The tumor vasculature is essential for tumor growth and metastasis, and is a prime target of several anti-cancer agents. Increasing evidence indicates that tumor angiogenesis is stimulated by ...extracellular vesicles (EVs) that are secreted or shed by cancer cells. These EVs encapsulate a variety of biomolecules with angiogenic properties, and have been largely thought to stimulate vessel formation by transferring this luminal cargo into endothelial cells. However, recent studies have revealed that EVs can also signal to recipient cells via proteins on the vesicular surface. This review discusses and integrates emerging insights into the diverse mechanisms by which proteins associate with the EV membrane, the biological functions of EV membrane-associated proteins in tumor angiogenesis, and the clinical significance of these proteins in anti-angiogenic therapy.
The problem of fine-grained sketch-based image retrieval (FG-SBIR) is defined and investigated in this paper. In FG-SBIR, free-hand human sketch images are used as queries to retrieve photo images ...containing the same object instances. It is thus a cross-domain (sketch to photo) instance-level retrieval task. It is an extremely challenging problem because (i) visual comparisons and matching need to be executed under large domain gap, i.e., from black and white line drawing sketches to colour photos; (ii) it requires to capture the fine-grained (dis)similarities of sketches and photo images while free-hand sketches drawn by different people present different levels of deformation and expressive interpretation; and (iii) annotated cross-domain fine-grained SBIR datasets are scarce, challenging many state-of-the-art machine learning techniques, particularly those based on deep learning. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based object instance retrieval application. Specifically, a new large-scale FG-SBIR database is introduced which is carefully designed to reflect the real-world application scenarios. A deep cross-domain matching model is then formulated to solve the intrinsic drawing style variability, large domain gap issues, and capture instance-level discriminative features. It distinguishes itself by a carefully designed attention module. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed model and validate the need for a rigorous definition of the FG-SBIR problem and collecting suitable datasets.
In this study, a strategy that can result in the polyaniline (PANI) solely confined within the nanopores of a metal–organic framework (MOF) without forming obvious bulk PANI between MOF crystals is ...developed. A water‐stable zirconium‐based MOF, UiO‐66‐NH2, is selected as the MOF material. The polymerization of aniline is initiated in the acidic suspension of UiO‐66‐NH2 nanocrystals in the presence of excess poly(sodium 4‐styrenesulfonate) (PSS). Since the pore size of UiO‐66‐NH2 is too small to enable the insertion of the bulky PSS, the quick formation of pore‐confined solid PANI and the slower formation of well dispersed PANI:PSS occur within the MOF crystals and in the bulk solution, respectively. By taking advantage of the resulting homogeneous PANI:PSS polymer solution, the bulk PANI:PSS can be removed from the PANI/UiO‐66‐NH2 solid by successive washing the sample with fresh acidic solutions through centrifugation. As this is the first time reporting the PANI solely confined in the pores of a MOF, as a demonstration, the obtained PANI/UiO‐66‐NH2 composite material is applied as the electrode material for supercapacitors. The PANI/UiO‐66‐NH2 thin films exhibit a pseudocapacitive electrochemical characteristic, and their resulting electrochemical activity and charge‐storage capacities are remarkably higher than those of the bulk PANI thin films.
Polymer power: Polyaniline (PANI) solely confined within the nanopores of a water‐stable metal–organic framework (MOF) without forming obvious bulk PANI between MOF crystals is synthesized. The PANI/UiO‐66‐NH2 thin films exhibit a pseudocapacitive electrochemical characteristic, and their resulting electrochemical activity is remarkably higher than those of the bulk PANI thin films.
