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SA–BA eutectic mixture based shape-stabilized composite PCMs composed of BN and EG for thermal conductivity enhancement are provided.

Tumor metastasis is a fundamental cause of the poor prognosis of gastric carcinoma (GC). In order to study the problems affecting metastasis and recurrence of gastric cancer, the paper expose that TNF alpha induced protein 6 (TNFAIP6) is aberrantly overexpressed in GC, and patients with high-TNFAIP6 levels exhibited inferior overall survival. Mechanistically, overexpression of TNFAIP6 raised β-catenin ectopic nuclear distribution and activated the Wnt/β-catenin signal pathway. The experimental results show that TNFAIP6 facilitates the aggressive potential of GC cells through modulating PTX3 expression.

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address the above issues, we propose a Line Graph Contrastive Learning(LGCL) method to obtain rich information with multiple perspectives. LGCL obtains a subgraph view by h-hop subgraph sampling with target node pairs. After transforming the sampled subgraph into a line graph, the link prediction task is converted into a node classification task, which graph convolution progress can learn edge embeddings from graphs more effectively. Then we design a novel cross-scale contrastive learning framework on the line graph and the subgraph to maximize the mutual information of them, so that fuses the structure and feature information. The experimental results demonstrate that the proposed LGCL outperforms the state-of-the-art methods and has better performance on generalization and robustness., Comment: 37 pages

The multicolor-based point-of-care testing (POCT) of tumor cell-derived exosomes is of vital importance for understanding tumor growth and metastasis. Multicolor-based ratiometric signals most often rely on molecular optics, such as fluorescence resonance energy transfer (FRET)-dependent molecular fluorescence and localized surface plasmon resonance (LSPR)-related molecular colorimetry. However, finding acceptable FRET donor-acceptor fluorophore pairs and the kinetically slow color responses during size-related molecular colorimetry have greatly impeded POCT applications. Herein, an atomic flame was used to develop a visual sensing platform for the POCT of tumor-cell-derived exosomes. In comparison with common molecular optics, the atomic flame possessed the advantages of providing both a variety of ratiometric flame signals and fast response sensitivity. The integration of a gas-pressure-assisted flame reaction and dual-aptamer recognition guaranteed the sensitive and selective analysis of exosomes with a low limit of detection (LOD) of 7.6 × 102 particles per mL. Such a novel optical signal will inspire the development of more user-friendly POCT approaches.

Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is challenging due to large memory capacity and bandwidth requirements on a single compute node and high communication volumes across multiple nodes. In this paper, we present DistGNN that optimizes the well-known Deep Graph Library (DGL) for full-batch training on CPU clusters via an efficient shared memory implementation, communication reduction using a minimum vertex-cut graph partitioning algorithm and communication avoidance using a family of delayed-update algorithms. Our results on four common GNN benchmark datasets: Reddit, OGB-Products, OGB-Papers and Proteins, show up to 3.7× speed-up using a single CPU socket and up to 97× speed-up using 128 CPU sockets, respectively, over baseline DGL implementations running on a single CPU socket.

The stearic acid (SA) – hexanamide (HA) binary mixtures were prepared, and the phase diagram and Tammann plot were constructed. The differential scanning calorimetry results indicated that a eutectic composition formed by 46.2 mol% SA and 53.8 mol% HA melted at the temperature of 331.15 K with a latent heat of 176.62 J·g−1. Fourier transform infrared spectroscopy and X-ray diffraction results revealed that SA and HA were just physical mixture. The results of thermogravimetric analysis and 100 thermal cycles test respectively manifested that eutectic mixture had good thermal stability and reliability. Therefore, eutectic mixture is promising for thermal energy storage application.

