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Abstract Background Ductal carcinoma in situ (DCIS) of the breast is an early stage of breast cancer, and preventing its progression to invasive ductal carcinoma (IDC) is crucial for the early detection and treatment of breast cancer. Although single-cell transcriptome analysis technology has been widely used in breast cancer research, the biological mechanisms underlying the transition from DCIS to IDC remain poorly understood. Results We identified eight cell types through cell annotation, finding significant differences in T cell proportions between DCIS and IDC. Using this as a basis, we performed pseudotime analysis on T cell subpopulations, revealing that differentially expressed genes primarily regulate immune cell migration and modulation. By intersecting WGCNA results of T cells highly correlated with the subtypes and the differentially expressed genes, we identified six key genes: FGFBP2, GNLY, KLRD1, TYROBP, PRF1, and NKG7. Excluding PRF1, the other five genes were significantly associated with overall survival in breast cancer, highlighting their potential as prognostic biomarkers. Conclusions We identified immune cells that may play a role in the progression from DCIS to IDC and uncovered five key genes that can serve as prognostic markers for breast cancer. These findings provide insights into the mechanisms underlying the transition from DCIS to IDC, offering valuable perspectives for future research. Additionally, our results contribute to a better understanding of the biological processes involved in breast cancer progression.

Abstract Background A promoter is a specific sequence in DNA that has transcriptional regulatory functions, playing a role in initiating gene expression. Identifying promoters and their strengths can provide valuable information related to human diseases. In recent years, computational methods have gained prominence as an effective means for identifying promoter, offering a more efficient alternative to labor-intensive biological approaches. Results In this study, a two-stage integrated predictor called “msBERT-Promoter” is proposed for identifying promoters and predicting their strengths. The model incorporates multi-scale sequence information through a tokenization strategy and fine-tunes the DNABERT model. Soft voting is then used to fuse the multi-scale information, effectively addressing the issue of insufficient DNA sequence information extraction in traditional models. To the best of our knowledge, this is the first time an integrated approach has been used in the DNABERT model for promoter identification and strength prediction. Our model achieves accuracy rates of 96.2% for promoter identification and 79.8% for promoter strength prediction, significantly outperforming existing methods. Furthermore, through attention mechanism analysis, we demonstrate that our model can effectively combine local and global sequence information, enhancing its interpretability. Conclusions msBERT-Promoter provides an effective tool that successfully captures sequence-related attributes of DNA promoters and can accurately identify promoters and predict their strengths. This work paves a new path for the application of artificial intelligence in traditional biology.

Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.

Understanding protein corona composition is essential for evaluating their potential applications in biomedicine. Relative protein abundance (RPA), accounting for the total proteins in the corona, is an important parameter for describing the protein corona. For the first time, we comprehensively predicted the RPA of multiple proteins on the protein corona. First, we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle, which is dichotomous prediction. Then, we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA, which is regression prediction. Meanwhile, we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis. Finally, we mined important features about the RPA prediction, which provided effective suggestions for the preliminary design of protein corona. The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.

In this paper, a novel combined heat and power system based on compressed air energy storage (CAES) coupled with a solar collector system was introduced. Based on the significant effect of heat pumps in improving heat quality, the heat pump and solar energy are adopted to improve energy storage and economic performance. To maximize the effective utilization of solar energy, the energy dispatch scheme of the system has been studied in detail. The system's performance under multiple operating conditions has been evaluated. Moreover, to further explore the optimization potential of the solar-coupled compressed air energy storage system, important factors such as the distribution proportion of thermal oil and key temperature have been emphasized and analyzed. The optimization of key temperature, distribution proportion of thermal oil, and operating conditions can enhance the cascade utilization of energy, and also provide a comprehensive approach for the design and optimization of the proposed system. Parametric sensitivity analysis was first conducted. Results show that the charging pressure should be close to the discharging pressure and there is no local optimal distribution ratio for the thermal oil. The optimal condition under design constraints is also determined, the system power efficiency, energy efficiency, and power-to-power efficiency reached 0.297, 0.851, and 0.570, while the levelized cost of storage of the system is 0.527 $/kWh, the dynamic payback period is 9.516 and simple payback period is 7.428 under the specified conditions. The output power and heat in 6 h of discharging are 3829.50 kWh and 7147.48 kWh, respectively, with a total investment cost of 7373.57k$. Moreover, the exergy and economic analyses were then conducted. The highest exergy losses were caused by the solar collector system with 29.01% of the total loss. The turbines and the solar system also had the highest economic cost, indicating directions for further optimization.

