A Brief Review of OPT101 Sensor Application in Near-Infrared Spectroscopy Instrumentation for Intensive Care Unit Clinics

The optoelectronic sensor OPT101 have merits in advanced optoelectronic response characteristics at wavelength range for medical near-infrared spectroscopy and small-size chip design with build-in trans-impedance amplifier. Our lab is devoted to developing a series of portable near-infrared spectroscopy (NIRS) devices embedded with OPT101 for applications in intensive care unit clinics, based on NIRS principle. Here we review the characteristics and advantages of OPT101 relative to clinical NIRS instrumentation, and the most recent achievements, including early-diagnosis and therapeutic effect evaluation of thrombus, noninvasive monitoring of patients\u27 shock severity, and fatigue evaluation. The future prospect on OPT101 improvements in noninvasive clinical applications is also discussed

A real-time electricity price decision model for demand side management in wind power heating mode

The problem of wind power curtailment (WPC) during winter heating periods in China’s "Three-North regions" is becoming worse. Wind power heating, though being an effective way to increase wind power consumptions, is constrained by high electric heating costs under a peak-to-valley electricity price pattern. This study develops a real-time price (RTP) decision model which adjusts the time-varying RTPs within an acceptable range of heating users based on the WPC distribution over a particular dispatch day. The lower RTPs accompanying the higher WPC can guide the electric heating user side equipped with regenerative electric boilers (REBs) to actively increase REB imports to absorb additional wind generation. Then, the demand side response using REBs under the RTP scheme is optimized to minimize the total heating cost met by electric heating users while assisting in the large-scale wind generation accommodation. The total heating costs and WPC reductions under different heating scenarios are compared and discussed alongside the effectiveness of the RTP-based demand side management in terms of reducing the WPC and heating costs and increasing the feasibility of wind power heating during winter heating periods

Genome Sequence and Metabolic Analysis of a Fluoranthene-Degrading Strain Pseudomonas aeruginosa DN1

Pseudomonas aeruginosa DN1, isolated from petroleum-contaminated soil, showed excellent degradation ability toward diverse polycyclic aromatic hydrocarbons (PAHs). Many studies have been done to improve its degradation ability. However, the molecular mechanisms of PAHs degradation in DN1 strain are unclear. In this study, the whole genome of DN1 strain was sequenced and analyzed. Its genome contains 6,641,902 bp and encodes 6,684 putative open reading frames (ORFs), which has the largest genome in almost all the comparative Pseudomonas strains. Results of gene annotation showed that this strain harbored over 100 candidate genes involved in PAHs degradation, including those encoding 25 dioxygenases, four ring-hydroxylating dioxygenases, five ring-cleaving dioxygenases, and various catabolic enzymes, transcriptional regulators, and transporters in the degradation pathways. In addition, gene knockout experiments revealed that the disruption of some key PAHs degradation genes in DN1 strain, such as catA, pcaG, pcaH, and rhdA, did not completely inhibit fluoranthene degradation, even though their degradative rate reduced to some extent. Three intermediate metabolites, including 9-hydroxyfluorene, 1-acenaphthenone, and 1, 8-naphthalic anhydride, were identified as the dominating intermediates in presence of 50 μg/mL fluoranthene as the sole carbon source according to gas chromatography mass spectrometry analysis. Taken together, the genomic and metabolic analysis indicated that the fluoranthene degradation by DN1 strain was initiated by dioxygenation at the C-1, 2-, C-2, 3-, and C-7, 8- positions. These results provide new insights into the genomic plasticity and environmental adaptation of DN1 strain

Associations between smoke exposure and kidney stones: results from the NHANES (2007–2018) and Mendelian randomization analysis

PurposeIt is currently controversial whether smoke exposure is associated with the risk of kidney stones. Herein, publicly available databases were combined to explore relationships with the risk of nephrolithiasis in terms of smoking status and serum cotinine concentrations.Materials and methodsFirst, we conducted an observational study using data from 2007 to 2018, based on the National Health and Nutrition Examination Survey (NHANES) database. Univariate analysis, multivariate logistic regression, trend testing, restricted cubic spline (RCS), and multiple imputation (MI) were the main analytical methods of our study. Then, A Mendelian randomization (MR) analysis was performed to explore the causal relationship between serum cotinine and nephrolithiasis. Genetic instruments for serum cotinine and pooled data for kidney stones were derived from publicly available large-scale genome-wide association studies (GWAS). Inverse-variance weighting (IVW) was the primary method for our MR analysis.ResultsA total of 34,657 and 31,352 participants were included in the observational study based on smoking status and serum cotinine concentrations, respectively. Under full adjustment of covariates, current smokers had an increased risk of kidney stones compared to non-smokers [OR = 1.17 (1.04–1.31), P = 0.009, P for trend = 0.010]. Compared with serum cotinine of <0.05 ng/ml, serum cotinine levels of 0.05–2.99 ng/ml [OR = 1.15 (1.03–1.29), P = 0.013] and ≥3.00 ng/ml [OR = 1.22 (1.10–1.37), P < 0.001] were observed to have a higher risk of nephrolithiasis (P for trend < 0.001). In addition, a non-linear relationship between log2-transformed serum cotinine and the risk of nephrolithiasis was found (P for non-linearity = 0.028). Similar results were found when serum cotinine (log2 transformation) was used as a continuous variable [OR = 1.02 (1.01–1.03), P < 0.001] or complete data was used to analyze after MI. In the MR analysis, genetically predicted high serum cotinine was causally related to the high risk of nephrolithiasis [IVW: OR = 1.09 (1.00–1.19), P = 0.044].ConclusionCurrent smoking and high serum cotinine concentrations may be associated with an increased risk of kidney stones. Further research is needed to validate this relationship and explore its underlying mechanisms