A series of stearic acid (SA)/benzamide (BA) binary mixtures were prepared and demonstrated, and then the phase diagram and Tammann plot were established. The results showed that the molar fraction of SA in SA-BA eutectic mixture was 0.757. The eutectic mixture melted at 339.05 K with 200.15 J·g−1 latent heat. Expanded graphite (EG) was added to eutectic mixture to form composite phase change materials (PCMs) owing to the eutectic mixture has low thermal conductivity. It was found that the binary mixture and EG had good chemical compatibility, and the thermal conductivity of the composite with 12 wt% EG was significant increased by 12.30 times compared to eutectic mixture. Besides, the phase change properties of eutectic mixture/12 wt% EG composite phase change material (PCM) had a little change after 100 thermal cycles, and had negligible mass loss after leakage test. These results demonstrated that the composite PCM had good thermal reliability and stability. Based on the above results, the composite PCM has great potential in thermal energy storage application.

Kinetics of rubidium extraction by 4-tert-butyl-2-(α-methylbenzy) phenol (t-BAMBP)/sulfonated kerosene in brine solutions were investigated using the rising single drop technique. Parameters affecting the mass transfer mechanism, including the interfacial area, initial alkalinity of aqueous phase, concentration of rubidium ions and t-BAMBP, and temperature were studied. The results indicated that the process of extraction was controlled by the diffusion process, while the chemical reaction occurred at the interfacial area. Dynamic equation of the extraction system, Re = 2.901 × 10−5[Rb+]1.022[OH−]1.179[t-BAMBP]0.991 was obtained by non-linear fitting the experimental data. Moreover, a new mechanism of the rubidium extraction with t-BAMBP/sulfonated kerosene was proposed.

Online reviews provide product evaluations for customers to make decisions. Unfortunately, the evaluations can be manipulated using fake reviews ("spams") by professional spammers, who have learned increasingly insidious and powerful spamming strategies by adapting to the deployed detectors. Spamming strategies are hard to capture, as they can be varying quickly along time, different across spammers and target products, and more critically, remained unknown in most cases. Furthermore, most existing detectors focus on detection accuracy, which is not well-aligned with the goal of maintaining the trustworthiness of product evaluations. To address the challenges, we formulate a minimax game where the spammers and spam detectors compete with each other on their practical goals that are not solely based on detection accuracy. Nash equilibria of the game lead to stable detectors that are agnostic to any mixed detection strategies. However, the game has no closed-form solution and is not differentiable to admit the typical gradient-based algorithms. We turn the game into two dependent Markov Decision Processes (MDPs) to allow efficient stochastic optimization based on multi-armed bandit and policy gradient. We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal. Our code is available at https://github.com/YingtongDou/Nash-Detect., Comment: Accepted by KDD 2020

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.

In this study, a series of binary mixtures of stearic acid (SA) and acetanilide (AA) at various mass fractions were prepared. A novel binary mixture of SA and AA in the ratio of 81.0:19.0 mol% is shown to be an eutectic mixture with melting temperature of 342.45 ± 0.25 K, melting latent heat of 220.04 ± 10.02 J·g-1. In order to improve the thermal properties of SA-AA eutectic mixture so as to obtain a better composite phase change materials (CPCM) with high thermal conductivity, large heat storage capacity, good thermal stability and reliability for heat energy storage, expanded graphite (EG) at different mass fractions is added into SA-AA eutectic mixture. The results exhibited it remarkable energy storage capacity with a latent heat of CPCM of 176.14 J·g-1 compared to most of the organic eutectic mixture composite. The thermal conductivity of the CPCM was enhanced by 14.5 times comparing to SA-AA eutectic mixture. And the thermo-physical properties of the SA-AA/EG CPCM after 100 accelerated thermal cycles were slightly decreased which did not distinct influence on heat storage. Thus, the prepared SA-AA/EG CPCM is a promising material for latent heat storage systems.

The thermal properties of the binary system composed of lauric acid (LA) and nonanoic acid (NA) and its binary phase diagram were studied. To put this binary system into practice, the mixture with 96wt% of LA was selected, and it has a phase change temperature of 41oC and a latent heat of 163 J g-1, which is suitable for domestic water heating application. Besides, its thermal and chemical structure stability, thermal conductivity, degree of supercooling and thermal reliability were investigated by thermogravimetric analyzer (TGA), fourier transform infrared (FT-IR) spectrophotometer, thermal conductivity apparatus (TCA), T-history method and accelerated thermal cycle test for 1000 melt/freeze cycles, separately. The results of above tests indicated that the mixture has a remarkable thermal stability, a good chemical structure stability, no supercooling and an excellent thermal reliability. Therefore, the mixture can be a potential PCM used for thermal energy storage.

An oxalic acid dihydrate/boric acid (OCD-BA) binary eutectic mixtures containing 88 wt% OCD and 12 wt% BA was investigated as a novel phase change material (PCM) with high latent heat and thermal stability. The solid-liquid phase diagram of binary systems is established, and the phase change temperature of OCD-BA binary eutectic mixture is 87.3 °C and the latent heats of the melting and crystallization process are 344 J g−1 and 262 J g−1, respectively. Its thermophysical properties and thermal stability were studied using differential scanning calorimetry (DSC), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), and Scanning electron microscopy-Energy dispersive spectroscopy (SEM-EDS). Its thermal stability was investigated in situ through 100 cycles using DSC and 1000 thermal cycling tests using Refrigerated and heating circulators (RHC). The latent heat of melting still exceed 280 J g−1 after 1000 heating/cooling cycles. The heat release platform sustained 5 h at more than 70.0 °C, and the degree of supercooling was only 1.0 °C according to T-history method. These results indicate that the newly prepared OCD-BA binary eutectic mixture is a potential PCM for thermal storage tanks.

Summary To satisfy the application demands for latent heat storage in the temperature range from 5°C to 15°C, an original composite phase change material (PCM), CA-NA/EG (caprylic-nonanoic acid/expanded graphite), was prepared and characterized. For CA-NA/EG, the mass ratio of CA and NA was 8:2, and the mass percentage of the CA-NA in CA-NA/EG composite PCM was determined as 90% by leakage test. The melting and freezing points of the CA-NA/EG were 6.84°C and 9.34°C, and corresponding latent heats were 108.75 kJ/kg and 107.67 kJ/kg. In addition, its thermal conductivity, thermal stability and reliability were investigated by thermal conductivity apparatus (TCA), thermal gravimetric analyzer (TGA), and accelerated thermal cycle test for 100 melt/freeze cycles, respectively. The results showed that the CA-NA/EG had a good thermal stability and an excellent thermal reliability. Moreover, the thermal conductivity of CA-NA/EG had an improvement of 25% than that of the CA-NA. On the other hand, the accelerated thermal cycle test also indicated that the CA-NA/EG had no supercooling during all melt/freeze cycles. Therefore, the prepared composite PCM, CA-NA/EG, can be applied for low-temperature thermal energy storage owing to its proper melting temperature, acceptable latent heat and thermal conductivity, excellent thermal stability and reliability.

The thermal properties of caprylic acid, nonanoic acid and their binary system were investigated by differential scanning calorimetry (DSC). The phase diagram for the binary system was constructed, and the experiment results showed that the eutectic temperature and the molar fraction of caprylic acid for the eutectic mixture are 1.83 °C and 0.483, respectively. To satisfy the application demands of the temperature scope from 5 °C to 15 °C, another binary mixture for the binary system with a molar fraction of 0.814 for caprylic acid component is found successfully, and it has good thermal properties that the melting temperature and the latent heat are 7.6 °C and 123 J g−1, respectively. What is more, the latent heat and melting temperature for the binary mixture do not change at all nearly after being cycled 100 times during the whole melting process. As a novel phase change material (PCM) for application in the field of air conditioning and cooling, the binary mixture has own unique thermal advantages.

