Catalytic Para‐Xylene Maximization. V. Toluene Methylation with Methanol and Disproportionation of Toluene Using Pt/ZSM‐5 and Pt/Mordenite Catalysts

Toluene was methylated with methanol and disproportionated using catalysts containing different Pt contents (0.2, 0.4 and 0.6%) supported on H‐ZSM‐5 or H‐mordenite (H‐M) zeolites in a fixed‐bed flow‐reactor operated atmospherically at temperatures of 300–500 °C in a flow of hydrogen. Platinum dispersion in the zeolite supports and acid sites strength distribution were evaluated using hydrogen chemisorption (1:1 stoichiometry) and ammonia temperature programmed desorption (TPD) in a differential scanning calorimeter (DSC). Toluene methylation was much faster on all catalysts than toluene disproportionation (DISP). Both reactions were more accelerated using H‐ZSM‐5 containing catalysts than H‐M containing catalysts. The yield of xylenes, and in particular para‐xylene, was significantly influenced by the yield of trimethylbenzenes (TMBs) in product. The selectivities for para‐, ortho‐ and meta‐xylenes production were found largely dependent on the Pt content in the catalysts, particularly when supported on H‐ZSM5‐zeolite. However, using Pt/H‐M catalysts, these selectivities were not strictly controlled by Pt content in the catalysts.     

Microbial transformation of prednisone

The microbial transformation of prednisone (17alpha,21-dihydroxy-pregna-1,4-diene-3,11,20-trione) (1) by Cunninghamella elegans afforded two metabolites, 17alpha,21-dihydroxy-5alpha-pregn-1-ene-3,11,20-trione (2) and 17alpha,20S,21-trihydroxy-5alpha-pregn-1-ene-3,11-dione (3), while the fermentation of 1 with Fusarium lini, Rhizopus stolonifer and Curvularia lunata afforded a metabolite 1,4-pregnadiene-17alpha,20S,21-triol-3,11-dione (4). Compound 3 was found to be a new metabolite. Their structures were elucidated on the basis of spectroscopic techniques. Compound 3 showed inhibitory activity against lipoxygenase enzyme.     

Determination of Br,Rb, Cs,Sc and Na in various plant leaves located in an urban park by neutron activation analysis

In order to determine the effect of airborne emission of trace elements, e.g. Br, Rb Cs, Sc and Na on roadside ecosystem, concentrations were determined in different species of plants from an urban park at Lahore. For monitoring purpose attention was paid to the leaves ofSapindus mukorossis, Alstonia scholaris andDiospyros embryopteris. Different varieties of tissues were found to have different concentration of trace elements when compared among themselves. The concentration of trace element deposition was compared with the values reported in literature. Significant pollution was observed.     

Reactions of [(Cp)2Ti(NCS)2] as a potential metalloligand; synthesis and physico-chemical investigations of novel heterobimetallic complexes

Summary Bis(cyclopentadienyl)titanium(IV) diisothiocyanate [(Cp)₂-Ti(NCS)₂] reacts with MCl₂ (M = Cu, Pd or Pt), [CuCl(PPh₃)₃], [RuCl₂(PPh₃)₃] and [RuCl₂(DMSO)₄] (DMSO = dimethylsulphoxide) giving solid compounds of stochiometry [(Cp)₂Ti(-NCS)₂MCl₂] (M = Cu, Pd or Pt), [(Cp)₂Ti(-NCS)₂CuCl(PPh₃)], [(Cp)₂Ti(-NCS)₂-RuCl(PPh₃)₂]Cl and [(Cp)₂Ti(-NCS)₂RuCl₂(DMSO)2]. These products have been characterized by physicochemical and spectroscopic methods.     

Synthesis of natural pulvinic acids based on a ‘[3+2] cyclization-Suzuki cross-coupling’ strategy

A number of pulvinic acid natural products were prepared based on Suzuki cross coupling reactions of α-hydroxy-γ-alkylidenebutenolides which are readily available by cyclization of 1,3-bis-silyl enol ethers with oxalyl chloride. The formal total synthesis of pulvinic acid, atromentic acid, gomphidic acid and of 4-hydroxypulvinic acid, 4′-hydroxypulvinic acid and iso-gomphidic acid are reported. In addition, total syntheses of pinastric acid, xerocomic acid and variegatic acid were accomplished.     

Synthesis and pharmacological activity of 4-(4'-(chlorophenyl)-4-hydroxypiperidine) derivatives

A new series of 4-(4'-chlorophenyl)-4-hydroxypiperidine derivatives (2-5), substituted at nitrogen, were synthesized and tested as potential analgesic compounds as well as evaluated for their effect on hypotensive activity. Results showed that all the derivatives exhibit significant analgesic activity in male Wistar rats at a dose of 50 mg/kg of body weight after intramuscular injection, when tested by thermal stimuli (tail flick test). Pethidine was used as reference drug. Compounds 2, 3 and 5 produced reduction in blood pressure in normotensive rat.     

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.     

Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.     

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking,DFT, and MD Simulation Analysis

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of −5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a −4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO–LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.