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Demyelination and failure of remyelination in the central nervous system (CNS) characterize a number of neurological disorders. Spontaneous remyelination in demyelinating diseases is limited, as oligodendrocyte precursor cells (OPCs), which are often present in demyelinated lesions in abundance, mostly fail to differentiate into oligodendrocytes, the myelinating cells in the CNS. In addition to OPCs, the lesions are assembled numbers of activated resident microglia/infiltrated macrophages; however, the mechanisms and potential role of interactions between the microglia/macrophages and OPCs are poorly understood. Here, we generated a transcriptional profile of exosomes from activated microglia, and found that miR-615-5p was elevated. miR-615-5p bound to 3'UTR of myelin regulator factor (MYRF), a crucial myelination transcription factor expressed in oligodendrocyte lineage cells. Mechanistically, exosomes from activated microglia transferred miR-615-5p to OPCs, which directly bound to MYRF and inhibited OPC maturation. Furthermore, an effect of AAV expressing miR-615-5p sponge in microglia was tested in experimental autoimmune encephalomyelitis (EAE) and cuprizone (CPZ)-induced demyelination model, the classical mouse models of multiple sclerosis. miR-615-5p sponge effectively alleviated disease progression and promoted remyelination. This study identifies miR-615-5p/MYRF as a new target for the therapy of demyelinating diseases.

Yu Zhou,1– 3,* Yong Ma,2– 4,* Dongliang Qian,2,3,* Zhou Zhou,5,6,* Bin Li,2,3,* Erqing Chai1– 3 1First Clinical Medical College, Lanzhou University, Lanzhou, People’s Republic of China; 2Cerebrovascular Disease Center, Gansu Provincial Hospital, Lanzhou, People’s Republic of China; 3Key Laboratory of Cerebrovascular Diseases, Lanzhou, People’s Republic of China; 4Clinical Medicine College, Ningxia Medical University, Yinchuan, People’s Republic of China; 5First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 6National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Erqing Chai, First Clinical Medical College, Lanzhou University, No. 204, Donggang West Road, Chengguan District, Lanzhou City, 730000, People’s Republic of China, Tel +86 13893439044, Email ldyy_chaieq@lzu.edu.cnPurpose: To explore the predictive value of atherogenic index of plasma(AIP) for carotid in-stent restenosis(ISR).Methods: Patients who underwent carotid artery stenting (CAS) in hospital from January 2016 to January 2021 were retrospectively enrolled. They were randomly divided into training and validation sets. Based on the results of carotid digital subtraction angiography (DSA) during the follow-up period, the patients were divided into ISR group and non-ISR group. The differences of AIP and lipid levels between the two groups were compared. The independent risk factors of ISR and the predictive value of AIP for ISR were analyzed. A nomogram was developed based on the independent risk factors, and the receiver operating characteristic (ROC) curve, the calibration curve and the decision curve analysis were conducted to assess the predictive ability and clinical practicability of the nomogram in both the training set and validation sets.Results

The properties and applications of ionic liquids (ILs) have been widely investigated when they are confined within nanochannels such as carbon nanotubes (CNTs). The confined ILs exhibit very different properties from their bulk state due to a nanoconfinement effect, which plays an important role in the performances of devices with ILs. In this work, we studied the effect of the charge carried by CNTs on confined ILs inside CNTs using molecular dynamics simulations. In charged CNTs, cations and anions are distributed separately along the radial directions, and the transition of orientations of the cations between parallel and vertical to CNTs occurs by changing the charge state of CNTs. The number of hydrogen bonds (HBs) formed by the confined ILs can be reduced by switching the surface charge of CNTs from positive to negative due to the contact modes between cations and anions as well as the distributions of cations in CNTs. The diffusivities along and vertical to the axial direction of CNTs were found to be non-monotonic owing to the "trade-off" effect from both ion pair interlocking and anchoring ILs on the CNT walls. Additionally, the region-dependent dynamics of ILs were also related to the intermolecular interactions in different regions of CNTs. Furthermore, the vibrational modes of ILs were obviously influenced in highly charged CNTs as determined by calculating the density of vibrational states, which demonstrated the transitions in the structure and interactions. The density distributions changed from single layer to double layers when increasing the pore size of neutral CNTs while the hydrogen bonds exhibited a non-monotonic tendency versus the pore sizes. Our results might help to understand the structure and dynamics of confined ILs as well as aid optimizing the performance of devices with ILs. Exploration of the impact of charge states of carbon nanotubes on region-dependent properties of confined ionic liquids and the relation between ionic dynamics an, Funding Agencies|Basic and Applied Basic Research Foundation of Guangdong Province [2022A1515010873, 2023A1515012535]; Guangdong Basic and Applied Basic Research Foundation [22003080]; National Natural Science Foundation of China

The heart, which is the first organ to develop, is highly dependent on its form to function1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.

Qiongdan Zhang,1,2,* Huizhen Wang,1,2,* Qingyi Liu,1,2 Ni Zeng,1,2 Gang Fu,1,2 Yixing Qiu,1,2 Yupei Yang,1,2 Hanwen Yuan,1,2 Wei Wang,1,2 Bin Li1,2 1TCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, People’s Republic of China; 2School of Pharmacy, Hunan University of Chinese Medicine, Changsha, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Wang; Bin Li, Tel +86-136-5743-8606 ; +86-158-7410-0917, Email wangwei402@hotmail.com; libin@hnucm.edu.cnAbstract: Exosomes, small extracellular vesicles derived from cells, are known to carry important bioactive molecules such as proteins, nucleic acids, and lipids. These bioactive components play crucial roles in cell signaling, immune response, and tumor metastasis, making exosomes potential diagnostic biomarkers for various diseases. However, current methods for detecting tumor exosomes face scientific challenges including low sensitivity, poor specificity, complicated procedures, and high costs. It is essential to surmount these obstacles to enhance the precision and dependability of diagnostics that rely on exosomes. Merging DNA signal amplification techniques with the signal boosting capabilities of nanomaterials presents an encouraging strategy to overcome these constraints and improve exosome detection. This article highlights the use of DNA signal amplification technology and nanomaterials’ signal enhancement effect to improve the detection of exosomes. This review seeks to offer valuable perspectives for the enhancement of amplification methods applied in practical cancer diagnosis and prognosis by providing an overview of how these novel technologies are utilized in exosome-based diagnostic procedures. Keywords: tumor, exosomes, nanomaterials, DNA signal amplification

