Macroporous Materials from Sintering Capillary Aggregate Networks

Capillary aggregate is one of the morphologies that appear in ternary mixtures of particles and two immiscible fluids. Capillary aggregates appear when two conditions are satisfied: the particles are fully-wetted by one of the two liquid phases, and furthermore, the wetting fluid has a volume fraction that is roughly equal to the particle volume fraction. Under these conditions, the wetting fluid creates highly compact particle aggregates called capillary aggregates. Recent research suggests that capillary aggregates can stick to one another to create a network in which capillary aggregates act as building blocks. The aim of this study is to develop a macro-porous material from sintering capillary aggregate networks. In this study, morphologies of ternary mixtures in which the continuous phase is ethylene glycol, the wetting phase is light mineral oil and the solid phase is hydrophobic particles of low melting temperature polymer were studied. Capillary aggregate networks were prepared by suitable mixing methods, and then the mixtures were sintered to obtain macro-porous materials. Such macro-porous materials may be used as scaffolds for cells growth.iv This thesis reported the implementation of capillary aggregate networks and the procedures of sintering and washing process. The effects of composition of ternary mixtures on porosity, pore sizes and number of aggregates were studied. This study demonstrates that by sintering capillary aggregate networks, we can obtain high porosity materials with low particle loading, and obtain large pore sizes without using different size particles. Moreover, result shows that cells can grow well in the macro-porous materials

Enhanced cancer therapy with cold-controlled drug release and photothermal warming enabled by one nanoplatform

Stimuli-responsive nanoparticles hold great promise for drug delivery to improve the safety and efficacy of cancer therapy. One of the most investigated stimuli-responsive strategies is to induce drug release by heating with laser, ultrasound, or electromagnetic field. More recently, cryosurgery (also called cryotherapy and cryoablation), destruction of diseased tissues by first cooling/freezing and then warming back, has been used to treat various diseases including cancer in the clinic. Here we developed a cold-responsive nanoparticle for controlled drug release as a result of the irreversible disassembly of the nanoparticle when cooled to below ∼10 °C. Furthermore, this nanoparticle can be used to generate localized heating under near infrared (NIR) laser irradiation, which can facilitate the warming process after cooling/freezing during cryosurgery. Indeed, the combination of this cold-responsive nanoparticle with ice cooling and NIR laser irradiation can greatly augment cancer destruction both in vitro and in vivo with no evident systemic toxicity

MeshNet: Mesh Neural Network for 3D Shape Representation

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation

Rosavin exerts an antitumor role and inactivates the MAPK/ERK pathway in small-cell lung carcinoma in vitro

This study attempts to explore the function and mechanism of action of rosavin in small-cell lung cancer (SCLC) in vitro. The viability and clone formation of SCLC cells were assessed using cell counting kit-8 and colony formation assays, respectively. Apoptosis and cell cycle were detected using flow cytometry and cell cycle analysis, respectively. Wound healing and transwell assays were performed to evaluate the migration and invasion of SCLC cells. Besides, protein levels of p-ERK, ERK, p-MEK and MEK were determined using western blot analysis. Rosavin repressed the viability and clone formation of SCLC cells, and promoted apoptosis and G0/G1 arrest of SCLC cells. At the same time, rosavin suppressed migration and invasion of SCLC cells. Moreover, protein levels of p-ERK/ERK and p-MEK/MEK were decreased after rosavin addition in SCLC cells. Rosavin impaired malignant behaviors of SCLC cells, which may be associated with inhibition of the MAPK/ERK pathway in vitro

A local resampling trick for focused molecular dynamics

We describe a method that focuses sampling effort on a user-defined selection of a large system, which can lead to substantial decreases in computational effort by speeding up the calculation of nonbonded interactions. A naive approach can lead to incorrect sampling if the selection depends on the configuration in a way that is not accounted for. We avoid this pitfall by introducing appropriate auxiliary variables. This results in an implementation that is closely related to configurational freezing and elastic barrier dynamical freezing. We implement the method and validate that it can be used to supplement conventional molecular dynamics in free energy calculations (absolute hydration and relative binding)

More than Classification: A Unified Framework for Event Temporal Relation Extraction

Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events. For example, relation \textit{Includes} could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3\% on both datasets. By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios. When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations. The code is released at \url{https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE}

UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training

This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data. Particularly focusing on visual and linguistic modalities, we categorize data knowledge into five unit types, namely, in-image, in-text, cross-image, cross-text, and image-text. Following this protocol, we collect, from public international news, a large-scale multimodal knowledge graph dataset that consists of 1,388,568 nodes (with 571,791 vision-related ones) and 3,673,817 triplets. The dataset is also annotated with rich event tags, including 96 coarse labels and 9,185 fine labels, expanding its potential usage. To further verify that UKnow can serve as a standard protocol, we set up an efficient pipeline to help reorganize existing datasets under UKnow format. Finally, we benchmark the performance of some widely-used baselines on the tasks of common-sense reasoning and vision-language pre-training. Results on both our new dataset and the reformatted public datasets demonstrate the effectiveness of UKnow in knowledge organization and method evaluation. Code, dataset, conversion tool, and baseline models will be made public

Fiber absorption measurement errors resulting from re-emission of radiation

We show that errors in the absorption measured in rare-earth-doped fibers can exceed 50% and severely distort the spectral shape. This is a result of re-emission in fibers with overlapping absorption and emission spectra