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Abstract Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m3 for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission.

Abstract This paper addresses the challenge of low detection accuracy of grape clusters caused by scale differences, illumination changes, and occlusion in realistic and complex scenes. We propose a multi-scale feature fusion and augmentation YOLOv7 network to enhance the detection accuracy of grape clusters across variable environments. First, we design a Multi-Scale Feature Extraction Module (MSFEM) to enhance feature extraction for small-scale targets. Second, we propose the Receptive Field Augmentation Module (RFAM), which uses dilated convolution to expand the receptive field and enhance the detection accuracy for objects of various scales. Third, we present the Spatial Pyramid Pooling Cross Stage Partial Concatenation Faster (SPPCSPCF) module to fuse multi-scale features, improving accuracy and speeding up model training. Finally, we integrate the Residual Global Attention Mechanism (ResGAM) into the network to better focus on crucial regions and features. Experimental results show that our proposed method achieves a mAP $$_{0.5}$$ 0.5 of 93.29% on the GrappoliV2 dataset, an improvement of 5.39% over YOLOv7. Additionally, our method increases Precision, Recall, and F1 score by 2.83%, 3.49%, and 0.07, respectively. Compared to state-of-the-art detection methods, our approach demonstrates superior detection performance and adaptability to various environments for detecting grape clusters.

Abstract Emerging diagnostic scenarios, such as population surveillance by pooled testing and on‐site rapid diagnosis, highlight the importance of advanced microfluidic systems for in vitro diagnostics. However, the widespread adoption of microfluidic technology faces challenges due to the lack of standardized design paradigms, posing difficulties in managing macro‐micro fluidic interfaces, reagent storage, and complex macrofluidic operations. This paper introduces a novel modular‐based mesoscopic design paradigm, featuring a core “needle‐plug/piston” structure with versatile variants for complex fluidic operations. These structures can be easily coupled with various microfluidic platforms to achieve truly self‐contained microsystems. Incorporated into a “3D extensible” design architecture, the mesoscopic design meets the demands of function integration, macrofluid manipulations, and flexible throughputs for point‐of‐care nucleic acid testing. Using this approach, an ultra‐sensitive nucleic acid detection system is developed with a limit of detection of ten copies of SARS‐CoV‐2 per mL. This system efficiently conducts large‐scale pooled testing from 50 pharyngeal swabs in a tube with an uncompromised sensitivity, enabling a truly “sample‐in‐answer‐out” microsystem with exceptional performance.

The stratification of cancer into subtypes that are significantly associated with clinical outcomes is beneficial for targeted prognosis and treatment. In this study, we integrated somatic mutation and gene expression data to identify clusters of patients. In contrast to previous studies, we constructed cancer-type-specific significant co-expression networks (SCNs) rather than using a fixed gene network across all cancers, such as the network-based stratification (NBS) method, which ignores cancer heterogeneity. For each type of cancer, the gene expression data were used to construct the SCN network, while the gene somatic mutation data were mapped onto the network, propagated, and used for further clustering. For the clustering, we adopted an improved network-regularized non-negative matrix factorization (netNMF) (netNMF_HC) for a more precise classification. We applied our method to various datasets, including ovarian cancer (OV), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC) cohorts derived from the TCGA (The Cancer Genome Atlas) project. Based on the results, we evaluated the performance of our method to identify survival-relevant subtypes and further compared it to the NBS method, which adopts priori networks and netNMF algorithm. The proposed algorithm outperformed the NBS method in identifying informative cancer subtypes that were significantly associated with clinical outcomes in most cancer types we studied. In particular, our method identified survival-associated UCEC subtypes that were not identified by the NBS method. Our analysis indicated valid subtyping of patient could be applied by mutation data with cancer-type-specific SCNs and netNMF_HC for individual cancers because of specific cancer co-expression patterns and more precise clustering.

Interferometric synthetic aperture radar (InSAR) is an effective technology for generating high-precision digital elevation models (DEMs). However, the vertical accuracy of InSAR DEMs is limited by the contradiction between height measurement sensitivity and phase unwrapping reliability in terms of normal baseline length as well as data voids caused by layover or shadow effects. In order to alleviate these two unfavorable factors, in this study, a novel InSAR DEM fusion method with guided filter is developed and assessed with multiple bistatic TanDEM-X InSAR data pairs of different normal baselines acquired from different orbits. Unlike the widely used fusion method based on pixel-by-pixel weighted average, the guided-filter-based method incorporates local spatial context information into the fusion and can thus effectively alleviate the noise effect and automatically fill in data voids. As a result of the local edge-preserving capability of the guided filter, the proposed fusion method can preserve terrain details by maintaining gradient consistency and introducing terrain features as guidance image. Furthermore, the proposed fusion method is computationally efficient owing to the linear time complexity of guided filter. The experimental results show that the fused DEM with guided filter can depict terrain details well and smooth the “salt-and-pepper” noise and fill in almost all of the data voids. The root mean square error (RMSE) of the fused InSAR DEM with guided filter is lower than those of the weighted average fused InSAR DEM and the TanDEM-X DEM released by the German Aerospace Center (DLR), thus validating the effectiveness of the fusion method proposed in this study.

Anterior plate fusion is an effective procedure for the treatment of cervical spinal diseases but is accompanied by a high incidence of postoperative dysphagia. A zero profile (Zero-P) spacer is increasingly being used to reduce postoperative dysphagia and other potential complications associated with surgical intervention. Studies comparing the Zero-P spacer and anterior plate have reported conflicting results.A meta-analysis was conducted to compare the safety, efficacy, radiological outcomes and complications associated with the use of a Zero-P spacer versus an anterior plate in anterior cervical spine fusion for the treatment of cervical spinal disease. We comprehensively searched PubMed, Embase, the Cochrane Library and other databases and performed a meta-analysis of all randomized controlled trials (RCTs) and prospective or retrospective comparative studies assessing the two techniques.Ten studies enrolling 719 cervical spondylosis patients were included. The pooled data showed significant differences in the operation time [SMD = -0.58 (95% CI = -0.77 to 0.40, p < 0.01)] and blood loss [SMD = -0.40, 95% CI (-0.59 to -0.21), p < 0.01] between the two groups. Compared to the anterior plate group, the Zero-P group exhibited a significantly improved JOA score and reduced NDI and VAS. However, anterior plate fusion had greater postoperative segmental and cervical Cobb's angles than the Zero-P group at the last follow-up. The fusion rate in the two groups was similar. More importantly, the Zero-P group had a lower incidence of earlier and later postoperative dysphagia.Compared to anterior plate fusion, Zero-P is a safer and effective procedure, with a similar fusion rate and lower incidence of earlier and later postoperative dysphagia. However, the results of this meta-analysis should be accepted with caution due to the limitations of the study. Further evaluation and large-sample RCTs are required to confirm and update the results of this study.