High-cadence, multiwavelength observations have continuously revealed the diversity of tidal disruption events (TDEs), thus greatly advancing our knowledge and understanding of TDEs. In this work, we conducted an intensive optical-UV and X-ray follow-up campaign of TDE AT2023lli, and found a remarkable month-long bump in its UV/optical light curve nearly two months prior to maximum brightness. The bump represents the longest separation time from the main peak among known TDEs to date. The main UV/optical outburst declines as $t^{-4.10}$, making it one of the fastest decaying optically selected TDEs. Furthermore, we detected sporadic X-ray emission 30 days after the UV/optical peak, accompanied by a reduction in the period of inactivity. It is proposed that the UV/optical bump could be caused by the self-intersection of the stream debris, whereas the primary peak is generated by the reprocessed emission of the accretion process. In addition, our results suggest that episodic X-ray radiation during the initial phase of decline may be due to the patched obscurer surrounding the accretion disk, a phenomenon associated with the inhomogeneous reprocessing process. The double TDE scenario, in which two stars are disrupted in sequence, is also a possible explanation for producing the observed early bump and main peak. We anticipate that the multicolor light curves of TDEs, especially in the very early stages, and the underlying physics can be better understood in the near future with the assistance of dedicated surveys such as the deep high-cadence survey of the 2.5-meter Wide Field Survey Telescope (WFST)., Comment: 14 pages, 8 figures,accepted for publication by ApJL

Yuping Zhu,1 Xunfu Lai,1 Mengting Wang,2 Xin Tang,1 Tianyi Wan,3 Bin Li,1 Xiaoming Liu,1 Jialin Wu,1 Lei He,1 Yulin He1 1Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 2Department of Radiology, Yichang Central People’s Hospital, Yichang, People’s Republic of China; 3Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, People’s Republic of ChinaCorrespondence: Yulin He, Department of Radiology, The First Affiliated Hospital of Nanchang University, 17 Yongwaizheng Street, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86 0791 8869 3802, Email ndyfy02171@ncu.edu.cnObjective: To separate the resting-state network of patients with dental pain using independent component analysis (ICA) and analyze abnormal changes in functional connectivity within as well as between the networks.Patients and Methods: Twenty-three patients with dental pain and 30 healthy controls participated in this study. We extracted the resting-state functional network components of both using ICA. Functional connectivity differences within 14 resting-state brain networks were analyzed at the voxel level. Directional interactions between networks were analyzed using Granger causality analysis. Subsequently, functional connectivity values and causal coefficients were assessed for correlations with clinical parameters.Results: Compared to healthy controls, we found enhanced functional connectivity in the left superior temporal gyrus of anterior protrusion network and the right Rolandic operculum of auditory network in patients with dental pain (p< 0.01 and cluster-level p< 0.05, Gaussian random field corrected). In contrast, functional connectivity of the right precuneus in the precuneus network was reduced, and were significantly as well as negatively correlated to those of the Visual Analogue Scale (r=− 4.93, p=0.017), Hamilton Anxiety Scale (r=− 0.46, p=0.027), and Hamilton Depr

Microplastics (MPs) are a global concern regarding environmental pollution. This study evaluated the impacts of MPs with two sizes (5 µm and 0.5 µm) on hatching performance and microbiota of Sepiella japonica. The presence of MPs increased the hatching rate at some stages of the fertilization process and reduced the oxygen consumption rate at the gastrula stage. No size-dependent impact was observed. The 16S rRNA gene was sequenced to identify the flora. Clustering tags assessed species diversity in the samples with 97% similarity. Proteobacteria was the most abundant phylum in all three groups. MPs publicity appreciably modified flower structure. The most variable genera were Ralstonia, Methylophilus, and Pseudorhodoferax, which can furnish nutrients and modify the host's immune response. MPs exposure appeared to enrich beneficial bacteria in this study. The presence of MPs with a size of 5µm played a greater role in this process, which is supported by presumptive functions. However, since the adsorption of suspended MPs on aquatic eggs can have cascading effects on specific life stages of oviparous animals, regular monitoring of microbial communities is necessary after juvenile S. japonica formation to prevent disease outbreaks.

Deep learning-based video compression is a challenging task, and many previous state-of-the-art learning-based video codecs use optical flows to exploit the temporal correlation between successive frames and then compress the residual error. Although these two-stage models are end-to-end optimized, the epistemic uncertainty in the motion estimation and the aleatoric uncertainty from the quantization operation lead to errors in the intermediate representations and introduce artifacts in the reconstructed frames. This inherent flaw limits the potential for higher bit rate savings. To address this issue, we propose an uncertainty-aware video compression model that can effectively capture the predictive uncertainty with deep ensembles. Additionally, we introduce an ensemble-aware loss to encourage the diversity among ensemble members and investigate the benefits of incorporating adversarial training in the video compression task. Experimental results on 1080p sequences show that our model can effectively save bits by more than 20% compared to DVC Pro., Comment: Published on IEEE Transactions on Multimedia

Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image. However, existing methods primarily integrate reference images within the text embedding space, leading to a complex entanglement of image and text information, which poses challenges for preserving both identity fidelity and semantic consistency. To tackle this challenge, we propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. Specifically, we introduce identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information while deactivating the original text cross-attention module of the diffusion model. This ensures that the image stream faithfully represents the identity provided by the reference image while mitigating interference from textual input. Additionally, we introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams. This mechanism not only enhances the fidelity of identity and semantic consistency but also enables convenient control over the styles of the generated images. Extensive experimental results on both raw photo generation and style image generation demonstrate the superior performance of our proposed method.

