Machine learning active-nematic hydrodynamics

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics. Seldom is this challenge more apparent than in active matter where the energy cascade mechanisms responsible for autonomous large-scale dynamics are poorly understood. Here, we use active nematics to demonstrate that neural networks can extract the spatio-temporal variation of hydrodynamic parameters directly from experiments. Our algorithms analyze microtubule-kinesin and actin-myosin experiments as computer vision problems. Unlike existing methods, neural networks can determine how multiple parameters such as activity and elastic constants vary with ATP and motor concentration. In addition, we can forecast the evolution of these chaotic many-body systems solely from image-sequences of their past by combining autoencoder and recurrent networks with residual architecture. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists.Comment: SI Movie 1: https://www.youtube.com/watch?v=9WzIT7OG9pY SI Movie 2: https://youtu.be/Trc4RyU7-dw SI Movie 3: https://youtu.be/Epm_P_EakH

Empagliflozin rescues pro-arrhythmic and Ca 2+ homeostatic effects of transverse aortic constriction in intact murine hearts

We explored physiological effects of the sodium-glucose co-transporter-2 inhibitor empagliflozin on intact experimentally hypertrophic murine hearts following transverse aortic constriction (TAC). Postoperative drug (2–6 weeks) challenge resulted in reduced late Na+ currents, and increased phosphorylated (p-)CaMK-II and Nav1.5 but not total (t)-CaMK-II, and Na+/Ca2+ exchanger expression, confirming previous cardiomyocyte-level reports. It rescued TAC-induced reductions in echocardiographic ejection fraction and fractional shortening, and diastolic anterior and posterior wall thickening. Dual voltage- and Ca2+-optical mapping of Langendorff-perfused hearts demonstrated that empagliflozin rescued TAC-induced increases in action potential durations at 80% recovery (APD80), Ca2+ transient peak signals and durations at 80% recovery (CaTD80), times to peak Ca2+ (TTP100) and Ca2+ decay constants (Decay30–90) during regular 10-Hz stimulation, and Ca2+ transient alternans with shortening cycle length. Isoproterenol shortened APD80 in sham-operated and TAC-only hearts, shortening CaTD80 and Decay30–90 but sparing TTP100 and Ca2+ transient alternans in all groups. All groups showed similar APD80, and TAC-only hearts showed greater CaTD80, heterogeneities following isoproterenol challenge. Empagliflozin abolished or reduced ventricular tachycardia and premature ventricular contractions and associated re-entrant conduction patterns, in isoproterenol-challenged TAC-operated hearts following successive burst pacing episodes. Empagliflozin thus rescues TAC-induced ventricular hypertrophy and systolic functional, Ca2+ homeostatic, and pro-arrhythmogenic changes in intact hearts

Record Maximum Oscillation Frequency in C-face Epitaxial Graphene Transistors

The maximum oscillation frequency (fmax) quantifies the practical upper bound for useful circuit operation. We report here an fmax of 70 GHz in transistors using epitaxial graphene grown on the C-face of SiC. This is a significant improvement over Si-face epitaxial graphene used in the prior high frequency transistor studies, exemplifying the superior electronics potential of C-face epitaxial graphene. Careful transistor design using a high {\kappa} dielectric T-gate and self-aligned contacts, further contributed to the record-breaking fmax

Total synthesis of plagiochin G and derivatives as potential cancer chemopreventive agents

A new and efficient total synthesis has been developed to obtain plagiochin G (22), a macrocyclic bisbibenzyl, and four derivatives. The key 16-membered ring containing biphenyl ether and biaryl units was closed via an intramolecular SNAr reaction. All synthesized macrocyclic bisbibenzyls inhibited Epstein-Barr virus early antigen (EBVEA) activation induced by the tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA) in Raji cells and, thus, are potential cancer chemopreventive agents

Deleted in Colorectal Cancer Is a Putative Conditional Tumor-Suppressor Gene Inactivated by Promoter Hypermethylation in Head and Neck Squamous Cell Carcinoma

Deleted in colorectal cancer (DCC) is a candidate tumor-suppressor gene located at chromosome 18q21. However, DCC gene was found to have few somatic mutations and the heterozygous mice (DCC+/−) showed a similar frequency of tumor formation compared with the wild-type mice (DCC+/+). Recently, DCC came back to the spotlight as a better understating of its function and relationship with its ligand (netrin-1) had shown that DCC may act as a conditional tumor-suppressor gene. We evaluated hypermethylation as a mechanism for DCC inactivation in head and neck squamous cell carcinoma (HNSCC). DCC promoter region hypermethylation was found in 75% of primary HNSCC. There was a significant correlation between DCC promoter region hypermethylation and DCC expression (assessed by immunohistochemistry; P = 0.021). DCC nonexpressing HNSCC cell lines JHU-O12 and JHU-O19 with baseline hypermethylation of the DCC promoter were treated with 5-aza-2′-deoxycytidine (a demethylating agent) and reexpression of DCC was noted. Transfection of DCC into DCC-negative HNSCC cell lines resulted in complete abrogation of growth in all cell lines, whereas additional cotransfection of netrin-1 resulted in rescue of DCC-mediated growth inhibition. These results suggest that DCC is a putative conditional tumor-suppressor gene that is epigenetically inactivated by promoter hypermethylation in a majority of HNSCC. (Cancer Res 2006; 66(19): 9401-07

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

[[abstract]]Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子