A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate

Silicate weathering, which is of great importance in regulating the global carbon cycle, has been found to be affected by complicated factors, including climate, tectonics and vegetation. However, the exact transfer function between these factors and the silicate weathering rate is still unclear, leading to large model–data discrepancies in the CO2 consumption associated with silicate weathering. Here we propose a simple parameterization for the influence of vegetation cover on erosion rate to improve the model–data comparison based on a state-of-the-art silicate weathering model. We found out that the current weathering model tends to overestimate the silicate weathering fluxes in the tropical region, which can hardly be explained by either the uncertainties in climate and geomorphological conditions or the optimization of model parameters. We show that such an overestimation of the tropical weathering rate can be rectified significantly by parameterizing the shielding effect of vegetation cover on soil erosion using the leaf area index (LAI), the high values of which are coincident with the distribution of leached soils. We propose that the heavy vegetation in the tropical region likely slows down the erosion rate, much more so than thought before, by reducing extreme streamflow in response to precipitation. The silicate weathering model thus revised gives a smaller global weathering flux which is arguably more consistent with the observed value and the recently reconstructed global outgassing, both of which are subject to uncertainties. The model is also easily applicable to the deep-time Earth to investigate the influence of land plants on the global biogeochemical cycle.</p

Rapid identification of early renal damage in asymptomatic hyperuricemia patients based on urine Raman spectroscopy and bioinformatics analysis

Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease.Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed.Results: The Raman spectra showed significant differences in the levels of uric acid (640 cm−1), urea, creatinine (1,608, 1,706 cm−1), proteins/amino acids (642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,615 cm−1), and ketone body (1,643 cm−1) (p &lt; 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD (ALB, MYC, IL10, FOS, TOP2A, PLG, REN, FGA, CCNA2, and BUB1) were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD.Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Doctoral Recitals

Recital presented at the UNT College of Music Voertman Hall in partial fulfillment of the Doctor of Musical Arts (DMA) degree

Simulated Study of High-Sensitivity Gas Sensor with a Metal-PhC Nanocavity via Tamm Plasmon Polaritons

An optical configuration was designed and simulated with a metal-photonic crystal (PhC) nanocavity, which had high sensitivity on gas detection. The simulated results shows that this configuration can generate a strong photonic localization through exciting Tamm plasmon polaritons. The strong photonic localization highly increases the sensitivity of gas detection. Furthermore, this configuration can be tuned to sense gases at different conditions through an adjustment of the detection light wavelength, the period number of photonic crystal and the thickness of the gas cavity. The sensing routes to pressure variations of air were revealed. The simulation results showed that the detection precision of the proposed device for gas pressure could reach 0.0004 atm

Simulated Study of High-Sensitivity Gas Sensor with a Metal-PhC Nanocavity via Tamm Plasmon Polaritons

An optical configuration was designed and simulated with a metal-photonic crystal (PhC) nanocavity, which had high sensitivity on gas detection. The simulated results shows that this configuration can generate a strong photonic localization through exciting Tamm plasmon polaritons. The strong photonic localization highly increases the sensitivity of gas detection. Furthermore, this configuration can be tuned to sense gases at different conditions through an adjustment of the detection light wavelength, the period number of photonic crystal and the thickness of the gas cavity. The sensing routes to pressure variations of air were revealed. The simulation results showed that the detection precision of the proposed device for gas pressure could reach 0.0004 atm

Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia

Acute myeloid leukemia (AML) and T cell acute lymphoblastic leukemia (T-ALL) are two of the most prevalent hematological malignancies diagnosed among adult leukemia patients, with both being difficult to treat and associated with high rates of recurrence and mortality. In the present study, bioinformatics approaches were used to analyze both of these types of leukemia in an effort to identify characteristic gene expression patterns that were subsequently validated via Raman spectroscopy. For these analyses, four Gene Expression Omnibus datasets (GSE13204, GSE51082, GSE89565, and GSE131184) pertaining to acute leukemia were downloaded, and differentially expressed genes (DEGs) were then identified through comparisons of AML and T-ALL patient samples using the R Bioconductor package. Shared DEGs were then subjected to Gene Ontology (GO) enrichment analyses and were used to establish a protein-protein interaction (PPI) network analysis. In total, 43 and 129 upregulated and downregulated DEGs were respectively identified. Enrichment analyses indicated that these DEGs were closely tied to immune function, collagen synthesis and decomposition, inflammation, the synthesis and decomposition of lipopolysaccharide, and antigen presentation. PPI network module clustering analyses further led to the identification of the top 10 significantly upregulated and downregulated genes associated with disease incidence. These key genes were then validated in patient samples via Raman spectroscopy, ultimately confirming the value of these genes as tools that may aid the differential diagnosis and treatment of AML and T-ALL. Overall, these results thus highlight a range of novel pathways and genes that are linked to the incidence and progression of AML and T-ALL, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of these devastating malignancies