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A 56-year-old man with a 5-year history of paroxysmal palpitations, which have worsened over the past year, was diagnosed with atrial fibrillation. During evaluation, transesophageal echocardiography revealed a left atrium (LA) tumoral mass attached to the atrial septal fossa ovale, with intra-tumoral blood flow and blood stream draining from the mass. Both coronary computed tomography angiography and coronary angiography demonstrated a coro-cameral fistula connection between the left circumflex artery (LCX) branch and the LA. In addition, they showed feeding arteries of the mass arising from the LCX. The patient underwent surgical resection of the LA mass and repair of the coronary artery fistula. Intraoperative exploration revealed a 1.7 cm × 1.0 cm jelly-like, brittle LA mass and confirmed a rupture of the supplying artery, leading to a coronary artery–left atrial fistula. Surgical ligation was executed to ensure complete sealing of the supplying coronary branch within the atrial septum. Histopathological examination confirmed the diagnosis of left atrial myxoma. The 6-month follow-up indicated no recurrence of the myxoma and restoration of sinus rhythm after radiofrequency ablation. In the literature, cases of a left circumflex artery branch–left atrial fistula due to rupture of the artery supplying a left atrial myxoma are rare.

Abstract Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.

Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Abstract Background Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological “haystack” merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. Results Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. Conclusion Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.

Owing to early diagnosis and rapid development of treatments for cancers, the five-year survival rate of all cancer types has markedly improved worldwide. Over time, however, there has been an increase in the number of cancer patients who develop coronary artery disease (CAD) due to different causes. First, many risk factors are shared between cancer and CAD. Second, inflammation and oxidative stress are common underlying pathogeneses in both disorders. Lastly, cancer therapy can result in endothelial injury, coronary artery spasm, and coagulation, thereby increasing the risk of CAD. As more cancer patients are being diagnosed with CAD, specialized cardiac care should be established to minimize the cardiovascular mortality of cancer survivors. Keywords: Cancer, Coronary artery disease, Cardio-oncology

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at “pollen localization problem”) and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at “pollen classification problem”). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.

Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines. Keywords: drug response, cell line, graph regularization, matrix factorization, response prediction

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations. Keywords: microRNA, small molecule, association prediction, HeteSim algorithm, heterogeneous network

Abstract Background l-2-aminobutyric acid (l-ABA) is an unnatural amino acid that is a key intermediate for the synthesis of several important pharmaceuticals. To make the biosynthesis of l-ABA environmental friendly and more suitable for the industrial-scale production. We expand the nature metabolic network of Escherichia coli using metabolic engineering approach for the production of l-ABA. Results In this study, Escherichia coli THR strain with a modified pathway for threonine-hyperproduction was engineered via deletion of the rhtA gene from the chromosome. To redirect carbon flux from 2-ketobutyrate (2-KB) to l-ABA, the ilvIH gene was deleted to block the l-isoleucine pathway. Furthermore, the ilvA gene from Escherichia coli W3110 and the leuDH gene from Thermoactinomyces intermedius were amplified and co-overexpressed. The promoter was altered to regulate the expression strength of ilvA* and leuDH. The final engineered strain E. coli THR ΔrhtAΔilvIH/Gap-ilvA*-Pbs-leuDH was able to produce 9.33 g/L of l-ABA with a yield of 0.19 g/L/h by fed-batch fermentation in a 5 L bioreactor. Conclusions This novel metabolically tailored strain offers a promising approach to fulfill industrial requirements for production of l-ABA.

Shoot multiplication induced by exogenous cytokinins (CKs) has been commonly used in Phalaenopsis micropropagation for commercial production. Despite this, mechanisms of CKs action on shoot multiplication remain unclear in Phalaenopsis. In this study, we first identified key CKs metabolic genes, including six isopentenyltransferase (PaIPTs), six cytokinin riboside 5′ monophosphate phosphoribohydrolase (PaLOGs), and six cytokinin dehydrogenase (PaCKXs), from the Phalaenopsis genome. Then, we investigated expression profiles of these CKs metabolic genes and endogenous CKs dynamics in shoot proliferation by thidiazuron (TDZ) treatments (an artificial plant growth regulator with strong cytokinin-like activity). Our data showed that these CKs metabolic genes have organ-specific expression patterns. The shoot proliferation in vitro was effectively promoted with increased TDZ concentrations. Following TDZ treatments, the highly expressed CKs metabolic genes in micropropagated shoots were PaIPT1, PaLOG2, and PaCKX4. By 30 days of culture, TDZ treatments significantly induced CK-ribosides levels in micropropagated shoots, such as tZR and iPR (2000-fold and 200-fold, respectively) as compared to the controls, whereas cZR showed only a 10-fold increase. Overexpression of PaIPT1 and PaLOG2 by agroinfiltration assays resulted in increased CK-ribosides levels in tobacco leaves, while overexpression of PaCKX4 resulted in decreased CK-ribosides levels. These findings suggest de novo biosynthesis of CKs induced by TDZ, primarily in elevation of tZR and iPR levels. Our results provide a better understanding of CKs metabolism in Phalaenopsis micropropagation.

Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.

Abstract Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule–MiRNA association prediction (TLHNSMMA) to uncover potential SM–miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM–miRNA associations and miRNA–disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM–miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM–miRNA associations.

With increasingly fierce competition for jobs, the pressures on people have risen in recent years, leading to lifestyle and diet disorders that result in significantly higher risks of cardiovascular disease. Hypertension is one of the common chronic cardiovascular diseases; however, mainstream blood pressure measurement devices are relatively heavy. When multiple measurements are required, the user experience and the measurement results may be unsatisfactory. In this paper, we describe the design of a signal collection module that collects pulse waves and electrocardiograph (ECG) signals. The collected signals are input into a signal processing module to filter the noise and amplify the useful physiological signals. Then, we use a wavelet transform to eliminate baseline drift noise and detect the feature points of the pulse waves and ECG signals. We propose the concept of detecting the wave shape associated with an instance, an approach that minimizes the impact of atypical pulse waves on blood pressure measurements. Finally, we propose an improved method for measuring blood pressure based on pulse wave velocity that improves the accuracy of blood pressure measurements by 58%. Moreover, the results meet the american medical instrument promotion association standards, which demonstrate the feasibility of our measurement system.

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.

Background: Previous studies revealed that culprit vessels of ST-segment elevation myocardial infarction (STEMI) were often related to mild or moderate stenosis. However, recent studies suggested that severe stenosis was primarily found in culprit lesions. The objective of this study was to analyze the stenosis severity of culprit lesions in STEMI patients and to clarify the paradoxical results. Methods: A total of 489 consecutive STEMI patients who underwent primary percutaneous coronary intervention were retrospectively studied from January 2012 to December 2014. The patients were divided into three groups based on stenosis severity using quantitative coronary analysis: Group A, 314 cases, stenosis ≥70%; Group B, 127 cases, stenosis 50–70%; and Group C, 48 cases, stenosis ≤50%. The clinical, demographic, and angiographic data of all groups were analyzed. Results: Patients in Group A exhibited a significantly higher prevalence of history of angina pectoris (95.9% vs. 62.5%, P< 0.001), multivessel disease (73.2% vs. 54.2%, P = 0.007), and lower cardiac ejection fraction (53.3 ± 8.6 vs. 56.8 ± 8.4, P= 0.009) than those in Group C. Multivariable analysis revealed that history of angina pectoris (odds ratio [OR]: 13.89, 95% confidence interval [CI]: 6.21–31.11) and multivessel disease (OR: 2.32, 95% CI: 1.25–4.31) were correlated with severe stenosis of the culprit lesion in Group A. Conclusions: Most culprit lesions in STEMI patients were severe stenosis. These patients exhibited a higher prevalence of angina history and multivessel diseases.

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.

Individualized therapies ask for the most effective regimen for each patient, while the patients' response may differ from each other. However, it is impossible to clinically evaluate each patient's response due to the large population. Human cell lines have harbored most of the same genetic changes found in patients' tumors, thus are widely used to help understand initial responses of drugs. Based on the more credible assumption that similar cell lines and similar drugs exhibit similar responses, we formulated drug response prediction as a recommender system problem, and then adopted a hybrid interpolation weighted collaborative filtering (HIWCF) method to predict anti-cancer drug responses of cell lines by incorporating cell line similarity and drug similarity shown from gene expression profiles, drug chemical structure as well as drug response similarity. Specifically, we estimated the baseline based on the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. The similarity scores were then shrunk and weighted by the correlation coefficients drawn from the know response between each pair. Before used to find the K most similar neighbors for further prediction, they went through the case amplification strategy to emphasize high similarity and neglect low similarity. In the last step for prediction, cell line-oriented and drug-oriented collaborative filtering models were carried out, and the average of predicted values from both models was used as the final predicted sensitivity. Through 10-fold cross validation, this approach was shown to reach accurate and reproducible outcome for those missing drug sensitivities. We also found that the drug response similarity between cell lines or drugs may play important role in the prediction. Finally, we discussed the biological outcomes based on the newly predicted response values in GDSC dataset.

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.

Adaptation is a characteristic that enhances the survival or reproduction of organisms; selection is the critical process leading to adaptive evolution. Therefore, detecting selection is important in studying evolutionary biology. Changes in allele frequency are fundamental to adaptive evolution. The allele frequency of entire genes at the genomic scale is more intensive and precise for analyzing selection effects, compared with simple sequence repeat and single nucleotide polymorphism (SNP) alleles from nuclear gene fragments. Here, we analyzed 29,094 SNPs derived from 80 individuals of 14 L. Liou ex S.L. Chen & Renvoize populations planted near their native habitat (Jiangxia, Hubei Province, JH) and a stressful environment (Qingyang, Gansu Province, QG) to detect selection during initial adaptation. The nucleotide diversity of over 60% of genes was decreased in QG compared with JH, suggesting that most genes were undergoing selection in the stressful environment. We explored a new approach based on haplotype data inferred from RNA-seq data to analyze the change in frequency between two sites and to detect selection signals. In total, 402 and 51 genes were found to be targets of positive and negative selection, respectively. Among these candidate genes, the enrichment of abiotic stress-response genes and photosynthesis-related genes might have been responsible for establishment in the stressful environment. This is the first study assessing the change in allele frequency at the genomic level during adaptation. The method in which allele frequency detects selection during initial adaptation using population RNA-seq data would be useful for developing evolutionary biology.

For shortcomings of current smart home system and power line carrier insufficient data transferring, an intelligent sensor networks system based on intelligent gateway is studied in this paper. Smart home system includes internal network, intelligent gateway, and external network. Multiple servers are designed in gateway, which start at the same time and receive various forms of data. Then data is processed for internal network format. External network includes several modes of communication such as TCP/IP, UDP, and pipe. Internal network includes ZigBee wireless and power line carrier (PLC) communication. Power line carrier protocol is improved, and it expands data transmission capacity to accommodate communication needs of modern family. Smart home system is designed as modules, such as intelligent gateway module, power line carrier communication module, ZigBee wireless communication modules, and appliance within electric equipment. Finally this system is physically designed and verified. The smart home system introduced in this paper has easily used and has strong compatibility.

The early-diverging eudicot order Trochodendrales contains only two monospecific genera, Tetracentron and Trochodendron. Although an extensive fossil record indicates that the clade is perhaps 100 million years old and was widespread throughout the Northern Hemisphere during the Paleogene and Neogene, the two extant genera are both narrowly distributed in eastern Asia. Recent phylogenetic analyses strongly support a clade of Trochodendrales, Buxales, and Gunneridae (core eudicots), but complete plastome analyses do not resolve the relationships among these groups with strong support. However, plastid phylogenomic analyses have not included data for Tetracentron. To better resolve basal eudicot relationships and to clarify when the two extant genera of Trochodendrales diverged, we sequenced the complete plastid genome of Tetracentron sinense using Illumina technology. The Tetracentron and Trochodendron plastomes possess the typical gene content and arrangement that characterize most angiosperm plastid genomes, but both genomes have the same unusual ∼4 kb expansion of the inverted repeat region to include five genes (rpl22, rps3, rpl16, rpl14, and rps8) that are normally found in the large single-copy region. Maximum likelihood analyses of an 83-gene, 88 taxon angiosperm data set yield an identical tree topology as previous plastid-based trees, and moderately support the sister relationship between Buxaceae and Gunneridae. Molecular dating analyses suggest that Tetracentron and Trochodendron diverged between 44-30 million years ago, which is congruent with the fossil record of Trochodendrales and with previous estimates of the divergence time of these two taxa. We also characterize 154 simple sequence repeat loci from the Tetracentron sinense and Trochodendron aralioides plastomes that will be useful in future studies of population genetic structure for these relict species, both of which are of conservation concern.

In this paper, the resistance and the gene homology of Acinetobacter calcoaceticus-baumannii strains which were isolated clinically from the Fourth and the Third People's Hospital of Wuxi, China was studied. The prevalence and distribution was observed and recorded. The susceptibility testing was performed with agar dilution technique and compared with the genotype was investigated with randomly amplified polymorphic DNA (RAPD) technique. To these strains, the resistant rates of Ampicillin, Piperacillin, Cefazolin, Nitrofurantoin were 100%, those of Imipenem, Meropenem and Cefoperazone/Sulbactam were 27%, 15% and 12%, respectively. The resistant rates of other β-lactams and quinolones were higher than 60%. The resistant rates of aminoglycosides were higher than 75%. The type A and D included 35 strains and 22 strains, respectively. The same genotype separated from the same and the different endemic areas. It is sensitive to Imipenem, Meropenem and Cefoperazone/Sulbactam among the 100 strains of Acinetobacter calcoaceticus –baumannii. Maybe the 2 strains are Acinetobacter calcoaceticus and 98 strains are Acinetobacter baumannii . The type A and D are the major types, which show cross-transmission in two hospitals in Wuxi.

Coronary artery perforation (CAP) poses a significant challenge for interventional cardiologists. Management of CAP depends on the location and severity of the perforation. The conventional method for addressing the perforation of large vessels involves the placement of a covered stent, while the perforation of distal and collateral vessels is typically managed using coils, autologous skin, subcutaneous fat, microspheres, gelatin sponge, thrombin or other substances. However, the above techniques have certain limitations and are not applicable in all scenarios. Our team has developed a range of innovative strategies for effectively managing CAP. This article provides an insightful review of the various tips and tricks for the treatment of CAP. [ABSTRACT FROM AUTHOR]