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The Walrus Optimization (WO) algorithm, as an emerging metaheuristic algorithm, has shown excellent performance in problem-solving, however it still faces issues such as slow convergence and susceptibility to getting trapped in local optima. To this end, the study proposes a novel WO enhanced by quasi-oppositional-based learning and chaotic local search mechanisms, called QOCWO. The study aims to prevent premature convergence to local optima and enhance the diversity of the population by integrating the quasi-oppositional-based learning mechanism into the original Walrus Optimization (WO) algorithm, thereby improving the global search capability and expanding the search range. Additionally, the chaotic local search mechanism is introduced to accelerate the convergence speed of WO. To test the capabilities, the QOCWO algorithm is applied to the 23 standard functions and compared with seven other algorithms. Furthermore, the Wilcoxon rank-sum test is utilized to evaluate the significance of the results, which demonstrates the superior performance of the proposed algorithm. To assess the practicality in solving real-world problems, the QOCWO is applied to two engineering design issues, and the results indicated that QOCWO achieved lower costs compared to other algorithms.

Optical transparency methods can facilitate biological tissue optical imaging, which enabled accurate three-dimensional (3D) signal visualization and quantification of complex biological structures. Unfortunately, existing optical clearing approaches present a compromise between maximizing clearing capability, the preservation of fluorescent protein emission and the speed of sample processing. To address this challenge, we synthesis of a 2-hydroxyethyl methacrylate (HEMA)-acrylamide (AAm) copolymer using antipyrine (ATP) and 2,2′-thiodiethanol (TDE) as solvent, which could embed tissue samples rapidly and highly transparent, and compatible with multiple fluorescence labeling. It can enable volumetric imaging of tissue up to the scale of mice organs, shrinkage duration of the clearing and preserve emission from fluorescent proteins and dyes. This copolymer with suitable toughness and plasticity allows the tissue of interest to be sectioned into thin slices, and histological techniques provide high-resolution two-dimensional (2D) images of cells and subcellular structures. Furthermore, HEMA-AAm copolymer -tissue transparent could distinguish cell structures between healthy and diabetic disease in dye-labeled liver tissues, which provides new insights into pathological diagnosis and analysis. Copolymer provides an environment to facilitate high-resolution 3D/2D fluorescence imaging, which enables the study of cellular and tissue morphology in experimental and clinical conditions of interest.

Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice. An artificial intelligence (AI) model was developed to discriminate between fibroadenomas and phyllodes tumors on core biopsy images. It employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction, and a recurrent neural network (RNN) component for whole-slide classification, with an overall slide-level accuracy of 87.5%.

The “SIP” syncytium is a multicellular system in which smooth muscle cells (“S”), interstitial cells of Cajal (“I”), and platelet-derived growth factor receptor alpha-positive cells (“P”) are coupled via gap junctions. This electrical coupling allows changes in electrical conductance of cell type to modulate the excitability of other cell types in the syncytium. Gastrointestinal (GI) motility is further regulated by inputs from the enteric nervous system (ENS) through both excitatory and inhibitory enteric motor neurons. The basal excitability of the GI musculature is believed to be the result of a balance between the excitatory and inhibitory influences exerted by the interstitial cells. A disruption to this balance, caused by dysfunction of interstitial cells, could explain some of the symptoms observed in motility disorders. A lack of understanding of the mechanisms underlying GI disorders such as constipation, irritable bowel syndrome (IBS), idiopathic gastroparesis, and functional dyspepsia has hindered the effectiveness of clinical diagnosis and therapy. Computational models can succinctly describe complex biological systems with input from experimental data and help in developing a better understanding of the underlying physiological and pathophysiological processes.In this work, we aim to describe electrical activity in the SIP syncytium and its potential correlation to motility disorders. First, we constructed a phenomenological model to describe the Ca2+ transients in platelet-derived growth factor receptor alpha-positive (PDGFRα +) cells and investigated effect of Ca2+ transients on tonic inhibition in the GI musculature through tissue simulations. Second, we constructed a biophysically-based intramuscular interstitial cell of Cajal (ICC-IM) model with descriptions of the major ion channels, receptors, and intracellular process necessary to describe their role in mediating cholinergic neurotransmission. We have adapted our Ca2+ transient model to study the depolarising influence of Ca2+ transients in ICCIM on the GI musculature. Finally, we have integrated the excitatory and inhibitory effects exerted by both types of interstitial cells on the GI smooth muscle by developing a modelling framework which is capable of describing the electrophysiology of tissues with three cell types.From the in silico experiments, we demonstrate that the basal electrical activity observed in the GI smooth muscle tissue may be the result of a balance between excitatory and inhibitory influences within the SIP syncytium. We also demonstrate that disabling various inhibitory components within the SIP syncytium could produce an analogue similar to the symptoms of diarrhoea predominant IBS.The models developed in this work have been validated against experimental recordings. These models provide a basis for a better understanding of the underlying pathophysiology of GI motility disorders.