Ovarian cancer preferentially metastasizes to the omentum, a fatty tissue characterized by immune structures called milky spots, but the cellular dynamics that direct this tropism are unknown. Here, ...we identified that neutrophil influx into the omentum is a prerequisite premetastatic step in orthotopic ovarian cancer models. Ovarian tumor-derived inflammatory factors stimulated neutrophils to mobilize and extrude chromatin webs called neutrophil extracellular traps (NETs). NETs were detected in the omentum of ovarian tumor-bearing mice before metastasis and of women with early-stage ovarian cancer. NETs, in turn, bound ovarian cancer cells and promoted metastasis. Omental metastasis was decreased in mice with neutrophil-specific deficiency of peptidylarginine deiminase 4 (PAD4), an enzyme that is essential for NET formation. Blockade of NET formation using a PAD4 pharmacologic inhibitor also decreased omental colonization. Our findings implicate NET formation in rendering the premetastatic omental niche conducive for implantation of ovarian cancer cells and raise the possibility that blockade of NET formation prevents omental metastasis.
Inside-out NMR with two concentric ring magnets Utsuzawa, Shin; Tang, Yiqiao; Song, Yi-Qiao
Journal of magnetic resonance (1997),
December 2021, 2021-12-00, 20211201, 2021-12-01, Letnik:
333, Številka:
C
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•Inside-out NMR magnet with a remote sensitive region.•Enlarged saddle point by combing two ring magnets.•Saddle point B0 allows the measurement of T2 = 1.5 s with a 100 μs echo ...time.•A palm-size inside-out NMR sensor built with inexpensive magnets achieved SNR ∼ 23 per two scans with ∼ 10 W peak RF power.
We report the design and the implementation of an inside-out NMR sensor that produces a large sensitive region with substantially uniform magnetic field at a remote location. The construction using a pair of ring magnets is simple yet provides multiple benefits, including large sample volume, operation with low RF power, and the ability to measure samples with long T2 and high diffusivity. A palm-size inside-out NMR sensor (57 mm OD × 29 mm height, 420 g including the housing and the coil PCB) was built with inexpensive magnets. The sweet spot is located ∼5 mm above the magnet surface with ∼4 mm width and ∼5 mm height assuming t180 = 18 μs. The field strength at that point is 0.16 T and achieved SNR ∼23 per two scans when operated with ∼10 W peak RF power. Its quasi-uniform B0 around the saddle point allows the measurement of T2 = 1.5 s with a 100 μs echo time.
Deep Learning for Free-Hand Sketch: A Survey Xu, Peng; Hospedales, Timothy M.; Yin, Qiyue ...
IEEE transactions on pattern analysis and machine intelligence,
2023-Jan.-1, 2023-Jan, 2023-1-1, 20230101, Letnik:
45, Številka:
1
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Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made ...sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.
Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain. Amongst its many variants, open set domain adaptation (OSDA) is ...perhaps the most challenging one, as it further assumes the presence of unknown classes in the target domain. In this paper, we study OSDA with a particular focus on enriching its ability to traverse across larger domain gaps, and we show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps, especially on a new dataset (PACS) that we re-purposed for OSDA. Exploring this is pivotal for OSDA as with increasing domain shift, identifying unknown samples in the target domain becomes harder for the model, thus making negative transfer between source and target domains more challenging. Accordingly, we propose a Mutual-to-Separate (MTS) framework to address the larger domain gaps. Essentially we design two networks - (a) Sample Separation Network (SSN): which is trained to learn a hyperplane for separating unknown samples from known ones, and (b) Distribution Matching Network (DMN): which is trained to maximise domain confusion between source and target domains without unknown samples under the guidance of the SSN. The key insight lies in how we exploit the mutually beneficial information between these two networks. On closer observation, we see that SSN can reveal which samples in the target domain belong to the unknown class by instance weighting whereas, DMN pushes apart the samples that most likely belong to the unknown class in the target domain, which in turn reduces the difficulty of SSN in identifying unknown samples. It follows that (a) and (b) will mutually supervise each other and alternate until convergence, which can better align the source and target domains in the shared label space. Extensive experiments on five datasets (Office-31, Office-Home, PACS, VisDA, and <inline-formula> <tex-math notation="LaTeX">mini </tex-math></inline-formula>_DomainNet) demonstrate the efficiency of the proposed method. Detailed ablation experiments also validate the effectiveness of each component and the generality of the proposed framework. Codes are available at: https://github.com/PRIS-CV/Mutual-to-Separate .
