Cell surface-specific N-glycan profiling in breast cancer

Aberrant changes in specific glycans have been shown to be associated with immunosurveillance, tumorigenesis, tumor progression and metastasis. In this study, the N-glycan profiling of membrane proteins from human breast cancer cell lines and tissues was detected using modified DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis (DSA-FACE). The N-glycan profiles of membrane proteins were analyzed from 7 breast cancer cell lines and MCF 10A, as well as from 100 pairs of breast cancer and corresponding adjacent tissues. The results showed that, compared with the matched adjacent normal tissue samples, two biantennary N-glycans (NA2 and NA2FB) were significantly decreased (p <0.0001) in the breast cancer tissue samples, while the triantennary glycan (NA3FB) and a high-mannose glycan (M8) were dramatically increased (p = 0.001 and p <0.0001, respectively). Moreover, the alterations in these specific N-glycans occurred through the oncogenesis and progression of breast cancer. These results suggested that the modified method based on DSA-FACE is a high-throughput detection technology that is suited for analyzing cell surface N-glycans. These cell surface-specific N-glycans may be helpful in recognizing the mechanisms of tumor cell immunologic escape and could be potential targets for new breast cancer drugs

Nonlinear wave equations and applications from control theory

In 1961 Joergens [14]  rst proved that the Cauchy problem to the wave equation with power type nonlinearity (1.1) utt − u + uSuSp−1 = 0 inRn × R in the case of R3 and when p C2 for any Cauchy data u0 > C3, u1 > C2. Our research in this paper will be centred on  nding the connection between the above results and the closely related equations from the control theory in a more abstract setting. In particular we examine the quasipotential formula in [7] and investigate whether the result is applicable to (1.1) or its generalisations. To this end we  rst extend the existence theorems in [10] to controlled equations, and then establish the  nding that the quasipotential admits of analogous expression for our well-posed problem. Along the way there is also a remarkable discovery concerning the large time behaviour of the solution

Lightweight Vision Transformer with Cross Feature Attention

Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks. Convolutional neural networks (CNNs) exploit spatial inductive bias to learn visual representations, but these networks are spatially local. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for mobile devices. In this paper, we propose cross feature attention (XFA) to bring down computation cost for transformers, and combine efficient mobile CNNs to form a novel efficient light-weight CNN-ViT hybrid model, XFormer, which can serve as a general-purpose backbone to learn both global and local representation. Experimental results show that XFormer outperforms numerous CNN and ViT-based models across different tasks and datasets. On ImageNet1K dataset, XFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters, which is 2.2% and 6.3% more accurate than EfficientNet-B0 (CNN-based) and DeiT (ViT-based) for similar number of parameters. Our model also performs well when transferring to object detection and semantic segmentation tasks. On MS COCO dataset, XFormer exceeds MobileNetV2 by 10.5 AP (22.7 -> 33.2 AP) in YOLOv3 framework with only 6.3M parameters and 3.8G FLOPs. On Cityscapes dataset, with only a simple all-MLP decoder, XFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.Comment: Technical Repor

DanZero+: Dominating the GuanDan Game through Reinforcement Learning

The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period. Recent advancements have propelled AI programs to showcase expertise in intricate card games such as Mahjong, DouDizhu, and Texas Hold'em. In this work, we aim to develop an AI program for an exceptionally complex and popular card game called GuanDan. This game involves four players engaging in both competitive and cooperative play throughout a long process to upgrade their level, posing great challenges for AI due to its expansive state and action space, long episode length, and complex rules. Employing reinforcement learning techniques, specifically Deep Monte Carlo (DMC), and a distributed training framework, we first put forward an AI program named DanZero for this game. Evaluation against baseline AI programs based on heuristic rules highlights the outstanding performance of our bot. Besides, in order to further enhance the AI's capabilities, we apply policy-based reinforcement learning algorithm to GuanDan. To address the challenges arising from the huge action space, which will significantly impact the performance of policy-based algorithms, we adopt the pre-trained model to facilitate the training process and the achieved AI program manages to achieve a superior performance.Comment: arXiv admin note: text overlap with arXiv:2210.1708

Merino: Entropy-driven Design for Generative Language Models on IoT Devices

Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, directly deploying LLMs in resource-constrained hardware, such as Internet-of-Things (IoT) devices, is difficult due to their high computational cost. In this paper, we propose a novel information-entropy framework for designing mobile-friendly generative language models. Our key design paradigm is to maximize the entropy of transformer decoders within the given computational budgets. The whole design procedure involves solving a mathematical programming (MP) problem, which can be done on the CPU within minutes, making it nearly zero-cost. We evaluate our designed models, termed MeRino, across nine NLP downstream tasks, showing their competitive performance against the state-of-the-art autoregressive transformer models under the mobile setting. Notably, MeRino achieves similar or better zero performance compared to the 350M parameter OPT while being 4.9x faster on NVIDIA Jetson Nano with 5.5x reduction in model size. Code will be made available soon

Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding

To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-annotated data with diverse base classes. In this paper, we propose a new open-world semantic segmentation pipeline that makes the first attempt to learn to segment semantic objects of various open-world categories without any efforts on dense annotations, by purely exploiting the image-caption data that naturally exist on the Internet. Our method, Vision-language-driven Semantic Segmentation (ViL-Seg), employs an image and a text encoder to generate visual and text embeddings for the image-caption data, with two core components that endow its segmentation ability: First, the image encoder is jointly trained with a vision-based contrasting and a cross-modal contrasting, which encourage the visual embeddings to preserve both fine-grained semantics and high-level category information that are crucial for the segmentation task. Furthermore, an online clustering head is devised over the image encoder, which allows to dynamically segment the visual embeddings into distinct semantic groups such that they can be classified by comparing with various text embeddings to complete our segmentation pipeline. Experiments show that without using any data with dense annotations, our method can directly segment objects of arbitrary categories, outperforming zero-shot segmentation methods that require data labeling on three benchmark datasets.Comment: Accepted to ECCV 202

