A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

An interpretability model for syndrome differentiation of HBV-ACLF in traditional Chinese medicine using small-sample imbalanced data

Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.

Transfer RNA-derived small RNA serves as potential non-invasive diagnostic marker and a novel therapeutic target for acute pancreatitis

Transfer RNA(tRNA)-derived fragments,a new type of tRNA-derived small RNA(tsRNA),can be cleaved from tRNA by enzymes to regulate target gene expression at the transcriptional and translational levels.tsRNAs are not only degradation fragments but also have biological functions,including those in immune inflammation,metabolic disorders,and cell death.tsRNA dysregulation is closely associated with multiple diseases,including various cancers and acute pancreatitis(AP).AP is a common gastrointestinal disease,and its incidence increases annually.AP development is associated with tsRNAs,which regulate cell injury and induce inflammation,especially pyroptosis and ferroptosis.Notably,serum tRF36 has the potential to serve as a non-invasive diagnostic biomarker and leads to pancreatic acinar cell ferroptosis causing inflammation to promote AP.We show the characteristics of tsRNAs and their diagnostic value and function in AP,and discuss the potential opportunities and challenges of using tsRNAs in clinical applications and research.

Small extracellular vesicles derived from cerebral endothelial cells with elevated microRNA 27a promote ischemic stroke recovery

Axonal remodeling is a critical aspect of ischemic brain repair processes and contributes to spontaneous functional recovery.Our previous in vitro study demonstrated that exosomes/small extracellular vesicles(sEVs)isolated from cerebral endothelial cells(CEC-sEVs)of ischemic brain promote axonal growth of embryonic cortical neurons and that microRNA 27a(miR-27a)is an elevated miRNA in ischemic CEC-sEVs.In the present study,we investigated whether normal CEC-sEVs engineered to enrich their levels of miR-27a(27a-sEVs)further enhance axonal growth and improve neurological outcomes after ischemic stroke when compared with treatment with non-engineered CEC-sEVs.27a-sEVs were isolated from the conditioned medium of healthy mouse CECs transfected with a lentiviral miR-27a expression vector.Small EVs isolated from CECs transfected with a scramble vector(Scra-sEVs)were used as a control.Adult male mice were subjected to permanent middle cerebral artery occlusion and then were randomly treated with 27a-sEVs or Scra-sEVs.An array of behavior assays was used to measure neurological function.Compared with treatment of ischemic stroke with Scra-sEVs,treatment with 27a-sEVs significantly augmented axons and spines in the peri-infarct zone and in the corticospinal tract of the spinal grey matter of the denervated side,and significantly improved neurological outcomes.In vitro studies demonstrated that CEC-sEVs carrying reduced miR-27a abolished 27a-sEV-augmented axonal growth.Ultrastructural analysis revealed that 27a-sEVs systemically administered preferentially localized to the pre-synaptic active zone,while quantitative reverse transcription-polymerase chain reaction and Western Blot analysis showed elevated miR-27a,and reduced axonal inhibitory proteins Semaphorin 6A and Ras Homolog Family Member A in the peri-infarct zone.Blockage of the Clathrin-dependent endocytosis pathway substantially reduced neuronal internalization of 27a-sEVs.Our data provide evidence that 27a-sEVs have a therapeutic effect on stroke recovery by promoting axonal remodeling and improving neurological outcomes.Our findings also suggest that suppression of axonal inhibitory proteins such as Semaphorin 6A may contribute to the beneficial effect of 27a-sEVs on axonal remodeling.

A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

Small cell lung cancer with peripheral neuropathy as the first symptom:Two case reports

BACKGROUND Small cell lung cancer(SCLC)is the most malignant type of lung cancer.Even in the latent period and early stage of the tumor,SCLC is prone to produce distant metastases with complex and diverse clinical manifestations.SCLC is most closely related to paraneoplastic syndrome,and some cases present as paraneoplastic peripheral neuropathy(PPN).PPN in SCLC appears early,lacks specificity,and often occurs before diagnosis of the primary tumor.It is easy to be misdiagnosed as a primary disease of the nervous system,leading to missed diagnosis and delayed diagnosis and treatment.CASE SUMMARY This paper reports two cases of SCLC with limb weakness as the first symptom.The first symptoms of one patient were rash,limb weakness,and abnormal electromyography.The patient was repeatedly referred to the hospital for limb weakness and rash for>1 year,during which time,treatment with hormones and immunosuppressants did not lead to significant improvement,and the condition gradually aggravated.The patient was later diagnosed with SCLC,and the dyskinesia did not worsen as the dermatomyositis improved after antineoplastic and hormone therapy.The second case presented with limb numbness and weakness as the first symptom,but the patient did not pay attention to it.Later,the patient was diagnosed with SCLC after facial edema caused by tumor thrombus invading the vein.However,he was diagnosed with extensive SCLC and died 1 year after diagnosis.CONCLUSION The two cases had PPN and abnormal electromyography,highlighting its correlation with early clinical indicators of SCLC.

