Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Two parallel cycling experiments with a 6 h interval are performed for each binary TC case, and the difference between the two experiments is whether satellite radiance observations are assimilated. Satellite radiance observations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)'s three-dimensional variational (3D-Var) system, which includes the observation operator, quality control procedures, and bias correction algorithm for radiance observations. On average, radiance assimilation results in slight improvements of environmental fields and track forecasts of binary TC cases, but the detailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimilation leads to better depictions of environmental fields, and finally it results in improved track forecasts. However, positive effects of radiance assimilation on track forecasts can be reduced when there exists a direct interaction between binary TCs and intensities/structures of binary TCs are not represented well. An initialization method (e.g., dynamic initialization) combined with radiance assimilation and/or more advanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations

The Basic Helix-Loop-Helix Transcription Factor Family in the Pea Aphid, Acyrthosiphon pisum

The basic helix-loop-helix (bHLH) proteins play essential roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in over 20 organisms, including fruit fly, zebrafish, and human. This study identified 54 bHLH family members in the pea aphid, Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae), genome. Phylogenetic analyses revealed that they belong to 37 bHLH families with 21, 13, 9, 1, 9, and 1 members in group A, B, C, D, E, and F, respectively. Through in-group phylogenetic analyses, all of the identified A. pisum bHLH members were assigned into their correspondent bHLH families with confidence, among which 51 were defined according to phylogenetic analyses with orthologs from Drosophila melanogaster Meigen (Diptera: Drosophilidae), and 3 of them were defined according to phylogenetic analyses with orthologs from Bombyx mori L. (Lepidoptera: Bombycidae) and Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae). Analyses on genomic coding regions revealed that the number and average length of introns in A. pisum bHLH motifs are higher than those in other insects. The present study provides useful background information for future studies on structure and function of bHLH proteins in the regulation of A. pisum development

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

LATS2 is De-methylated and Overexpressed in Nasopharyngeal Carcinoma and Predicts Poor Prognosis

<p>Abstract</p> <p>Background</p> <p>LATS2, which encodes a novel serine/threonine kinase, is known to be important in centrosome duplication and in the maintenance of genomic stability. Recently, a potential role for LATS2 in cancer has been reported. In breast cancer and acute lymphoblastic leukemia (ALL), LATS2 mRNA is downregulated and has been suggested to be a tumor suppressor. However, the role of LATS2 in nasopharyngeal carcinoma has not been investigated. In this study, we aimed to investigate the expression pattern of LATS2 and its clinicopathological involvement in nasopharyngeal carcinoma to understand its effect on cell survival.</p> <p>Methods</p> <p>Using quantitative real time PCR and immunoblotting, the expression of LATS2 was detected in nasopharyngeal carcinoma cell lines and in the immortalized nasopharyngeal epithelial cell line NP69. Using immunohistochemistry, we analyzed LATS2 protein expression in 220 nasopharyngeal carcinoma cases. The association of LATS2 protein expression with the clinicopathological characteristics and the prognosis of nasopharyngeal carcinoma were subsequently assessed. Using methylation specific PCR, we detected the methylation status of the LATS2 promoter. RNA interference was performed by transfecting siRNA to specifically knock down LATS2 expression in 5-8F and CNE2.</p> <p>Results</p> <p>LATS2 protein was detected in 178 of 220 (80.91%) cases of nasopharyngeal carcinoma. LATS2 overexpression was a significant, independent prognosis predictor (<it>P </it>= 0.037) in nasopharyngeal carcinoma patients. Methylation specific PCR revealed that 36.7% (11/30) of nasopharyngeal carcinoma tissues and all of the chronic nasopharyngeal inflammation samples were methylated. Functional studies showed that the suppression of LATS2 expression in nasopharyngeal carcinoma (5-8F and CNE2) cell lines by using specific small interfering (siRNA) resulted in the inhibition of growth, induction of apoptosis and S-phase cell cycle increase. Overexpression of LATS2 in NP69 stimulated cell proliferation.</p> <p>Conclusions</p> <p>Our results indicate that LATS2 might play a role in the tumorigenesis of nasopharyngeal carcinoma by promoting the growth of nasopharyngeal carcinoma cells. Transfection with specific siRNA might be feasible for the inhibition of growth, induction of apoptosis and S phase increase in nasopharyngeal carcinoma.</p

