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1. A concave four-arc honeycomb with enhanced stiffness and desirable negative Poisson’s effect

Author

Ning Feng, Yuanhao Tie, Ronghui Guo, Qingwen Yuan, Fengling Xue, Cheng Li, Liwen Lv, and Weibo Xie

Subjects
Medicine, Science
Abstract

Abstract The conventional star-shaped honeycomb (CSSH) structure is inherently rich in mechanical properties. Based on the CSSH structure, the Poisson's ratio and Young’s modulus can be improved by adding the tip re-entrant angle (ISSH). In this paper, a new concave four-arc honeycomb (CFAH) structure is proposed by designing the straight rod as a curved rod and retaining the tip re-entrant angle from the ISSH structure. The Young's modulus, specific stiffness and Poisson’s ratio of CFAH structures are derived from Castigliano’s second theorem and Moore’s theorem. The theoretical results show good agreement with the numerical and experimental results. The results show that the normalized effective specific stiffness and normalized effective Young’s modulus of the CFAH structure are further improved by about 12.95% and 16.86%, respectively, compared with the ISSH structure, and more significant auxiliary effects are obtained. CFAH structures show good promise in aerospace, construction and other applications due to their enhanced mechanical property. Meanwhile, the present work provides guidance for the study of concave four-arc honeycomb structures.

Published
2023
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2. Evaluation of product conceptual design based on Pythagorean fuzzy set under big data environment

Author

Lian-Dan Ma, Wei-Xing Wang, Jing-Wen Xie, Ning Zhang, Ning-Feng Hu, and Zi-Ao Wang

Subjects
Medicine, Science
Abstract

Abstract The concept design evaluation phase of the new product launch is extremely important. However, current evaluation information relies mainly on the a priori knowledge of decision makers and is subjective and ambiguous. For this reason, a conceptual design solution decision model based on Pythagorean fuzzy sets in a big data environment is proposed. Firstly, we use the ability of big data to mine and analyze information to construct a new standard for product concept design evaluation in the big data environment. Secondly, the Pythagorean fuzzy set (PFS), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated into a decision model. AHP, extended by the Pythagorean fuzzy set, is used to determine the weights of new conceptual design criteria in a big data environment. The Pythagorean fuzzy TOPSIS is used to prioritize alternative conceptual design solutions. The feasibility of the approach is proven with a practical case, the generalizability of the method is confirmed with two descriptive digital cases, and the reliability, validity, and superiority of the process are demonstrated with sensitivity analysis, comparative analysis, and computational complexity analysis.

Published
2022
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3. Genetic and environmental influences on human height from infancy through adulthood at different levels of parental education

Author

Jelenkovic, Aline, Sund, Reijo, Yokoyama, Yoshie, Latvala, Antti, Sugawara, Masumi, Tanaka, Mami, Matsumoto, Satoko, Freitas, Duarte L, Maia, José Antonio, Knafo-Noam, Ariel, Mankuta, David, Abramson, Lior, Ji, Fuling, Ning, Feng, Pang, Zengchang, Rebato, Esther, Saudino, Kimberly J, Cutler, Tessa L, Hopper, John L, Ullemar, Vilhelmina, Almqvist, Catarina, Magnusson, Patrik KE, Cozen, Wendy, Hwang, Amie E, Mack, Thomas M, Nelson, Tracy L, Whitfield, Keith E, Sung, Joohon, Kim, Jina, Lee, Jooyeon, Lee, Sooji, Llewellyn, Clare H, Fisher, Abigail, Medda, Emanuela, Nisticò, Lorenza, Toccaceli, Virgilia, Baker, Laura A, Tuvblad, Catherine, Corley, Robin P, Huibregtse, Brooke M, Derom, Catherine A, Vlietinck, Robert F, Loos, Ruth JF, Burt, S Alexandra, Klump, Kelly L, Silberg, Judy L, Maes, Hermine H, Krueger, Robert F, McGue, Matt, Pahlen, Shandell, Gatz, Margaret, Butler, David A, Harris, Jennifer R, Brandt, Ingunn, Nilsen, Thomas S, Harden, K Paige, Tucker-Drob, Elliot M, Franz, Carol E, Kremen, William S, Lyons, Michael J, Lichtenstein, Paul, Bartels, Meike, Beijsterveldt, Catharina EM van, Willemsen, Gonneke, Öncel, Sevgi Y, Aliev, Fazil, Jeong, Hoe-Uk, Hur, Yoon-Mi, Turkheimer, Eric, Boomsma, Dorret I, Sørensen, Thorkild IA, Kaprio, Jaakko, and Silventoinen, Karri

