Crystal structure and Hirshfeld surface analysis of (S)-N-methyl-1-phenylethan-1-aminium chloride

The title compound C9H14N+·Cl−, (1), can be synthesized starting from (S)-N-methyl-1-phenylethan-1-amine (2). Compound 2 upon addition of HCl·Et2O leads to crystallization of compound 1 as colorless blocks. The configuration of compound 1 is stable as well as preserved in space group P212121. Ammonium chlorides, like the title compound, are often observed as undesirable by-products in aminosilylation of chlorosilanes. Additionally, these by-products are usually soluble in selected organic solvents, which require difficult separation steps. Therefore, detailed studies on structural features and intermolecular interactions performed by Hirshfeld atom refinement (HAR) using NoSpherA2 [Kleemiss et al. (2021). Chem. Sci. 12, 1675–1692] and Hirshfeld surface analysis were used to address structural issues on that separation problem.

Rolling Circle RNA Synthesis Catalysed by RNA

RNA-catalysed RNA replication is widely considered a key step in the emergence of life's first genetic system. However, RNA replication can be impeded by the extraordinary stability of duplex RNA products, which must be dissociated for re-initiation of the next replication cycle. Here we have explored rolling circle synthesis (RCS) as a potential solution to this strand separation problem. RCS on small circular RNAs - as indicated by molecular dynamics simulations - induces a progressive build-up of conformational strain with destabilisation of nascent strand 5′ and 3′ ends. At the same time, we observe sustained RCS by a triplet polymerase ribozyme on small circular RNAs over multiple orbits with strand displacement yielding concatemeric RNA products. Furthermore, we show RCS of a circular Hammerhead ribozyme capable of self-cleavage and re-circularisation. Thus, all steps of a viroid-like RNA replication pathway can be catalysed by RNA alone. Our results have implications for the emergence of RNA replication and for understanding the potential of RNA to support complex genetic processes.

Optimizing over the Closure of Rank Inequalities with a Small Right-Hand Side for the Maximum Stable Set Problem via Bilevel Programming

In the context of the maximum stable set problem, rank inequalities impose that the cardinality of any set of vertices contained in a stable set be, at most, as large as the stability number of the subgraph induced by such a set. Rank inequalities are very general, as they subsume many classical inequalities such as clique, hole, antihole, web, and antiweb inequalities. In spite of their generality, the exact separation of rank inequalities has never been addressed without the introduction of topological restrictions on the induced subgraph and the tightness of their closure has never been investigated systematically. In this work, we propose a methodology for optimizing over the closure of all rank inequalities with a right-hand side no larger than a small constant without imposing any restrictions on the topology of the induced subgraph. Our method relies on the exact separation of a relaxation of rank inequalities, which we call relaxed k-rank inequalities, whose closure is as tight. We investigate the corresponding separation problem, a bilevel programming problem asking for a subgraph of maximum weight with a bound on its stability number, whose study could be of independent interest. We first prove that the problem is [Formula: see text]-hard and provide some insights on its polyhedral structure. We then propose two exact methods for its solution: a branch-and-cut algorithm (which relies on a family of faced-defining inequalities which we introduce in this paper) and a purely combinatorial branch-and-bound algorithm. Our computational results show that the closure of rank inequalities with a right-hand side no larger than a small constant can yield a bound that is stronger, in some cases, than Lovász’s Theta function, and substantially stronger than bounds obtained with standard inequalities that are valid for the stable set problem, including odd-cycle inequalities and wheel inequalities. Summary of Contribution: This paper proposes two original methods for solving a challenging cut-separation problem (of bilevel type) for a large class of inequalities valid for one of the key operations research problems, namely, the max stable set problem. An extensive set of experimental results validates the proposed methods. All the source code and data sets are available online on GitHub.

A New Criterion for Bounded Component Analysis

<div>In this paper we present a new criterion for bounded component analysis, a quite new approach for the Blind Source Separation problem. For the determined case, we show that the `1-norm of the estimated sources can be used as a contrast for the problem. We present a blind algorithm for the source separation of independents sources or mixtures of correlated sources by only a rotation matrix. We also present a variety of simulations assessing the performance of the proposed approach.</div>

A New Criterion for Bounded Component Analysis

<div>In this paper we present a new criterion for bounded component analysis, a quite new approach for the Blind Source Separation problem. For the determined case, we show that the `1-norm of the estimated sources can be used as a contrast for the problem. We present a blind algorithm for the source separation of independents sources or mixtures of correlated sources by only a rotation matrix. We also present a variety of simulations assessing the performance of the proposed approach.</div>

Unchaining mini Bacillus PG10: relief of FlgM-mediated repression of autolysin genes

Cell chaining in Bacillus subtilis is naturally observed in a subset of cells during exponential growth and during biofilm formation. However, the recently constructed large-scale genome-minimized B. subtilis strain PG10 displays a severe and permanent defect in cell separation, as it exclusively grows in the form of long filaments of non-separated cells. In this study, we investigated the underlying mechanisms responsible for the incomplete cell division of PG10 by genomic and transcriptomic analyses. Repression of the SigD-regulon, including the major autolysin lytF , was identified as the cause for the cell separation problem of PG10. It appeared that SigD-regulated genes are downregulated in PG10 due to the absence of the flagellar export apparatus, which normally is responsible for secretion of FlgM, the anti-sigma factor of SigD. Although mild negative effects on growth and cell morphology were observed, deletion of flgM could revert the aberrant cell chaining phenotype and increased the transformation efficiency. Interestingly, our work also demonstrates the occurrence of increased antisense transcription of slrR , a transcriptional repressor of autolysin genes, in PG10, and provides further understanding for this observation. In addition to revealing the molecular basis of the cell separation defect in PG10, our work provides novel targets for subsequent genome reduction efforts and future directions for further optimization of mini Bacillus PG10. IMPORTANCE Reduction of the size of bacterial genomes is relevant for understanding the minimal requirements for cellular life as well as from a biotechnological point of view. Although the genome-minimized Bacillus subtilis strain PG10 displays several beneficial traits as a microbial cell factory compared to its parental strain, a defect at the final stage of cell division was introduced during the genome reduction process. By genetic and transcriptomic analyses, we identified the underlying reasons for the cell separation problem of PG10. In addition to enabling PG10 to grow in a similar way as B. subtilis wild type strains, our work points towards subsequent targets for fine-tuning and further reduction of the genome of PG10. Moreover, solving the cell separation defect facilitates laboratory handling of PG10 by increasing the transformation efficiency amongst others. Overall, our work contributes to understanding and improving biotechnologically attractive minimal bacterial cell factories.

Removing residual airborne sensor motion for measuring seismic signals from a drone

Acquiring seismic data using drones requires excellent knowledge of the drone’s motion since positional measurements made from an airborne sensor represent a combination of sensor and ground motion. Recent advancements in laser Doppler vibrometry and repeat lidar surveys show that the frequency and resolution of non-contact motion measurements is increasing to the point necessary for measuring seismic signals. We explore the conditions under which separation of sensor motion from ground motion can be accomplished in practice. We assume (i) that the translation and rotation of a stabilized airborne sensor follows an analytic form in time that is either known or can be estimated from the sensor’s measurements, (ii) that the seismic signal we observe has compact support contained within the measurement window, and (iii) that the ground motion can be described by a rigid translation. We analyze the effectiveness of our signal separation problem as a function of peak signal, sensor noise level, sensor rotation angle, and sensor point sampling density by defining a boundary where SNR = 0 dB for various combinations of these parameters. We find that under the set of assumptions, lower rotation angles, lower sensor noise, and denser point samplings on the ground provide better signal separation using our method.

Under-Determined Blind Source Separation

The Under-determined Blind Source Separation problem aims at estimating N source signals, with only a given set of M known mixtures, where M < N. The problem is solved by a two-stage approach. The rst stage is the estimation of the unknown mixing matrix. The contributions made unravel a more precise and accurate tool which directly relates to the initialization of the clustering algorithm. Di erent schemes such as segmentation, correlation and least square curve tting are used to take advantage of the sparsity of the sources. A signi cant addition involves applying linear transforms to produce a higher sparse domain. Further, the second stage is the sparse source recovery using a Matching Pursuit algorithm. The contributions involve a Matching Pursuit algorithm with di

Under-Determined Blind Source Separation

The Under-determined Blind Source Separation problem aims at estimating N source signals, with only a given set of M known mixtures, where M < N. The problem is solved by a two-stage approach. The rst stage is the estimation of the unknown mixing matrix. The contributions made unravel a more precise and accurate tool which directly relates to the initialization of the clustering algorithm. Di erent schemes such as segmentation, correlation and least square curve tting are used to take advantage of the sparsity of the sources. A signi cant addition involves applying linear transforms to produce a higher sparse domain. Further, the second stage is the sparse source recovery using a Matching Pursuit algorithm. The contributions involve a Matching Pursuit algorithm with di