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Can a bacterial cell model vet large datasets from disparate sources? Macklin et al. explored whether a comprehensive mathematical model can be used to verify or find conflicts in massive amounts of data that have been reported for the bacterium Escherichia coli , produced in thousands of papers from hundreds of labs. Although most data were consistent, there were data that could not accommodate known biological results, such as insufficient output of RNA polymerases and ribosomes to produce measured cell-doubling times. Other analyses showed that for some essential proteins, no RNA may be transcribed or translated in a cell's lifetime, but viability can be maintained without certain enzymes through a pool of stable metabolites produced earlier. Science , this issue p. eaav3751 Construction of a large-scale mechanistic model of Escherichia coli brings models and large datasets together to enhance knowledge in biology. The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Whole-cell modeling is an approach for computational simulation of the known inner workings of a living cell. As our lab developed a large-scale model of Escherichia coli in 2020, we also brought to fruition the role of whole-cell modeling as an engineering tool for studying cell biology. By describing the biological processes occurring inside the E. coli cell with mathematical equations and by parameterizing those equations with measurements from literature, we were presented with the opportunity to perform a large-scale cross-evaluation of the data reported by numerous labs over the past decades using the theories describing E. coli cell physiology -- a technique we call Deep Curation. The Deep Curation approach has enabled us to identify intriguing incompatibilities between the measurements used to parameterize the E. coli model; for example, we identified that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that the measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle -- and the cell is robust to this absence. Finally, these data as a whole led to successful predictions of protein half-lives.The E. coli Model provided a foundation upon which new functionalities could be incorporated, in this case tRNA aminoacylation, which facilitated investigations of the inconsistencies between in vitro measurements and in vivo demands concerning tRNA aminoacylation and protein synthesis -- first reported almost forty years ago. We found that in vitro measurements of tRNA synthetase activities were insufficient to support the in vivo demands for protein synthesis, and optimization of tRNA synthetase kinetic parameters yielded kcat estimations that were on average 14.5-fold higher than their highest measurements. Simulating cell growth with perturbed kcat values for individual synthetases enabled us to determine the global impact of these in vitro measurements on cellular phenotypes. For the case of the CysRS synthetase, insufficient kcats caused protein synthesis to be less robust to the natural variability in tRNA synthetase expression in single cells. Surprisingly, insufficient ArgRS kinetic capacity led to catastrophic impacts on cellular phenotype via DNA replication due to a replisome subunit whose translation was biased to Arginine codons.Taken together, the work presented in this dissertation furthers our understanding of incompatibilities amongst the data characterizing E. coli, a model organism of scientific research. The expansion to the E. coli Model reported here advances the depth of mechanistic detail incorporated into the translational machinery and enhances the breadth of potential for generating predictions and accelerating biological discovery. We anticipate that as other functionalities are incorporated into the E. coli Model, similarly remarkable and unexpected phenotypes will continue to emerge.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model ofEscherichia colienabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Type 2 diabetes mellitus (T2DM) is a chronic disease that requires long-term therapy and regular check-ups to prevent complications. In this study, insurance claim data from the National Health Insurance Service (NHIS) of Korea were used to investigate insulin use in T2DM patients according to the economic status of patients and their access to primary physicians, operationally defined as the frequently used medical care providers at the time of T2DM diagnosis. A total of 91,810 participants were included from the NHIS claims database for the period between 2002 and 2013. The utilization pattern of insulin was set as the dependent variable and classified as one of the following: non-use of antidiabetic drugs, use of oral antidiabetic drugs only, or use of insulin with or without oral antidiabetic drugs. The main independent variables of interest were level of income and access to a frequently-visited physician. Multivariate Cox proportional hazards analysis was performed. Insulin was used by 9,281 patients during the study period, while use was 2.874 times more frequent in the Medical-aid group than in the highest premium group [hazard ratio (HR): 2.874, 95% confidence interval (CI): 2.588-3.192]. Insulin was also used ~50% more often in the patients managed by a frequently-visited physician than in those managed by other healthcare professionals (HR: 1.549, 95% CI: 1.434-1.624). The lag time to starting insulin was shorter when the patients had a low income and no frequently-visited physicians. Patients with a low level of income were more likely to use insulin and to have a shorter lag time from diagnosis to starting insulin. The likelihood of insulin being used was higher when the patients had a frequently-visited physician, particularly if they also had a low level of income. Therefore, the economic statuses of patients should be considered to ensure effective management of T2DM. Utilizing frequently-visited physicians might improve the management of T2DM, particularly for patients with a low income.

A sensitive and reproducible liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was developed and fully validated for the simultaneous determination of ephedrine and pseudoephedrine in human plasma after oral administration of the herbal prescription Ojeok-san (OJS); 2-phenylethylamine was used as the internal standard (IS). Both compounds presented a linear calibration curve (r2 ≥ 0.99) over a concentration range of 0.2–50 ng/mL. The developed method was fully validated in terms of selectivity, lower limit of quantitation, precision, accuracy, recovery, matrix effect, and stability, according to the regulatory guidelines from the U.S. Food and Drug Administration and the Korea Ministry of Food and Drug Safety. This validated method was successfully applied for the pharmacokinetic assessment of ephedrine and pseudoephedrine in 20 healthy Korean volunteers administered OJS.

The proportion of packed malware has been growing rapidly and now comprises more than 80 % of all existing malware. In this paper, we propose a method for classifying the packing algorithms of given unknown packed executables, regardless of whether they are malware or benign programs. First, we scale the entropy values of a given executable and convert the entropy values of a particular location of memory into symbolic representations. Our proposed method uses symbolic aggregate approximation (SAX), which is known to be effective for large data conversions. Second, we classify the distribution of symbols using supervised learning classification methods, i.e., naive Bayes and support vector machines for detecting packing algorithms. The results of our experiments involving a collection of 324 packed benign programs and 326 packed malware programs with 19 packing algorithms demonstrate that our method can identify packing algorithms of given executables with a high accuracy of 95.35 %, a recall of 95.83 %, and a precision of 94.13 %. We propose four similarity measurements for detecting packing algorithms based on SAX representations of the entropy values and an incremental aggregate analysis. Among these four metrics, the fidelity similarity measurement demonstrates the best matching result, i.e., a rate of accuracy ranging from 95.0 to 99.9 %, which is from 2 to 13  higher than that of the other three metrics. Our study confirms that packing algorithms can be identified through an entropy analysis based on a measure of the uncertainty of the running processes and without prior knowledge of the executables.

The aim of this study was to characterize the physicochemical properties and microbial communities of particulate matter (PM) in Seoul, Korea. We collected long-term (2017–2019) precipitation samples and PM 10 and PM 2.5 monitoring data to determine the impact of soluble and insoluble chemical species on the soil surface. Ambient PM 10 concentrations were higher than PM 2.5 concentrations during the monitoring period, but both decreased during rainfall due to the washing effect of precipitation. PM 2.5 particles had a “fluffy” shape and contained sulfur (0.2%), but suspended particles (SPs) contained many carbon particles (approximately 60%). Spherical particles containing metal oxides, Fe and Al, might be originated from coal combustion, wild fires, and metal-refining processes under high-temperature conditions. Dissolved ions in precipitation included those eluted from salts and coal combustion based on the correlation coefficients of Na and Cl ( R = 0.953) and F and NO 3 ( R = 0.706). The δ 15 N–NO 3 and δ 34 S–SO 4 of precipitation were enriched as the atmospheric temperature decreased from 9.8 to −1.6°C, implying the influence of domestic coal combustion. Backward trajectories showed that, in winter, air parcels passed through industrialized cities from China to South Korea. The microbial communities associated with PM were strongly influenced by atmospheric conditions. Proteobacteria (range from 4.6 to 76.7%) and Firmicutes (range from 6.0 to 91.4%) were the most dominant phyla and were significantly affected by changes in the PM 2.5 environment. The results indicate that the acidity of precipitation and the composition of aerosols were affected by fossil fuel combustion and mineral dust, and that atmospheric conditions may change as PM 2.5 concentrations increase.

Abstract Invasive drywood termites are one of the most challenging species to detect in the early invasion process as they can infest a small piece of wood and be transported by human activity. Cryptotermes domesticus (Haviland, 1898) is native to south Asia and Australia and has been introduced into many other Asian countries and pacific islands, where they cause damage to furniture and wooden structures. Recently, an established colony of C. domesticus has been found in the Seoul metropolitan area, Republic of Korea, where drywood termites were not thought to be able to establish due to low winter temperatures. The discovery of C. domesticus was initiated from a post on an internet forum in which an anonymous homeowner collected alates in an apartment and asked for pest identification. This information was readily delivered to professional entomologists, and a task force was formed for inspection. During the thorough inspection, an infested sliding door frame was identified and a colony of C. domesticus was found. Instead of fumigation and localized pesticide treatment, the door and frame were removed and replaced, which was the most cost-effective control measure as the colony was only found there. Since the potential spread of C. domesticus is uncertain, the task force collaborated with the media, including newspapers and broadcasting news, to disseminate information to help recognize any additional unreported infestations. This study provides insights on how to cooperate with the media and citizens when a new invasive species is found.