Catalytic Pathways and Kinetic Requirements for Alkanal Deoxygenation on Solid Tungstosilicic Acid Clusters

Kinetic measurements and acid site titrations were carried out to interrogate the reaction network, probe the mechanism of several concomitant catalytic cycles, and explain their connection during deoxygenation of light alkanals (C_<i>n</i>H_2<i>n</i>O, <i>n</i> = 3–6) on tungstosilicic acid clusters (H₄SiW₁₂O₄₀) that leads to hydrocarbons (e.g., light alkenes, dienes, and larger aromatics) and larger oxygenates (e.g., alkenals). The three primary pathways are (1) intermolecular CC bond formation, which couples two alkanal molecules in aldol-condensation reactions followed by rapid dehydration, forming a larger alkenal (C_2<i>n</i>H_{4<i>n</i>–2}O), (2) intramolecular CC bond formation, which converts an alkanal directly to an <i>n</i>-alkene (C_<i>n</i>H_2<i>n</i>) by accepting a hydride ion from H donor and ejecting a H₂O molecule, and (3) isomerization–dehydration, which involves self-isomerization of an alkanal to form an allylic alcohol and then rapid dehydration to produce an <i>n</i>-diene (C_<i>n</i>H_{2<i>n</i>–2}). The initial intermolecular CC bond formation is followed by a series of sequential intermolecular CC bond formation steps; during each of these steps an additional alkanal unit is added onto the carbon chain to evolve a larger alkenal (C_3<i>n</i>H_{6<i>n</i>–4}O and C_4<i>n</i>H_{8<i>n</i>–6}O), which upon its cyclization–dehydration reaction forms hydrocarbons (C_<i>tn</i>H_{2<i>tn</i>–2<i>t</i>}, <i>t</i> = 2–4, including cycloalkadienes or aromatics). The intermolecular and intramolecular CC bond formation cycles are catalytically coupled through intermolecular H-transfer events, whereas the intermolecular CC bond formation and isomerization–dehydration pathways share a coadsorbed alkanal–alkenol pair as the common reaction intermediate. The carbon number of alkanals determines their hydride ion affinities, the stabilities of their enol tautomers, and the extent of van der Waals interactions with the tungstosilicic clusters; these factors influence the stabilities of the transition states or the abundances of reaction intermediates in the kinetically relevant steps and in turn the reactivities and selectivities of the various cycles

Table1_De novo assembly and comparative analysis of the mitochondrial genome of Reynoutria japonica.XLSX

Reynoutria japonica Houtt. is an important medical plant with a long history of thousands of years in China, however, its mitochondrial genome (mitogenome) has not been reported yet. In this work, we reported and analyzed the R. japonica mitogenome. The main results include: The R. japonica mitogenome was 302,229 bp in length and encoded 48 genes, including 27 protein-coding genes (PCGs), 3 rRNA genes, and 18 tRNA genes. Repeat sequence analysis revealed that there were 54 repeat sequences ranging from 193 bp to 1,983 bp in the R. japonica mitogenome. Relative synonymous codon usage (RSCU) analysis showed that leucine (900, 11.01%) and serine (732, 8.96%) were the two most abundant amino acids, and the codons with RSCU values showed the preference of A or T ending when greater than 1. The RNA editing sites of PCGs in the R. japonica mitogenome were characterized, and 299 RNA editing sites were found. Extensive sequences transfer between mitochondrion and chloroplast were found in R. japonica, where 11 complete plastid-derived tRNA genes stayed intact in the R. japonica mitogenome. Three genes (ccmFC, cox1, and nad1) were seen to play essential roles in the evolution through selection pressure analysis. The phylogenetic analysis showed that Fallopia multiflora was the closest species with R. japonica, in consistency with the results of chloroplast genome. Overall, the current work presents the first mitogenome of R. japonica and could contribute to the phylogenetic analysis of the family Polygonaceae.</p

Table_1_Association of Coffee, Tea, and Caffeine Consumption With All-Cause Risk and Specific Mortality for Cardiovascular Disease Patients.DOCX

AimThe aim of the study was to examine the relationship between coffee, tea, caffeine consumption and risk of all-cause death and cardiovascular disease (CVD) death in CVD population.MethodsThis cohort study included 626 CVD participants aged ≥18 years old who derived from the National Health and Nutrition Examination Surveys (NHANES) database 2003–2006. The end time of follow-up was 2015, and with a median follow-up time of 113.5 (63, 133) months. CVD death was defined as a death caused by congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris, heart attack or stroke. Cox model and competitive-risk model were used to explore the relationship of coffee, tea, caffeine, decaffeinated coffee/tea on the risk of the all-cause death and CVD death for CVD population, respectively. Additionally, we explored the effect of urinary caffeine and caffeine metabolites on all-cause death.ResultsAll patients were divided into survival group (n = 304), non-CVD death group (n = 223), and CVD death group (n = 99). The incidence of all-cause death and CVD death was ~51.44 and 15.81% in the study. After adjusting age, body mass index (BMI), cancer, estimated glomerular filtration rate (eGFR), energy, the history of CVD medications, carbohydrate and family income to poverty ratio (PIR), the results suggested coffee, caffeine, iced tea and hot tea consumption (≥4 cups per day) were associated with an increased risk of the all-cause death in CVD patients; while hot tea (1–3 cups per day), decaffeinated coffee/iced tea/hot tea could reduce the risk of the all-cause death. Likewise, coffee, caffeine, iced tea (≥4 cups per day), hot tea, decaffeinated iced tea/ hot tea (Always) could enhance the risk of the CVD death in CVD population. We also found that 1-methylxanthine showed a significant positive association on the risk of all-cause death in CVD population.ConclusionOur study indicated that higher consumption of coffee, tea and caffeine could increase the risk of all-cause and CVD death for CVD patients.</p

