Universal fluctuations in growth dynamics of economic systems

The growth of business firms is an example of a system of complex interacting units that resembles complex interacting systems in nature such as earthquakes. Remarkably, work in econophysics has provided evidence that the statistical properties of the growth of business firms follow the same sorts of power laws that characterize physical systems near their critical points. Given how economies change over time, whether these statistical properties are persistent, robust, and universal like those of physical systems remains an open question. Here, we show that the scaling properties of firm growth previously demonstrated for publicly-traded U.S. manufacturing firms from 1974 to 1993 apply to the same sorts of firms from 1993 to 2015, to firms in other broad sectors (such as materials), and to firms in new sectors (such as Internet services). We measure virtually the same scaling exponent for manufacturing for the 1993 to 2015 period as for the 1974 to 1993 period and virtually the same scaling exponent for other sectors as for manufacturing. Furthermore, we show that fluctuations of the growth rate for new industries self-organize into a power law over relatively short time scales.Comment: 15 pages, 7 figure

Graph Contrastive Learning for Materials

Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competitive with engineered fingerprinting methods. We also demonstrate that via model finetuning, contrastive pretraining can improve the performance of graph neural networks for prediction of material properties and significantly outperform traditional ML models that use engineered fingerprints. Lastly, we observe that CrystalCLR produces material representations that form clusters by compound class.Comment: 7 pages, 3 figures, NeurIPS 2022 AI for Accelerated Materials Design Worksho

Countercurrent Chromatography Fractions of Plant Extracts with Anti-Tuberculosis Activity

Samples of numerous plant species were received from the southwestern part of the USA, from Richard Spjut, and plant samples were collected here in Illinois. All were extracted with typical solvents, giving crude residues, some of which were subjected to chromatographic methods. Some of the crude residues and some of the fractions were tested for anti-tuberculosis activity and/or antibacterial activity. In a general way, bioactive natural products are dealt with very well by Liang & Fang. More specifically, the southwestern part of the United States has a large variety of indigenous plants many of which have not been investigated for their medicinal potential, and only very few have had their extracts separated into the individual compounds they may contain. But, some information is available for Native American herbal uses (Moerman,2003)

SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers

We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task aids in supervised training of Euclidean neural networks (E3NNs) and increases manifold smoothness (MS) around point-cloud geometries. We demonstrate this property for multiple drug-activity prediction tasks while maintaining relevant performance metrics, and propose an extension of MS to probabilistic and regression settings. We provide an analysis of representation collapse, finding substantial effects of task-weighting, latent dimension, and regularization. We expect the presented protocol to aid in the development of reliable E3NNs from molecular conformers, even for small-data drug discovery programs.Comment: Submitted to the MLDD workshop, ICLR 202

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Clinical considerations for the treatment of secondary differentiated thyroid carcinoma in childhood cancer survivors

The incidence of differentiated thyroid carcinoma (DTC) has increased rapidly over the past several years. Thus far, the only conclusively established risk factor for developing DTC is exposure to ionizing radiation, especially when the exposure occurs in childhood. Since the number of childhood cancer survivors (CCS) is increasing due to improvements in treatment and supportive care, the number of patients who will develop DTC after surviving childhood cancer (secondary thyroid cancer) is also expected to rise. Currently, there are no recommendations for management of thyroid cancer specifically for patients who develop DTC as a consequence of cancer therapy during childhood. Since complications or late effects from prior cancer treatment may elevate the risk of toxicity from DTC therapy, the medical history of CCS should be considered carefully in choosing DTC treatment. In this paper, we emphasize how the occurrence and treatment of the initial childhood malignancy affects the medical and psychosocial factors that will play a role in the diagnosis and treatment of a secondary DTC. We present considerations for clinicians to use in the management of patients with secondary DTC, based on the available evidence combined with experience -based opinions of the authors

Length-Independent Charge Transport in Chimeric Molecular Wires

Advanced molecular electronic components remain vital for the next generation of miniaturized integrated circuits. Thus, much research effort has been devoted to the discovery of lossless molecular wires, for which the charge transport rate or conductivity is not attenuated with length in the tunneling regime. Herein, we report the synthesis and electrochemical interrogation of DNA-like molecular wires. We determine that the rate of electron transfer through these constructs is independent of their length and propose a plausible mechanism to explain our findings. The reported approach holds relevance for the development of high-performance molecular electronic components and the fundamental study of charge transport phenomena in organic semiconductors

Distinguishing electronic contributions of surface and sub-surface transition metal atoms in Ti-based MXenes

MXenes are a rapidly-expanding family of 2D transition metal carbides and nitrides that have attracted attention due to their excellent performance in applications ranging from energy storage to electromagnetic interference shielding. Numerous other electronic and magnetic properties have been computationally predicted, but not yet realized due to the experimental difficulty in obtaining uniform surface terminations (Tx), necessitating new design approaches for MXenes that are independent of surface terminations. In this study, we distinguished the contributions of surface and sub-surface Ti atoms to the electronic structure of four Ti-containing MXenes (Ti2CTx, Ti3C2Tx, Cr2TiC2Tx, and Mo2TiC2Tx) using soft x-ray absorption spectroscopy. For MXenes with no Ti atoms on the surface transition metal layers, such as Mo2TiC2Tx and Cr2TiC2Tx, our results show minimal changes in the spectral features between the parent MAX phase and its MXene. In contrast, for MXenes with surface Ti atoms, here Ti3C2Tx and Ti2CTx, the Ti L-edge spectra are significantly modified compared to their parent MAX phase compounds. First principles calculations provide similar trends in the partial density of states derived from surface and sub-surface Ti atoms, corroborating the spectroscopic measurements. These results reveal that electronic states derived from sub-surface M-site layers are largely unperturbed by the surface terminations, indicating a relatively short length scale over which the Tx terminations alter the nominal electron count associated with Ti atoms and suggesting that desired band features should be hosted by sub-surface M-sites that are electronically more robust than their surface M-site counterparts

Radical SAM enzyme QueE defines a new minimal core fold and metal-dependent mechanism

7-carboxy-7-deazaguanine synthase (QueE) catalyzes a key S-adenosyl-L-methionine (AdoMet)- and Mg[superscript 2+]-dependent radical-mediated ring contraction step, which is common to the biosynthetic pathways of all deazapurine-containing compounds. QueE is a member of the AdoMet radical superfamily, which employs the 5′-deoxyadenosyl radical from reductive cleavage of AdoMet to initiate chemistry. To provide a mechanistic rationale for this elaborate transformation, we present the crystal structure of a QueE along with structures of pre- and post-turnover states. We find that substrate binds perpendicular to the [4Fe-4S]-bound AdoMet, exposing its C6 hydrogen atom for abstraction and generating the binding site for Mg[superscript 2+], which coordinates directly to the substrate. The Burkholderia multivorans structure reported here varies from all other previously characterized members of the AdoMet radical superfamily in that it contains a hypermodified ([β [subscript 6] over α [subscript 3]]) protein core and an expanded cluster-binding motif, CX[subscript 14]CX[subscript 2]C.United States. Dept. of Energy. Office of Biological and Environmental ResearchUnited States. Dept. of Energy. Office of Basic Energy SciencesNational Center for Research Resources (U.S.) (P41RR012408)National Institute of General Medical Sciences (U.S.) (P41GM103473)National Center for Research Resources (U.S.) (5P41RR015301-10)National Institute of General Medical Sciences (U.S.) (8 P41 GM 103403-10)United States. Dept. of Energy (Contract DE-AC02-06CH11357