Identification of a novel marker and its associated laccase gene for regulating ear length in tropical and subtropical maize lines.

KEY MESSAGE: This study revealed the identification of a novel gene, Zm00001d042906, that regulates maize ear length by modulating lignin synthesis and reported a molecular marker for selecting maize lines with elongated ears. Maize ear length has garnered considerable attention due to its high correlation with yield. In this study, six maize inbred lines of significant importance in maize breeding were used as parents. The temperate maize inbred line Ye107, characterized by a short ear, was crossed with five tropical or subtropical inbred lines featuring longer ears, creating a multi-parent population displaying significant variations in ear length. Through genome-wide association studies and mutation analysis, the A/G variation at SNP_183573532 on chromosome 3 was identified as an effective site for discriminating long-ear maize. Furthermore, the associated gene Zm00001d042906 was found to correlate with maize ear length. Zm00001d042906 was functionally annotated as a laccase (Lac4), which showed activity and influenced lignin synthesis in the midsection cells of the cob, thereby regulating maize ear length. This study further reports a novel molecular marker and a new gene that can assist maize breeding programs in selecting varieties with elongated ears.

Exploring simple triplet representation learning.

Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size. With the meaningful representation extracted from unlabelled data, our model demonstrates exemplary performance across two medical image datasets. It achieves this using only partial labels and outperforms other state-of-the-art methods. The method we present herein offers a novel paradigm for unsupervised representation learning, establishing a baseline that is poised to inspire the development of more intricate SimTrip-based methods across a spectrum of computer vision applications. Code and user guide are released at https://github.com/JerryRollingUp/SimTripSystem, the system also runs at http://43.131.9.159:5000/.

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T2-weighted MR imaging (T2WI) for gastric cancer (GC). METHODS: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T2WI and BLADE-T2WI of stomach were scanned with same spatial resolution. Three radiologists independently evaluated the image qualities using a 5-scale Likert scales (IQS) in terms of lesion delineation, gastric wall boundary conspicuity, and overall image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in measurable lesions. T staging was conducted based on the results of both sequences for GC patients with gastrectomy. Pairwise comparisons between DLSB-T2WI and BLADE-T2WI were performed using the Wilcoxon signed-rank test, paired t-test, and chi-squared test. Kendall's W, Fleiss' Kappa, and intraclass correlation coefficient values were used to determine inter-reader reliability. RESULTS: Against BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05). CONCLUSIONS: DLSB-T2WI demonstrated shorter acquisition time, better image quality, and comparable staging accuracy, which could be an alternative to BLADE-T2WI for gastric cancer imaging.

Asiatic acid and madecassic acid cause cardiotoxicity via inflammation and production of excessive reactive oxygen species in zebrafish.

Centella asiatica (L.) Urban is a famous Chinese traditional medicine, which is widely used for treating various chronic inflammatory diseases. Although there are reports that Centella total glycosides exhibit heart-protective properties, our previous experiment showed that it has cardiac toxic effects in zebrafish. The components of Centella total glycosides are complex, so we recommend further research to determine their key components and mechanisms. In this study, sample quantification was done using liquid chromatography-tandem mass spectrometry. The cardiotoxicity of Centella total glycosides, asiaticoside, madecassoside, asiatic acid, and madecassic acid was evaluated using zebrafish and cell models. The zebrafish oxidative stress model and myocarditis model were used to explore further the mechanisms through which cardiotoxicity is achieved. Asiatic acid and madecassic acid caused zebrafish cardiotoxicity and H9C2 cell death. However, no toxicity effects were observed for asiaticoside and madecassoside in zebrafish, until the solution was saturated. The results from the cell model study showed that asiatic acid and madecassic acid changed the expression of apoptosis-related genes in myocardial cells. In the zebrafish model, high concentrations of these components raised the levels of induced systemic inflammation, neutrophils gathered in the heart, and oxidative stress injury. Asiatic acid and madecassic acid are the main components causing cardiotoxicity in zebrafish. This may be due to enhanced inflammation and reactive oxygen species injury, which causes myocardial cell apoptosis, which further leads to cardiac toxicity.

Prognostic Evaluation for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus Patients Treated with Transarterial Chemoembolization Plus Molecular Targeted Therapies-Development and Validation of the ABPS Score.

RATIONALE AND OBJECTIVES: Transarterial chemoembolization (TACE) plus molecular targeted therapies has emerged as the main approach for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). A robust model for outcome prediction and risk stratification of recommended TACE plus molecular targeted therapies candidates is lacking. We aimed to develop an easy-to-use tool specifically for these patients. METHODS: A retrospective analysis was conducted on 384 patients with HCC and PVTT who underwent TACE plus molecular targeted therapies at 16 different institutions. We developed and validated a new prognostic score which called ABPS score. Additionally, an external validation was performed on data from 200 patients enrolled in a prospective cohort study. RESULTS: The ABPS score (ranging from 0 to 3 scores), which involves only Albumin-bilirubin (ALBI, grade 1: 0 score; grade 2: 1 score), PVTT(I-II type: 0 score; III-IV type: 1 score), and systemic-immune inflammation index (SII,<550 × 1012: 0 score; ≥550 × 1012: 1 score). Patients were categorized into three risk groups based on their ABPS score: ABPS-A, B, and C (scored 0, 1-2, and 3, respectively). The concordance index (C-index) of the ABPS scoring system was calculated to be 0.802, significantly outperforming the HAP score (0.758), 6-12 (0.712), Up to 7 (0.683), and ALBI (0.595) scoring systems (all P < 0.05). These research findings were further validated in the external validation cohorts. CONCLUSION: The ABPS score demonstrated a strong association with survival outcomes and radiological response in patients undergoing TACE plus molecular targeted therapy for HCC with PVTT. The ABPS scoring system could serve as a valuable tool to guide treatment selection for these patients.

Flexible Grid Graphene Electrothermal Films for Real-Time Monitoring Applications.

Flexible electrothermal films are crucial for protecting equipment and systems in cold weather, such as ice blockages in natural gas pipelines and icing on aircraft wings. Therefore, a flexible electric heater is one of the essential devices in industrial operations. One of the main challenges is to develop flexible electrothermal films with low operating voltage, high steady-state temperature, and good mechanical stability. In this study, a flexible electrothermal film based on graphene-patterned structures was manufactured by combining the laser induction method and the transfer printing process. The grid structure design provides accurate real-time monitoring for the application of electrothermal films and shows potential in solving problems related to deicing and clearing ice blockages in pipelines. The flexible electrothermal film can reach a high heating temperature of 165 °C at 15 V and exhibits sufficient heating stability. By employing a simple and efficient method to create a flexible, high-performance electrothermal film, we provide a reliable solution for deicing and monitoring applications.

A self-supervised network for image denoising and watermark removal.

In image watermark removal, popular methods depend on given reference non-watermark images in a supervised way to remove watermarks. However, reference non-watermark images are difficult to be obtained in the real world. At the same time, they often suffer from the influence of noise when captured by digital devices. To resolve these issues, in this paper, we present a self-supervised network for image denoising and watermark removal (SSNet). SSNet uses a parallel network in a self-supervised learning way to remove noise and watermarks. Specifically, each sub-network contains two sub-blocks. The upper sub-network uses the first sub-block to remove noise, according to noise-to-noise. Then, the second sub-block in the upper sub-network is used to remove watermarks, according to the distributions of watermarks. To prevent the loss of important information, the lower sub-network is used to simultaneously learn noise and watermarks in a self-supervised learning way. Moreover, two sub-networks interact via attention to extract more complementary salient information. The proposed method does not depend on paired images to learn a blind denoising and watermark removal model, which is very meaningful for real applications. Also, it is more effective than the popular image watermark removal methods in public datasets. Codes can be found at https://github.com/hellloxiaotian/SSNet.

