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In order to explore the movement and distribution of particles in the tank of jet and agitation co-flotation machine, a geometric model of the tank of jet and agitation co-flotation machine was established. The COMSOL software was used to simulate the flow field distribution and particle movement in the flotation tank under impeller agitation, and a test system of jet and agitation co-flotation device was set up to test the particle dispersion effect. The results showed that some particles pass through the gap between the impeller blades and spiral downward under the action of flow field, the other part of the particles are thrown out by the blade and move helically around and up under the coupling effect of the blade and the flow field. The trajectory curves of all particles were drawn to explore the particle movement and distribution law during the operation of the flotation machine. It was found that the trajectory curves of particles at the bottom of the flotation cell, around the wall and at the corner of the tank have a high coincidence degree, which indicates that the probability of particles moving there is large. The coincidence degree of particle motion trajectories in the upper part of the flotation tank and the area between the wall and the impeller is small, and the probability of particle movement there is low, and only a few particles are distributed there. The uniformity of particle distribution does not increase with the increase of impeller stirring speed, but there is an optimal impeller working parameter. The results of simulation and test showed that the jet and agitation co-flotation machine has better particle dispersion performance under the condition of impeller speed of 6.25 r/s. The particle dispersion is affected by the shape and internal structure of the tank. Adding baffle or using column flotation cell can effectively promote the uniform distribution of particles. The study of the movement and distribution of particles in the tank is beneficial to optimize the structure of the jet and agitation co-flotation machine and improve its flotation efficiency.

Abstract Background Learning to perform strabismus surgery is an essential aspect of ophthalmologists’ surgical training. Automated classification strategy for surgical steps can improve the effectiveness of training curricula and the efficient evaluation of residents’ performance. To this end, we aimed to develop and validate a deep learning (DL) model for automated detecting strabismus surgery steps in the videos. Methods In this study, we gathered 479 strabismus surgery videos from Shanghai Children’s Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, spanning July 2017 to October 2021. The videos were manually cut into 3345 clips of the eight strabismus surgical steps based on the International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubrics (ICO-OSCAR: strabismus). The videos dataset was randomly split by eye-level into a training (60%), validation (20%) and testing dataset (20%). We evaluated two hybrid DL algorithms: a Recurrent Neural Network (RNN) based and a Transformer-based model. The evaluation metrics included: accuracy, area under the receiver operating characteristic curve, precision, recall and F1-score. Results DL models identified the steps in video clips of strabismus surgery achieved macro-average AUC of 1.00 (95% CI 1.00–1.00) with Transformer-based model and 0.98 (95% CI 0.97-1.00) with RNN-based model, respectively. The Transformer-based model yielded a higher accuracy compared with RNN-based models (0.96 vs. 0.83, p

Claudin18.2 (CLDN18.2) is a tight junction protein that is overexpressed in a variety of solid tumors such as gastrointestinal cancer and oesophageal cancer. It has been identified as a promising target and a potential biomarker to diagnose tumor, evaluate efficacy, and determine patient prognosis. TST001 is a recombinant humanized CLDN18.2 antibody that selectively binds to the extracellular loop of human Claudin18.2. In this study, we constructed a solid target radionuclide zirconium-89 (89Zr) labled-TST001 to detect the expression of in the human stomach cancer BGC823CLDN18.2 cell lines. The [89Zr]Zr-desferrioxamine (DFO)-TST001 showed high radiochemical purity (RCP, >99%) and specific activity (24.15 ± 1.34 GBq/μmol), and was stable in 5% human serum albumin, and phosphate buffer saline (>85% RCP at 96 h). The EC50 values of TST001 and DFO-TST001 were as high as 0.413 ± 0.055 and 0.361 ± 0.058 nM (P > 0.05), respectively. The radiotracer had a significantly higher average standard uptake values in CLDN18.2-positive tumors than in CLDN18.2-negative tumors (1.11 ± 0.02 vs. 0.49 ± 0.03, P = 0.0016) 2 days post injection (p.i.). BGC823CLDN18.2 mice models showed high tumor/muscle ratios 96 h p.i. with [89Zr]Zr-DFO-TST001 was much higher than those of the other imaging groups. Immunohistochemistry results showed that BGC823CLDN18.2 tumors were highly positive (+++) for CLDN18.2, while those in the BGC823 group did not express CLDN18.2 (−). The results of ex vivo biodistribution studies showed that there was a higher distribution in the BGC823CLDN18.2 tumor bearing mice (2.05 ± 0.16 %ID/g) than BGC823 mice (0.69 ± 0.02 %ID/g) and blocking group (0.72 ± 0.02 %ID/g). A dosimetry estimation study showed that the effective dose of [89Zr]Zr-DFO-TST001 was 0.0705 mSv/MBq, which is within the range of acceptable doses for nuclear medicine research. Taken together, these results suggest that Good Manufacturing Practices produced by this immuno-positron emission tomography probe can detect CLDN18.2-overexpressing tumors.