The proliferation of malicious deepfake applications has ignited substantial public apprehension, casting a shadow of doubt upon the integrity of digital media. Despite the development of proficient deepfake detection mechanisms, they persistently demonstrate pronounced vulnerability to an array of attacks. It is noteworthy that the pre-existing repertoire of attacks predominantly comprises adversarial example attack, predominantly manifesting during the testing phase. In the present study, we introduce a pioneering paradigm denominated as Bad-Deepfake, which represents a novel foray into the realm of backdoor attacks levied against deepfake detectors. Our approach hinges upon the strategic manipulation of a delimited subset of the training data, enabling us to wield disproportionate influence over the operational characteristics of a trained model. This manipulation leverages inherent frailties inherent to deepfake detectors, affording us the capacity to engineer triggers and judiciously select the most efficacious samples for the construction of the poisoned set. Through the synergistic amalgamation of these sophisticated techniques, we achieve an remarkable performance-a 100% attack success rate (ASR) against extensively employed deepfake detectors., Comment: BigDIA 2023

We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst-case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning-augmented algorithm achieves both consistency and robustness., Comment: A preliminary version of this work is to be presented at IEEE INFOCOM 2024 Age and Semantics of Information Workshop

Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typically decomposed the UCIR problem into two distinct tasks: intra-domain representation learning and cross-domain feature alignment. However, these segregated strategies overlook the potential synergies between these tasks. This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR, which integrates intra-domain feature representation learning and cross-domain alignment into a unified framework. ProtoOT leverages the strengths of the K-means clustering method to effectively manage distribution imbalances inherent in UCIR. By utilizing K-means for generating initial prototypes and approximating class marginal distributions, we modify the constraints in Optimal Transport accordingly, significantly enhancing its performance in UCIR scenarios. Furthermore, we incorporate contrastive learning into the ProtoOT framework to further improve representation learning. This encourages local semantic consistency among features with similar semantics, while also explicitly enforcing separation between features and unmatched prototypes, thereby enhancing global discriminativeness. ProtoOT surpasses existing state-of-the-art methods by a notable margin across benchmark datasets. Notably, on DomainNet, ProtoOT achieves an average P@200 enhancement of 24.44%, and on Office-Home, it demonstrates a P@15 improvement of 12.12%. Code is available at https://github.com/HCVLAB/ProtoOT., Comment: Accepted by AAAI2024

The emerging conditional coding-based neural video codec (NVC) shows superiority over commonly-used residual coding-based codec and the latest NVC already claims to outperform the best traditional codec. However, there still exist critical problems blocking the practicality of NVC. In this paper, we propose a powerful conditional coding-based NVC that solves two critical problems via feature modulation. The first is how to support a wide quality range in a single model. Previous NVC with this capability only supports about 3.8 dB PSNR range on average. To tackle this limitation, we modulate the latent feature of the current frame via the learnable quantization scaler. During the training, we specially design the uniform quantization parameter sampling mechanism to improve the harmonization of encoding and quantization. This results in a better learning of the quantization scaler and helps our NVC support about 11.4 dB PSNR range. The second is how to make NVC still work under a long prediction chain. We expose that the previous SOTA NVC has an obvious quality degradation problem when using a large intra-period setting. To this end, we propose modulating the temporal feature with a periodically refreshing mechanism to boost the quality. %Besides solving the above two problems, we also design a single model that can support both RGB and YUV colorspaces. Notably, under single intra-frame setting, our codec can achieve 29.7\% bitrate saving over previous SOTA NVC with 16\% MACs reduction. Our codec serves as a notable landmark in the journey of NVC evolution. The codes are at https://github.com/microsoft/DCVC., Comment: CVPR 2024. Codes are at https://github.com/microsoft/DCVC

Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect the causal relationships between these physical variables, failing to fully capture the spatial and temporal patterns required for intensity forecasting. To address this issue, we propose a Multi-modal multi-Scale Causal AutoRegressive model (MSCAR), which is the first model that combines causal relationships with large-scale multi-modal data for global TC intensity autoregressive forecasting. Furthermore, given the current absence of a TC dataset that offers a wide range of spatial variables, we present the Satellite and ERA5-based Tropical Cyclone Dataset (SETCD), which stands as the longest and most comprehensive global dataset related to TCs. Experiments on the dataset show that MSCAR outperforms the state-of-the-art methods, achieving maximum reductions in global and regional forecast errors of 9.52% and 6.74%, respectively. The code and dataset are publicly available at https://anonymous.4open.science/r/MSCAR.

Recent advances in large language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, their effective application in the medical domain is hampered by a lack of medical domain knowledge. In this study, we present SA-MDKIF, a scalable and adaptable framework that aims to inject medical knowledge into general-purpose LLMs through instruction tuning, thereby enabling adaptability for various downstream tasks. SA-MDKIF consists of two stages: skill training and skill adaptation. In the first stage, we define 12 basic medical skills and use AdaLoRA to train these skills based on uniformly formatted instructional datasets that we have constructed. In the next stage, we train the skill router using task-specific downstream data and use this router to integrate the acquired skills with LLMs during inference. Experimental results on 9 different medical tasks show that SA-MDKIF improves performance by 10-20% compared to the original LLMs. Notably, this improvement is particularly pronounced for unseen medical tasks, showing an improvement of up to 30%.

Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explanation strategy, they have the danger of revealing spurious correlations and redundancy issue. In this paper, we propose Coca, a general framework aiming to 1) enhance the robustness of existing GNN-based vulnerability detection models to avoid spurious explanations; and 2) provide both concise and effective explanations to reason about the detected vulnerabilities. \sysname consists of two core parts referred to as Trainer and Explainer. The former aims to train a detection model which is robust to random perturbation based on combinatorial contrastive learning, while the latter builds an explainer to derive crucial code statements that are most decisive to the detected vulnerability via dual-view causal inference as explanations. We apply Coca over three typical GNN-based vulnerability detectors. Experimental results show that Coca can effectively mitigate the spurious correlation issue, and provide more useful high-quality explanations., Comment: To appear in the Technical Track of ICSE 2024

The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across multiple sectors, including Software Engineering (SE). However, due to their black-box nature, these promising AI-driven SE models are still far from being deployed in practice. This lack of explainability poses unwanted risks for their applications in critical tasks, such as vulnerability detection, where decision-making transparency is of paramount importance. This paper endeavors to elucidate this interdisciplinary domain by presenting a systematic literature review of approaches that aim to improve the explainability of AI models within the context of SE. The review canvasses work appearing in the most prominent SE & AI conferences and journals, and spans 63 papers across 21 unique SE tasks. Based on three key Research Questions (RQs), we aim to (1) summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches. Based on our findings, we identified a set of challenges remaining to be addressed in existing studies, together with a roadmap highlighting potential opportunities we deemed appropriate and important for future work., Comment: submitted to ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2202.06840 by other authors

Endowing machines with abstract reasoning ability has been a long-term research topic in artificial intelligence. Raven's Progressive Matrix (RPM) is widely used to probe abstract visual reasoning in machine intelligence, where models will analyze the underlying rules and select one image from candidates to complete the image matrix. Participators of RPM tests can show powerful reasoning ability by inferring and combining attribute-changing rules and imagining the missing images at arbitrary positions of a matrix. However, existing solvers can hardly manifest such an ability in realistic RPM tests. In this paper, we propose a deep latent variable model for answer generation problems through Rule AbstractIon and SElection (RAISE). RAISE can encode image attributes into latent concepts and abstract atomic rules that act on the latent concepts. When generating answers, RAISE selects one atomic rule out of the global knowledge set for each latent concept to constitute the underlying rule of an RPM. In the experiments of bottom-right and arbitrary-position answer generation, RAISE outperforms the compared solvers in most configurations of realistic RPM datasets. In the odd-one-out task and two held-out configurations, RAISE can leverage acquired latent concepts and atomic rules to find the rule-breaking image in a matrix and handle problems with unseen combinations of rules and attributes.

Learning neural operators for solving partial differential equations (PDEs) has attracted great attention due to its high inference efficiency. However, training such operators requires generating a substantial amount of labeled data, i.e., PDE problems together with their solutions. The data generation process is exceptionally time-consuming, as it involves solving numerous systems of linear equations to obtain numerical solutions to the PDEs. Many existing methods solve these systems independently without considering their inherent similarities, resulting in extremely redundant computations. To tackle this problem, we propose a novel method, namely Sorting Krylov Recycling (SKR), to boost the efficiency of solving these systems, thus significantly accelerating data generation for neural operators training. To the best of our knowledge, SKR is the first attempt to address the time-consuming nature of data generation for learning neural operators. The working horse of SKR is Krylov subspace recycling, a powerful technique for solving a series of interrelated systems by leveraging their inherent similarities. Specifically, SKR employs a sorting algorithm to arrange these systems in a sequence, where adjacent systems exhibit high similarities. Then it equips a solver with Krylov subspace recycling to solve the systems sequentially instead of independently, thus effectively enhancing the solving efficiency. Both theoretical analysis and extensive experiments demonstrate that SKR can significantly accelerate neural operator data generation, achieving a remarkable speedup of up to 13.9 times.

This paper presents an innovative exploration of the application potential of large language models (LLM) in addressing the challenging task of automatically generating behavior trees (BTs) for complex tasks. The conventional manual BT generation method is inefficient and heavily reliant on domain expertise. On the other hand, existing automatic BT generation technologies encounter bottlenecks related to task complexity, model adaptability, and reliability. In order to overcome these challenges, we propose a novel methodology that leverages the robust representation and reasoning abilities of LLMs. The core contribution of this paper lies in the design of a BT generation framework based on LLM, which encompasses the entire process, from data synthesis and model training to application developing and data verification. Synthetic data is introduced to train the BT generation model (BTGen model), enhancing its understanding and adaptability to various complex tasks, thereby significantly improving its overall performance. In order to ensure the effectiveness and executability of the generated BTs, we emphasize the importance of data verification and introduce a multilevel verification strategy. Additionally, we explore a range of agent design and development schemes with LLM as the central element. We hope that the work in this paper may provide a reference for the researchers who are interested in BT generation based on LLMs.

Visual scenes are extremely diverse, not only because there are infinite possible combinations of objects and backgrounds but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a multi-object visual scene from multiple viewpoints, humans can perceive the scene compositionally from each viewpoint while achieving the so-called ``object constancy'' across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have a similar ability. In this paper, we consider a novel problem of learning compositional scene representations from multiple unspecified (i.e., unknown and unrelated) viewpoints without using any supervision and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. During the inference, latent representations are randomly initialized and iteratively updated by integrating the information in different viewpoints with neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method can effectively learn from multiple unspecified viewpoints., Comment: arXiv admin note: substantial text overlap with arXiv:2112.03568

In real-world scenarios, person Re-IDentification (Re-ID) systems need to be adaptable to changes in space and time. Therefore, the adaptation of Re-ID models to new domains while preserving previously acquired knowledge is crucial, known as Lifelong person Re-IDentification (LReID). Advanced LReID methods rely on replaying exemplars from old domains and applying knowledge distillation in logits with old models. However, due to privacy concerns, retaining previous data is inappropriate. Additionally, the fine-grained and open-set characteristics of Re-ID limit the effectiveness of the distillation paradigm for accumulating knowledge. We argue that a Re-ID model trained on diverse and challenging pedestrian images at a large scale can acquire robust and general human semantic knowledge. These semantics can be readily utilized as shared knowledge for lifelong applications. In this paper, we identify the challenges and discrepancies associated with adapting a pre-trained model to each application domain, and introduce the Distribution Aligned Semantics Adaption (DASA) framework. It efficiently adjusts Batch Normalization (BN) to mitigate interference from data distribution discrepancy and freezes the pre-trained convolutional layers to preserve shared knowledge. Additionally, we propose the lightweight Semantics Adaption (SA) module, which effectively adapts learned semantics to enhance pedestrian representations. Extensive experiments demonstrate the remarkable superiority of our proposed framework over advanced LReID methods, and it exhibits significantly reduced storage consumption. DASA presents a novel and cost-effective perspective on effectively adapting pre-trained models for LReID.