DataSheet1_De novo assembly and comparative analysis of the mitochondrial genome of Reynoutria japonica.docx

Reynoutria japonica Houtt. is an important medical plant with a long history of thousands of years in China, however, its mitochondrial genome (mitogenome) has not been reported yet. In this work, we reported and analyzed the R. japonica mitogenome. The main results include: The R. japonica mitogenome was 302,229 bp in length and encoded 48 genes, including 27 protein-coding genes (PCGs), 3 rRNA genes, and 18 tRNA genes. Repeat sequence analysis revealed that there were 54 repeat sequences ranging from 193 bp to 1,983 bp in the R. japonica mitogenome. Relative synonymous codon usage (RSCU) analysis showed that leucine (900, 11.01%) and serine (732, 8.96%) were the two most abundant amino acids, and the codons with RSCU values showed the preference of A or T ending when greater than 1. The RNA editing sites of PCGs in the R. japonica mitogenome were characterized, and 299 RNA editing sites were found. Extensive sequences transfer between mitochondrion and chloroplast were found in R. japonica, where 11 complete plastid-derived tRNA genes stayed intact in the R. japonica mitogenome. Three genes (ccmFC, cox1, and nad1) were seen to play essential roles in the evolution through selection pressure analysis. The phylogenetic analysis showed that Fallopia multiflora was the closest species with R. japonica, in consistency with the results of chloroplast genome. Overall, the current work presents the first mitogenome of R. japonica and could contribute to the phylogenetic analysis of the family Polygonaceae.</p

Additional file 1 of Chinese ASCVD risk equations rather than pooled cohort equations are better to identify macro- and microcirculation abnormalities

Additional file 1: Supplemental Table 1. comparison of CHINA-PAR and PCE model

DataSheet_1_Integration of bulk RNA sequencing and single-cell analysis reveals a global landscape of DNA damage response in the immune environment of Alzheimer’s disease.docx

BackgroundWe developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer’s disease (AD).MethodsWe thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.ResultsThe progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively,ConclusionsIn conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.</p

Image_4_Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning.tif

BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.</p

Table_2_Phenylpropanoid Biosynthesis Gene Expression Precedes Lignin Accumulation During Shoot Development in Lowland and Upland Switchgrass Genotypes.XLSX

Efficient conversion of lignocellulosic biomass into biofuels is influenced by biomass composition and structure. Lignin and other cell wall phenylpropanoids, such as para-coumaric acid (pCA) and ferulic acid (FA), reduce cell wall sugar accessibility and hamper biochemical fuel production. Toward identifying the timing and key parameters of cell wall recalcitrance across different switchgrass genotypes, this study measured cell wall composition and lignin biosynthesis gene expression in three switchgrass genotypes, A4 and AP13, representing the lowland ecotype, and VS16, representing the upland ecotype, at three developmental stages [Vegetative 3 (V3), Elongation 4 (E4), and Reproductive 3 (R3)] and three segments (S1–S3) of the E4 stage under greenhouse conditions. A decrease in cell wall digestibility and an increase in phenylpropanoids occur across development. Compared with AP13 and A4, VS16 has significantly less lignin and greater cell wall digestibility at the V3 and E4 stages; however, differences among genotypes diminish by the R3 stage. Gini correlation analysis across all genotypes revealed that lignin and pCA, but also pectin monosaccharide components, show the greatest negative correlations with digestibility. Lignin and pCA accumulation is delayed compared with expression of phenylpropanoid biosynthesis genes, while FA accumulation coincides with expression of these genes. The different cell wall component accumulation profiles and gene expression correlations may have implications for system biology approaches to identify additional gene products with cell wall component synthesis and regulation functions.</p

Structural diversity and anti-lung cancer activity evaluation of two Co(II) and Cu(II) containing mixed-ligand coordination polymers

In this study, we report the synthesis and structural characterization of two new Co(II) and Cu(II) containing coordination polymers {[Co2(btc)(mbtz)2]·7H2O}n (1), [Cu2(btc)(mbtz)2]n (2) by using the N donor ligand 1,3-bis(1H-1,2,4-triazol-1-ylmethyl)benzene (mbtz) and the O-donor ligand 1,2,4,5-benzenetetracarboxylic acid (H4btc) as the co-linkers. Furthermore, a sonochemical process was used to produce the nanostructures of coordination polymers 1 and 2. Then, we evaluated the anti-viability of nano 1 and 2 on LTEP lung cancer cells. And the Annexin V-FITC/PI staining was performed to detect the apoptosis induced by nano 1 and 2 on LTEP cells. The ROS level in LTEP cells was measured by DCFH-DA assay.</p

Image_3_Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning.tif

BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.</p