sOCP: a framework predicting smORF coding potential based on TIS and in-frame features and effectively applied in the human genome.

Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72 000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.

Bacterium-enabled transient gene activation by artificial transcription factors for resolving gene regulation in maize.

Understanding gene regulatory networks is essential to elucidate developmental processes and environmental responses. Here, we studied regulation of a maize (Zea mays) transcription factor gene using designer transcription activator-like effectors (dTALes), which are synthetic Type III TALes of the bacterial genus Xanthomonas and serve as inducers of disease susceptibility gene transcription in host cells. The maize pathogen Xanthomonas vasicola pv. vasculorum was used to introduce 2 independent dTALes into maize cells to induced expression of the gene glossy3 (gl3), which encodes a MYB transcription factor involved in biosynthesis of cuticular wax. RNA-seq analysis of leaf samples identified, in addition to gl3, 146 genes altered in expression by the 2 dTALes. Nine of the 10 genes known to be involved in cuticular wax biosynthesis were upregulated by at least 1 of the 2 dTALes. A gene previously unknown to be associated with gl3, Zm00001d017418, which encodes aldehyde dehydrogenase, was also expressed in a dTALe-dependent manner. A chemically induced mutant and a CRISPR-Cas9 mutant of Zm00001d017418 both exhibited glossy leaf phenotypes, indicating that Zm00001d017418 is involved in biosynthesis of cuticular waxes. Bacterial protein delivery of dTALes proved to be a straightforward and practical approach for the analysis and discovery of pathway-specific genes in maize.

Ciliogenesis requires sphingolipid-dependent membrane and axoneme interaction.

Cilium formation and regeneration requires new protein synthesis, but the underlying cytosolic translational reprogramming remains largely unknown. Using ribosome footprinting, we performed global translatome profiling during cilia regeneration in Chlamydomonas and uncovered that flagellar genes undergo an early transcriptional activation but late translational repression. This pattern guided our identification of sphingolipid metabolism enzymes, including serine palmitoyltransferase (SPT), as essential regulators for ciliogenesis. Cryo-electron tomography showed that ceramide loss abnormally increased the membrane-axoneme distance and generated bulged cilia. We found that ceramides interact with intraflagellar transport (IFT) particle proteins that IFT motors transport along axoneme microtubules (MTs), suggesting that ceramide-IFT particle-IFT motor-MT interactions connect the ciliary membrane with the axoneme to form rod-shaped cilia. SPT-deficient vertebrate cells were defective in ciliogenesis, and SPT mutations from patients with hereditary sensory neuropathy disrupted cilia, which could be restored by sphingolipid supplementation. These results reveal a conserved role of sphingolipid in cilium formation and link compromised sphingolipid production with ciliopathies.

Genome analysis and hyphal movement characterization of the hitchhiker endohyphal Enterobacter sp. from Rhizoctonia solani.

Bacterial-fungal interactions are pervasive in the rhizosphere. While an increasing number of endohyphal bacteria have been identified, little is known about their ecology and impact on the associated fungal hosts and the surrounding environment. In this study, we characterized the genome of an Enterobacter sp. Crenshaw (En-Cren), which was isolated from the generalist fungal pathogen Rhizoctonia solani, and examined the genetic potential of the bacterium with regard to the phenotypic traits associated with the fungus. Overall, the En-Cren genome size was typical for members of the genus and was capable of free-living growth. The genome was 4.6 MB in size, and no plasmids were detected. Several prophage regions and genomic islands were identified that harbor unique genes in comparison with phylogenetically closely related Enterobacter spp. Type VI secretion system and cyanate assimilation genes were identified from the bacterium, while some common heavy metal resistance genes were absent. En-Cren contains the key genes for indole-3-acetic acid (IAA) and phenylacetic acid (PAA) biosynthesis, and produces IAA and PAA in vitro, which may impact the ecology or pathogenicity of the fungal pathogen in vivo. En-Cren was observed to move along hyphae of R. solani and on other basidiomycetes and ascomycetes in culture. The bacterial flagellum is essential for hyphal movement, while other pathways and genes may also be involved.IMPORTANCEThe genome characterization and comparative genomics analysis of Enterobacter sp. Crenshaw provided the foundation and resources for a better understanding of the ecology and evolution of this endohyphal bacteria in the rhizosphere. The ability to produce indole-3-acetic acid and phenylacetic acid may provide new angles to study the impact of phytohormones during the plant-pathogen interactions. The hitchhiking behavior of the bacterium on a diverse group of fungi, while inhibiting the growth of some others, revealed new areas of bacterial-fungal signaling and interaction, which have yet to be explored.

Patient-reported quality of life and working status outcomes in ambulatory patients with idiopathic inflammatory myopathy.

OBJECTIVE: To investigate the health-related quality of life (HR-QoL), work productivity and activity impairment and associated factors among patients with idiopathic inflammatory myopathy (IIM). METHODS: This was an observational, cross-sectional study. The 189 ambulatory patients with IIM were recruited from May 2019 to May 2022. HR-QoL was measured by the European Quality of Life 5-Dimension (EQ-5D) questionnaire. The Work Productivity and Activity Impairment (WPAI) questionnaire was used to evaluate work productivity and activity impairment. The IIM-related parameters were assessed by the 8-item Manual Muscle Test (MMT-8), Myositis Disease Activity Assessment visual analogue scale (MYOACT), Myositis Damage Index (MDI), Disease Activity Score (DAS) and Physician/Patient Global Assessment (PhGA/PtGA). Quantile regression and ordinal logistic regression were performed to identify the factors, considering EQ-5D or WPAI scores as dependent variables, respectively. RESULTS: Of the 189 IIM patients enrolled, 60% had DM, 13% had PM and 27% had clinical amyopathic DM. The median EQ-5D score was 1.00 (95% CI 0.73, 1.00), 28% were employed and 45% of overall work was impaired due to health problems. EQ-5D values were positively associated with MMT-8 and negatively with MYOACT, DAS, MDI-global and PhGA/PtGA. For the WPAI, activity impairment was associated with a lower MMT-8 score, older onset age and higher PhGA only in 25th-75th percentile. Increased PtGA was associated with increased activity and overall working productivity impairment in most quantiles (P<0.05). CONCLUSION: Multiple disease characteristics were associated with reduced HR-QoL or working productivity impairment in patients with IIM, especially for PtGA.

CyanoMapDB: a database integrating experimentally validated protein-protein interactions in cyanobacteria.

As one of the essential life forms in the biosphere, research on cyanobacteria has been growing remarkably for decades. Biological functions in organisms are often accomplished through protein-protein interactions (PPIs), which help to regulate interacting proteins or organize them into an integral machine. However, the study of PPIs in cyanobacteria falls far behind that in mammals and has not been integrated for ease of use. Thus, we built CyanoMapDB (http://www.cyanomapdb.msbio.pro/), a database providing cyanobacterial PPIs with experimental evidence, consisting of 52,304 PPIs among 6,789 proteins from 23 cyanobacterial species. We collected available data in UniProt, STRING, and IntAct, and mined numerous PPIs from co-fractionation MS data in cyanobacteria. The integrated data are accessible in CyanoMapDB (http://www.cyanomapdb.msbio.pro/), enabling users to easily query proteins of interest, investigate interacting proteins with evidence from different sources, and acquire a visual network of the target protein. We believe that CyanoMapDB will promote research involved with cyanobacteria and plants.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI.

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population.

This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.

Gouqi-derived nanovesicles (GqDNVs) inhibited dexamethasone-induced muscle atrophy associating with AMPK/SIRT1/PGC1α signaling pathway.

With the increasing trend of global aging, sarcopenia has become a significant public health issue. Goji berry, also known as "Gou qi zi" in China, is a traditional Chinese herb that can enhance the structure and function of muscles and bones. Otherwise, previous excellent publications illustrated that plant-derived exosome-like nanoparticles can exert good bioactive functions in different aging or disease models. Thus, we issued the hypothesis that Gouqi-derived nanovesicles (GqDNVs) may also have the ability to improve skeletal muscle health, though the effect and its mechanism need to be explored. Hence, we have extracted GqDNVs from fresh berries of Lycium barbarum L. (goji) and found that the contents of GqDNVs are rich in saccharides and lipids. Based on the pathway annotations and predictions in non-targeted metabolome analysis, GqDNVs are tightly associated with the pathways in metabolism. In muscle atrophy model mice, intramuscular injection of GqDNVs improves the cross-sectional area of the quadriceps muscle, grip strength and the AMPK/SIRT1/PGC1α pathway expression. After separately inhibiting AMPK or PGC1α in C2C12 cells with dexamethasone administration, we have found that the activated AMPK plays the chief role in improving cell proliferation induced by GqDNVs. Furthermore, the energy-targeted metabolome analysis in the quadriceps muscle demonstrates that the GqDNVs up-regulate the metabolism of amino sugar and nucleotide sugar, autophagy and oxidative phosphorylation process, which indicates the activation of muscle regeneration. Besides, the Spearman rank analysis shows close associations between the quality and function of skeletal muscle, metabolites and expression levels of AMPK and SIRT1. In this study, we provide a new founding that GqDNVs can improve the quality and function of skeletal muscle accompanying the activated AMPK/SIRT1/PGC1α signaling pathway. Therefore, GqDNVs have the effect of anti-aging skeletal muscle as a potential adjuvant or complementary method or idea in future therapy and research.

Development of Loop-Mediated Isothermal Amplification Assays for the Rapid and Accurate Diagnosis of Exserohilum turcicum for Field Applications.

Northern corn leaf blight (NCLB), caused by Exserohilum turcicum, is one of the most devastating foliar diseases of maize. Rapid and accurate diagnosis for this disease is urgently needed but still limited. Here, we establish a field-deployable diagnostic method to detect E. turcicum based on loop-mediated isothermal amplification (LAMP) assays. A software application called K-mer Elimination by Cross-reference was used to search for the specific sequences belonging to E. turcicum by comparing the whole genome sequence between E. turcicum and other known maize pathogens. Five LAMP primer sets were designed based on specific and single-copy fragments of E. turcicum. Post-LAMP analyses indicated that only the primer set, Et9468_set1, was the most suitable, producing a ladder-like amplification pattern in the agarose gel electrophoresis and a strong fluorescence signal in the presence of SYBR Green I. The LAMP assay using Et9468_set1 primers demonstrated a high level of specificity in distinguishing E. turcicum from six other common fungal pathogens of maize, as well as 12 more fungal and oomycete strains including the epiphytic fungi from maize leaves and other crop pathogens. Moreover, it exhibited remarkable sensitivity by detecting five copies per reaction, which was approximately 104 times more sensitive compared with conventional PCR. The LAMP assay successfully detected E. turcicum in field maize leaves without DNA extraction, demonstrating its suitability for rapid on-spot detection of NCLB. Our study provides a direct LAMP diagnostic method to detect E. turcicum, which enables on-site pathogen detection in the field and the development of preventive strategies for NCLB management.

Deep learning based automatic internal gross target volume delineation from 4D-CT of hepatocellular carcinoma patients.

BACKGROUND: The location and morphology of the liver are significantly affected by respiratory motion. Therefore, delineating the gross target volume (GTV) based on 4D medical images is more accurate than regular 3D-CT with contrast. However, the 4D method is also more time-consuming and laborious. This study proposes a deep learning (DL) framework based on 4D-CT that can achieve automatic delineation of internal GTV. METHODS: The proposed network consists of two encoding paths, one for feature extraction of adjacent slices (spatial slices) in a specific 3D-CT sequence, and one for feature extraction of slices at the same location in three adjacent phase 3D-CT sequences (temporal slices), a feature fusion module based on an attention mechanism was proposed for fusing the temporal and spatial features. Twenty-six patients' 4D-CT, each consisting of 10 respiratory phases, were used as the dataset. The Hausdorff distance (HD95), Dice similarity coefficient (DSC), and volume difference (VD) between the manual and predicted tumor contour were computed to evaluate the model's segmentation accuracy. RESULTS: The predicted GTVs and IGTVs were compared quantitatively and visually with the ground truth. For the test dataset, the proposed method achieved a mean DSC of 0.869 ± 0.089 and an HD95 of 5.14 ± 3.34 mm for all GTVs, with under-segmented GTVs on some CT slices being compensated by GTVs on other slices, resulting in better agreement between the predicted IGTVs and the ground truth, with a mean DSC of 0.882 ± 0.085 and an HD95 of 4.88 ± 2.84 mm. The best GTV results were generally observed at the end-inspiration stage. CONCLUSIONS: Our proposed DL framework for tumor segmentation on 4D-CT datasets shows promise for fully automated delineation in the future. The promising results of this work provide impetus for its integration into the 4DCT treatment planning workflow to improve hepatocellular carcinoma radiotherapy.

Effects of Timing and Types of Protein Supplementation on Improving Muscle Mass, Strength, and Physical Performance in Adults Undergoing Resistance Training: A Network Meta-Analysis.

Precise protein supplementation strategies for muscle improvement are still lacking. The timing or type of protein supplementation has been debated as a window of opportunity to improve muscle mass, strength, and physical performance. We conducted a network meta-analysis of randomized controlled trials with protein supplements and resistance training. PubMed, Web of Science, Cochrane Library, and SPORTDiscus databases were searched until May 1, 2023. We included 116 eligible trials with 4,711 participants that reported on 11 timing and 14 types of protein supplementation. Compared with placebo, protein supplementation after exercise (mean difference [MD]: 0.54 kg [95% confidence intervals 0.10, 0.99] for fat-free mass, MD: 0.34 kg [95% confidence intervals 0.10, 0.58] for skeletal muscle mass) and at night (MD: 2.85 kg [0.49, 5.22] for handgrip strength, MD: 12.12 kg [3.26, 20.99] for leg press strength) was most effective in improving muscle mass and strength, respectively (moderate certainty). Milk proteins (milk, whey protein, yogurt, casein, and bovine colostrum), red meat, and mixed protein were effective for gains in both muscle mass and strength (moderate certainty). No timing or type of protein showed a significant enhancement in physical performance (timed up-to-go test, 6-min walk test, and gait speed). Pre/postexercise and Night are key recommended times of protein intake to increase muscle mass and strength, respectively. Milk proteins are the preferred types of protein supplements for improving muscle mass and strength. Future randomized controlled trials that directly compare the effects of protein timing or types are needed. This trial was registered at International Prospective Register of Systematic Reviews as CRD42022358766.

Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults.

OBJECTIVES: To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation (MPR), and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. METHODS: The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction (VR) and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures (both left and right) and lambdoid sutures (both left and right) were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation (gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression) were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. RESULTS: Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. CONCLUSIONS: The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.

Identification of novel smORFs and microprotein acting in response to rehydration of Nostoc flagelliforme.

Nostoc flagelliforme, a terrestrial cyanobacterium spread throughout arid and semi-arid areas, has been long known for its outstanding adaptability to extremely dry conditions. This microorganism is able to recover biological activities within hours after months of anhydrobiosis state, attracting investigation through proteomic analysis. Except for canonical proteome, microproteins encoded by small ORFs (smORFs) have recently been regarded as indispensable participants in metabolic processes. However, the involvement of smORFs in N. flagelliforme remains unknown. Here we first constructed a smORF database in N. flagelliforme using bioinformatic prediction, resulting in 6072 novel smORFs. Then LS-MS/MS analysis was applied to identify expression patterns of microproteins and seek smORFs and their encoded microprotein playing a role during rehydration. In total, 18 novel microproteins were mined based on a smORF searching strategy combined with three proteomic assays, of which five were annotated as ribosomal proteins, one as RNA polymerase subunit, and one as acetohydroxy acid isomeroreductase. We also suggested the possible functions of smORFs according to their expression pattern and discovered two neighboring and homologous smORFs. All these results will expand our knowledge of smORFs-encoded microproteins and their relation to the stress response of extremophilic microorganisms.

Systematic Proteomics Study on the Embryonic Development of Danio rerio.

The early development of zebrafish (Danio rerio) is a complex and dynamic physiological process involving cell division, differentiation, and movement. Currently, the genome and transcriptome techniques have been widely used to study the embryonic development of zebrafish. However, the research of proteomics based on proteins that directly execute functions is relatively vacant. In this work, we apply label-free quantitative proteomics to explore protein profiling during zebrafish's embryogenesis, and a total of 5961 proteins were identified at 10 stages of zebrafish's early development. The identified proteins were divided into 11 modules according to weighted gene coexpression network analysis (WGCNA), and the characteristics between modules were significantly different. For example, mitochondria-related functions enriched the early development of zebrafish. Primordial germ cell-related proteins were identified at the 4-cell stage, while the eye development event is dominated at 5 days post fertilization (dpf). By combining with published transcriptomics data, we discovered some proteins that may be involved in activating zygotic genes. Meanwhile, 137 novel proteins were identified. This study comprehensively analyzed the dynamic processes in the embryonic development of zebrafish from the perspective of proteomics. It provided solid data support for further understanding of the molecular mechanism of its development.

Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

OBJECTIVE: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. METHODS: Chest CT were reconstructed using volume rendering (VR) and maximum intensity projection (MIP) separately. Retrospective data of 2500 patients aged 20.00-69.99 years were obtained. The cohort was split into training (80%) and validation (20%) sets. Additional independent data from 200 patients were used as the test set and external validation set. Different modality DL models were developed accordingly. Comparisons were hierarchically performed by VR versus MIP, single-modality versus multi-modality, and DL versus manual method. Mean absolute error (MAE) was the primary parameter of comparison. RESULTS: A total of 2700 patients (mean age = 45.24 years ± 14.03 [SD]) were evaluated. Of single-modality models, MAEs yielded by VR were lower than MIP. Multi-modality models generally yielded lower MAEs than the optimal single-modality model. The best-performing multi-modality model obtained the lowest MAEs of 3.78 in males and 3.40 in females. On the test set, DL achieved MAEs of 3.78 in males and 3.92 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. For the external validation, MAEs were 6.05 in males and 6.68 in females for DL, and 6.93 and 8.28 for the manual method. CONCLUSIONS: DL demonstrated better performance than the manual method in AAE based on CT reconstruction of the costal cartilage. CLINICAL RELEVANCE STATEMENT: Aging leads to diseases, functional performance deterioration, and both physical and physiological damage over time. Accurate AAE may aid in diagnosing the personalization of aging processes. KEY POINTS: • VR-based DL models outperformed MIP-based models with lower MAEs and higher R2 values. • All multi-modality DL models showed better performance than single-modality models in adult age estimation. • DL models achieved a better performance than expert assessments.

Human milk extracellular vesicles enhance muscle growth and physical performance of immature mice associating with Akt/mTOR/p70s6k signaling pathway.

Extracellular vesicles (EVs) play an important role in human and bovine milk composition. According to excellent published studies, it also exerts various functions in the gut, bone, or immune system. However, the effects of milk-derived EVs on skeletal muscle growth and performance have yet to be fully explored. Firstly, the current study examined the amino acids profile in human milk EVs (HME) and bovine milk EVs (BME) using targeted metabolomics. Secondly, HME and BME were injected in the quadriceps of mice for four weeks (1 time/3 days). Then, related muscle performance, muscle growth markers/pathways, and amino acids profile were detected or measured by grip strength analysis, rotarod performance testing, Jenner-Giemsa/H&E staining, Western blotting, and targeted metabolomics, respectively. Finally, HME and BME were co-cultured with C2C12 cells to detect the above-related indexes and further testify relative phenomena. Our findings mainly demonstrated that HME and BME significantly increase the diameter of C2C12 myotubes. HME treatment demonstrates higher exercise performance and muscle fiber densities than BME treatment. Besides, after KEGG and correlation analyses with biological function after HME and BME treatment, results showed L-Ornithine acts as a "notable marker" after HME treatment to affect mouse skeletal muscle growth or functions. Otherwise, L-Ornithine also significantly positively correlates with the activation of the AKT/mTOR pathway and myogenic regulatory factors (MRFs) and can also be observed in muscle and C2C12 cells after HME treatment. Overall, our study not only provides a novel result for the amino acid composition of HME and BME, but the current study also indicates the advantage of human milk on skeletal muscle growth and performance.

Identification and analysis of smORFs in Chlamydomonas reinhardtii.

Small open reading frames (smORFs) have been acknowledged as an important partner in organism functions ranging from bacteria to higher eukaryotes. However, there is a lack of investigation of smORFs in green algae, despite their importance in ecology and evolution. We applied bioinformatic analysis, ribosome profiling, and small peptide proteomics to provide a genome-wide and high-confident smORF database in the model green alga Chlamydomonas reinhardtii. The whole genome was screened first to mine potential coding smORFs. Then conservative analysis, ribosome profiling, and proteomics data were processed to identify conserved smORFs and generate translation evidence. The combination of procedures resulted in 2014 smORFs that might exist in the C. reinhardtii genome. The expression of smORFs in Cd treatment suggested that two smORFs might participate in redox reaction, three in inorganic phosphate transport, and one in DNA repair under stress. Our study built a genome-widely database in C. reinhardtii, providing target smORFs for further research.