iAMP-CRA: Identifying Antimicrobial Peptides Using Convolutional Recurrent Neural Network with Self-Attention.

Antimicrobial peptides (AMPs) are natural polypeptides with antibacterial activity and are an important part of the innate immune system. In order to solve the growing problem of conventional antibiotic resistance, AMPs have been applied in different fields as highly potential alternatives. It is of great significance that deep learning-based methods can quickly screen out candidate samples of AMPs from massive protein sequences to help discover new AMPs.

Spring pair method of finding saddle points using the minimum energy path as a compass.

Finding index-1 saddle points is crucial for understanding phase transitions. In this work, we propose a simple yet efficient approach, the spring pair method (SPM), to accurately locate saddle points. Without requiring the Hessian information, the SPM evolves a single pair of spring-coupled particles on an energy surface.

Assessment of intratumor heterogeneity for preoperatively predicting the invasiveness of pulmonary adenocarcinomas manifesting as pure ground-glass nodules.

Background: The increased use of low-dose computed tomography (CT) for lung cancer screening has improved the detection of ground-glass nodules. However, as the clinical utility of CT findings to predict the invasiveness of pure ground-glass nodules (pGGNs) is currently limited, differentiating pGGNs that indicate invasive adenocarcinoma (IAC) from those that represent other histological entities is challenging. We aimed to quantify intratumor heterogeneity of lung adenocarcinomas characterized by pGGNs on CT to assess its efficacy in predicting IACs before surgery.

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.

Background: This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

Methods: In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method.

Prediction of mucinous adenocarcinoma in colorectal cancer with mucinous components detected in preoperative biopsy diagnosis.

Objectives: Endoscopic biopsy diagnosis for the preoperative assessment of mucinous components in patients with colorectal cancer is limited. This study investigated a radiomics model and established an explainable prediction model by using machine learning to differentiate between adenocarcinoma with mucinous components and mucinous adenocarcinoma.

Methods: The derivation cohort included 312 patients with colorectal cancer with mucinous components detected during preoperative endoscopic biopsy diagnosis.

Novel predefined-time stability theory and its application in sliding mode control of synchronizing chaotic systems.

Aiming at predefined-time synchronization for chaotic systems, a new predefined-time sliding mode control method is proposed. First, based on the definition of predefined-time stability, a novel predefined-time inequality is proposed, along with a detailed mathematical proof. This inequality differs from existing Lyapunov inequalities and offers greater flexibility.

LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.

Alzheimer's disease (AD) is a complex disease with its genetic etiology not fully understood. Gene network-based methods have been proven promising in predicting AD genes. However, existing approaches are limited in their ability to model the nonlinear relationship between networks and disease genes, because (i) any data can be theoretically decomposed into the sum of a linear part and a nonlinear part, (ii) the linear part can be best modeled by a linear model since a nonlinear model is biased and can be easily overfit, and (iii) existing methods do not separate the linear part from the nonlinear part when building the disease gene prediction model.

Phase behavior of symmetric diblock copolymers under 3D soft confinement.

The phase behavior of symmetric diblock copolymers under three-dimensional (3D) soft confinement is investigated using self-consistent field theory. Soft confinement is realized in binary blends composed of AB diblock copolymers and C homopolymers, where the copolymers self-assemble to form a droplet embedded in a homopolymer matrix. The phase behavior of the confined block copolymers is regulated by the degree of confinement and the selectivity of the homopolymers, resulting in a rich variety of novel structures.

Nucleation and phase transition of decagonal quasicrystals.

In this work, we study the nucleation of quasicrystals from liquid or periodic crystals by developing an efficient order-order phase transition algorithm, namely, the nullspace-preserving saddle search method. In particular, we focus on nucleation and phase transitions of the decagonal quasicrystal (DQC) based on the Lifshitz-Petrich model. We present the nucleation path of DQC from the liquid and demonstrate one- and two-stage transition paths between DQC and periodic crystals.

A cascaded FAS-UNet+ framework with iterative optimization strategy for segmentation of organs at risk.

Segmentation of organs at risks (OARs) in the thorax plays a critical role in radiation therapy for lung and esophageal cancer. Although automatic segmentation of OARs has been extensively studied, it remains challenging due to the varying sizes and shapes of organs, as well as the low contrast between the target and background. This paper proposes a cascaded FAS-UNet+ framework, which integrates convolutional neural networks and nonlinear multi-grid theory to solve a modified Mumford-shah model for segmenting OARs.

Stable Jumping Control Based on Deep Reinforcement Learning for a Locust-Inspired Robot.

Biologically inspired jumping robots exhibit exceptional movement capabilities and can quickly overcome obstacles. However, the stability and accuracy of jumping movements are significantly compromised by rapid changes in posture. Here, we propose a stable jumping control algorithm for a locust-inspired jumping robot based on deep reinforcement learning.

Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study.

Purpose: We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC).

Methods: A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared.

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA-disease association.

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation.

Novel flexible fixed-time stability theorem and its application to sliding mode control nonlinear systems.

For the fixed-time nonlinear system control problem, a new fixed-time stability (FxTS) theorem and an integral sliding mode surface are proposed to balance the control speed and energy consumption. We discuss the existing fixed time inequalities and set up less conservative inequalities to study the FxTS theorem. The new inequality differs from other existing inequalities in that the parameter settings are more flexible.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer.

Objective: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features.

Methods: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively.

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer.

Rationale And Objectives: Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC.

Materials And Methods: In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.

A static precise single-point positioning method based on carrier phase zero-baseline self-differencing.

Satellite navigation positioning has become an indispensable component of everyday life, where precise pinpointing and rapid convergence are crucial in delivering timely and accurate location information. However, due to the damping of integer ambiguities and system residual errors, the rapid convergence of Precise Point Positioning (PPP) implementation is a significant challenge. To address this, this paper proposes a novel Carrier Phase Zero-Baseline Self-Differencing Precise Point Positioning (CZS-PPP) technique and its ionosphere-free fusion model.

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography.

Rationale And Objectives: To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes.

Materials And Methods: An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values.

The research of river basin ecological compensation based on water emissions trading mechanism.

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution.

Interval-based sparse ensemble multi-class classification algorithm for terahertz data.

Terahertz time-domain spectroscopy (THz-TDS) has been widely used for food and drug identification. The classification information of a THz spectrum usually does not exist in the whole spectral band but exists only in one or several small intervals. Therefore, feature selection is indispensable in THz-based substance identification.