Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China's experience.

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China's eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran's index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China.

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system.

In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it's effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.

Stochastic dynamical analysis for the complex infectious disease model driven by multisource noises.

This paper mainly studies the dynamical behavior of the infectious disease model affected by white noise and Lévy noise. First, a stochastic model of infectious disease with secondary vaccination affected by noises is established. Besides, the existence and uniqueness of the global positive solution for the stochastic model are proved based on stochastic differential equations and Lyapunov function, then the asymptotic behavior of the disease-free equilibrium point is studied. Moreover, the sufficient conditions for the extinction of the disease are obtained and the analysis showed that different noise intensity could affect the extinction of infectious disease on different degree. Finally, the theoretical results are verified by numerical simulation and some suggestions have been put forward on how to prevent the spread of diseases are presented.

Comprehensive analysis of m6A regulators characterized by the immune microenvironment in Duchenne muscular dystrophy.

BACKGROUND: Duchenne muscular dystrophy (DMD) is an X-linked, incurable, degenerative neuromuscular disease that is exacerbated by secondary inflammation. N6-methyladenosine (m6A), the most common base modification of RNA, has pleiotropic immunomodulatory effects in many diseases. However, the role of m6A modification in the immune microenvironment of DMD remains elusive. METHODS: Our study retrospectively analyzed the expression data of 56 muscle tissues from DMD patients and 26 from non-muscular dystrophy individuals. Based on single sample gene set enrichment analysis, immune cells infiltration was identified and the result was validated by flow cytometry analysis and immunohistochemical staining. Then, we described the features of genetic variation in 26 m6A regulators and explored their relationship with the immune mircoenvironment of DMD patients through a series of bioinformatical analysis. At last, we determined subtypes of DMD patients by unsupervised clustering analysis and characterized the molecular and immune characteristics in different subgroups. RESULTS: DMD patients have a sophisticated immune microenvironment that is significantly different from non-DMD controls. Numerous m6A regulators were aberrantly expressed in the muscle tissues of DMD and inversely related to most muscle-infiltrating immune cell types and immune response-related signaling pathways. A diagnostic model involving seven m6A regulators was established using LASSO. Furthermore, we determined three m6A modification patterns (cluster A/B/C) with distinct immune microenvironmental characteristics. CONCLUSION: In summary, our study demonstrated that m6A regulators are intimately linked to the immune microenvironment of muscle tissues in DMD. These findings may facilitate a better understanding of the immunomodulatory mechanisms in DMD and provide novel strategies for the treatment.

Stochastic dynamical behavior of COVID-19 model based on secondary vaccination.

This paper mainly studies the dynamical behavior of a stochastic COVID-19 model. First, the stochastic COVID-19 model is built based on random perturbations, secondary vaccination and bilinear incidence. Second, in the proposed model, we prove the existence and uniqueness of the global positive solution using random Lyapunov function theory, and the sufficient conditions for disease extinction are obtained. It is analyzed that secondary vaccination can effectively control the spread of COVID-19 and the intensity of the random disturbance can promote the extinction of the infected population. Finally, the theoretical results are verified by numerical simulations.

The clinical, myopathological, and molecular characteristics of 26 Chinese patients with dysferlinopathy: a high proportion of misdiagnosis and novel variants.

BACKGROUND: Dysferlinopathy is an autosomal recessive muscular dystrophy caused by pathogenic variants in the dysferlin (DYSF) gene. This disease shows heterogeneous clinical phenotypes and genetic characteristics. METHODS: We reviewed the clinical and pathological data as well as the molecular characteristics of 26 Chinese patients with dysferlinopathy screened by immunohistochemistry staining and pathogenic variants in DYSF genes. RESULTS: Among 26 patients with dysferlinopathy, 18 patients (69.2%) presented as Limb-girdle Muscular Dystrophy Type R2 (LGMD R2), 4 (15.4%) had a phenotype of Miyoshi myopathy (MM), and 4 (15.4%) presented as asymptomatic hyperCKemia. Fifteen patients (57.7%) were originally misdiagnosed as inflammatory myopathy or other diseases. Fifteen novel variants were identified among the 40 variant sites identified in this cohort. CONCLUSION: Dysferlinopathy is a clinically and genetically heterogeneous group of disorders with various phenotypes, a high proportion of novel variants, and a high rate of misdiagnosis before immunohistochemistry staining and genetic analysis.

