MVPCR: Multiplex Visual Detection Strategy Based on Ultrafast PCR for Point-of-Care Pathogens Detection Within 10 Min

Pathogens pose significant threats to biosecurity and environmental health due to their potential for widespread outbreaks. Effective pathogen detection requires methods that are rapid, sensitive, specific, and informative. Here, we proposed a multiplex visual detection system that integrated ultrafast polymerase chain reaction (PCR) and molecular beacons, allowing the simultaneous detection of three pathogens in a one-pot reaction. The ultrafast PCR protocol employed cycles of just 7 s each, allowing the entire process-from sampling to result-to be completed within only 10 min. Molecular beacons hybridized with target sequences during ultrafast PCR, generating fluorescence signals that are visually detectable without specialized equipment. Additionally, we developed a compact, portable cartridge integrated with online software for fluorescence visualization and direct result output, eliminating the need for bulky instruments and specialized personnel, thereby facilitating point-of-care testing (POCT). The method demonstrated high specificity and sensitivity, with a limit of detection (LOD) as low as 23 copies per reaction. It achieved a 100% positive detection rate in practical applications, performing comparably to standard PCR. Furthermore, the method effectively identified low concentrations of pathogens in animal infection samples. This ultrafast, highly sensitive, specific, and informative method shows significant potential for POCT applications, including food safety monitoring and clinical diagnostics. © International Human Phenome Institutes (Shanghai) 2025

Influence of cobalt redox couple concentration on the characteristics of liquid and quasi-solid electrolytes and on the photovoltaic parameters of dye-sensitised solar cells

Dye-sensitised solar cell (DSSC) is a next-generation solar energy conversion device. The electrolyte, which is one of the key components of a DSSC, greatly affects its short-circuit current density (Jsc) and open-circuit voltage (Voc) and hence, its overall performance. In this work, bis(trifluoromethane)sulfonimide (TFSI) cobalt complex was used for the first time as redox couple in DSSC, and an effort was carried out to study the effects of the varying concentration of cobalt complex redox ions on the characteristics of the prepared liquid electrolytes (LEs) and quasi-solid electrolytes (QEs), and on the photovoltaic parameters of DSSCs. Specifically, the electrolyte characteristics include the viscosity and electrical conductivity, while the photovoltaic parameters of DSSCs include Jsc, Voc, fill factor (FF) and power conversion efficiency (PCE). The viscosity of electrolytes was found to increase with increasing molar concentrations and then further increased with the addition of polyethylene oxide (PEO); the highest viscosity of 2.49 cP was obtained at 44 rpm for QE-50. The highest conductivity measured by electrochemical impedance spectroscopy was 83 mS cm− 1 for LE-50. Finally, zinc oxide-based DSSCs with platinum counter electrodes were fabricated for current-voltage measurements. Among the synthesised electrolytes, QE-35-based DSSC showed a better combination of Jsc and Voc, resulting in a PCE of 0.48%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025

Central Limit Theorem for Linear Eigenvalue Statistics of Random Matrices from Binary Linear Codes

It was known that the empirical spectral distribution of random matrices constructed from binary linear codes of increasing length converges to the Marchenko-Pastur law as long as the dual distance of the codes is at least 5, and the condition of the dual distance 5 is optimal because there are binary linear codes of dual distance 4 that do not satisfy this property. In this article, we push this result a little further: we show that a Gaussian central limit theorem holds for the linear spectral statistics associated with such random matrices from binary linear codes of increasing length when the dual distance is at least 7. We also show that the condition of dual distance 7 is optimal as there are binary linear codes of dual distance 6 that do not satisfy this property. This result can be interpreted as that pseudorandom sequences constructed from long binary linear codes of dual distance 7 in general satisfy a more stringent pseudorandom test than those from binary linear codes of dual distance 5

Plasma N-terminal tau fragment is an amyloid-dependent biomarker in Alzheimer's disease

