) is the most frequent cancer among women worldwide and is the leading cause of cancer-related deaths in women. Cancer cells with stem cell-like features and tumor-initiating potential contribute to drug resistance, tumor recurrence, and metastasis. To achieve better clinical outcomes, it is crucial to eradicate both bulk BC cells and breast cancer stem cells (BCSCs). Salinomycin, a monocarboxylic polyether antibiotic isolated from Streptomyces albus, can precisely kill cancer stem cells (CSCs), particularly BCSCs, by various mechanisms, including apoptosis, autophagy, and necrosis. There is increasing evidence that salinomycin can inhibit cell proliferation, invasion, and migration in BC and reverse the immune-inhibitory microenvironment to prevent tumor growth and metastasis. Therefore, salinomycin is a promising therapeutic drug for BC. In this review, we summarize established mechanisms by which salinomycin protects against BC and discuss its future clinical applications.

Interfacial Solvation and Surface pH of Phenol and Dihydroxybenzene Aqueous Nanoaerosols Unveiled by Aerosol VUV Photoelectron Spectroscopy.

Although the significance of aqueous interfaces has been recognized in numerous important fields, it can be even more prominent for nanoscaled aqueous aerosols because of their large surface-to-volume ratios and prevalent existence in nature. Also, considering that organic species are often mixed with aqueous aerosols in nature, a fundamental understanding of the electronic and structural properties of organic species in aqueous nanoaerosols is essential to learn the interplay between water and organic solutes under the nanoscaled size regime. Here, we report for the first time the vacuum ultraviolet photoelectron spectroscopy of phenol and three dihydroxybenzene (DHB) isomers including catechol, resorcinol, and hydroquinone in the aqueous nanoaerosol form. By evaluating two photoelectron features of the lowest vertical ionization energies originated from the b1(π) and a2(π) orbitals for phenolic aqueous nanoaerosols, their interfacial solvation characteristics are unraveled. Phenolic species appear to reside primarily on/near the aqueous nanoaerosol interface, where they appear only partially hydrated on the aqueous interface with the hydrophilic hydroxyl group more solvated in water. An appreciable proportion of phenol is found to coexist with phenolate at/near the nanoaerosol interface even under a high bulk pH of 12.0, indicating that the nanoaerosol interface exhibits a composition distribution and pH drastically different from those of the bulk. The surface pH of phenol-containing aqueous nanoaerosols is found to be ∼2.2 ± 0.1 units more acidic than that of the bulk interior, as measured at the bulk pH of 12.0. From the photoelectron spectra of DHB aqueous nanoaerosols, the effects of numbers/arrangements of -OH groups are assessed. This study shows that the hydration extents, pH values, deprotonation status, and numbers/relative arrangements of -OH groups are crucial factors affecting the ionization energies of phenolic aqueous nanoaerosols and thus their redox-based activities. The multifaceted implications of the present study in the aerosol science, atmospheric/marine chemistry, and biological science are also addressed.

Molecular Basis of the Antioxidant Capability of Glutathione Unraveled via Aerosol VUV Photoelectron Spectroscopy.

Glutathione (GSH), the most abundant nonenzymatic antioxidant in living systems, actively scavenges various exogenous/endogenous oxidizing species, defending important biomolecules against oxidative damages. Although it is well established that the antioxidant activity of GSH originates from the cysteinyl thiol (-SH) group, the molecular origin that makes the thiol group of GSH a stronger reducing agent than other thiol-containing proteins is unclear. To gain insights into the molecular basis underlying GSH's superior antioxidant capability, here we report, for the first time, the valence electronic structures of solvated GSH in the aqueous aerosol form via the aerosol vacuum ultraviolet photoelectron spectroscopy technique. The pH-dependent electronic evolution of GSH is obtained, and the possible correlations between GSH and its constituting amino acids are interrogated. The valence band maxima (VBMs) for GSH aqueous aerosols are found at 7.81, 7.61, 7.52, and 5.51 ± 0.10 eV at a pH of 1.00, 2.74, 7.00, and 12.00, respectively, which appear to be lower than the values of their corresponding hybrid counterparts collectively contributed from the three isolated constituting amino acids of GSH. An additional photoelectron feature is observed for GSH aqueous aerosols at pH = 12.00, where the thiol group on its Cys residue becomes deprotonated and the relatively well-separated feature allows its vertical ionization energy (VIE) to be determined as 6.70 ± 0.05 eV. Compared to a VIE of 6.97 ± 0.05 eV obtained for a similar thiolate feature observed previously for isolated Cys aqueous aerosols ( Su et al. VUV Photoelectron Spectroscopy of Cysteine Aqueous Aerosols: A Microscopic View of Its Nucleophilicity at Varying pH Conditions . J. Phys. Chem. Lett. 2015 , 6 , 817 - 823 ), a 0.27 eV reduction in the VIE is found for GSH, indicating that the outermost electron corresponding to the nonbonding electron on the thiolate group can be removed more readily from the GSH tripeptide than that from Cys alone. The possible origins underlying the decrease in the VBM of GSH with respect to that of each corresponding hybrid counterpart and the decrease in the VIE of the thiolate feature of GSH with respect to that of the isolated Cys are discussed, providing hints to understand the superior antioxidant capability of GSH from a molecular level.