A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists. METHODS: This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists. RESULTS: The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively). CONCLUSION: The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

Analysis of the acoustoelectric response of SAW gas sensors using a COM model.

Surface acoustic wave (SAW) gas sensors based on the acoustoelectric effect exhibit wide application prospects for in situ gas detection. However, establishing accurate models for calculating the scattering parameters of SAW gas sensors remains a challenge. Here, we present a coupling of modes (COM) model that includes the acoustoelectric effect and specifically explains the nonmonotonic variation in the center frequency with respect to the sensing film's sheet conductivity. Several sensing parameters of the gas sensors, including the center frequency, insertion loss, and phase, were experimentally compared for accuracy and practicality. Finally, the frequency of the phase extremum (FPE) shift was determined to vary monotonically, and the range of selectable test points was wide, making the FPE an appropriate response parameter for leveraging in SAW gas sensors. The simulation results of the COM model were highly consistent with the experimental results. Our study is proposed to provide theoretical guidance for the future development of gas SAW sensors.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.

OBJECTIVE: To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA. MATERIALS AND METHODS: This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists' performance without and with model assistance was compared to assess the clinical utility of the models. RESULTS: Our segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811-0.942) and 0.799 (95% CI: 0.696-0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05). CONCLUSIONS: DL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist's performance, which might assist in improving diagnosis and progression of axSpA. CRITICAL RELEVANCE STATEMENT: DL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA. KEY POINTS: • Deep learning was used for automatic segmentation of fat metaplasia on MRI. • UNet-based models achieved automatic and accurate segmentation of fat metaplasia. • Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.

OBJECTIVES: To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center. METHODS: This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations. RESULT: On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%). CONCLUSION: The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists. CLINICAL RELEVANCE STATEMENT: Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients. KEY POINTS: • The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Highly Sensitive Chemiresistive H2S Detection at Subzero Temperature over the Sb-Doped SnO2@g-C3N4 Heterojunctions under UV Illumination.

NASA has detected H2S in the persistently shadowed region of the lunar South Pole through NIR and UV/vis spectroscopy remotely, but in situ detection is generally considered to be more accurate and convincing. However, subzero temperatures in space drastically reduce chemisorbed oxygen ions for gas sensing reactions, making gas sensing at subzero temperature something that has rarely been attempted. Herein, we report an in situ semiconductor H2S gas sensor assisted by UV illumination at subzero temperature. We constructed a g-C3N4 network to wrap the porous Sb doped SnO2 microspheres to form type II heterojunctions, which facilitate the separation and transport of photoinduced charge carriers under UV irradiation. This UV-driven technique affords the gas sensor a fast response time of 14 s and a response value of 20.1 toward 2 ppm H2S at -20 °C, realizing the sensitive response of the semiconductor gas sensor at subzero temperature for the first time. Both the experimental observations and theoretical calculation results provide evidence that UV irradiation and the formation of type II heterojunctions together promote the performance at subzero temperature. This work fills the gap of semiconductor gas sensors working at subzero temperature and suggests a feasible method for deep space gas detection.

Extracting Lamb wave vibrating modes with convolutional neural network.

In recent years, micro-acoustic devices, such as surface acoustic wave (SAW) devices, and bulk acoustic wave (BAW) devices have been widely used in the areas of Internet of Things and mobile communication. With the increasing demand of information transmission speed, working frequencies of micro-acoustic devices are becoming much higher. To meet the emerging demand, Lamb wave devices with characteristics that are fit for high working frequency come into being. However, Lamb wave devices have more complicated vibrating modes than SAW and BAW devices. Methods used for SAW and BAW devices are no longer suitable for the mode extraction of Lamb wave devices. To solve this difficulty, this paper proposed a method based on machine learning with convolutional neural network to achieve automatic identification. The great ability to handle large amount of images makes it a good option for vibrating mode recognition and extraction. With a pre-trained model, we are able to identify and extract the first two anti-symmetric and symmetric modes of Lamb waves in varisized plate structures. After the successful use of this method in Lamb wave modes automatic extraction, it can be extended to all micro-acoustic devices and all other wave types. The proposed method will further promote the application of the Lamb wave devices.