With the continuous expansion of intelligent surveillance networks, lifelong person re-identification (LReID) has received widespread attention, pursuing the need of self-evolution across different domains. However, existing LReID studies accumulate knowledge with the assumption that people would not change their clothes. In this paper, we propose a more practical task, namely lifelong person re-identification with hybrid clothing states (LReID-Hybrid), which takes a series of cloth-changing and cloth-consistent domains into account during lifelong learning. To tackle the challenges of knowledge granularity mismatch and knowledge presentation mismatch that occurred in LReID-Hybrid, we take advantage of the consistency and generalization of the text space, and propose a novel framework, dubbed $Teata$, to effectively align, transfer and accumulate knowledge in an "image-text-image" closed loop. Concretely, to achieve effective knowledge transfer, we design a Structured Semantic Prompt (SSP) learning to decompose the text prompt into several structured pairs to distill knowledge from the image space with a unified granularity of text description. Then, we introduce a Knowledge Adaptation and Projection strategy (KAP), which tunes text knowledge via a slow-paced learner to adapt to different tasks without catastrophic forgetting. Extensive experiments demonstrate the superiority of our proposed $Teata$ for LReID-Hybrid as well as on conventional LReID benchmarks over advanced methods.

Intelligent vision control systems for surgical robots should adapt to unknown and diverse objects while being robust to system disturbances. Previous methods did not meet these requirements due to mainly relying on pose estimation and feature tracking. We propose a world-model-based deep reinforcement learning framework "Grasp Anything for Surgery" (GAS), that learns a pixel-level visuomotor policy for surgical grasping, enhancing both generality and robustness. In particular, a novel method is proposed to estimate the values and uncertainties of depth pixels for a rigid-link object's inaccurate region based on the empirical prior of the object's size; both depth and mask images of task objects are encoded to a single compact 3-channel image (size: 64x64x3) by dynamically zooming in the mask regions, minimizing the information loss. The learned controller's effectiveness is extensively evaluated in simulation and in a real robot. Our learned visuomotor policy handles: i) unseen objects, including 5 types of target grasping objects and a robot gripper, in unstructured real-world surgery environments, and ii) disturbances in perception and control. Note that we are the first work to achieve a unified surgical control system that grasps diverse surgical objects using different robot grippers on real robots in complex surgery scenes (average success rate: 69%). Our system also demonstrates significant robustness across 6 conditions including background variation, target disturbance, camera pose variation, kinematic control error, image noise, and re-grasping after the gripped target object drops from the gripper. Videos and codes can be found on our project page: https://linhongbin.github.io/gas/.

Cloth-changing person Re-IDentification (Re-ID) aims at recognizing the same person with clothing changes across non-overlapping cameras. Conventional person Re-ID methods usually bias the model's focus on cloth-related appearance features rather than identity-sensitive features associated with biological traits. Recently, advanced cloth-changing person Re-ID methods either resort to identity-related auxiliary modalities (e.g., sketches, silhouettes, keypoints and 3D shapes) or clothing labels to mitigate the impact of clothes. However, relying on unpractical and inflexible auxiliary modalities or annotations limits their real-world applicability. In this paper, we promote cloth-changing person Re-ID by effectively leveraging abundant semantics present within pedestrian images without the need for any auxiliaries. Specifically, we propose the Content and Salient Semantics Collaboration (CSSC) framework, facilitating cross-parallel semantics interaction and refinement. Our framework is simple yet effective, and the vital design is the Semantics Mining and Refinement (SMR) module. It extracts robust identity features about content and salient semantics, while mitigating interference from clothing appearances effectively. By capitalizing on the mined abundant semantic features, our proposed approach achieves state-of-the-art performance on three cloth-changing benchmarks as well as conventional benchmarks, demonstrating its superiority over advanced competitors.

This paper examines the problem of distributing rewards on social networks to improve the efficiency of crowdsourcing tasks for sponsors. To complete the tasks efficiently, we aim to design reward mechanisms that incentivize early-joining agents to invite more participants to the tasks. Nonetheless, participants could potentially engage in strategic behaviors, e.g., not inviting others to the tasks, misreporting their capacity for the tasks, or creaking fake identities (aka Sybil attacks), to maximize their own rewards. The focus of this study is to address the challenge outlined above by designing effective reward mechanisms. To this end, we propose a novel reward mechanism, called Propagation Reward Distribution Mechanism (PRDM), for the general information propagation model with limited budgets. It is proved that the PRDM can not only incentivize all agents to contribute their full efforts to the tasks and share the task information to all their neighbors in the social networks, but can also prevent them from Sybil attacks.

Tooth segmentation is a key step for computer aided diagnosis of dental diseases. Numerous machine learning models have been employed for tooth segmentation on dental panoramic radiograph. However, it is a difficult task to achieve accurate tooth segmentation due to complex tooth shapes, diverse tooth categories and incomplete sample set for machine learning. In this paper, we propose ViSTooth, a visualization framework for tooth segmentation on dental panoramic radiograph. First, we employ Mask R-CNN to conduct preliminary tooth segmentation, and a set of domain metrics are proposed to estimate the accuracy of the segmented teeth, including tooth shape, tooth position and tooth angle. Then, we represent the teeth with high-dimensional vectors and visualize their distribution in a low-dimensional space, in which experts can easily observe those teeth with specific metrics. Further, we expand the sample set with the expert-specified teeth and train the tooth segmentation model iteratively. Finally, we conduct case study and expert study to demonstrate the effectiveness and usability of our ViSTooth, in aiding experts to implement accurate tooth segmentation guided by expert knowledge.

