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Bitter gourd, being perishable, requires timely harvesting. Delayed harvesting can result in a substantial reduction in fruit quality. while premature harvesting leads to underdeveloped fruit and decreased yields, the continuous flowering pattern in bitter gourd underscores the significance of accurately assessing fruit growth and ensuring timely harvesting for subsequent fruit setting and development. The current reliance on the experience of production personnel represents a substantial inefficiency. We present an improved real-time instance segmentation model based on YOLOv5-seg. The utilization of dynamic snake convolution enables the extraction of morphological features from the curved and elongated structure of bitter gourd. Diverse branch blocks enhance feature space diversity without inflating model size and inference time, contributing to improved recognition of expansion stages during bitter gourd growth. Additionally, the introduction of Focal-EIOU loss accurately locates the boundary box and mask, addressing sample imbalances in the L2 stage. Experimental results showcase remarkable accuracy rates of 99.3%, 93.8%, and 98.3% for L1, L2, and L3 stages using mAP@0.5. In comparison, our model outperforms other case segmentation models, excelling in both detection accuracy and inference speed. The improved YOLOv5-seg model demonstrates strong performance in fine-grained recognition of bitter gourd during the expansion stage. It efficiently segments bitter gourd in real-time under varying lighting and occlusion conditions, providing crucial maturity information. This model offers reliable insights for agricultural workers, facilitating precise harvesting decisions.

Discoidin domain receptor 1 (DDR1), a collagen-binding receptor tyrosine kinase, plays a key role in extracellular matrix remodeling, tumor progression, and immune evasion. However, DDR1’s comprehensive role across diverse cancers and its therapeutic potential in immune-resistant tumors remain poorly defined. We performed a pan-cancer analysis integrating bulk transcriptomic datasets, single-cell RNA sequencing, and pathway enrichment to evaluate DDR1 expression, genetic alterations, and its associations with immune cell infiltration and clinical outcomes. DDR1 was consistently overexpressed in 21 cancer types, correlating with poor prognosis and reduced immune cell infiltration. Mechanistically, DDR1 promoted collagen remodeling, immune exclusion, and upregulated immunosuppressive pathways. Single-cell analysis in pancreatic ductal adenocarcinoma (PDAC) revealed DDR1-high ductal cells associated with reduced cytotoxic T cell infiltration and increased regulatory T cell populations. Therapeutic blockade of DDR1 in an immunocompetent KPC mouse model of PDAC disrupted collagen architecture, enhanced CD8+ T cell infiltration, and improved responses to chemotherapy, highlighting a direct link between DDR1 inhibition and immune reactivation. These findings establish DDR1 as a key mediator of collagen-driven immune resistance and a promising therapeutic target for overcoming immune exclusion, especially in PDAC and other collagen-rich solid tumors.

Corn–soybean strip intercropping (abbr. CSSI system) can enhance species biodiversity and ecological services for ecological control of insect pests. To improve its effectiveness and fully utilize it to improve ecological control of insect pests and crop production, two monoculture types of corn (C) and soybean (S), and two strip intercropping patterns (i.e., C3S3 and C3S4, indicating three rows of corn strip intercropped with three and four rows of soybeans respectively), were conducted to assess the CSSI system’s (i.e., C3S3 and C3S4) impacts on the abundance of insect pests and crop yields by a two-year field experiment. The results indicated that a total of 11 species of insect pests were found in the CSSI system. Compared with C or S monoculture, the community indexes of insect pests (including the Shannon–Wiener diversity index (H), the Pielou’s evenness index (E), and the Margalef’s richness index (D)) increased, and the Simpson’s dominance index (C) decreased in the C3S3 and C3S4 patterns in 2022. Compared to the C and S monoculture, the CSSI system decreased the population dynamics of total insect pests and the key insect pests Trialeurodes vaporariorum on corn and soybean plants, respectively. In the CSSI system, T. vaporariorum exhibited higher population dynamics on corn plants than on soybean plants, indicating a preference for corn plants under the CSSI system. Moreover, the corn yield per hectare in the C3S4 pattern was significantly higher than that of the C monoculture in 2022–2023. The biomass per plant and the 1000-grain weight of corn in the C3S3 pattern were significantly lower than that in the C monoculture and C3S4 pattern in 2022. The biomass per plant, the 1000-grain weight and yield per hectare of soybean in the C3S3 and C3S4 patterns were significantly lower than that in the S monoculture in 2022–2023. The land equivalent ratio (LER) was <1.0 in the CSSI system, posing yield loss risk for soybeans in the CSSI system. The competitive ratio (CR) of corn was greater than soybean in the CSSI system. In addition, the yield of corn and soybeans were not significantly correlated with the abundance of total insect pests, while the soybean yield was significantly positively correlated to the abundance of T. vaporariorum. In conclusion, it is presumed that the CSSI system can decrease the abundances of insect pests, particularly key insect pests, and maintain their community stability, thereby preventing insect pests’ outbreak. However, the CSSI system is disadvantageous for soybean yield, as it cannot fully utilize land resources and may pose a risk of system yield loss.