Performance improvement of atherosclerosis risk assessment based on feature interaction.

BACKGROUND AND OBJECTIVE: Cardiovascular disease is a leading cause of mortality and premature death. Early intervention in asymptomatic individuals through risk assessment can reduce the incidence of disease. Atherosclerosis is a major cause of cardiovascular disease and early detection can effectively prevent and treat it. In this study, we used real patient data to evaluate the risk of atherosclerosis, assisting doctors in diagnosis and reducing the incidence of cardiovascular disease. METHODS: We proposed a multi-stage atherosclerosis risk assessment model that includes three main stages: (i) SMOTE and decorrelation weighting algorithm technology were added to the causal stability middle layer to address class imbalance in the dataset and reduce the impact of feature-induced dataset distribution shifts on model differences. (ii) The feature interaction layer considered possible feature interactions and classified features by different categories. By adding more effective feature information, the accuracy and generalizability of the model were improved. (iii) In the integrated model layer, we chose LightGBM as the decision tree integration model for risk assessment because it has higher accuracy and robustness compared to other machine learning algorithms. RESULTS: The final model used a dataset containing 21 original features and 17 interaction features, achieving excellent performance under a 10-fold cross-validation strategy. The macro accuracy reached 93.86%, macro precision was 94.82%, macro recall was 93.52%, and macro F1 score was as high as 93.37%. These indicators demonstrate the accuracy and robustness of the model in atherosclerosis risk assessment. CONCLUSION: The model provides strong support for the prevention and diagnosis of cardiovascular disease. Through atherosclerosis risk assessment, the model can help doctors develop personalized prevention and treatment plans, which is of great significance for the prevention and treatment of cardiovascular disease.

Improved neural network with multi-task learning for Alzheimer's disease classification.

Alzheimer's disease(AD) poses a significant challenge due to its widespread prevalence and the lack of effective treatments, highlighting the urgent need for early detection. This research introduces an enhanced neural network, named ADnet, which is based on the VGG16 model, to detect Alzheimer's disease using two-dimensional MRI slices. ADNet incorporates several key improvements: it replaces traditional convolution with depthwise separable convolution to reduce model parameters, replaces the ReLU activation function with ELU to address potential issues with exploding gradients, and integrates the SE(Squeeze-and-Excitation) module to enhance feature extraction efficiency. In addition to the primary task of MRI feature extraction, ADnet is simultaneously trained on two auxiliary tasks: clinical dementia score regression and mental state score regression. Experimental results demonstrate that compared to the baseline VGG16, ADNet achieves a 4.18% accuracy improvement for AD vs. CN classification and a 6% improvement for MCI vs. CN classification. These findings highlight the effectiveness of ADnet in classifying Alzheimer's disease, providing crucial support for early diagnosis and intervention by medical professionals. The proposed enhancements represent advancements in neural network architecture and training strategies for improved AD classification.

Laryngeal mask airway combined with bronchial blocker achieved 1-lung ventilation in a patient with bilateral vocal cord paralysis: A case report.

INTRODUCTION: One-lung ventilation (OLV) is a commonly used technique to facilitate surgical visualization during thoracic surgical procedures. Double-lumen endotracheal tubes and one-lumen tracheal tube combined with bronchial blocker might lead to intubation-related laryngeal injury. PATIENT CONCERNS: In the perioperative period, how to avoid further damage to the vocal cord while achieving OLV during operation is challenging work. DIAGNOSIS: She was diagnosed with systemic lupus erythematosus, bilateral vocal cord paralysis, and lung tumor. INTERVENTIONS: We used a combination of a laryngeal mask airway with bronchial blocker to avoid further damage to the vocal cord when achieving OLV. OUTCOMES: At 1-month follow-up, she had fully recovered without obvious abnormalities. CONCLUSION: When OLV was required for patients with bilateral vocal cord paralysis, a combination of a laryngeal mask airway with bronchial blocker was considered a better choice.