The home energy management system (HEMS), which utilizes multi-modal data from multiple sensors to generate the knowledge about decision making, is essential to the optimization of home energy management efficiency. Load scheduling based on HEMS can improve the utilization efficiency of multi-modal data and derived knowledge, achieve power supply-demand balance, and reduce users’ electricity costs. This paper proposes a distributed load optimization scheduling method for the load scheduling problem in HEMS based on multi-modal data-driven algorithm. Additionally, a two-stage data-driven optimization method is proposed, including a first-stage optimization model based on minimizing electricity costs and a second-stage optimization model based on minimizing system load fluctuations. In the first stage, cost self-optimization is performed based on energy storage devices. In the second stage, a load optimization instruction is issued by the control center, and each user optimizes the load fluctuations based on the system load data. Compared to centralized control methods, this approach reduces the computational overhead of the control center. Finally, simulation experiments based on load scheduling in the HEMS are conducted. The results of the first optimization stage show that when the battery capacity integrated into the system increases from 3.68 kWh to 6.68 kWh, user costs can be reduced from 57.572 cents to 42.064 cents. It is not only evident that the proposed method can effectively save users on electricity costs, but the introduction of larger capacity batteries also lowers these costs. The second stage of load fluctuation optimization results show that the proposed method can effectively optimize the usage data of a group of users and decrease the absolute peak-valley difference by 8.8%.

Compressed air energy storage (CAES) is considered to be one of the most promising large-scale energy storage technologies to address the challenges of source-grid-load-storage integration. However, the integration strategies of CAES with renewable energy sources (RES), driven by the goal of enhancing system efficiency, have not been fully explored. In this paper, a unique energy allocation strategy is introduced for a CAES system when coupled with solar energy. Intermittent solar energy is transformed into a consistent heat source, jointly preheating the air entering the turbines with compression heat. Besides, three cogeneration systems with different waste heat recovery strategies are designed and compared. Based on the basic integration of CAES and solar collection system (SCS), heat pump (HP) and organic rankine cycle (ORC) are progressively introduced to recover waste heat from compressors and SCS, respectively. Finally, parametric studies and energy, exergy, economic (3E) analysis for the three cogeneration systems are conducted. The results show that the system integrated with HP presents the best system power efficiency (SPE) of 38.9% and system energy efficiency (SENE) of 99.6%. The basic integration system presents the best power-to-power (P2P) efficiency of 57.4%. Additionally, it offers the best economic performance with a levelized cost of storage (LCOS) of 0.520 $/kWh. Besides, the introduction of HP can improve the economic and electricity conversion performance when the air release pressure is in the low-pressure region, but also increases exergy destruction caused by the large temperature difference of heat exchangers. The introduction of ORC raises the overall costs caused by its complex components, but has little effect on system exergy destruction. Finally, by comparing with existing studies, the superiority of the proposed CAES and SCS integration strategy is validated.

The growing scale of electric vehicles (EVs) brings continuous challenges to the energy trading market. In the process of grid-connected charging of EVs, disorderly charging behavior of a large number of EVs will have a substantial impact on the grid load. Aiming to solve the problem of optimal scheduling for charging and discharging of EVs, this paper first establishes a model for the charging and discharging scheduling of EVs involving the grid, charging equipment, and EVs. Then, the established scheduling model is described as a partially observable Markov decision process (POMDP) in the multi-agent environment. This paper proposes an optimization objective that comprehensively considers various factors such as the cost of charging and discharging EVs, grid load stability, and user usage requirements. Finally, this paper introduces the long short-term memory enhanced multi-agent deep deterministic policy gra dient (LEMADDPG) algorithm to obtain the optimal scheduling strategy of EVs. Simulation results prove that the proposed LEMADDPG algorithm can obtain the fastest convergence speed, the smallest fluctuation and the highest cumulative reward compared with traditional deep deterministic policy gradient and DQN algorithms.

