A class of BVPS for first order impulsive functional integro-differential equations with a parameter

This paper is concerned with a class of boundary value problems for the nonlinear impulsive functional integro-differential equations with a parameter by establishing new comparison principles and using the method of upper and lower solutions together with monotone iterative technique. Sufficient conditions are established for the existence of extremal system of solutions for the given problem. Finally, we give an example that illustrates our results

More ConvNets in the 2020s:Scaling up Kernels Beyond 51x51 using Sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local-window attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31x31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer. In this paper, we explore the possibility of training extreme convolutions larger than 31x31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61x61 with better performance. Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with sparse factorized 51x51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as a wide range of downstream tasks including semantic segmentation on ADE20K, object detection on PASCAL VOC 2007, and object detection/segmentation on MS COCO

More ConvNets in the 2020s:Scaling up Kernels Beyond 51x51 using Sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local-window attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31×31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer. In this paper, we explore the possibility of training extreme convolutions larger than 31×31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61×61 with better performance. Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with sparse factorized 51×51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as a wide range of downstream tasks including semantic segmentation on ADE20K, object detection on PASCAL VOC 2007, and object detection/segmentation on MS COCO

More ConvNets in the 2020s:Scaling up Kernels Beyond 51x51 using Sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local-window attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31×31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer. In this paper, we explore the possibility of training extreme convolutions larger than 31×31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61×61 with better performance. Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with sparse factorized 51×51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as a wide range of downstream tasks including semantic segmentation on ADE20K, object detection on PASCAL VOC 2007, and object detection/segmentation on MS COCO

Sustained Release of IGF-1 by 3D Mesoporous Scaffolds Promoting Cardiac Stem Cell Migration and Proliferation

Background/Aims: C-kit-positive cardiac stem cells (CSCs) may have potential as a treatment for cardiovascular disease. However, the low survival rates of c-kit-positive CSCs present a major challenge during the transplantation process. Methods: The hierarchical structure of the 3D cell scaffold was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and N2 adsorption-desorption isotherms. Analyses of the proliferation and migration performances of the IGF-1 scaffold on c-kit-positive CSCs were conducted by experiments including QuantiT PicoGreen dsDNA and transwell assays. Results: In this study, we synthesized for the first time a novel hierarchical macro-mesoporous silica material (denoted MS15-c) in a one-pot procedure for the release of insulin-like growth factor-1 (IGF-1) and a three-dimensional (3D) cell scaffold. Both macropores and mesopores were visible in MS15-c and enabled the sustained release of IGF-1, extending its half-life and enhancing CSC proliferation and migration. Proliferation and migration were detected by QuantiT PicoGreen dsDNA and transwell assays, respectively. Moreover, an in vivo experiment was conducted to detect heart function with the addition of MS15-c. The new strategy proposed in this paper may extend the bio-applications of 3D cell scaffolds, thus permitting the sustained release of growth factors and efficient promotion of cell proliferation. Conclusion: This work successfully demonstrated an effective strategy for the construction of MS15-c cell scaffolds with hierarchical macro-mesoporous structures. The macro-mesoporous structures gave cell scaffolds the ability to release a growth factor to facilitate cell growth, while the scaffold structure promoted cell proliferation

Ischemic colitis presenting as a colonic mass: a case report and diagnostic challenges

Ischemic colitis (IC) is a multifaceted condition that often manifests with nonspecific symptoms such as abdominal pain and bloody diarrhea, particularly in older adults with vascular risk factors. Diagnosis is supported by elevated levels of white blood cells, lactate, and C-reactive protein (CRP). Computed tomography (CT) imaging typically reveals wall thickening and fat stranding in watershed areas. Colonoscopy may demonstrate mucosal erythema, ulceration, or necrosis. IC can be differentiated from inflammatory bowel disease (IBD), diverticulitis, and colorectal cancer based on symptom patterns and imaging findings. The absence of specific biomarkers can complicate diagnosis, potentially causing delays. Illustrating these challenges is the case of a 53-year-old male patient who arrived at the hospital exhibiting abdominal pain and diarrhea. Enhanced CT scans and colonoscopy identified a mass in the ileocecal region of the colon, and subsequent tissue biopsy revealed ischemic lesions in the submucosa. Initially diagnosed with IC, the patient’s symptoms gradually improved with conservative treatment, which included antibiotics, fluid resuscitation, and bowel rest. Follow-up endoscopy showed significant lesion improvement, and no recurrence was detected during subsequent follow-ups. This case illustrates the healing process of IC as manifested by colon mass under endoscopy. Also, it highlights the critical importance of timely diagnosis and personalized treatment strategies in atypical presentations to improve patient outcomes

Insights into the senescent mechanisms of harvested strawberry fruit at the physiological, molecular and metabolic levels

Strawberry (Fragaria × ananassa) is a worldwide cultivated horticultural crop, however, its short preservative life of the harvested fruit remains a challenge to be addressed. Currently, although much progress has been made toward understanding the senescence of harvested strawberry fruit, the defined mechanisms remain unclear. Therefore, we performed a series of morphological, physiological and biochemical, as well as transcriptome and proteome analyses using the widely-cultivated strawberry 'Benihoppe' during 0−8 d at room temperature. The results showed not only the shorter storage of harvested strawberry fruit resulted from the rapid perishability, softening, and water loss, but also an increase in soluble sugars within 2 d and a coordination of ABA with JA at the early stage, BR at the middle stage and ethylene at the later stage, respectively. The RNA-seq data highlighted on ABA with NCEDs and PYLs, auxin with IAAs and AUXs, ethylene with ACSs, EIN3 and ERFs, BR with BZRs, and JA with JMTs; while proteome data highlighted on ABA with PYL/SnRK2/ABF, JA with JAR1/JAZ, GA with GID1, BR with BSK, and ethylene with ETR/CTR/EIN2, suggesting an important role of ABA, JA, and ethylene in the senescence of harvested strawberries. Interestingly, higher contents of nerolidalyl caproate and threonine represented characteristic signs of ripening and senescence. Finally, a physiological, molecular and metabolic model for strawberry fruit senescence is proposed, providing comprehensive insights into the preservative mechanisms

Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models

IntroductionAntibiotic resistance is emerging as a critical global public health threat. The precise prediction of bacterial antibiotic resistance genes (ARGs) and phenotypes is essential to understand resistance mechanisms and guide clinical antibiotic use. Although high-throughput DNA sequencing provides a foundation for identification, current methods lack precision and often require manual intervention.MethodsWe developed a novel deep learning model for ARG prediction by integrating bacterial protein sequences using two protein language models, ProtBert-BFD and ESM-1b. The model further employs data augmentation techniques and Long Short-Term Memory (LSTM) networks to enhance feature extraction and classification performance.ResultsThe proposed model demonstrated superior performance compared to existing methods, achieving higher accuracy, precision, recall, and F1-score. It significantly reduced both false negative and false positive predictions in identifying ARGs, providing a robust computational tool for reliable gene-level resistance detection. Moreover, the model was successfully applied to predict bacterial resistance phenotypes, demonstrating its potential for clinical applicability.DiscussionThis study presents an accurate and automated approach for predicting antibiotic resistance genes and phenotypes, reducing the need for manual verification. The model offers a powerful technical tool that can support clinical decision-making and guide antibiotic use, thereby addressing an urgent need in the fight against antimicrobial resistance