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Abstract Although deep convolution neural network (DCNN) has achieved great success in computer vision field, such models are considered to lack interpretability in decision‐making. One of fundamental issues is that its decision mechanism is considered to be a “black‐box” operation. The authors design the binary tree structure convolution (BTSC) module and control the activation level of particular neurons to build the interpretable DCNN model. First, the authors design a BTSC module, in which each parent node generates two independent child layers, and then integrate them into a normal DCNN model. The main advantages of the BTSC are as follows: 1) child nodes of the different parent nodes do not interfere with each other; 2) parent and child nodes can inherit knowledge. Second, considering the activation level of neurons, the authors design an information coding objective to guide neural nodes to learn the particular information coding that is expected. Through the experiments, the authors can verify that: 1) the decision‐making made by both the ResNet and DenseNet models can be explained well based on the "decision information flow path" (known as the decision‐path) formed in the BTSC module; 2) the decision‐path can reasonably interpret the decision reversal mechanism (Robustness mechanism) of the DCNN model; 3) the credibility of decision‐making can be measured by the matching degree between the actual and expected decision‐path.

Abstract Background In several eukaryotes, DNA methylation occurs within the coding regions of many genes, termed gene body methylation (GbM). Whereas the role of DNA methylation on the silencing of transposons and repetitive DNA is well understood, gene body methylation is not associated with transcriptional repression, and its biological importance remains unclear. Results We report a newly discovered type of GbM in plants, which is under constitutive addition and removal by dynamic methylation modifiers in all cells, including the germline. Methylation at Dynamic GbM genes is removed by the DRDD demethylation pathway and added by an unknown source of de novo methylation, most likely the maintenance methyltransferase MET1. We show that the Dynamic GbM state is present at homologous genes across divergent lineages spanning over 100 million years, indicating evolutionary conservation. We demonstrate that Dynamic GbM is tightly associated with the presence of a promoter or regulatory chromatin state within the gene body, in contrast to other gene body methylated genes. We find Dynamic GbM is associated with enhanced gene expression plasticity across development and diverse physiological conditions, whereas stably methylated GbM genes exhibit reduced plasticity. Dynamic GbM genes exhibit reduced dynamic range in drdd mutants, indicating a causal link between DNA demethylation and enhanced gene expression plasticity. Conclusions We propose a new model for GbM in regulating gene expression plasticity, including a novel type of GbM in which increased gene expression plasticity is associated with the activity of DNA methylation writers and erasers and the enrichment of a regulatory chromatin state.

Abstract This paper focuses on the development trend of industrial carbon emissions in Bengbu city, Anhui Province in the next ten years, and how to help the industry reach the carbon peak as soon as possible. The research process and conclusions are as follows: (1) Through literature review and carbon emission index method, five main factors affecting industrial carbon emission are identified. (2) The resistance model is used to analyze the main resistance factors of industrial carbon emission reduction in Bengbu city. (3) Based on the existing data of Bengbu city from 2011 to 2020, the grey prediction EGM (1,1) model is used to predict the industrial carbon emissions of Bengbu city from 2021 to 2030. The results show that among the five factors, the urbanization rate has the most significant impact on industrial carbon emissions, while energy intensity has the least impact. Bengbu’s industrial carbon emissions will continue to increase in the next decade, but the growth rate will be flat. Based on the findings of the analysis, specific recommendations on urbanization development, energy structure, and industrial structure of Bengbu city are put forward.

This study presents a novel soft-switching inverter distinguished by a simplified topology and an innovative modulation approach. The design aims to optimize the energy conversion processes commonly found in auxiliary snubber circuits. By minimizing the number of auxiliary switches, the control method is streamlined, thereby enhancing system reliability and cost-efficiency. The principles of operation and conditions for soft-switching are thoroughly analyzed using equivalent circuit models. A 3 kW/16 kHz inverter prototype was constructed, and the experimental results confirm the effectiveness and benefits of the proposed inverter.

Abstract In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, these deep neural models are still considered as “black box” for most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these features. It is widely believed that CNN models combine low‐level features to form complex shapes until the object can be readily classified, however, several recent studies have argued that texture features are more important than other features. In this paper, we assume that the importance of certain features varies depending on specific tasks, that is, specific tasks exhibit feature bias. We designed two classification tasks based on human intuition to train deep neural models to identify the anticipated biases. We designed experiments comprising many tasks to test these biases in the Res Net and Dense Net models. From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased towards a specific anticipated feature.

Xenia effects describe the genetic contribution of pollen to seed phenotypes. Here the authors show that paternal imprinting of Ded1 contributes to the xenia effect in maize by setting the pace of endosperm development.

