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

Protein Design with Guided Discrete Diffusion

A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided diffusion modeling is a promising foundation for protein design, leading many to develop guided diffusion models for structure with inverse folding to recover sequences. In this work, we propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models that follows gradients in the hidden states of the denoising network. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods, including scarce data and challenging inverse design. Moreover, we use NOS to generalize LaMBO, a Bayesian optimization procedure for sequence design that facilitates multiple objectives and edit-based constraints. The resulting method, LaMBO-2, enables discrete diffusions and stronger performance with limited edits through a novel application of saliency maps. We apply LaMBO-2 to a real-world protein design task, optimizing antibodies for higher expression yield and binding affinity to several therapeutic targets under locality and developability constraints, attaining a 99% expression rate and 40% binding rate in exploratory in vitro experiments

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

Effect of Aspirin Versus Low-Molecular-Weight Heparin Thromboprophylaxis on Medication Satisfaction and Out-of-Pocket Costs: A Secondary Analysis of a Randomized Clinical Trial

BACKGROUND: Current guidelines recommend low-molecular-weight heparin for thromboprophylaxis after orthopaedic trauma. However, recent evidence suggests that aspirin is similar in efficacy and safety. To understand patients\u27 experiences with these medications, we compared patients\u27 satisfaction and out-of-pocket costs after thromboprophylaxis with aspirin versus low-molecular-weight heparin. METHODS: This study was a secondary analysis of the PREVENTion of CLots in Orthopaedic Trauma (PREVENT CLOT) trial, conducted at 21 trauma centers in the U.S. and Canada. We included adult patients with an operatively treated extremity fracture or a pelvic or acetabular fracture. Patients were randomly assigned to receive 30 mg of low-molecular-weight heparin (enoxaparin) twice daily or 81 mg of aspirin twice daily for thromboprophylaxis. The duration of the thromboprophylaxis, including post-discharge prescription, was based on hospital protocols. The study outcomes included patient satisfaction with and out-of-pocket costs for their thromboprophylactic medication measured on ordinal scales. RESULTS: The trial enrolled 12,211 patients (mean age and standard deviation [SD], 45 ± 18 years; 62% male), 9725 of whom completed the question regarding their satisfaction with the medication and 6723 of whom reported their out-of-pocket costs. The odds of greater satisfaction were 2.6 times higher for patients assigned to aspirin than those assigned to low-molecular-weight heparin (odds ratio [OR]: 2.59; 95% confidence interval [CI]: 2.39 to 2.80; p \u3c 0.001). Overall, the odds of incurring any out-of-pocket costs for thromboprophylaxis medication were 51% higher for patients assigned to aspirin compared with low-molecular-weight heparin (OR: 1.51; 95% CI: 1.37 to 1.66; p \u3c 0.001). However, patients assigned to aspirin had substantially lower odds of out-of-pocket costs of at least $25 (OR: 0.15; 95CONCLUSIONS: Use of aspirin substantially improved patients\u27 satisfaction with their medication after orthopaedic trauma. While aspirin use increased the odds of incurring any out-of-pocket costs, it protected against costs of ≥$25, potentially improving health equity for thromboprophylaxis. LEVEL OF EVIDENCE: Therapeutic Level II . See Instructions for Authors for a complete description of levels of evidence

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