Effect of radius-dependent diffusion behavior of various gold nanoparticles on photothermal therapy.

Among the various anti-cancer treatments, photothermal therapy (PTT) is gaining traction as it is a non-invasive treatment. PTT is a treatment technique involving the use of a laser to raise the temperature of the target tumor until it dies. In this study, the effects of PTT under various conditions of squamous cell carcinoma (SCC) occurring in the skin were numerically analyzed and optimized. Gold nanoparticles (AuNPs) with different radii were injected into the center of the SCC. Subsequently, the diffusion behavior of the AuNPs was analyzed to calculate the distribution area of the AuNPs that changed over time. Furthermore, at each elapsed time point after injection, the temperature distribution in the tissue was calculated, as treatment was performed using varying laser intensities. The diffusion coefficient of AuNPs was calculated using the Stokes-Einstein equation, and diffusion behavior of AuNPs in biological tissues was analyzed using the convection-diffusion equation. Additionally, temperature distribution was analyzed using the Pennes bioheat equation. The effect of PTT under each condition was quantitatively analyzed using apoptotic variables. As a result, As the radius of AuNPs increased, the optimal treatment start time was derived as 2 h, 8 h, 8 h, and 12 h, respectively, and the laser intensity at that time was derived as 0.44 W, 0.46 W, 0.42 W, and 0.42 W, respectively. The study findings will provide reference for the optimization of the efficacy of PTT.

Numerical study on the three-dimensional temperature distribution according to laser conditions in photothermal therapy of peri-implantitis.

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.

Analysis of Optimal Treatment Starting Time for Photothermal Therapy Through Analysis of Diffusion Behavior of Gold Nanoparticles.

Introduction: Due to its distinct advantage of non-invasive application in treatment, photothermal therapy (PTT) is being studied by many researchers to reduce the need for surgical incisions. It is characterized by the injection of nanoparticles into biological tissue as photothermal agents (PTAs) which diffuse within the tissue. In this study, the diffusion behavior of various doses of gold nanoparticles (AuNPs) injected into tumor tissues is analyzed and the effectiveness of PTT at each elapsed time after injection is confirmed by numerical analysis. Methods: The diffusion behavior of AuNPs within biological tissues is assessed using the convection-diffusion equation, while the temperature distribution is determined using the Pennes bioheat transfer equation. In addition, the effect of the diffusion behavior of AuNPs on the effectiveness of PTT is quantitatively confirmed by analyzing the temperature distribution in the medium through the apoptotic variable. Numerical simulation parameters are selected with doses ranging from 100 to 400 µg/mL, elapsed time after injection from 1 min to 24 h, and laser power ranging from 0 to 1 W. Results: After evaluating PTT's efficacy in every situation, it was discovered that a dosage of 100-300 µg/mL produced the best therapeutic result, with the highest impact occurring 12 hours after injection. In contrast, when the dosage was 400 µg/mL, the highest therapeutic effect was achieved after 18 hours post-injection. Additionally, it was discovered that the ideal laser power at each injection dose was 0.22, 0.14, 0.12, and 0.12 W, respectively. Conclusion: The conditions required to achieve the optimal treatment effect at each dosage, presented here, are expected to accelerate the commercialization of PTT.

Numerical study of the induction of intratumoral apoptosis under microwave ablation by changing slot length of microwave coaxial antenna.

Recent advances in technology have led to an increase in the detection of previously undetected deep-located tumor tissue. As a result, the medical field is using a variety of methods to treat deep-located tumors, and minimally invasive treatment techniques are being explored. In this study, therapeutic effect of microwave ablation (MWA) on tumor generated inside liver tissue was analyzed through numerical analysis. The distribution of electromagnetic fields in biological tissues emitted by microwave coaxial antenna (MCA) was calculated through the wave equation, and the thermal behavior of the tissue was analyzed through the Pennes bioheat equation. Among various treatment conditions constituting MWA, tumor radius and the slot length inside the MCA were changed, and the resulting treatment effect was quantitatively confirmed through three apoptotic variables. As a result, each tumor radius has optimal power condition for MWA, 2.6W, 2.4W, and 3.0W respectively. This study confirmed optimal therapeutic conditions for MWA. Three apoptotic variables were used to quantitatively identify apoptotic temperature maintenance inside tumor tissue and thermal damage to surrounding normal tissue. The findings of this study are expected to serve as a standard for treatment based on actual MWA treatment.

Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure.

Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)-a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.

Straightforward Magnetic Resonance Temperature Measurements Combined with High Frame Rate and Magnetic Susceptibility Correction.

