Ewingsharma5895

Comprehensive sequence analysis, annotation, chromosomal locations and epitope mapping enabled us to build a LC-MRM-MS method to monitor and quantify the immunostimulatory ATI proteins potentially related to NCWS, autoimmune diseases and metabolic disorders. This provides an opportunity to select wheat cultivars with significantly lower level of ATIs.Stimuli-responsive mesoporous silica films were prepared by evaporation-induced self-assembly through the physical entrapment of a functional metalloblock copolymer structuring agent, which simultaneously served to functionalize the mesopore. After end-functionalization with a silane group, the applied functional metalloblock copolymers were covalently integrated into the silica mesopore wall. In addition, they were partly degraded after the formation of the mesoporous film, which enabled the precise design of accessible mesopores. These polymer-silica hybrid materials exhibited remarkable and gating ionic permselectivity and offer the potential for highly precise pore filling design and combination with high-throughput printing techniques. This in situ functionalization strategy of mesoporous silica using responsive metalloblock copolymers has the potential to improve how we approach the design of complex architectures at the nanoscale for tailored transport. This functionalization strategy paves the way for a variety of technologies based on molecular transport in nanoscale pores, including separation, sensing, catalysis, and energy conversion.Binding between protein and DNA is an essential process to regulate different biological activities. Two puzzling questions in protein-DNA recognition are (i) how the protein's binding domain identifies the DNA sequence in an aqueous solution and (ii) how the formation of the complex alters the dynamical environment around it. In this work, we present results obtained from molecular dynamics simulations of the N-terminal α-helical domain of the λ-repressor protein (in dimeric form) bound to the corresponding operator DNA. Effects of formation of the complex in modifying the microscopic dynamics of water as well as the kinetics of hydrogen bonds at the interface have been explored. Locally heterogeneous restricted water motions at the complex interface have been observed, the extent of restriction being more significant around the directly bound residues of the protein and the DNA. In particular, the calculation revealed the existence of significantly constrained motionally restricted water layer that can form either bridges around the directly bound residues of the protein and DNA or are engaged in forming water-mediated contacts between a fraction of the unbound residues. More importantly, it is observed that the restricted water motion around the complex is correlated with the hydrogen bond relaxation time scale at the interface. It is further demonstrated that the kinetics of water-water hydrogen bonds involving the bridged water are influenced more due to complex formation.New derivatives of α-phenyl-N-tert-butyl nitrone (PBN) bearing a hydroxyl, an acetate, or an acetamide substituent on the N-tert-butyl moiety and para-substituted phenyl or naphthlyl moieties were synthesized. Their ability to trap hydroxymethyl radical was evaluated by electron paramagnetic resonance spectroscopy. The presence of two electron-withdrawing substituents on both sides of the nitronyl function improves the spin-trapping properties, with 4-HOOC-PBN-CH2OAc and 4-HOOC-PBN-CH2NHAc being ∼4× more reactive than PBN. The electrochemical properties of the derivatives were further investigated by cyclic voltammetry and showed that the redox potentials of the nitrones are largely influenced by the nature of the substituents both on the aromatic ring and on the N-tert-butyl function. The acetamide derivatives PBN-CH2NHAc, 4-AcNHCH2-PBN-CH2NHAc, and 4-MeO-PBN-CH2NHAc were the easiest to oxidize. A computational approach was used to rationalize the effect of functionalization on the free energies of nitrone reactivity with hydroxymethyl radical as well as on the electron affinity and ionization potential. Finally, the neuroprotection of the derivatives was evaluated in an in vitro model of cellular injury on cortical neurons. Five derivatives showed good protection at very low concentrations (0.1-10 μM), with PBN-CH2NHAc and 4-HOOC-PBN being the two most promising agents.The photoexcitation of α-diazocarbonyl compounds produces ketenes by both concerted and stepwise Wolff rearrangements. The stepwise mechanism proceeds through singlet carbene intermediates which can also participate in bimolecular reactions such as ylide formation with nucleophiles. Here, ultrafast transient infrared absorption spectroscopy is used to show competitive production of singlet carbene and ketene intermediates from the photoexcitation of ethyl diazoacetoacetate. We provide direct spectroscopic evidence for ylide formation by singlet α-carbonyl carbene capture in aprotic nucleophilic solvents (with ylide bands at 1625 cm-1 in acetonitrile and 1586 and 1635 cm-1 in tetrahydrofuran) and report an enol-mediated pathway for singlet α-carbonyl carbene reaction with alcohols (ethanol or tert-butanol) identified by an absorption band at 1694 cm-1; however, we find no evidence for a previously proposed ylide pathway. The α-carbonyl carbene is monitored by using a band with solvent-dependent wavenumber in the range 1627-1645 cm-1. A computed two-dimensional cut of the potential energy surface for the reaction of the singlet α-carbonyl carbene with methanol shows that the enol forms without a barrier and that this reaction is promoted by an intermolecular hydrogen bond from methanol to the carbonyl oxygen atom. The corresponding ylide structure lies higher in energy, with a barrierless downhill path to isomerization to the enol.Transfer learning is a subfield of machine learning that leverages proficiency in one or more prediction tasks to improve proficiency in a related task. For chemical property prediction, transfer learning models represent a promising approach for addressing the data scarcity limitations of many properties by utilizing potentially abundant data from one or more adjacent applications. Transfer learning models typically utilize a latent variable that is common to several prediction tasks and provides a mechanism for information exchange between tasks. For chemical applications, it is still largely unknown how correlation between the prediction tasks affects performance, the limitations on the number of tasks that can be simultaneously trained in these models before incurring performance degradation, and if transfer learning positively or negatively affects ancillary model properties. Here we investigate these questions using an autoencoder latent space as a latent variable for transfer learning models for predicting properties from the QM9 dataset that have been supplemented with semi-empirical quantum chemistry calculations. We demonstrate that property prediction can be counter-intuitively improved by utilizing a simpler linear predictor model, which has the effect of forcing the latent space to reorganize linearly with respect to each property. In data scarce prediction tasks, the transfer learning improvement is dramatic, whereas in data rich prediction tasks, there appears to be little to no adverse impact of transfer learning on prediction performance. The transfer learning approach demonstrated here thus represents a highly advantageous supplement to property prediction models with no downside in implementation.Fast ion conduction in solid-state matrices constitutes the foundation for a wide spectrum of electrochemical systems that use solid electrolytes (SEs), examples of which include solid-state batteries (SSBs), solid oxide fuel cells (SOFCs), and diversified gas sensors. Mixing different solid conductors to form composite solid electrolytes (CSEs) introduces unique opportunities for SEs to possess exceptional overall performance far superior to their individual parental solids, thanks to the abundant chemistry and physics at the new interfaces thus created. In this review, we provide a comprehensive and in-depth examination of the development and understanding of CSEs for SSBs, with special focus on their physiochemical properties and mechanisms of ion transport therein. The origin of the enhanced ionic conductivity in CSEs relative to their single-phase parents is discussed in the context of defect chemistry and interfacial reactions. The models/theories for ion movement in diversified composites are critically reviewed to interrogate a general strategy to the design of novel CSEs, while properties such as mechanical strength and electrochemical stability are discussed in view of their perspective applications in lithium metal batteries and beyond. As an integral component of understanding how ions interact with their composite environments, characterization techniques to probe the ion transport kinetics across different temporal and spatial time scales are also summarized.The chemical and electrochemical reduction of CO2 to value added chemicals entails the development of efficient and selective catalysts. Synthesis, characterization and electrochemical CO2 reduction activity of a air-stable cobalt(III) diphenylphosphenethano-bis(2-pyridinethiolate)chloride [Co(dppe)(2-PyS)2Cl, 1-Cl] complex is divulged. The complex reduces CO2 under homogeneous electrocatalytic conditions to produce CO with high Faradaic efficiency (FE > 92%) and selectivity in the presence of water. Through detailed electrochemical investigations, product analysis, and mechanistic investigations supported by theoretical calculations, it is established that complex 1-Cl reduces CO2 in its Co(I) state. A reductive cleavage leads to a dangling protonated pyridine arm which enables facile CO2 binding through a H-bond donation and facilitates the C-O bond cleavage via a directed protonation. A systematic benchmarking of this catalyst indicates that it has a modest overpotential (∼180 mV) and a TOF of ∼20 s-1 for selective reduction of CO2 to CO with H2O as a proton source.Some parasites are expected to have beneficial impacts on wild populations in polluted environments, because of their bioaccumulation potential of pollutants from their hosts. The fate of organic micropollutants in host-parasite systems and the combined effect of parasitism and pollution were investigated in chub Squalius cephalus, a freshwater fish, infected (n = 73) or uninfected (n = 45) by acanthocephalan parasites Pomphorhynchus sp. from differently-contaminated riverine sites. Several ubiquitous pollutants (polychlorinated biphenyls PCBs, organochlorine pesticides OCPs, polybrominated diphenyl-ethers PBDEs, polycyclic aromatic hydrocarbons PAHs, phthalates, insecticides pyrethroids and N,N-diethyl-meta-toluamide DEET) and some of their metabolites were characterized for the first time in the parasites and various fish matrices (muscle, liver and stomach content). Most organic pollutants reached higher levels in parasites than in chub matrices. In contrast, metabolite levels were lower in parasite tissues compared to fish matrices. Infected and uninfected chub exhibited no significant differences in their pollutant load. read more Body condition, organo-somatic indices and immunity were not affected by parasitism and few correlations were found with chemical pollution. Interestingly, infected chub exhibited lower oxidative damage compared to uninfected fish, irrespective of their pollutant load. In light of these results, this correlative study supports the hypothesis that acanthocephalan parasites could bring benefits to their hosts to cope with organic pollution.