Application of bronchoscopic argon plasma coagulation in the treatment of tumorous endobronchial tuberculosis: Historical controlled trial

ObjectiveThe purpose of this study was to evaluate the efficacy and safety of bronchoscopic argon plasma coagulation for tumorous endobronchial tuberculosis.MethodsWe analyzed the records of 115 patients with tumorous endobronchial tuberculosis who did not show luminal narrowing of the bronchus at diagnosis. Of these 115 patients, 41 patients received bronchoscopic argon plasma coagulation plus routine antituberculosis chemotherapy (argon plasma coagulation group) and the other 74 patients received only routine antituberculosis chemotherapy (chemotherapy group). The treatment effects between these 2 groups were compared based on changes in lesions, rate of lesion disappearance, and complications associated with bronchoscopic argon plasma coagulation.ResultsThe complete removal rate was 100% in patients in argon plasma coagulation group. About 84.6% lesions disappeared completely in patients in the chemotherapy group. The rate of disappearance of lesions in the argon plasma coagulation group was faster than that of the chemotherapy group. There were no severe complications in the argon plasma coagulation group.ConclusionsBronchoscopic argon plasma coagulation can accelerate the healing of tumorous endobronchial tuberculosis and can help prevent progressive bronchial stenosis resulting from tumorous endobronchial tuberculosis, and it is a very safe method

Feature selection using enhanced particle swarm optimisation for classification models.

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets

Guaranteed Cost Control for Multirate Networked Control Systems with Both Time-Delay and Packet-Dropout

Compared with traditional networked control systems, the sampling rates of the nodes are not the same in the multirate networked control systems (NCSs). This paper presents a new stabilization method for multirate NCSs. A multirate NCSs with simultaneous considering time-delay and packet-dropout is modeled as a time-varying sampling system with time-delay. The proposed Lyapunov function deceases at each input signal updating point, which is largely ignored in prior works. Sufficient condition for the stochastic mean-square stability of the multirate NCSs is given, and the cost function value is less than a bound. Numerical examples are presented to illustrate the effectiveness of the proposed control scheme

A new framework for the integrative analytics of intravascular ultrasound and optical coherence tomography images

Abstract:The integrative analysis of multimodal medical images plays an important role in the diagnosis of coronary artery disease by providing additional comprehensive information that cannot be found in an individual source image. Intravascular ultrasound (IVUS) and optical coherence tomography (IV-OCT) are two imaging modalities that have been widely used in the medical practice for the assessment of arterial health and the detection of vascular lumen lesions. IV-OCT has a high resolution and poor penetration, while IVUS has a low resolution and high detection depth. This paper proposes a new approach for the fusion of intravascular ultrasound and optical coherence tomography pullbacks to significantly improve the use of those two types of medical images. It also presents a new two-phase multimodal fusion framework using a coarse-to-fine registration and a wavelet fusion method. In the coarse-registration process, we define a set of new feature points to match the IVUS image and IV-OCT image. Then, the improved quality image is obtained based on the integration of the mutual information of two types of images. Finally, the matched registered images are fused with an approach based on the new proposed wavelet algorithm. The experimental results demonstrate the performance of the proposed new approach for significantly enhancing both the precision and computational stability. The proposed approach is shown to be promising for providing additional information to enhance the diagnosis and enable a deeper understanding of atherosclerosis

Improving K-means clustering with enhanced Firefly Algorithms

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for undertaking the obstinate problems of initialization sensitivity and local optima traps of the K-means clustering model. To enhance the capability of both exploitation and exploration, matrix-based search parameters and dispersing mechanisms are incorporated into the two proposed FA models. We first replace the attractiveness coefficient with a randomized control matrix in the IIEFA model to release the FA from the constraints of biological law, as the exploitation capability in the neighbourhood is elevated from a one-dimensional to multi-dimensional search mechanism with enhanced diversity in search scopes, scales, and directions. Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. This dispersing mechanism ensures sufficient variance between fireflies in comparison to increase search efficiency. The ALL-IDB2 database, a skin lesion data set, and a total of 15 UCI data sets are employed to evaluate efficiency of the proposed FA models on clustering tasks. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality. The empirical results indicate that the proposed FA models demonstrate statistically significant superiority in both distance and performance measures for clustering tasks in comparison with conventional K-means clustering, five classical search methods, and five advanced FA variants

