Dual-Comb Gas Sensor Integrated with a Neural Network-Based Spectral Decoupling Algorithm of Overlapped Spectra for Gas Mixture Sensing.

Cross-interference among absorptions severely affects the ability to achieve accurate gas concentration retrieval through gas molecular specificity. In this study, a novel dual gas sensor was proposed to separate methane and water absorbance from the blended spectra of their mixture in the mid-infrared (MIR) band by employing a neural network algorithm. To address the scarcity of experimental data, the neural network was trained over a simulated data set constructed with the same distribution as the experimental ones. The system takes advantages of the broadband spectra to provide high-quality comb data and allows the neural network to establish an accurate spectral decoupling function. In addition, a feature absorption peak screening mechanism was proposed to achieve more accurate concentration retrieval, which avoids the prediction error introduced by interrogating the only peak of the separated spectra. The promising results of the systematic evaluation have demonstrated the feasibility of our methods in practical detections.

A Multi-Task Convolutional Neural Network for Lesion Region Segmentation and Classification of Non-Small Cell Lung Carcinoma.

Targeted therapy is an effective treatment for non-small cell lung cancer. Before treatment, pathologists need to confirm tumor morphology and type, which is time-consuming and highly repetitive. In this study, we propose a multi-task deep learning model based on a convolutional neural network for joint cancer lesion region segmentation and histological subtype classification, using magnified pathological tissue images. Firstly, we constructed a shared feature extraction channel to extract abstract information of visual space for joint segmentation and classification learning. Then, the weighted losses of segmentation and classification tasks were tuned to balance the computing bias of the multi-task model. We evaluated our model on a private in-house dataset of pathological tissue images collected from Qilu Hospital of Shandong University. The proposed approach achieved Dice similarity coefficients of 93.5% and 89.0% for segmenting squamous cell carcinoma (SCC) and adenocarcinoma (AD) specimens, respectively. In addition, the proposed method achieved an accuracy of 97.8% in classifying SCC vs. normal tissue and an accuracy of 100% in classifying AD vs. normal tissue. The experimental results demonstrated that our method outperforms other state-of-the-art methods and shows promising performance for both lesion region segmentation and subtype classification.

Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors.

Noise significantly limits the accuracy and stability of retrieving gas concentration with the traditional direct absorption spectroscopy (DAS). Here, we developed an adaptively optimized gas analysis model (AOGAM) composed of a neural sequence filter (NSF) and a neural concentration retriever (NCR) based on deep learning algorithms for extraction of methane absorption information from the noisy transmission spectra and obtaining the corresponding concentrations from the denoised spectra. The model was trained on two data sets, including a computationally generated one and the experimental one. We have applied this model for retrieving methane concentration from its transmission spectra in the near-infrared (NIR) region. The NSF was implemented through an encoder-decoder structure enhanced by the attention mechanism, improving robustness under noisy conditions. Further, the NCR was employed based on a combination of a principal component analysis (PCA) layer, which focuses the algorithm on the most significant spectral components, and a fully connected layer for solving the nonlinear inversion problem of the determination of methane concentration from the denoised spectra without manual computation. Evaluation results show that the proposed NSF outperforms widely used digital filters as well as the state-of-the-art filtering algorithms, improving the signal-to-noise ratio by 7.3 dB, and the concentrations determined with the NCR are more accurate than those determined with the traditional DAS method. With the AOGAM enhancement, the optimized methane sensor features precision and stability in real-time measurements and achieves the minimum detectable column density of 1.40 ppm·m (1σ). The promising results of the present study demonstrate that the combination of deep learning and absorption spectroscopy provides a more effective, accurate, and stable solution for a gas monitoring system.

Recovery integral absorbance method in the full concentration range to eliminate the interference of background gas.

At present, gas sensors are extremely susceptible to interference from background gases in the field environment, which leads to greatly reduced accuracy. For this reason, we propose an improved method of recovering integral absorbance (IA) using Y component of first harmonic to achieve accurate prediction of the full range of concentration (not reaching absorption saturation). This approach can eliminate the interference of background gas at a low modulation depth (m < 0.25). When the background gas is pure nitrogen and a mixture of nitrogen and carbon dioxide, the recovery effect of this method on methane is both close to the theoretical value when the background gas is air. The linear fitting coefficients for the methane concentration range of 2000-7000 ppm are all greater than 0.999. The prediction effect is satisfactory regardless of the background gas, with a relative error of less than 1%. In summary, this method has considerable application prospects.

Cystic tumour of the atrioventricular node: a case report and review of the literature.

Cystic tumour of the atrioventricular node is a rare primary cardiac tumour that can cause complete heart block and sudden death. Here, we describe a male case aged 42 years who suddenly died without a medical and family history of cardiac illnesses. After detailed macroscopic and microscopic examinations, a cystic mass was found in the atrioventricular nodal region. The small lesion was less than 1 cm in diameter, and consisted of small and large cystic spaces and tubular structures lined by flat, cuboidal or squamous epithelium. Immunohistochemical staining revealed the tumour epithelium positive for epithelial membrane antigen, carcinoembryonic antigen, antigen epitopes AE1/AE3, cytokeratins CK5/6 and CK7, but negative for calretinin, HBME-1, Wilms' tumor 1, factor VIII, chromogranin, synaptophysin or smooth muscle actin, suggesting an endodermal rather than mesothelial origin.