Novel biomarkers of inflammation-associated immunity in cervical cancer

BackgroundCervical cancer (CC) is a highly malignant gynecological cancer with a direct causal link to inflammation, primarily resulting from persistent high-risk human papillomavirus (HPV) infection. Given the challenges in early detection and mid to late-stage treatment, our research aims to identify inflammation-associated immune biomarkers in CC.MethodsUsing a bioinformatics approach combined with experimental validation, we integrated two CC datasets (GSE39001 and GSE63514) in the Gene Expression Omnibus (GEO) to eliminate batch effects. Immune-related inflammation differentially expressed genes (DGEs) were obtained by R language identification.ResultsThis analysis identified 37 inflammation-related DEGs. Subsequently, we discussed the different levels of immune infiltration between CC cases and controls. Weighted gene co-expression network analysis (WGCNA) identified seven immune infiltration-related modules in CC. We identified 15 immune DEGs associated with inflammation at the intersection of these findings. In addition, we constructed a protein interaction network using the String database and screened five hub genes using "CytoHubba": CXC chemokine ligand 8 (CXCL8), CXC chemokine ligand 10 (CXCL10), CX3C chemokine receptor 1 (CX3CR1), Fc gamma receptors 3B (FCGR3B), and SELL. The expression of these five genes in CC was determined by PCR experiments. In addition, we assessed their diagnostic value and further analyzed the association of immune cells with them.ConclusionsFive inflammation- and immune-related genes were identified, aiming to provide new directions for early diagnosis and mid to late-stage treatment of CC from multiple perspectives

A Transformer-Based Multi-Entity Load Forecasting Method for Integrated Energy Systems

Energy load forecasting is a critical component of energy system scheduling and optimization. This method, which is classified as a time-series forecasting method, uses prior features as inputs to forecast future energy loads. Unlike a traditional single-target scenario, an integrated energy system has a hierarchy of many correlated energy consumption entities as prediction targets. Existing data-driven approaches typically interpret entity indexes as suggestive features, which fail to adequately represent interrelationships among entities. This paper, therefore, proposes a neural network model named Cross-entity Temporal Fusion Transformer (CETFT) that leverages a cross-entity attention mechanism to model inter-entity correlations. The enhanced attention module is capable of mapping the relationships among entities within a time window and informing the decoder about which entity in the encoder to concentrate on. In order to reduce the computational complexity, shared variable selection networks are adapted to extract features from different entities. A data set obtained from 13 buildings on a university campus is used as a case study to verify the performance of the proposed approach. Compared to the comparative methods, the proposed model achieves the smallest error on most horizons and buildings. Furthermore, variable importance, temporal correlations, building relationships, and time-series patterns in data are analyzed with the attention mechanism and variable selection networks, therefore the rich interpretability of the model is verified

Fungal Community as a Bioindicator to Reflect Anthropogenic Activities in a River Ecosystem

The fungal community interacts with the ambient environment and can be used as a bioindicator to reflect anthropogenic activities in aquatic ecosystems. Several studies have investigated the impact of anthropogenic activities on the fungal community and found that community diversity and composition are influenced by such activities. Here we combined chemical analysis of water properties and sequencing of fungal internal transcribed spacer regions to explore the relationship between water quality indices and fungal community diversity and composition in three river ecosystem areas along a gradient of anthropogenic disturbance (i.e., less-disturbed mountainous area, wastewater-discharge urban area, and pesticide and fertilizer used agricultural area). Results revealed that the level of anthropogenic activity was strongly correlated to water quality and mycoplankton community. The increase in organic carbon and nitrogen concentrations in water improved the relative abundance of Schizosaccharomyces, which could be used as a potential biomarker to reflect pollutant and nutrient discharge. We further applied a biofilm reactor using water from the three areas as influent to investigate the differences in fungal communities in the formed biofilms. Different community compositions were observed among the three areas, with the dominant fungal phyla in the biofilms found to be more sensitive to seasonal effects than those found in water. Finally, we determined whether the fungal community could recover following water quality restoration. Our biofilm reactor assay revealed that the recovery of fungal community would occur but need a long period of time. Thus, this study highlights the importance of preserving the original natural aquatic ecosystem

Variation of Tensor Force due to Nuclear Medium Effect

The enhancement of $J^{\pi}(T)$=3$^{+}$(0) state with isospin $T=0$ excited by the tensor force in the free $^{6}$Li nucleus has been observed, for the first time, relative to a shrinkable excitation in the $^{6}$Li cluster component inside its host nucleus. Comparatively, the excitation of $J^{\pi}(T)$=0$^{+}$(1) state with isospin $T=1$ for these two $^{6}$Li formations take on an approximately equal excitation strength. The mechanism of such tensor force effect was proposed due to the intensive nuclear medium role on isospin $T$=0 state.Comment: 6 pages, 4 figure

Aspect of Clusters Correlation at Light Nuclei Excited State

The correlation of $\alpha\alpha$ was probed via measuring the transverse momentum $p_{T}$ and width $\delta p_{T}$ of one $\alpha$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$\alpha$ reflected by the magnitudes of their relative momentums and relative emission angles proves the isosceles triangle configuration for 3$\alpha$ at the high excited energy analogous Hoyle states.Comment: 8 pages, 9 figure

Multi-alpha Boson Gas state in Fusion Evaporation Reaction and Three-body Force

The experimental evidence for the $\alpha$ Boson gas state in the $^{11}$C+$^{12}$C$\rightarrow$$^{23}$Mg$^{\ast}$ fusion evaporation reaction is presented. By measuring the $\alpha$ emission spectrum with multiplicity 2 and 3, we provide insight into the existence of a three-body force among $\alpha$ particles. The observed spectrum exhibited distinct tails corresponding to $\alpha$ particles emitted in pairs and triplets consistent well with the model-calculations of AV18-UX and chiral effective field theory of NV2-3-la*, indicating the formation of $\alpha$ clusters with three-body force in the Boson gas state.Comment: 7 pages, 6 figure

Pd–Ce/ZIF-8 Nanocomposite for Catalytic Extraction of Sinomenine from Sinomenium acutum

Sinomenine is a naturally occurring alkaloid and commonly used as one of the bioactive drug components in rheumatoid arthritis (RA) treatment in the clinic. Varying supported palladium-based catalysts have been synthesized and examined as heterogeneous catalysts for catalytic extraction of sinomenine from Sinomenium acutum. Among various examined supported catalysts, Pd–Ce/ZIF-8 (zeolitic imidazolate framework-8) demonstrates promising catalytic activity in the extraction reaction with an improved yield of 2.15% under optimized conditions. The catalyst composite can be recovered by centrifuging, and reused. A total of three catalyst recycling processes were performed with constant activity. The catalyst Pd–Ce/ZIF-8 has a particle size range of 2–12 nm and a total Pd–Ce loading amount of 5.1 wt% (ZIF-8)

Resilient and sustainable production of peanut (Arachis hypogaea) in phosphorus-limited environment by using exogenous gamma-aminobutyric acid to sustain photosynthesis

Globally, many low to medium yielding peanut fields have the potential for further yield improvement. Low phosphorus (P) limitation is one of the significant factors curtailing Arachis hypogaea productivity in many regions. In order to demonstrate the effects of gamma-aminobutyric acid (GABA) on peanuts growing under P deficiency, we used a pot-based experiment to examine the effects of exogenous GABA on alleviating P deficiency-induced physiological changes and growth inhibition in peanuts. The key physiological parameters examined were foliar gas exchange, photochemical efficiency, proton motive force, reactive oxygen species (ROS), and adenosine triphosphate (ATP) synthase activity of peanuts under cultivation with low P (LP, 0.5 mM P) and control conditions. During low P, the cyclic electron flow (CEF) maintained the high proton gradient (∆pH) induced by low ATP synthetic activity. Applying GABA during low P conditions stimulated CEF and reduced the concomitant ROS generation and thereby protecting the foliar photosystem II (PSII) from photoinhibition. Specifically, GABA enhanced the rate of electronic transmission of PSII (ETRII) by pausing the photoprotection mechanisms including non-photochemical quenching (NPQ) and ∆pH regulation. Thus, GABA was shown to be effective in restoring peanut growth when encountering P deficiency. Exogenous GABA alleviated two symptoms (increased root-shoot ratio and photoinhibition) of P-deficient peanuts. This is possibly the first report of using exogenous GABA to restore photosynthesis and growth under low P availability. Therefore, foliar applications of GABA could be a simple, safe and effective approach to overcome low yield imposed by limited P resources (low P in soils or P-fertilizers are unavailable) for sustainable peanut cultivation and especially in low to medium yielding fields

Tracking a Rain-Induced Low-Salinity Pool in the South China Sea Using Satellite and Quasi-Lagrangian Field Observations

A low-salinity pool (LSP) was observed in the northeastern South China Sea on 8–10 August 2018. Employing satellite and field observations, as well as widely used HYbrid Coordinate Ocean Model (HYCOM) Analysis data, we investigated the distribution, origin and evolution of the LSP. A bowl-like structure of the LSP was observed from field observations and is also indicated by the HYCOM Analysis data. Spatially, the LSP extended 20 m deep vertically and spread at least 45 km laterally. Particle tracking simulations using satellite-observed precipitation and surface currents revealed the origin and evolution of the LSP. It is found that the LSP was induced by a heavy rainfall event two days prior to the field observations, evidenced by the significant correlation between the rainfall and salinity anomaly. The vertical expansion of the LSP was favored by nocturnal convection, but was restricted by the strong stratification at its base, which appeared to have prohibited development of convective instabilities as indicated by the observed vertical variation of the turbulent dissipation rate. The formation of a barrier layer due to the LSP restricted vertical heat exchanges, and as a result a thin temperature inversion layer was formed as the surface temperature dropped due to the nighttime cooling and mixing with the cold rainwater. The thermohaline structure favored development of diffusive convection, which is evidenced by the observation that the diapycnal diffusivity for heat (KT) was one order of magnitude larger than that for density (Kρ). Overall, this study provides novel insights into how the upper ocean responds to rainfall with satellite and field observations