Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.

Genetic Variation in Long-Range Enhancers.

Cis-regulatory elements (CREs), including insulators, promoters, and enhancers, play critical roles in the establishment and maintenance of normal cellular function. Within each cell, the 3D structure of chromatin is arranged in specific patterns to expose the CREs required for optimal spatiotemporal regulation of gene expression. CREs can act over large distances along the linear genome, facilitated by looping of the intervening chromatin to allow direct interaction between distal regulatory elements and their target genes. A number of pathologies are associated with dysregulation of CRE function, including developmental disorders, cancers, and neuropsychiatric disease. A majority of known neuropsychiatric disease risk loci are noncoding, and increasing evidence suggests that they contribute to disease through disruption of CREs. As such, rather than directly altering the amino acid content of proteins, these variants are instead thought to affect where, when, and to what extent a given gene is expressed. The distances over which CREs can operate often render their target genes difficult to identify. Furthermore, as many risk loci contain multiple variants in high linkage disequilibrium, identification of the causative single nucleotide polymorphism(s) therein is not straightforward. Thus, deciphering the genetic etiology of complex neuropsychiatric disorders presents a significant challenge.

Single-cell profiling reveals that eRNA accumulation at enhancer-promoter loops is not required to sustain transcription.

Enhancers are intergenic DNA elements that regulate the transcription of target genes in response to signaling pathways by interacting with promoters over large genomic distances. Recent studies have revealed that enhancers are bi-directionally transcribed into enhancer RNAs (eRNAs). Using single-molecule fluorescence in situ hybridization (smFISH), we investigated the eRNA-mediated regulation of transcription during estrogen induction in MCF-7 cells. We demonstrate that eRNAs are localized exclusively in the nucleus and are induced with similar kinetics as target mRNAs. However, eRNAs are mostly nascent at enhancers and their steady-state levels remain lower than those of their cognate mRNAs. Surprisingly, at the single-allele level, eRNAs are rarely co-expressed with their target loci, demonstrating that active gene transcription does not require the continuous transcription of eRNAs or their accumulation at enhancers. When co-expressed, sub-diffraction distance measurements between nascent mRNA and eRNA signals reveal that co-transcription of eRNAs and mRNAs rarely occurs within closed enhancer-promoter loops. Lastly, basal eRNA transcription at enhancers, but not E2-induced transcription, is maintained upon depletion of MLL1 and ERα, suggesting some degree of chromatin accessibility prior to signal-dependent activation of transcription. Together, our findings suggest that eRNA accumulation at enhancer-promoter loops is not required to sustain target gene transcription.

Does NETosis Contribute to the Bacterial Pathoadaptation in Cystic Fibrosis?

Significant advances in our understanding of neutrophil biology were made in the past several years. The exciting discovery that neutrophils deploy neutrophil extracellular traps (NETs) to catch pathogens paved the way for a series of additional studies to define the molecular mechanisms of NET generation and the biological significance of NETosis in acute and chronic pathologic conditions. This review highlights the latest knowledge regarding NET structures, deployment, and function, with an emphasis on current understanding of NET proteomes, their conservation, and significance in the context of cystic fibrosis (CF), a condition characterized by excessive extracellular DNA/NET presence. We also discuss how our understanding of NETosis yields novel therapeutic approaches and their applicability to CF.