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While global transcription factors (TFs) have been studied extensively in Escherichia coli model strains, conservation and diversity in TF regulation between strains is still unknown. Here we use a combination of ChIP-exo-to define ferric uptake regulator (Fur) binding sites-and differential gene expression-to define the Fur regulon in nine E. coli strains. We then define a pan-regulon consisting of 469 target genes that includes all Fur target genes in all nine strains. The pan-regulon is then divided into the core regulon (target genes found in all the strains, n = 36), the accessory regulon (target found in two to eight strains, n = 158) and the unique regulon (target genes found in one strain, n = 275). Thus, there is a small set of Fur regulated genes common to all nine strains, but a large number of regulatory targets unique to a particular strain. Many of the unique regulatory targets are genes unique to that strain. This first-established pan-regulon reveals a common core of conserved regulatory targets and significant diversity in transcriptional regulation amongst E. coli strains, reflecting diverse niche specification and strain history.

The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia coli transposon mutant library comprising approximately 400,000 different mutants. Determination of transposon insertion sites using next-generation sequencing provided a high-resolution essentiality landscape of the E. coli genome. We identified false negatives of essential gene discovery due to the permissive insertion of transposons in the C-terminal region. Comparisons between the transposon insertion landscape with binding profiles of DNA-binding proteins revealed interference of nucleoid-associated proteins to transposon insertion, generating false positives of essential gene discovery. Consideration of these findings is required to avoid the misinterpretation of transposon mutagenesis results.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Anticancer treatment is performed in various ways, and photothermal therapy (PTT) is gaining traction from a noninvasive treatment perspective. PTT is a treatment technique based on the photothermal effect that kills tumors by increasing their temperature. In this study, gold nanoparticles (AuNPs), which are photothermal agents, were used in numerical simulations to determine the PTT effect by considering diffusion induced changes in the distribution area of the AuNPs. The treatment effect was confirmed by varying the initial injection radius of AuNPs represented by the injection volume, the elapsed time after injection of AuNPs, and the laser intensity. The degree of maintenance of the apoptotic temperature band in the tumor was quantitatively analyzed by the apoptotic variable. Ultimately, if the initial injection radius of AuNPs is 0.7 mm or less, the optimal time to start treatment is 240 min after injection, and for 1.0 and 1.2 mm, it is optimal to start treatment when the elapsed time after injection is 90 and 30 min, respectively. This study identified the optimal treatment conditions for dosage of AuNPs and treatment start time in PTT using AuNPs, which will serve as a reference point for future PTT studies. [ABSTRACT FROM AUTHOR]

Purpose: Dental implants have been successfully implemented as a treatment for tooth loss. However, peri-implantitis, an inflammatory reaction owing to microbial deposition around the implant, can lead to implant failure. So, it is necessary to treat peri-implantitis. Therefore, this numerical study is aimed at investigating conditions for treating peri-implantitis. Methods: Photothermal therapy, a laser treatment method, utilizes photothermal effect, in which light is converted to heat. This technique has advantage of selectively curing inflamed tissues by increasing their temperature. Accordingly, herein, photothermal effect on peri-implantitis is studied through numerical analysis with using Arrhenius damage integral and Arrhenius thermal damage ratio. Results: Through numerical analysis on peri-implantitis treatment, we explored temperature changes under varied laser settings (laser power, radius, irradiation time). We obtained the temperature distribution on interface of artificial tooth root and inflammation and determined whether temperature exceeds or does not exceed 47℃ to know which laser power affects alveolar bone indirectly. We defined the Arrhenius thermal damage ratio as a variable and determined that the maximum laser power that does not exceed 47℃ at the AA' line is 1.0 W. Additionally, we found that the value of the Arrhenius thermal damage ratio is 0.26 for a laser irradiation time of 100 s and 0.50 for 500 s. Conclusion: The result of this numerical study indicates that the Arrhenius thermal damage ratio can be used as a standard for determining the treatment conditions to help assisted laser treatment for peri-implantitis in each numerical analysis scenario. [ABSTRACT FROM AUTHOR]