Sponge-like loose and porous SnO2 microspheres with rich oxygen vacancies and their enhanced room-temperature gas-sensing performance.

Structure and surface modification of semiconductor materials are of great importance in gas sensors. In this study, a facile citric acid-assisted solvothermal method via a precise calcination process was leveraged to synthesize sponge-like loose and porous SnO2 microspheres with rich oxygen vacancies (denoted as LP-SnO2-Ov). When this material was used in a gas sensor, it exhibited an extremely high response to 10 ppm hydrogen sulfide gas at room temperature (Ra/Rg = 9688), which was 54 times higher than that of commercial SnO2. Furthermore, the response time of LP-SnO2-Ov was 5 s, while the recovery time was 177 s. Moreover, it displayed such high selectivity and stability for hydrogen sulfide gas that its properties remained almost unchanged after 1 month. This method paves a new way to fabricate materials possessing a sponge-like loose and porous structure with oxygen vacancies, which is promising for many other scientific fields such as lithium-ion batteries and photocatalysis.

DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer.

The morphological evaluation of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H& E)-stained histopathological images is the key to breast cancer (BCa) diagnosis, prognosis, and therapeutic response prediction. For now, the qualitative assessment of TILs is carried out by pathologists, and computer-aided automatic lymphocyte measurement is still a great challenge because of the small size and complex distribution of lymphocytes. In this paper, we propose a novel dense dual-task network (DDTNet) to simultaneously achieve automatic TIL detection and segmentation in histopathological images. DDTNet consists of a backbone network (i.e., feature pyramid network) for extracting multi-scale morphological characteristics of TILs, a detection module for the localization of TIL centers, and a segmentation module for the delineation of TIL boundaries, where a boundary-aware branch is further used to provide a shape prior to segmentation. An effective feature fusion strategy is utilized to introduce multi-scale features with lymphocyte location information from highly correlated branches for precise segmentation. Experiments on three independent lymphocyte datasets of BCa demonstrate that DDTNet outperforms other advanced methods in detection and segmentation metrics. As part of this work, we also propose a semi-automatic method (TILAnno) to generate high-quality boundary annotations for TILs in H& E-stained histopathological images. TILAnno is used to produce a new lymphocyte dataset that contains 5029 annotated lymphocyte boundaries, which have been released to facilitate computational histopathology in the future.

An Algorithm for Solving the Phase Ambiguity Problem of SAW Delay-Line Sensor Systems.

Solving the phase ambiguity problem is crucial to achieving a wide-range and high-precision measurement for the frequency-domain sampling (FDS)-based surface acoustic wave (SAW) delay-line sensor systems. This study proposes an improved phase estimation algorithm called dual-band phase estimation (DBPE) to solve the problem. By using DBPE, the SAW sensor system can obtain an extensive and alterable measuring range without further requirements for sensor design or transmitted signals. Thus, it can be widely used in various FDS-based SAW delay-line sensor systems. Monte Carlo simulations and temperature measuring experiments, based on a YZ-cut LiNbO3 SAW delay-line sensor and a switched frequency-stepped continuous wave (S-FSCW) reader, are performed to demonstrate the algorithm's validity. The Monte Carlo simulations show that DBPE can effectively solve the phase ambiguity problem and has better performance than frequency estimation in measuring precision at a low signal-to-noise ratio (SNR). The temperature-sensing experiments show that DBPE has a good performance in measuring range and precision, serving as a phase ambiguity solver in the temperature sensor system.

Prediction of Treatment Response According to ASAS-EULAR Management Recommendations in 1 Year for Hip Involvement in Axial Spondyloarthritis Based on MRI and Clinical Indicators.

