Development of Phosphodiesterase–Protein-Kinase Complexes as Novel Targets for Discovery of Inhibitors with Enhanced Specificity

Phosphodiesterases (PDEs) hydrolyze cyclic nucleotides to modulate multiple signaling events in cells. PDEs are recognized to actively associate with cyclic nucleotide receptors (protein kinases, PKs) in larger macromolecular assemblies referred to as signalosomes. Complexation of PDEs with PKs generates an expanded active site that enhances PDE activity. This facilitates signalosome-associated PDEs to preferentially catalyze active hydrolysis of cyclic nucleotides bound to PKs and aid in signal termination. PDEs are important drug targets, and current strategies for inhibitor discovery are based entirely on targeting conserved PDE catalytic domains. This often results in inhibitors with cross-reactivity amongst closely related PDEs and attendant unwanted side effects. Here, our approach targeted PDE–PK complexes as they would occur in signalosomes, thereby offering greater specificity. Our developed fluorescence polarization assay was adapted to identify inhibitors that block cyclic nucleotide pockets in PDE–PK complexes in one mode and disrupt protein-protein interactions between PDEs and PKs in a second mode. We tested this approach with three different systems—cAMP-specific PDE8–PKAR, cGMP-specific PDE5–PKG, and dual-specificity RegA–R_D complexes—and ranked inhibitors according to their inhibition potency. Targeting PDE–PK complexes offers biochemical tools for describing the exquisite specificity of cyclic nucleotide signaling networks in cells.     

One-dimensional ZnO nanorods doped with neodymium for enhanced resorcinol degradation under sunlight irradiation

Neodymium-doped ZnO nanorods (Nd/ZNRs) were prepared for the first time by a simple and surfactant-free solvothermal route. The synthesized samples were characterized using different instrumental techniques. The photoluminescence results showed that the Nd(2.0 at%)/ZNRs exhibited the highest separation rate of charge carriers and the highest formation rate of hydroxyl radicals. The photocatalytic activities of synthesized samples were investigated toward the degradation of endocrine disrupting chemical resorcinol under natural sunlight irradiation. Among the samples, Nd(2.0 at%)/ZNRs showed considerable improvement in the photocatalytic activity for the resorcinol degradation as compared to ZNRs and commercial TiO₂. Kinetic studies revealed that the photocatalytic degradation of resorcinol obeyed pseudo-first-order kinetic. The high-performance liquid chromatography and total organic carbon analyses also demonstrated the progressive mineralization of resorcinol into carbon dioxide and water. Furthermore, high stability of Nd/ZNRs in photocatalytic reaction also demonstrated promising potential toward practical applications in purifying environmental pollutants.     

Photoactive Earth‐Abundant Iron Pyrite Catalysts for Electrocatalytic Nitrogen Reduction Reaction

The generation of ammonia, hydrogen production, and nitrogen purification are considered as energy intensive processes accompanied with large amounts of CO₂ emission. An electrochemical method assisted by photoenergy is widely utilized for the chemical energy conversion. In this work, earth‐abundant iron pyrite (FeS₂) nanocrystals grown on carbon fiber paper (FeS₂/CFP) are found to be an electrochemical and photoactive catalyst for nitrogen reduction reaction under ambient temperature and pressure. The electrochemical results reveal that FeS₂/CFP achieves a high Faradaic efficiency (FE) of ≈14.14% and NH₃ yield rate of ≈0.096 µg min^?1 at ?0.6 V versus RHE electrode in 0.25 m LiClO₄. During the electrochemical catalytic reaction, the crystal structure of FeS₂/CFP remains in the cubic pyrite phase, as analyzed by in situ X‐ray diffraction measurements. With near‐infrared laser irradiation (808 nm), the NH₃ yield rate of the FeS₂/CFP catalyst can be slightly improved to 0.1 µg min^?1 with high FE of 14.57%. Furthermore, density functional theory calculations demonstrate that the N₂ molecule has strong chemical adsorption energy on the iron atom of FeS₂. Overall, iron pyrite‐based materials have proven to be a potential electrocatalyst with photoactive behavior for ammonia production in practical applications.     

Allele-Specific Inhibition of Histone Demethylases

Histone demethylases play a critical role in mammalian gene expression by removing methyl groups from lysine residues in degree- and site-specific manner. To specifically interrogate members and isoforms of this class of enzymes, we have developed demethylase variants with an expanded active site. The mutant enzymes are capable of performing lysine demethylation with wild-type proficiency, but are sensitive to inhibition by cofactor-competitive molecules embellished with a complementary steric “bump”. The selected inhibitors show more than 20-fold selectivity over the wild-type demethylase, thus overcoming issues typical to pharmacological and genetic approaches. The mutant–inhibitor pairs are shown to act on a physiologically relevant full-length substrate. By engineering a conserved amino acid to achieve member-specific perturbation, this study provides a general approach for studying histone demethylases in diverse cellular processes.     

Potential influence of microorganisms on the corrosion of carbon steel in the French high- and intermediate-level long-lived radioactive waste disposal context

In the context of the high-level radioactive waste disposal CIGEO, the corrosion rate due to microbially influenced corrosion (MIC) has to be evaluated. In France, it is envisaged to dispose of high- and intermediate-level long-lived radioactive waste at a depth of 500 m in a deep geological disposal, drilled in the Callovo-Oxfordian claystone (Cox) formation. To do so, a carbon steel casing will be inserted inside disposal cells, which are horizontal tunnels drilled in the Cox. A specific cement grout will be injected between the carbon steel casing and the claystone. A study was conducted to evaluate the possibility of MIC on carbon steel in the foreseeable high radioactive waste disposal. The corrosiveness of various environments was investigated at 50°C and 80°C with or without microorganisms enriched from samples of Andra's underground research laboratory. The monitoring of corrosion during the experiments was ensured using gravimetric method and real-time corrosion monitoring using sensors based on the measurements of the electrical resistance. The corrosion data were completed with microbiological analyses including cultural and molecular characterizations.     

Suppressing Ion Migration across Perovskite Grain Boundaries by Polymer Additives

Passivation of organometal halide perovskites with polar molecules has been recently demonstrated to improve the photovoltaic device efficiency and stability. However, the mechanism is still elusive. Here, it is found that both polymers with large and small dipole moment of 3.7 D and 0.6 D have negligible defect passivation effect on the MAPbI₃ perovskite films as evidenced by photothermal deflection spectroscopy. The photovoltaic devices with and without the polymer additives also have comparable power conversion efficiencies around 19%. However, devices with the additives have noticeable improvement in stability under continuous light irradiation. It is found that although the initial mobile ion concentrations are comparable in both devices with and without the additives, the additives can strongly suppress the ion migration during the device operation. This contributes to the significantly enhanced electrical-field stress tolerance of the perovskite solar cells (PVSCs). The PVSCs with polymer additives can operate up to −2 V reverse voltage bias which is much larger than the breakdown voltage of −0.5 V that has been commonly observed. This study provides insight into the role of additives in perovskites and the corresponding device degradation mechanism.     

Effect of processing method on vitamin profile,antioxidant properties and total phenolic content of coconut (Cocos nucifera L.) sugar syrup

Coconut (Cocos nucifera L.) sugar is a more nutritious alternative sugar source as compared to sugar palm (Borassus flabellifer) and sugarcane (Saccharum officinarum L.). This work was aimed to investigate the browning index (BI), vitamin profile and antioxidant properties of coconut sap sugar syrups, which were produced by different processing methods: rotary evaporation (RE), microwave evaporation (ME) and open-heat evaporation (OHE). The results obtained showed that coconut sugar syrup produced by RE-60 contained high antioxidant activities [DPPH (36.71%) and ABTS (34.84%), TPC (299.87 mg per 100 g sample) and FRAP (3.74 mm )]. These values were slightly lower than those of ME and OHE. Coconut sugar syrup (RE-60) also contained higher amounts of vitamin C (1587.27 mg L⁻¹), vitamin B1 (97.44 mg L⁻¹) and vitamin B3 (19.84 mg L⁻¹) compared with those of ME and OHE coconut sugar syrups. RE-60 was the best method to produce coconut sugar syrup in a shorter time with lower browning index and higher vitamin contents.     

Filling the gap between Continuous and Discrete Time Dynamics of Autoregressive Processes

Discretization of continuous time autoregressive (AR) processes driven by a Brownian motion and embedding of discrete time AR sequences driven by a Gaussian white noise are classical issues. The article aims at establishing and using such discretization and embedding formulae between extended AR continuous time processes and discrete time sequences. The continuous-time processes are driven by either Brownian or jump processes, and may have random coefficients depending on time; Lévy-driven processes are also considered. The innovation of the discrete time processes may be of many types – including Gaussian. In one way, observing the continuous time AR process at discrete times leads the AR dynamics of the discretized process to be characterized. The other way round, AR sequences can be embedded, in the almost sure sense, into continuous time AR processes with the same dynamics. Illustration is provided through many examples and simulation.     

Comparative analysis of formaldehyde and toluene sorption on indoor floorings and consequence on Indoor Air Quality

Indoor surfaces may be adsorptive sinks with the potential to change Indoor Air Quality. To estimate this effect, the sorption parameters of formaldehyde and toluene were assessed on five floorings by an experimental method using solid-phase microextraction in an airtight emission cell. Adsorption rate constants ranged from 0.003 to 0.075 m·h⁻¹, desorption rate constants from 0.019 to 0.51 h⁻¹, and the partition coefficient from 0.005 to 3.9 m, and these parameters vary greatly from one volatile organic compound/material couple to another indicating contrasted sorption behaviors. A rubber was identified as a sink of formaldehyde characterized by a very low desorption constant close to 0. For these sorbent floorings identified, the adsorption rates of formaldehyde are from 2 to 4 times higher than those of toluene. Two models were used to evaluate the sink effects of floorings on indoor pollutant concentrations in one room from different realistic conditions. The scenarios tested came to the conclusion that the formaldehyde sorption on one rubber (identified as a sink) has a maximum contribution from 15% to 21% for the conditions of low air exchange rate. For other floorings, the sorption has a minor contribution less than or equal to 5%, regardless of the air exchange rate.     

Broad learning for nonparametric spatial modeling with application to seismic attenuation

Spatial modeling is a core element in geographical information science. It incorporates geographic information to construct the relationship for interpreting the behavior of spatial phenomena. In this paper, a broad learning framework for nonparametric spatial modeling is presented. Broad learning overcomes the obstacle of expensive computational consumption in deep learning and provides a powerful computationally efficient alternative. In contrast to the deep learning architecture that is configured with stacks of hierarchical layers, broad learning networks are established in a flat manner that can be flexibly reconfigured with the inherited information from the trained network. To develop the broad learning network, a simple prototype network is established as the initial trial and it is modified incrementally to enhance its data fitting capacity. Consequently, complex relationship of unstructured spatial data can be modeled efficiently. To demonstrate the efficacy and applicability of the broad learning framework, we will present a simulated example and a real application using the strong ground motion records on the 2008 great Wenchuan earthquake.