This study introduces an innovative approach by incorporating statistical offset features, range profiles, time–frequency analyses, and azimuth–range–time characteristics to effectively identify various human daily activities. Our technique utilizes nine feature vectors consisting of six statistical offset features and three principal component analysis network (PCANet) fusion attributes. These statistical offset features are derived from combined elevation and azimuth data, considering their spatial angle relationships. The fusion attributes are generated through concurrent 1D networks using CNN-BiLSTM. The process begins with the temporal fusion of 3D range–azimuth–time data, followed by PCANet integration. Subsequently, a conventional classification model is employed to categorize a range of actions. Our methodology was tested with 21,000 samples across fourteen categories of human daily activities, demonstrating the effectiveness of our proposed solution. The experimental outcomes highlight the superior robustness of our method, particularly when using the Margenau–Hill Spectrogram for time–frequency analysis. When employing a random forest classifier, our approach outperformed other classifiers in terms of classification efficacy, achieving an average sensitivity, precision, F1, specificity, and accuracy of 98.25%, 98.25%, 98.25%, 99.87%, and 99.75%, respectively.

The prosperity of microelectronics has intensified the requirement for miniaturized power systems using capacitors with high capacity and broad frequency ranges. Electrochemical supercapacitors stand out with their superior capacitance density, surpassing traditional electrolytic capacitors by at least two orders of magnitude. However, the intrinsic slow ion dynamics of electrical double layer effects greatly limit supercapacitors characteristic frequency, constraining their applicability in microsystems. This work constructs a near-ideal micro electrochemical supercapacitor, featuring the monolayer graphene as a working electrode, to reveal the ceiling of electrochemical capacitance characteristic frequency. To address this limitation, we introduce a Hybrid Electrochemical Electrolytic Capacitor design, which asymmetrically coupling the electrochemical and dielectric effects. At low frequencies, the electrochemical segment provides sufficient capacity, while its electrolytic segment takes over at high frequencies, broadening the frequency range. Consequently, the hybrid design boasts considerable capacitance density across a broad frequency range. Employing our wafer-scale microfabrication techniques, we showcase a device, achieving a characteristic frequency of 44 kHz and a volume capacitance density of 800 μ F / cm 3 . To demonstrate its practicality in microsystems, the device is integrated with a power management chip and buck circuit module, respectively, with only 2 % space usage compared to commercial electrolytic capacitor, achieving the same performance. The characteristic frequency of electrochemical supercapacitors is limited by ion dynamics of electrical double layer. Here, authors propose a hybrid design of electrochemical and electrolytic capacitors, operating over 44 kHz, that enables it to surpass such limitation.

