Abstract Automatic heartbeat classification is an important technique to assist doctors to identify ectopic heartbeats in long-term Holter recording. In this paper, we introduce a novel ...disease-specific feature selection method which consists of a one-versus-one (OvO) features ranking stage and a feature search stage wrapped in the same OvO-rule support vector machine (SVM) binary classifier. The proposed method differs from traditional approaches in that it focuses on the selection of effective feature subsets for distinguishing a class from others by making OvO comparison. The electrocardiograms (ECG) from the MIT-BIH arrhythmia database (MIT-BIH-AR) are used to evaluate the proposed feature selection method. The ECG features adopted include inter-beat and intra-beat intervals, amplitude morphology, area morphology and morphological distance. Following the recommendation of the Advancement of Medical Instrumentation (AAMI), all the heartbeat samples of MIT-BIH-AR are grouped into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The division of training and testing data complies with the inter-patient schema. Experimental results show that the average classification accuracy of the proposed feature selection method is 86.66%, outperforming those methods without feature selection. The sensitivities for the classes N, S, V and F are 88.94%, 79.06%, 85.48% and 93.81% respectively, and the corresponding positive predictive values are 98.98%, 35.98%, 92.75% and 13.74% respectively. In terms of geometric means of sensitivity and positive predictivity, the proposed method also demonstrates better performance than other state-of-the-art feature selection methods.
Image fusion has the capability to integrate useful information from source images into a more comprehensive image. How to obtain the effective representation of source images is a key step to image ...fusion. Due to the loss of the dependence of coefficients, most of traditional multi-scale decomposition-based image fusion methods suffer from an inaccurate image representation. To solve this problem, a novel image fusion method with contextual statistical similarity in nonsubsampled shearlet transform (NSST) is presented. The key contributions include: 1) the dependence of NSST coefficients is captured by the contextual hidden Markov model (CHMM); 2) the contextual statistical similarity of coefficients is proposed; 3) an effective fusion rule based on the characteristic of CHMM is developed for high-frequency subbands in NSST domain. By the visual analysis and quantitative evaluations on experimental results, the superiority of the proposed method is demonstrated.
Multi-scale-based image fusion is one of main fusion methods, in which multi-scale decomposition tool and feature extraction play very important roles. The quaternion wavelet transform (QWT) is one ...of the effective multi-scale decomposition tools. Therefore, this paper proposes a novel multimodal image fusion method using QWT and multiple features. First, we perform QWT on each source image to obtain low-frequency coefficients and high-frequency coefficients. Second, a weighted average fusion rule based on the phase and magnitude of low-frequency subband and spatial variance is proposed to fuse the low-frequency subbands. Next, a choose-max fusion rule based on the contrast and energy of coefficient is proposed to integrate the high-frequency subbands. Finally, the final fused image is constructed by inverse QWT. The proposed method is conducted on multi-focus images, medical images, infrared-visible images, and remote sensing images, respectively. Experimental results demonstrate the effectiveness of the proposed method.
While genes with an excess of de novo mutations (DNMs) have been identified in children with neurodevelopmental disorders (NDDs), few studies focus on DNM patterns where the sex of affected children ...is examined separately. We considered ∼8,825 sequenced parent-child trios (n ∼26,475 individuals) and identify 54 genes with a DNM enrichment in males (n = 18), females (n = 17), or overlapping in both the male and female subsets (n = 19). A replication cohort of 18,778 sequenced parent-child trios (n = 56,334 individuals) confirms 25 genes (n = 3 in males, n = 7 in females, n = 15 in both male and female subsets). As expected, we observe significant enrichment on the X chromosome for females but also find autosomal genes with potential sex bias (females, CDK13, ITPR1; males, CHD8, MBD5, SYNGAP1); 6.5% of females harbor a DNM in a female-enriched gene, whereas 2.7% of males have a DNM in a male-enriched gene. Sex-biased genes are enriched in transcriptional processes and chromatin binding, primarily reside in the nucleus of cells, and have brain expression. By downsampling, we find that DNM gene discovery is greatest when studying affected females. Finally, directly comparing de novo allele counts in NDD-affected males and females identifies one replicated genome-wide significant gene (DDX3X) with locus-specific enrichment in females. Our sex-based DNM enrichment analysis identifies candidate NDD genes differentially affecting males and females and indicates that the study of females with NDDs leads to greater gene discovery consistent with the female-protective effect.
Complementary and redundant relationships inherently exist between multi-modal medical images captured from the same brain. Fusion processes conducted on intermingled representations can cause ...information distortion and the loss of discriminative modality information. To fully exploit the interdependency between source images for better feature representation and improve the fusion accuracy, we present the multi-modal brain medical image fusion method in a disentangled pipeline under the deep learning framework. A three-branch auto-encoder with two complementary branches and a redundant branch is designed to extract the exclusive modality features and common structure features from input images. Especially, to promote the disentanglement of complement and redundancy, a complementary group lasso penalty is proposed to constrain the extracted feature maps. Then, based on the disentangled representations, different fusion strategies are adopted for complementary features and redundant features, respectively. The experiments demonstrate the superior performance of the proposed fusion method in terms of structure preservation, visual quality, and running efficiency.
Exome sequencing (ES) is increasingly used for the diagnosis of rare genetic disease. However, some pathogenic sequence variants within the exome go undetected due to the technical difficulty of ...identifying them. Mobile element insertions (MEIs) are a known cause of genetic disease in humans but have been historically difficult to detect via ES and similar targeted sequencing methods.
We developed and applied a novel MEI detection method prospectively to samples received for clinical ES beginning in November 2017. Positive MEI findings were confirmed by an orthogonal method and reported back to the ordering provider. In this study, we examined 89,874 samples from 38,871 cases.
