Highly localized continuous wave optical vortex with controllable orbital angular momentum orientation and topological charge.

We report an approach to simultaneously control orbital angular momentum (OAM) orientation and topological charge in highly localized optical vortices by employing a 4π focusing system. The required continuous wave illumination field in the pupil planes is derived by superimposing the radiation pattern of only one dipole placed at the focal point of the high numerical aperture lens and the corresponding tailored spiral phase factor. The topological charge and OAM orientation of the obtained focused fields are quantitatively evaluated based on the focal field distributions calculated by the Richards-Wolf vector diffraction integration theory. Results show that the OAM of the generated optical vortices can be tailored by changing the oscillation orientation of the mimic dipole and the topological charge of the superimposed spiral phase term. The presented method may find potential applications in optical trapping, optical tweezers, light-matter interaction, etc.

Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer.

Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.

High-Q refractive index sensors based on all-dielectric metasurfaces.

Possessing fantastic abilities to freely manipulate electromagnetic waves on an ultrathin platform, metasurfaces have aroused intense interest in the academic circle. In this work, we present a high-sensitivity refractive index sensor excited by the guided mode of a two-dimensional periodic TiO2 dielectric grating structure. Numerical simulation results show that the optimized nanosensor can excite guided-mode resonance with an ultra-narrow linewidth of 0.19 nm. When the thickness of the biological layer is 20 nm, the sensitivity, Q factor, and FOM values of the nanosensor can reach 82.29 nm RIU-1, 3207.9, and 433.1, respectively. In addition, the device shows insensitivity to polarization and good tolerance to the angle of incident light. This demonstrates that the utilization of low-loss all-dielectric metasurfaces is an effective way to achieve ultra-sensitive biosensor detection.

A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer.

Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.

O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification.

The application of deep learning in the medical field has continuously made huge breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net framework has become the benchmark of the medical image segmentation task. However, this framework cannot fully learn global information and remote semantic information. The transformer structure has been demonstrated to capture global information relatively better than the U-Net, but the ability to learn local information is not as good as CNN. Therefore, we propose a novel network referred to as the O-Net, which combines the advantages of CNN and transformer to fully use both the global and the local information for improving medical image segmentation and classification. In the encoder part of our proposed O-Net framework, we combine the CNN and the Swin Transformer to acquire both global and local contextual features. In the decoder part, the results of the Swin Transformer and the CNN blocks are fused to get the final results. We have evaluated the proposed network on the synapse multi-organ CT dataset and the ISIC 2017 challenge dataset for the segmentation task. The classification network is simultaneously trained by using the encoder weights of the segmentation network. The experimental results show that our proposed O-Net achieves superior segmentation performance than state-of-the-art approaches, and the segmentation results are beneficial for improving the accuracy of the classification task. The codes and models of this study are available at https://github.com/ortonwang/O-Net.

Whole-Genome/Exome Sequencing Uncovers Mutations and Copy Number Variations in Primary Diffuse Large B-Cell Lymphoma of the Central Nervous System.

Background/objective: Identification of key genetic alterations is of importance in the targeted therapies of primary central nervous system lymphoma (PCNSL). However, only a small number of studies have been carried out in PCNSL. In this study, we further described the genetic mutations and copy number variations (CNVs) in PCNSL patients using whole-genome/exome sequencing (WGS/WES), as well as revealed their associations with patients' clinicopathological features and prognosis. Methods: Tumor specimens from 38 patients with primary diffuse large B-cell lymphoma of the central nervous system (CNS DLBCL) were enrolled to WGS (n = 24) or WES (n = 14). The CNVs and mutations of 24 samples (WGS) and 38 samples (WGS/WES) were characterized, respectively. The associations between CNVs and mutations with the overall survival rates of PCNSL patients were also evaluated. Results: The most common mutations were identified in IGLL5 (68%), PIM1 (63%), MYD88 (55%), CD79B (42%), BTG2 (39%), PCLO (39%), KMT2D (34%), and BTG1 (29%) genes. Among the mutated genes, EP300, ETV6, and HIST1H1E mutations were exclusively detected in the elderly, while DUSP2 mutations were associated with the immune microenvironment indicators. In addition, KMT2D mutation was associated with a poor prognosis. In addition, 488 CNVs including 91 gains and 397 deletions were observed across 24 samples from WGS results. Notably, 1q31.3 amplification was closely associated with the poor prognosis of PCNSL patients. Conclusion: This study further characterizes the genomic landscape of primary CNS DLBCL using WGS/WES, which provides insight into understanding the pathogenesis of PCNSL and fosters new ideas for the targeted treatment of PCNSL.

Ultra-Wideband and Wide-Angle Perfect Solar Energy Absorber Based on Titanium and Silicon Dioxide Colloidal Nanoarray Structure.

In this paper, we designed an ultra-wideband solar energy absorber and approved it numerically by the finite-difference time-domain simulation. The designed solar energy absorber can achieve a high absorption of more than 90% of light in a continuous 3.506 µm (0.596 µm-4.102 µm) wavelength range. The basic structure of the absorber is based on silicon dioxide colloidal crystal and Ti. Since the materials have a high melting point, the designed solar energy absorber can work normally under high temperature, and the structure of this solar energy absorber is simpler than most solar energy absorbers fabricated with traditional metal. In the entire wavelength band researched, the average absorption of the colloidal crystal-based solar energy absorber is as high as 94.3%, demonstrating an excellent performance under the incidence light of AM 1.5 solar spectrum. In the meantime, the absorption spectrum of the solar energy absorber is insensitive to the polarization of light. In comparison to other similar structures, our designed solar energy absorber has various advantages, such as its high absorption in a wide spectrum range and that it is low cost and easy to make.

Focal shift in tightly focused hybridly polarized Laguerre-Gaussian vector beams with zero radial index.

Hybridly polarized Laguerre-Gaussian vector beams (HPLGVBs) with zero radial index are obtained based on the third type of Laguerre-Gaussian vector beams. Polarization distributions of HPLGVBs are controlled by the phase retardation of a wave plate. The ellipticity angle and polarization orientation angle are used to describe the polarization distributions of the HPLGVBs. The electric field intensity distributions of tightly focused HPLGVBs are analyzed in the focal plane by the Richards-Wolf vectorial diffraction method. It is found that the tightly focused HPLGVBs have a focal shift phenomenon. The dependence of the focal shift of the HPLGVBs on the different parameters is discussed in detail. The simulation results show that the magnitude of the focal shift is related to the polarization distribution, and the focal shift is quite obvious for large azimuthal mode index, long focal length, large numerical aperture, and narrow beam waist. The effective control of the focal shift will have great potential applications in optical micromanipulation.

Dimensional properties of Laguerre-Gaussian vortex beams.

We derive and analyze the dimensional properties of Laguerre-Gaussian vortex beams theoretically and numerically. The analytical expressions of inner and outer radii are given out and proved to be proportional to the transverse beam size, when the topological charge remains. The ratio of the outer and inner radius only depends on the topological charge, having nothing to do with the waist radius and the propagation distance, and the ratio decreases as the topological charge increases. Using a spatial light modulator loaded with computer-generated holograms, we generate vortex beams. The experimental results are in good agreement with our numerical simulations. This research may provide useful insights into the study of the optical vortex beam and its further applications.