Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Research on Distributed Energy Operation Model of Distribution Network in China

With the development of China's social economy, the total demand for energy is increasing. Compared with traditional thermal power plants, natural gas distributed energy stations have many ad-vantages, which can realize the cascade utilization of energy, the efficiency of energy utilization can be im-proved. Using natural gas as fuel, SO2 and dust pollution can be eliminated, and improve the safety and reli-ability of power supply. However, the distributed energy station needs more capital investment during the project construction, but the distributed energy station has difficulty financing. Firstly, this paper introduces the environment of distributed energy policy in China, analyzes the current status of the China's natural gas distributed energy. Then, this paper summarizes the development of the Germany the energy industry pro-ject financing, and provides experience for the use of the financing methods of China's energy stations. Fi-nally, the model of China's natural gas energy construction is introduced from three aspects of investment, financing and engineering construction. The research results of this paper can provide experience for the project financing of the distributed energy station in China

Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features

Protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of cells; thus, detecting PPIs is one of the most important issues in current molecular biology. Although much effort has been devoted to using high-throughput techniques to identify protein-protein interactions, the experimental methods are both time-consuming and costly. In addition, they yield high rates of false positive and false negative results. In addition, most of the proposed computational methods are limited in information about protein homology or the interaction marks of the protein partners. In this paper, we report a computational method only using the information from protein sequences. The main improvements come from novel protein sequence representation by combing the continuous and discrete wavelet transforms and from adopting weighted sparse representation-based classifier (WSRC). The proposed method was used to predict PPIs from three different datasets: yeast, human and H. pylori. In addition, we employed the prediction model trained on the PPIs dataset of yeast to predict the PPIs of six datasets of other species. To further evaluate the performance of the prediction model, we compared WSRC with the state-of-the-art support vector machine classifier. When predicting PPIs of yeast, humans and H. pylori dataset, we obtained high average prediction accuracies of 97.38%, 98.92% and 93.93% respectively. In the cross-species experiments, most of the prediction accuracies are over 94%. These promising results show that the proposed method is indeed capable of obtaining higher performance in PPIs detection

Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

Contemporary distribution networks can be seen with diverse dispatchable and nondispatchable energy resources. The coordinated scheduling of these dispatchable resources, together with nondispatchable resources, can provide several technoeconomic and social benefits. Since battery energy storage systems (BESSs) and microturbine units (MT units) are capital-intensive, a thorough investigation of their coordinated scheduling under the economic criterion will be a challenging task while considering dynamic electricity prices and uncertainties of renewable power generation and load demand. This paper proposes a comprehensive methodological framework for optimal coordinated scheduling of BESSs with MT unit considering existing renewable energy resources and dynamic electricity prices to maximize the daily profit function of the utility by employing a recently explored modified African buffalo optimization algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps track of system states as a priori, thus resulting in an artificial intelligence-based solution technique for sequential optimization. Further, a novel concept of fictitious charges is also proposed to restrict the counterproductive operational management of BESSs. The proposed model and method are demonstrated on the 33-bus distribution system, and the obtained results verify the effectiveness of the proposed methodology

Comparative study of biomarkers for the early identification of Epstein–Barr virus-associated hemophagocytic lymphohistiocytosis in infectious mononucleosis