Background: To predict the treatment response for axial spondyloarthritis (axSpA) with hip involvement in 1 year based on MRI and clinical indicators. Methods: A total of 77 axSpA patients with hip involvement (60 males; median age, 25 years; interquartile, 22-31 years old) were treated with a drug recommended by the Assessment of SpondyloArthritis international Society and the European League Against Rheumatism (ASAS-EULAR) management. They were prospectively enrolled according to Assessment in SpondyloArthritis international Society (ASAS) criteria. Clinical indicators, including age, gender, disease duration, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), were collected at baseline and in 3 months to 1-year follow-up. Treatment response was evaluated according to ASAS response criteria. MRI indicators consisting of bone marrow edema (BME) in acetabulum and femoral head, hip effusion, fat deposition, thickened synovium, bone erosion, bone proliferation, muscle involvement, enthesitis and bony ankylosis were assessed at baseline. Spearman's correlation analysis was utilized for indicator selection. The selected clinical and MRI indicators were integrated with previous clinical knowledge to develop multivariable logistic regression models. Receiver operator characteristic curve and area under the curve (AUC) were used to assess the performance of the constructed models. Results: The model combining MR indicators comprising hip effusion, BME in acetabulum and femoral head and clinical indicators consisting of disease duration, ESR and CRP yielded AUC values of 0.811 and 0.753 for the training and validation cohorts, respectively. Conclusion: The model combining MRI and clinical indicators could predict treatment response for axSpA with hip involvement in 1 year.

High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning.

Phase-measuring phase-sensitive optical time-domain reflectometry (OTDR) has been widely used for the distributed acoustic sensing. However, the demodulated phase signals are generally noisy due to the laser frequency drift, laser phase noise, and interference fading. These issues are usually addressed individually. In this paper, we propose to address them simultaneously using supervised learning. We first use numerical simulations to generate the corresponding noisy differential phase signals for the given acoustic signals. Then we use the generated acoustic signals and noises together with some real noise data to train an end-to-end convolutional neutral network (CNN) for the acoustic signal enhancement. Three experiments are conduct to evaluate the performance of the proposed signal enhancement method. After enhancement, the average signal-to-noise ratio (SNR) of the recovered PZT vibration signals is improved from 13.4 dB to 42.8 dB, while the average scale-invariant signal-to-distortion ratio (SI-SDR) of the recovered speech signals is improved by 7.7 dB. The results show that, the proposed method can well suppress the noise and signal distortion caused by the laser frequency drift, laser phase noise, and interference fading, while recover the acoustic signals with high fidelity.

Numerical Analysis of Viscous Dissipation in Microchannel Sensor Based on Phononic Crystal.

Phononic crystals with phononic band gaps varying in different parameters represent a promising structure for sensing. Equipping microchannel sensors with phononic crystals has also become a great area of interest in research. For building a microchannels system compatible with conventional micro-electro-mechanical system (MEMS) technology, SU-8 is an optimal choice, because it has been used in both fields for a long time. However, its mechanical properties are greatly affected by temperature, as this affects the phononic bands of the phononic crystal. With this in mind, the viscous dissipation in microchannels of flowing liquid is required for application. To solve the problem of viscous dissipation, this article proposes a simulation model that considers the heat transfer between fluid and microchannel and analyzes the frequency domain properties of phononic crystals. The results show that when the channel length reaches 1 mm, the frequency shift caused by viscous dissipation will significantly affect detecting accuracy. Furthermore, the temperature gradient also introduces some weak passbands into the band gap. This article proves that viscous dissipation does influence the band gap of phononic crystal chemical sensors and highlights the necessity of temperature compensation in calibration. This work may promote the application of microchannel chemical sensors in the future.