This study explored the application of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis to rapidly assess soil fertility, focusing on critical parameters such as available B, organic carbon (OC), available Mn, available S, and the sulfur availability index (SAI). Analyzing 1,133 soil samples from various agro-climatic zones in Eastern India, the research combined color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results indicated that integrating image features (IFs) with auxiliary variables (AVs) significantly enhanced prediction accuracy for available B (R^2 = 0.80) and OC (R^2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further improved predictions for available Mn and SAI with R^2 values of 0.72 and 0.70, respectively. The study demonstrated how these integrated technologies have the potential to provide quick and affordable options for soil testing, opening up access to more sophisticated prediction models and a better comprehension of the fertility and health of the soil. Future research should focus on the application of deep learning models on a larger dataset of soil images, developed using soils from a broader range of agro-climatic zones under field condition., Comment: 37 pages, 10 figures; manuscript under peer-review for publication in the jounral 'Computers and Electronics in Agriculture'

End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driving methods on sub-tasks is still far behind the single-task methods. Meanwhile, the widely used dense BEV features in previous end-to-end methods make it costly to extend to more modalities or tasks. In this paper, we propose a Sparse query-centric paradigm for end-to-end Autonomous Driving (SparseAD), where the sparse queries completely represent the whole driving scenario across space, time and tasks without any dense BEV representation. Concretely, we design a unified sparse architecture for perception tasks including detection, tracking, and online mapping. Moreover, we revisit motion prediction and planning, and devise a more justifiable motion planner framework. On the challenging nuScenes dataset, SparseAD achieves SOTA full-task performance among end-to-end methods and significantly narrows the performance gap between end-to-end paradigms and single-task methods. Codes will be released soon.

Heart rate is an important physiological indicator of human health status. Existing remote heart rate measurement methods typically involve facial detection followed by signal extraction from the region of interest (ROI). These SOTA methods have three serious problems: (a) inaccuracies even failures in detection caused by environmental influences or subject movement; (b) failures for special patients such as infants and burn victims; (c) privacy leakage issues resulting from collecting face video. To address these issues, we regard the remote heart rate measurement as the process of analyzing the spatiotemporal characteristics of the optical flow signal in the video. We apply chaos theory to computer vision tasks for the first time, thus designing a brain-inspired framework. Firstly, using an artificial primary visual cortex model to extract the skin in the videos, and then calculate heart rate by time-frequency analysis on all pixels. Our method achieves Robust Skin Tracking for Heart Rate measurement, called HR-RST. The experimental results show that HR-RST overcomes the difficulty of environmental influences and effectively tracks the subject movement. Moreover, the method could extend to other body parts. Consequently, the method can be applied to special patients and effectively protect individual privacy, offering an innovative solution., Comment: 8 pages, 10 figures

Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods., Comment: 16 pages, 14figures

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270 degrees and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space and constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods., Comment: Accepted to ICRA 2024

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in color space. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces {\em FreqBlender}, a new method that can generate pseudo-fake faces by blending frequency knowledge. Specifically, we investigate the major frequency components and propose a Frequency Parsing Network to adaptively partition frequency components related to forgery traces. Then we blend this frequency knowledge from fake faces into real faces to generate pseudo-fake faces. Since there is no ground truth for frequency components, we describe a dedicated training strategy by leveraging the inner correlations among different frequency knowledge to instruct the learning process. Experimental results demonstrate the effectiveness of our method in enhancing DeepFake detection, making it a potential plug-and-play strategy for other methods.

To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts the post-reception beamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method is designed to implement our new estimator. Theoretical analysis shows that the extra gain achieved by our method over the linear MMSE estimator grows according to the rule of O($\log_{10}M$), while its computational complexity is linearly scalable to the number of antennas $M$. Numerical simulations also validate the theoretical results. Our new method substantially extends the accuracy-complexity region and constitutes a promising channel estimation solution to the emerging massive MIMO communications., Comment: 15 pages, 12 figures, accepted to appear on IEEE Transactions on Communications, Apr. 2024

Topologically associating domain (TAD) boundaries partition the genome into distinct regulatory territories. Anecdotal evidence suggests that their disruption may interfere with normal gene expression and cause disease phenotypes1-3, but the overall extent to which this occurs remains unknown. Here we demonstrate that targeted deletions of TAD boundaries cause a range of disruptions to normal in vivo genome function and organismal development. We used CRISPR genome editing in mice to individually delete eight TAD boundaries (11-80 kb in size) from the genome. All deletions examined resulted in detectable molecular or organismal phenotypes, which included altered chromatin interactions or gene expression, reduced viability, and anatomical phenotypes. We observed changes in local 3D chromatin architecture in 7 of 8 (88%) cases, including the merging of TADs and altered contact frequencies within TADs adjacent to the deleted boundary. For 5 of 8 (63%) loci examined, boundary deletions were associated with increased embryonic lethality or other developmental phenotypes. For example, a TAD boundary deletion near Smad3/Smad6 caused complete embryonic lethality, while a deletion near Tbx5/Lhx5 resulted in a severe lung malformation. Our findings demonstrate the importance of TAD boundary sequences for in vivo genome function and reinforce the critical need to carefully consider the potential pathogenicity of noncoding deletions affecting TAD boundaries in clinical genetics screening.

Introduction: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed. Methods: To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few mu L of plasma sample. Results: Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon gamma (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion: Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.

Introduction: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed. Methods: To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few mu L of plasma sample. Results: Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon gamma (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion: Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.

Introduction: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed. Methods: To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few mu L of plasma sample. Results: Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon gamma (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion: Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.