Elastically-Constrained Meta-Learner for Federated Learning

Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients. Meta-learning, such as Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns shared initial parameters for all clients. Each client employs gradient descent to adapt the initialization to local data distributions quickly to realize model personalization. However, due to non-convex loss function and randomness of sampling update, meta-learning approaches have unstable goals in local adaptation for the same client. This fluctuation in different adaptation directions hinders the convergence in meta-learning. To overcome this challenge, we use the historical local adapted model to restrict the direction of the inner loop and propose an elastic-constrained method. As a result, the current round inner loop keeps historical goals and adapts to better solutions. Experiments show our method boosts meta-learning convergence and improves personalization without additional calculation and communication. Our method achieved SOTA on all metrics in three public datasets.Comment: FL-IJCAI'2

MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation

Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally may distort the essential structural information of the target-domain data, leading to inferior performance. To address this issue, we firstly propose to introduce active sample selection to assist domain adaptation regarding the semantic segmentation task. By innovatively adopting multiple anchors instead of a single centroid, both source and target domains can be better characterized as multimodal distributions, in which way more complementary and informative samples are selected from the target domain. With only a little workload to manually annotate these active samples, the distortion of the target-domain distribution can be effectively alleviated, achieving a large performance gain. In addition, a powerful semi-supervised domain adaptation strategy is proposed to alleviate the long-tail distribution problem and further improve the segmentation performance. Extensive experiments are conducted on public datasets, and the results demonstrate that the proposed approach outperforms state-of-the-art methods by large margins and achieves similar performance to the fully-supervised upperbound, i.e., 71.4% mIoU on GTA5 and 71.8% mIoU on SYNTHIA. The effectiveness of each component is also verified by thorough ablation studies.Comment: Accepted by TPAMI-IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2108.0801

Formation of highly oxygenated organic molecules from chlorine-atom-initiated oxidation of alpha-pinene

Highly oxygenated organic molecules (HOMs) from atmospheric oxidation of alpha-pinene can irreversibly condense to particles and contribute to secondary organic aerosol (SOA) formation. Recently, the formation of nitryl chloride (C1NO(2)) from heterogeneous reactions, followed by its subsequent photolysis, is suggested to be an important source of chlorine atoms in many parts of the atmosphere. However, the oxidation of monoterpenes such as alpha-pinene by chlorine atoms has received very little attention, and the ability of this reaction to form HOMs is completely unstudied. Here, chamber experiments were conducted with alpha-pinene and chlorine under low- and high-nitrogen-oxide (NOx, NOx = NO+NO2) conditions. A nitrate-based CI-APi-ToF (chemical ionization-atmospheric pressure interface-time of flight) mass spectrometer was used to measure HOM products. Clear distributions of monomers with 9-10 carbon atoms and dimers with 18-20 carbon atoms were observed under low-NOx conditions. With increased concentration of NOx within the chamber, the formation of dimers was suppressed due to the reactions of peroxy radicals with NO. We estimated the HOM yields from chlorine-initiated oxidation of alpha-pinene under low-NOx conditions to be around 1.8 %, though with a substantial uncertainty range (0.8 %-4 %) due to lack of suitable calibration methods. Corresponding yields at high NOx could not be determined because of concurrent ozonolysis reactions. Our study demonstrates that also the oxidation of alpha-pinene by chlorine atoms and yield low-volatility organic compounds.Peer reviewe

Detection of herb-symptom associations from traditional chinese medicine clinical data

YesTraditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients. We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data. To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure. To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations. The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians. Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations. Symptoms play significant roles for the prescriptions of herb treatment. The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations