Integrated CO2 capture and electrochemical upgradation: the underpinning mechanism and techno-chemical analysis.

Coupling post-combustion CO2 capture with electrochemical utilization (CCU) is a quantum leap in renewable energy science since it eliminates the cost and energy involved in the transport and storage of CO2. However, the major challenges involved in industrial scale implementation are selecting an appropriate solvent/electrolyte for CO2 capture, modeling an appropriate infrastructure by coupling an electrolyser with a CO2 point source and a separator to isolate CO2 reduction reaction (CO2RR) products, and finally selection of an appropriate electrocatalyst. In this review, we highlight the major difficulties with detailed mechanistic interpretation in each step, to find out the underpinning mechanism involved in the integration of electrochemical CCU to achieve higher-value products. In the past decades, most of the studies dealt with individual parts of the integration process, i.e., either selecting a solvent for CO2 capture, designing an electrocatalyst, or choosing an ideal electrolyte. In this context, it is important to note that solvents such as monoethanolamine, bicarbonate, and ionic liquids are often used as electrolytes in CO2 capture media. Therefore, it is essential to fabricate a cost-effective electrolyser that should function as a reversible binder with CO2 and an electron pool capable of recovering the solvent to electrolyte reversibly. For example, reversible ionic liquids, which are non-ionic in their normal forms, but produce ionic forms after CO2 capture, can be further reverted back to their original non-ionic forms after CO2 release with almost 100% efficiency through the chemical or thermal modulations. This review also sheds light on a focused techno-economic evolution for converting the electrochemically integrated CCU process from a pilot-scale project to industrial-scale implementation. In brief, this review article will summarize a state-of-the-art argumentation of challenges and outcomes over the different segments involved in electrochemically integrated CCU to stimulate urgent progress in the field.

Resultant inward imbalanced seeding force (RIISF)-induced concave gold nanostar (CAuNS) for non-enzymatic electrocatalytic detection of serotonin and Kynurenine in human serum.

CTAC-based gold nanoseed-induced concave curvature evolution of surface boundary planes from concave gold nanocube (CAuNC) to concave gold nanostar (CAuNS) has been achieved by a novel synthetic methodology simply by controlling the extent of seed used and hence the generated 'Resultant Inward Imbalanced Seeding Force (RIISF)'. The resultant CAuNS shows an excellent enhancement in catalytic activity compared to CAuNC and other intermediates as a function of curvature-induced anisotropy. Detailed characterization evaluates the presence of an enhanced number of multiple defect sites, high energy facets, larger surface area, and roughened surface which ultimately results in an increased mechanical strain, coordinately unsaturation, and multifacet-oriented anisotropic behavior suitable for positive influence on the binding affinity of CAuNSs. While different crystalline and structural parameters improve their catalytic activity, the resultant uniform three-dimensional (3D) platform shows comparatively easy pliability and well absorptivity on the glassy carbon electrode surface for increased shelf life, a uniform structure to confine a large extent of stoichiometric systems, and long-term stability under ambient conditions for making this newly developed material a unique nonenzymatic scalable universal electrocatalytic platform. With the help of various electrochemical measurements, the ability of the platform has been established by performing highly specific and sensitive detection of the two most important human bio messengers: Serotonin (STN) and Kynurenine (KYN) which are metabolites of L-Tryptophan in the human body system. The present study mechanistically surveys the role of seed-induced RIISF-modulated anisotropy in controlling the catalytic activity which offers a universal 3D electrocatalytic sensing tenet by an electrocatalytic approach.

A high-quality Ixodes scapularis genome advances tick science.