Green purchasing is a critical factor in sustainable enterprise development, and it often affects a company's business performance and environmental protection practices. An enterprise must have an ...appropriate assessment model to address the complexities of green purchasing. Most green purchasing studies have focused on the use of green criteria in the selection of suppliers to develop sustainable operations. By contrast, there have been few articles on green supply chain management discussing both green supplier evaluation and order allocation. This study proposes a novel model that integrates the best–worst method (BWM), modified fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and fuzzy multi-objective linear programming (FMOLP) to solve problems in green supplier selection and order allocation. We demonstrated the proposed method using actual data provided by an electronics company. The results indicate that this model can effectively evaluate the performance of green suppliers and can also optimize order allocation for qualified suppliers.
Accurate simulation and prediction of occupants’ energy use behavior are crucial in building energy consumption research. However, few studies have focused on household energy use behavior in ...severely cold regions that have unique energy use patterns because of the low demand of cooling in summer and the use of central heating system in winter. Thus, we developed an agent-based model to simulate the household electricity use behavior in severely cold regions, according to data for Harbin, China. The model regards apartments, residents, household appliances, and energy-management departments as agents and generates the household electricity consumption with respect to time, temperature, and energy-saving events. The simulation parameters include basic information of the residents, their energy-saving awareness, their appliance use behaviors, and the impact of energy-saving management. Electricity use patterns are described by decision-making mechanisms and probabilities obtained through a questionnaire survey. In the end, the energy-saving effects of different management strategies are evaluated. The results indicate that the model can visually present and accurately predict the dynamic energy use behavior of residents. The energy-saving potential of household electricity use in severely cold regions is mainly concentrated in lighting and standby waste, rather than cooling and heating, since the cooling demand in summer is low and the heating in winter mainly relies on central heating system of the city, not on household electricity appliances. Energy-saving promotion can significantly reduce the amount of energy waste (41.89% of lighting and 97.79% of standby energy consumption), and the best frequency of promotional events is once every four months. Residents prefer incentive policies, in which energy-saving effect is 57.7% larger than that of increasing electricity prices. This study realized the re-presentation of the changes of energy consumption in a large number of households and highlighted the particularity of household energy-saving potential in severely cold regions. The proposed model has a simple structure and high output accuracy; it can help cities in severely cold regions formulate energy-saving management policies and evaluate their effects.
With the expansion of the Internet of Things (IoT), security incidents about exploiting vulnerabilities in IoT devices have become prominent. However, due to the characteristics of IoT devices such ...as low power and low performance, it is difficult to apply existing security solutions to IoT devices. As a result, IoT devices have easily become targets for cyber attackers, and malware attacks on IoT devices are increasing every year. The most representative is the Mirai malware that caused distributed denial of service (DDoS) attacks by creating a massive IoT botnet. Moreover, Mirai malware has been released on the Internet, resulting in increasing variants and new malicious codes. One of the ways to mitigate distributed denial of service attacks is to render the creation of massive IoT botnets difficult by preventing the spread of malicious code. For IoT infrastructure security, security solutions are being studied to analyze network packets going in and out of IoT infrastructure to detect threats, and to prevent the spread of threats within IoT infrastructure by dynamically controlling network access to maliciously used IoT devices, network equipment, and IoT services. However, there is a great risk to apply unverified security solutions to real-world environments. In this paper, we propose a malware simulation tool that scans vulnerable IoT devices assigned a private IP address, and spreads malicious code within IoT infrastructure by injecting malicious code download command into vulnerable devices. The malware simulation tool proposed in this paper can be used to verify the functionality of network threat detection and prevention solutions.
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