INTRODUCTION: Novel fluid biomarkers for tracking neurodegeneration specific to Alzheimer's disease (AD) are greatly needed. METHODS: Using two independent well-characterized cohorts (n = 881 in total), we investigated the group differences in plasma N-terminal tau (NT1-tau) fragments across different AD stages and their association with cross-sectional and longitudinal amyloid beta (Aβ) plaques, tau tangles, brain atrophy, and cognitive decline. RESULTS: Plasma NT1-tau significantly increased in symptomatic AD and displayed positive associations with Aβ PET (positron emission tomography) and tau PET. Higher baseline NT1-tau levels predicted greater tau PET, with 2- to 10-year intervals and faster longitudinal Aβ PET increases, AD-typical neurodegeneration, and cognitive decline. Plasma NT1-tau showed negative correlations with baseline regional brain volume and thickness, superior to plasma brain-derived tau (BD-tau) and neurofilament light (NfL) in Aβ-positive participants. DISCUSSION: This study suggests that plasma NT1-tau is an Aβ-dependent biomarker and outperforms BD-tau and NfL in detecting cross-sectional neurodegeneration in the AD continuum. Highlights: Plasma N-terminal tau (NT1-tau) was specifically increased in the A+/T+ stage. Plasma NT1-tau was positively associated with greater amyloid beta (Aβ) and tau PET (positron emission tomography) accumulations. Higher plasma NT1-tau predicted greater tau burden and faster Aβ increases. Plasma NT1-tau was more related to neurodegeneration than plasma brain-derived tau (BD-tau) and neurofilament light (NfL). © 2025 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association

A hybrid graph neural network-based federated learning method for personalized manufacturing service composition recommendation

The demand for personalized manufacturing service recommendations is expanding with the popularity and application of industrial Internet platforms. However, the recommendation system has drawbacks in data privacy and security when exchanging parameters of clients. Therefore, this paper proposes a hybrid graph neural network-based federated learning method for personalized manufacturing service composition recommendation (FLGRC). First, a hybrid differential privacy algorithm based on federated learning is designed to solve the data island problem and achieve collaborative training. Second, an improved method of data mining is established to discover the collaborative relationships between different enterprises. Third, the graph neural network algorithm is employed to predict missing QoS (Quality of Service) data, and the lists of recommendations are generated in accordance with fast non-dominated sorting and Top-N sorting rules. Finally, a real industry Internet platform case is adopted in this paper. The experiments analyze the accuracy of the prediction results. Moreover, the results obtained from the proposed algorithm are compared with those collected from other recommendation algorithms to verify the recommendation effect of the model. © 2025 Informa UK Limited, trading as Taylor & Francis Group

Curcumin/kaolin composite for advanced latent fingerprint imaging with fluorescence quantification

Latent fingerprints (LFPs) are invisible impressions that need to be developed before being used for criminal investigation; however, existing fingerprint visualization techniques face challenges, such as complex preparation and poor contrast. To advance practical fingerprint detection, green-emissive micron-sized curcumin/kaolin composites were synthesized via a facile and cost-effective one-step physical cross-linking method, which exhibited unprecedented performance in developing diversified marks, including LFPs, knuckle prints, palm prints, and footprints, with clear three-level details on various substrates. Notably, the powders successfully developed LFPs that were aged for 30 days and even up to 100 days, meeting the stringent requirements for comprehensive forensic application. Afterward, a novel method, termed Fingerprint Fluorescence Intensity Ratio (FFIR), was developed to quantify the contrast between fingerprint signals and background noise and to compare the efficacy of full-color developing agents. Compared with the existing grayscale conversion strategy, the proposed FFIR method achieved tunable multi-color fingerprint image enhancement for the first time, which helped to eliminate background fluorescence interference and improved visual perception. The feasibility of FFIR and its sensitivity in tracking image capture parameters were demonstrated by the established mathematical model. Hence, the newly synthesized modified composites and the mathematical model-validated method demonstrate profound practical significance in comprehensive fingerprint imaging. © 2025 The Royal Society of Chemistry

SnS2/WSe2 van der Waals single-detector spectrometer with a dynamically selecting spectral reconstruction strategy

The single-detector spectrometers based on 2D layer van der Waals (vdW) heterojunctions offer advantages in spectral reconstruction due to their high sensitivity, tunable optical properties, and the ability to cover a broad spectral range. There exist two principal algorithms dominating spectrum reconstruction for this kind spectrometer: the Tikhonov regularization method combined with the Least Squares Method (LSM) and neural network-based approaches, particularly Deep Learning (DL). However, both of the algorithms exhibit inherent limitations in spectral reconstruction, which constrain the versatility of computational spectrometers that rely solely on a single algorithm for reconstructing diverse spectral profiles. To overcome this limitation, we introduce an artificial neural network (ANN)-based classification model capable of dynamically selecting the optimal algorithm throughout the reconstruction process. This enables highly accurate spectral reconstruction within the 440-700 nm wavelength range, achieving a spectral resolution of 6 nm. By harnessing the complementary strengths of multiple algorithms, our approach proposes a novel strategy for combining techniques to enhance the precision of spectral reconstructions, laying the groundwork for more sophisticated methods in the future. © 1980-2012 IEEE