Diagnostic MEIs were present in 0.03% (95% binomial test confidence interval: 0.02-0.06%) of all cases and account for 0.15% (95% binomial test confidence interval: 0.08-0.25%) of cases with a molecular diagnosis. One diagnostic MEI was a novel founder event. Most patients with pathogenic MEIs had prior genetic testing, three of whom had previous negative DNA sequencing analysis of the diagnostic gene.
MEI detection from ES is a valuable diagnostic tool, reveals molecular findings that may be undetected by other sequencing assays, and increases diagnostic yield by 0.15%.
An accurate representation of the source image is vital in image fusion processing. However, among most of the traditional multi-scale decomposition based image fusion methods, they fail to ...accurately interpret the source images, only capturing local information without considering the regional and global information. To overcome this drawback, a novel image fusion method based on the global-regional-local rule is proposed in the nonsubsampled shearlet transform (NSST) domain. First, the source paired images are decomposed into the low-pass subbands and high-pass subbands with the NSST transformation. Second, for comprehensively representing the statistical correlation of source image, three-level statistical models, that is, global contextual hidden Markov model (G-CHMM), regional contextual hidden Markov model (R-CHMM), and local contextual hidden Markov model (L-CHMM) are established for the high-pass subbands. In the R-CHMM, a novel feature vector is extracted for getting the region map of source image by the fuzzy cluster method (FCM). Third, to get accurate active measures of the source image, a global-regional-local fusion rule based on the statistical characteristics extracted from global-regional-local CHMM is designed, which is used to fuse the high-pass subbands. The low-pass subbands are fused with the choose-max rule based on the local gradient measure. Finally, the fused image is obtained by the inverse NSST. Experimental results on serials of infrared and visible images, medical images and multi-focus images demonstrate the advantage of the proposed method in terms of detail preserving.
This study employs deep learning and artificial intelligence (AI) clustering analysis techniques to evaluate the suitability of integrated rural land for three industries. Diverse datasets pertaining ...to rural development, encompassing land use, agricultural production, and rural tourism, are gathered and harmoniously amalgamated. An innovative land suitability assessment model, merging ResNet-50 with the k-means algorithm, is devised. Specifically, ResNet-50 is harnessed for the classification and recognition of rural land-use images, thus deriving feature vectors for each sample. These feature vectors are subsequently fed into the k-means algorithm to cluster samples with akin land-use patterns. The ensuing examination of land use composition within each cluster facilitates the evaluation of rural land's suitability for three-industry integration. Experimental scrutiny discloses that this study achieves an accuracy rate of 88.3% in rural land-use classification and recognition, outperforming alternative algorithms by at least 3.1%. Furthermore, it yields an average intersection over union (IoU) of 67.29%. Remarkably, the k-means algorithm exhibits superior clustering outcomes. Consequently, the model introduces herein demonstrated substantial enhancements in rural land-use classification and recognition accuracy, average IoU, and clustering performance. It offers an innovative tool for policymakers to advance rural industry integration, fostering economic diversification. Additionally, this model aids decision-makers in identifying prospective opportunities and challenges, thus facilitating the formulation of forward-thinking and viable rural development strategies.
The synthesis of all 13 mitochondrial DNA (mtDNA)-encoded protein subunits of the human oxidative phosphorylation (OXPHOS) system is carried out by mitochondrial ribosomes (mitoribosomes). Defects in ...the stability of mitoribosomal proteins or mitoribosome assembly impair mitochondrial protein translation, causing combined OXPHOS enzyme deficiency and clinical disease. Here we report four autosomal-recessive pathogenic mutations in the gene encoding the small mitoribosomal subunit protein, MRPS34, in six subjects from four unrelated families with Leigh syndrome and combined OXPHOS defects. Whole-exome sequencing was used to independently identify all variants. Two splice-site mutations were identified, including homozygous c.321+1G>T in a subject of Italian ancestry and homozygous c.322−10G>A in affected sibling pairs from two unrelated families of Puerto Rican descent. In addition, compound heterozygous MRPS34 mutations were identified in a proband of French ancestry; a missense (c.37G>A p.Glu13Lys) and a nonsense (c.94C>T p.Gln32∗) variant. We demonstrated that these mutations reduce MRPS34 protein levels and the synthesis of OXPHOS subunits encoded by mtDNA. Examination of the mitoribosome profile and quantitative proteomics showed that the mitochondrial translation defect was caused by destabilization of the small mitoribosomal subunit and impaired monosome assembly. Lentiviral-mediated expression of wild-type MRPS34 rescued the defect in mitochondrial translation observed in skin fibroblasts from affected subjects, confirming the pathogenicity of MRPS34 mutations. Our data establish that MRPS34 is required for normal function of the mitoribosome in humans and furthermore demonstrate the power of quantitative proteomic analysis to identify signatures of defects in specific cellular pathways in fibroblasts from subjects with inherited disease.
In this work, diamondoid metal-organic frameworks (MOFs) were efficiently prepared by sonochemical synthesis and grown on polyimide (PI), aiming to improve the anti-wear performance of the PI matrix. ...By introducing MOFs into the PI matrix, the free movement of PI molecular chains were restricted, and its hardness and elastic modulus were improved. It was found that the wear rate of the 3 wt.% MOFs/PI composites was reduced by 72.6% compared to pure PI at a load of 4 N after tribological testing by using a ball-on-disk tribometer. This can be attributed to the excellent load-bearing and shear resistance of the fourfold-interpenetrated diamondoid networks, in which the transition metal elements can favor the formation of transfer films. It is worth noting that the 3 wt.% MOFs/PI composites still exhibited great tribological properties under high loads or high speeds. The findings of the present study indicate that diamondoid metal-organic frameworks can be used as efficient modifiers to enhance the tribological properties of PI.
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