The tropical climate variabilities, such as Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO), are accompanied by changes in the tropical deep convection which can influence the atmospheric circulation in the Southern Hemisphere (SH). To investigate each role of IOD and ENSO in the September‐November (SON) circulation, we examine teleconnection patterns associated with IOD and ENSO events using the ERA5 monthly averaged data from 1979 to 2020. Our approach is to calculate the power spectral density (PSD) of the sea level pressure (SLP) and meridional wind and geopotential height at 300 hPa that are decomposed by zonal wave numbers (ZWNs), and to compute their correlations with IOD and ENSO at each latitudinal band. The main results are that IOD (ENSO) is negatively (positively) correlated with PSDs of ZWN2 and ZWN3 (ZWN1) at 300 hPa in the SH middle latitudes. Considering the Rossby wave train, IOD (ENSO) considerably affects the variability of the ZWN3 (ZWN1) pattern, which influences the meridional exchange of momentum. Additionally, the relationship between IOD and ZWN3 has become tighter in recent years, which is not seen in that with ENSO. The IOD and ENSO events also modify the SLP patterns and meridional surface winds, modulating the sea ice extent in the Southern Ocean. During the highly positive 2019 IOD event, the variability of the middle latitudes atmospheric circulation was considerably larger than climatology, suggesting a higher chance of more extreme weather patterns associated with more frequent intense IOD events in the warming climate. Plain Language Summary: The two tropical climate variabilities, Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO), can change the location and intensity of tropical convection, generate anomalous Rossby wave trains heading to the Southern Hemisphere (SH), and the zonal wave pattern of the middle latitudes' atmospheric circulation and further the Antarctic climate. We analyzed this low‐high latitudes teleconnection in the austral spring when IOD and ENSO strongly affect the wave patterns using atmospheric reanalysis data. The results show that ENSO is positively correlated with zonal wave number 1 (ZWN1) activity, but IOD negatively correlates with ZWN2 and ZWN3 activities in the SH middle latitudes. The relationship between IOD and ZWN3 has become tighter in recent years, which is not seen in that with ENSO. The IOD and ENSO events also modify the sea level pressure patterns and meridional surface winds in the Southern Ocean, leaving the ZWN3 features. The surface wind patterns affect the sea ice concentration, but with a difference between IOD and ENSO. Key Points: Indian Ocean Dipole (IOD)'s Rossby wave train is partially similar to the climatology; a negative IOD enormously increases zonal wavenumber 3IOD (El Niño Southern Oscillation) is negatively (positively) correlated with zonal wavenumber 2 and 3 (1) in austral springThe extremely positive IOD in 2019 exhibited more significant increases and unusual changes in wave patterns [ABSTRACT FROM AUTHOR]

As the electric vehicle market continues to surge, the proper assessment of used batteries has become increasingly important. However, current technologies for assessing used batteries, which involve separately estimating the State-of-Health (SoH) of the pack and its individual modules, require multiple times of cycling tests and lead to time inefficiency and power consumption. The proposed DeepSUGAR, a deep learning-based framework for SoH estimation using a generative algorithm based on graphical representation techniques to reveal individual module health, offers the advantage of estimating the status of internal modules replying on battery pack SoH. The cycling profiles of a simultaneously measured 14S7P pack and its constituent modules were analyzed, and a convolutional neural network (CNN) was trained by spatializing cycling curves to estimate SoH. DeepSUGAR, trained on pack data, showed outstanding performance with an RMSE of 5.31 × 10−3 and its applicability was validated by testing with module data, resulting in an RMSE of 7.38 × 10−3. Furthermore, the generated module cycling profiles from pack SoH using the deep generative model were fed into the trained CNN and showed a remarkable performance with an RMSE of 8.38 × 10−3. DeepSUGAR can significantly reduce power consumption, processing cost, and carbon dioxide emissions by integrating module-level diagnosis within the pack-level assessment process. [ABSTRACT FROM AUTHOR]