Utilization of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for noninvasive assessment of chronic kidney disease in type 2 diabetes.

BACKGROUNDS: Accumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD). OBJECTIVES: We aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD. METHODS: Total 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance. RESULTS: All enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10-6 mm2/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (ß = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (ß = -3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10-6 mm2/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI. CONCLUSIONS: ΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10-6 mm2/s and <66 × 10-6 mm2/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.

Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population.

Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS.

Personalized Mobile Health-Enhanced Cognitive Behavioral Intervention for Maternal Distress: Examining the Moderating Role of Adverse Childhood Experiences.

BACKGROUND: Maternal history of trauma is a risk factor for distress during pregnancy. The purpose of this paper was to examine the theorized differential impact of a cognitive behavioral intervention (Mothers and Babies Personalized; MB-P) on maternal distress and emotional regulation for those with ≥ 1 adverse childhood experiences (ACEs; vs no ACEs) from pregnancy to 3 months postpartum. METHODS: Between August 2019 and August 2021, eligible pregnant individuals aged ≥ 18 years, < 22 weeks' gestation, and English-speaking were recruited from 6 university-affiliated prenatal clinics. Participants (N = 100) were randomized to MB-P (n = 49) or control (n = 51). Analyzable data were collected for 95 participants. Analyses tested progression of change (slope) and at individual timepoints (panel analysis) for perinatal mental health outcomes. RESULTS: The majority of participants (n = 68, 71%) reported experiencing > 1 ACE (median = 1, range: 0-11). Participants demonstrated significant differential effects for depressive symptoms in absence of ACEs (standardized mean differences [SMD] = 0.82; 95% confidence interval [CI] = [0.13-1.51]) vs in presence of ACEs (SMD = 0.39; 95% CI = [-0.20 to 0.97]) and perceived stress in absence of ACEs (SMD = 0.92; 95% CI = [0.23-1.62]) vs in presence of ACEs (SMD = -0.05; 95% CI = [-0.63 to 0.53]). A panel analysis showed significantly reduced depressive symptoms postintervention and increased negative mood regulation at 3 months postpartum for individuals with ACEs. CONCLUSIONS: Findings support effectiveness of the MB-P intervention to reduce prenatal distress for all pregnant individuals. Preliminary exploration suggests the possibility that individuals with ACEs may benefit from enhanced trauma-informed content to optimize the effects of a perinatal intervention.

Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer.

BACKGROUND: Vesical Imaging-Reporting and Data System (VI-RADS) is a pathway for the standardized imaging and reporting of bladder cancer staging using multiparametric (mp) MRI. PURPOSE: To investigate additional role of morphological (MOR) measurements to VI-RADS for the detection of muscle-invasive bladder cancer (MIBC) with mpMRI. STUDY TYPE: Retrospective. POPULATION: A total of 198 patients (72 MIBC and 126 NMIBC) underwent bladder mpMRI was included. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted imaging with fast-spin-echo sequence, spin-echo-planar diffusion-weighted imaging and dynamic contrast-enhanced imaging with fast 3D gradient-echo sequence. ASSESSMENT: VI-RADS score and MOR measurement including tumor location, number, stalk, cauliflower-like surface, type of tumor growth, tumor-muscle contact margin (TCM), tumor-longitudinal length (TLL), and tumor cellularity index (TCI) were analyzed by three uroradiologists (3-year, 8-year, and 15-year experience of bladder MRI, respectively) who were blinded to histopathology. STATISTICAL TESTS: Significant MOR measurements associated with MIBC were tested by univariable and multivariable logistic regression (LR) analysis with odds ratio (OR). Area under receiver operating characteristic curve (AUC) with DeLong's test and decision curve analysis (DCA) were used to compared the performance of unadjusted vs. adjusted VI-RADS. A P-value <0.05 was considered statistically significant. RESULTS: TCM (OR 9.98; 95% confidence interval [CI] 4.77-20.8), TCI (OR 5.72; 95% CI 2.37-13.8), and TLL (OR 3.35; 95% CI 1.40-8.03) were independently associated with MIBC at multivariable LR analysis. VI-RADS adjusted by three MORs achieved significantly higher AUC (reader 1 0.908 vs. 0.798; reader 2 0.906 vs. 0.855; reader 3 0.907 vs. 0.831) and better clinical benefits than unadjusted VI-RADS at DCA. Specially in VI-RADS-defined equivocal lesions, MOR-based adjustment resulted in 55.5% (25/45), 70.4% (38/54), and 46.4% (26/56) improvement in accuracy for discriminating MIBC in three readers, respectively. DATA CONCLUSION: MOR measurements improved the performance of VI-RADS in detecting MIBC with mpMRI, especially for equivocal lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Genetic dissection of resistance to gray leaf spot by genome-wide association study in a multi-parent maize population.

BACKGROUND: Understanding the genetic mechanisms underlying gray leaf spot (GLS) resistance in maize is crucial for breeding GLS-resistant inbred lines and commercial hybrids. Genome-wide association studies (GWAS) and gene functional annotation are valuable methods for identifying potential SNPs (single nucleotide polymorphism) and candidate genes associated with GLS resistance in maize. RESULTS: In this study, a total of 757 lines from five recombinant inbred line (RIL) populations of maize at the F7 generation were used to construct an association mapping panel. SNPs obtained through genotyping-by-sequencing (GBS) were used to perform GWAS for GLS resistance using a linear mixture model in GEMMA. Candidate gene screening was performed by analyzing the 10 kb region upstream and downstream of the significantly associated SNPs linked to GLS resistance. Through GWAS analysis of multi-location phenotypic data, we identified ten candidate genes that were consistently detected in two locations or from one location along with best linear unbiased estimates (BLUE). One of these candidate genes, Zm00001d003257 that might impact GLS resistance by regulating gibberellin content, was further identified through haplotype-based association analysis, candidate gene expression analysis, and previous reports. CONCLUSIONS: The discovery of the novel candidate gene provides valuable genomic resources for elucidating the genetic mechanisms underlying GLS resistance in maize. Additionally, these findings will contribute to the development of new genetic resources by utilizing molecular markers to facilitate the genetic improvement and breeding of maize for GLS resistance.

DF-dRVFL: A novel deep feature based classifier for breast mass classification.

Amongst all types of cancer, breast cancer has become one of the most common cancers in the UK threatening millions of people's health. Early detection of breast cancer plays a key role in timely treatment for morbidity reduction. Compared to biopsy, which takes tissues from the lesion for further analysis, image-based methods are less time-consuming and pain-free though they are hampered by lower accuracy due to high false positivity rates. Nevertheless, mammography has become a standard screening method due to its high efficiency and low cost with promising performance. Breast mass, as the most palpable symptom of breast cancer, has received wide attention from the community. As a result, the past decades have witnessed the speeding development of computer-aided systems that are aimed at providing radiologists with useful tools for breast mass analysis based on mammograms. However, the main issues of these systems include low accuracy and require enough computational power on a large scale of datasets. To solve these issues, we developed a novel breast mass classification system called DF-dRVFL. On the public dataset DDSM with more than 3500 images, our best model based on deep random vector functional link network showed promising results through five-cross validation with an averaged AUC of 0.93 and an average accuracy of 81.71%. Compared to sole deep learning based methods, average accuracy has increased by 0.38. Compared with the state-of-the-art methods, our method showed better performance considering the number of images for evaluation and the overall accuracy.

Physical Contacts Between Mitochondria and WPBs Participate in WPB Maturation.

BACKGROUND: Weibel-Palade bodies (WPBs) are endothelial cell-specific cigar-shaped secretory organelles containing various biologically active molecules. WPBs play crucial roles in thrombosis, hemostasis, angiogenesis, and inflammation. The main content of WPBs is the procoagulant protein vWF (von Willebrand factor). Physical contacts and functional cross talk between mitochondria and other organelles have been demonstrated. Whether an interorganellar connection exists between mitochondria and WPBs is unknown. METHODS: We observed physical contacts between mitochondria and WPBs in human umbilical vein endothelial cells by electron microscopy and living cell confocal microscopy. We developed an artificial intelligence-assisted method to quantify the duration and length of organelle contact sites in live cells. RESULTS: We found there existed physical contacts between mitochondria and WPBs. Disruption of mitochondrial function affected the morphology of WPBs. Furthermore, we found that Rab3b, a small GTPase on the WPBs, was enriched at the mitochondrion-WPB contact sites. Rab3b deficiency reduced interaction between the two organelles and impaired the maturation of WPBs and vWF multimer secretion. CONCLUSIONS: Our results reveal that Rab3b plays a crucial role in mediating the mitochondrion-WPB contacts, and that mitochondrion-WPB coupling is critical for the maturation of WPBs in vascular endothelial cells.

Developmental Trajectories of Irritability across the Transition to Toddlerhood: Associations with Effortful Control and Psychopathology.

Preschool-age irritability is a transdiagnostic marker of internalizing and externalizing problems. However, researchers have generally been reluctant to examine irritability within a clinically salient framework at younger ages due to some instability during the "terrible twos" period. Developmentally sensitive and dense measurements to capture intra- and inter-individual variability, as well as exploration of developmental processes that predict change, are needed. This study aimed to examine (1) the trajectories of irritability at the transition to toddlerhood (12-24 months of age) using repeated measures, (2) whether effortful control was associated with individual differences in level and growth rate of irritability, and (3) whether individual differences in the irritability trajectories were associated with later psychopathology. Families were recruited when the child was 12-18 months old (N = 333, 45.65% female). Mothers reported on their toddler's irritability at baseline and every two months until a follow-up laboratory assessment approximately one year later. Effortful control was measured at baseline. Clinical internalizing/externalizing symptoms were measured at the follow-up assessment. Hierarchical linear models revealed an increase in irritability over time, yet there was relatively little within-person variability. Effortful control was only associated with the level of irritability and not growth rate. Level of irritability was associated with internalizing, externalizing, and combined symptoms, but growth rate was not. Findings suggest intraindividual stability in irritability at the transition to toddlerhood and the possibility that screening for elevated irritability at toddler age is meaningful.

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. The purpose of this study was to develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (283 women, 119 men; mean age, 53.2 years) with a total of 448 pGGNs on noncontrast chest CT that were resected from January 2019 to June 2022 and were histologically diagnosed as AAH (n = 29), AIS (n = 83), MIA (n = 235), or IAC (n = 101). Lung-PNet, a 3D deep learning model, was developed for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules resected from January 2019 to December 2021 were randomly allocated to training (n = 327) and internal test (n = 82) sets. Nodules resected from January 2022 to June 2022 formed a holdout test set (n = 39). Segmentation performance was assessed with Dice coefficients with radiologists' manual segmentations as reference. Classification performance was assessed by ROC AUC and precision-recall AUC (PR AUC) and compared with that of four readers (three radiologists, one surgeon). The code used is publicly available (https://github.com/XiaodongZhang-PKUFH/Lung-PNet.git). RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0.838, and ROC AUC and PR AUC for classification as IAC were 0.911 and 0.842. At threshold probability of 50.0% or greater for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. In the holdout test set, accuracy and F1 score (p values vs Lung-PNet) for individual readers were as follows: reader 1, 51.3% (p = .02) and 48.6% (p = .008); reader 2, 79.5% (p = .75) and 75.0% (p = .10); reader 3, 66.7% (p = .35) and 68.3% (p < .001); reader 4, 71.8% (p = .48) and 42.1% (p = .18). CONCLUSION. Lung-PNet had robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGGN management.