Proton resonance frequency shift (PRFS) is an MRI-based simple temperature mapping method that exhibits higher spatial and temporal resolution than temperature mapping methods based on T1 relaxation time and diffusion. PRFS temperature measurements are validated against fiber-optic thermal sensors (FOSs). However, the use of FOSs may introduce temperature errors, leading to both underestimation and overestimation of PRFS measurements, primarily due to material susceptibility changes caused by the thermal sensors. In this study, we demonstrated susceptibility-corrected PRFS (scPRFS) with a high frame rate and accuracy for suitably distributed temperatures. A single-echo-based background removal technique was employed for phase variation correction, primarily owing to magnetic susceptibility, which enabled fast temperature mapping. The scPRFS was used to validate the temperature fidelity by comparing the temperatures of fiber-optic sensors and conventional PRFS through phantom-mimicked human and ex vivo experiments. This study demonstrates that scPRFS measurements in agar-gel are in good agreement with the thermal sensor readings, with a root mean square error (RMSE) of 0.33-0.36 °C in the phantom model and 0.12-0.16 °C in the ex vivo experiment. These results highlight the potential of scPRFS for precise thermal monitoring and ablation in both low- and high-temperature non-invasive therapies.

Analysis of temperature behavior in biological tissue in photothermal therapy according to laser irradiation angle.

The type of death of biological tissue varies with temperature and is broadly classified as apoptosis and necrosis. A new treatment called photothermal therapy is being studied on this basis. Photothermal therapy is a treatment technique based on photothermal effects and has the advantage of not requiring incisions and, therefore, no bleeding. In this study, a numerical analysis of photothermal therapy for squamous cell carcinoma was performed. Photothermal agents used were gold nanoparticles, and the photothermal therapy effect was confirmed by changing the angle of the laser irradiating the tumor tissue. The effectiveness of photothermal therapy was quantitatively assessed on the basis of three apoptotic variables. Further, the volume fraction of gold nanoparticles in the tumor tissue and laser intensity with optimal therapeutic effect for different laser irradiation angles were studied. Thus, the findings of this study can aid the practical implementation of photothermal therapy in the future.

Numerical study on optimization of quantitative treatment conditions for skin cancer photothermal therapy considering multiple blood vessels.

BACKGROUND: In recent years, lasers have gained considerable attention as a potential treatment option in the medical field. Photothermal therapy, in particular, has been investigated as a technique to remove tumor tissue by leveraging photothermal effects. The method involves raising the temperature of the tumor tissue to destroy it and has primarily been studied for skin cancer treatment. OBJECTIVE: This study aimed to simulate a skin layer with squamous cell carcinoma by using numerical modeling and investigate the effect of different numbers of blood vessels on the temperature distribution in the medium under conditions such as varied laser intensity and gold nanoparticle volume fraction. METHODS: Optical properties of the light absorption enhancer were calculated using the discrete dipole approximation method, and the temperature and velocity distribution were computed using continuity, momentum, and energy equations. RESULTS: Quantitative determination of the apoptotic variable was performed to evaluate the treatment effect for each case, and the treatment condition with the maximum treatment effect was identified. Laser intensity with optimal therapeutic effect was confirmed to be 0.13 W, 0.15 W, 0.18 W, and 0.24 W, depending on the number of vessels, respectively, and the volume fraction of injected GNRs was confirmed to be 10-6 for all vessel numbers. CONCLUSION: The results of this study can serve as a guide for selecting appropriate treatment conditions when conducting photothermal therapy in the future.

Effect of gold nanoparticles distribution radius on photothermal therapy efficacy.

Lasers are used in various fields, however, in the medical field, they are mainly used for incision or chemotherapy. Photothermal therapy (PTT) is an anti-cancer treatment technique that uses lasers and the photothermal effect to increase the temperature of tumor tissue and induce its death. In this study, the therapeutic effect of PTT using gold nanoparticles as a photothermal converter was analyzed numerically for the occurrence of squamous cell carcinoma inside a skin section consisting four layers. Numerical modeling was implemented to calculate the temperature distribution inside the biological tissue while varying the distribution radius of gold nanoparticles in the tumor tissue, the number of injections, and the intensity of the irradiating laser. For the given situation, the optimal treatment effect was observed when the distribution radius ratio of the injected gold nanoparticles (GNPs) was 1, the number of injections was 7, and the intensity of the irradiated laser was 52 mW. Three apoptotic variables were used to quantitively evaluate the effect of PTT in each case and thus suggest the optimal treatment effect. However, although the temperature range at which apoptosis occurs is known, the maintenance of that temperature range is still under research and the temporal influence of apoptosis remains to be determined.