Abstract Iris presentation attack detection (PAD) is still an unsolved problem mainly due to the various spoof attack strategies and poor generalisation on unseen attackers. In this paper, the merits of both light field (LF) imaging and deep learning (DL) are leveraged to combine 2D texture and 3D geometry features for iris liveness detection. By exploring off‐the‐shelf deep features of planar‐oriented and sequence‐oriented deep neural networks (DNNs) on the rendered focal stack, the proposed framework excavates the differences in 3D geometric structure and 2D spatial texture between bona fide and spoofing irises captured by LF cameras. A group of pre‐trained DL models are adopted as feature extractor and the parameters of SVM classifiers are optimised on a limited number of samples. Moreover, two‐branch feature fusion further strengthens the framework's robustness and reliability against severe motion blur, noise, and other degradation factors. The results of comparative experiments indicate that variants of the proposed framework significantly surpass the PAD methods that take 2D planar images or LF focal stack as input, even recent state‐of‐the‐art (SOTA) methods fined‐tuned on the adopted database. Presentation attacks, including printed papers, printed photos, and electronic displays, can be accurately detected without fine‐tuning a bulky CNN. In addition, ablation studies validate the effectiveness of fusing geometric structure and spatial texture features. The results of multi‐class attack detection experiments also verify the good generalisation ability of the proposed framework on unseen presentation attacks.

Abstract Combining electron‐ and hole‐selective materials in one crystalline silicon (Si) solar cell, thereby avoiding any dopants, is not considered for application to photovoltaic industry until only comparable efficiency and stable performance are achievable. Here, it is demonstrated how a conventionally unstable electron‐selective contact (ESC) is optimized with huge boost in stability as well as improved electron transport. With the introduction of a Ti thin film between a‐Si:H(i)/LiF and Al electrode, high‐level passivation (Seff = 4.6 cm s–1) from a‐Si:H(i) and preferential band alignment (ρC = 7.9 mΩ cm2) from low work function stack of LiF/Ti/Al are both stably retained in the newly constructed n‐Si/a‐Si:H(i)/LiF/Ti/Al ESC. A detailed interfacial elements analysis reveals that the efficiently blocked inward diffusion of Al from electrode by the Ti protecting layer balances transport and recombination losses in general. This excellent electron‐selective properties in combination with large process tolerance that enable remarkable device performance, particularly high efficiencies of 22.12% and 23.61%, respectively, are successfully approached by heterojunction solar cells with dopant‐free ESC and dopant‐free contacts for both polarities.

Crystalline silicon solar cells produced by doping processes have intrinsic shortages of high Auger recombination and/or severe parasitic optical absorption. Dopant-free carrier-selective contacts (DF-CSCs) are alternative routines for the next generation of highly efficient solar cells. However, it is difficult to achieve both good passivating and low contact resistivity for most DF-CSCs. In this paper, a high-quality dopant-free electron-selective passivating contact made from ultra-low concentration water solution is reported. Both low recombination current (J0) ~10 fA/cm2 and low contact resistivity (ρc) ~31 mΩ·cm2 are demonstrated with this novel contact on intrinsic amorphous silicon thin film passivated n-Si. The electron selectivity is attributed to relieving of the interfacial Fermi level pinning because of dielectric properties (decaying of the metal-induced gap states (MIGS)). The full-area implementation of the novel passivating contact shows 20.4% efficiency on a prototype solar cell without an advanced lithography process. Our findings offer a very simple, cost-effective, and efficient solution for future semiconductor devices, including photovoltaics and thin-film transistors.

