Elucidating the molecular mechanisms of sepsis: Identifying key aging-related biomarkers and potential therapeutic targets in the treatment of sepsis.

BACKGROUND: Sepsis remains a crucial global health issue characterized by high mortality rates and a lack of specific treatments. This study aimed to elucidate the molecular mechanisms underlying sepsis and to identify potential therapeutic targets and compounds. METHODS: High-throughput sequencing data from the GEO database (GSE26440 as the training set and GSE13904 and GSE32707 as the validation sets), weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, alongside a combination of PPI and machine learning methods (LASSO and SVM) were utilized. RESULTS: WGCNA identified the black module as positively correlated, and the green module as negatively correlated with sepsis. Further intersections of these module genes with age-related genes yielded 57 sepsis-related genes. GO and KEGG pathway enrichment analysis, PPI, LASSO, and SVM selected six hub aging-related genes: BCL6, FOS, ETS1, ETS2, MAPK14, and MYC. A diagnostic model was constructed based on these six core genes, presenting commendable performance in both the training and validation sets. Notably, ETS1 demonstrated significant differential expression between mild and severe sepsis, indicating its potential as a biomarker of severity. Furthermore, immune infiltration analysis of these six core genes revealed their correlation with most immune cells and immune-related pathways. Additionally, compounds were identified in the traditional Chinese medicine Danshen, which upon further analysis, revealed 354 potential target proteins. GO and KEGG enrichment analysis of these targets indicated a primary enrichment in inflammation and immune-related pathways. A Venn diagram intersects these target proteins, and our aforementioned six core genes yielded three common genes, suggesting the potential efficacy of Danshen in sepsis treatment through these genes. CONCLUSIONS: This study highlights the pivotal roles of age-related genes in the molecular mechanisms of sepsis, offers potential biomarkers, and identifies promising therapeutic compounds, laying a robust foundation for future studies on the treatment of sepsis.

Development of a distributed group control strategy for pumping well groups connected by multisource DC microgrids.

Due to the alternating loads on pumping units and the integration of new energy sources, multisource DC microgrid pumping unit well groups experience increased fluctuations in voltage and power as well as superimposed peak and valley values. This work presents a distributed control strategy for pumping unit well groups on a multisource DC microgrid based on the weighted moving average algorithm. A centralized control program is implanted in the RTU of the single-well controller of each pumping unit, and communication with each well is realized via SCADA and multicast communication, resulting in a distributed well group system. The real-time power values of the pumping well group are calculated by grouping the power values, and each group is weighted using the total power fluctuation threshold of the well group as the control target. Then, a weighted moving average algorithm is used to predict the next power value and form a table of predicted real-time power spectra. According to the power values in the community power spectrum table, the inverter frequency is proportionally adjusted downwards to reach the power peak before deceleration; after the power peak is crossed, the frequency is increased in the same way to reach the power valley before acceleration. Finally, the peak and valley power values of the bus system level off and further learn to reach the set impulse; ultimately, a stable impulse is formed. In laboratory testing and field application in the Shengli Oilfield XIN-11 block, the group control software module effectively suppressed the active power peak and valley values and voltage fluctuations of the bus system, the active power fluctuation rate range decreased by more than 70%, and the DC bus voltage fluctuation range decreased by more than 80%; moreover, the active power decreased by approximately 6% without additional hardware costs.

Prognostic impact of lactylation-associated gene modifications in clear cell renal cell carcinoma: Insights into molecular landscape and therapeutic opportunities.

Clear cell renal cell carcinoma (ccRCC) stands as a challenging subtype of kidney cancer, frequently complicating patient prognosis due to factors like postsurgical recurrences or late-stage diagnoses. In this study, we employed bioinformatics to investigate lactylation modifications in ccRCC, focusing on the TCGA-KIRC cohort. Out of 328 lactylation-associated genes, 31 emerged as differentially expressed, with 16 showing a marked correlation with overall survival. These genes exhibited strong protein-protein interactions and significant expression correlations. Intriguingly, a notable loss of gene copy numbers suggests potential implications in tumor progression. Utilizing unsupervised clustering, KIRC samples were grouped into two distinct subcategories, each showcasing different survival outcomes. While pathway enrichment highlighted an aggressive, inflammation-driven profile for subgroup 2, subgroup 1 was characterized by metabolic prominence. Furthermore, subgroup 2 presented an intensified inflammatory response, hinting at potential immune exhaustion. Capitalizing on machine learning, we developed a risk model using the TCGA-KIRC dataset, efficiently categorizing ccRCC patients into high- and low-risk clusters. Notably, those in the low-risk group indicated a more favorable survival trajectory. Clinical evaluations further corroborated these findings, linking better outcomes with reduced risk scores. Additionally, observed mutation patterns allude to a potential association between elevated risk scores and cytokine storms. TIDE analysis illuminated possible immunotherapeutic benefits for the low-risk group, underscored by an evident rise in microsatellite instability. Finally, our drug sensitivity evaluations revealed distinct therapeutic responses between the groups. In summary, this research underscores the pivotal role of lactylation modifications in ccRCC and introduces a promising prognostic model. These revelations pave the way for enhanced prognostic precision, presenting a promising path toward personalized treatment strategies and enriching our comprehension of the multifaceted molecular landscape of the disease.

Bioinformatics-based analysis of programmed cell death pathway and key prognostic genes in gastric cancer: Implications for the development of therapeutics.

BACKGROUND: Gastric cancer (GC) represents a major global health burden as a result of its high incidence and poor prognosis. The present study examined the role of the programmed cell death (PCD) pathway and identified key genes influencing the prognosis of patients with GC. METHODS: Bioinformatics analysis, machine learning techniques and survival analysis were systematically integrated to identify core prognostic genes from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset. A prognostic model was then developed to stratify patients into high-risk and low-risk groups, and further validated in the GSE84437 dataset. The model also demonstrated clinical relevance with tumor staging and histopathology. Immune infiltration analysis and the potential benefits of immunotherapy for each risk group were assessed. Finally, subgroup analysis was performed based on the expression of three key prognostic genes. RESULTS: Three core prognostic genes (CAV1, MMP9 and MAGEA3) were identified. The prognostic model could effectively differentiate patients into high-risk and low-risk groups, leading to significantly distinct survival outcomes. Increased immune cell infiltration was observed in the high-risk group, and better potential for immunotherapy outcomes was observed in the low-risk group. Pathways related to cancer progression, such as epithelial-mesenchymal transition and tumor necrosis factor-α signaling via nuclear factor-kappa B, were enriched in the high-risk group. By contrast, the low-risk group showed a number of pathways associated with maintenance of cell functionality and immune responses. The two groups differed in gene mutation patterns and drug sensitivities. Subgroup analysis based on the expression of the three key genes revealed two distinct clusters with distinct survival outcomes, tumor immune microenvironment characteristics and pathway enrichment. CONCLUSIONS: The present study offers novel insights into the significance of PCD pathways and identifies key genes associated with the prognosis of patients with GC. This robust prognostic model, along with the delineation of distinct risk groups and molecular subtypes, provides valuable tools for risk stratification, treatment selection and personalized therapeutic interventions for GC.

Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning.

BACKGROUND: Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of this dysfunction in SLE are not fully understood. Our study aims to explore the molecular characteristics of mitochondria-related genes (MRGs) in SLE, with a focus on identifying reliable biomarkers for classification and therapeutic purposes. METHODS: We sourced six SLE-related microarray datasets (GSE61635, GSE50772, GSE30153, GSE99967, GSE81622, and GSE49454) from the Gene Expression Omnibus (GEO) database. Three of these datasets (GSE61635, GSE50772, GSE30153) were integrated into a training set for differential analysis. The intersection of differentially expressed genes with MRGs yielded a set of differentially expressed MRGs (DE-MRGs). We employed machine learning algorithms-random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression-to select key hub genes. These genes' classifying potential was validated in the training set and three other validation sets (GSE99967, GSE81622, and GSE49454). Further analyses included differential expression, co-expression, protein-protein interaction (PPI), gene set enrichment analysis (GSEA), and immune infiltration, centered on these hub genes. We also constructed TF-mRNA, miRNA-mRNA, and drug-target networks based on these hub genes using the ChEA3, miRcode, and PubChem databases. RESULTS: Our investigation identified 761 differentially expressed genes (DEGs), mainly related to viral infection, inflammatory, and immune-related signaling pathways. The interaction between these DEGs and MRGs led to the identification of 27 distinct DE-MRGs. Key among these were FAM210B, MSRB2, LYRM7, IFI27, and SCO2, designated as hub genes through machine learning analysis. Their significant role in SLE classification was confirmed in both the training and validation sets. Additional analyses included differential expression, co-expression, PPI, GSEA, immune infiltration, and the construction of TF-mRNA, miRNA-mRNA, and drug-target networks. CONCLUSIONS: This research represents a novel exploration into the MRGs of SLE, identifying FAM210B, MSRB2, LYRM7, IFI27, and SCO2 as significant candidates for classifying and therapeutic targeting.

Comparison of clinical characteristics between adult-onset and juvenile-onset non-radiographic axial spondyloarthritis in Chinese patients: results from the COCAS cohort.

BACKGROUND: Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease predominantly affecting the axial skeleton. We aimed to describe the clinical characteristics of patients with non-radiographic axSpA (nr-axSpA) in China and compare the differences between adult- and juvenile-onset cases. METHODS: A cross-sectional study was conducted using data from 776 patients with nr-axSpA in the Clinical Characteristic and Outcome in Chinese Axial Spondyloarthritis (COCAS) study cohort. Patients were divided into two groups including the adult-onset group (n = 662) and the juvenile-onset group (n = 114) according to age at disease onset. Baseline demographics and clinical characteristics were compared between patients with adult-onset and juvenile-onset nr-axSpA. RESULTS: Overall, the male-to-female ratio was 1.26:1, the prevalence of HLA-B27 positivity was 72.2%, and the median age at disease onset of nr-axSpA was 22 years. Nearly 75% of nr-axSpA patients had peripheral arthritis in the disease course, and the prevalence of extra-articular manifestations was 10.4%. The juvenile-onset group contained a higher proportion of men (66.7% vs. 53.9%, P = 0.011) and a longer baseline disease duration (4.0 [4.0] vs. 1.6 [3.5], P < 0.001) than the adult-onset group. A family history of spondyloarthritis was more frequent in the juvenile-onset group than in the adult-onset group (23.7% vs. 15.4%, P = 0.028), but no significant difference in the prevalence of HLA-B27 positivity was observed between the two groups (P = 0.537). Regarding initial symptoms, peripheral arthritis occurred more often in patients with juvenile-onset nr-axSpA, whereas patients with adult-onset nr-axSpA presented more frequently with axial involvement. The prevalence of inflammatory back pain (IBP) was higher in the adult-onset group than in the juvenile-onset group (85.0% vs. 75.4%, P = 0.010), whereas the juvenile-onset group showed a higher prevalence of peripheral arthritis and enthesitis than the adult-onset group (67.5% vs. 48.5%, P < 0.001; 35.1% vs. 23.3%, P = 0.007, respectively). CONCLUSIONS: Compared with adult-onset nr-axSpA, juvenile-onset nr-axSpA was more common in men and those with a family history of spondyloarthritis. Juvenile-onset nr-axSpA presents with a "peripheral predominant" mode at disease onset and a higher frequency of peripheral arthritis and enthesitis during the disease course.

Elucidating the role of T-cell exhaustion-related genes in colorectal cancer: a single-cell bioinformatics perspective.

Colorectal cancer (CRC) remains a significant global health issue. In this study, the role of T-cell exhaustion-related genes (TEXs) in CRC was investigated using single-cell and bulk RNA-seq analysis. This research involved extensive data analysis using multiple databases, including the TCGA-COAD cohort, GSE14333, and GSE39582. Through single-cell analysis, distinct cell populations within CRC samples were identified and classified T-cells into four subgroups: regulatory T-cells (Tregs), conventional CD4+ T-cells (CD4+ T conv), CD8+ T, and CD8+ T exhausted cells. Intercellular communication networks and signaling pathways associated with TEXs using computational tools such as CellChat and PROGENy. Additionally, TEX-related alterations in tumor gene pathways were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Prognostic models were developed, and their correlation with immune infiltration was assessed. The study revealed the presence of distinct cell populations within CRC, with TEXs playing a crucial role in the tumor microenvironment. CD8+ T exhausted cells exhibited expression of specific markers, indicating their involvement in tumor immune evasion. CellChat and PROGENy analyses revealed intricate communication networks and signaling pathways associated with TEXs, including RNA splicing and viral carcinogenesis. Furthermore, the prognostic risk model developed on the basis of TEXs demonstrated its efficacy in stratifying CRC patients. This risk model exhibited strong correlations with immune infiltration by various effector immune cells, highlighting the influence of TEXs on the tumor immune response. The complex interactions and signaling pathways underlying TEX-associated immune dysregulation in CRC were revealed by employing advanced analytical approaches. The development of a prognostic risk model based on TEXs offers a promising tool for prognostic stratification in patients with CRC. Furthermore, the correlations observed between TEXs and immune infiltration provide valuable insights into the potential of TEXs as therapeutic targets and highlight the need for further investigation into TEX-mediated immune evasion mechanisms. This study thus provides valuable insights into the role of TEXs in CRC.

Identification of the shared gene signatures and molecular pathways in systemic lupus erythematosus and diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) incidence is higher in systemic lupus erythematosus (SLE) patients than the general population, but the molecular mechanisms behind this link remain ambiguous. The aim of this study was to investigate shared gene signatures and molecular pathways between SLE and DLBCL. METHODS: We procured expression profiles of SLE and DLBCL from public databases and identified common differentially expressed genes (DEGs). Functional pathway enrichment and protein-protein interaction (PPI) analyses were performed on these shared genes. The molecular complex detection technology (MCODE) and eXtreme Gradient Boosting (XGBoost) machine learning algorithm were used to select core shared genes, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. RESULTS: We identified 54 DEGs as shared genes, among which CD177, CEACAM1, GPR84 and IFIT3 were identified as core shared genes. These genes showed strong associations with inflammatory and immune response pathways. We found a significant positive correlation between GPR84 and IFIT3 expression levels and the immune microenvironment. Decreased expression levels of GPR84 and IFIT3 were linked to enhanced immune therapy sensitivity, potentially due to lower dysregulation scores during low expression. We also discovered that TP53 mutations might elevate CD177 and GPR84 expression and that reduced expression levels of GPR84 and IFIT3 were linked with better overall survival and progression-free survival in DLBCL patients. CONCLUSIONS: Our study provides valuable insights into the shared molecular mechanisms underpinning the pathogenesis of SLE and DLBCL. These findings could potentially offer new biomarkers and therapeutic targets for SLE and DLBCL.

CRISPR-Cas9 screening identified lethal genes enriched in necroptosis pathway and of prognosis significance in osteosarcoma.

BACKGROUND: The present study aimed to identify indispensable genes associated with tumor cell viability according to the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) datasets, which may support new therapeutic targets for patients with osteosarcoma. METHODS: The transcriptome patterns between tumor and normal tissues, which were obtained from the Therapeutically Applicable Research to Generate Effective Treatments dataset, were overlapped with the genomics associated with cell viability screened by CRISPR-Cas9 technology. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were employed to determine enrichment pathways related to lethal genes. Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a risk model related to lethal genes for predicting clinical outcomes of osteosarcoma. Univariate and multivariate Cox regressions were conducted to assess the prognostic value of this feature. Weighted gene co-expression network analysis was performed to identify modules associated with patients with high-risk score. RESULTS: In total, 34 lethal genes were identified in this investigation. These genes were enriched in the necroptosis pathway. The risk model based on LASSO regression algorithm distinguishes patients with high-risk score from patients with low-risk score. Compared with low-risk patients, high-risk patients showed a shorter overall survival rate in both the training and validation sets. The time-dependent receiver operating characteristic curves of 1, 3 and 5 years displayed that the risk score has great prediction performance. The necroptosis pathway represents the main difference in biological behavior between the high-risk group and the low-risk group. Meanwhile, CDK6 and SMARCB1 may serve as important targets for detecting osteosarcoma progression. CONCLUSIONS: The present study developed a predictive model that outperformed classical clinicopathological parameters for predicting the clinical outcomes of osteosarcoma patients and identified specific lethal genes, including CDK6 and SMARCB1, as well as the necroptosis pathway. These findings may serve as potential targets for future osteosarcoma treatments.

