Impact of fintech and environmental regulation on green innovation: inspiration from prefecture-level cities in China

Environmental regulations may promote regional ecological evolution, but they also increase the need for financing green innovation activities. This study uses panel data from prefecture-level cities in China to examine the impact of fintech and environmental regulation on regional green innovation in the digital economy era. Empirical evidence shows that fintech significantly promotes regional green innovation, and fintech has a positive interaction effect with environmental regulation. While the evidence generally supports the role of environmental regulations in promoting green innovation, the evidence is insignificant in some models. The synergistic effect of fintech and environmental regulation on utility model green innovation is significant, but not on invention type green innovation. Climate policy, as a carbon regulatory policy, does not directly lead to green innovation, but it significantly collaborates with fintech to promote green innovation. The effects of fintech and environmental regulation on green innovation also have heterogeneity effects between resource-based and non-resource-based cities, and non-resource-based cities have a greater effect on achieving green innovation through fintech and environmental regulation. Our findings contribute to optimizing the coordination system between financial and environmental policies, thereby driving regional green innovation development with fintech in the digital age

Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis

IntroductionThe comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection).MethodsIn this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis.ResultsWe have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037–3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021–1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction.DiscussionOur findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19

The reporting quality of randomized controlled trials in Chinese herbal medicine (CHM) formulas for diabetes based on the consort statement and its extension for CHM formulas

Background: This study aimed to assess the overall reporting quality of randomized controlled trials (RCTs) in Chinese herbal medicine (CHM) formulas for patients with diabetes, and to identify factors associated with better reporting quality.Methods: Four databases including PubMed, Embase, Cochrane Library and Web of Science were systematically searched from their inception to December 2022. The reporting quality was assessed based on the Consolidated Standards of Reporting Trials (CONSORT) statement and its CHM formula extension. The overall CONSORT and its CHM formula extension scores were calculated and expressed as proportions separately. We also analyzed the pre-specified study characteristics and performed exploratory regressions to determine their associations with the reporting quality.Results: Seventy-two RCTs were included. Overall reporting quality (mean adherence) were 53.56% and 45.71% on the CONSORT statement and its CHM formula extension, respectively. The strongest associations with reporting quality based on the CONSORT statement were multiple centers and larger author numbers. Compliance with the CHM formula extension, particularly regarding the disclosure of the targeted traditional Chinese medicine (TCM) pattern (s), was generally insufficient.Conclusion: The reporting quality of RCTs in CHM formulas for diabetes remains unsatisfactory, and the adherence to the CHM formula extension is even poorer. In order to ensure transparent and standardized reporting of RCTs, it is essential to advocate for or even mandate adherence of the CONSORT statement and its CHM formula extension when reporting trials in CHM formulas for diabetes by both authors and editors

Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms

MA-SNP -- A New Genotype Calling Method for Oligonucleotide SNP Arrays Modeling the Batch Effect with a Normal Mixture Model

Genome-wide association studies hold great promise in identifying disease-susceptibility variants and understanding the genetic etiology of complex diseases. Microarray technology enables the genotyping of millions of single nucleotide polymorphisms. Many factors in microarray studies, such as probe selection, sample quality, and experimental process and batch, have substantial effect on the genotype calling accuracy, which is crucial for downstream analyses. Failure to account for the variability of these sources may lead to inaccurate genotype calls and false positive and false negative findings. In this study, we develop a SNP-specific genotype calling algorithm based on the probe intensity composite representation (PICR) model, while using a normal mixture model to account for the variability of batch effect on the genotype calls. We demonstrate our method with SNP array data in a few studies, including the HapMap project, the coronary heart disease and the UK Blood Service Control studies by the Wellcome Trust Case-Control Consortium, and a methylation profiling study. Our single array based approach outperforms PICR and is comparable to the best multi-array genotype calling methods.

An Imputation Approach for Oligonucleotide Microarrays

<div><p>Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various sources, observed as “bright spots”, “dark clouds”, and “shadowy circles”, etc. It is crucial that these image defects are correctly identified and properly processed. Existing approaches mainly focus on detecting defect areas and removing affected intensities. In this article, we propose to use a mixed effect model for imputing the affected intensities. The proposed imputation procedure is a single-array-based approach which does not require any biological replicate or between-array normalization. We further examine its performance by using Affymetrix high-density SNP arrays. The results show that this imputation procedure significantly reduces genotyping error rates. We also discuss the necessary adjustments for its potential extension to other oligonucleotide microarrays, such as gene expression profiling. The R source code for the implementation of approach is freely available upon request.</p> </div

Application of metaPRS and APOE&#x03B5;4 to Optimize Genetic Risk Prediction Modeling Strategy for Mild Cognitive Impairment

Background Mild cognitive impairment (MCI) is an important stage to intervene and delay the progression of dementia, and it has been shown closely associated with genetic factors, among which apolipoprotein E (APOE) &#x03B5;4 is recognized as an important risk allele of MCI in the medical field. Due to the lack of Genome-Wide Association Study (GWAS) summary data of MCI, it is common to use the GWAS summary data of Alzheimer&apos;s disease (AD) as the base dataset to calculate the polygenic risk score (PRS) of MCI, resulting in suboptimal PRS genetic risk prediction for MCI. Objective To explore the and optimize the statistical modeling strategy of genetic risk in MCI from the perspective of generalized linear model and machine learning, using meta-polygenic risk score (metaPRS) and APOE&#x03B5;4 as important predictors. Methods PRS for the 12 MCI-related traits were calculated and integrated into metaPRS for MCI by elastic-net Logistic regression model. SCOREAPOE was calculated by weighting the APOE&#x03B5;4 effect size with age correction. XGBoost, GBM, Logistic regression and Lasso regression were used as statistical modeling methods to verify the inclusion strategies of different predictors based on metaPRS, SCOREAPOE and basic demographic information (age, gender, education level) . AUC and F-measure were used to evaluate the predictive effect of statistical modeling of genetic risk of MCI. Results metaPRS and SCOREAPOE have high predictive value for the genetic risk of MCI. After including metaPRS, SCOREAPOE and basic demographic information (age, gender, education level) , the predictive effect of each statistical modeling method is XGBoost (AUC=0.69, F-measure=0.88) , GBM (AUC=0.76, F-measure=0.87) , Logistic regression (AUC=0.77, F-measure=0.89) , and Lasso regression (AUC=0.76, F-measure=0.92) . Conclusion When the sample size is 325 (less than 500) , the Lasso regression model constructed by including metaPRS, SCOREAPOE and basic demographic information (age, gender, education level) as predictors has the best effect on MCI genetic risk prediction, providing a new idea and perspective for statistical modeling of genetic risk of complex diseases such as MCI