Short-term effect of low-dose roxadustat combined with erythropoiesis-stimulating agent treatment for erythropoietin-resistant anemia in patients undergoing maintenance hemodialysis

BackgroundErythropoietin resistance is present in some patients with chronic kidney disease, especially in those undergoing hemodialysis, and is often treated using roxadustat rather than iron supplements and erythropoiesis-stimulating agents (ESAs). However, some patients cannot afford full doses of roxadustat. This retrospective study investigated the efficacy of low-dose roxadustat combined with recombinant human erythropoietin (rhuEPO) therapy in 39 patients with erythropoietin-resistant renal anemia undergoing maintenance hemodialysis (3-4 sessions/week).MethodsThe ability of the combination of low-dose roxadustat and rhuEPO to increase the hemoglobin concentration over 12 weeks was assessed. Markers of iron metabolism were evaluated. Eligible adults received 50–60% of the recommended dose of roxadustat and higher doses of rhuEPO.ResultsThe mean hemoglobin level increased from 77.67 ± 11.18 g/dL to 92.0 ± 8.35 g/dL after treatment, and the hemoglobin response rate increased to 72%. The mean hematocrit level significantly increased from 24.26 ± 3.99% to 30.04 ± 3.69%. The soluble transferrin receptor level increased (27.29 ± 13.60 mg/L to 38.09 ± 12.78 mg/L), while the total iron binding capacity (49.22 ± 11.29 mg/L to 43.91 ± 12.88 mg/L) and ferritin level (171.05 ± 54.75 ng/mL to 140.83 ± 42.03 ng/mL) decreased.ConclusionTherefore, in patients with ESA-resistant anemia who are undergoing hemodialysis, the combination of low-dose roxadustat and rhuEPO effectively improves renal anemia and iron metabolism

Retinal status analysis method based on feature extraction and quantitative grading in OCT images

Background: Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. Methods: This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. Results: This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. Conclusions: This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosi

Optimized energy storage system configuration for voltage regulation of distribution network with PV access

With the large-scale integration of renewable energy such as wind power and PV, it is necessary to maintain the voltage stability of power systems while increasing the use of intermittent renewable energy sources. The rapid development of energy storage technologies permits the deployment of energy storage systems (ESS) for voltage regulation support. This paper develops an ESS optimization method to estimate the optimal capacity and locations of distributed ESS supporting the voltage regulation of a distribution network. The electrical elements of the network integrated with PV and ESS are first modelled to simulate the voltage profile of the network. Then an improved multi-objective particle swarm optimization (PSO) algorithm is employed to minimise a weighted sum of the overall nodal voltage deviation from the nominal level across the network and across the time horizon and the energy capacity of ESS reflecting the associated investment. The improved PSO algorithm adaptively adjusts the inertia weight associated with each particle based on its distance from the best known particle of the population and introduces the cross-mutation operation for a small distance to avoid falling into local optimal solutions. Then the dynamic dense distance arrangement is taken to update the non-inferior solution set and indicate potential global optimal solutions so as to keep the scale and uniformity of the optimal Pareto solution set. To mitigate the impact of decision makers’ preference, the information entropy based technique for order of preference by similarity to ideal solution is used to select the optimal combination of the ESS access scheme and capacity from the Pareto solution set. The proposed ESS optimization method is tested based on the IEEE 24-bus system with additional imports from high-voltage power supply. The voltage profile of the network simulated without the ESS or with the random or optimized ESS placement is compared to illustrate the effectiveness of the optimized ESS in performing voltage regulation under normal operation and supporting emergency power supply during high-voltage transmission failures

Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-Spectral Manifold Learning

The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm called spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consistency property of HSI, SSMRPE utilizes a new spatial-spectral combined distance (SSCD) to fuse the spatial structure and spectral information for selecting effective spatial-spectral neighbors of HSI pixels. Then, it explores the spatial relationship between each point and its neighbors to adjust the reconstruction weights to improve the efficiency of manifold reconstruction. As a result, the proposed method can extract the discriminant features and subsequently improve the classification performance of HSI. The experimental results on the PaviaU and Salinas hyperspectral data sets indicate that SSMRPE can achieve better classification results in comparison with some state-of-The-Art methods