Pretreatment carcinoembryonic antigen level is a risk factor for para-aortic lymph node recurrence in addition to squamous cell carcinoma antigen following definitive concurrent chemoradiotherapy for squamous cell carcinoma of the uterine cervix

<p>Abstract</p> <p>Background</p> <p>To identify pretreatment carcinoembryonic antigen (CEA) levels as a risk factor for para-aortic lymph node (PALN) recurrence following concurrent chemoradiotherapy (CCRT) for cervical cancer.</p> <p>Methods</p> <p>From March 1995 to January 2008, 188 patients with squamous cell carcinoma (SCC) of the uterine cervix were analyzed retrospectively. No patient received PALN irradiation as the initial treatment. CEA and squamous cell carcinoma antigen (SCC-Ag) were measured before and after radiotherapy. PALN recurrence was detected by computer tomography (CT) scans. We analyzed the actuarial rates of PALN recurrence by using Kaplan-Meier curves. Multivariate analyses were carried out with Cox regression models. We stratified the risk groups based on the hazard ratios (HR).</p> <p>Results</p> <p>Both pretreatment CEA levels ≥ 10 ng/mL and SCC-Ag levels < 10 ng/mL (<it>p </it>< 0.001, HR = 8.838), SCC-Ag levels ≥ 40 ng/mL (<it>p </it>< 0.001, HR = 12.551), and SCC-Ag levels of 10-40 ng/mL (<it>p </it>< 0.001, HR = 4.2464) were significant factors for PALN recurrence. The corresponding 5-year PALN recurrence rates were 51.5%, 84.8%, and 27.5%, respectively. The 5-year PALN recurrence rate for patients with both low (< 10 ng/mL) SCC and CEA was only 9.6%. CEA levels ≥ 10 ng/mL or SCC-Ag levels ≥ 10 ng/mL at PALN recurrence were associated with overall survival after an isolated PALN recurrence. Pretreatment CEA levels ≥ 10 ng/mL were also associated with survival after an isolated PALN recurrence.</p> <p>Conclusions</p> <p>Pretreatment CEA ≥ 10 ng/mL is an additional risk factor of PALN relapse following definitive CCRT for SCC of the uterine cervix in patients with pretreatment SCC-Ag levels < 10 ng/mL. More comprehensive examinations before CCRT and intensive follow-up schedules are suggested for early detection and salvage in patients with SCC-Ag or CEA levels ≥ 10 ng/mL.</p

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

a retrospective case–control study from the PARADIGM registry

Funding Information: Dr. Jonathon A. Leipsic serves as a consultant and has stock options in HeartFlow and Circle Cardiovascular Imaging; he also receives grant support from GE Healthcare and speaking fees from Philips. Dr. Habib Samady has an equity interest in Covanos. Dr. Daniel Berman receives software royalties from Cedars-Sinai Medical Center. Dr. James K. Min receives funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare. Dr. Min serves on the scientific advisory board of Arineta and GE Healthcare and has an equity interest in Cleerly. All other authors declare that they have no competing interests. Funding Information: This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT; Ministry of Trade, Industry and Energy; Ministry of Health & Welfare, Republic of Korea; and Ministry of Food and Drug Safety; Project Number: 202016B02). Publisher Copyright: © 2022, The Author(s).Background: The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. Methods: A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). Results: During a median inter-scan period of 3.6 years (interquartile range: 2.7–5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048–0.149; P < 0.001). The multiple logistic regression models showed that the serum hemoglobin A1c level had an independent and positive association with the risk of RPP. The optimal predictive cut-off value of the hemoglobin A1c level for RPP was 7.05% (sensitivity: 80.0%, specificity: 86.7%; area under curve: 0.816 [95% CI: 0.574–0.999]; P = 0.017). Conclusion: In this retrospective case–control study, the glycemic control status was strongly associated with the risk of RPP in patients without a baseline coronary plaque burden. This suggests that regular monitoring of the glycemic control status might be helpful for preventing the rapid progression of coronary atherosclerosis irrespective of the baseline risk factors. Further randomized investigations are necessary to confirm the results of our study. Trial registration: ClinicalTrials.gov NCT02803411.publishersversionpublishe