Ixodes spp. and related ticks transmit prevalent infections, although knowledge of their biology and development of anti-tick measures have been hindered by the lack of a high-quality genome. In the present study, we present the assembly of a 2.23-Gb Ixodes scapularis genome by sequencing two haplotypes within one individual, complemented by chromosome-level scaffolding and full-length RNA isoform sequencing, yielding a fully reannotated genome featuring thousands of new protein-coding genes and various RNA species. Analyses of the repetitive DNA identified transposable elements, whereas the examination of tick-associated bacterial sequences yielded an improved Rickettsia buchneri genome. We demonstrate how the Ixodes genome advances tick science by contributing to new annotations, gene models and epigenetic functions, expansion of gene families, development of in-depth proteome catalogs and deciphering of genetic variations in wild ticks. Overall, we report critical genetic resources and biological insights impacting our understanding of tick biology and future interventions against tick-transmitted infections.

Epigenetic Regulation by Polycomb Complexes from Drosophila to Human and Its Relation to Communicable Disease Pathogenesis.

Although all cells in the human body are made of the same DNA, these cells undergo differentiation and behave differently during development, through integration of external and internal stimuli via 'specific mechanisms.' Epigenetics is one such mechanism that comprises DNA/RNA, histone modifications, and non-coding RNAs that regulate transcription without changing the genetic code. The discovery of the first Polycomb mutant phenotype in Drosophila started the study of epigenetics more than 80 years ago. Since then, a considerable number of Polycomb Group (PcG) genes in Drosophila have been discovered to be preserved in mammals, including humans. PcG proteins exert their influence through gene repression by acting in complexes, modifying histones, and compacting the chromatin within the nucleus. In this article, we discuss how our knowledge of the PcG repression mechanism in Drosophila translates to human communicable disease research.

Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence.

The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small in comparison to the number of possible materials. Here, we show how the subgroup-discovery (SGD) artificial-intelligence approach can be applied to an experimental plus theoretical data set to identify constraints on key physicochemical parameters, the so-called SG rules, which exclusively describe materials and reaction conditions with outstanding catalytic performance. By using high-throughput experimentation, 120 SiO2-supported catalysts containing ruthenium, tungsten, and phosphorus were synthesized and tested in the catalytic oxidation of propylene. As candidate descriptive parameters, the temperature and 10 parameters related to the composition and chemical nature of the catalyst materials, derived from calculated free-atom properties, were offered. The temperature, the phosphorus content, and the composition-weighted electronegativity are identified as key parameters describing high yields toward the value-added oxygenate products acrolein and acrylic acid. The SG rules not only reflect the underlying processes particularly associated with high performance but also guide the design of more complex catalysts containing up to five elements in their composition.

Genome-wide chromosomal association of Upf1 is linked to Pol II transcription in Schizosaccharomyces pombe.

Although the RNA helicase Upf1 has hitherto been examined mostly in relation to its cytoplasmic role in nonsense mediated mRNA decay (NMD), here we report high-throughput ChIP data indicating genome-wide association of Upf1 with active genes in Schizosaccharomyces pombe. This association is RNase sensitive, correlates with Pol II transcription and mRNA expression levels. Changes in Pol II occupancy were detected in a Upf1 deficient (upf1Δ) strain, prevalently at genes showing a high Upf1 relative to Pol II association in wild-type. Additionally, an increased Ser2 Pol II signal was detected at all highly transcribed genes examined by ChIP-qPCR. Furthermore, upf1Δ cells are hypersensitive to the transcription elongation inhibitor 6-azauracil. A significant proportion of the genes associated with Upf1 in wild-type conditions are also mis-regulated in upf1Δ. These data envisage that by operating on the nascent transcript, Upf1 might influence Pol II phosphorylation and transcription.

Epigenetic Regulation of Tick Biology and Vectorial Capacity.

Ticks exist across diverse environments and transmit numerous pathogens. Due to their long and unique life cycles, these arthropods likely evolved robust epigenetic mechanisms that provide sustainable responses and buffers against extreme environmental conditions. Herein, we highlight how the study of the epigenetic basis of tick biology and vectorial capacity will enrich our knowledge of tick-borne infections.

Defining the Boundaries of Polycomb Domains in Drosophila.

Polycomb group (PcG) proteins are an important group of transcriptional repressors that act by modifying chromatin. PcG target genes are covered by the repressive chromatin mark H3K27me3. Polycomb repressive complex 2 (PRC2) is a multiprotein complex that is responsible for generating H3K27me3. In Drosophila, PRC2 is recruited by Polycomb Response Elements (PREs) and then trimethylates flanking nucleosomes, spreading the H3K27me3 mark over large regions of the genome, the "Polycomb domains." What defines the boundary of a Polycomb domain? There is experimental evidence that insulators, PolII, and active transcription can all form the boundaries of Polycomb domains. Here we divide the boundaries of larval Polycomb domains into six different categories. In one category, genes are transcribed toward the Polycomb domain, where active transcription is thought to stop the spreading of H3K27me3. In agreement with this, we show that introducing a transcriptional terminator into such a transcription unit causes an extension of the Polycomb domain. Additional data suggest that active transcription of a boundary gene may restrict the range of enhancer activity of a Polycomb-regulated gene.

Dynamic Competition of Polycomb and Trithorax in Transcriptional Programming.

Predicting regulatory potential from primary DNA sequences or transcription factor binding patterns is not possible. However, the annotation of the genome by chromatin proteins, histone modifications, and differential compaction is largely sufficient to reveal the locations of genes and their differential activity states. The Polycomb Group (PcG) and Trithorax Group (TrxG) proteins are the central players in this cell type-specific chromatin organization. PcG function was originally viewed as being solely repressive and irreversible, as observed at the homeotic loci in flies and mammals. However, it is now clear that modular and reversible PcG function is essential at most developmental genes. Focusing mainly on recent advances, we review evidence for how PcG and TrxG patterns change dynamically during cell type transitions. The ability to implement cell type-specific transcriptional programming with exquisite fidelity is essential for normal development.

Machine Learning-Guided Approach for Studying Solvation Environments.

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1-, and 2-, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.

The RNA helicase UPF1 associates with mRNAs co-transcriptionally and is required for the release of mRNAs from gene loci.

UPF1 is an RNA helicase that is required for nonsense-mediated mRNA decay (NMD) in eukaryotes, and the predominant view is that UPF1 mainly operates on the 3'UTRs of mRNAs that are directed for NMD in the cytoplasm. Here we offer evidence, obtained from Drosophila, that UPF1 constantly moves between the nucleus and cytoplasm by a mechanism that requires its RNA helicase activity. UPF1 is associated, genome-wide, with nascent RNAs at most of the active Pol II transcription sites and at some Pol III-transcribed genes, as demonstrated microscopically on the polytene chromosomes of salivary glands and by ChIP-seq analysis in S2 cells. Intron recognition seems to interfere with association and translocation of UPF1 on nascent pre-mRNAs, and cells depleted of UPF1 show defects in the release of mRNAs from transcription sites and their export from the nucleus.

Structure and function of an ectopic Polycomb chromatin domain.

Polycomb group proteins (PcGs) drive target gene repression and form large chromatin domains. In Drosophila, DNA elements known as Polycomb group response elements (PREs) recruit PcGs to the DNA. We have shown that, within the invected-engrailed (inv-en) Polycomb domain, strong, constitutive PREs are dispensable for Polycomb domain structure and function. We suggest that the endogenous chromosomal location imparts stability to this Polycomb domain. To test this possibility, a 79-kb en transgene was inserted into other chromosomal locations. This transgene is functional and forms a Polycomb domain. The spreading of the H3K27me3 repressive mark, characteristic of PcG domains, varies depending on the chromatin context of the transgene. Unlike at the endogenous locus, deletion of the strong, constitutive PREs from the transgene leads to both loss- and gain-of function phenotypes, demonstrating the important role of these regulatory elements. Our data show that chromatin context plays an important role in Polycomb domain structure and function.

Chemical shifts in molecular solids by machine learning.

