An electron density clustering based adaptive segmentation method for protein Raman spectrum calculation.

Raman spectroscopy is a powerful technique for protein detection, but the calculation of Raman spectrum is a longstanding challenging problem due to the large sizes and complex structures of protein molecules. Dividing proteins into fragments can greatly accelerate the calculation, but this usually introduces large errors originating from ignored interactions between fragments into obtained spectra. In this paper, we proposed a new adaptive segmentation method based on the strength of interactions and molecular shapes and structures, i.e., electron density clustering, to divide proteins. It can reduce errors of obtained Raman spectra by about 20% compared to the uniform segmentation method without a significant increase in computational cost. This method can facilitate the validation and analysis of detected Raman spectra of proteins and promote the application of Raman spectroscopy in biological detection.

Rapid Detection of SARS-CoV-2 in Clinical and Environmental Samples via a Resonant Cavity SERS Platform within 20 min.

The coronavirus disease 2019 (COVID-19) epidemic has given a warning that it is important to explore the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical specimens or environmental samples for public health strategies and future variants. The surface-enhanced Raman spectroscopy (SERS) technique was demonstrated to achieve this goal. However, the consistency of signals originating from the poor compatibility of virions with SERS hotspots remains a key scientific challenge for the practical applications of SERS. Herein, we develop a SERS platform for the ultrasensitive and rapid detection of SARS-CoV-2 antigen within 20 min by the combination of a highly consistent SERS substrate and a supervised deep learning algorithm. A V-shaped resonant cavity array (VRC) substrate was fabricated to trap SARS-CoV-2 virions in the periodic V cavity array and stimulate the integral SERS signal of the virus via a resonance coupling effect. Benefiting from the unique architecture of the VRC substrate, we were able to directly detect the SARS-CoV-2 virus with high sensitivity and high consistency. These excellent performances enabled us to identify five different kinds of SARS-CoV-2 variants and detect SARS-CoV-2 from clinical and environmental samples with high accuracies.

Ecological function zoning based on ecosystem service bundles and trade-offs: a study of Dongjiang Lake Basin, China.

Although the research framework of ecological function zoning is complex and diverse, there are not many spatially continuous zoning results, which can be effectively applied to watershed management practices. Ecosystem service bundles and trade-offs can identify interactions among multiple ecosystem services, and achieve better social-ecosystem management when applying to ecological function zoning. Taking the Dongjiang Lake Basin, China, as research area, the study used the InVEST model to investigate the trade-offs and synergies of ecosystem services at township and grid scales, respectively. Then, the study conducted ecological function zoning based on the bundles and trade-off intensity among ecosystem services. The results showed that food production showed extremely significant trade-offs with other services in the two scales, in which the trade-off intensity between food production and water purification was the largest, and the water areas were the hotspots of trade-off intensity. Based on the ecosystem service bundles at the township, combined with the trade-off intensity, the watershed was finally divided into four ecological functional zones, namely, agricultural product supply area (southern part in the study area), economic forestry area (northeast regions in the study area), water supply area (western areas of the study area), and forest conservation area (northern areas in the study area), accounting for 29.27%, 14.63%, 17.07%, and 39.03%, respectively. The study contributed to the ecological function maintenance and sustainable development in Dongjiang Lake Basin and provided an important reference in ecological zoning.

Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering.

Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.

Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis.

The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.

Constructing high-accuracy theoretical Raman spectra of SARS-CoV-2 spike proteins based on a large fragment method.

In order to control COVID-19, rapid and accurate detection of the pathogenic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an urgent task. The target spike proteins of SARS-CoV-2 have been detected experimentally via Raman spectroscopy. However, there lacks high-accuracy theoretical Raman spectra of the spike proteins to as a standard reference for the clinic diagnostic purpose. In this paper, we propose a large fragment method to construct the high-precision Raman spectra for the spike proteins. The large fragment method not only reduces the calculation error but also improves the accuracy of the protein Raman spectra by completely calculating the interactions within the large fragment. The Pearson correlation coefficient of theoretical Raman spectra is greater than 0.929 or more. Compared with the experimental spectra, the characteristic patterns are easily visible. This work provides a detection standard for the spike proteins which shall bring a step closer to the fast recognition of SARS-CoV-2 via Raman spectroscopy method.

Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing.

Road networks affect the spatial structure of urban landscapes, and with continuous expansion, it will also exert more widespread influences on the regional ecological environment. With the support of geographic information system (GIS) technology, based on the application of various spatial analysis methods, this study analyzed the spatiotemporal changes of road networks and landscape ecological risk in the research area of Beijing to explore the impacts of road network expansion on ecological risk in the urban landscape. The results showed the following: 1) In the dynamic processes of change in the overall landscape pattern, the changing differences in landscape indices of various landscape types were obvious and were primarily related to land-use type. 2) For the changes in a time series, the expansion of the road kernel area was consistent with the extension of the sub-low-risk area in the urban center, but some differences were observed during different stages of development. 3) For the spatial position, the expanding changes in the road kernel area were consistent with the grade changes of the urban central ecological risk, primarily because both had a certain spatial correlation with the expressways. 4) The influence of road network expansion on the ecological risk in the study area had obvious spatial differences, which may be closely associated with the distribution of ecosystem types.