Subjects
Biological Psychology, Health Sciences, Psychology, Pediatric, Genetics, 2.1 Biological and endogenous factors, Quality Education, Adolescent, Adult, Body Height, Child, Child, Preschool, Environment, Female, Gene-Environment Interaction, Genetic Background, Humans, Infant, Infant, Newborn, Male, Parenting, Parents, Quantitative Trait Loci, Quantitative Trait, Heritable, Young Adult
Abstract

Genetic factors explain a major proportion of human height variation, but differences in mean stature have also been found between socio-economic categories suggesting a possible effect of environment. By utilizing a classical twin design which allows decomposing the variation of height into genetic and environmental components, we tested the hypothesis that environmental variation in height is greater in offspring of lower educated parents. Twin data from 29 cohorts including 65,978 complete twin pairs with information on height at ages 1 to 69 years and on parental education were pooled allowing the analyses at different ages and in three geographic-cultural regions (Europe, North America and Australia, and East Asia). Parental education mostly showed a positive association with offspring height, with significant associations in mid-childhood and from adolescence onwards. In variance decomposition modeling, the genetic and environmental variance components of height did not show a consistent relation to parental education. A random-effects meta-regression analysis of the aggregate-level data showed a trend towards greater shared environmental variation of height in low parental education families. In conclusion, in our very large dataset from twin cohorts around the globe, these results provide only weak evidence for the study hypothesis.

Published
2020

4. Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases

Author

Cancan Chen, Shan Zheng, Lei Guo, Xuebing Yang, Yan Song, Zhuo Li, Yanwu Zhu, Xiaoqi Liu, Qingzhuang Li, Huijuan Zhang, Ning Feng, Zuxuan Zhao, Tinglin Qiu, Jun Du, Qiang Guo, Wensheng Zhang, Wenzhao Shi, Jianhui Ma, and Fenglong Sun

Subjects
Medicine, Science
Abstract

Abstract The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate (p 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

Published
2022
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6. Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases

Author

Cancan Chen, Shan Zheng, Lei Guo, Xuebing Yang, Yan Song, Zhuo Li, Yanwu Zhu, Xiaoqi Liu, Qingzhuang Li, Huijuan Zhang, Ning Feng, Zuxuan Zhao, Tinglin Qiu, Jun Du, Qiang Guo, Wensheng Zhang, Wenzhao Shi, Jianhui Ma, and Fenglong Sun

Subjects
Multidisciplinary, Sentinel Lymph Node Biopsy, Lymphatic Metastasis, Humans, Breast Neoplasms, Female, Neoplasms, Second Primary, Lymph Nodes, Neural Networks, Computer, Diagnostic Errors
Abstract

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate (p p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

