Programmable synthetic biomolecular condensates for cellular control.

The formation of biomolecular condensates mediated by a coupling of associative and segregative phase transitions plays a critical role in controlling diverse cellular functions in nature. This has inspired the use of phase transitions to design synthetic systems. While design rules of phase transitions have been established for many synthetic intrinsically disordered proteins, most efforts have focused on investigating their phase behaviors in a test tube. Here, we present a rational engineering approach to program the formation and physical properties of synthetic condensates to achieve intended cellular functions. We demonstrate this approach through targeted plasmid sequestration and transcription regulation in bacteria and modulation of a protein circuit in mammalian cells. Our approach lays the foundation for engineering designer condensates for synthetic biology applications.

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

BACKGROUND: Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. OBJECTIVE: The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. METHODS: This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). RESULTS: Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. CONCLUSIONS: Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Tunable Mott Dirac and Kagome Bands Engineered on 1T-TaS2.

Strongly interacting electrons in hexagonal and kagome lattices exhibit rich phase diagrams of exotic quantum states, including superconductivity and correlated topological orders. However, material realizations of these electronic states have been scarce in nature or by design. Here, we theoretically propose an approach to realize artificial lattices by metal adsorption on a 2D Mott insulator 1T-TaS2. Alkali, alkaline-earth, and group 13 metal atoms are deposited in (√3 × âˆš3)R30° and 2 × 2 TaS2 superstructures of honeycomb- and kagome-lattice symmetries exhibiting Dirac and kagome bands, respectively. The strong electron correlation of 1T-TaS2 drives the honeycomb and kagome systems into correlated topological phases described by Kane-Mele-Hubbard and kagome-Hubbard models. We further show that the 2/3 or 3/4 band filling of Mott Dirac and flat bands can be achieved with a proper concentration of Mg adsorbates. Our proposal may be readily implemented in experiments, offering an attractive condensed-matter platform to exploit the interplay of correlated topological order and superconductivity.

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations.

Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to the 2021 U.S. Preventive Services Task Force (USPSTF) recommendations in a health check-up sample. Materials and Methods This single-center retrospective study included consecutive current and former smokers aged 50-80 years who underwent chest radiography during a health check-up between January 2004 and June 2018. Discrimination performance, including receiver operating characteristic curve analysis and area under the receiver operating characteristic curve (AUC) calculation, of the model for incident lung cancers was evaluated. The added value of the model to the 2021 USPSTF recommendations was investigated for lung cancer inclusion rate, proportion of selected CT screening candidates, and positive predictive value (PPV). Results For model validation, a total of 19 488 individuals (mean age, 58 years ± 6 [SD]; 18 467 [95%] men) and the subset of USPSTF-eligible individuals (n = 7835; mean age, 57 years ± 6; 7699 [98%] men) were assessed, and the AUCs for incident lung cancers were 0.68 (95% CI: 0.62, 0.73) and 0.75 (95% CI: 0.68, 0.81), respectively. In individuals with pack-year information (n = 17 390), when excluding low- and indeterminate-risk categories from the USPSTF-eligible sample, the proportion of selected CT screening candidates was reduced to 35.8% (6233 of 17 390) from 45.1% (7835 of 17 390, P < .001), with three missed lung cancers (0.2%). The cancer inclusion rate (0.3% [53 of 17 390] vs 0.3% [56 of 17 390], P = .85) and PPV (0.9% [53 of 6233] vs 0.7% [56 of 7835], P = .42) remained unaffected. Conclusion An externally validated deep learning model showed the added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening in reducing the number of screening candidates while maintaining the inclusion rate and positive predictive value for incident lung cancer. © RSNA, 2022 Online supplemental material is available for this article.

Augmented Reality-Based Visual Cue for Guiding Central Catheter Insertion in Pediatric Oncologic Patients.

BACKGROUND: Pediatric hemato-oncologic patients require central catheters for chemotherapy, and the junction of the superior vena cava and right atrium is considered the ideal location for catheter tips. Skin landmarks or fluoroscopic supports have been applied to identify the cavoatrial junction; however, none has been recognized as the gold standard. Therefore, we aim to develop a safe and accurate technique using augmented reality technology for the location of the cavoatrial junction in pediatric hemato-oncologic patients. METHODS: Fifteen oncology patients who underwent chest computed tomography were enrolled for Hickman catheter or chemoport insertion. With the aid of augmented reality technology, three-dimensional models of the internal jugular veins, external jugular veins, subclavian veins, superior vena cava, and right atrium were constructed. On inserting the central vein catheters, the cavoatrial junction identified using the three-dimensional models were marked on the body surface, the tip was positioned at the corresponding location, and the actual insertion location was confirmed using a portable x-ray machine. The proposed method was evaluated by comparing the distance from the cavoatrial junction to the augmented reality location with that to the conventional location on x-ray. RESULTS: The mean distance between the cavoatrial junction and augmented reality location on x-ray was 1.2 cm, which was significantly shorter than that between the cavoatrial junction and conventional location (1.9 cm; P = 0.027). CONCLUSIONS: Central catheter insertion using augmented reality technology is more safe and accurate than that using conventional methods and can be performed at no additional cost in oncology patients.

Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps by endoscopists of different skill levels. METHODS: We developed convolutional neural networks (CNNs) for evaluation of diminutive colorectal polyps, based on efficient neural architecture searches via parameter sharing with augmentation using NBIs of diminutive (≤5 mm) polyps, collected from October 2015 through October 2017 at the Seoul National University Hospital, Healthcare System Gangnam Center (training set). We trained the CNN using images from 1100 adenomatous polyps and 1050 hyperplastic polyps from 1379 patients. We then tested the system using 300 images of 180 adenomatous polyps and 120 hyperplastic polyps, obtained from January 2018 to May 2019. We compared the accuracy of 22 endoscopists of different skill levels (7 novices, 4 experts, and 11 NBI-trained experts) vs the CNN in evaluation of images (adenomatous vs hyperplastic) from 180 adenomatous and 120 hyperplastic polyps. The endoscopists then evaluated the polyp images with knowledge of the CNN-processed results. We conducted mixed-effect logistic and linear regression analyses to determine the effects of AI assistance on the accuracy of analysis of diminutive colorectal polyps by endoscopists (primary outcome). RESULTS: The CNN distinguished adenomatous vs hyperplastic diminutive polyps with 86.7% accuracy, based on histologic analysis as the reference standard. Endoscopists distinguished adenomatous vs hyperplastic diminutive polyps with 82.5% overall accuracy (novices, 73.8% accuracy; experts, 83.8% accuracy; and NBI-trained experts, 87.6% accuracy). With knowledge of the CNN-processed results, the overall accuracy of the endoscopists increased to 88.5% (P < .05). With knowledge of the CNN-processed results, the accuracy of novice endoscopists increased to 85.6% (P < .05). The CNN-processed results significantly reduced endoscopist time of diagnosis (from 3.92 to 3.37 seconds per polyp, P = .042). CONCLUSIONS: We developed a CNN that significantly increases the accuracy of evaluation of diminutive colorectal polyps (as adenomatous vs hyperplastic) and reduces the time of diagnosis by endoscopists. This AI assistance system significantly increased the accuracy of analysis by novice endoscopists, who achieved near-expert levels of accuracy without extra training. The CNN assistance system can reduce the skill-level dependence of endoscopists and costs.

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.

Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists.

OBJECTIVES: To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model and expert radiologists. METHODS: This study included 525 patients (309 women; median, 62 years) to develop models, and an independent cohort of 101 patients (57 women; median, 66 years) was used for validation. A size-based logistic model and deep learning models using 2.5-dimension (2.5D) and three-dimension (3D) CT images were developed to discriminate IAC from less invasive pathologies. Overall performance, discrimination, and calibration were assessed. Diagnostic performances of the three thoracic radiologists were compared with those of the deep learning model. RESULTS: The overall performances of the deep learning models (Brier score, 0.122 for the 2.5D DenseNet and 0.121 for the 3D DenseNet) were superior to those of the size-based logistic model (Brier score, 0.198). The area under the receiver operating characteristic curve (AUC) of the 2.5D DenseNet (0.921) was significantly higher than that of the 3D DenseNet (0.835; p = 0.037) and the size-based logistic model (0.836; p = 0.009). At equally high sensitivities of 90%, the 2.5D DenseNet showed significantly higher specificity (88.2%; all p < 0.05) and positive predictive value (97.4%; all p < 0.05) than other models. Model calibration was poor for all models (all p < 0.05). The 2.5D DenseNet had a comparable performance with the radiologists (AUC, 0.848-0.910). CONCLUSION: The 2.5D DenseNet model could be used as a highly sensitive and specific diagnostic tool to differentiate IACs among SSNs for surgical candidates. KEY POINTS: • The deep learning model developed using 2.5D DenseNet showed higher overall performance and discrimination than the size-based logistic model for the differentiation of invasive adenocarcinomas among subsolid nodules for surgical candidates. • The 2.5D DenseNet demonstrated a thoracic radiologist-level diagnostic performance and had higher specificity (88.2%) at equal sensitivities (90%) than the size-based logistic model (specificity, 52.9%). • The 2.5D DenseNet could be used to reduce potential overtreatment for the indolent subsolid nodules or to select candidates for sublobar resection instead of the standard lobectomy.

The influence of heteromultivalency on lectin-glycan binding behavior.

We recently discovered that the nature of lectin multivalency and glycolipid diffusion on cell membranes could lead to the heteromultivalent binding (i.e., a single lectin simultaneously binding to different types of glycolipid ligands). This heteromultivalent binding may even govern the lectin-glycan recognition process. To investigate this, we developed a kinetic Monte Carlo simulation, which only considers the fundamental physics/chemistry principles, to model the process of lectin binding to glycans on cell surfaces. We found that the high-affinity glycan ligands could facilitate lectin binding to other low-affinity glycan ligands, even though these low-affinity ligands are barely detectable in microarrays with immobilized glycan ligands. Such heteromultivalent binding processes significantly change lectin binding behaviors. We hypothesize that living organisms probably utilize this mechanism to regulate the downstream lectin functions. Our finding not only offers a mechanism to describe the concept that lectins are pattern recognition molecules, but also suggests that the two overlooked parameters, surface diffusion of glycan ligand and lectin binding kinetics, can play important roles in glycobiology processes. In this paper, we identified the critical parameters that influence the heteromultivalent binding process. We also discussed how our discovery can impact the current lectin-glycan analysis.

Melaminivora jejuensis sp. nov., isolated from Swinery waste.

A sulfur-oxidizing bacterium, designated strain KBB12T, was isolated from swinery waste collected in Jeju, Republic of Korea. The cells were Gram-stain-negative, flagellated and rod-shaped. Growth occurred at 15-45 °C (optimum, 30-37 °C), at pH 6-9 (optimum, pH 7.0) and in the presence of 0-1 % (w/v) NaCl. The major cellular fatty acids were summed feature 3 (iso-C15 : 0 2-OH and/or C16 : 1 ω7c, C16 : 0 and C18 : 1ω7c. The polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phospholipid and an unidentified lipid. The major isoprenoid quinone was ubiquinone-8 (Q-8) and the DNA G+C content of the genomic DNA was 69.6 mol%. Phylogenetic analyses, based on 16S rRNA gene sequences, showed that the novel isolate belongs to the genus Melaminivora and was most closely related to Melaminivora alkalimesophila CY1T (97.2 % similarity). The DNA-DNA relatedness values between strain KBB12T and M. alkalimesophila DSM26005T was 43.4±2.7 %. On the basis of phylogenetic and phenotypic evidence, it is proposed that strain KBB12T represents a novel species of the genus Melaminivora, for which the name Melaminivora jejuensis sp. nov. is proposed. The type strain is KBB12T (=KCTC 32230T=JCM 18740T).

Case Report of a Large Common Hepatic Artery Aneurysm.

Hepatic artery aneurysm is rare, but appropriate treatment is mandatory. We report a 50-year-old woman with an asymptomatic large hepatic artery aneurysm. The aneurysm was found as a result of abdominal computed tomography (CT) performed as a part of the screening. An open surgery was performed due to the size of the aneurysm. Aneurysmectomy was achieved, and the proper hepatic artery was anastomosed with gastroduodenal artery for adequate blood flow to the liver. Adequate hepatic circulation was confirmed postoperatively by duplex ultrasonography and CT. The patient was discharged on the 9th postoperative day.

The effect of prenatal TVOC exposure on birth and infantile weight: the Mothers and Children's Environmental Health study.

BACKGROUNDVolatile organic compounds (VOCs) might restrict prenatal and postnatal growth. However, the effect of the exposure of prenatal VOCs on postnatal growth has not been studied sufficiently. Thus, we investigated the relationship between the exposure of total volatile organic compounds (TVOCs) during pregnancy and its effects on postnatal growth.METHODSA total of 383 pregnant participants were enrolled from 2006 to 2008. We investigated maternal characteristics using a questionnaire. Personal air samples of TVOCs were obtained in mid or late pregnancy. After these mothers had given birth, 360 singleton newborns were selected and postnatal follow-up data were collected at 6, 12, 24, and 36 months, as well as anthropometric factors including body weight. Multiple general linear and mixed models were applied for statistical analyses.RESULTSThe mean concentration of prenatal exposure to TVOCs was 284.2 µg/m3 and that of formaldehyde was 81.6 µg/m3. The birth weight of newborns decreased significantly with prenatal TVOC exposure (ß=-45.89, P=0.04). The adjusted mean body weight was 300 g lower in the high-TVOC group (⩾75th) compared with that in the low-exposure group (<75th).CONCLUSIONThese results indicate that elevated exposure to TVOCs during the prenatal period may adversely influence early postnatal growth.

Shewanella algicola sp. nov., a marine bacterium isolated from brown algae.

A Gram-stain-negative, aerobic, rod-shaped bacterium motile by means of a single polar flagella, strain ST-6T, was isolated from a brown alga (Sargassum thunbergii) collected in Jeju, Republic of Korea. Strain ST-6T was psychrotolerant, growing at 4-30 °C (optimum 20 °C). Phylogenetic analysis based on 16S rRNA and gyrB gene sequences revealed that strain ST-6T belonged to a distinct lineage in the genus Shewanella. Strain ST-6T was related most closely to Shewanella basaltis J83T, S. gaetbuli TF-27T, S. arctica IT12T, S. vesiculosa M7T and S. aestuarii SC18T, showing 96-97 % and 85-70 % 16S rRNA and gyrB gene sequences similarities, respectively. DNA-DNA relatedness values between strain ST-6T and the type strains of two species of the genus Shewanella were <22.6 %. The major cellular fatty acids (>5 %) were summed feature 3 (comprising C16:1ω7c and/ or iso-C15:0 2-OH), C16:0, iso-C13:0 and C17:1ω8c. The DNA G+C content of strain ST-6Twas 42.4 mol%, and the predominant isoprenoid quinones were menaquinone MK-7 and ubiquinones Q-7 and Q-8. On the basis of its phenotypic properties and phylogenetic distinctiveness, strain ST-6T is considered to represent a novel species of the genus Shewanella, for which the name Shewanella algicola sp. nov. is proposed. The type strain is ST-6T (= KCTC 23253T = JCM 31091T).

Beef peptides mitigate skeletal muscle atrophy in C2C12 myotubes through protein degradation, protein synthesis, and the oxidative stress pathway.

This study aimed to investigate the potential of beef peptides (BPs) in mitigating muscle atrophy induced by dexamethasone (DEX) with underlying three mechanisms in vitro (protein degradation, protein synthesis, and the oxidative stress pathway). Finally, the anti-atrophic effect of BPs was enhanced through purification and isolation. BPs were generated using beef loin hydrolyzed with alcalase/ProteAX/trypsin, each at a concentration of 0.67%, followed by ultrafiltration through a 3 kDa cut-off. BPs (10-100 µg mL-1) dose-dependently counteracted the DEX-induced reductions in myotube diameters, differentiation, fusion, and maturation indices (p < 0.05). Additionally, BPs significantly reduced FoxO1 protein dephosphorylation, thereby suppressing muscle-specific E3 ubiquitin ligases such as muscle RING-finger containing protein-1 and muscle atrophy F-box protein in C2C12 myotubes at concentrations exceeding 25 µg mL-1 (p < 0.05). BPs also enhanced the phosphorylation of protein synthesis markers, including mTOR, 4E-BP1, and p70S6K1, in a dose-dependent manner (p < 0.05) and increased the mRNA expression of antioxidant enzymes. Fractionated peptides derived from BPs, through size exclusion and polarity-based fractionation, also demonstrated enhanced anti-atrophic effects compared to BPs. These peptides downregulated the mRNA expression of primary muscle atrophy markers while upregulated that of antioxidant enzymes. Specifically, peptides GAGAAGAPAGGA (MW 924.5) and AFRSSTKK (MW 826.4) were identified from fractionated peptides of BPs. These findings suggest that BPs, specifically the peptide fractions GAGAAGAPAGGA and AFRSSTKK, could be a potential strategy to mitigate glucocorticoid-induced skeletal muscle atrophy by reducing the E3 ubiquitin ligase activity.