Flood mitigation by permeable pavements in Chinese sponge city construction

It is important to evaluate the effectiveness of permeable pavements on flood mitigation at different spatial scales for their effective application, for example, sponge city construction in China. This study evaluated the effectiveness of three types of permeable pavements (i.e., permeable asphalts (PA), permeable concretes (PC), and permeable interlocking concrete pavers (PICP)) on flood mitigation at a community scale in China using a hydrological model. In addition, the effects of clogging and initial water content in permeable pavements on flood mitigation performance were assessed. The results indicated that in 12 scenarios, permeable pavements reduced total surface runoff by 1–40% and peak flow by 7–43%, respectively. The hydrological performance of permeable pavements was limited by clogging and initial water content. Clogging resulted in the effectiveness on total surface runoff reduction and peak flow reduction being decreased by 62–92% and 37–65%, respectively. By increasing initial water content at the beginning of the simulation, the effectiveness of total runoff reduction and peak flow reduction decreased by 57–85% and 37–67%, respectively. Overall, among the three types of permeable pavements, PC without clogging had the best performance in terms of flood mitigation, and PICP was the least prone to being clogged. Our findings demonstrate that both the type and the maintenance of permeable pavements have significant effects on their performance in the flood mitigation

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Accurate power consumption assessment is of critical importance in the fast-evolving world of cloud and edge computing. These technologies enable rapid data processing and storage but they also require huge amounts of energy. This energy requirement directly impacts operational costs, as well as environmental responsibility. We are conducting research to develop a specialized cloud-edge power measurement and security model. This model delivers reliable power usage data from these systems while maintaining security for the data they process and store. A combination of simulation-based analysis and real-world experimentation helped us to deliver these results. Monte Carlo based simulations produced power usage predictions under various conditions and Load Testing validated their real-world performance. A Threat Modeling-based security study identified potential vulnerabilities and suggested protection protocols. A collaborative approach enhances power measurements accuracy and encourages secure operation of the combined cloud-edge systems. By fusing these metrics, a more efficient and secure operation of computing resources becomes possible. This research underscores the critical importance of developing advanced techniques for power metering and security in cloud-edge computing systems. Future research may focus on both expanding the model’s use to an array of larger, more complex networks, as well as the inclusion of AI driven predictive analytics to amplify accuracy of power management

Small Needle-Knife Versus Extracorporeal Shock Wave Therapy for the Treatment of Plantar Fasciitis: A Systematic Review and Meta-Analysis

BACKGROUND: Plantar fasciitis (PF) is the most common cause of chronic heel pain among adults. Extracorporeal shock wave therapy (ESWT) is the recommended in the current guidelines, and the small needle-knife yields acceptable clinical effects for musculoskeletal pain. OBJECTIVE: To systematically compare the efficacy of the small needle-knife versus ESWT for the treatment of PF. METHODS: The present review was registered in the International Prospective Register of Systematic Reviews (i.e., PROSPERO , CRD42023448813). Two of the authors searched electronic databases for randomized controlled trials (RCTs) comparing the small needle-knife versus ESWT for the treatment of PF, and collected outcomes including curative effect, pain intensity, and function. Risk of bias was assessed using the Cochrane Handbook Risk of Bias tool and the quality of the RCTs was evaluated according to the Jadad Scale. The same authors independently performed data extraction from the included studies, which were imported into Review Manager version 5.4.1(Copenhagen: Nordic Cochrane Centre, The Cochrane Collaboration, 2020) for meta-analysis. RESULTS: The initial literature search retrieved 886 studies, of which 6 were eventually included in this study. Meta-analysis revealed no significant difference in curative effect (OR = 1.87; 95 % CI [0.80, 4.37], p = .15) nor short-term pain improvement (MD = 2.20; 95 % CI [-2.77, 7.16], p = .39) between the small needle-knife and ESWT. However, the small needle-knife may be more effective than ESWT for pain improvement in mid-term (MD = 9.11; 95 % CI [5.08, 13.15], p＜ .00001) and long-term follow-ups (MD = 10.71; 95 % CI [2.18, 19.25], p＜ .00001). Subgroup analysis revealed that the small needle-knife combined with a corticosteroid injection yielded a statistically significant difference in reduction of pain intensity at all follow-ups (MD = 4.84; 95 % CI [1.33, 8.36], p = .007; MD = 10.99; 95 % CI [8.30, 13.69], p＜ .00001; MD = 17.87; 95 % CI [15.26, 20.48], p＜ .00001). Meta-analysis revealed no statistical differences in short-term (MD = 1.34; 95 % CI [-3.19, 5.86], p = .56) and mid-term (MD = 2.75; 95 % CI [-1.21, 6.72], p = . 17) functional improvement between the needle-knife and ESWT groups. In a subgroup analysis of moderate-quality studies, the small needle-knife demonstrated a favorable effect on mid-term functional improvement (MD = 1.58; 95 % CI [0.52, 2.65], p = .004), with low heterogeneity (