Dimensions of fractals related to languages defined by tagged strings in complete genomes

A representation of frequency of strings of length K in complete genomes of many organisms in a square has led to seemingly self-similar patterns when K increases. These patterns are caused by under-represented strings with a certain "tag"-string and they define some fractals when K tends to infinite. The Box and Hausdorff dimensions of the limit set are discussed. Although the method proposed by Mauldin and Williams to calculate Box and Hausdorff dimension is valid in our case, a different and simpler method is proposed in this paper.Comment: 9 pages with two figure

A Few-Shot Learning-Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks

Impact of quadrupole deformation on intermediate-energy heavy-ion collisions

This study employs the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to simulate intermediate-energy heavy-ion collisions between prolate nuclei $^{24}$Mg. The emphasis is on investigating the influence of centrality and orientation in several collision scenarios. The final-state particle multiplicities and anisotropic flows are primarily determined by the eccentricity and the area of the initial overlap. This not only provides feedback on the collision systems, but also, to some extent, provides a means to explore the fine structure inside deformed nuclei. Additionally, non-polarized collisions have been further discussed. These results contribute to the understanding of the geometric effects in nuclear reactions, and aid in the exploration of other information on reaction systems, such as the equation of state and nuclear high-momentum tail

Observed deep energetic eddies by seamount wake

Despite numerous surface eddies are observed in the ocean, deep eddies (a type of eddies which have no footprints at the sea surface) are much less reported in the literature due to the scarcity of their observation. In this letter, from recently collected current and temperature data by mooring arrays, a deep energetic and baroclinic eddy is detected in the northwestern South China Sea (SCS) with its intensity, size, polarity and structure being characterized. It remarkably deepens isotherm at deep layers by the amplitude of ~120 m and induces a maximal velocity amplitude about 0.18 m/s, which is far larger than the median velocity (0.02 m/s). The deep eddy is generated in a wake when a steering flow in the upper layer passes a seamount, induced by a surface cyclonic eddy. More observations suggest that the deep eddy should not be an episode in the area. Deep eddies significantly increase the velocity intensity and enhance the mixing in the deep ocean, also have potential implication for deep-sea sediments transport

Sulforaphane induces adipocyte browning and promotes glucose and lipid utilization

Scope: Obesity is closely related to the imbalance of white adipose tissue storing excess calories, and brown adipose tissue dissipating energy to produce heat in mammals. Recent studies revealed that acquisition of brown characteristics by white adipocytes, termed “browning,” may positively contribute to cellular bioenergetics and metabolism homeostasis. The goal was to investigate the putative effects of natural antioxidant sulforaphane (1-isothiocyanate-4-methyl-sulfonyl butane; SFN) on browning of white adipocytes. Methods and Results: 3T3-L1 mature white adipocytes were treated with SFN for 48 h, and then the mitochondrial content, function, and energy utilization were assessed. SFN was found to induce 3T3-L1 adipocytes browning based on the increased mitochondrial content and activity of respiratory chain enzymes, whereas the mechanism involved the upregulation of nuclear factor E2-related factor 2/ sirtuin1/ peroxisome proliferator-activated receptor gamma coactivator 1 alpha signaling. SFN enhanced uncoupling protein 1 expression, a marker for brown adipocyte, leading to the decrease in cellular ATP. SFN also enhanced glucose uptake and oxidative utilization, lipolysis and fatty acid oxidation in 3T3-L1 adipocytes. Conclusion: SFN-induced browning of white adipocytes enhanced the utilization of cellular fuel, and the application of SFN is a promising strategy to combat obesity and obesity-related metabolic disorder

An LMI-Based H

Due to the bandwidth constraints in the networked control systems (NCSs), a deadband scheduling strategy is proposed to reduce the data transmission rate of network nodes. A discrete-time model of NCSs is established with both deadband scheduling and network-induced time-delay. By employing the Lyapunov functional and LMI approach, a state feedback H∞ controller is designed to ensure the closed-loop system asymptotically to be stable with H∞ performance index. Simulation results show that the introduced deadband scheduling strategy can ensure the control performance of the system and effectively reduce the node's data transmission rate

Fault-Tolerant Control for Networked Control Systems with Limited Information in Case of Actuator Fault

This paper is concerned with the problem of designing a fault-tolerant controller for uncertain discrete-time networked control systems against actuator possible fault. The step difference between the running step k and the time stamp of the used plant state is modeled as a finite state Markov chain of which the transition probabilities matrix information is limited. By introducing actuator fault indicator matrix, the closed-loop system model is obtained by means of state augmentation technique. The sufficient conditions on the stochastic stability of the closed-loop system are given and the fault-tolerant controller is designed by solving a linear matrix inequality. A numerical example is presented to illustrate the effectiveness of the proposed method

Artificial intelligence-aided diagnosis and treatment in the field of optometry

With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice

Construction and Validation of an Autophagy-Related Prognostic Signature and a Nomogram for Bladder Cancer

ObjectiveBladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients’ risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature.MethodsWe screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets.ResultsThree prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage).ConclusionsThree ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients