Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to ...specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans.
Distributed trajectory similarity search Xie, Dong; Li, Feifei; Phillips, Jeff M.
Proceedings of the VLDB Endowment,
08/2017, Letnik:
10, Številka:
11
Journal Article
Recenzirano
Mobile and sensing devices have already become ubiquitous. They have made tracking moving objects an easy task. As a result, mobile applications like Uber and many IoT projects have generated massive ...amounts of trajectory data that can no longer be processed by a single machine efficiently. Among the typical query operations over trajectories, similarity search is a common yet expensive operator in querying trajectory data. It is useful for applications in different domains such as traffic and transportation optimizations, weather forecast and modeling, and sports analytics. It is also a fundamental operator for many important mining operations such as clustering and classification of trajectories. In this paper, we propose a distributed query framework to process trajectory similarity search over a large set of trajectories. We have implemented the proposed framework in Spark, a popular distributed data processing engine, by carefully considering different design choices. Our query framework supports both the Hausdorff distance the Fréchet distance. Extensive experiments have demonstrated the excellent scalability and query efficiency achieved by our design, compared to other methods and design alternatives.
Human epidermal growth factor receptor 2 (HER2)-positive breast cancer has a high metastatic potential. Monoclonal antibodies (mAbs) that target HER2, such as trastuzumab and pertuzumab, are the ...cornerstone of adjuvant therapy for HER2-positive breast cancer. A growing body of preclinical and clinical evidence points to the importance of innate immunity mediated by antibody-dependent cellular cytotoxicity (ADCC) in the clinical effect of mAbs on the resulting anti-tumor response. In this review, we provide an overview of the role of natural killer (NK) cells and ADCC in targeted therapy of HER2-positive breast cancer, including the biological functions of NK cells and the role of NK cells and ADCC in anti-HER2 targeted drugs. We then discuss regulatory mechanisms and recent strategies to leverage our knowledge of NK cells and ADCC as an immunotherapy approach for HER2-positive breast cancer.
Radiotherapy is one of the major therapeutic strategies for cancer treatment. In the past decade, there has been growing interest in using high Z (atomic number) elements (materials) as ...radiosensitizers. New strategies in nanomedicine could help to improve cancer diagnosis and therapy at cellular and molecular levels. Metal-based nanoparticles usually exhibit chemical inertness in cellular and subcellular systems and may play a role in radiosensitization and synergistic cell-killing effects for radiation therapy. This review summarizes the efficacy of metal-based
against cancers in both
and
systems for a range of ionizing radiations including gamma-rays, X-rays, and charged particles. The potential of translating preclinical studies on metal-based nanoparticles-enhanced radiation therapy into clinical practice is also discussed using examples of several metal-based
(such as CYT-6091, AGuIX, and NBTXR3). Also, a few general examples of theranostic multimetallic nanocomposites are presented, and the related biological mechanisms are discussed.
The sluggish oxygen evolution reaction (OER) is a pivotal process for renewable energy technologies, such as water splitting. The discovery of efficient, durable, and earth‐abundant electrocatalysts ...for water oxidation is highly desirable. Here, a novel trimetallic nitride compound grown on nickel foam (CoVFeN @ NF) is demonstrated, which is an ultra‐highly active OER electrocatalyst that outperforms the benchmark catalyst, RuO2, and most of the state‐of‐the‐art 3D transition metals and their compounds. CoVFeN @ NF exhibits ultralow OER overpotentials of 212 and 264 mV at 10 and 100 mA cm−2 in 1 m KOH, respectively, together with a small Tafel slop of 34.8 mV dec−1. Structural characterization reveals that the excellent catalytic activity mainly originates from: 1) formation of oxyhydroxide species on the surface of the catalyst due to surface reconstruction and phase transition, 2) promoted oxygen evolution possibly activated by peroxo‐like (O22−) species through a combined lattice‐oxygen‐oxidation and adsorbate escape mechanism, 3) an optimized electronic structure and local coordination environment owing to the synergistic effect of the multimetal system, and 4) greatly accelerated electron transfer as a result of nitridation. This study provides a simple approach to rationally design cost‐efficient and highly catalytic multimetal compound systems as OER catalysts for electrochemical energy devices.