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy.

Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.

Selective interactions of Co2+-Ca2+-concanavalin A with high mannose N-glycans.

Concanavalin A (ConA) has an intrinsic binding affinity to carbohydrates. Here, we obtained Co2+-Ca2+-ConA (2.83 Å, PDB: 8I7Q) via X-ray crystallography by substituting native ConA (Mn2+-Ca2+); it has binding selectivity for high-mannose N-glycan similar to native ConA. Our findings may thus inform antiviral reagent design.

Development of orthogonal aminoacyl-tRNA synthetase mutant for incorporating a non-canonical amino acid.

Genetic code expansion involves introducing non-canonical amino acids (ncAAs) with unique functional groups into proteins to broaden their applications. Orthogonal aminoacyl tRNA synthetase (aaRS), essential for genetic code expansion, facilitates the charging of ncAAs to tRNA. In this study, we developed a new aaRS mutant from Methanosaeta concilii tyrosyl-tRNA synthetase (Mc TyrRS) to incorporate para-azido-L-phenylalanine (AzF). The development involved initial site-specific mutations in Mc TyrRS, followed by random mutagenesis. The new aaRS mutant with amber suppression was isolated through fluorescence-activated cell sorting. The M. concilii aaRS mutant structure was further analyzed to interpret the effect of mutations. This research provides a novel orthogonal aaRS evolution pipeline for highly efficient ncAA incorporation that will contribute to developing novel aaRS from various organisms.

Essential elements of physical fitness analysis in male adolescent athletes using machine learning.

Physical fitness (PF) includes various factors that significantly impacts athletic performance. Analyzing PF is critical in developing customized training methods for athletes based on the sports in which they compete. Previous approaches to analyzing PF have relied on statistical or machine learning algorithms that focus on predicting athlete injury or performance. In this study, six machine learning algorithms were used to analyze the PF of 1,489 male adolescent athletes across five sports, including track & field, football, baseball, swimming, and badminton. Furthermore, the machine learning models were utilized to analyze the essential elements of PF using feature importance of XGBoost, and SHAP values. As a result, XGBoost represents the highest performance, with an average accuracy of 90.14, an area under the curve of 0.86, and F1-score of 0.87, demonstrating the similarity between the sports. Feature importance of XGBoost, and SHAP value provided a quantitative assessment of the relative importance of PF in sports by comparing two sports within each of the five sports. This analysis is expected to be useful in analyzing the essential PF elements of athletes in various sports and recommending personalized exercise methods accordingly.

Texture-preserving low dose CT image denoising using Pearson divergence.

Objective.The mean squared error (MSE), also known asL2loss, has been widely used as a loss function to optimize image denoising models due to its strong performance as a mean estimator of the Gaussian noise model. Recently, various low-dose computed tomography (LDCT) image denoising methods using deep learning combined with the MSE loss have been developed; however, this approach has been observed to suffer from the regression-to-the-mean problem, leading to over-smoothed edges and degradation of texture in the image.Approach.To overcome this issue, we propose a stochastic function in the loss function to improve the texture of the denoised CT images, rather than relying on complicated networks or feature space losses. The proposed loss function includes the MSE loss to learn the mean distribution and the Pearson divergence loss to learn feature textures. Specifically, the Pearson divergence loss is computed in an image space to measure the distance between two intensity measures of denoised low-dose and normal-dose CT images. The evaluation of the proposed model employs a novel approach of multi-metric quantitative analysis utilizing relative texture feature distance.Results.Our experimental results show that the proposed Pearson divergence loss leads to a significant improvement in texture compared to the conventional MSE loss and generative adversarial network (GAN), both qualitatively and quantitatively.Significance.Achieving consistent texture preservation in LDCT is a challenge in conventional GAN-type methods due to adversarial aspects aimed at minimizing noise while preserving texture. By incorporating the Pearson regularizer in the loss function, we can easily achieve a balance between two conflicting properties. Consistent high-quality CT images can significantly help clinicians in diagnoses and supporting researchers in the development of AI-diagnostic models.