Shape-stabilized composite phase change materials composed of sebacic acid (SA) and MCM-41 were prepared by a facile blending and impregnating method. Various characterization techniques were carried out to characterize their structural and thermal properties, including thermogravimetry (TG) analyses and differential scanning calorimetry (DSC), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy. The XRD and FT-IR results indicated the interactions between SA and MCM-41, such as the capillary force and the hydrogen bonding, resulting in the confined crystallization process. As a result, the SA that was confined was amorphous. The TG analysis results showed that the presence of MCM-41 practically resulted in an overlapping of the two decomposition stages. The DSC analysis demonstrated that a decrease in the SA content was accompanied by a continuous decrease in the melting point and phase change enthalpy of the composites.

Thermal properties and stabilities of the eutectic mixture: 1,6-hexanediol (HE) and lauric acid (LA) as a new phase change material (PCM) for latent heat thermal energy storage (TES) were investigated. Differential scanning calorimetry (DSC) results indicated that the aforementioned HE/LA mixture with eutectic composition (70/30 wt.%) was a suitable PCM in terms of melting point (Tpeak = 36.92 ± 0.71 °C) and latent heat of fusion (ΔHm = 177.11 ± 7.93 J g−1). After 1000 thermal cycles, the change in melting point for the eutectic mixture was in the range of −0.49% to −1.19%, and the change in latent heat of fusion was in the range of −0.22% to −3.24%. The eutectic mixture was thermally and chemically stable according to results of thermogravimetric analysis (TGA), volatile test and Fourier Transform Infrared (FT-IR) spectroscopic analysis. Therefore, the HE/LA eutectic mixture is an effective TES material to reduce energy consumption.

An adipic acid/boric acid (AA–BA) binary eutectic mixture containing 80 wt % adipic acid and 20 wt % boric acid was investigated as a new phase change material (PCM) with high latent heat, high thermal stability, and low cost. The material′s thermophysical properties and thermal stability were studied using differential scanning calorimetry (DSC), X-Ray Diffraction (XRD), and thermogravimetric analysis (TGA). The thermal stability was investigated in situ over 100 cycles using DSC. The phase change temperature of the AA–BA binary eutectic mixture was 135.04 °C and the latent heats of the melting and crystallization process were 283.42 and 272.16 J g−1, respectively. The latent heat of melting only dropped 4.94 % after in situ 100 cycles using DSC. Following comparison of thermal properties and price of the AA–BA eutectic mixture PCM with those of some composite PCMs, the results indicate that the newly prepared AA–BA eutectic mixture is a potential phase change material for thermal storage tanks.

We investigated the thermal properties and thermal reliability of stearic acid- n -butyramide and stearic acid- n -octanamide binary eutectic mixtures, as phase change materials for low temperature solar heat energy storage. The solid-liquid phase diagrams of two binary systems were established, and the eutectic compositions of binary mixtures and corresponding thermal properties were verified: (1) stearic acid- n -butyramide, the eutectic composition is 0.551 ± 0.005 mol fraction stearic acid and (2) stearic acid- n -octanamide, the eutectic composition is 0.589 ± 0.005 mol fraction stearic acid. The eutectic compositions have high latent heat of fusion Δ H m = 198.38 ± 10.22 J·g −1 , Δ H m = 198.98 ± 8.56 J·g −1 and melting temperature T peak = 337.41 ± 1.93 K, T peak = 336.28 ± 2.00 K for stearic acid- n -butyramide and stearic acid- n -octanamide binary mixture, respectively. A modeling approach on the bases of thermodynamics is employed to predict solid-liquid equilibria of the studied mixtures and their latent heats of fusion. The simulated results are in good agreement with experimental results. The heat capacity of the solid phase measured are 1.69 ± 0.2 J·g −1 ·K −1 , 1.61 ± 0.2 J·g −1 ·K −1 and the thermal conductivity of the solid phase are 0.222 ± 0.03 W·m −1 ·K −1 , 0.276 ± 0.034 W·m −1 ·K −1 at 298.15 K for stearic acid- n -butyramide and stearic acid- n -octanamide binary mixture, respectively. The mixture has a good thermal stability after 100 melt-freeze cycles. As novel phase change materials for solar heat applications, the studied eutectic mixtures have attractive thermal performance and great utility.