This multimetal nitride system demonstrates greatly optimized electronic structure and local coordination environment, and enhanced the conductivity and active surface area as a catalyst in the oxygen evolution reaction (OER). The metal oxyhydroxide formed in the OER process due to the surface reconstruction and phase transition is an intrinsically active species for the OER. In addition, the promoted oxygen evolution is possibly activated by peroxo‐like (O22−) species.
A two-stage decomposition approach based on a novel multi-agent system (MAS) is proposed for the distributed resource constrained multi-project scheduling problem (DRCMPSP). In stage one, from the ...point of view of each local project manager, a forward-backward hybrid genetic algorithm (FBHGA) is developed to generate an initial local schedule with the objective of minimizing individual project makespan. In stage two, from the global perspective of project management office, a sequential game-based negotiation mechanism is employed to eliminate global resource conflicts with the objective of minimizing total tardiness cost (TTC). The proposed approach is tested on 140 benchmark problem instances. According to the computational results, high-quality local project schedules can be obtained by FBHGA in stage one. Furthermore, it is observed that our method is capable of dealing with various complex multi-project instances under different degrees of resource conflicts in reasonable CPU running time. Compared to the existing decentralized methods for DRCMPSP, the proposed approach with sequential game-based negotiation mechanism shows the superiority in producing multi-project schedules with lower TTC, especially for large-size and strong conflicting instances.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Although chemotherapy has advanced into the era of targeted drugs, the antitumor efficacies of current therapies are limited, most likely because of the high degree of cancer clonal heterogeneity, ...intratumor genetic heterogeneity and cell signal complexity. As shutdown of a single target does not necessarily eradicate the cancer, the use of combinations of molecular‐targeted agents (MATs) has been proposed, and some pioneering research has been conducted to examine the efficacy of this strategy. In this article, the clinical and preclinical studies that are underway in an attempt to improve the anticancer efficacy of chemotherapies through combination strategies are summarized. Studies of combining cytotoxic agents with MATs, coinhibiting two or more targets in a single pathway or coinhibiting parallel or compensatory pathways as well as specific combinations will be introduced, and the antitumor potentials of each combination strategy will be evaluated.
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•The non-petroleum-based carbene polymerization toward functional polyolefins.•The stereoselective polymerization, controlled polymerization, and copolymerization.•The carbene ...insertion polymerization versus carbene radical polymerization.•The tertiary structures, properties, and applications of polycarbene.
Carbene polymerization from the catalyzed decomposition of diazo compounds is a unique non-petroleum-based C1 polymerization pathway toward high-density functional polyolefins. The last 20 years have seen vigorous development in carbene polymerization, such as various efficient initiating systems, high molecular weight and stereoregular polycarbene, controlled carbene polymerization, and the combined copolymerization approaches with other polymerization methodologies. This review highlights the recent advancements in developing the new/old disciplines of carbene polymerization, i.e., carbene insertion polymerization and carbene radical polymerization. The representative copolymerization methodologies, aggregation structures, properties, and applications of polycarbene are also summarized. Finally, the future trends in expanding carbene polymerization potentials are discussed.
Scalable Keyword Search on Large RDF Data Wangchao Le; Feifei Li; Kementsietsidis, Anastasios ...
IEEE transactions on knowledge and data engineering,
11/2014, Letnik:
26, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Keyword search is a useful tool for exploring large RDF data sets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summaries from the RDF ...graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF data with tens of millions of triples. Furthermore, the existing summarization techniques may lead to incorrect/incomplete results. To address these issues, we propose an effective summarization algorithm to summarize the RDF data. Given a keyword query, the summaries lend significant pruning powers to exploratory keyword search and result in much better efficiency compared to previous works. Unlike existing techniques, our search algorithms always return correct results. Besides, the summaries we built can be updated incrementally and efficiently. Experiments on both benchmark and large real RDF data sets show that our techniques are scalable and efficient.
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