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Peaches are an ancient fruit in China, with great nutritional and commercial value. Peach fruit is a typical respiratory climacteric fruit, which is prone to browning within a short period of time after harvesting, and rapid browning and high browning degree occur during peach processing, which are difficult to control, affecting the quality and economic value of the fruit. At present, systematic research on the characteristics and mechanism of peach browning is lacking, as well as effective control techniques and methods. In this paper, the current status of research on the characteristics and mechanism of and the key factors for enzymatic browning in peach fruit is reviewed, and the techniques used for its control are summarized, aiming to provide a reference for the regulation of enzymatic browning during peach storage and processing and consequently to promote the quality improvement of fresh and processed peach products.

Simple Summary: Drugs work by binding to a specific 3D structure on a protein. Drug discovery has historically been driven by prior knowledge of function, either of a protein or chemical. This knowledge of function then drives investigations to probe chemical/protein interactions. We undertook a different approach. We first identified unique 3D structures, agnostic of function, and investigated whether they could lead us to innovative therapeutics. Using a synchrotron-based X-ray source, we first determined high-resolution structures of hundreds of proteins. With a supercomputer running analytical programs created by us, we identified novel 3D structures and screened for chemicals binding them. We then tested their ability to inhibit cancer growth without damaging normal cells. We identified a potent inhibitor of a deadly cancer, melanoma. It was not toxic to normal cells even at 2100-fold higher doses. It worked by inducing anoikis, a fundamental process of known importance for cancer. Therapeutics that selectively induce anoikis are needed. In summary, we demonstrate the power of using a 3D protein structure as the starting point to discover new biology and drugs. Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable. [ABSTRACT FROM AUTHOR]

Targeting the dynamic image-motion problem of aerial cameras in the process of swing imaging, the image-motion compensation technology of aerial cameras based on the multi-dimensional motion of the secondary mirror was adopted. The secondary mirror was used as the image-motion compensation element, and the comprehensive image-motion compensation of the aerial camera was realized through the multi-dimensional motion of the secondary mirror. However, in the process of compensating for the image motion, the secondary mirror would be eccentric and inclined, which would cause the secondary mirror to be off-axis and affect the image quality. Therefore, a misalignment optical system model was established to study the relationship between the deviation vector and the misalignment of the secondary mirror, and the influence of the secondary mirror's motion on the distribution of the aberration was analyzed. In order to verify the image-motion compensation ability of the multi-dimensional motion of the secondary mirror, an experimental platform was built to conduct a laboratory imaging experiment and flight experiment on the aerial camera. The experimental results showed that the dynamic resolution of the aerial camera using the image-motion compensation technology could reach 74 lp/mm, and the image-motion compensation accuracy was better than 0.5 pixels, which met the design expectation. In conclusion, the image-motion compensation technology is expected to be applied to various high-precision optical imaging as well as optical detection systems. [ABSTRACT FROM AUTHOR]

In the athermal and apochromatic design of optical systems, the distribution of lens' optical powers and the selection of optical glass and structural materials are crucial. In this paper, an athermal and apochromatic design method is proposed for optical systems with a long focal length, large relative aperture, and wide spectrum. Firstly, a complex optical system composed of multiple lenses is equivalent to a two-component, single-lens system consisting of a replacement and an equivalent lens group. The optical glass for the replacement lens group is selected based on weight and the principle of material replacement in the 3D glass diagram, thus achieving an athermal and apochromatic design. Secondly, an athermal and apochromatic optical system with a focal length of 130 mm, an F-number of 2.0, a spectral range of 480 nm~800 nm, a field of view angle of 22°, and an operating temperature of −40 °C~+60 °C is designed. The modulation transfer function (MTF) at each field of view is greater than 0.6 at 50 lp/mm in the −40 °C~+60 °C temperature range, and the secondary spectrum aberration is 0.0056 mm, which is within the focal depth range of the optical system. [ABSTRACT FROM AUTHOR]