Bo Li,1 Weiqing Wu,1 Aijun Liu,1 Lifeng Feng,1 Bin Li,1 Yong Mei,1 Li Tan,1 Chaoyang Zhang,2 Yangtao Tian1 1Department of Pancreatic Surgery, Shangluo Center Hospital, Shangluo, Shaanxi, 726000, People’s Republic of China; 2Department of Ultrasound Medicine, Shangluo Center Hospital, Shangluo, Shaanxi, 726000, People’s Republic of ChinaCorrespondence: Yangtao Tian, Department of Pancreatic Surgery, Shangluo Center Hospital, 37 Shangyang, Shangzhou, Shangluo, Shaanxi, 726000, People’s Republic of China, Email 928763821@qq.comBackground: Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction, and blood clotting within 48 hours of its onset, and is associated with a high mortality rate. The aim of this study was to establish a reliable diagnostic prediction model for the early stage of severe pancreatitis.Methods: The clinical data of patients diagnosed with acute pancreatitis from October 2017 to June 2022 at the Shangluo Central Hospital were collected. The risk factors were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. A novel nomogram model was then established by multivariable logistic regression analysis.Results: The data of 436 patients with acute pancreatitis, 45 (10.3%) patients had progressed to SAP. Through univariate and LASSO regression analyses, the neutrophils (P < 0.001), albumin (P < 0.001), blood glucose (P < 0.001), serum calcium (P < 0.001), serum creatinine (P < 0.001), blood urea nitrogen (P < 0.001) and procalcitonin (P = 0.005) were identified as independent predictive factors for SAP. The nomogram built on the basis of these factors predicted SAP with sensitivity of 0.733, specificity of 0.9, positive predictive value of 0.458 and negative predictive value of 0.967. Furthermore, the concordance index of the nomogram reached 0.889 (95% CI, 0.837– 0.941), and the area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis was signifi

The design principle of osteogenic bone grafts has shifted from immunological inertness to limiting foreign body response to combined osteoimmunomodulatory activity to promote high-quality endogenous bone regeneration. Recently developed immunomodulatory mucin hydrogels have been shown to elicit very low complement activation and suppress macrophage release and activation after implantation in vivo. However, their immunoregulatory activity has not yet been studied in the context of tissue repair. Herein, we synthesized mucinmonetite composite materials and investigated their early osteoimmunomodulation using a critical-size rat bone defect model. We demonstrated that the composites can polarize macrophages towards the M2 phenotype at weeks 1 and 2. The early osteoimmunomodulation enhanced early osteogenesis and angiogenesis and ultimately promoted fracture healing and engraftment (revascularization of the host vasculature) at weeks 6 and 12. Overall, we demonstrated the applicability of mucin-based immunomodulatory biomaterials to enhance tissue repair in tissue engineering and regenerative medicine., QC 20230404

Ultraviolet cured-in-place-pipe (UV-CIPP) materials are commonly used in trenchless pipeline rehabilitation. Their bending strength is a crucial indicator to evaluate the curing quality. Studies show that this indicator is affected by multiple factors, including the curing time, UV lamp curing power, curing distance, and material thickness. Laboratory experiments have limitations in analyzing the effect of multiple factors on the bending strength of UV-CIPP materials and quantitatively predicting the optimum curing parameters. Aiming at resolving these shortcomings, resolve machine learning techniques were applied to predict the bending strength. In this regard, the surface curing reaction temperature monitoring data and three-point bending data of 30 groups of UV-CIPP material under the influence of different curing parameters were used as a dataset to predict the bending strength of UV-CIPP material. The results show that the influence degree of each factor on the bending strength of the UV-CIPP material, from high to low, is as follows: UV lamp power (−0.439), the temperature at the illuminated side (−0.392), curing time (−0.323), the temperature at the back side (−0.233), curing distance (0.143) and material thickness (−0.140). The best penalty parameter c (44.435) and width g (0.072) of the kernel function in the support vector machine (SVM) model were obtained using the genetic algorithm (GA) optimization, and the results were compared with the grey wolf optimizer (GWO) and particle swarm optimization (PSO). The performed analyses revealed that the developed GA-SVM model exhibits the best prediction results compared to other machine learning algorithms. The optimum bending strength of the UV-CIPP material used in this test is 294.77 MPa, which corresponds to the curing time, UV lamp power, curing distance, material thickness, light side temperature, and back side temperature of 7.59 min, 157.33 mW/cm2, 189.99 mm, 4.38 mm, 79.49 °C, and 76.59 °C, respectively.

Introduction: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed. Methods: To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few mu L of plasma sample. Results: Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon gamma (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion: Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.

Construction of organic-inorganic composites similar to natural bone in terms of structure and composition has attracted extensive attention. However, the clinical applications of these composites are limited due to the insufficient osteogenic and mechanical properties. In nature, the presence of amorphous iron-calcium phosphate (Fe-ACP) strengthens the mechanical properties of some biominerals, and our previous study has revealed its synthetic route and in vitro osteogenic properties. However, the potential role of Fe-ACP on biomineralization and constructing biomimetic scaffolds for bone regeneration has not been studied. Herein, a biomimetic scaffold with good osteogenic property was fabricated based on the mineralization of Fe-ACP, with the assistance of ice-templated freeze-casting. The in vitro study showed that the mineralized scaffolds possessed favorable biocompatibility and osteogenic property. Moreover, the scaffolds promoted cell chemotaxis and angiogenic property by upregulating the hypoxia inducible factor-1 alpha (HIF-1 alpha). In vivo experiment demonstrated potent early osteogenesis along with angiogenesis and ultimately promoted bone regeneration. Overall, the mineralized scaffold mediated by Fe-ACP precursors provide a unique platform to enhance bone tissue repair.

