The hot streak-loosely defined as 'winning begets more winnings'-highlights a specific period during which an individual's performance is substantially better than his or her typical performance. Although hot streaks have been widely debated in sports, gambling and financial markets over the past several decades, little is known about whether they apply to individual careers. Here, building on rich literature on the lifecycle of creativity, we collected large-scale career histories of individual artists, film directors and scientists, tracing the artworks, films and scientific publications they produced. We find that, across all three domains, hit works within a career show a high degree of temporal regularity, with each career being characterized by bursts of high-impact works occurring in sequence. We demonstrate that these observations can be explained by a simple hot-streak model, allowing us to probe quantitatively the hot streak phenomenon governing individual careers. We find this phenomemon to be remarkably universal across diverse domains: hot streaks are ubiquitous yet usually unique across different careers. The hot streak emerges randomly within an individual's sequence of works, is temporally localized, and is not associated with any detectable change in productivity. We show that, because works produced during hot streaks garner substantially more impact, the uncovered hot streaks fundamentally drive the collective impact of an individual, and ignoring this leads us to systematically overestimate or underestimate the future impact of a career. These results not only deepen our quantitative understanding of patterns that govern individual ingenuity and success, but also may have implications for identifying and nurturing individuals whose work will have lasting impact.
D-allulose, which is one of the important rare sugars, has gained significant attention in the food and pharmaceutical industries as a potential alternative to sucrose and fructose. Enzymes belonging to the D-tagatose 3-epimerase (DTEase) family can reversibly catalyze the epimerization of D-fructose at the C3 position and convert it into D-allulose by a good number of naturally occurring microorganisms. However, microbial synthesis of D-allulose is still at its immature stage in the industrial arena, mostly due to the preference of slightly acidic conditions for Izumoring reactions. Discovery of novel DTEase that works at acidic conditions is highly preferred for industrial applications. In this study, a novel DTEase, DTE-CM, capable of catalyzing D-fructose into D-allulose was applications. In this study, a novel DTEase, DTE-CM, capable of catalyzing D-fructose into D-allulose was DTE-CM on D-fructose was found to be remarkably influenced and modulated by the type of metal ions (co-factors). The DTE-CM on D-fructose was found to be remarkably influenced and modulated by the type of metal ions (co-factors). The 50°C from 0.5 to 3.5 h at a concentration of 0.1 mM. The enzyme exhibited its maximum catalytic activity on D-fructose at pH 6.0 and 50°C from 0.5 to 3.5 h at a concentration of 0.1 mM. The enzyme exhibited its maximum catalytic activity on -fructose at pH 6.0 and 50°C with a Kcat/Km value of 45 mM−1min−1. The 500 g/L D-fructose, which corresponded to 30% conversion rate. With these interesting catalytic properties, this enzyme could be a promising candidate for industrial biocatalytic applications.
Applying simulations with structure-based Go À like ð Þmodels has proven to be an effective strategy for investigating the factors that control biomolecular dynamics. The common element of these models is that some (or all) of the intra/inter-molecular interactions are explicitly defined to stabilize an experimentally determined structure. To facilitate the development and application of this broad class of models, we previously released the SMOG 2 software package. This suite allows one to easily customize and distribute structurebased (i.e., SMOG) models for any type of polymer-ligand system. The force fields generated by SMOG 2 may then be used to perform simulations in highly optimized MD packages, such as Gromacs, NAMD, LAMMPS, and OpenMM.Here, we describe extensions to the software and demonstrate the capabilities of the most recent version (SMOG v2.4.2). Changes include new tools that aid user-defined customization of force fields, as well as an interface with the OpenMM simulation libraries (OpenSMOG v1.1.0). The OpenSMOG module allows for arbitrary user-defined contact potentials and non-bonded potentials to be employed in SMOG models, without source-code modifications. To illustrate the utility of these advances, we present applications to systems with millions of atoms, long polymers and explicit ions, as well as models that include non-structure-based (e.g., AMBER-based) energetic terms. Examples include Antonio B. de Oliveira Jr and Vinícius G. Contessoto contributed equally to this work.
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