Background Acute kidney injury (AKI) is a prevalent and severe complication of sepsis contributing to high morbidity and mortality among critically ill patients. In this retrospective study, we develop a novel risk-predicted nomogram of sepsis associated-AKI (SA-AKI). Methods A total of 2,871 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database were randomly assigned to primary (2,012 patients) and validation (859 patients) cohorts. A risk-predicted nomogram for SA-AKI was developed through multivariate logistic regression analysis in the primary cohort while the nomogram was evaluated in the validation cohort. Nomogram discrimination and calibration were assessed using C-index and calibration curves in the primary and external validation cohorts. The clinical utility of the final nomogram was evaluated using decision curve analysis. Results Risk predictors included in the prediction nomogram included length of stay in intensive care unit (LOS in ICU), baseline serum creatinine (SCr), glucose, anemia, and vasoactive drugs. Nomogram revealed moderate discrimination and calibration in estimating the risk of SA-AKI, with an unadjusted C-index of 0.752, 95 %Cl (0.730–0.774), and a bootstrap-corrected C index of 0.749. Application of the nomogram in the validation cohort provided moderate discrimination (C-index, 0.757 [95 % CI, 0.724–0.790]) and good calibration. Besides, the decision curve analysis (DCA) confirmed the clinical usefulness of the nomogram. Conclusions This study developed and validated an AKI risk prediction nomogram applied to critically ill patients with sepsis, which may help identify reasonable risk judgments and treatment strategies to a certain extent. Nevertheless, further verification using external data is essential to enhance its applicability in clinical practice.

The development of optical interconnect techniques greatly expands the communication bandwidth and decreases the power consumption at the same time. It provides a prospective solution for both intra-chip and inter-chip links. Herein reported is an integrated wavelength-division multiplexing (WDM)-compatible multimode optical switching system-on-chip (SoC) for large-capacity optical switching among processors. The interfaces for the input and output of the processor signals are electrical, and the on-chip data transmission and switching process are optical. It includes silicon-based microring optical modulator arrays, mode multiplexers/de-multiplexers, optical switches, microring wavelength de-multiplexers and germanium-silicon high-speed photodetectors. By introducing external multi-wavelength laser sources, the SoC achieved the function of on-chip WDM and mode-division multiplexing (MDM) hybrid-signal data transmission and switching on a standard silicon photonics platform. As a proof of concept, signals with a 25 Gbps data rate are implemented on each microring modulator of the fabricated SoC. We illustrated 25 × 3 × 2 Gbps on-chip data throughput with two-by-two multimode switching functionality through implementing three wavelength-channels and two mode-channel hybrid-multiplexed signals for each multimode transmission waveguide. The architecture of the SoC is flexible to scale, both for the number of supported processors and the data throughput. The demonstration paves the way to a large-capacity multimode optical switching SoC.

With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios. To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency-domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised methods, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have extremely fast inference speed (near 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3). Furthermore, the potential benefits of our method to object detection in the dark are discussed.

Background Hemorrhagic transformation (HT), especially symptomatic HT (sHT), seriously affects the functional prognosis in patients following mechanical thrombectomy (MT). The present study aimed to investigate the association of HT with heart rate variability (HRV) using multiple parameters in patients undergoing MT. Methods Between November 2019 and April 2024, we enrolled eligible patients according to the inclusion criteria and exclusion criteria. Linear parameters and nonlinear approaches reflecting HRV were calculated and analyzed. Results A total of 254 patients were included in the analysis, including 56 patients with HT and 198 patients without, and 24 patients with sHT and 230 patients without. Nonlinear approaches including SD1/SD2 ratio, Higuchi fractal dimension (HFD), large-scale multiscale entropy index (MSE LS ), multiscale entropy area under the curve (MSE AUC ), large-scale composite multiscale entropy index (CMSE LS ), and composite multiscale entropy area under the curve (CMSE AUC ) were showed to be independently associated with sHT (p < 0.05). Meanwhile, receiver operating characteristic curves suggested that CMSE LS was a better indicator to predict sHT than other HRV parameters, with the area under the curve of 0.708 (95%CI 0.581–0.835). Conclusions Nonlinear parameters of HRV including SD1/SD2 ratio, HFD, MSE LS , MSE AUC , CMSE LS , and CMSE AUC provide an independent indicator for predicting sHT in patients following MT.

Mode (de)multiplexer is an essential device in integrated multimode photonic systems. Here, we present a dual-mode (de)multiplexer that separates two input modes, TE0 and TE1, into two output ports while converting TE1 to TE0 mode. Based on the adjoint and level set method, the device features a small footprint of 9.4 μm × 2.9 μm, and a minimum feature size over 200 nm is achieved, affirming stable and reliable fabrication. Through simulations, we observed insertion losses of less than 0.28 dB for TE0 mode and 0.35 dB for TE1 mode within the wavelength range of 1500–1600 nm, accompanied by crosstalk levels lower than −30 dB. In our experimental tests, we achieved insertion losses of less than 0.89 dB for TE0 mode and 0.44 dB for TE1 modes within the 1530 nm to 1570 nm range, with crosstalk maintained below −25 dB. Furthermore, we conducted an experimental verification of the differences between the standard device and the boundary dilation/erosion device, observing an insertion loss degradation by 0.61 dB within a deviation range of ±40 nm, which demonstrates the device’s robustness to the fabrication. The proposed devices exhibit exceptional performance and feature a compact structure, thus holding significant potential for the development of future multimode integrated photonic circuits.

Ants are one of the largest insect groups, with the most species and individuals in the world, and they have an important ecological function. Ants are not only an important part of the food chains but are also one of the main decomposers on the Earth; they can also improve soil fertility, etc. However, some species of ants are harmful to human beings, which leads to people’s panic or worry about coming into contact with these insects during their daily home life or in their tourism or leisure activities. The presence of ants in indoor living facilities and in outdoor green spaces, parks, gardens, and tourist attractions seriously interferes with the leisure life and entertainment activities of all people (especially children). How can we control ants in these environments? Do we kill them by spraying insecticides, or do we adopt green prevention and control technology for the ecological management of ants? This topic is related to healthy life for the public and the protection of the ecological environment. In this paper, the species and diversity of ants are introduced, and research progress regarding ant tropism is introduced according to the three aspects of phototaxis, chromotaxis, and chemotaxis (i.e., “3-tropisms”). The research on repellent substances from plants and insects and the related ant attractants are also summarized, analyzed, and discussed, in order to help the research and application of green prevention and control technology for ant diversity protection and conservation.

Fishery resources are among the most economically valuable assets from aquatic ecosystems, underpinning global food security, nutrition, and livelihoods. However, their sustainable management is increasingly challenged by anthropogenic pressures, including overexploitation, and pollution, which not only deplete stocks, but also compromise the health of human and aquatic organism. In this context, the One Health framework, an integrated approach recognizing the interdependence of human, aquatic organism, and environmental health, provides a critical lens for fisheries governance to ensure long-term resource sustainability. It calls for coordinated surveillance of biological and environmental indicators across trophic levels to anticipate and mitigate risks such as pathogen emergence, biodiversity loss, and fishery resource depletion. Environmental DNA (eDNA) has emerged as a promising exploratory tool in fisheries science and aquatic ecology, offering a non-invasive and system-wide monitoring mean to detect presence and composition of cross-domain organisms (from microbes to aquatic animals) and even inferring relative or absolute abundance. Its ability to simultaneously interrogate multiple components of the aquatic biosphere aligns uniquely with the multisectoral objectives of One Health. As such, eDNA functions not as a standalone solution, but as a synergistic component within integrated assessment frameworks that link ecosystem status, fishery productivity, and public health outcomes. Nonetheless, methodological challenges remain, particularly in designing primers, expanding and curating reference databases, standardizing sampling and bioinformatic protocols, and developing robust quantitative models translating eDNA signals into actionable stock or risk assessments. This review critically examines the applications, limitations, and future trajectories of eDNA technology in fisheries science through the lens of One Health, with emphasis on its potential to inform cross-scale, interdisciplinary strategies for sustainable fishery management.