In order to actively respond to the global climate and environmental challenges, and to help achieve the goal of carbon peaking and carbon neutrality, China aims to achieve carbon peaking by 2030. As the main contributor of energy consumption, construction industry transformation is imminent. This paper focuses on the development trend of carbon emissions in Anhui construction industry in the next 20 years, and how to help Anhui construction industry achieve the carbon peak target. The research process and conclusions are as follows: 1) Through literature review and gray correlation analysis, seven important factors affecting the construction industry are identified. 2) The gray prediction EGM(1,1) model was adopted to forecast the carbon emissions of the construction industry in Anhui Province from 2021 to 2040. The research results show that carbon emissions will increase year by year in the next 20 years. 3) The EGM-BP neural network model was used to further predict the carbon emissions from the construction industry in 2021–2040. It is expected that the carbon emissions from construction industry in Anhui Province will show an “inverted U″ trend in the next 20 years, and is expected to reach its peak in 2031, after which it will start to decline. Based on the results of the above study, we provide support paths for achieving the carbon peak in the construction industry from the perspective of improving and developing the construction system, and provide policy suggestions for relevant departments to accelerate the construction industry and carbon peak construction in Anhui Province.

Recently, the deep neural network has achieved considerable success in the field of machine learning. However, for most tasks, the current neural network models are still considered as the “black-box” structure, essentially because the knowledge a neural network learns from data is often unpredictable and unexplainable. Similar to neural coding in the brain, one neuron may be simultaneously involved in encoding one or even several tasks. Information may be transmitted between neurons along a particular neural-path (neural circuit) to end neurons that encode a specific decision. Inspired by this, we aim to explore the reason for the performance of a neural model based on a neural-path. First, for a trained neural model, we quantify the neural-path in which the role of a neuron was assumed to control the amount of information that can be passed through; Second, we define a Euclidean distance (ED) for every two neural-paths, and by analyzing the EDs between two classes of a neural classifier, we explain the ease of prediction for some classes, whereas others are not. We performed extensive experiments for architectures of ResNet and DenseNet models on several benchmark datasets, and determined that the shorter the distance between the neural-paths of two classes, the easier it is to make mistakes. Finally, we proposed a method for controlling the formation of a “neural-path” to build a partially understandable neural model. For the new ResNet model, each feature map in redirect layers was assigned to participate in encoding only one class. The feasibility of the method is also verified through experiments.

Background/Aims: Cyclin D1 (CCND1) is frequently overexpressed in malignant gliomas. We have previously shown ectopic overexpression of CCND1 in human malignant gliomas cell lines. Methods: Quantitative reverse transcriptase PCR (qRT-PCR) and Western Blot (WB) was performed to investigate the expression of CCND1 in glioma tissues and cell lines. The biological function of CCND1 was also investigated through knockdown and overexpression of BCYRN1 in vitro. Results: Here we reported that CCND1 expression was positively associated with the pathological grade and proliferative activity of astrocytomas, as the lowest expression was found in normal brain tissue (N = 3) whereas the highest expression was in high-grade glioma tissue (N = 25). Additionally, we found that the expression level of CCND1 was associated with IC50 values in malignant glioma cell lines. Forced inhibition of CCND1 increased temozolomide efficacy in U251 and SHG-44 cells. After CCND1 overexpression, the temozolomide efficacy decreased in U251 and SHG-44 cells. Colony survival assay and apoptosis analysis confirmed that CCND1 inhibition renders cells more sensitive to temozolomide treatment and temozolomide-induced apoptosis in U251 and SHG-44 cells. Inhibition of P-gp (MDR1) by Tariquidar overcomes the effects of CCND1 overexpression on inhibiting temozolomide-induced apoptosis. Inhibition of CCND1 inhibited cell growth in vitro and in vivo significantly more effectively after temozolomide treatments than single temozolomide treatments. Finally, inhibition of CCND1 in glioma cells reduced tumor volume in a murine model. Conclusion: Taken together, these data indicate that CCND1 overexpression upregulate P-gp and induces chemoresistance in human malignant gliomas cells and that inhibition of CCND1 may be an effective means of overcoming CCND1 associated chemoresistance in human malignant glioma cells.

The cyclin family protein CCNG2 has an important inhibitory role in cancer initiation and progression, but the exact mechanism is still unknown. In this study, we examined the relationship between CCNG2 and the malignancy of astrocytomas and whether the AKT pathway, which is upregulated in astrocytomas, may inhibit CCNG2 expression. CCNG2 expression was found to be negatively associated with the pathological grade and proliferative activity of astrocytomas, as the highest expression was found in control brain tissue (N = 31), whereas the lowest expression was in high-grade glioma tissue (N = 31). Additionally, CCNG2 overexpression in glioma cell lines, T98G and U251 inhibited proliferation and arrested cells in the G0/G1 phase. Moreover, CCNG2 overexpression could increase glioma cells apoptosis. In contrast, AKT activity increased in glioma cells that had low CCNG2 expression. Expression of CCNG2 was higher in cells treated with the AKT kinase inhibitor MK-2206 indicating that the presence of phosphorylated AKT may inhibit the expression of CCNG2. Inhibition of AKT also led to decreased colony formation in T98G and U251 cells and knocked down of CCNG2 reversed the result. Finally, overexpression of CCNG2 in glioma cells reduced tumor volume in a murine model. To conclude, low expression of CCNG2 correlated with the severity astrocytoma and CCNG2 overexpression could induce apoptosis and inhibit proliferation. Inhibition of AKT activity increased the expression of CCNG2. The present study highlights the regulatory consequences of CCNG2 expression and AKT activity in astrocytoma tumorigenesis and the potential use of CCNG2 in anticancer treatment.

BACKGROUND: To assessed the significance of thoracic injury on the 30-day mortality and outcome of traumatic brain injury (TBI). METHODS: TBI patients admitted to our department were retrospectively evaluated. We developed two prognostic models based on admission predictors with logistic regression analysis to assess the significance of thoracic injuries in determining the 30-day mortality and outcome. The internal validity of the models was evaluated with the bootstrap re-sampling technique. We also validated the models in an external series of 165 patients that collected from our center. Discriminative ability was evaluated with C statistic. Calibrative ability was assessed with the Hosmer-Lemeshow test (H-L test). RESULTS: Among 505 TBI patients admitted, 102 (20.2%) had thoracic injuries. Patients with a PCS ≥ 6 had a 3.142 and 8.065 times higher odds of mortality and poor outcome compared with patients with a PCS 0.05). CONCLUSION: Thoracic injury diagnosed by CT has a negative impact on the 30-day mortality and functional outcome of TBI patients. The extent of PC and the TTS are the predictors for TBI outcome.

An intermediate level between neural circuits and behaviors isneural computations, various behaviors that animals exhibitfollowing some basic control laws can be implemented by somecanonical neural computations [Carandini, 2012]. To explore howthe microscopic activity of neurons leads to macroscopicbehavioral control strategy, we consider basic logic-like operationsas some canonical computations in the brain. In this paper, firstlywe designed the functional circuits for basic logic-like operationsbased on the known neurophysiological properties. Secondly,using basic functional circuits constructed a possible neuralnetwork for decision logic of animal’s behavior. This studyprovides a general approach for constructing the neural circuits toimplement the behavioral control rules. Furthermore, this studywill help us to establish a transitional bridge between themicroscopic activity of the nervous system and the macroscopicanimal behavior.

The present study aimed to identify microRNAs (miRNAs) that are involved in neuropathic pain and predict their corresponding roles in the pathogenesis and development process of neuropathic pain. The rat model of neuropathic pain caused by spared nerve injury (SNI) was established in Sprague-Dawley male rats, followed by small RNA sequencing of the L3–L6 dorsal root ganglion. Real-time PCR was performed to validate the differently expressed miRNAs. Functional verification was performed by intrathecally injecting the animals with miRNA agomir. A total of 72 differentially expressed miRNAs were identified in the SNI rats, including 33 upregulated and 39 downregulated miRNAs. The results of qPCR further verified the expression levels of rno-miR-6215 (P=0.015), rno-miR-1224 (P=0.030), rno-miR-1249 (P=0.038), and rno-miR-488-3p (P=0.048), which were all significantly downregulated in the SNI rats compared to the control ones. The majority of differentially expressed miRNAs were associated with phosphorylation, intracellular signal transduction, and cell death. Target prediction, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses suggested that these differentially expressed miRNAs targeted genes that are related to axon guidance, focal adhesion, and Ras and Wnt signaling pathways. Moreover, miR-1224 agomir significantly alleviated SNI-induced neuropathic pain. The current findings provide new insights into the role of miRNAs in the pathogenesis of neuropathic pain.

The environmental problems with climate change have become the focus of social attention. Based on STIRPAT model, ridge regression analysis is used to analyze the impact of five factors, such as resident population, per capita GDP, energy intensity, energy structure and urbanization rate, on the carbon emissions of Anhui Province. Scenario-analysis is used to set up three scenarios to forecast the carbon emissions of Anhui Province from 2020 to 2035. The results show that when other economic conditions remain unchanged, the energy structure has the most significant impact on carbon emissions, followed by the number of permanent residents. Under the high carbon scenario, there is no peak. Under the medium carbon scenario, Anhui's carbon emissions will peak in 2027, and under the low carbon scenario, the peak time will be advanced to 2025. Finally, the policy recommendations are put forward from the energy structure and urbanization development. [ABSTRACT FROM AUTHOR]