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which  comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid.

Correction: Crystal-defect-induced facet-dependent electrocatalytic activity of 3D gold nanoflowers for the selective nanomolar detection of ascorbic acid.

Correction for 'Crystal-defect-induced facet-dependent electrocatalytic activity of 3D gold nanoflowers for the selective nanomolar detection of ascorbic acid' by Sandip Kumar De, et al., Nanoscale, 2018, 10, 11091-11102.

Crystal-defect-induced facet-dependent electrocatalytic activity of 3D gold nanoflowers for the selective nanomolar detection of ascorbic acid.

Understanding and exploring the decisive factors responsible for superlative catalytic efficiency is necessary to formulate active electrode materials for improved electrocatalysis and high-throughput sensing. This research demonstrates the ability of bud-shaped gold nanoflowers (AuNFs), intermediates in the bud-to-blossom gold nanoflower synthesis, to offer remarkable electrocatalytic efficiency in the oxidation of ascorbic acid (AA) at nanomolar concentrations. Multicomponent sensing in a single potential sweep is measured using differential pulse voltammetry while the kinetic parameters are estimated using electrochemical impedance spectroscopy. The outstanding catalytic activity of bud-structured AuNF [iAuNFp(Bud)/iGCp ≅ 100] compared with other bud-to-blossom intermediate nanostructures is explained by studying their structural transitions, charge distributions, crystalline patterns, and intrinsic irregularities/defects. Detailed microscopic analysis shows that density of crystal defects, such as edges, terraces, steps, ledges, kinks, and dislocation, plays a major role in producing the high catalytic efficiency. An associated ab initio simulation provides necessary support for the projected role of different crystal facets as selective catalytic sites. Density functional theory corroborates the appearance of inter- and intra-molecular hydrogen bonding within AA molecules to control the resultant fingerprint peak potentials at variable concentrations. Bud-structured AuNF facilitates AA detection at nanomolar levels in a multicomponent pathological sample.

Machine learning for the structure-energy-property landscapes of molecular crystals.

Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol-1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure-property relations in molecular crystal engineering.

Machine learning unifies the modeling of materials and molecules.

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.

Mapping and classifying molecules from a high-throughput structural database.

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational searches, as well as the agglomeration of data of heterogeneous provenance leads to considerable challenges when it comes to navigating the database, representing its structure at a glance, understanding structure-property relations, eliminating duplicates and identifying inconsistencies. Here we present a case study, based on a data set of conformers of amino acids and dipeptides, of how machine-learning techniques can help addressing these issues. We will exploit a recently-developed strategy to define a metric between structures, and use it as the basis of both clustering and dimensionality reduction techniques-showing how these can help reveal structure-property relations, identify outliers and inconsistent structures, and rationalise how perturbations (e.g. binding of ions to the molecule) affect the stability of different conformers.

Formation of a Polycomb-Domain in the Absence of Strong Polycomb Response Elements.

Polycomb group response elements (PREs) in Drosophila are DNA-elements that recruit Polycomb proteins (PcG) to chromatin and regulate gene expression. PREs are easily recognizable in the Drosophila genome as strong peaks of PcG-protein binding over discrete DNA fragments; many small but statistically significant PcG peaks are also observed in PcG domains. Surprisingly, in vivo deletion of the four characterized strong PREs from the PcG regulated invected-engrailed (inv-en) gene complex did not disrupt the formation of the H3K27me3 domain and did not affect inv-en expression in embryos or larvae suggesting the presence of redundant PcG recruitment mechanism. Further, the 3D-structure of the inv-en domain was only minimally altered by the deletion of the strong PREs. A reporter construct containing a 7.5kb en fragment that contains three weak peaks but no large PcG peaks forms an H3K27me3 domain and is PcG-regulated. Our data suggests a model for the recruitment of PcG-complexes to Drosophila genes via interactions with multiple, weak PREs spread throughout an H3K27me3 domain.