Hepatocellular carcinoma (HCC) is characterized by a high rate of incidence and recurrence, and resistance to chemotherapy may aggravate the poor prognosis of HCC patients. Sorafenib resistance is a conundrum to the treatment of advanced/recurrent HCC. Therefore, studies on the molecular pathogenesis of HCC and the resistance to sorafenib are of great interest. Here, we report that GINS1 was highly expressed in HCC tumors, associated with tumor grades, and predicted poor patient survival using Gene Expression Omnibus (GEO) databases exploration. Cell cycle, cell proliferation assay and in vivo xenograft mouse model indicated that knocking down GINS1 induced in G1/S phase cell cycle arrest and decreased tumor cells proliferation in vitro and in vivo. Spheroid formation assay results showed that GINS1 promoted the stem cell activity of HCC tumor cells. Furthermore, GEO database (GSE17112) analysis showed that HRAS oncogenic gene set was enriched in GINS1 high-expressed cancer cells, and quantitative real-time PCR, and Western blot results proved that GINS1 enhanced HCC progression through regulating HRAS signaling pathway. Moreover, knocking down endogenous GINS1 with shGINS1 increased the sensitivity of HCC cells to sorafenib, and restoring HRAS or stem associated pathway partly recovered the sorafenib resistance. Overall, the collective findings highlight GINS1 functions in hepatocarcinogenesis and sorafenib resistance, and indicate its potential use of GINS1 in drug-resistant HCC.

The ACE2 receptor, as the potential entrance site of SARS-CoV-2-affected cells, plays a crucial role in spreading infection. The DX600 peptide is a competitive inhibitor of ACE2. We previously constructed the 68Ga-labeled DOTA-DX600 (also known as 68Ga-HZ20) peptide and confirmed its ACE2 binding ability both in vitro and in vivo. In this research, we aimed to investigate the noninvasive mapping of ACE2 expression in fowl using 68Ga-HZ20 micro-PET. We chose pigeons as an animal model and first studied the administration method of 68Ga-HZ20 by direct site injection or intravenous injection. Then, the dynamic micro-PET scan of 68Ga-HZ20 was conducted at 0–40 min. Additionally, 18F-FDG was used for comparison. Finally, the pigeons were sacrificed, and the main organs were collected for further immunoPET and IHC staining. Micro PET/CT imaging results showed that 68Ga-HZ20 uptake was distributed from the heart at the preliminary injection to the kidneys, liver, stomach, and lungs over time, where the highest uptake was observed in the kidneys (SUVmax = 6.95, 20 min) and lung (SUVmax = 1.11, 20 min). Immunohistochemical experiments were carried out on its main organs. Compared to the SUVmax data, the IHC results showed that ACE2 was highly expressed in both kidneys and intestines, and the optimal imaging time was determined to be 20 min after injection through correlation analysis. These results indicated that 68Ga-HZ20 is a potential target molecule for SARS-CoV-2 in fowl, which is worthy of promotion and further study.

Research on photovoltaic devices with a high performance-to-cost ratio requires efforts not only on efficiency improvement but also on manufacturing cost reduction. Recently, a record efficiency of 26.6% on crystalline silicon solar cells (SCs) has been achieved by combining the heterojunctions (HJs) with a device structure of interdigitated back contacts. However, the technology that integrates the interdigital p- and n-type amorphous silicon (a-Si:H) layers on the rear surface of the Si substrate is challenging. This issue has motivated researchers to search dopant-free carrier-selective contacts with alternative materials to completely replace doped a-Si:H layers. Transition metal oxides, graphene, and poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), all having high work functions and hole conducting properties, can play the role of hole-selective layers (HSLs). In this review, we focus on the latest advances and the future trends in these HSLs and their applications in silicon HJ SCs. The main issues and challenges encountered are discussed.

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.