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A novel approach to use two-dimensional correlation spectroscopy (2D-COS) to analyze bilinear data is proposed. A phenomenon called Systematic Absence of Cross Peaks (SACPs) is observed in a 2D asynchronous spectrum. Two theorems relevant to SACPs have been derived. The SACP-based 2D-COS method has been successfully applied on analyzing bilinear data from mixed samples (including one model system and two real systems). Implicit isolated peaks can be identified and assigned to different components based on characteristic pattern of SACPs even if the time-related profiles of different components are severely overlapped. Based on the results of SACPs, spectra of pure components can be retrieved. Identification of SACPs can still be achieved in the presence of artifacts. Thus, neither noise nor baseline drift can produce significant influence on the results obtained from the approach described in this paper. We have used several well-established chemometric methods, including N-Findr, VCA, and MCR with various initial settings, on two systems that can be successfully solved using the 2D-COS method. The chemometric methods mentioned above cannot provide correct spectra of pure components because of severe problem of rotational ambiguity derived from severe overlapping of the time-related profiles. Only when the information from SACPs in 2D-COS is used as additional constraints in MCR calculation, correct spectra can be obtained. That is to say, the SACP-based 2D-COS method provides intrinsic information which is crucial in the analysis of chromatographic-spectroscopic and analogous data even if the time-related profiles of different components overlap severely.
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http://dx.doi.org/10.1016/j.saa.2019.05.008 | DOI Listing |
Front Plant Sci
September 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Accurate identification of cherry maturity and precise detection of harvestable cherry contours are essential for the development of cherry-picking robots. However, occlusion, lighting variation, and blurriness in natural orchard environments present significant challenges for real-time semantic segmentation.
Methods: To address these issues, we propose a machine vision approach based on the PIDNet real-time semantic segmentation framework.
Comput Biol Med
August 2025
Department of Radiation Oncology, UTSW, United States of America. Electronic address:
Accurate prediction of head and neck cancer recurrence across medical institutions remains challenging due to inherent domain shifts in imaging data. Current domain generalization methods primarily focus on learning domain-invariant features from medical images, often overlooking structured clinical information that inherently exhibits cross-institutional consistency. To leverage clinical data and enhance the model's generalization, we propose an end-to-end Language-Guided Multimodal Domain Generalization (LGMDG) method.
View Article and Find Full Text PDFBrief Bioinform
July 2025
Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
RNA interference (RNAi) is a technique for precisely silencing the expression of specific genes by means of small RNA molecules and is essential in functional genomics. Among the commonly used RNAi molecules, short hairpin RNAs (shRNAs) exhibit advantages over small interfering RNAs, including longer half-life, comparable silencing efficiency, fewer off-target effects, and greater safety. However, traditional screening of potent shRNAs is costly and time-consuming.
View Article and Find Full Text PDFFront Genet
August 2025
Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China.
Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ecological health, and drug development.
Methods: In this manuscript, we introduce an innovative computational model named BANSMDA, which integrates Bilinear Attention Networks with sparse autoencoder to uncover hidden connections between microbes and diseases. In BANSMDA, we first constructed a heterogeneous microbe-disease network by integrating multiple Gaussian similarity measures for diseases and microbes, along with known microbe-disease associations.
IEEE Trans Comput Biol Bioinform
August 2025
Drug-target interaction (DTI) prediction is a pivotal task in the realm of drug discovery. As the volume of biological data has increased rapidly, the integration of multiple data sources to increase prediction accuracy has become increasingly important. However, few methods exploit the heterogeneous information network in the drug-target network by integrating multisource information to address the task of drug-target interaction prediction.
View Article and Find Full Text PDF