Abstract Background and aim Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) and infectious mononucleosis (EBV-IM) share mimic symptoms in the early stages of childhood development. We aimed to examine the clinical features and laboratory indices of these two diseases in children and uncover unique indicators to assist pediatricians in identifying these diseases early. Methods We collected clinical data from 791 pediatric patients diagnosed with EBV-IM or EBV-HLH, compared the clinical traits and laboratory biomarkers presented in the two groups, and constructed predictive models based on them. Results Patients with EBV-IM had greater ratios of cervical lymphadenopathy, eyelid edema, and tonsillitis, whereas individuals with EBV-HLH were more likely to have hepatomegaly and splenomegaly. When using the criteria of interleukin (IL)-10 > 89.6 pg/mL, interferon (IFN)-γ > 45.6 pg/mL, ferritin > 429 μg/L, D-dimer > 3.15 mg/L and triglycerides > 2.1 mmol/L, the sensitivity was 87.9%, 90.7%, 98.1%, 91.1% and 81.5% to predict EBV-HLH, while the specificity was 98.4%, 96.3%, 96.5%, 94.1% and 80.6%, respectively. A logistic regression model based on four parameters (IL-10, ferritin, D-dimer, and triglycerides) was established to distinguish EBV-HLH patients from EBV-IM patients, with a sensitivity of 98.0% and a specificity of 98.2%. Conclusions IL-10, IFN-γ, ferritin and D-dimer levels are significantly different between EBV-HLH and EBV-IM. Predictive models based on clinical signs and laboratory findings provide simple tools to distinguish the two situations

Research on the Influence of Distributed Generation on Voltage in Rural Distribution Network

With the development of photovoltaic projects for poverty alleviation, large number of distributed photovoltaic power plant is incorporated rural grid. On the one hand, photovoltaic poverty project can increase the income of farmers, on the other hand, it can improve the low voltage problem of rural distribution network. While photovoltaic power plant incorporating makes the distribution network from traditional radial network become active network, and causes a change of voltage distribution in feeder. Rural grid distribution network is the weakest link in the entire grid. So that it is necessary to analyse the influence factors of voltage distribution and the interconnection principles of photovoltaic power station. In this paper, through the modeling and simulation of distributed photovoltaic access to rural distribution network, analyses the law of the capacity and access location of photovoltaic power integration in the distribution network, and summarizes the interconnection requirements of the photovoltaic power station. The results show that only reasonable and proper use the distributed photovoltaic power plant, can play the role of voltage support and solve the problem of low voltage in rural distribution network

Research on AC/DC Distribution Network Planning Method

The transition from traditional AC distribution system to future mature AC/DC hybrid distribution mode is an effective means to solve the challenges of high penetration of distributed energy, diversification of energy types, high requirements of power quality and power supply reliability.AC / DC hybrid distribution system can be used in many scenarios by virtue of its technical advantages. This paper analyzes the typical application scenarios of AC / DC distribution network from the perspective of "source network load". Combined with the research status of AC / DC hybrid distribution network, this paper introduces the planning method from three aspects: paper, standard and typical algorithm. Finally, a demonstration project is taken as an example to illustrate the key points of the planning

Research on the Fluctuation Characteristics of Social Media Message Sentiment with Time Before and During the COVID-19 Epidemic

“Tree hole” refers to a social media formed after the death of a social media user, in which other users continue to leave messages due to emotional resonance. This paper focuses on exploring the fluctuation of emotions with time in a “tree hole” of social media such as Microblog, and provides ideas and support for suicide warning, rescue, and user portraits of patients with depression in the “tree hole”. In this paper, the dataset of 2,356,066 messages captured from the “tree hole” Microblog with the “tree hole” agent (i.e., an AI program) and pre-processed. Subsequently, the effective dataset was labeled by a text sentiment analysis model based on BERT and BiLSTM, and accordingly the sentiment was scored. Then the scored data was visualized and analyzed in the time dimension. Finally, it was found that the sentiment of the “tree hole” messages reached a trough at 4:00 am and a peak around 8:00 am. In addition, the overall trend of “tree hole” sentiment has fluctuated downwards from Monday to Sunday. We have concluded that the sentiment of patients with depression fluctuates regularly at some special time points, and special events such as the outbreak of COVID-19 and so on, have a great impact on the emotions of patients with depression. Therefore, it is necessary to strengthen warning and intervention for those who has expressed thoughts of suicide at special points to prevent the spread and fermentation of suicidal emotions in the “tree hole” in time. In addition, the rescue volunteers for patients with depression as Tree Hole Rescue Team should make corresponding adjustments to the rescue strategy when special events occur. This research is of great significance for the emergency response of “tree hole” depressed users in major events such as COVID-19 epidemic