Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy.

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System.

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.

Analysis on the Structure and Function of the Bacterial Community in the Replanting Soil of Basswood of Ganoderma lingzhi Medicinal Mushroom (Agaricomycetes).

This study aimed to investigate the impact of the replanting of basswood Ganoderma lingzhi on the soil bacterial community and reveal the obstacle phenomenon of replanting basswood G. lingzhi. In this study, the soil bacterial community of wild 20 cm (N0a) and 40 cm deep soil (N0b), cultivated once (N1a, N1b) and twice (N2a, N2b), were investigated by Illumina MiSeq sequencing. The predominant bacterial phyla at the phylum classification level were Acidobacteria, Chloracidobacteria, Nitrospira, Spartobacteria, Gemmatimonadetes, Acidobacteria-6. Still, only the relative abundance of Chloracidobacteria and Acidobacteriia increased after two years of replanting of basswood G. lingzhi. At the genus level, the dominant genus included many unclassified bacteria. Among the known genera, the best genus was DA101, which showed a decreasing trend after two years of replanting. Network analysis showed that more connections of bacterial communities were observed in soil samples of the group "a," indicating that the replanting of basswood G. lingzhi can affect the relationship between soil bacterial communities at depths. The phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis showed that the gene metabolism function of soil bacteria was quite different after one year of replanting basswood G. lingzhi. The replanting of basswood G. lingzhi changed the composition and function of the soil bacterial community, and also affected the bacterial community diversity in the soil at different depths.

Degradation of lignocelluloses in straw using AC-1, a thermophilic composite microbial system.

In composting, the degradation of lignocellulose in straw is problematic due to its complex structures such as lignin. A common solution to this problem is the addition of exogenous inoculants. AC-1, a stable thermophilic microbial composite, was isolated from high temperature compost samples that can decompose lignocellulose at 50-70 °C. AC-1 had a best degradation efficiency of rice straw at 60 °C (78.92%), of hemicellulose, cellulose and lignin were 82.49%, 97.20% and 20.12%, respectively. It showed degrad-ability on both simple (filter paper, absorbent cotton) and complex (rice straw) cellulose materials. It produced acetic and formic acid during decomposition process and the pH had a trend of first downward then upward. High throughput sequencing revealed the main bacterial components of AC-1 were Tepidimicrobium, Haloplasma, norank-f-Limnochordaceae, Ruminiclostridium and Rhodothermus which provides major theoretical basis for further application of AC-1.