Demand response technologies can achieve the objective of optimizing resource allocation and ensuring efficient operation of the smart grid by motivating the energy users to change their power usage behavior. However, the increasing uncertainty of smart grid environment brings great challenges to the development of demand response technique. In this study, we build a long short-term memory (LSTM) network as a load forecasting model to predict user load data in order to obtain accurate consumption behavior of energy users. Then, we utilize a Stackelberg game model based on the load forecasting model to dynamically optimize the electricity prices set by power suppliers at different times, enhancing the efficiency of demand response between users and suppliers. Extensive simulation experiments demonstrate that the LSTM-based load forecasting model achieves an accuracy of up to 96.37% in predicting user load demand. And the game model reduces the overall expenditure of users by 30% compared with the general pricing model.

BackgroundHealthcare systems had an exceptionally difficult time during the early COVID-19 pandemic. Nurse managers in particular made enormous contributions to ensuring the safety of patients and front-line nurses while being under excessive psychological stress. However, little is known about their experiences during this time.ObjectiveThe aim of this study was thus to assess the level of stress overload and psychological feelings of nurse managers during the early COVID-19 pandemic.MethodsA mixed methods sequential explanatory design study with non-random convenience sampling was performed, following the STROBE and COREQ checklists. The study was conducted at the Affiliated Dongyang Hospital, Wenzhou Medical University, with data collected from six provinces in southern China (Zhejiang, Hubei, Shanghai, Jiangsu, Hunan and Jiangxi) during March 2020 and June 2020. A total of 966 nurse managers completed the Stress Overload Scale and Work-Family Support Scale. In addition, a nested sample of nurse managers participated in semi-structured face-to-face interviews. The data were then analyzed using qualitative content analysis, Pearson correlation, and multiple linear regression.ResultsThe quantitative results showed that nurse managers experienced a moderate level of stress load. There was a significant negative correlation between work-family support and stress load (r = −0.551, p

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.

The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it is impractical to detect FDIAs by fixed methods. This paper proposed a false data injection attack model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP). A deep reinforcement learning-based method is proposed to detect FDIAs with a combined dynamic-static detection mechanism. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception capability. Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of attack model and efficiency of detection method. Results proved efficacy of the detection method in different scenarios.

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.

Machine learning has been widely applied in the fields of biomedicine, computational biology, bioinformatics, image processing, and so on. The performance of machine learning methods mainly relies on feature representation that is the mapping from various types of raw data (i.e., image and genomic data) to a discriminant high-dimensional data space, bridging the raw data with the input of learning/inference algorithms. A good representation is often one that captures the discriminative information from the data and supports effective machine learning. However, over the last few decades, most representation learning approaches are labor-intensive and heavily dependent on the professional knowledge of researchers (dependent on handcrafted feature engineering). To conduct more novel applications in bioinformatics and biomedicine, the weakness of current learning algorithms should be overcome by developing novel feature representation learning algorithms, including supervised representation learning algorithms that are learning features from labeled data, unsupervised feature representation strategies that are learning feature representatives from unlabeled data, and deep feature representation learning algorithms that are learning representative features from data using deep learning architectures.