Given the impressive achievement of convolutional neural networks (CNNs) in grasping image priors from extensive datasets, they have been widely utilized for tasks related to image restoration. Recently, there is been significant progress in another category of neural architectures—Transformers. These models have demonstrated remarkable performance in natural language tasks and higher‐level vision applications. Despite their ability to address some of CNNs limitations, such as restricted receptive fields and adaptability issues, Transformer models often face difficulties when processing images with a high level of detail. This is because the complexity of the computations required increases significantly with the image's spatial resolution. As a result, their application to most high‐resolution image restoration tasks becomes impractical. In our research, we introduce a novel Transformer model, named DehFormer, by implementing specific design modifications in its fundamental components, for example, the multi‐head attention and feed‐forward network. Specifically, the proposed architecture consists of the three modules, that is, (a) multi‐scale feature aggregation network (MSFAN), (b) the gated‐Dconv feed‐forward network (GFFN), (c) and the multi‐Dconv head transposed attention (MDHTA). For the MDHTA module, our objective is to scrutinize the mechanics of scaled dot‐product attention through the utilization of per‐element product operations, thereby bypassing the need for matrix multiplications and operating directly in the frequency domain for enhanced efficiency. For the GFFN module, which enables only the relevant and valuable information to advance through the network hierarchy, thereby enhancing the efficiency of information flow within the model. Extensive experiments are conducted on the SateHazelk, RS‐Haze, and RSID datasets, resulting in performance that significantly exceeds that of existing methods. [ABSTRACT FROM AUTHOR]

For the purpose of achieving long-range, high-resolution, and ultra-wide-swath airborne earth imaging at extremely low-light levels (0.01 Lux), a low-light imaging system built on multi-detector optical butting was researched. Having decomposed the system's specifications and verified its low-light imaging capability, we proposed to employ an optical system with a large relative aperture and low distortion and achieve imaging through the field-of-view (FOV) butting facilitated by eight 1080P high-sensitivity scientific complementary metal-oxide semiconductor (SCMOS) detectors. This paper elaborates on the design concept of the mechanical configuration of the imaging system; studies the calculation method of the structural parameters of the reflection prism; provides mathematical expressions for geometric parameters, such as the length and width of the splicing prism; and designs in detail the splicing structure of six reflection prisms for eight-channel beam splitting. Based on the design and computational results, a high-resolution, wide-swath imaging system for an ambient illuminance of 0.01 Lux was developed. Exhibiting a ground sampling distance (GSD) of 0.5 m (at a flight height of 5 km), this low-light imaging system keeps the FOV overlap ratio between adjacent detectors below 3% and boasts an effective image resolution of 4222 × 3782. The results from flight testing revealed that the proposed imaging system is capable of generating wide-swath, high-contrast resolution imagery under airborne and low-light conditions. As such, the way the system is prepared can serve as a reference point for the development of airborne low-light imaging devices. [ABSTRACT FROM AUTHOR]

Leveraging space-based optical platforms for space debris and defunct spacecraft detection presents several advantages, including a wide detection range, immunity to cloud cover, and the ability to maintain continuous surveillance on space targets. As a result, it has become an essential approach for accomplishing tasks related to space situational awareness. However, the prediction of the orbits of space objects is crucial for the success of such missions, and current technologies face challenges related to accuracy, reliability, and practical efficiency. These challenges limit the performance of space-based optical space situational awareness systems. To drive progress in this field and establish a more effective and reliable space situational awareness system based on space optical platforms, this paper conducts a retrospective overview of research advancements in this area. It explores the research landscape of orbit determination methods, encompassing orbit association methods, initial orbit determination methods, and precise orbit determination methods, providing insights from international perspectives. The article concludes by highlighting key research areas, challenges, and future trends in current space situational awareness systems and orbit determination methods. [ABSTRACT FROM AUTHOR]

The previous study has researched that combining YKL-5-124, CDK7 specific target drug with anti-PD-1 treatment had a survival advantage in various Small Cell Lung Cancer models. However, the molecular mechanism that YKL-5-124 triggers inflammatory response remains unknown. This study investigates the effects of CDK7 inhibition by YKL-5-124 on two DNA damage pathways (ATM and ATR) of SCLC in vitro. The result of this study will reveal the effects of CDK7 inhibition by YKL-5-124 on the molecular mechanisms of SCLC and potentially promote the development of new anticancer treatment strategies based on immunity manipulation.

The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method. [ABSTRACT FROM AUTHOR]