[Compositional Characteristics of Volatile Organic Compounds in Typical Industrial Areas of the Pearl River Delta: Importance of Oxygenated Volatile Organic Compounds].

Volatile organic compounds (VOCs) are key components of tropospheric chemistry, of which industrial emissions are an important source of atmospheric VOCs. In this study, online measurements of 74 VOCs were made in a typical industrial area of the Pearl River Delta in southern China during the early summer of 2021. The mean volume mixing ratio of total volatile organic compounds (TVOC) was (81.9±45.4)×10-9 during the campaign. Among them, oxygenated volatile organic compounds (OVOCs) accounted for the largest fraction of TVOC, with an average of 51.5%, followed by aromatics, accounting for 19.4% of TVOC. The proportion of OVOCs in TVOC gradually increased with the increase in TVOC concentration. Industry-related emissions were the main contributors to aromatics and OVOCs in this region. Aromatics and OVOCs were the two major contributors to the ozone formation potential (OFP), accounting for 56.4% and 26.7%, respectively. Furthermore, OVOCs also contributed 40.0% of the total ·OH reactivity from VOCs. Xylenes, toluene, acrolein, and ethyl acetate had a greater contribution to the formation of secondary pollution; thus, these species should be given priority for controlling secondary pollution. Our results underscore the severity of OVOCs pollution in industrial areas and the important roles of OVOCs in secondary pollution.

Molecular and morphological evidence for a new species of Stachys (Lamiaceae) from Hunan, China.

Stachysyingzuijieensis, a new species from western Hunan, China, is described and illustrated. Molecular phylogenetic analyses based on three nuclear ribosomal DNA loci (ETS, ITS and 5S-NTS) recovered S.yingzuijieensis within the Stachys clade and as a sister group of S.arrecta. The two species can be easily distinguished by the morphology of lamina, corolla and nutlet. A key to all species of Stachydeae from China is also provided.

Crystalline Hydrate Dehydration Sensing Based on Integrated Terahertz Whispering Gallery Mode Resonators.

Water molecules play a very important role in the hydration and dehydration process of hydrates, which may lead to distinct physical and chemical properties, affecting their availability in practical applications. However, miniaturized, integrated sensors capable of the rapid, sensitive sensing of water molecules in the hydrate are still lacking, limiting their proliferation. Here, we realize the high-sensitivity sensing of water molecules in copper sulfate pentahydrate (CuSO4·5H2O), based on an on-chip terahertz whispering gallery mode resonator (THz-WGMR) fabricated on silicon material via CMOS-compatible technologies. An integrated THz-WGMR with a high-Q factor of 3305 and a resonance frequency of 410.497 GHz was proposed and fabricated. Then, the sensor was employed to distinguish the CuSO4·xH2O (x = 5, 3, 1). The static characterization from the CuSO4·5H2O to the copper sulfate trihydrate (CuSO4·3H2O) experienced blueshifts of 0.55 GHz/µmol, whereas the dehydration process of CuSO4·3H2O to copper sulfate monohydrate (CuSO4·H2O) exhibited blueshifts of 0.21 GHz/µmol. Finally, the dynamic dehydration processes of CuSO4·5H2O to CuSO4·3H2O at different temperatures were monitored. We believe that our proposed THz-WGMR sensors with highly sensitive substance identification capabilities can provide a versatile and integrated platform for studying the transformation between substances, contributing to hydrated/crystal water-assisted biochemical applications.

Terahertz anisotropy in fascia and lean meat tissues.

Terahertz (THz) spectroscopy provides multifaceted capabilities for observing low-energy responses of macromolecules, cells and tissues, understanding THz biophysical effects, and expecting to realize the application of THz technology in biomedicine. However, its high-frequency characteristics of limited penetration depth and strong absorption of water in the body comparable to microwaves are impeding the proliferation of THz spectroscopy. Here we show that THz spectroscopy makes possible the observation of THz anisotropy phenomena for the first time in fascia and lean tissue. Through optical microscopy, we infer that the microscopic mechanism of THz anisotropy comes from the periodic stripe structure of the biological tissue. The above related experimental findings may be expected to promote the application of THz technology in biomedicine.

Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis.

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

Dental caries diagnosis using terahertz spectroscopy and birefringence.

Dental caries is a widespread chronic infectious disease which may induce a series of oral and general problems if untreated. As a result, early diagnosis and follow-up following radiation-free dental caries therapy are critical. Terahertz (THz) waves with highly penetrating and non-ionizing properties are ideally suited for dental caries diagnosis, however related research in this area is still in its infancy. Here, we successfully observe the existence of THz birefringence phenomenon in enamel and demonstrate the feasibility of utilizing THz spectroscopy and birefringence to realize caries diagnosis. By comparing THz responses between healthy teeth and caries, the transmitted THz signals in caries are evidently reduced. Concomitantly, the THz birefringence is also unambiguously inhibited when caries occurs due to the destruction of the internal hydroxyapatite crystal structure. This THz anisotropic activity is position-dependent, which can be qualitatively understood by optical microscopic imaging of dental structures. To increase the accuracy of THz technology in detecting dental caries and stimulate the development of THz caries instruments, the presence of significant THz birefringence effect induced anisotropy in enamel, in combination with the strong THz attenuation at the caries, may be used as a new tool for caries diagnosis.

Deep learning enhanced terahertz imaging of silkworm eggs development.

Terahertz (THz) technology lays the foundation for next-generation high-speed wireless communication, nondestructive testing, food safety inspecting, and medical applications. When THz technology is integrated by artificial intelligence (AI), it is confidently expected that THz technology could be accelerated from the laboratory research stage to practical industrial applications. Employing THz video imaging, we can gain more insights into the internal morphology of silkworm egg. Deep learning algorithm combined with THz silkworm egg images, rapid recognition of the silkworm egg development stages is successfully demonstrated, with a recognition accuracy of ∼98.5%. Through the fusion of optical imaging and THz imaging, we further improve the AI recognition accuracy of silkworm egg development stages to ∼99.2%. The proposed THz imaging technology not only features the intrinsic THz imaging advantages, but also possesses AI merits of low time consuming and high recognition accuracy, which can be extended to other application scenarios.

LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer.

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium-calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

Identification of GSN and LAMC2 as Key Prognostic Genes of Bladder Cancer by Integrated Bioinformatics Analysis.

Bladder cancer is a common malignancy with mechanisms of pathogenesis and progression. This study aimed to identify the prognostic hub genes, which are the central modulators to regulate the progression and proliferation in the specific subtype of bladder cancer. The identification of the candidate hub gene was performed by weighted gene co-expression network analysis to construct a free-scale gene co-expression network. The gene expression profile of GSE97768 from the Gene Expression Omnibus database was used. The association between prognosis and hub gene was evaluated by The Cancer Genome Atlas database. Four gene-expression modules were significantly related to bladder cancer disease: the red module (human adenocarcinoma lymph node metastasis), the darkturquioise module (grade 2 carcinoma), the lightgreen module (grade 3 carcinoma), and the royalblue module (transitional cell carcinoma lymphatic metastasis). Based on betweenness centrality and survival analysis, we identified laminin subunit gamma-2 (LAMC2) in the grade 2 carcinoma, gelsolin (GSN) in the grade 3 carcinoma, and homeodomain-interacting protein kinase 2 (HIPK2) in the transitional cell carcinoma lymphatic metastasis. Subsequently, the protein levels of LAMC2 and GSN were respectively down-regulated and up-regulated in tumor tissue with the Human Protein Atlas (HPA) database. Our results suggested that LAMC2 and GSN are the central modulators to transfer information in the specific subtype of the disease.

Identification of Hub Genes Associated With Development of Head and Neck Squamous Cell Carcinoma by Integrated Bioinformatics Analysis.

Improved insight into the molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify significant genes associated with HNSCC and to further analyze its prognostic significance. In our study, the cancer genome atlas (TCGA) HNSCC database and the gene expression profiles of GSE6631 from the Gene Expression Omnibus (GEO) were used to explore the differential co-expression genes in HNSCC compared with normal tissues. A total of 29 differential co-expression genes were screened out by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. As suggested in functional annotation analysis using the R clusterProfiler package, these genes were mainly enriched in epidermis development and differentiation (biological process), apical plasma membrane and cell-cell junction (cellular component), and enzyme inhibitor activity (molecular function). Furthermore, in a protein-protein interaction (PPI) network containing 21 nodes and 25 edges, the ten hub genes (S100A8, S100A9, IL1RN, CSTA, ANXA1, KRT4, TGM3, SCEL, PPL, and PSCA) were identified using the CytoHubba plugin of Cytoscape. The expression of the ten hub genes were all downregulated in HNSCC tissues compared with normal tissues. Based on survival analysis, the lower expression of CSTA was associated with worse overall survival (OS) in patients with HNSCC. Finally, the protein level of CSTA, which was validated by the Human Protein Atlas (HPA) database, was down-regulated consistently with mRNA levels in head and neck cancer samples. In summary, our study demonstrated that a survival-related gene is highly correlated with head and neck cancer development. Thus, CSTA may play important roles in the progression of head and neck cancer and serve as a potential biomarker for future diagnosis and treatment.

Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis.

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.