Bayesian Recursive Information Optical Imaging: A Ghost Imaging Scheme Based on Bayesian Filtering

Computational imaging~(CI) has been attracting a lot of interest in recent years for its superiority over traditional imaging in various applications. In CI systems, information is generally acquired in an encoded form and subsequently decoded via processing algorithms, which is quite in line with the information transmission mode of modern communication, and leads to emerging studies from the viewpoint of information optical imaging. Currently, one of the most important issues to be theoretically studied for CI is to quantitatively evaluate the fundamental ability of information acquisition, which is essential for both objective performance assessment and efficient design of imaging system. In this paper, by incorporating the Bayesian filtering paradigm, we propose a framework for CI that enables quantitative evaluation and design of the imaging system, and demonstate it based on ghost imaging. In specific, this framework can provide a quantitative evaluation on the acquired information through Fisher information and Cram\'er-Rao Lower Bound (CRLB), and the intrinsic performance of the imaging system can be accessed in real-time. With simulation and experiments, the framework is validated and compared with existing linear unbiased algorithms. In particular, the image retrieval can reach the CRLB. Furthermore, information-driven adaptive design for optimizing the information acquisition procedure is also achieved. By quantitative describing and efficient designing, the proposed framework is expected to promote the practical applications of CI techniques

FvMYB79 Positively Regulates Strawberry Fruit Softening via Transcriptional Activation of FvPME38

Strawberry is a soft fruit with short postharvest life, due to a rapid loss of firmness. Pectin methylesterase (PME)-mediated cell wall remodeling is important to determine fruit firmness and softening. Previously, we have verified the essential role of FvPME38 in regulation of PME-mediated strawberry fruit softening. However, the regulatory network involved in PME-mediated fruit softening is still largely unknown. Here, we identified an R2R3-type MYB transcription factor FvMYB79, which activates the expression level of FvPME38, thereby accelerating fruit softening. During fruit development, FvMYB79 co-expressed with FvPME38, and this co-expression pattern was opposite to the change of fruit firmness in the fruit of ‘Ruegen’ which significantly decreased during fruit developmental stages and suddenly became very low after the color turning stage. Via transient transformation, FvMYB79 could significantly increase the transcriptional level of FvPME38, leading to a decrease of firmness and acceleration of fruit ripening. In addition, silencing of FvMYB79 showed an insensitivity to ABA-induced fruit ripening, suggesting a possible involvement of FvMYB79 in the ABA-dependent fruit softening process. Our findings suggest FvMYB79 acts as a novel regulator during strawberry ripening via transcriptional activation of FvPME38, which provides a novel mechanism for improvement of strawberry fruit firmness

Nanostructured back reflectors produced using polystyrene assisted lithography for enhanced light trapping in silicon thin film solar cells

We study light trapping in hydrogenated amorphous silicon thin film solar cells fabricated by plasma-enhanced chemical vapor deposition on various nanostructured back reflectors. The back reflectors are patterned using polystyrene assisted lithography. We have investigated the correlation between the back reflector optical properties and the corresponding solar cell performance. We have introduced double size polystyrene sphere patterned back reflectors and have provided experimental evidence for improved light trapping performance compared to single size polystyrene sphere patterned back reflectors. We have achieved high performing nanostructured amorphous silicon solar cells with an initial power conversion efficiency of 7.53% and over 20% enhancement of the short-circuit current compared with the reference flat solar cell

Atomically dispersed intrinsic hollow sites of M-M₁-M (M₁ = Pt, Ir; M = Fe, Co, Ni, Cu, Pt, Ir) on FeCoNiCuPtIr nanocrystals enabling rapid water redox

