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The extensive existing knowledge on bi-directional communication between astrocytes and neurons led us to hypothesize that not only ischemia-preconditioned (IP) astrocytes can protect neurons but also IP neurons protect astrocytes from lethal ischemic injury. Here, we demonstrated for the first time that neurons have a significant role in protecting astrocytes from ischemic injury. The cultured medium from IP neurons (IPcNCM) induced a remarkable reduction in LDH and an increase in cell viability in ischemic astrocytes in vitro. Selective neuronal loss by kainic acid injection induced a significant increase in apoptotic astrocyte numbers in the brain of ischemic rats in vivo. Furthermore, TUNEL analysis, DNA ladder assay, and the measurements of ROS, GSH, pro- and anti-apoptotic factors, anti-oxidant enzymes and signal molecules in vitro and/or in vivo demonstrated that IP neurons protect astrocytes by an EPO-mediated inhibition of pro-apoptotic signals, activation of anti-apoptotic proteins via the P13K/ERK/STAT5 pathways and activation of anti-oxidant proteins via up-regulation of anti-oxidant enzymes. We demonstrated the existence of astro-protection by IP neurons under ischemia and proposed that the bi-directionally protective communications between cells might be a common activity in the brain or peripheral organs under most if not all pathological conditions.

Electrocardiogram (ECG) signal is a direct and effective way to find cardiovascular disease timely, which can intuitively reflect changes of the heart beat and activities of different parts. Due to the noise interference from surroundings, acquisition of real-time and high-quality ECG signal is a big challenge for portable mobile medical systems. Integer coefficients infinite impulse response (IIR) digital filter is suitable for portable mobile platform, but it will be a little bit of distortion. So, an improved integer IIR filter for portable ECG monitors is presented in this paper. The proposed IIR filter can instantaneously and effectively eliminate baseline drift of main interference frequency band and 50-Hz power frequency interference in ECG signal. The method is verified by a specific example and MIT/BIH Arrhythmia Database. The improved integer coefficients IIR filter was applied in a portable mobile medical system. It can meet the requirements of ECG filtering in real-time and filtering performance.

In recent years, deep convolutional neural networks (CNN) have been widely used in computer vision and significantly improved the performance of image recognition tasks. Most works use softmax loss to supervise the training of CNN and then adopt the output of last layer as features. However, the discriminative capability of the softmax loss is limited. Here, the authors analyse and improve the softmax loss by manipulating the cosine value and input feature length. As the approach does not change the principle of the softmax loss, the network can easily be optimised by typical stochastic gradient descent. The MNIST handwritten digits dataset is employed to visualise the features learned by the improved softmax loss. The CASIA-WebFace and FER2013 training set are adopted to train deep CNN for face and expression recognition, respectively. Results on both the LFW dataset and FER2013 test set show that the proposed softmax loss can learn more discriminative features and achieve better performance.

This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems.

Decomposition-based multiobjective evolutionary algorithms (MOEA/Ds) have become increasingly popular in recent years. In these MOEA/Ds, evolutionary search is guided by the used weight vectors in decomposition function to approximate the Pareto front (PF). Generally, the decomposition function will be constructed by the weight vectors and the reference point, which play an important role to balance convergence and diversity during the evolutionary search. However, in most existing MOEA/Ds, only one ideal point is used as the reference point for all the evolutionary search, which is harmful to search the entire PF when tackling the problems with difficult-to-approximate PF boundaries. To address the above problem, this paper proposes an evolutionary search method with multiple utopian reference points in MOEA/Ds. Similar to the existing MOEA/Ds, each solution is associated with one weight vector, which provides an evolutionary search direction, while the novelty of our approach is to use multiple utopian reference points, which can provide evolutionary search directions for different weight vectors. Corner solutions are used to approximate the nadir point and then multiple utopian reference points for evolutionary search can be constructed based on the ideal point and the nadir point, which are uniformly distributed on the coordinate axis or planes. The use of these utopian points can prevent solutions to gather in the same region of PF and helps to strike a good balance of exploration and exploitation in the search space. The performance of our proposed algorithm is validated on tackling 16 recently proposed test problems with difficult-to-approximate PF boundaries and empirically compared to eight state-of-the-art multiobjective evolutionary algorithms. The experimental results demonstrate the superiority of the proposed algorithm on solving most of the test problems adopted.

The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health.

This paper studies the prediction of interbank offered rate changes in each working day. Using the actual data of each working day of China’s interbank offered rate from 2007 to 2019, this paper sets up ARIMA, Prophet, grey model and MTGNN to study and verify the time series data, and make a comparison between these models. The limitation of this paper is that it does not consider the impact of macroeconomic characteristics but only considers the predict changes in time series. The results of this paper are expected to be helpful for bank management and interbank transaction decision making.

Cancer cells often overexpress CD47, which triggers the inhibitory receptor SIRPα expressed on macrophages, to elude phagocytosis and antitumor immunity. Pharmacological blockade of CD47 or SIRPα is showing promise as anticancer therapy, although CD47 blockade has been associated with hematological toxicities that may reflect its broad expression pattern on normal cells. Here we found that, in addition to triggering SIRPα, CD47 suppressed phagocytosis by a SIRPα-independent mechanism. This mechanism prevented phagocytosis initiated by the pro-phagocytic ligand, SLAMF7, on tumor cells, due to a cis interaction between CD47 and SLAMF7. The CD47-SLAMF7 interaction was disrupted by CD47 blockade and by a first-in-class agonist SLAMF7 antibody, but not by SIRPα blockade, thereby promoting antitumor immunity. Hence, CD47 suppresses phagocytosis not only by engaging SIRPα, but also by masking cell-intrinsic pro-phagocytic ligands on tumor cells and knowledge of this mechanism may influence the decision between CD47 blockade or SIRPα blockade for therapeutic purposes.

People loss is one of the most important information that the government concerns after an earthquake, because it affects appropriate rescue levels. However, existing evaluation methods often consider an entire stricken region as a whole assessment area but disregard the spatial disparity of influencing factors. As a consequence, results are inaccurately evaluated. In order to address this problem, this paper proposes a post-earthquake evaluation approach of people loss based on the seismic multi-level hybrid grid (SMHG). In SMHG, the whole area is divided into grids at different levels with various sizes. In this manner, the efficiency of data management is improved. With SMHG, disaster statistics can be easily counted under both the administrative unit and per unit area. The proposed approach was then applied to investigate Yushu Ms7.1 earthquake in China. Results revealed that the number of deaths varied with different exposure grids. Among all the different grids, we found that using the 50×50 exposure grid can get the most satisfactory results, and the estimated number of deaths was 2,203, with an 18.3% deviation from the actual loss. People loss results obtained through the proposed approach were more accurate than those obtained through traditional GIS-based methods.

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.

MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2′-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.