Small extracellular vesicles derived from human induced pluripotent stem cell-differentiated neural progenitor cells mitigate retinal ganglion cell degeneration in a mouse model of optic nerve injury

Several studies have found that transplantation of neural progenitor cells(NPCs)promotes the survival of injured neurons.However,a poor integration rate and high risk of tumorigenicity after cell transplantation limits their clinical application.Small extracellular vesicles(sEVs)contain bioactive molecules for neuronal protection and regeneration.Previous studies have shown that stem/progenitor cell-derived sEVs can promote neuronal survival and recovery of neurological function in neurodegenerative eye diseases and other eye diseases.In this study,we intravitreally transplanted sEVs derived from human induced pluripotent stem cells(hiPSCs)and hiPSCs-differentiated NPCs(hiPSC-NPC)in a mouse model of optic nerve crush.Our results show that these intravitreally injected sEVs were ingested by retinal cells,especially those localized in the ganglion cell layer.Treatment with hiPSC-NPC-derived sEVs mitigated optic nerve crush-induced retinal ganglion cell degeneration,and regulated the retinal microenvironment by inhibiting excessive activation of microglia.Component analysis further revealed that hiPSC-NPC derived sEVs transported neuroprotective and anti-inflammatory miRNA cargos to target cells,which had protective effects on RGCs after optic nerve injury.These findings suggest that sEVs derived from hiPSC-NPC are a promising cell-free therapeutic strategy for optic neuropathy.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Small molecule inhibitor DDQ-treated hippocampal neuronal cells show improved neurite outgrowth and synaptic branching

The process of neurite outgrowth and branching is a crucial aspect of neuronal development and regeneration.Axons and dendrites,sometimes referred to as neurites,are extensions of a neuron's cellular body that are used to start networks.Here we explored the effects of diethyl(3,4-dihydroxyphenethylamino)(quinolin-4-yl)methylphosphonate(DDQ)on neurite developmental features in HT22 neuronal cells.In this work,we examined the protective effects of DDQ on neuronal processes and synaptic outgrowth in differentiated HT22cells expressing mutant Tau(mTau)cDNA.To investigate DDQ chara cteristics,cell viability,biochemical,molecular,western blotting,and immunocytochemistry were used.Neurite outgrowth is evaluated through the segmentation and measurement of neural processes.These neural processes can be seen and measured with a fluorescence microscope by manually tracing and measuring the length of the neurite growth.These neuronal processes can be observed and quantified with a fluorescent microscope by manually tracing and measuring the length of the neuronal HT22.DDQ-treated mTau-HT22 cells(HT22 cells transfected with cDNA mutant Tau)were seen to display increased levels of synaptophysin,MAP-2,andβ-tubulin.Additionally,we confirmed and noted reduced levels of both total and p-Tau,as well as elevated levels of microtubule-associated protein 2,β-tubulin,synaptophysin,vesicular acetylcholine transporter,and the mitochondrial biogenesis protein-pe roxisome prolife rator-activated receptor-gamma coactivator-1α.In mTa u-expressed HT22 neurons,we observed DDQ enhanced the neurite characteristics and improved neurite development through increased synaptic outgrowth.Our findings conclude that mTa u-HT22(Alzheimer's disease)cells treated with DDQ have functional neurite developmental chara cteristics.The key finding is that,in mTa u-HT22 cells,DDQ preserves neuronal structure and may even enhance nerve development function with mTa u inhibition.

基于MobileNetV3Small-ECA的水稻病害轻量级识别研究

为实现水稻病害的轻量化识别与检测,使用ECA注意力机制改进MobileNetV3Small模型,并使用共享参数迁移学习对水稻病害进行智能化轻量级识别和检测。在PlantVillage数据集上进行预训练,将预训练得到的共享参数迁移到对水稻病害识别模型上微调优化。在开源水稻病害数据集上进行试验测试,试验结果表明,在非迁移学习下,识别准确率达到97.47%,在迁移学习下识别准确率达到99.92%,同时参数量减少26.69%。其次,通过Grad-CAM进行可视化,本文方法与其他注意力机制CBAM和SENET相比,ECA模块生成的结果与图像中病斑的位置和颜色更加一致,表明网络可以更好地聚焦水稻病害的特征,并且通过可视化和各水稻病害分析了误分类原因。本文方法实现了水稻病害识别模型的轻量化,使其能够在移动设备等资源受限的场景中部署,达到快速、高效、便携的目的。同时开发了基于Android的水稻病害识别系统,方便于在边缘端进行水稻病害识别分析。

An analytical model for estimating rock strength parameters from small-scale drilling data

The small-scale drilling technique can be a fast and reliable method to estimate rock strength parameters. It needs to link the operational drilling parameters and strength properties of rock. The parameters such as bit geometry, bit movement, contact frictions and crushed zone affect the estimated parameters.An analytical model considering operational drilling data and effective parameters can be used for these purposes. In this research, an analytical model was developed based on limit equilibrium of forces in a Tshaped drag bit considering the effective parameters such as bit geometry, crushed zone and contact frictions in drilling process. Based on the model, a method was used to estimate rock strength parameters such as cohesion, internal friction angle and uniaxial compressive strength of different rock types from operational drilling data. Some drilling tests were conducted by a portable and powerful drilling machine which was developed for this work. The obtained results for strength properties of different rock types from the drilling experiments based on the proposed model are in good agreement with the results of standard tests. Experimental results show that the contact friction between the cutting face and rock is close to that between bit end wearing face and rock due to the same bit material. In this case,the strength parameters, especially internal friction angle and cohesion, are estimated only by using a blunt bit drilling data and the bit bluntness does not affect the estimated results.

