Blind Image Quality Assessment for Pathological Microscopic Image Under Screen and Immersion Scenarios.

The high-quality pathological microscopic images are essential for physicians or pathologists to make a correct diagnosis. Image quality assessment (IQA) can quantify the visual distortion degree of images and guide the imaging system to improve image quality, thus raising the quality of pathological microscopic images. Current IQA methods are not ideal for pathological microscopy images due to their specificity. In this paper, we present deep learning-based blind image quality assessment model with saliency block and patch block for pathological microscopic images. The saliency block and patch block can handle the local and global distortions, respectively. To better capture the area of interest of pathologists when viewing pathological images, the saliency block is fine-tuned by eye movement data of pathologists. The patch block can capture lots of global information strongly related to image quality via the interaction between different image patches from different positions. The performance of the developed model is validated by the home-made Pathological Microscopic Image Quality Database under Screen and Immersion Scenarios (PMIQD-SIS) and cross-validated by the five public datasets. The results of ablation experiments demonstrate the contribution of the added blocks. The dataset and the corresponding code are publicly available at: https://github.com/mikugyf/PMIQD-SIS.

Application of next-generation sequencing in clinical oncology to advance personalized treatment of cancer.

With the development and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research over the past decade. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is used to identify novel and rare cancer mutations, detect familial cancer mutation carriers, and provide molecular rationale for appropriate targeted therapy. Compared to traditional sequencing, NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) in a single test at a relatively low cost. However, significant challenges, particularly with respect to the requirement for simpler assays, more flexible throughput, shorter turnaround time, and most importantly, easier data analysis and interpretation, will have to be overcome to translate NGS to the bedside of cancer patients. Overall, continuous dedication to apply NGS in clinical oncology practice will enable us to be one step closer to personalized medicine.

[Assessment of humic and fulvic acids in black soils using near-infrared reflectance spectroscopy].

The organic carbon content and optical densities of humic acids in black soils of China were predicted and assessed using near infrared spectroscopy technique. The contents of humic acid (HA) and fulvic acid (FA) in 136 black soil samples in China were analyzed and the NIR spectra were collected using a VECTOR/22 (Fourier transform infrared spectroscopy). Partial least squares (PLS) regression with cross validation was used to develop prediction models with reference data and soil NIRS spectra, and the model was validated using an independent set of samples. NIRS well predicted (HAC+FAC), HAC and FAC contents, with R2 = 0.92, 0.92 and 0.86, RPD = 3.66, 3.82 and 2.69, and high correlation coefficients between predicted and measured values (r = 0.90, 0.85 and 0.82). Predictions for the E4 values of HA and FA were also good (R2 = 0.85, 0.85; RPD = 2.88, 2.65; r = 0.92, 0.80). Predictions for optical densities of HA and FA at 665 nm (E6) was acceptable. Generally, NIRS showed a good potential to predict C content and optical densities of humic acid and fulvic acid in blacks soils and may reveal information on SOC quality.