Groundwater depletion has become increasingly challenging, and many cities worldwide have adopted drastic policies to relieve water stress due to socioeconomic growth. Located on the declining aquifer of the North China Plain, Beijing, for example, has developed plans to limit the size of the city's population. However, the effect of population displacement under uncertain macroeconomic and climate change remains ambiguous. We adopt a sociohydrological model, with explicit consideration of the dynamics of human-water interactions, to explore the groundwater vulnerability of Beijing. We investigate how human response might shape the development trajectories of the groundwater-population-economy system under different macroscale economic and climate scenarios. Furthermore, we use a machine learning algorithm to identify the decisive factors to be considered for reducing groundwater vulnerability. Our results show that while rapid external economic development or larger annual average precipitation would enable recovery of the groundwater table in the short term, they may slacken human water shortage awareness and result in more acute groundwater depletion in the long run. Strengthening policymaker perceptions of groundwater depletion would prompt timely response policies for controlling population size. Improving the quantity and quality of labor force input to economic development would avoid downturns in the economy due to labor shortages. The outcomes of this study suggest that these strategies would effectively reduce groundwater vulnerability in the long run without causing severe socioeconomic recession. These findings highlight the importance of endogenizing human behavioral dynamics in sustainable urban water management. Beijing's groundwater vulnerability to external economic and climate change is assessed using a coupled sociohydro-economic modelRapid external economic development or larger precipitation might unintendedly lead to more severe groundwater dep

The Earth close approach of near-Earth asteroid 2005 LW3 on 2022 November 23 represented a good opportunity for a second observing campaign to test the timing accuracy of astrometric observation. With 82 participating stations, the International Asteroid Warning Network collected 1046 observations of 2005 LW3 around the time of the close approach. Compared to the previous timing campaign targeting 2019 XS, some individual observers were able to significantly improve the accuracy of their reported observation times. In particular, U.S. surveys achieved good timing performance. However, no broad, systematic improvement was achieved compared to the previous campaign, with an overall negative bias persisting among the different observers. The calibration of observing times and the mitigation of timing errors should be important future considerations for observers and orbit computers, respectively.

Ovarian cancer is the leading cause of death among gynecological malignancies. Checkpoint blockade immunotherapy has so far only shown modest efficacy in ovarian cancer and platinum-based chemotherapy remains the front-line treatment. Development of platinum resistance is one of the most important factors contributing to ovarian cancer recurrence and mortality. Through kinome-wide synthetic lethal RNAi screening combined with unbiased datamining of cell line platinum response in CCLE and GDSC databases, here we report that Src-Related Kinase Lacking C-Terminal Regulatory Tyrosine And N-Terminal Myristylation Sites (SRMS), a non-receptor tyrosine kinase, is a novel negative regulator of MKK4-JNK signaling under platinum treatment and plays an important role in dictating platinum efficacy in ovarian cancer. Suppressing SRMS specifically sensitizes p53-deficient ovarian cancer cells to platinum in vitro and in vivo. Mechanistically, SRMS serves as a sensor for platinum-induced ROS. Platinum treatment-induced ROS activates SRMS, which inhibits MKK4 kinase activity by directly phosphorylating MKK4 at Y269 and Y307, and consequently attenuates MKK4-JNK activation. Suppressing SRMS leads to enhanced MKK4-JNK-mediated apoptosis by inhibiting MCL1 transcription, thereby boosting platinum efficacy. Importantly, through a drug repurposing strategy, we uncovered that PLX4720, a small molecular selective inhibitor of B-RafV600E, is a novel SRMS inhibitor that can potently boost platinum efficacy in ovarian cancer in vitro and in vivo. Therefore, targeting SRMS with PLX4720 holds the promise to improve the efficacy of platinum-based chemotherapy and overcome chemoresistance in ovarian cancer.

Mobile Virtual Reality (VR) and panoramic video streaming rely on interactive panoramic scene delivery to provide desirable user experiences. However, it is pretty challenging to support multiple users via the wireless network since a panoramic scene typically consumes $4\sim 6\times$ bandwidth compared with a regular video with the same resolution. Motivated by the fact that users only perceive the Field-of-View (FoV), we employ the autoregressive process to predict the user's motion and stream only part of the panoramic content. Notably, we analytically characterize the effect of the delivered portion on the user's successful viewing probability. Then, we formulate an optimization problem to maximize the application-level throughput (which measures the average rate for successful viewing the desired content instead of raw network throughput) while providing a regular service. In addition, we impose three main constraints to our problem: minimum required service rate, maximum allowable energy consumption, and wireless interference. We then propose a novel scheduling algorithm that incorporates users' successful viewing probabilities and asymptotically maximizes application-level throughput while providing service regularity guarantees. We conduct real-trace simulations to evaluate the efficiency of our algorithm.

Groundwater depletion has become increasingly challenging, and many cities worldwide have adopted drastic policies to relieve water stress due to socioeconomic growth. Located on the declining aquifer of the North China Plain, Beijing, for example, has developed plans to limit the size of the city’s population. However, the effect of population displacement under uncertain macroeconomic and climate change remains ambiguous. We adopt a sociohydrological model, with explicit consideration of the dynamics of human-water interactions, to explore the groundwater vulnerability of Beijing. We investigate how human response might shape the development trajectories of the groundwater-population-economy system under different macroscale economic and climate scenarios. Furthermore, we use a machine learning algorithm to identify the decisive factors to be considered for reducing groundwater vulnerability. Our results show that while rapid external economic development or larger annual average precipitation would enable recovery of the groundwater table in the short term, they may slacken human water shortage awareness and result in more acute groundwater depletion in the long run. Strengthening policymaker perceptions of groundwater depletion would prompt timely response policies for controlling population size. Improving the quantity and quality of labor force input to economic development would avoid downturns in the economy due to labor shortages. The outcomes of this study suggest that these strategies would effectively reduce groundwater vulnerability in the long run without causing severe socioeconomic recession. These findings highlight the importance of endogenizing human behavioral dynamics in sustainable urban water management., Water Resources