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Background: Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucial. Methods: In this study, mRNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes and prognosis-related genes. Best prognostic genes were selected using the least absolute shrinkage and selection operator Cox regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent receiver operating characteristic analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological functions and pathways. External validation was conducted using data from the Gene Expression Omnibus and ArrayExpress databases. Results: FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage and clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external datasets. Conclusions: This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients.

Nanomechanical resonator based on nanomaterials has great potential for high sensitive, high resolution and negligible energy consumed sensing devices. In this work, high frequency SiC nanobeam resonators with a diameter of ∼100 nm and with lengths of tens of micrometers were designed and demonstrated, which was produced based on the high quality monocrystal 3C-SiC nanowire material with high aspect ratio characteristics. The resonance behaviors of the nanobeam resonator was studied using an integrated mixing circuit measurement system with the pressure from 10−3 to 10−1 Torr. It was observed that the resonant frequency was up to 9.16 MHz, which was a distinguished essential feature for high sensitivity detection. The tunability of SiC nanobeam resonators was about 22%, which can be conveniently controlled by adjusting DC voltage on the gate. It was examined that the detection value of the SiC nanobeam resonator can be as low as zeptogram, which indicated its ultra-sensitive mass detection behaviors. The results show that the SiC nanobeam resonator has high resonant frequency, large tunability and high sensitivity, which is expected to be a high-performance device for biosensing and mechanical sensing.

Introduction: The SARS-CoV-2 pandemic has endangered global health, the world economy, and societal values. Despite intensive measures taken around the world, morbidity and mortality remain high as many countries face new waves of infection and the spread of new variants. Worryingly, more and more variants are now being identified, such as 501Y.V1 (B.1.1.7) in the UK, 501Y.V2 (B.1.351) in South Africa, 501Y.V3 in Manaus, Brazil, and B.1.617/B.1.618 in India, which could lead to a severe epidemic rebound. Moreover, some variants have a stronger immune escape ability. To control the new SARS-CoV-2 variant, we may need to develop and redesign new vaccines repeatedly. So it is important to investigate how our immune system combats and responds to SARS-CoV-2 infection to develop safe and effective medical interventions. Objectives: In this study, we performed a longitudinal and proteome-wide analysis of antibodies in the COVID-19 patients to revealed some immune processes of COVID-19 patients against SARS-CoV-2 and found some dominant epitopes of a potential vaccine. Methods: Microarray assay, Antibody depletion assays, Neutralization assay. Results: We profiled a B-cell linear epitope landscape of SARS-CoV-2 and identified the epitopes specifically recognized by either IgM, IgG, or IgA. We found that epitopes more frequently recognized by IgM are enriched in non-structural proteins. We further identified epitopes with different immune responses in severe and mild patients. Moreover, we identified 12 dominant epitopes eliciting antibodies in most COVID-19 patients and identified five key amino acids of epitopes. Furthermore, we found epitope S-82 and S-15 are perfect immunogenic peptides and should be considered in vaccine design. Conclusion: This data provide useful information and rich resources for improving our understanding of viral infection and developing a novel vaccine/neutralizing antibodies for the treatment of SARS-CoV-2.

To study the mechanical behavior and energy-absorption capability, different lattices structures were designed and fabricated by using SLS technology with a raw material of Nylon. The quasi-static compression experiment and finite element simulation revealed the mechanical response and deformation mechanism of these structures. Meanwhile, the energy absorption capacity of the lattices structure was assessed by the impact resistance and corresponding calculations of specific energy absorption. The different design schemes were compared and analyzed under the same conditions. The results show that a minimal surface structure (MSS) has the best performance of compression properties. The designed composite scheme composed of the MSS and the BCC structures effectively enhances the impacting-bearing capability and improves the energy absorption performances.

The photovoltaic (PV) industry boom and increased PV applications call for better planning based on accurate and updated data on the installed capacity. Compared with the manual statistical approach, which is often time-consuming and labor-intensive, using satellite/aerial images to estimate the existing PV installed capacity offers a new method with cost-effective and data-consistent features. Previous studies investigated the feasibility of segmenting PV panels from images involving machine learning technologies. However, due to the particular characteristics of PV panel semantic-segmentation, the machine learning tools need to be designed and applied with careful considerations of the issue formulation, data quality, and model explainability. This paper investigated the characteristics of PV panel semantic-segmentation from the perspective of computer vision. The results reveal that the PV panel image data has several specific characteristics: highly class-imbalance and non-concentrated distribution; homogeneous texture and heterogenous color features; and the notable resolution threshold for effective semantic-segmentation. Moreover, this paper provided recommendations for data obtaining and model design, aiming at each observed character from the viewpoints of recent solutions in computer vision, which can be helpful for future improvement of the PV panel semantic-segmentation.