Oxalic acid dihydrate (OAD) which has the high initial phase transition enthalpy is a promising PCM for thermal energy storage (TES). However, large degradations of thermophysical properties over time have restricted further application of OAD. Cyclic stability of OAD, which means thermal cycling stability, needs to be improved to give full play to its potential for TES. In this paper, cyclic stability of OAD was determined by using ordinary aluminum pans and high pressure gold-plated pans in DSC measurement. In addition, PEG (with different molecular weights) was added to improve cyclic stability of OAD. Results show that the containers with good sealing performance and strong corrosion resistance are vital for the application of OAD. Meanwhile, adding 2.5% PEG 6000 gives better cyclic stability to OAD without affecting its melting temperature neither decreasing significantly its heat of fusion.

Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for clinical diagnostic applications. Structural brain network (e.g. DTI) and functional brain network (e.g. fMRI) are the primary networks of interest. Most existing works in brain network analysis focus on either structural or functional connectivity, which cannot leverage the complementary information from each other. Although multi-view learning methods have been proposed to learn from both networks (or views), these methods aim to reach a consensus among multiple views, and thus distinct intrinsic properties of each view may be ignored. How to jointly learn representations from structural and functional brain networks while preserving their inherent properties is a critical problem. In this paper, we propose a framework of Siamese community-preserving graph convolutional network (SCP-GCN) to learn the structural and functional joint embedding of brain networks. Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity. Moreover, we propose to preserve the community structure of brain networks in the graph convolutions by considering the intra-community and inter-community properties in the learning process. Furthermore, we use Siamese architecture which models the pair-wise similarity learning to guide the learning process. To evaluate the proposed approach, we conduct extensive experiments on two real brain network datasets. The experimental results demonstrate the superior performance of the proposed approach in structural and functional joint embedding for neurological disorder analysis, indicating its promising value for clinical applications.

We propose an end-to-end graph similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks. Our proposed framework performs higher-order convolutions by incorporating higher-order proximity in graph convolutional networks to characterize and learn the community structure in brain connectivity networks. To the best of our knowledge, this is the first community-preserving graph similarity learning framework for multi-subject brain network analysis. Experimental results on four real fMRI datasets demonstrate the potential use cases of the proposed framework for multi-subject brain analysis in health and neuropsychiatric disorders. Our proposed approach achieves an average AUC gain of $75$% compared to PCA, an average AUC gain of $65.5$% compared to Spectral Embedding, and an average AUC gain of $24.3$% compared to S-GCN across the four datasets, indicating promising applications in clinical investigation and brain disease diagnosis.

Reviews spams are prevalent in e-commerce to manipulate product ranking and customers decisions maliciously. While spams generated based on simple spamming strategy can be detected effectively, hardened spammers can evade regular detectors via more advanced spamming strategies. Previous work gave more attention to evasion against text and graph-based detectors, but evasions against behavior-based detectors are largely ignored, leading to vulnerabilities in spam detection systems. Since real evasion data are scarce, we first propose EMERAL (Evasion via Maximum Entropy and Rating sAmpLing) to generate evasive spams to certain existing detectors. EMERAL can simulate spammers with different goals and levels of knowledge about the detectors, targeting at different stages of the life cycle of target products. We show that in the evasion-defense dynamic, only a few evasion types are meaningful to the spammers, and any spammer will not be able to evade too many detection signals at the same time. We reveal that some evasions are quite insidious and can fail all detection signals. We then propose DETER (Defense via Evasion generaTion using EmeRal), based on model re-training on diverse evasive samples generated by EMERAL. Experiments confirm that DETER is more accurate in detecting both suspicious time window and individual spamming reviews. In terms of security, DETER is versatile enough to be vaccinated against diverse and unexpected evasions, is agnostic about evasion strategy and can be released without privacy concern.