Endometriosis is one of the most common disorders that causes infertility in women. Iron is overloaded in endometriosis peritoneal fluid (PF), with harmful effects on early embryo development. However, the mechanism by which endometriosis peritoneal fluid affects embryonic development remains unclear. Hence, this study investigated the effect of iron overload on mouse embryos and elucidated the molecular mechanism. Iron overload in endometriosis PF disrupted blastocyst formation, decreased GPX4 expression and induced lipid peroxidation, suggesting that iron overload causes embryotoxicity and induces ferroptosis. Moreover, mitochondrial damage occurs in iron overload-treated embryos, presenting as decreased ATP levels, increased ROS levels and MMP hyperpolarization. The cytotoxicity of iron overload is attenuated by the ferroptosis inhibitor Fer-1. Furthermore, Smart-seq analysis revealed that HMOX1 is upregulated in embryo ferroptosis and that HMOX1 suppresses ferroptosis by maintaining mitochondrial function. This study provides new insight into the mechanism of endometriosis infertility and a potential target for future endometriosis infertility treatment efforts.

Object detection in point clouds is a critical component in most autonomous driving systems. In this paper, in order to improve the effectiveness of image feature extraction and the accuracy of detection of point clouds, a pillar-based 3D point cloud object detection algorithm with multiattention mechanism is proposed, which includes three attention mechanisms SOCA, SOPA, and SAPI. The results show that the recognition accuracy of the optimized algorithm for cars, pedestrians, and cyclists on KITTI dataset is significantly improved on the detection benchmarks of BEV and 3D. Despite using only LiDAR, our algorithm outperforms PointPillars, which is one of the state-of-the-art algorithms for 3D object detection, with respect to both 3D and BEV view KITTI benchmarks while maintaining a relatively competitive speed.

Oxidative damage induced granulosa cells (GCs) apoptosis was considered as a significant cause of compromised follicle quality, antioxidants therapy has emerged as a potential method for improving endometriosis pregnancy outcomes. Here, we found that GCs from endometriosis patients show increased oxidative stress level. Methyl 3,4-dihydroxybenzoate (MDHB), a small molecule compound that is extracted from natural plants, reversed tert-butyl hydroperoxide (TBHP) induced GCs oxidative damage. Therefore, the aim of this study was to assess the protective effect of MDHB for GCs and its potential mechanisms. TUNEL staining and immunoblotting of cleaved caspase-3/7/9 showed MDHB attenuated TBHP induced GCs apoptosis. Mechanistically, MDHB treatment decreased cellular and mitochondria ROS production, improved the mitochondrial function by rescuing the mitochondrial membrane potential (MMP) and ATP production. Meanwhile, MDHB protein upregulated the expression of vital antioxidant transcriptional factor Nrf2 and antioxidant enzymes SOD1, NQO1 and GCLC to inhibited oxidative stress state, further beneficial to oocytes and embryos quality. Therefore, MDHB may represent a potential drug candidate in protecting granulosa cells in endometriosis, which can improve pregnancy outcomes for endometriosis-associated infertility.

Auricularia auricula polysaccharides used in Pinus koraiensis polyphenol encapsulation and delivery under weightlessness are rarely reported. In this study, an anionic polysaccharide fragment named AAP Iα with a molecular weight of 133.304 kDa was isolated and purified to construct a polyphenol encapsulation system. Nanoparticles named NPs-PP loaded with a rough surface for Pinus koraiensis polyphenol (PP) delivery were fabricated by AAP Iα and ε-poly-L-lysine (ε-PL). SEM and the DLS tracking method were used to observe continuous changes in AAP Iα, ε-PL and PP on the nanoparticles’ rough surface assembly, as well as the dispersion and stability. Hydrophilic, monodisperse and highly negative charged nanoparticles can be formed at AAP Iα 0.8 mg/mL, ε-PL 20 μg/mL and PP 80 μg/mL. FT-IR was used to determine their electrostatic interactions. Release kinetic studies showed that nanoparticles had an ideal gastrointestinal delivery effect. NPs-PP loaded were assembled through electrostatic interactions between polyelectrolytes after hydrogen bonding formation in PP-AAP Iα and PP-ε-PL, respectively. Colon adhesion properties and PP delivery in vivo of nanoparticles showed that NPs-PP loaded had high adhesion efficiency to the colonic mucosa under simulated microgravity and could enhance PP bioavailability. These results suggest that AAP Iα can be used in PP encapsulation and delivery under microgravity in astronaut food additives.