Optimized End Functionality of Silane-Terminated Liquid Butadiene Rubber for Silica-Filled Rubber Compounds.

As the world is shifting from internal combustion engine vehicles to electric vehicles in response to environmental pollution, the tire industry has been conducting research on tire performance to meet the requirements of electric vehicles. In this experiment, functionalized liquid butadiene rubber (F-LqBR) with triethoxysilyl groups at both ends was introduced into a silica-filled rubber compound as a substitute for treated distillate aromatic extract (TDAE) oil, and comparative evaluation was conducted according to the number of triethoxysilyl groups. The results showed that F-LqBRs improved silica dispersion in the rubber matrix through the formation of chemical bonds between silanol groups and the base rubber, and reduced rolling resistance by limiting chain end mobility and improving filler-rubber interaction. However, when the number of triethoxysilyl groups in F-LqBR was increased from two to four, self-condensation increased, the reactivity of the silanol groups decreased, and the improvement of properties was reduced. As a result, the optimized end functionality of triethoxysilyl groups for F-LqBR in silica-filled rubber compound was two. The 2-Azo-LqBR with the optimized functionality showed an improvement of 10% in rolling resistance, 16% in snow traction, and 17% in abrasion resistance when 10 phr of TDAE oil was substituted.

Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms.

Comparative Genomic Analysis and BTEX Degradation Pathways of a Thermotolerant Cupriavidus cauae PHS1.

Volatile organic compounds such as benzene, toluene, ethylbenzene, and isomers of xylenes (BTEX) constitute a group of monoaromatic compounds that are found in petroleum and have been classified as priority pollutants. In this study, based on its newly sequenced genome, we reclassified the previously identified BTEX-degrading thermotolerant strain Ralstonia sp. PHS1 as Cupriavidus cauae PHS1. Also presented are the complete genome sequence of C. cauae PHS1, its annotation, species delineation, and a comparative analysis of the BTEX-degrading gene cluster. Moreover, we cloned and characterized the BTEX-degrading pathway genes in C. cauae PHS1, the BTEX-degrading gene cluster of which consists of two monooxygenases and meta-cleavage genes. A genome-wide investigation of the PHS1 coding sequence and the experimentally confirmed regioselectivity of the toluene monooxygenases and catechol 2,3-dioxygenase allowed us to reconstruct the BTEX degradation pathway. The degradation of BTEX begins with aromatic ring hydroxylation, followed by ring cleavage, and eventually enters the core carbon metabolism. The information provided here on the genome and BTEX-degrading pathway of the thermotolerant strain C. cauae PHS1 could be useful in constructing an efficient production host.

Novel Activity of ODZ10117, a STAT3 Inhibitor, for Regulation of NLRP3 Inflammasome Activation.

The NLRP3 inflammasome serves as a host defense mechanism against various pathogens, but there is growing evidence linking its activation in sterile condition to diverse inflammatory diseases. Therefore, the identification of specific inhibitors that target NLRP3 inflammasome activation is meaningful and important for novel therapies for NLRP3 inflammasome-associated diseases. In this study, we identified a chemical compound, namely ODZ10117 (ODZ), that showed NLRP3 inflammasome-targeting anti-inflammatory effects during the screening of a chemical library for anti-inflammatory activity. Although ODZ was initially discovered as a STAT3 inhibitor, here we found it also has inhibitory activity on NLRP3 inflammasome activation. ODZ inhibited the cleavage of caspase-1 and IL-1ß-induced canonical NLRP3 inflammasome triggers, but had no effect on those induced by AIM2 or NLRC4 triggers. Mechanistically, ODZ impairs NLRP3 inflammasome activation through the inhibition of NLRP3-NEK7 interaction that is required for inflammasome formation. Moreover, the results obtained from the in silico docking experiment suggested that ODZ targets NLRP3 protein, which provides evidence for the specificity of ODZ to the NLRP3 inflammasome. Furthermore, ODZ administration significantly reduced MSU-induced IL-1ß release and the mortality rate of mice with LPS-induced sepsis. Collectively, these results demonstrate a novel effect of ODZ10117 in regulating NLRP3 inflammasome activation both in vitro and in vivo, making it a promising candidate for the treatment of NLRP3-inflammasome-associated immune disorders and cancer.

The Escherichia coli Fur pan-regulon has few conserved but many unique regulatory targets.

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.

Quantitative Analysis of Photothermal Therapy of Tumor Tissue Using Various Gold Nanoparticle Injection Schemes.