Case Report: A Novel Splice-Site Mutation in DNAJB6 Associated With Juvenile-Onset Proximal-Distal Myopathy in a Chinese Patient.

DNAJB6 was identified as the causative gene of limb-girdle muscular dystrophy type 1D. In recent years, the phenotypic and molecular spectrum of DNAJB6-myopathy has been expanded, and several mutations of DNAJB6 have been identified in Europe, North America, and Asia. Interestingly, almost all identified mutations in previous reports were point mutations, and most of them were clustered in exon 5, which encodes the G/F domain of DNAJB6. The so-far unique splice site mutation eliminating the entire G/F domain was reported to cause a severe, early-onset phenotype. Here, we report a juvenile-onset Chinese patient who presented with proximal-distal myopathy as well as esotropia and facial weakness. Muscle pathology showed rimmed vacuolation and myofibrillar disarrangement. A novel splice-site mutation NM_058246:c.236-1_240delGGTGGA of the DNAJB6 gene was identified by targeted exome sequencing, which results in a severe defect of the G/F domain. This rare mutation type expands the molecular spectrum of DNAJB6-myopathy and further underlines the importance of the G/F region.

Identification of Auxiliary Biomarkers and Description of the Immune Microenvironmental Characteristics in Duchenne Muscular Dystrophy by Bioinformatical Analysis and Experiment.

Background: Duchenne muscular dystrophy (DMD) is a genetic muscle disorder characterized by progressive muscle wasting associated with persistent inflammation. In this study, we aimed to identify auxiliary biomarkers and further characterize the immune microenvironment in DMD. Methods: Differentially expressed genes (DEGs) were identified between DMD and normal muscle tissues based on Gene Expression Omnibus (GEO) datasets. Bioinformatical analysis was used to screen and identify potential diagnostic signatures of DMD which were further validated by real-time quantitative reverse transcription PCR (RT-qPCR). We also performed single-sample gene-set enrichment analysis (ssGSEA) to characterize the proportion of tissue-infiltrating immune cells to determine the inflammatory state of DMD. Results: In total, 182 downregulated genes and 263 upregulated genes were identified in DMD. C3, SPP1, TMSB10, TYROBP were regarded as adjunct biomarkers and successfully validated by RT-qPCR. The infiltration of macrophages, CD4+, and CD8+ T cells was significantly higher (p < 0.05) in DMD compared with normal muscle tissues, while the infiltration of activated B cells, CD56dim natural killer cells, and type 17 T helper (Th17) cells was lower. In addition, the four biomarkers (C3, SPP1, TMSB10, TYROBP) were strongly associated with immune cells and immune-related pathways in DMD muscle tissues. Conclusion: Analyses demonstrated C3, SPP1, TMSB10, and TYROBP may serve as biomarkers and enhance our understanding of immune responses in DMD. The infiltration of immune cells into the muscle microenvironment might exert a critical impact on the development and occurrence of DMD.

Qualitative analysis of a two-group SVIR epidemic model with random effect.

In this paper, we investigate the dynamical behavior of a two-group SVIR epidemic model with random effect. Firstly, the two-group SVIR epidemic model with random perturbation of natural death rate is established. The existence and uniqueness of positive solution are proved by using stopping time theory and the Lyapunov analysis method. Secondly, a property of the system solution is obtained by using the law of strong numbers and the continuous local martingale. Finally, a new combination of Lyapunov functions is applied. The solution of the model we obtained is oscillating around a steady state if the basic reproduction number is less than one, which is the disease-free equilibrium of the corresponding deterministic model. A numerical simulation is presented to verify our theoretical results.