Benchmarking reconstructive spectrometer with multi-resonant cavities

Recent years have seen the rapid development of miniaturized reconstructive spectrometers (RSs), yet they still confront a range of technical challenges, such as bandwidth/resolution ratio, sensing speed, and/or power efficiency. Reported RS designs often suffer from insufficient decorrelation between sampling channels, which results in limited compressive sampling efficiency, in essence, due to inadequate engineering of sampling responses. This in turn leads to poor spectral-pixel-to-channel ratios (SPCRs), typically restricted at single digits. So far, there lacks a general guideline for manipulating RS sampling responses for the effectiveness of spectral information acquisition. In this study, we shed light on a fundamental parameter from the compressive sensing theory - the average mutual correlation coefficient v - and provide insight into how it serves as a critical benchmark in RS design with regards to the SPCR and reconstruction accuracy. To this end, we propose a novel RS design with multi-resonant cavities, consisting of a series of partial reflective interfaces. Such multi-cavity configuration offers an expansive parameter space, facilitating the superlative optimization of sampling matrices with minimized v. As a proof-of-concept demonstration, a single-shot, dual-band RS is implemented on a SiN platform, tailored for capturing signature spectral shapes across different wavelength regions, with customized photonic crystal nanobeam mirrors. Experimentally, the device demonstrates an overall operation bandwidth of 270 nm and a <0.5 nm resolution with only 15 sampling channels per band, leading to a record high SPCR of 18.0. Moreover, the proposed multi-cavity design can be readily adapted to various photonic platforms. For instance, we showcase that by employing multi-layer coatings, an ultra-broadband RS can be optimized to exhibit a 700 nm bandwidth with an SPCR of over 100

Pioglitazone Improves Mitochondrial Function in the Remnant Kidney and Protects against Renal Fibrosis in 5/6 Nephrectomized Rats

Pioglitazone is a type of peroxisome proliferator-activated receptor γ (PPARγ) agonist and has been demonstrated to be effective in chronic kidney diseases (CKD) treatment. However, the underlying mechanism involved in the renoprotection of pioglitazone has not been fully revealed. In the present study, the renoprotective mechanism of pioglitazone was investigated in 5/6 nephrectomized (Nx) rats and TGF-β1-exposed HK-2 cells. Pioglitazone attenuated renal injury and improved renal function, as examined by 24 h urinary protein, blood urea nitrogen and plasma creatinine in Nx rats. Renal fibrosis and enhanced expressions of profibrotic proteins TGF-β1, fibronectin and collagen I caused by Nx were significantly alleviated by pioglitazone. In addition, pioglitazone protected mitochondrial functions by stabilizing the mitochondrial membrane potential, inhibiting ROS generation, maintaining ATP production and the activities of complexes I and III, and preventing cytochrome C leakage from mitochondria. Pioglitazone also upregulated the expression levels of ATP synthase β, COX I and NDUFB8, which were downregulated in the kidney of Nx rats and TGF-β1-exposed HK-2 cells. Furthermore, pioglitazone increased fusion proteins Opa-1 and Mfn2 expressions and decreased fission protein Drp1 expression. The results imply that pioglitazone may exert the renoprotective effects through modulating mitochondrial electron transport chain and mitochondrial dynamics in CKD. Finally, these recoveries were completely or partly inhibited by GW9662, which suggests that these effects at least partly PPARγ dependent. This study provides evidence for the pharmacological mechanism of pioglitazone in the treatment of CKD

Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging

Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system.Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function.Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions.Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Prediction for hepatitis trends in Chongqing based on multisource data: a study of delayed input neural network

Objective To construct a time series analysis fusion tool using multisource internet data and then accurately predict the incidence trend of hepatitis in Chongqing. Methods The incidence rate of hepatitis were obtained from the database of the Centre for Health and Disease Control. Air pollutant data were obtained from the official website of the China Environmental Monitoring Station, climate data were obtained from the National Meteorological Galaxy Center, and network index data were obtained through Baidu search engine. The time duration was from November 2013 to May 2023. Based on existing time series analysis methods, multisource data were used to correct the residual part of the decomposition model. A delayed input neural network (DINN) was constructed based on the respective advantages of non autoregressive (NAR) and long short-term memory (LSTM) recurrent neural networks. Afterwards, optimization modules such as the Nutcracker Optimization Algorithm (NOA) and Joint Quantile Huber Loss (JQHL) were added to the foundation, and then DINN+ was constructed. Results Compared to common single-input models and synchronous multi-input models, DINN achieved the best prediction performance. After adding hyperparameters and loss function optimization, the predictive performance of DINN+ was further improved, with a mean-square error (MSE) of 0.170 9, a mean absolute error (MAE) of 0.461 2, a root-mean-square error (RMSE) of 0.582 1, a mean absolute percentage error (MAPE) of 0.062 6, and a R-square (R2) of 0.884 0 in a testing set. Conclusion Based on the ideas of diverse methods and multidimensional data fusion, we propose a DINN+ optimization model with good accuracy and generalization ability on the basis of previous time series analysis. This model enriches and supplements the methodological research content of using multisource data to calibrate infectious disease time series prediction analysis and can serve as a new benchmark for future analysis of influencing factors and trend prediction of infectious disease public health

