Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single-or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

An unusual correlation function is conjectured by M. Campostrini et al. (Phys. Rev. E 91, 042123 (2015)) for the ground state of a transverse Ising chain with geometrical frustration in one of the translationally invariant cases. Later, we demonstrated the correlation function and showed its non-local nature in the thermodynamic limit based on the rigorous evaluation of a Toeplitz determinant (J. Stat. Mech. 113102 (2016)). In this paper, we prove rigorously that all the states that forming the lowest gapless spectrum (including the ground state) in the kink phase exhibit the same asymptotic correlation function. So, in a point of view of cannonical ensemble, the thermal correlation function is inert to temperature within the energy range of the lowest gapless spectrum.

Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening.

On a ring, a single Jordan-Wigner transformation between the Kitaev model and the spin model suffers redundant degrees of freedom. However, we can establish a complete quaternary Jordan-Wigner mapping involving two Kitaev rings and two spin rings with periodic or antiperiodic boundary conditions in the transverse direction. This mapping facilitates us to demonstrate exactly how a topological extended-kink (TEK) phase develops in the interacting Kitaev ring with geometrical ring frustration. Unlike the usual topological phases protected by energy gap in noninteracting systems, this topological phase is gapless. And because the spectra of low energy excitations are quadratic, the specific heat per site approaches a half of Boltzmann constant near absolute zero temperature. More important, the ground state is unique, immune to spontaneous symmetry breaking, and exhibits long-range correlation but without local order parameter. We also demonstrate that concomitant localized kink zero modes (KZM's) take place by introducing a type of bond defect.

The protein kinase Cs (PKCs) are a family of Ser/Thr protein kinases categorized into three subfamilies: classical, novel, and atypical. The phosphorylation of PKC in germ cells is not well defined. In this study, we described the subcellular localization of phopho-PKC in the process of mouse oocyte maturation, fertilization, and early embryonic mitosis. Confocal microscopy revealed that phospho-PKC (pan) was distributed abundantly in the nucleus at the germinal vesicle stage. After germinal vesicle breakdown, phospho-PKC was localized in the vicinity of the condensed chromosomes, distributed in the whole meiotic spindle, and concentrated at the spindle poles. After metaphase I, phospho-PKC was translocated gradually to the spindle mid-zone during emission of the first polar body. After sperm penetration and electrical activation, the distribution of phospho-PKC was moved from the spindle poles to the spindle mid-zone. After the extrusion of the second polar body (PB2) phospho-PKC was localized in the area between the oocyte and the PB2. In fertilized eggs, phospho-PKC was concentrated in the pronuclei except for the nucleolus. Phospho-PKC was dispersed after pronuclear envelope breakdown, but distributed on the entire spindle at mitotic metaphase. The results suggest that PKC activation may play important roles in regulating spindle organization and stabilization, polar-body extrusion, and nuclear activity during mouse oocyte meiosis, fertilization, and early embryonic mitosis.