Data processing of small samples based on grey distance information approach

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is difficult and complex to determine the probability distribution of small samples, it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the different samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

miRNA对褐飞虱鞘脂质代谢基因表达的影响及沉默NlSPT1和Nl SMase4的small RNA分析

【背景】鞘脂质是第二大类膜脂,作为信号转导物质参与细胞生长发育、繁殖及凋亡等过程。鞘脂质代谢酶调控鞘脂质代谢以维持生物体内代谢的稳态。【目的】以重要水稻害虫褐飞虱(Nilaparvata lugens)为研究对象,通过RNA干扰(RNA interference,RNAi)技术检测微小RNA(microRNA,miRNA)生物合成通路核心组分NlAgo1、NlDicer1和NlDrosha沉默后褐飞虱鞘脂质代谢通路相关基因的相对转录水平,结合small RNA测序分析沉默丝氨酸棕榈酰转移酶1(serine palmitoyltransferase 1,SPT1)和鞘磷脂酶4(sphingomyelinase 4,SMase4)基因的差异miRNA,探究miRNA在褐飞虱鞘脂质代谢中的作用,为害虫防治提供新的分子靶标。【方法】利用RNAi技术,分别对羽化后第1天(1 PAE,post adult eclosion)的雌成虫NlAgo1、NlDicer1和NlDrosha进行dsRNA注射,以ds GFP为对照;分别解剖羽化后第5天的卵巢组织,以β-actin作为内参基因,采用实时荧光定量PCR(qRT-PCR)方法检测NlAgo1、NlDicer1和NlDrosha沉默后鞘脂质代谢通路相关基因的表达量变化;根据已有的小RNA文库联合miRNA-靶基因预测软件对可能调控NlSPT1和NlSMase4表达的miRNA进行预测;通过small RNA测序技术对沉默NlSPT1和NlSMase4的差异miRNA进行鉴定和靶基因富集性分析。【结果】与对照组相比,沉默NlAgo1、NlDicer1或NlDrosha显著上调卵巢中NlSPT1和NlSMase4等鞘脂质代谢通路相关基因的表达;靶基因预测结果显示,有6条miRNA能与NlSPT1结合,13条miRNA能与NlSMase4结合;沉默NlSPT1和NlSMase4的差异miRNA的靶基因显著富集在细胞核和蛋白质结合等生物学过程以及内吞作用、内质网加工、MAPK信号通路、TOR信号通路、凋亡、脂质代谢等代谢通路。【结论】NlAgo1、NlDicer1和NlDrosha依赖性的miRNA通过影响鞘脂质代谢相关基因的表达影响鞘脂质代谢。NlSPT1和NlSMase4沉默引起褐飞虱卵巢miRNA表达水平的改变。研究结果可为基于鞘脂质代谢基因为靶标的害虫防治提供理论依据。

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence.

A LoRa-based protocol for connecting IoT edge computing nodes to provide small-data-based services

Data is becoming increasingly personal.Individuals regularly interact with a variety of structured data,ranging from SQLite databases on the phone to personal sensors and open government data.The“digital traces left by individuals through these interactions”are sometimes referred to as“small data”.Examples of“small data”include driving records,biometric measurements,search histories,weather forecasts and usage alerts.In this paper,we present a flexible protocol called LoRaCTP,which is based on LoRa technology that allows data“chunks”to be transferred over large distances with very low energy expenditure.LoRaCTP provides all the mechanisms necessary to make LoRa transfer reliable by introducing a lightweight connection setup and allowing the ideal sending of an as-long-as necessary data message.We designed this protocol as communication support for small-data edge-based IoT solutions,given its stability,low power usage,and the possibility to cover long distances.We evaluated our protocol using various data content sizes and communication distances to demonstrate its performance and reliability.

A new package:MySAS for small angle scattering data analysis

In this paper,A MySAS package,which is verified on Windows XP,can easily convert two-dimensional data in small angle neutron and X-ray scattering analysis,operate individually and execute one particular operation as numerical data reduction or analysis,and graphical visualization.This MySAS package can implement the input and output routines via scanning certain properties,thus recalling completely sets of repetition input and selecting the input files.On starting from the two-dimensional files,the MySAS package can correct the anisotropic or isotropic data for physical interpretation and select the relevant pixels.Over 50 model functions are fitted by the POWELL code using x^2 as the figure of merit function.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet

A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.