Fabrication of advanced electrocatalysts acting as an electrode for simultaneous hydrogen and oxygen evolution reactions (i.e., HER and OER) in an overall cell has attracted massive attention but still faces enormous challenges. This study reports a significant strategy for the rapid synthesis of high-entropy alloys (HEAs) by pulsed laser irradiation. Two types of intrinsic atomic hollow sites over the surface of HEAs are revealed that enable engaging bifunctional activities for water splitting. In this work, a novel senary HEA electrocatalyst made of FeCoNiCuPtIr facilitates the redox of water at only 1.51 V to achieve 10 mA cm−2 and still remains steadily catalytic and durable after being subjected to a 1m KOH solution for more than 20 h. First-principles calculations reveal that the incorporation of Ir and Pt atoms with neighboring elements donate valence electrons to hollow sites weakening the coupling strength between adsorbate and alloy surface and, consequently accelerating both HER and OER. This work delivers a powerful technique to synthesize highly efficient HEA catalysts and unravels the formation mechanism of active sites across the surface of HEA catalysts.Ministry of Education (MOE)The authors gratefully acknowledge the financial support from MOE Tier 1 RG193/17, MOE Tier 1 RG 79/20 (2020-T1-001-045), the Natural Science Foundation of Beijing Municipality (Grant No. 2212037), the National Natural Science Foundation of China (Grant No.51771027), and the Fundamental Research Funds for the Central Universities (Grant No. FRF-AT-20-07)

The Combination of Ketorolac with Local Anesthesia for Pain Control in Day Care Retinal Detachment Surgery: A Randomized Controlled Trial

This study aims to evaluate the efficacy of ketorolac with local anesthesia compared to local anesthesia alone for perioperative pain control in day care retinal detachment surgery. The randomized controlled trial included 59 eyes of 59 participants for retinal detachment surgery who were randomly assigned (1 : 1) into the ketorolac (K) group and control (C) group. All participants underwent conventional local anesthesia while patients in the K group received an extra administration of preoperative ketorolac. Participants in the K group had a statistically significantly lower intraoperative NRS score (median 1.0 versus 3.0, P=0.003), lower postoperative NRS score (median 0 versus 1.0, P=0.035), fewer proportion of rescue analgesic requirement (10% versus 34.5%, P=0.023), and lower incidence of postoperative nausea and vomiting (13.3% versus 41.4%, P=0.015) compared to the C group. Intraocular pressure (IOP) changes (△IOP) were significantly reduced in the K group (median 1.9 versus 3.0, P=0.038) compared to the C group 24 hours postoperatively. In conclusion, the combination of local anesthesia with ketorolac provides better pain control in retinal detachment surgery compared to local anesthesia alone. The beneficial effect of ketorolac with local anesthesia may contribute to a wider-spread adoption of day care retinal detachment surgery. This trial is registered with ClinicalTrials.gov NCT02729285

Optical Study and Experimental Realization of Nanostructured Back Reflectors with Reduced Parasitic Losses for Silicon Thin Film Solar Cells

We study light trapping and parasitic losses in hydrogenated amorphous silicon thin film solar cells fabricated by plasma-enhanced chemical vapor deposition on nanostructured back reflectors. The back reflectors are patterned using polystyrene assisted lithography. By using O2 plasma etching of the polystyrene spheres, we managed to fabricate hexagonal nanostructured back reflectors. With the help of rigorous modeling, we study the parasitic losses in different back reflectors, non-active layers, and last but not least the light enhancement effect in the silicon absorber layer. Moreover, simulation results have been checked against experimental data. We have demonstrated hexagonal nanostructured amorphous silicon thin film solar cells with a power conversion efficiency of 7.7% and around 34.7% enhancement of the short-circuit current density, compared with planar amorphous silicon thin film solar cells

Domain Generalization for Mammographic Image Analysis with Contrastive Learning

The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and qualities. The diversity of data often comes from the use of various scanners of vendors. But, in practice, it is impractical to collect a sufficient amount of diverse data for training. To this end, a novel contrastive learning is developed to equip the deep learning models with better style generalization capability. Specifically, the multi-style and multi-view unsupervised self-learning scheme is carried out to seek robust feature embedding against style diversity as a pretrained model. Afterward, the pretrained network is further fine-tuned to the downstream tasks, e.g., mass detection, matching, BI-RADS rating, and breast density classification. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets. The experimental results suggest that the proposed domain generalization method can effectively improve performance of four mammographic image tasks on the data from both seen and unseen domains, and outperform many state-of-the-art (SOTA) generalization methods.Comment: arXiv admin note: text overlap with arXiv:2111.1082

Deep learning from “passive feeding” to “selective eating” of real-world data

Abstract Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems