Profiling Datasets Research Articles

Quantifying stratospheric ozone trends over 1984–2020: a comparison of ordinary and regularized multivariate regression models

Abstract. Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, and non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis; however, the complex coupling in many atmospheric processes can make it prone to the issue of over-fitting when using the conventional ordinary-least-squares (OLS) approach. To overcome this issue, here we adopt a regularized (ridge) regression method to estimate ozone trends and quantify the influence of individual processes. We use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged dataset (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Besides SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT) and output from a chemical transport model (TOMCAT) simulation forced with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. For 1984–1997, we observe smaller negative trends in the SWOOSH stratospheric ozone profile using ridge regression compared to OLS. Except for the tropical lower stratosphere, the largest differences arise in the mid-latitude lowermost stratosphere (>4 % per decade difference at 100 hPa). From 1998 and the onset of ozone recovery in the upper stratosphere, the positive trends estimated using the ridge regression model (∼1 % per decade near 2 hPa) are smaller than those using OLS (∼2 % per decade). In the lower stratosphere, post-1998 negative trends with large uncertainties are observed and ridge-based trend estimates are somewhat smaller and less variable in magnitude compared to the OLS regression. Aside from the tropical lower stratosphere, the largest difference is around 2 % per decade at 100 hPa (with ∼3 % per decade uncertainties for individual trends) in northern mid-latitudes. For both time periods the SWOOSH data produce large negative trends in the tropical lower stratosphere with a correspondingly large difference between the two trend methods. In both cases the ridge method produces a smaller trend. The regression coefficients from both OLS and ridge models, which represent ozone variations associated with natural processes (e.g. the quasi-biennial oscillation, solar variability, El Niño–Southern Oscillation, Arctic Oscillation, Antarctic Oscillation, and Eliassen–Palm flux), highlight the dominance of dynamical processes in controlling lower-stratospheric ozone concentrations. Ridge regression generally yields smaller regression coefficients due to correlated explanatory variables, and care must be exercised when comparing fit coefficients and their statistical significance across different regression methods. Comparing the ML-TOMCAT-based trend estimates with the ERA5-forced model simulation, we find ML-TOMCAT shows significant improvements with much better consistency with the SWOOSH dataset, despite the ML-TOMCAT training period overlapping with SWOOSH only for the Microwave Limb Sounder (MLS) measurement period. The largest inconsistencies with respect to SWOOSH-based trends post-1998 appear in the lower stratosphere where the ERA5-forced model simulation shows positive trends for both the tropics and the mid-latitudes. The large differences between satellite-based data and the ERA5-forced model simulation confirm significant uncertainties in ozone trend estimates, especially in the lower stratosphere, underscoring the need for caution when interpreting results obtained with different regression methods and datasets.

Identification of novel potential genes in testicular germ cell tumors: A transcriptome analysis.

Testicular germ cell tumors (TGCTs), containing pure seminoma and non-seminoma, occupy the most majority of testicular cancers in adolescents and young men, which has increased dramatically in recent decades. Therefore, it is important to find crucial genes for improving diagnosis and prognosis in TGCTs. However, the diagnostic and prognostic markers of TGCTs are limited. In this study, our main objective is to explore novel potential genes that can be used as diagnostic and prognostic biomarkers in TGCTs. Our study detected 732 differentially expressed genes (DEGs) using three microarray expression profiling datasets from Gene Expression Omnibus (GEO). Multiple analysis was performed to identify the roles of DEGs, including pathway and functional enrichment analysis, protein-protein interaction (PPI) network analysis, module analysis, and survival analysis. In total, 322 upregulated genes and 406 downregulated genes were identified as DEGs The functional and pathway enrichment analysis shows that DEGs were highly enriched in multiple biological attributes such as T cell activation, reproduction in multicellular organism, sperm flagellum, antigen processing and presentation Then, seven potential crucial genes were identified via PPI network analysis, module analysis, and survival analysis. Furthermore, 7 potential crucial genes had shown to play a key role in regulating immune cell infiltration level in patients with TGCTs. We identified seven potential crucial genes (LAPTM5, NCF2, PECAM1, CD14, COL4A2, ANPEP and RGS1), which may be molecular markers in improving the way of diagnosis and prognosis in TGCTs.

Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain.

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities.

ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors

The combination of ongoing urban expansion and electrification of uses challenges the power grid. In such a context, information regarding customers’ consumption is vital to assess the expected load at strategic nodes over time, and to guide power system planning strategies. Comprehensive household consumption databases are widely available today thanks to the roll-out of smart meters, while the consumption of tertiary premises is seldom shared mainly due to privacy concerns. To fill this gap, the French main distribution system operator, Enedis, commissioned Mines Paris to derive load profiles of industrial and tertiary sectors for its prospective tools. The ELMAS dataset is an open dataset of 18 electricity load profiles derived from hourly consumption time series collected continuously over one year from a total of 55,730 customers. These customers are divided into 424 fields of activity, and three levels of capacity subscription. A clustering approach is employed to gather activities sharing similar temporal patterns, before averaging the associated time series to ensure anonymity.

Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration.

Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease. The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed. A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively. ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.

Fake Instagram Profile Identification and Classification Using Machine Learning

Fake Instagram profiles are a growing problem, as they can be used to spread false information, deceive people, and harass and threaten others. The project aims to develop a robust and scalable system for identifying and classifying fake Instagram profiles using machine learning. This system will collect a large and diverse dataset of Instagram profiles, both real and fake. The data will be preprocessed and cleaned, and then relevant features will be extracted. Various machine learning algorithms will be evaluated to select the best model for identifying and classifying fake Instagram profiles. The trained model will be evaluated on a hold-out test set to ensure that it is able to generalize to new data. Once the model is evaluated and deemed satisfactory, it will be deployed in a production environment to identify and classify fake Instagram profiles in real time. The system is expected to have a significant impact on the fight against fake Instagram profiles. By identifying and classifying fake Instagram profiles, the system can help make the social media platform safer and more secure for everyone.. Key Words: Profile identification, Data preprocessing, Online security, User authentication, Model training, Machine learning.

Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the "AUCell" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using "CellChat." The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.

Exploring potential genes and pathways related to lung cancer: a graph theoretical analysis.

Lung cancer is the primary and third most frequently detected form of cancer in both males and females. The present study tries to perform integrated analysis in male as well as female patients inclusively both smoker and non-smokers. This study aims to identify diagnostic biomarkers and therapeutic targets for lung cancer patients using human microarray profile datasets. Differentially expressed genes (DEGs) were identified using a PPI network from the String database, and major modules or clusters were extracted using MCODE. The Cytohubba plug-in was used to find hub genes from the PPI network using centralities approaches. Twenty significant hub genes (CCND1, CDK1, CCNB1, CDH1, TP53, CTNNB1, EGFR, ESR1, CDK2, CCNA2, RHOA, EGF, FN1, HSP90AA1, STAT3, JUN, NOTCH1, IL6, SRC, and CD44) were identified as promising diagnostic biomarkers and therapeutic targets for lung cancer treatment. Survival analysis and hub gene validation were also conducted. GO enrichment and pathway analysis were conducted to identify their important functions. These hub genes were also used to identify targeted drugs. The findings suggest that the identified genes have the potential to be used as diagnostic biomarkers and therapeutic targets for lung cancer treatment.

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.

Identification and experimental validation of autophagy-related genes in abdominal aortic aneurysm

AimAutophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis.MethodsTwo gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein–protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients.ResultsAccording to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip.ConclusionBioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Identifying target ion channel-related genes to construct a diagnosis model for insulinoma.

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein-protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug-gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674-0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.

ENO2 as a Biomarker Regulating Energy Metabolism to Promote Tumor Progression in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and metastatic type of renal cell carcinoma. Despite significant advancements, the current diagnostic biomarkers for ccRCC lack the desired specificity and sensitivity, necessitating the identification of novel biomarkers and elucidation of their underlying mechanisms. Three gene expression profile datasets were obtained from the GEO database, and differentially expressed genes (DEGs) were screened. Gene Ontology and KEGG pathway analysis were conducted in ccRCC. To clarify the diagnosis and prognostic role of ENO2, Kaplan-Meier analysis and Cox proportional hazards regression analysis were performed. Functional experiments were also carried out to verify the significant role of ENO2 in ccRCC. Finally, tumor mutational burden analysis was utilized to investigate the potential role of ENO2 in gene mutations in ccRCC. The study showed that ENO2 is a potential biomarker for the diagnosis of ccRCC and can independently predict the clinical prognosis of ccRCC. Furthermore, we found that ENO2 can promote the occurrence and progression of ccRCC by affecting the glycolysis level of cells through the "Warburg effect". These findings provide new theories for the occurrence and development of ccRCC and can help formulate new strategies for its diagnosis and treatment.

JS04.5.A HEMIZYGOUSCDKN2A DELETION CONFERS WORSE SURVIVAL OUTCOMES IN IDH-MUTANT GLIOMAS

Abstract BACKGROUND The tumor suppressor gene CDKN2A is frequently deactivated in cancer. Homozygous CDKN2A deletion is a Grade 4 defining criterion for IDH-mutant gliomas. However, the prognostic value of hemizygous CDKN2A deletions is unclear. We assessed CNV profiles of 1238 initial and 479 recurrent IDHmut gliomas to evaluate the association of CDKN2A hemizygous deletions with overall survival (OS) outcomes. MATERIAL AND METHODS The GLASS primary/recurrent glioma dataset was used to infer CDKN2A status with DNAseq (n=210) and DNA methylation array (n=100) data. Validation was conducted using DFCI, MSKCC, TCGA and DKFZ cohorts. RESULTS Longitudinal analyses of IDH-mutant gliomas without 1p-/19q-codeletion showed significant increases in CDKN2A homozygous deletion from n=6 to n=23 (5% to 20%, P=0.0001, Fisher’s-exact test) and hemizygous deletion from n=19 to n=34 (17% to 30%, P=0.002) at recurrence. CDKN2A hemizygous deletions were significantly associated with poor OS at initial diagnosis (P<0.0001, log-rank test) and recurrence (P<0.0001), remaining significant in multivariable cox regression model that accounted for age, treatment with radiotherapy and/or alkylating agents, and 1p/19q-codeletion status: HR 2.40 (95%-CI: 1.31-4.37, P=0.004). Validation in independent datasets confirmed increased frequencies of CDKN2A hemizygous deletions in recurrent cases as well as the association between CDKN2A hemizygous deletion and worse OS in IDHmut gliomas. Using the DKFZ DNA methylation profiling dataset we further confirmed that CDKN2A status can be accurately assessed through DNAseq and DNA methylation array profiling. CONCLUSION CDKN2A hemizygous deletion is associated with significantly worse OS in initial and recurrent IDHmut gliomas. Similar to CDKN2A homozygous deletion, CDKN2A hemizygous deletion is enriched in post-treatment, recurrent IDHmut gliomas, confirming the value of CDKN2A status (re-)assessment in recurrences. Our results highlight the importance of incorporating CDKN2A status in diagnostic workup to inform prognosis and treatment strategies, revealing the previously unrecognized prognostic value of hemizygous CDKN2A deletions.

Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India

ABSTRACT Soil inorganic carbon (SIC) is important carbon reservoirs in desert ecosystems. However, little attention was paid to estimate carbon stock in these regions. In the present study, the distribution of SIC stock was investigated using digital soil mapping in Bikaner district, Rajasthan, India. A total of 187 soil profiles were used for SIC estimation by Quantile regression forest model. Landsat data, terrain attributes and bioclimatic variables were used as environmental variables. Ten-fold cross-validation was used to evaluate model. Equal-area quadratic splines were fitted to soil profile datasets to estimate SIC at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). The SIC in the study area ranged from 0.27 to 27.85 g kg−1 in 0–5 cm and 0.31 to 27.84 g kg−1 in 5–15 cm, respectively. The model could capture reasonable variability (R2 = 11–21%) while predicting SIC for different depths. The Lin’s concordance correlation coefficient values ranged from 0.20 to 0.32, indicating poor relationship between the predicted and observed values. The values of prediction interval coverage probability (PICP) recorded 86.4–91.1% for SIC at different depths. Annual precipitation and precipitation seasonality were the most important covariates in soil below the 30 cm depth. The predicted SIC stocks were 10.3 ± 0.01, 81.6 ± 0.07 and 186.7 ± 0.13 Mg ha−1 at 0–15, 0–100 and 0–200 cm, depth, respectively. The uncertainty analysis suggests that there is room to improve the current spatial predictions of SIC. It is anticipated that this digital mapping of SIC will be useful for assessment of carbon cycle in arid regions.

The Sympathetic Nervous System Promotes Hepatic Lymphangiogenesis, which Is Protective Against Liver Fibrosis

Liver is the largest lymph-producing organ. In cirrhotic patients, lymph production significantly increases concomitant with lymphangiogenesis. The aim of this study was to determine the mechanism of lymphangiogenesis in liver and its implication in liver fibrosis. Liver biopsies from portal hypertensive patients with portal-sinusoidal vascular disease (n = 22) and liver cirrhosis (n = 5) were evaluated for lymphangiogenesis and compared with controls (n = 9 and n = 6, respectively). For mechanistic studies, rats with partial portal vein ligation (PPVL) and bile duct ligation (BDL) were used. A gene profile data set (GSE77627), including 14 histologically normal liver, 18 idiopathic noncirrhotic portal hypertension, and 22 cirrhotic patients, was analyzed. Lymphangiogenesis was significantly increased in livers from patients with portal-sinusoidal vascular disease, cirrhotic patients, as well as PPVL and BDL rats. Importantly, Schwann cells of sympathetic nerves highly expressed vascular endothelial growth factor-C in PPVL rats. Vascular endothelial growth factor-C neutralizing antibody or sympathetic denervation significantly decreased lymphangiogenesis in livers of PPVL and BDL rats, which resulted in progression of liver fibrosis. Liver specimens from cirrhotic patients showed a positive correlation between sympathetic nerve/Schwann cell-positive areas and lymphatic vessel numbers, which was supported by gene set analysis from patients with noncirrhotic portal hypertension and cirrhotic patients. Sympathetic nerves promote hepatic lymphangiogenesis in noncirrhotic and cirrhotic livers. Increased hepatic lymphangiogenesis can be protective against liver fibrosis.

RNA-Sequencing and Bioinformatics Analysis of Exosomal Long Noncoding RNAs Revealed a Novel ceRNA Network in Stable COPD.

Exosomes are able to exchange their bioactive RNA cargo to recipient cells. In COPD, exosomes can be controlled and engineered for its use as targeted diagnostic and therapeutic tool.Our study explored novel lncRNAs and mRNAs in plasma exosomes that could be involved in the pathogenesis of COPD. High-throughput sequencing was conducted to detect the alterations in the expression of exosomal lncRNAs and mRNAs. Gene ontology (GO) functional analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to determine the significant functions and pathways associated with differentially expressed (DE) lncRNAs. The mRNA expression profile dataset, GSE76925, and microRNA expression profile dataset, GSE70080, were obtained from the GEO database. Venn diagrams were used to find common DE mRNAs between my mRNAs dataset and GSE76925. These common DEGs were subjected to PPI analyses to identify Hub genes. Subsequently, Venn diagrams were used to identify common genes between the target genes of DE-miRNAs and Hub genes as well as DE-miRNAs and my lncRNAs dataset. Finally, a lncRNA-miRNA-mRNA co-expression network was constructed byprediction using proprietary software. The lncRNA and mRNA expressions were then validated by quantitative reverse-transcription polymerase chain reaction (qRT-PCR). We identified 1578 differentially regulated lncRNAs and 3071 differentially regulated mRNAs. GO and KEGG pathway analyses suggested that the DE lncRNAs are involved in the pathogenesis of COPD. A lncRNA-miRNA-mRNA meshwork was established to predict the potential interactions among these RNAs. RP3-329A5.8 and MRPS11 expression was then subjected to qRT-PCR for validation. Correlations between MRPS11 and clinic-pathological features were explored. Our study provided a set of lncRNAs and mRNAs that may be involved in the pathogenesis of COPD, thereby highlighting the need for further research on both diagnostic biomarkers and molecular mechanisms.

CROSS-SHORE BEACH PROFILE SIMULATION IN THE SWASH ZONE OF NARRABEEN-COLLAROY BEACH

Sediment transport and, consequently, the beach profile changes in the swash zone are important to the overall sediment budget and beach profile evolution because the hydrodynamic condition in this zone is complex. One of the swash zone morphodynamic difficulties is estimating the beach profile accretion and erosion above the mean water level. Although there have been considerable studies and investigations into the swash zone morphodynamic, numerical models for predicting sediment transport rate and beach profile changes are limited. Therefore, this study examined a morphodynamic numerical model to predict beach profile evolution in the swash zone using a nearshore beach profile dataset measured at the Narrabeen-Collaroy beach, Australia.

Bioinformatics Research and qRT-PCR Verify Hub Genes and a Transcription Factor-MicroRNA Feedback Network in Intervertebral Disc Degeneration.

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies.

Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating

Optically Stimulated Luminescence (OSL) depth profiles can be used to extrapolate rock surface exposure ages by understanding a sample's light attenuation and OSL bleaching behaviors. However, the current measuring procedure for producing depth profiles, by measuring the luminescence of millimeter slices from surface core samples, offers limited resolution data, limiting parameter extrapolative precision. The use of OSL laser scanning measuring protocols on transverse core slices has potential for improving the data resolution of OSL depth profiles from core samples. An 11-year exposed rock surface was parameterized for light attenuation and luminescence bleaching rates using depth profile data from four surface cores collected via wafer and scan measuring techniques, to show the potential of OSL laser scanning to improve depth profile dataset resolutions and parameter extrapolative precision. Additionally, non-quartz OSL anomalies were filtered from scan datasets to determine if non-quartz mineral filtering can further improve the resolution and parameterization of depth profile datasets. OSL scanning measures improved the resolution of depth profiles over wafer derived depth profiles for the measured samples, and in comparing absolute uncertainties of parameters, broadly improved parameter precision using maximum likelihood estimate fitting protocols. Non-test dose corrected spatially resolved data error bounds were generally larger than from wafer derived data. When compared to unfiltered spatially resolved depth profile data, OSL anomaly filtered scanned depth profiles improved error bounds of anomaly affected data, and improved parameter extrapolative precision with roughly a similar effect as using non-filtered scan data. The absolute uncertainty in parameterization was impacted by scatter prevalent from scan datasets, which is caused by low OSL sensitivity from samples, the potential recording of remnant OSL, the lack of regenerative dose normalization applied to the scan dataset, as well as other scanning instrument measuring limitations. Still, these influences can be mitigated with optimized instrumentation and scanning protocols.

MiRNA profiling as a complementary diagnostic tool for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS), the most prevalent motor neuron disease characterized by its complex genetic structure, lacks a single diagnostic test capable of providing a conclusive diagnosis. In order to demonstrate the potential for genetic diagnosis and shed light on the pathogenic role of miRNAs in ALS, we developed an ALS diagnostic rule by training the model using 80% of a miRNA profiling dataset consisting of 253 ALS samples and 103 control samples. Subsequently, we validated the diagnostic rule using the remaining 20% of unseen samples. The diagnostic rule we developed includes miR-205-5p, miR-206, miR-376a-5p, miR-412-5p, miR-3927-3p, miR-4701-3p, miR-6763-5p, and miR-6801-3p. Remarkably, the rule achieved an 82% true positive rate and a 73% true negative rate when predicting the unseen samples. Furthermore, the identified miRNAs target 21 genes in the PI3K-Akt pathway and 27 genes in the ALS pathway, including notable genes such as BCL2, NEFH, and OPTN. We propose that miRNA profiling may serve as a complementary diagnostic tool to supplement the clinical presentation and aid in the early recognition of ALS.