On the Gaussian beam tracing method for long-distance sound wave propagation in non-uniform mean flows

This paper presents an improved Gaussian beam tracing method to efficiently compute long-distance sound wave propagation in non-uniform mean flows. New dynamic ray tracing equations are derived from the convected wave equation in the vicinity of the ray path under the high-frequency asymptotic and paraxial conditions, based on which the solutions of acoustic potential and the corresponding Gaussian beams are developed. The sound pressure at the observer is accurately computed by an integral superposition of all vicinal beams, through a revised weighting function that considers the convection effect on each beam. The proposed method is valid at caustics and can capture the wave diffractions at different sound frequencies. Benchmark problems of the acoustic monopole radiation and the broadband pulse propagation in a free space uniform flow, sound wave interactions with a semi-infinite rigid plate in a moving medium, sound propagations in inhomogeneous stratified flows and in a large-scale vortex flow are studied to validate the proposed method. The results are compared with analytical solutions and high-fidelity numerical results using the finite element method or the fast field program. Good agreements are obtained, showing its potential for the effective assessment of the long-distance sound propagation in complex inhomogeneous flows

Bayesian sequential learning of rock mass classifications along tunnel trajectory using TBM operational data and geo-data spatial correlation

During tunnel construction by a tunnel boring machine (TBM), it is a critical task to repeatedly predict rock mass classifications ahead of the tunnel face for each excavation step as the TBM advances along the designed tunnel trajectory. Such a prediction process is referred to as sequential prediction of rock mass classifications in this study. Previous studies have demonstrated the values of TBM operational data for predicting rock mass classifications using machine learning methods. However, although both TBM data and rock mass classifications are spatial data (i.e., with specific spatial coordinates along the tunnel trajectory), spatial correlation of geo-spatial data has not been utilized in existing machine learning models. To leverage geo-data spatial correlation, a data-driven Bayesian sequential learning method is proposed in this study for sequentially predicting rock mass classifications along the TBM tunnel trajectory. The proposed Bayesian method effectively integrates TBM data, rock mass classifications, and their corresponding spatial coordinates and correlation from completed tunnel segments for improving model fidelity. The proposed method is illustrated using data from the Songhua River water conveyance project in China and performs well

A novel corneal indentation device for comparison of corneal tangent modulus before and after FS-LASIK in vivo

Background: Corneal refractive laser surgery is widely used to correct myopia and astigmatism due to its safety and effectiveness. However, postoperative changes in corneal biomechanics, such as corneal ectasia, can occur, necessitating a deeper understanding of these changes. Finite Element Analysis has shown promise in predicting surgical outcomes based on corneal biomechanics. Devices like the Ocular Response Analyser (ORA) and Corvis ST provide noninvasive ways to measure corneal biomechanics, aiding in the assessment of corneal behavior post-surgery. Young's modulus and tangent modulus are crucial parameters for describing corneal elasticity, but there is limited data on the changes in tangent modulus following Femtosecond Laser-Assisted LASIK (FS-LASIK) in humans. This study aimed to investigate the effect of FS-LASIK on the corneal tangent modulus using a novel corneal indentation device (CID). The study sought to explore changes in corneal tangent modulus after FS-LASIK, taking into account central corneal thickness (CCT) and corneal radius, to enhance our understanding of the biomechanical changes induced by this surgical procedure. Results: Sixty-six patients (66 eyes) underwent FS-LASIK, resulting in significant changes in CCT, corneal radius, and Goldmann intraocular pressure (GAT IOP) 6 months post-surgery (△CCT = − 88 ± 31 µm, △corneal radius = 0.81 ± 0.30 mm, △GAT IOP = − 3.2 ± 2.4 mmHg, p < 0.001) 6 months after surgery. However, corneal stiffness did not significantly change (△ = − 0.002 ± 0.011, p < 0.2). The corneal tangent modulus showed a significant increase post-surgery (△ = 0.263 ± 0.146, p < 0.001), exhibiting a negative correlation with CCT (r = − 0.68, P < 0.001) and a positive correlation with corneal radius (r = 0.71, P < 0.001). For each 1 mm increase in corneal radius, there was a 0.23 MPa increase in corneal modulus, and for every 100 µm reduction in corneal thickness, there was a 0.14 MPa increase in corneal modulus. Conclusions: The corneal tangent modulus, influenced by corneal radius and CCT, increased significantly following FS-LASIK. This study highlights the biomechanical changes induced by FS-LASIK, with implications for understanding corneal behavior post-surgery and its potential impact on patient outcomes