ObjectiveBehavioral intentions to care for patients with infectious diseases are crucial for improving quality of care. However, there have been few studies of the behavioral intentions and factors influencing patient care by clinical nurses during the COVID-19 pandemic. This study aims to explore cognition, attitudes, subjective norms, self-efficacy, and behavioral intentions of clinical nurses while caring for COVID-19 patients and to explore any influencing factors.MethodA cross-sectional survey was conducted of nurses through convenience sampling in southeast China from February 2020 to March 2020. The questionnaire was developed based on the theory of planned behavior and self-efficacy.ResultsA total of 774 nurses completed the survey. Of these, 69.12% (535/774) reported positive behavioral intentions, 75.58% (585/774) reported a positive attitude, and 63.82% (494/774) reported having the confidence to care for patients. However, the lack of support from family and friends and special allowance affected their self-confidence. Attitude, self-efficacy, subjective norms, and ethical cognition were significantly positively correlated with behavioral intentions (r = 0.719, 0.690, 0.603, and 0.546, respectively, all P < 0.001). Structural equation model showed that self-efficacy, attitude, ethical cognition, and subjective norms had positive effects on behavioral intentions (β = 0.402, 0.382, 0.091, and 0.066, respectively, P < 0.01). The total effect of behavioral intentions was influenced by attitude, ethical cognition, self-efficacy, and subjective norms (β = 0.656, 0.630, 0.402, and 0.157, respectively, P < 0.01). In addition, ethical cognition had a positive mediating effect on behavioral intentions (β = 0.539, P < 0.001).ConclusionThe study results indicated that attitude, ethical cognition, and self-efficacy were the main factors influencing nurses' behavioral intention. Efforts should be made to improve nurses' attitude and self-efficacy through ethical education and training to increase behavioral intentions to care for patients with infectious diseases, which will improve the quality of nursing care.

The injection of carbon dioxide (CO2) into gas reservoirs has become an important way to enhance gas recovery and reduce CO2 emissions. Large discrepancies are observed when predicting natural gas compressibility factors with high CO2 content by several well-known empirical correlations. An explicit correlation is proposed to improve the prediction accuracy in the estimation of compressibility factors on condensate gases with variable CO2 contents. The analysis of the results is carried out on the basis of 202 experimental data from 9 various mixtures of natural gases. The results show that relative deviations of compressibility factors predicted by conventional empirical correlations increase with the increase in CO2 mole fraction with an average error of 8%. The average error of the new method is less than 4%. The effect of compressibility factors on the estimations of dynamic reserves is studied and the compressibility factor causes a 3% reduction in dynamic reserves estimation. The proposed correlation has fewer uncertainties and more accurate results than other correlations that involve the iterative process in calculating compressibility factors of natural gases with variable CO2 contents.

On January 3rd 2019, the Chang’e-4 mission successfully landed in the Von Kármán Crater inside the South Pole-Aitken (SPA) basin and achieved the first soft landing on the farside of the Moon. Lunar penetrating radar (LPR) equipped on the rover measured the shallow subsurface structure along the motion path for more than 700 m. LPR data could be used to obtain the dielectric properties of the materials beneath the exploration area, providing important clues as to the composition and source of the materials. Although the properties of the upper fine-grained regolith have been studied using various methods, the underlying coarse-grained materials still lack investigation. Therefore, this paper intends to estimate the loss tangent of the coarse-grained materials at depth ranges of ~12 and ~28 m. Stochastic media models with different rock distributions for the LPR finite-difference time-domain (FDTD) simulation are built to evaluate the feasibility of the estimation method. Our results show that the average loss tangent value of coarse-grained materials is 0.0104±0.0027, and the abundance of FeOT+TiO2 is 20.08 wt.%, which is much higher than the overlying fine-grained regolith, indicating different sources.

Biocomputing and synthetic biology have been two of the most exciting emerging fields in recent years [...]

Background. This meta-analysis was performed to assess the efficacy and safety of chewing gum in intestinal function recovery after colorectal cancer surgery. Methods. A systematic search was conducted in PubMed, Embase, Science Direct, and Cochrane library for relevant randomized controlled trials (RCTs) published until April 2017. Summary risk ratios or weighted mean differences with 95% confidence intervals were used for continuous and dichotomous outcomes, respectively. Results. 17 RCTs with a total number of 1845 patients were included. Gum chewing following colorectal cancer surgery significantly reduced the time to first passage of flatus (WMD −0.55; 95% CI −0.94 to −0.16; P=0.006), first bowel movement (WMD −0.60; 95% CI −0.87 to −0.33; P