Complexes of vitamin B6, part VII: Ternary complexes of cobalt(II), nickel(II), and copper(II) involving pyridoxamine and some amino acids

The stability constants of the ternary Cu(II), Ni(II), and Co(II) complexes containing pyridoxamine (PM) and as a second ligand (L) glycine, DL-alanine, DL-valine, and β-phenylalnine were determined by pH-metric titration in 0.50 M KNO₃ at 30°C. The corresponding constants of the equilibrium, log X, are greater than would be expected for purely statistical reasons (log X = 0.6), except for few complex cases of Co(II). It has been also concluded that amino acids compete more than pyridoxamine for Ni(II) and Co(II) through the formation of 1:2:1:0 species rather than 2:1:1:0 of PM⁻:L⁻:M²⁺:H⁺.     

Synthesis of 2-acetamido-2-deoxy-3-O-β-d-mannopyranosyl-d-glucose

Condensation of 4,6-di-O-acetyl-2,3-O-carbonyl-α-d-mannopyranosyl bromide with benzyl 2-acetamido-4,6-O-benzylidene-2-deoxy-α-d-glucopyranoside (2) gave an α-d-linked disaccharide, further transformed by removal of the carbonyl and benzylidene groups and acetylation into the previously reported benzyl 2-acetamido-4,6-O-benzylidene-2-deoxy-3-O-(2,3,4,6-tetra-O-acetyl-α-d-mannopyranosyl)-α-d-glucopyranoside. Condensation of 3,4,6-tri-O-benzyl-1,2-O-(1-ethoxyethylidene)-α-d-glucopyranose or 2-O-acetyl-3,4,6-tri-O-benzyl-α-d-glucopyranosyl bromide with 2 gave benzyl 2-acetamido-3-O-(2-O-acetyl-3,4,6-tri-O-benzyl-β-d-glucopyranosyl)-4,6-O-benzylidene-2-deoxy-α-d-glucopyranoside. Removal of the acetyl group at O-2, followed by oxidation with acetic anhydride-dimethyl sulfoxide, gave the β-d-arabino-hexosid-2-ulose 14. Reduction with sodium borohydride, and removal of the protective groups, gave 2-acetamido-2-deoxy-3-O-β-d-mannopyranosyl-d-glucose, which was characterized as the heptaacetate. The anomeric configuration of the glycosidic linkage was ascertained by comparison with the α-d-linked analog.     

Effect of glucose on the induction of nitrate reductase in corn roots

In Zea mays L., addition of glucose to the induction medium has no effect on the induction of nitrate reductase during the initial 3 hours either in root tips (0-10 mm) or mature root sections (25-35 mm). With longer times, higher levels of enzyme activity are recovered from both root segments when glucose is present in the incubation medium. The induction in root tips is saturated by 10 mm NO(3) (-). Higher concentrations of NO(3) (-) are required for saturation in mature root sections. The response to glucose is seen over a wide range of external NO(3) (-) concentrations.Nitrate reductase activity is lost rapidly when nitrate is withdrawn from the induction medium. Additions of glucose do not prevent this loss. Additions of glucose have no effect on total uptake of NO(3) (-) by the root segments but they increase the anaerobic NO(2) (-) production in both root tips and mature root segments. This latter measurement is considered to be an estimate of an active NO(3) (-) pool in the cytoplasm. Thus the results show that glucose alters the distribution of NO(3) (-) within the root sections. This may be an important factor in controlling the in vivo stability of the enzyme or its rate of synthesis.     

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer''s Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.     

Influence of Polymer Aggregation and Liquid Immiscibility on Morphology Tuning by Varying Composition in PffBT4T‐2DT/Nonfullerene Organic Solar Cells

The temperature‐dependent aggregation behavior of PffBT4T polymers used in organic solar cells plays a critical role in the formation of a favorable morphology in fullerene‐based devices. However, there is little investigation into the impact of donor/acceptor ratio on morphology tuning, especially for nonfullerene acceptors (NFAs). Herein, the influence of composition on morphology is reported for blends of PffBT4T‐2DT with two NFAs, O‐IDTBR and O‐IDFBR. The monotectic phase behavior inferred from differential scanning calorimetry provides qualitative insight into the interplay between solid–liquid and liquid–liquid demixing. Transient absorption spectroscopy suggests that geminate recombination dominates charge decay and that the decay rate is insensitive to composition, corroborated by negligible changes in open‐circuit voltage. Exciton lifetimes are also insensitive to composition, which is attributed to the signal being dominated by acceptor excitons which are formed and decay in domains of similar size and purity irrespective of composition. A hierarchical morphology is observed, where the composition dependence of size scales and scattering intensity from resonant soft X‐ray scattering (R‐SoXS) is dominated by variations in volume fractions of polymer/polymer‐rich domains. Results suggest an optimal morphology where polymer crystallite size and connectivity are balanced, ensuring a high probability of hole extraction via such domains.