Published
2021

7. Myocardial death and dysfunction after ischemia-reperfusion injury require CaMKIIδ oxidation

Author

Jonathan M. Granger, Mark E. Anderson, Yuejin Wu, Qinchuan Wang, and Ning Feng

Subjects
Male, 0301 basic medicine, Myocardial Ischemia, lcsh:Medicine, Pharmacology, Mice, Adenosine Triphosphate, Methionine, 0302 clinical medicine, Insulin, Myocyte, Myocytes, Cardiac, lcsh:Science, chemistry.chemical_classification, Glucose tolerance test, Multidisciplinary, medicine.diagnostic_test, Chemistry, Endocytosis, Mechanisms of disease, Echocardiography, Reperfusion Injury, Permeation and transport, Oxidation-Reduction, Cell Survival, Ischemia, Article, 03 medical and health sciences, Protein Domains, In vivo, Physical Conditioning, Animal, Ca2+/calmodulin-dependent protein kinase, medicine, Animals, Reactive oxygen species, Myocardium, lcsh:R, Hydrogen Peroxide, Glucose Tolerance Test, Translational research, medicine.disease, In vitro, Oxygen, Myocardial infarction, 030104 developmental biology, Potassium, lcsh:Q, Calcium-Calmodulin-Dependent Protein Kinase Type 2, Reactive Oxygen Species, Reperfusion injury, 030217 neurology & neurosurgery
Abstract

Reactive oxygen species (ROS) contribute to myocardial death during ischemia-reperfusion (I/R) injury, but detailed knowledge of molecular pathways connecting ROS to cardiac injury is lacking. Activation of the Ca2+/calmodulin-dependent protein kinase II (CaMKIIδ) is implicated in myocardial death, and CaMKII can be activated by ROS (ox-CaMKII) through oxidation of regulatory domain methionines (Met281/282). We examined I/R injury in mice where CaMKIIδ was made resistant to ROS activation by knock-in replacement of regulatory domain methionines with valines (MMVV). We found reduced myocardial death, and improved left ventricular function 24 hours after I/R injury in MMVV in vivo and in vitro compared to WT controls. Loss of ATP sensitive K+ channel (KATP) current contributes to I/R injury, and CaMKII promotes sequestration of KATP from myocardial cell membranes. KATP current density was significantly reduced by H2O2 in WT ventricular myocytes, but not in MMVV, showing ox-CaMKII decreases KATP availability. Taken together, these findings support a view that ox-CaMKII and KATP are components of a signaling axis promoting I/R injury by ROS.

Published
2019
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8. Birth size and gestational age in opposite-sex twins as compared to same-sex twins: An individual-based pooled analysis of 21 cohorts

Author

Jelenkovic, Aline, primary, Sund, Reijo, additional, Yokoyama, Yoshie, additional, Hur, Yoon-Mi, additional, Ullemar, Vilhelmina, additional, Almqvist, Catarina, additional, Magnusson, Patrik KE, additional, Willemsen, Gonneke, additional, Bartels, Meike, additional, Beijsterveldt, Catharina EM van, additional, Bogl, Leonie H., additional, Pietiläinen, Kirsi H, additional, Vuoksimaa, Eero, additional, Ji, Fuling, additional, Ning, Feng, additional, Pang, Zengchang, additional, Nelson, Tracy L, additional, Whitfield, Keith E., additional, Rebato, Esther, additional, Llewellyn, Clare H., additional, Fisher, Abigail, additional, Bayasgalan, Gombojav, additional, Narandalai, Danshiitsoodol, additional, Bjerregaard-Andersen, Morten, additional, Beck-Nielsen, Henning, additional, Sodemann, Morten, additional, Tarnoki, Adam D., additional, Tarnoki, David L., additional, Ooki, Syuichi, additional, Stazi, Maria A., additional, Fagnani, Corrado, additional, Brescianini, Sonia, additional, Dubois, Lise, additional, Boivin, Michel, additional, Brendgen, Mara, additional, Dionne, Ginette, additional, Vitaro, Frank, additional, Cutler, Tessa L, additional, Hopper, John L., additional, Krueger, Robert F., additional, McGue, Matt, additional, Pahlen, Shandell, additional, Craig, Jeffrey M., additional, Saffery, Richard, additional, Haworth, Claire MA, additional, Plomin, Robert, additional, Knafo-Noam, Ariel, additional, Mankuta, David, additional, Abramson, Lior, additional, Burt, S Alexandra, additional, Klump, Kelly L., additional, Vlietinck, Robert F., additional, Derom, Catherine A., additional, Loos, Ruth JF, additional, Boomsma, Dorret I., additional, Sørensen, Thorkild I. A., additional, Kaprio, Jaakko, additional, and Silventoinen, Karri, additional

