Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

Morphological features and nanostructures generated during SiC graphitization process

Surface morphological features and nanostructures generated during SiC graphitization process can significantly affect fabrication of high-quality epitaxial graphene on semiconductor substrates.In this work,we investigate the surface morphologies and atomic structures during graphitization process of 4H-SiC(0001) using scanning tunneling microscopy.Our high-magnified scanning-tunneling-microscope images exhibit the appearance and gradual developments of SiC(1 × 1)nanostructures after 1100℃ cleaning treatments,irregularly distributed among carbon nanocaps and(√3×√3) reconstruction domains.A model for the formation and growth progression of SiC(1 × 1) nanostructures has been proposed.When post-annealing temperature reaches 1300 ℃,the nanoholes and nanoislands can be observed on the surface,and multilayer graphene is often detected lying on the top surface of those nanoislands.These results provide profound insights into the complex evolution process of surface morphology during SiC thermal decomposition and will shed light on fabrication of SiC nanostructures and graphene nanoflakes.

Protective effect of DNA vaccine with the gene encoding 55kDa antigen fragment against Pneumocystis carinii in mice

Objective:To evaluate the protective effect of DNA vaccine with the gene encoding 55kDa antigen fragment of Pneumocystis carinii(P.carina) against P.carina in mice.Methods:The fragment of the antigen within p55(p55-582) was cloned.Then recombinant plasmid was constructed based on the eukaryotic expression vector pcDNA3.1(+).BALB/c mice were used as experimental models to examine the immunogenicity of pcDNA3.1(+)-p55-582.ELBA and RTPCR were used to evaluate the role of this kind of DNA vaccine.Results:The results of western blot indicated that the recombinant DNA[pcDNA3.1(+)-p55-582]could be expressed correctly and had antigenicity in transfected COS-7 cells.ELBA and RT-PCR showed that pcDNA3.1(+)- p55-582 elicited antibody production,stimulated lymphocyte proliferation and provided partial protection by reducing the P.carina burden.Conclusions:The data demonstrate that pcDNA3.1(+)-p55-582 might be potent vaccination that can afford the partial protection for the immunized animals.

Phase Transition and Oxygen Ion Diffusion in （La1-xLnx）2Mo2O9 （Ln=Nd, Gd, x=0.05-0.25） Using Dielectric Relaxation Method

Dielectric relaxation method was employed to study the properties of oxygen ion diffusion and phase transition in the oxide-ion conductors （Lal-xLnx）2Mo209 （Ln=Nd, Gd, x=0.05-0.25）. Two dielectric loss peaks were observed： peak Pd at about 600 K and peak P5 around 720 K. Peak Pd is a relaxational peak and associated with the short-range diffusion of oxygen ions, while peak P5 hardly changes its position and dramatically decreases in height with increasing frequency, exhibiting non-relaxational nature. With increasing Ln^3＋ concentration, the heights of peak Ph and Pd increase at first and then decrease after passing a maximum at 15% doping. It is suggested that peak P5 is related to the phase transition of a static disordered state to a dynamic disordered state in oxygen ions/vacancies distribution. It is found that the 15%Gd or 15%Nd doped La2Mo209 samples exhibit the highest conductivity in accordance with the highest height of peak Pd at this doping content.

High-brightness photo-injector with standing-wave buncher-based ballistic bunching scheme for inverse Compton scattering light source

We report our recent progress in the design and simulation of a high-brightness S-band photo-injector with a ballistic bunching scheme aimed at driving an inverse Compton scattering(ICS)X-ray source.By adding a short standing-wave buncher between the RF gun and first booster in a conventional S-band photo-injector,electron bunches with a 500 pC charge can be compressed to the sub-picosecond level with very limited input RF power and an unchanged basic layout of the photo-injector.Beam dynamics analysis indicates that fine tuning of the focusing strength of the gun and linac solenoid can well balance additional focusing provided by the standing wave buncher and generate a well-compensated transverse emittance.Thorough bunching dynamics simulations with different operating conditions of the buncher show that a buncher with more cells and a moderate gradient is suitable for simultaneously obtaining a short bunch duration and low emittance.In a typical case of a 9-cell buncher with a 38 MV/m gradient,an ultrashort bunch duration of 0.5 ps(corresponding to a compression ratio of>5)and a low emittance of<1 mm mrad can be readily obtained for a 500 pC electron pulse.This feasible ballistic bunching scheme will facilitate the implementation of an ultrashort pulse mode inverse Compton scattering X-ray source on most existing S-band photo-injectors.

MRTF-A transactivates COMT gene and decreases the anti-tumor effects of tamoxifen

Myocardin-related transcription factors A (MRTF-A) is a myocardin-related transcription factor that have been found strongly activated CarG box–containing genes through its direct binding to serum response factor (SRF). In the present study, the MRTF-A ex-pression vector was constructed. The MTT assay showed that transfection of MRTF-A could significantly decrease the anti-tumor effect of tamoxifen on MCF-7 breast cancer cells. The bioinformatics analysis found that the CarG element existed in the pro-moter region of COMT gene of many familiar verte-brates, including of human, rhesus macaque, chimpanzee, etc. The results of RT-PCR assay further showed that MRTF-A could enhance the transcrip-tion level of COMT. These results are the first to indicate that COMT might be a target gene which could be regulated by MRTF-A/SRF, and such transactivation event might be involved in the process of tamoxifen resistance.

Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys

Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium. However, it is still difficult to obtain novel near-β-Ti alloys with low modulus. This study developed a method that combines machine learning with calculation of phase diagrams(CALPHAD) to facilitate the design of near-β-Ti alloys. An elastic modulus database of Ti–Nb–Zr–Mo–Ta–Sn system was constructed first, and then three features(the electron to atom ratio, mean absolute deviation of atom mass, and mean electronegativity) were selected as the key factors of modulus by performing a three-step feature selection. With these features, a highly accurate model was built for predicting the modulus of near-β-Ti alloys. To further ensure the accuracy of modulus prediction, machine learning with the elastic constants calculated was leveraged by CALPHAD database. The root mean square error of the well-trained model can be as low as 6.75 GPa. Guided by the prediction of machine learning and CALPHAD, three novel near-β-Ti alloys with elastic modulus below 50 GPa were successfully designed in this study. The best candidate alloy(Ti–26Nb–4Zr–4Sn–1Mo–Ta) exhibits an ultra-low modulus(36.6 GPa) after cold rolling with a thickness reduction of 20%. Our method can greatly save time and resources in the development of novel Ti alloys, and experimental verifications have demonstrated the reliability of this method.

Extracorporeal Cardiopulmonary Resuscitation in Children of Asia Pacific： A Retrospective Analysis of Extracorporeal Life Support Organization Registry

Background： Recent advances in extracorporeal membrane oxygenation （ECMO） have led to increasing interest in its use during cardiopuhnonary resuscitation （CPR）. However, decisions regarding extracorporeal CPR （ECPR） in children are difficult as a result of limited studies, especially in Asia Pacific. The objective of this study was to investigate trends in survival and demographic details for children with ECPR in Asia Pacific recorded in the Extracorporeal Life Support Organization （ELSO） registry from 1999 to 2016 and identify the risk factors associated with in-hospital mortality. Methods： The data of children younger than 18 years of age who received ECPR over the past 18 years in Asia Pacific were retrospectively analyzed. The data were extracted from the ELSO registry and divided into two 9-year groups （Group 1 1999-2007 and Group 2： 2008-2016） to assess temporal changes using univariate analysis. Then, univariate and multiple logistic regression analyses were performed between survivors and nonsurvivors to identify factors independently associated with in-hospital mortality. Results： A total of 321 children were included in final analysis, with an overall survival rate of 50.8%. Although survival rates were similar between Group 1 and Group 2 （43.1% vs. 52.5%,χ^2= 1.67, P - 0.196）, the median age （1.7 [0.3, 19.2] months for Group 1 vs. 5.6 [0.8, 64.9] months for Group 2, t = 2.93, P = 0.003） and weight （3.7 [3.0, 11.5] kg for Group 1 vs. 6.0 [3.4, 20.3] kg for Group 2, t = -3.14, P 0.002） of children increased over time, while the proportion of congenital heart disease （75.9% for Group 1 vs. 57.8% for Group 2, χ^2=6.52, P 0.011 ） and cardiogenic shock （36.2% for Group 1 vs. 7.2% for Group 2, χ^2=36.59, P 〈 0.001 ） decreased. Patient conditions before ECMO were worse, while ECMO complications decreased across time periods, especially renal complications. Multiple logistic regress!on analysis of ECMO complications showed that disseminated intravascular coagulation （DIC）, myocardial stunning, and neurological complications were independently associated with increased odds of hospital mortality. Conclusions： The broader indications and decreased complication rates make EPCR to be applicated more and more extensive in children in Asia Pacific region. ECMO complications such as myocardial stunning are independently associated with decreased survival.

Controllability and Its Applications to Biological Networks

Biological elements usually exert their functions through interactions with others to form various types of biological networks.The ability of controlling the dynamics of biological networks is of enormous benefits to pharmaceutical and medical industry as well as scientific research.Though there are many mathematical methods for steering dynamic systems towards desired states,the methods are usually not feasible for applying to complex biological networks.The difficulties come from the lack of accurate model that can capture the dynamics of interactions between biological elements and the fact that many mathematical methods are computationally intractable for large-scale networks.Recently,a concept in control theory --controllability,has been applied to investigate the dynamics of complex networks.In this article, recent advances on the controllability of complex networks and applications to biological networks are reviewed.Developing dynamic models is the prior concern for analyzing dynamics of biological networks.First,we introduce a widely used dynamic model for investigating controllability of complex networks.Then recent studies of theorems and algorithms for having complex biological networks controllable in general or specific application scenarios are reviewed.Finally,applications to real biological networks manifest that investigating the controllability of biological networks can shed lights on many critical physiological or medical problems,such as revealing biological mechanisms and identifying drug targets,from a systematic perspective.

Organic Composite Crystal with Persistent Room-Temperature Luminescence Above 650 nm by Combining Triplet–Triplet Energy Transfer with Thermally Activated Delayed Fluorescence

Structural modifications are a successful and commonly used approach to tune the emission properties of diverse fluorophores,but extending this approach to heavy-atom-free persistent luminophores has so far been unsuccessful.Here we employed a novel strategy to demonstrate triplet–triplet energy transfer from an organic room-temperature phosphor(RTP)with persistent luminescence to an organic molecule with thermally activated delayed fluorescence(TADF).We illustrated this approach by preparing heavy-atom-free composite crystals of an RTP with a long-lifetime emission and a red emissive organic fluorophore with TADF to yield materials with emission above 650 nm.The emission arose from the triplet excited state of an acceptor undergoing thermally activated reverse intersystem crossing(RISC)to the emissive S1 state.Such composite crystal is the first organic material with persistent TADF,achieved by triplet–triplet energy transfer.

Decoding the Structural Keywords in Protein Structure Universe

Although the protein sequence-structure gap continues to enlarge due to the development of high-throughput sequencing tools,the protein structure universe tends to be complete without proteins with novel structural folds deposited in the protein data bank (PDB)recently.In this work,we identify a protein structural dictionary (Frag-K)composed of a set of backbone fragments ranging from 4 to 20 residues as the structural "keywords"that can effectively distinguish between major protein folds.We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large scale of high-quality,non-homologous protein structures available in PDB.We analyze the impacts of clustering cut-offs on the performance of the fragment hbraries.Then,the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins).Our results show that a structural dictionary with N400 4-to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy.

Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

Identifying the potential drug-target interactions(DTI)is critical in drug discovery.The drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction performance.However,many corresponding models cannot accurately express the relationship between similarity features and DTI features.In order to rationally represent the correlation,we propose a novel matrix factorization method,so-called collaborative matrix factorization with soft regularization(SRCMF).SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix factorization.In contrast to general collaborative matrix factorization,the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate,not identical.Specifically,SRCMF obtains low-rank feature representations of drug similarity and target similarity,and then uses a soft regularization term to constrain the approximation between drug(target)similarity features and drug(target)potential features of DTI.To comprehensively evaluate the prediction performance of SRCMF,we conduct cross-validation experiments under three different settings.In terms of the area under the precision-recall curve(AUPR),SRCMF achieves better prediction results than six state-of-the-art methods.Besides,under different noise levels of similarity data,the prediction performance of SRCMF is much better than that of collaborative matrix factorization.In conclusion,SRCMF is robust leading to performance improvement in drug-target interaction prediction.

Difluoroboron β-diketonate dye with intense red/near-infrared fluorescence in solutions and solid states

Difluoroboronβ-diketonate(BF2bdk)complexes have attracted much attention due to their outstanding photophysical properties.However,BF2bdk with near-infrared fluorescence usually suffer from emission quenching in solid state due to theπ-πstacking in aggregation.Herein,we report a BF2bdk dye exhibiting donor-acceptor(D-A)structure with the difluoroboron moiety acting as the electron acceptor and the aminonaphthalene as the electron donor.It processes intense molar extinction coefficient,large Stokes shift and strong fluorescence in red/NIR region in both solution and aggregations.It was used for NIR imaging in living cells.

Xanthogranulomatous Cholecystitis and Misdiagnosis Analysis

INTRODUCTION Gastric sub-epithelial masses （SEMs） are relatively common findings when receiving gastric endoscopy. It is a mass, bulge, or impression visible. Computed tomography （CT） and magnetic resonance imaging can assist in making a diagnosis,

Preface

It is our great honor to announce the publication of this special section on AI and big data analytics in biology and medicine in the Journal of Computing Science and Technology(JCST).As more and more modern biological and medical data are produced,artificial intelligence(AI)and big data analytics are playing an increasingly important role in helping to draw meaningful and logical conclusions about biology and medicine.