Identification of NETs-related biomarkers and molecular clusters in systemic lupus erythematosus.

Neutrophil extracellular traps (NETs) is an important process involved in the pathogenesis of systemic lupus erythematosus (SLE), but the potential mechanisms of NETs contributing to SLE at the genetic level have not been clearly investigated. This investigation aimed to explore the molecular characteristics of NETs-related genes (NRGs) in SLE based on bioinformatics analysis, and identify associated reliable biomarkers and molecular clusters. Dataset GSE45291 was acquired from the Gene Expression Omnibus repository and used as a training set for subsequent analysis. A total of 1006 differentially expressed genes (DEGs) were obtained, most of which were associated with multiple viral infections. The interaction of DEGs with NRGs revealed 8 differentially expressed NRGs (DE-NRGs). The correlation and protein-protein interaction analyses of these DE-NRGs were performed. Among them, HMGB1, ITGB2, and CREB5 were selected as hub genes by random forest, support vector machine, and least absolute shrinkage and selection operator algorithms. The significant diagnostic value for SLE was confirmed in the training set and three validation sets (GSE81622, GSE61635, and GSE122459). Additionally, three NETs-related sub-clusters were identified based on the hub genes' expression profiles analyzed by unsupervised consensus cluster assessment. Functional enrichment was performed among the three NETs subgroups, and the data revealed that cluster 1 highly expressed DEGs were prevalent in innate immune response pathways while that of cluster 3 were enriched in adaptive immune response pathways. Moreover, immune infiltration analysis also revealed that innate immune cells were markedly infiltrated in cluster 1 while the adaptive immune cells were upregulated in cluster 3. As per our knowledge, this investigation is the first to explore the molecular characteristics of NRGs in SLE, identify three potential biomarkers (HMGB1, ITGB2, and CREB5), and three distinct clusters based on these hub biomarkers.

Comparing clinical characteristics of systemic sclerosis with or without interstitial lung disease: A cross-sectional study from a single center of the Chinese Rheumatism Data Center.

Background: We aimed to compare the clinical characteristics of patients with systemic sclerosis (SSc) with or without interstitial lung disease (ILD) to identify relationships with the presence of ILD in SSc at a single center in China. Methods: A cross-sectional study was conducted using retrospective data from the Chinese Rheumatology Data Center. Patients diagnosed with SSc at the Second Affiliated Hospital of Nanchang University between 2013 and 2022 were included. Demographic and clinical characteristics were compared between patients with SSc with and without ILD. Logistic regression analyses were performed to explore these associations. Results: A total of 227 patients with SSc were included (male:female ratio = 1:4.82), of which 121 (53.3%) were accompanied with ILD. SSc patients with ILD had a higher percentage of diffuse cutaneous systemic sclerosis (dcSSc), sclerodactyly, loss of finger pad, muscle involvement, left ventricular diastolic dysfunction (LVDD), and pulmonary hypertension (PAH), elevated Krebs von den Lungen-6 (KL-6), and elevated ferritin than those without ILD, and a higher modified Rodnan skin score (mRSS), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) (all P < 0.05). Antinuclear antibody (ANA) and anti-scleroderma-70 (anti-Scl-70) positivity was presented frequently in SSc patients with ILD, while SSc patients without ILD were more often anti-centromere antibody (ACA) positive (all P < 0.05). On the multivariable analysis, muscle involvement [OR 2.551 (95% CI 1.054-6.175), P = 0.038], LVDD [OR 2.360 (95% CI 1.277-4.361), P = 0.006], PAH [OR 9.134 (95% CI 2.335-35.730), P = 0.001], dcSSc [OR 2.859 (95% CI 1.489-5.487), P = 0.002], PLR [OR 1.005 (95% CI 1.001-1.008), P = 0.020], elevated KL-6 [OR 2.033 (95% CI 1.099-3.763), P = 0.024], and anti-Scl-70 [OR 3.101 (95% CI 1.647-5.840), P < 0.001] were statistically significant associations with SSc patients with ILD. Conclusion: Systemic sclerosis was found mainly in females. Several important differences in clinical and laboratory characteristics have been demonstrated between SSc patients with or without ILD. Muscle involvement, LVDD, PAH, dcSSc, PLR, elevated KL-6, and Anti-Scl-70 antibody may be associated with SSc in patients with ILD.

Exploring the molecular mechanisms and shared gene signatures between rheumatoid arthritis and diffuse large B cell lymphoma.

The relationship between rheumatoid arthritis (RA) and diffuse large B-cell lymphoma (DLBCL) is well characterized, but the molecular mechanisms underlying this association have not been clearly investigated. Our study aimed to identify shared gene signatures and molecular mechanisms between RA and DLBCL. We selected multiple Gene Expression Omnibus (GEO) datasets (GSE93272, GSE83632, GSE12453, GSE1919) to obtain gene expression levels and clinical information about patients with RA and DLBCL. Weighted gene co-expression network analysis (WGCNA) was used to research co-expression networks associated with RA and DLBCL. Subsequently, we performed enrichment analysis of shared genes and screened the most significant core genes. We observed expression of the screened target gene, galectin 2 (LGALS2), in DLBCL patients and its impact on patient prognosis. Finally, we analyzed the molecular functional mechanism of LGALS2 and observed its relationship with the immune response in DLBCL using single-sample Gene Set Enrichment Analysis (ssGSEA). WGCNA recognized two major modules for RA and DLBCL, respectively. Shared genes (551) were identified for RA and DLBCL by observing the intersection. In addition, a critical shared gene, LGALS2, was acquired in the validation tests. Next, we found that the expression level of LGALS2 gradually decreased with tumor progression in DLBCL and that increased expression of LGALS2 predicted a better prognosis for DLBCL patients. ssGSEA revealed that LGALS2 is involved in immune-related pathways and has a significant regulatory effect on human immune responses. Additionally, we observed that LGALS2 is closely related to the sensitivity of multiple chemotherapeutic drugs. There is extremely little research on the molecular mechanism of correlation between RA and DLBCL. Our study identified that LGALS2 is a potential therapeutic target and an immune-related biomarker for patients with RA and DLBCL.

Risk factors for progression of juvenile-onset non-radiographic axial spondyloarthritis to juvenile-onset ankylosing spondylitis: a nested case-control study.

OBJECTIVE: To evaluate the clinical characteristics of juvenile-onset non-radiographic axial spondyloarthritis (nr-axSpA) and to investigate risk factors associated with progression to juvenile-onset ankylosing spondylitis (JoAS). METHODS: A nested case-control study was conducted using the retrospectively collected data of 106 patients with juvenile-onset nr-axSpA (age at disease onset, <16 years) in the Clinical characteristic and Outcome in Chinese Axial Spondyloarthritis study cohort. Baseline demographic and clinical characteristics and prognosis were reviewed. Logistic regression analyses were performed to investigate risk factors associated with progression to JoAS. RESULTS: Overall, 58.5% of patients with juvenile-onset nr-axSpA presented with peripheral symptoms at disease onset. In 82.1% of these patients, axial with peripheral involvement occurred during the disease course. The rate of disease onset at >12 years and disease duration of ≤10 years were significantly higher in those with progression to JoAS than in those without progression to JoAS (83.0% vs 52.8%, p=0.001; 92.5% vs 56.6%, p<0.001, respectively). Multivariable logistic regression analysis revealed that inflammatory back pain (IBP) (OR 13.359 (95% CI 2.549 to 70.013)), buttock pain (OR 10.171 (95% CI 2.197 to 47.085)), enthesitis (OR 7.113 (95% CI 1.670 to 30.305)), elevated baseline C reactive protein (CRP) levels (OR 7.295 (95% CI 1.984 to 26.820)) and sacroiliac joint-MRI (SIJ-MRI) positivity (OR 53.821 (95% CI 9.705 to 298.475)) were significantly associated with progression to JoAS. CONCLUSION: Peripheral involvement was prevalent in juvenile-onset nr-axSpA. IBP, buttock pain, enthesitis, elevated baseline CRP levels and SIJ-MRI positivity in patients with the disease are associated with higher risk of progression to JoAS.

Impact of Zeolite Structure on Entropic-Enthalpic Contributions to Alkane Monomolecular Cracking: An IR Operando Study.

The monomolecular cracking rates of propane and n-butane over MFI, CHA, FER and TON zeolites were determined simultaneously with the coverage of active sites at reaction condition using IR operando spectroscopy. This allowed direct determination of adsorption thermodynamics and intrinsic rate parameters. The results show that the zeolite confinement mediates enthalpy-entropy trade-offs only at the adsorbed state, leaving the true activation energy insensitive to the zeolite or alkane structure while the activation entropy was found to increase with the confinement. Hence, relative cracking rates of alkanes within zeolite pores are mostly governed by activation entropy.

A New Feature Extraction Method of Near-Infrared Spectra Based on the Addition of Historical Data.

In traditional qualitative analysis of near-infrared (NIR) spectra, the stability of recognition models is decreased when new varieties of samples are added into the model. In order to improve the robustness of the model, a new feature extraction method based on the addition of historical data was put forward. The NIR training samples will be collected first, after that the historical data of the same species is added to constitute a larger and richer dataset. Then, the pretreated data of these training samples is projected to the feature space, which is constructed by feature extraction using partial least squares (PLS) based on the above dataset. Subsequently, orthogonal linear discriminant analysis (OLDA) is employed to extract features of the projected data. 18 varieties of corn seeds were taken as study subject, the comparative experiments with and without historical data are implemented respectively, and then the biomimetic pattern recognition (BPR) method is applied to verify the efficiency of the method proposed. The results suggest that the method adopted can improve the robustness of recognition model more effectively compared with the method without historical data. It maintains the high correct recognition ratios when new varieties are added into the model. Besides that, the recognition effect on test sets of the different days remains the same basically in the condition of same PLS dimensions. Therefore, the dimension of feature extraction can be set to some fixed values in recognition software. In this way, it can keep out of the trouble of manually modifying the optimal PLS parameter in recognition software if new varieties need to be added into the model. The experiment results of the thesis manifested the effectiveness of the proposed method.

Effective bulk and surface temperatures of the catalyst bed of FT-IR cells used for in situ and operando studies.

The temperature prevailing in the catalyst bed of three different IR spectroscopic reaction cells was assessed by means of thermocouples, an optical pyrometer and reaction rate measurements. One of the cells was a custom-made transmission FT-IR cell for use with thin wafers and the two others were commercial Harrick and Spectra-Tech diffuse reflectance FT-IR (DRIFTS) cells used for the analysis of powdered samples. The rate of CO methanation measured over a 16 wt% Ni/alumina catalyst was used as a means to derive the effective temperature prevailing in the IR cells from that existing in a traditional (non-spectroscopic) reactor having a well-controlled temperature. The sample bed of these three IR cells exhibited a significantly lower temperature than that of the corresponding measure thermocouple, which was yet located in or close to the sample bed. The comparison of Arrhenius plots enabled us to determine a temperature correction valid over a large temperature range. The use of an optical pyrometer was assessed with a view to determining the temperature of the surface of the powdered beds and that at the centre of the wafer. The optical pyrometer proved useful in the case of the catalyst powder, which behaved as a black non-reflecting body. In contrast, the temperature reading was inaccurate in the case of the pressed wafer, probably due to the shiny surface and minute thickness of the wafer, which led to a significant portion of the IR radiation of the surroundings being reflected by and transmitted through the wafer. The optical pyrometer data showed that the temperature of the surface of the powdered beds was significantly lower than that of the bulk of the bed, and that the total flow rate and composition did not affect this value. This work emphasises that the effective bed temperature in spectroscopic cells can be significantly different from that given by measure thermocouples, even when located in the vicinity of the sample, but that the calibration curves derived from rate measurements can be used to overcome this problem.