Health assessment for railway switch systems

Because of the heavy workload and high failure rate of railway switch system (RSS), the traditional scheduled maintenance can not meet the actual operation needs of the railway. Therefore condition-based maintenance (CBM) and prognostic and health management(PHM), which have been mature in other fields should be introduced into RSS. Health assessment is of a great concern among all the technologies. This paper presents a novel method which can be utilized on the health status evaluation of RSS. First of all, RSS is briefly introduced and the connotation of PHM for RSS is analyzed. Secondly, health indicators (HIs) are extracted by different time domain features, and the best indicators are selected to establish the degradation model. By using clustering algorithm, the change point of state is detected which can be used as an instruction of advance maintenance. Finally, a ZYJ7 RSS is selected to test and verify the proposed method. Result indicates that the algorithm can be effectively applied to health assessment of RSS

Prognostic Value of Soluble Suppression of Tumorigenicity 2 in Chronic Kidney Disease Patients: A Meta-Analysis

Objective. Previous studies have controversial results about the prognostic role of soluble suppression of tumorigenicity 2 (sST2) in chronic kidney disease (CKD). Therefore, we conduct this meta-analysis to access the association between sST2 and all-cause mortality, cardiovascular disease (CVD) mortality, and CVD events in patients with CKD. Methods. The publication studies on the association of sST2 with all-cause mortality, CVD mortality, and CVD events from PubMed and Embase were searched through August 2020. We pooled the hazard ratio (HR) comparing high versus low levels of sST2 and subgroup analysis based on treatment, continent, and diabetes mellitus (DM) proportion, and sample size was also performed. Results. There were 15 eligible studies with 11,063 CKD patients that were included in our meta-analysis. Elevated level of sST2 was associated with increased risk of all-cause mortality (HR 2.05; 95% confidence interval (CI), 1.51–2.78), CVD mortality (HR 1.68; 95% CI, 1.35–2.09), total CVD events (HR 1.88; 95% CI, 1.26–2.80), and HF (HR 1.35; 95% CI, 1.11–1.64). Subgroup analysis based on continent, DM percentage, and sample size showed that these factors did not influence the prognostic role of sST2 levels to all-cause mortality. Conclusions. Our results show that high levels of sST2 could predict the all-cause mortality, CVD mortality, and CVD events in CKD patients

Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing.

The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socio-economic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms

The Canonical E2Fs Are Required for Germline Development in Arabidopsis

A number of cell fate determinations, including cell division, cell differentiation, and programmed cell death, intensely occur during plant germline development. How these cell fate determinations are regulated remains largely unclear. The transcription factor E2F is a core cell cycle regulator. Here we show that the Arabidopsis canonical E2Fs, including E2Fa, E2Fb, and E2Fc, play a redundant role in plant germline development. The e2fa e2fb e2fc (e2fabc) triple mutant is sterile, although its vegetative development appears normal. On the one hand, the e2fabc microspores undergo cell death during pollen mitosis. Microspores start to die at the bicellular stage. By the tricellular stage, the majority of the e2fabc microspores are degenerated. On the other hand, a wild type ovule often has one megaspore mother cell (MMC), whereas the majority of e2fabc ovules have two to three MMCs. The subsequent female gametogenesis of e2fabc mutant is aborted and the vacuole is severely impaired in the embryo sac. Analysis of transmission efficiency showed that the canonical E2Fs from both male and female gametophyte are essential for plant gametogenesis. Our study reveals that the canonical E2Fs are required for plant germline development, especially the pollen mitosis and the archesporial cell (AC)-MMC transition

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

Abstract Background Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. Results The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. Conclusions We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases