Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Mechanism of Wendan decoction in preventing obesity by regulating multiple signal pathway networks based on gene promoter methylation

Objective:To investigate the potential mechanism of Wendan decoction in obesity by screening target genes with promoter region methylation changes and constructing a multiple signaling pathways network based on promoter methylation.Methods:The methylation degree of Itgad,Col8a1,Adra2b,Jund,Rab2a,Wnt8b,Fzd9,B4galt7,Pik3cd,Creb1,Stard8,and Mmp1 in the abdominal adipose tissue of obese rats was determined using the Agena MassARRAY system.Western blot was performed to assess protein expression levels.Target genes were identified based on the methylation degree in the promoter region and protein expression.Enrichment analysis of signaling pathways was conducted to identify relevant target genes and obtain a multiple signaling pathway network associated with obesity.Core and terminal effector molecules in the pathway networks were selected as research targets for reverse transcription-polymerase chain reaction(RT-PCR)analysis.Results:Four genes(Adra2b,Creb1,Itgad,and Pik3cd)showed a degree of promoter methylation consistent with their respective protein expression levels.Among them,Adra2b,Creb1,and Pik3cd expression increased,while that of Itgad decreased.Enrichment analysis revealed that Creb1 and Pik3cd were involved in 6 signaling pathways related to obesity:tumor necrosis factor(TNF)signaling pathway,growth hormone synthesis/secretion and action,adenosine 5'-monophosphate-activated protein kinase(AMPK)signaling pathway,relaxin signaling pathway,cyclic nucleotide(cAMP)signaling pathway,and phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt)signaling pathway.Subsequently,a multiple signaling pathways network was constructed based on promoter methylation.Key molecules including protein kinase B(AKT),mechanistic target of rapamycin complex 1(mTORC1),and unc-51 like autophagy activating kinase 1(ULK1),as well as terminal effector molecules interleukin-1β(IL-1β),interleukin-6(IL-6),and chemokine(C-X-C motif)ligand 2(CXCL2)were selected as research targets.Wendan decoction decreased the expressions of AKT,mTORC1,IL-1β,IL-6,and CXCL2 while up-regulating ULK1 expression.Conclusion:The mechanism of Wendan decoction in preventing obesity involves the regulation of multiple signaling pathways through the control of Creb1 and Pik3cd gene promoter methylation.However,the associated multi-path gene regulation mechanism in preventing obesity is complex.Thus,further exploration is needed to elucidate the role of methylation changes in this mechanism.

Design of a Multifrequency Signal Parameter Estimation Method for the Distribution Network Based on HIpST

The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks.Therefore,it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks.By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing,a multifrequency signal estimation approach based on HT-IpDFT-STWLS(HIpST)for distribution networks is provided.First,by introducing the Hilbert transform(HT),the influence of noise on the estimation algorithm is reduced.Second,signal frequency components are obtained on the basis of the calculated signal envelope spectrum,and the interpolated discrete Fourier transform(IpDFT)frequency coarse estimation results are used as the initial values of symmetric Taylor weighted least squares(STWLS)to achieve high-precision parameter estimation under the dynamic changes of the signal,and the method increases the number of discrete Fourier.Third,the accuracy of this proposed method is verified by simulation analysis.Data show that this proposed method can accurately achieve the parameter estimation of multifrequency signals in distribution networks.This approach provides a solution for the application of phasor measurement units in distribution networks.

Netrin-1 signaling pathway mechanisms in neurodegenerative diseases

Netrin-1 and its receptors play crucial roles in inducing axonal growth and neuronal migration during neuronal development.Their profound impacts then extend into adulthood to encompass the maintenance of neuronal survival and synaptic function.Increasing amounts of evidence highlight several key points:(1)Diminished Netrin-1 levels exacerbate pathological progression in animal models of Alzheimer’s disease and Parkinson’s disease,and potentially,similar alterations occur in humans.(2)Genetic mutations of Netrin-1 receptors increase an individuals’susceptibility to neurodegenerative disorders.(3)Therapeutic approaches targeting Netrin-1 and its receptors offer the benefits of enhancing memory and motor function.(4)Netrin-1 and its receptors show genetic and epigenetic alterations in a variety of cancers.These findings provide compelling evidence that Netrin-1 and its receptors are crucial targets in neurodegenerative diseases.Through a comprehensive review of Netrin-1 signaling pathways,our objective is to uncover potential therapeutic avenues for neurodegenerative disorders.

Acoustic emission signal identification of different rocks based on SE-1DCNN-BLSTM network model

In order to study fracture mechanism of rocks in different brittle mineral contents,this study pro-poses a method to identify the acoustic emission signal released by rock fracture under different brittle miner-al content(BMC),and then determine the content of brittle matter in rock.To understand related interference such as the noises in the acoustic emission signals released by the rock mass rupture,a 1DCNN-BLSTM network model with SE module is constructed in this study.The signal data is processed through the 1DCNN and BLSTM networks to fully extract the time-series correlation features of the signals,the non-correlated features of the local space and the weak periodicity law.Furthermore,the processed signals data is input into the fully connected layers.Finally,softmax function is used to accurately identify the acoustic emission signals released by different rocks,and then determine the content of brittle minerals contained in rocks.Through experimental comparison and analysis,1DCNN-BLSTM model embedded with SE module has good anti-noise performance,and the recognition accuracy can reach more than 90 percent,which is better than the traditional deep network models and provides a new way of thinking for rock acoustic emission re-search.

Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.

Comparative study of anti-inflammatory effects of different processed products through the COX-2/PGE2 signaling pathway: based on network pharmacology and molecular docking

Background:Radix Aconiti Lateralis Preparata(Fu-zi)is a traditional Chinese medicinal herb,which has been widely used in the clinic and has potent anti-inflammatory activities.we aimed to explore the mechanisms of extract containing alkaloids from different Fu-zi Processed Products(FPP)in treating inflammation,especially rheumatoid arthritis(RA).Methods:Firstly,using network pharmacology technology,the ingredients,and targets of Fu-zi were obtained by searching and screening,the targets involving RA were acquired,the intersection targets were constructed a"component-target-pathway"network.A comprehensive investigation was conducted on the anti-rheumatoid arthritis mechanisms of 5 FPPs in lipopolysaccharide(LPS)induced RAW264.7 cells,which serve as a model for RA.The production of NO and inflammatory cytokines were measured by ELISA kit.Quantitative Real-time PCR(qRT-PCR)was utilized to measure the mRNA levels.COX-2/PGE2 signaling pathway-associated proteins were determined by western blot.Results:According to a network pharmacological study,16 chemical components and 43 common targets were found in Fu-zi and 6 key targets including PTGS2 were closely related to the mechanism of Fu-zi in treating RA.The in vitro study revealed that the levels of NO,TNF-α,and IL-1βwere substantially decreased by the 5 FPPs.The 5 FPPs significantly suppressed the expression of proteins COX-2,iNOS,and NF-κB,with particularly notable effects observed for PFZ and XFZ.Conclusion:Altogether,these results demonstrated that the 5 PPS containing alkaloids have a good anti-RA-related inflammatory effect,and the mechanism may be related to COX-2/PGE2 signaling pathway,particularly,Fu-zi prepared utilizing a traditional Chinese technique.

Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis

Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.

Anethole improves the developmental competence of porcine embryos by reducing oxidative stress via the sonic hedgehog signaling pathway

Background Anethole(AN)is an organic antioxidant compound with a benzene ring and is expected to have a positive impact on early embryogenesis in mammals.However,no study has examined the effect of AN on porcine embryonic development.Therefore,we investigated the effect of AN on the development of porcine embryos and the underlying mechanism.Results We cultured porcine in vitro-fertilized embryos in medium with AN(0,0.3,0.5,and 1 mg/mL)for 6 d.AN at 0.5 mg/mL significantly increased the blastocyst formation rate,trophectoderm cell number,and cellular survival rate compared to the control.AN-supplemented embryos exhibited significantly lower reactive oxygen species levels and higher glutathione levels than the control.Moreover,AN significantly improved the quantity of mitochondria and mitochondrial membrane potential,and increased the lipid droplet,fatty acid,and ATP levels.Interestingly,the levels of proteins and genes related to the sonic hedgehog(SHH)signaling pathway were significantly increased by AN.Conclusions These results revealed that AN improved the developmental competence of porcine preimplantation embryos by activating SHH signaling against oxidative stress and could be used for large-scale production of high-quality porcine embryos.

Zuo Gui Wan Promotes Osteogenesis via PI3K/AKT Signaling Pathway:Network Pharmacology Analysis and Experimental Validation

Objective Osteogenesis is vitally important for bone defect repair,and Zuo Gui Wan(ZGW)is a classic prescription in traditional Chinese medicine(TCM)for strengthening bones.However,the specific mechanism by which ZGW regulates osteogenesis is still unclear.The current study is based on a network pharmacology analysis to explore the potential mechanism of ZGW in promoting osteogenesis.Methods A network pharmacology analysis followed by experimental validation was applied to explore the potential mechanisms of ZGW in promoting the osteogenesis of bone marrow mesenchymal stem cells(BMSCs).Results In total,487 no-repeat targets corresponding to the bioactive components of ZGW were screened,and 175 target genes in the intersection of ZGW and osteogenesis were obtained.And 28 core target genes were then obtained from a PPI network analysis.A GO functional enrichment analysis showed that the relevant biological processes mainly involve the cellular response to chemical stress,metal ions,and lipopolysaccharide.Additionally,KEGG pathway enrichment analysis revealed that multiple signaling pathways,including the phosphatidylinositol-3-kinase/protein kinase B(PI3K/AKT)signaling pathway,were associated with ZGW-promoted osteogensis.Further experimental validation showed that ZGW could increase alkaline phosphatase(ALP)activity as well as the mRNA and protein levels of ALP,osteocalcin(OCN),and runt related transcription factor 2(Runx 2).What’s more,Western blot analysis results showed that ZGW significantly increased the protein levels of p-PI3K and p-AKT,and the increases of these protein levels significantly receded after the addition of the PI3K inhibitor LY294002.Finally,the upregulated osteogenic-related indicators were also suppressed by the addition of LY294002.Conclusion ZGW promotes the osteogenesis of BMSCs via PI3K/AKT signaling pathway.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.

Hypoglycemic mechanism of Tegillarca granosa polysaccharides on type 2 diabetic mice by altering gut microbiota and regulating the PI3K-akt signaling pathwaye

Type 2 diabetes mellitus(T2DM)is a complex metabolic disease threatening human health.We investigated the effects of Tegillarca granosa polysaccharide(TGP)and determined its potential mechanisms in a mouse model of T2DM established through a high-fat diet and streptozotocin.TGP(5.1×10^(3) Da)was composed of mannose,glucosamine,rhamnose,glucuronic acid,galactosamine,glucose,galactose,xylose,and fucose.It could significantly alleviate weight loss,reduce fasting blood glucose levels,reverse dyslipidemia,reduce liver damage from oxidative stress,and improve insulin sensitivity.RT-PCR and Western blotting indicated that TGP could activate the phosphatidylinositol-3-kinase/protein kinase B signaling pathway to regulate disorders in glucolipid metabolism and improve insulin resistance.TGP increased the abundance of Allobaculum,Akkermansia,and Bifidobacterium,restored the microbiota abundance in the intestinal tracts of mice with T2DM,and promoted short-chain fatty acid production.This study provides new insights into the antidiabetic effects of TGP and highlights its potential as a natural hypoglycemic nutraceutical.

Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health status.The physiological complexity of the cardiac system is related to age,disease,etc.For the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time series.We propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological properties.The obtained results suggest that these two network measures show distinct changes between different subjects.This is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac system.Consequently,the complexity of the cardiac system is reduced.After that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed approach.Accuracy of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two datasets.Therefore,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

The term Epilepsy refers to a most commonly occurring brain disorder after a migraine.Early identification of incoming seizures significantly impacts the lives of people with Epilepsy.Automated detection of epileptic seizures(ES)has dramatically improved the life quality of the patients.Recent Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in real-time.The EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording sessions.Further,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs.Artificial intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)approaches.This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection(OAOFS-DBNECD)technique using EEG signals.The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs.The suggested OAOFS-DBNECD technique transforms the EEG signals into.csv format at the initial stage.Next,the OAOFS technique selects an optimal subset of features using the preprocessed data.For seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)model.An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm.The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies.In addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Scaffold proteins of cancer signaling networks: The paradigm of FK506 binding protein 51 (FKBP51) supporting tumor intrinsic properties and immune escape

Scaffold proteins are crucial regulators of signaling networks,and their abnormal expression may favor the development of tumors.Among the scaffold proteins,immunophilin covers a unique role as‘protein-philin’(Greek‘philin’=friend)that interacts with proteins to guide their proper assembly.The growing list of human syndromes associated with the immunophilin defect underscores the biological relevance of these proteins that are largely opportunistically exploited by cancer cells to support and enable the tumor’s intrinsic properties.Among the members of the immunophilin family,the FKBP5 gene was the only one identified to have a splicing variant.Cancer cells impose unique demands on the splicing machinery,thus acquiring a particular susceptibility to splicing inhibitors.This review article aims to overview the current knowledge of the FKBP5 gene functions in human cancer,illustrating how cancer cells exploit the scaffolding function of canonical FKBP51 to foster signaling networks that support their intrinsic tumor properties and the spliced FKBP51s to gain the capacity to evade the immune system.