Photothermal therapy is a new chemotherapy technique using photothermal effects, a phenomenon in which light energy is converted into thermal energy. Since the treatment technique is performed without surgical incision, it does not cause bleeding and patients are expected to make rapid recoveries, which are significant advantages. In this study, photothermal therapy with direct injection of gold nanoparticles into tumor tissue was simulated through numerical modeling. The treatment effect resulting from changing the intensity of the irradiated laser, volume fraction of the injected gold nanoparticles, and number of gold nanoparticle injections was quantitatively evaluated. The discrete dipole approximation method was applied to calculate the optical properties of the entire medium, and the Monte Carlo method was applied to identify the absorption and scattering behavior of lasers in tissue. In addition, by confirming the temperature distribution of the entire medium through the calculated light absorption distribution, the treatment effect of photothermal therapy was evaluated, and the optimal treatment conditions were suggested. This is expected to accelerate the popularization of photothermal therapy in the future.

Polyphenols Coordinated with Cu (II) in an Aqueous System Build Ion-Channel Coatings on Hair Surfaces.

Recently, developments in the field of cosmetics have led to a renewed interest in hair dyeing. However, damage to the hair during the dyeing process has increased hesitation in attempting hair dyeing. As a result, hair dyes with minimal side effects have been in constant demand, and are being developed. In this study, natural-extract polyphenols, pyrogallol, and gallic acid are coordinated by CuCl2 in a NaCl aqueous solution to form an oligomer, which creates an ion-channel coating on the hair surface to protect it. This work attempts to develop fast, simple, and damage-free hair-dye ingredients based on pyrogallol and gallic acid. The morphology and elements of polyphenols coated on hair are characterized. The results reveal that the hair is dyed with the polyphenol-based dye reagent successfully. Moreover, the thickness of the dyed hair continuously rises ten times after dyeing. The tensile strength of the dyed hair is also measured, showing an upward and downward trend. These results reflect the fact that pyrogallol and gallic acid are considered to be the essential and functional polyphenols, and can build ion blocks on hair, which can create new multifunctional coating materials.

Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles.

Accurately estimating the state-of-health (SOH) of lithium-ion batteries is emerging as a hot topic because of the rapid increase in electric appliance usage. However, versatile applicability to various battery compositions and diverse cycling conditions, and prediction only with partial data still remain challenges. In this paper, a Deep-learning-based Graphical approach to Estimation of Lithium-ion batteries SOH (D-GELS) was developed to predict the SOH covering three cathode materials, LiFePO4, LiNiCoAlO2, and LiNiCOMnO2. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings.

A Machine Learning Approach Reveals a Microbiota Signature for Infection with Mycobacterium avium subsp. paratuberculosis in Cattle.

Although Mycobacterium avium subsp. paratuberculosis (MAP) has threatened public health and the livestock industry, the current diagnostic tools (e.g., fecal PCR and enzyme-linked immunosorbent assay [ELISA]) for MAP infection have some limitations, such as inconsistent results due to intermittent bacterial shedding or low sensitivity during the early stage of infection. Therefore, this study aimed to develop a novel biomarker focusing on elucidating the gut microbial signature of MAP-positive ruminants, since the clinical signs of MAP infection are closely related to dysbiosis. 16S rRNA-based gut microbial community analysis revealed both a decrease in microbial diversity and the emergence of several distinct taxa following MAP infection. To determine the discriminant taxa diagnostic of MAP infection, machine learning-based feature selection and predictive model construction were applied to taxon abundance data or their transformed derivatives. The selected taxa, such as Clostridioides (formerly Clostridium) difficile, were used to build models using a support vector machine, linear support vector classification, k-nearest neighbor, and random forest with 10-fold cross-validation. The receiver operating characteristic-area under the curve (ROC-AUC) analysis of the models revealed their high accuracy, up to approximately 96%. Collectively, taxonomic signatures of cattle gut microbiotas according to MAP infection status could be identified by feature selection tools and applied to establish a predictive model for the infection state. IMPORTANCE Due to the limitations, such as intermittent bacterial shedding or poor sensitivity, of the current diagnostic tools for Johne's disease, novel biomarkers are urgently needed to aid control of the disease. Here, we explored the fecal microbiota of Johne's disease-affected cattle and tried to discover distinct microbial characteristics which have the potential to be novel noninvasive biomarkers. Through 16S rRNA sequencing and machine learning approaches, a dozen taxa were selected as taxonomic signatures to discriminate the disease state. In addition, when constructing predictive models using relative abundance data of the corresponding taxa, the models showed high accuracy for classification, even including animals with subclinical infection. Thus, our study suggested novel noninvasive microbiological biomarkers that are robustly expressed regardless of subclinical infection and the applicability of machine learning for diagnosis of Johne's disease.

Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli.

The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling.

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users' specific needs.