Published
2018
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9. Genetic and environmental influences on height from infancy to early adulthood: An individual-based pooled analysis of 45 twin cohorts

Author

Jelenkovic, Aline, primary, Sund, Reijo, additional, Hur, Yoon-Mi, additional, Yokoyama, Yoshie, additional, Hjelmborg, Jacob v. B., additional, Möller, Sören, additional, Honda, Chika, additional, Magnusson, Patrik K. E., additional, Pedersen, Nancy L., additional, Ooki, Syuichi, additional, Aaltonen, Sari, additional, Stazi, Maria A., additional, Fagnani, Corrado, additional, D’Ippolito, Cristina, additional, Freitas, Duarte L., additional, Maia, José Antonio, additional, Ji, Fuling, additional, Ning, Feng, additional, Pang, Zengchang, additional, Rebato, Esther, additional, Busjahn, Andreas, additional, Kandler, Christian, additional, Saudino, Kimberly J., additional, Jang, Kerry L., additional, Cozen, Wendy, additional, Hwang, Amie E., additional, Mack, Thomas M., additional, Gao, Wenjing, additional, Yu, Canqing, additional, Li, Liming, additional, Corley, Robin P., additional, Huibregtse, Brooke M., additional, Derom, Catherine A., additional, Vlietinck, Robert F., additional, Loos, Ruth J. F., additional, Heikkilä, Kauko, additional, Wardle, Jane, additional, Llewellyn, Clare H., additional, Fisher, Abigail, additional, McAdams, Tom A., additional, Eley, Thalia C., additional, Gregory, Alice M., additional, He, Mingguang, additional, Ding, Xiaohu, additional, Bjerregaard-Andersen, Morten, additional, Beck-Nielsen, Henning, additional, Sodemann, Morten, additional, Tarnoki, Adam D., additional, Tarnoki, David L., additional, Knafo-Noam, Ariel, additional, Mankuta, David, additional, Abramson, Lior, additional, Burt, S. Alexandra, additional, Klump, Kelly L., additional, Silberg, Judy L., additional, Eaves, Lindon J., additional, Maes, Hermine H., additional, Krueger, Robert F., additional, McGue, Matt, additional, Pahlen, Shandell, additional, Gatz, Margaret, additional, Butler, David A., additional, Bartels, Meike, additional, van Beijsterveldt, Toos C. E. M., additional, Craig, Jeffrey M., additional, Saffery, Richard, additional, Dubois, Lise, additional, Boivin, Michel, additional, Brendgen, Mara, additional, Dionne, Ginette, additional, Vitaro, Frank, additional, Martin, Nicholas G., additional, Medland, Sarah E., additional, Montgomery, Grant W., additional, Swan, Gary E., additional, Krasnow, Ruth, additional, Tynelius, Per, additional, Lichtenstein, Paul, additional, Haworth, Claire M. A., additional, Plomin, Robert, additional, Bayasgalan, Gombojav, additional, Narandalai, Danshiitsoodol, additional, Harden, K. Paige, additional, Tucker-Drob, Elliot M., additional, Spector, Timothy, additional, Mangino, Massimo, additional, Lachance, Genevieve, additional, Baker, Laura A., additional, Tuvblad, Catherine, additional, Duncan, Glen E., additional, Buchwald, Dedra, additional, Willemsen, Gonneke, additional, Skytthe, Axel, additional, Kyvik, Kirsten O., additional, Christensen, Kaare, additional, Öncel, Sevgi Y., additional, Aliev, Fazil, additional, Rasmussen, Finn, additional, Goldberg, Jack H., additional, Sørensen, Thorkild I. A., additional, Boomsma, Dorret I., additional, Kaprio, Jaakko, additional, and Silventoinen, Karri, additional

Published
2016
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