Scalable and selective deuteration of (hetero)arenes

Isotope labelling, particularly deuteration, is an important tool for the development of new drugs, specifically for identification and quantification of metabolites. For this purpose, many efficient methodologies have been developed that allow for the small-scale synthesis of selectively deuterated compounds. Due to the development of deuterated compounds as active drug ingredients, there is a growing interest in scalable methods for deuteration. The development of methodologies for large-scale deuterium labelling in industrial settings requires technologies that are reliable, robust and scalable. Here we show that a nanostructured iron catalyst, prepared by combining cellulose with abundant iron salts, permits the selective deuteration of (hetero)arenes including anilines, phenols, indoles and other heterocycles, using inexpensive D2O under hydrogen pressure. This methodology represents an easily scalable deuteration (demonstrated by the synthesis of deuterium-containing products on the kilogram scale) and the air- and water-stable catalyst enables efficient labelling in a straightforward manner with high quality control.

Umpolung of Indoles: Triflic Acid-Mediated C3-Regioselective Hy-droarylation of N-H Indoles

The direct dearomative addition of arenes to the C3-position of unprotected indoles is reported under operationally simple conditions with triflic acid at room temperature. The present regioselective hydroarylation is a straightforward manner to gen-erate an electrophilic indole at the C3-position without the need to introduce a deactivating acetyl group on the indolic nitrogen as in previously reported strategies. This atom economy method delivers biologically relevant 3-aryl indolines and 3,3-spiroindolines in high yields and regioselectivities from both intra and intermolecular processes.

One-step pipetting of barcoded planar microparticles into compact monolayer assembling chip for efficient readout of multiplexed immunoassay

Barcoded planar microparticles have many qualities suitable for developing cost-efficient multiplexed immunoassays. But at the translational research level, there are a number of technical aspects yet remain to be addressed which includes robustness and efficiency of the assay readout process. Assay readout process involves automated barcode identification and signal intensity values from each planar microparticle. For this, each microparticle has to be correctly aligned for correct barcode readout while being, ideally, compactly assembled for maximum microparticle imaging efficiency. To simultaneously achieve such alignment and assembly of microparticles but in a straightforward manner, we designed a microfluidic microparticle assembling chip that only requires a single pipetting step. Our design utilizes capillary flow based guided particle assembly, which allows maximum microparticle-based immunoassay readout efficiency. With the aid of image processing algorithms, we obtained good multiplex immunoassay readout accuracy similar to conventional imaging platforms. Our approach is applicable to both soft elastomer materials (e.g. PDMS) and rigid materials (e.g. polystyrene), the latter of which is frequently used for injection molding based mass production. We anticipate our device could help developing facile and user-friendly platform technologies based on barcoded planar microparticles.

Time-independent perturbation theory with Lagrange multipliers

We derive the formulas for the energy and wavefunction of the time-independent Schrödinger equation with perturbation in a compact form. Unlike the conventional approaches based on Rayleigh–Schrödinger or Brillouin–Wigner perturbation theories, we employ a recently developed approach of matrix-valued Lagrange multipliers that regularizes an eigenproblem. The Lagrange-multiplier regularization makes the characteristic matrix for an eigenproblem invertible. After applying the constraint equation to recover the original equation, we find the solutions of the energy and wavefunction consistent with the conventional approaches. This formalism does not rely on an iterative way and the order-by-order corrections are easily obtained by taking the Taylor expansion. The Lagrange-multiplier regularization formalism for perturbation theory presented in this paper is completely new and can be extended to the degenerate perturbation theory in a straightforward manner. We expect that this new formalism is also pedagogically useful to give insights on the perturbation theory in quantum mechanics.

Construction of a Corpus of Rhetorical Devices in Slogans and Structural Analysis of Antitheses

An advertising slogan is a sentence that expresses a product or a work of art in a straightforward manner and is used for advertising and publicity. Moving the consumer's mind and attracting their interest can significantly influence sales. Although rhetorical techniques in a slogan are known to improve the effectiveness of advertising, not much attention has been devoted to analyze or automatically generate sentences with the techniques. Therefore, we constructed a large corpus of slogans and revealed the linguistic characteristics of the basic statistics and rhetorical devices. Another point of focus was antitheses, of which the usage rates are relatively high and which have a specific sentence structure and lexical constraints. The generation of a slogan that contains an antithesis necessitates the structure of sentences, known as templates, to be extracted and also requires knowledge of word pairs with semantic contrast. Thus, the next step involved analysis of the structure to extract the sentence structure and lexical knowledge about the antithesis. Despite its simple architecture, the proposed method exceeds the prediction accuracy and efficiency of a comparable method. Lexical knowledge that is not available in existing dictionaries was also extracted.

MicNet Toolbox: visualizing and deconstructing a microbial network

Understanding both global and local patterns in the structure and interplay of microbial communities has been a fundamental question in ecological research. In this paper, we present a python toolbox that combines two emerging techniques that have been proposed as useful when analyzing compositional microbial data. On one hand, we introduce a visualization module that incorporates the use of UMAP, a recent dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density. On the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple this with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) using simulated community networks with known topologies to show that we are able to discern between network topologies. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data.

Science and the Necessity of Faith: Notes on “Appendix to the Transcendental Dialectic”

Apropos Kant’s discussion of scientific practice in the section of the first Critique entitled “Appendix to the Transcendental Dialectic,” there has long been a tendency in Anglophone Kant scholarship to downplay the role of God or quickly brush aside the centrality of the idea in the Kantian system. As a way of setting the stage for evaluating the place of God in Kant’s philosophy, this paper, in a concise and straightforward manner, attempts to make the connection between science and the idea of God as it appears in the first Critique explicit and explain why Kant is driven to make that connection. In the first half of the paper, I summarize Kant’s discussion of scientific practice as presented in the first part of the Appendix, followed by a brief discussion of a problem his account raises. In the second half of the paper, I elaborate the connection between science and God as a response to that problem.

The congruent number problem

The congruent number problem is the oldest unsolved major mathematical problem to date. The problem aiming to determine whether or not some given integer n is congruent, which corresponds to a Pythagorean triangle with integer sides, can be settled in a finite number of steps. However, once we permit the triangles to acquire rational values for its sides, the degree of difficulty of the task changes dramatically. In this paper a basis is developed, to produce right Pythagorean triangles with rational sides and integral area in a straightforward manner. Determining whether or not a given natural number n is congruent, is equivalent to a search through an ordered 2D array.

A light tunable differentiation system for the creation and control of consortia in yeast

Artificial microbial consortia seek to leverage division-of-labour to optimize function and possess immense potential for bioproduction. Co-culturing approaches, the preferred mode of generating a consortium, remain limited in their ability to give rise to stable consortia having finely tuned compositions. Here, we present an artificial differentiation system in budding yeast capable of generating stable microbial consortia with custom functionalities from a single strain at user-defined composition in space and in time based on optogenetically-driven genetic rewiring. Owing to fast, reproducible, and light-tunable dynamics, our system enables dynamic control of consortia composition in continuous cultures for extended periods. We further demonstrate that our system can be extended in a straightforward manner to give rise to consortia with multiple subpopulations. Our artificial differentiation strategy establishes a novel paradigm for the creation of complex microbial consortia that are simple to implement, precisely controllable, and versatile to use.

Petri Net Modeling for Ising Model Formulation in Quantum Annealing

Quantum annealing is an emerging new platform for combinatorial optimization, requiring an Ising model formulation for optimization problems. The formulation can be an essential obstacle to the permeation of this innovation into broad areas of everyday life. Our research is aimed at the proposal of a Petri net modeling approach for an Ising model formulation. Although the proposed method requires users to model their optimization problems with Petri nets, this process can be carried out in a relatively straightforward manner if we know the target problem and the simple Petri net modeling rules. With our method, the constraints and objective functions in the target optimization problems are represented as fundamental characteristics of Petri net models, extracted systematically from Petri net models, and then converted into binary quadratic nets, equivalent to Ising models. The proposed method can drastically reduce the difficulty of the Ising model formulation.