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

BACKGROUND: To prospectively explore the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters of sacroiliitis in patients with axial spondyloarthritis (axSpA). METHODS: Patients with initially diagnosed axSpA prospectively underwent on 3.0 T MRI of sacroiliac joint (SIJ). The IVIM parameters (D, f, D *) were calculated using biexponential analysis. K trans, K ep, V e, and V p from DCE-MRI were obtained in SIJ. The uni-variable and multi-variable linear regression analyses were used to evaluate the correlation between the parameters from these two imaging methods after controlling confounders, such as bone marrow edema (BME), age, agenda, scopes, and localization of lesions, and course of the disease. Then, their correlations were measured by calculating the Pearson's correlation coefficient (r). RESULTS: The study eventually enrolled 234 patients (178 men, 56 women; mean age, 28.51 ± 9.50 years) with axSpA. With controlling confounders, D was independently related to K trans (regression coefficient [b] = 27.593, p < 0.001), K ep (b = -6.707, p = 0.021), and V e (b = 131.074, p = 0.003), whereas f and D * had no independent correlation with the parameters from DCE MRI. The correlations above were exhibited with Pearson's correlation coefficients (r) (r = 0.662, -0.408, and 0.396, respectively, all p < 0.001). CONCLUSION: There were independent correlations between D derived from IVIM DWI and K trans, K ep, and V e derived from DCE-MRI. The factors which affect their correlations mainly included BME, gender, and scopes of lesions.

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer.

BACKGROUND: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. METHODS: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. RESULTS: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features. CONCLUSION: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.

PURPOSE: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs. METHODS: We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)-positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes. RESULTS: The TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and CLTA4). Different breast cancer subtypes showed TIL score in association with mutations from different genes suggesting that different genetic alterations may lead to similar phenotypes. Spatial TIL features that represent density and distribution of TIL clusters were important indicators of the patient outcomes. CONCLUSION: Our pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development.

Integrative Analysis of Pathological Images and Multi-Dimensional Genomic Data for Early-Stage Cancer Prognosis.

The integrative analysis of histopathological images and genomic data has received increasing attention for studying the complex mechanisms of driving cancers. However, most image-genomic studies have been restricted to combining histopathological images with the single modality of genomic data (e.g., mRNA transcription or genetic mutation), and thus neglect the fact that the molecular architecture of cancer is manifested at multiple levels, including genetic, epigenetic, transcriptional, and post-transcriptional events. To address this issue, we propose a novel ordinal multi-modal feature selection (OMMFS) framework that can simultaneously identify important features from both pathological images and multi-modal genomic data (i.e., mRNA transcription, copy number variation, and DNA methylation data) for the prognosis of cancer patients. Our model is based on a generalized sparse canonical correlation analysis framework, by which we also take advantage of the ordinal survival information among different patients for survival outcome prediction. We evaluate our method on three early-stage cancer datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrated that both the selected image and multi-modal genomic markers are strongly correlated with survival enabling effective stratification of patients with distinct survival than the comparing methods, which is often difficult for early-stage cancer patients.

Low Computational Cost Distributed Acoustic Sensing Using Analog I/Q Demodulation.

Distributed acoustic sensing based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been widely used in many fields. Phase demodulation of the Φ-OTDR signal is essential for undistorted acoustic measurement. Digital coherent detection is a universal method to implement phase demodulation, but it may cause severe computational burden. In this paper, analog I/Q demodulation is introduced into the Φ-OTDR based DAS system to solve this problem, which can directly obtain the I and Q components of the beat signal without any digital processing, meaning that the computational cost can be sharply reduced. Besides, the sampling frequency of the data acquisition card can theoretically be lower than the beat frequency as the spectrum aliasing would not affect the demodulation results, thus further reducing the data volume of the system. Experimental results show that the proposed DAS system can demodulate the phase signal with good linearity and wide frequency response range. It can also adequately recover the sound signal sensed by the optical fiber, indicating that it can be a promising solution for computational-cost-sensitive distributed acoustic sensing applications.