MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning

Attention mechanism enables the Graph Neural Networks(GNNs) to learn the attention weights between the target node and its one-hop neighbors, the performance is further improved. However, the most existing GNNs are oriented to homogeneous graphs and each layer can only aggregate the information of one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise and easily lead to over smoothing. We propose a Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning method (MHNF). Specifically, we first propose a hybrid metapath autonomous extraction model to efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level heterogeneous Information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is proposed, which can efficiently integrate different-hop and different-path neighborhood information respectively. This paper can solve the problem of aggregating the multi-hop neighborhood information and can learn hybrid metapaths for target task, reducing the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information of different levels. Experimental results on real datasets show that MHNF is superior to state-of-the-art methods in node classification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93% relative improvement on average, respectively)

Can We Transfer Noise Patterns? An Multi-environment Spectrum Analysis Model Using Generated Cases

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system's learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://github.com/Magnomic/CNST

New tranylcypromine derivatives containing sulfonamide motif as potent LSD1 inhibitors to target acute myeloid leukemia: design, synthesis and biological evaluation

Lysine-specific demethylase 1 (LSD1) is frequently elevated in acute myeloid leukemia (AML) and often leads to tumorigenesis. In recent years, numerous LSD1 inhibitors based on tranylcypromine (TCP) scaffolding have reached clinical trials. Most TCP derivatives were modified at the amino site of cyclopropane motif. Herein, we for the first time introduced a sulfonamide group in TCP benzene ring of series a compounds and performed a systematical study on structure and activity relationships by varying sulfonamide groups. The introduction of sulfonamide significantly increased the targeting capacity of TCP against LSD1. Moreover, we discovered that the Boc attached LSD1 inhibitors (labelled as series b compounds) substantially improved their anti-proliferation capacity towards AML cells. The intracellular thermal shift and LC-MS/MS results implied that Boc enhanced the drug lipophilicity and might be removed under the cancerous acidic environment to release the real pharmacophore, evidenced by the fact that a structurally similar but acidic inert pivaloyl to replace Boc dramatically dropped the cellular anti-proliferation effect. Finally, a benzyl group installed at the amino site to appropriately increase lipophilicity led to trans-4-(2-(benzylamino)-cyclopropyl)-N,N-diethylbenzenesulfonamide a10 that showed better anti-proliferation activity in AML cells and enzymatic inhibition against LSD1. Taken together, our work offers a novel TCP-based structure and provides a prodrug strategy for the discovery of potent LSD1 inhibitors by having appropriate lipophilicity

Cyanidin-3-o-Glucoside Pharmacologically Inhibits Tumorigenesis via Estrogen Receptor β in Melanoma Mice

Expression patterns of estrogen receptors [ERα, ERβ, and G-protein associated ER (GPER)] in melanoma and skin may suggest their differential roles in carcinogenesis. Phytoestrogenic compound cyanidin-3-o-glucoside (C3G) has been shown to inhibit the growth and metastatic potential of melanoma, although the underlying molecular mechanism remains unclear. The aim of this study was to clarify the mechanism of action of C3G in melanoma in vitro and in vivo, as well as to characterize the functional expressions of ERs in melanoma. In normal skin or melanoma (n = 20/each), no ERα protein was detectable, whereas expression of ERβ was high in skin but weak focal or negative in melanoma; and finally high expression of GPER in all skin vs. 50% melanoma tissues (10/20) was found. These results correspond with our analysis of the melanoma survival rates (SRs) from Human Protein Atlas and The Cancer Genome Atlas GDC (362 patients), where low ERβ expression in melanoma correlate with a poor relapse-free survival, and no correlations were observed between SRs and ERα or GPER expression in melanoma. Furthermore, we demonstrated that C3G treatment arrested the cell cycle at the G2/M phase by targeting cyclin B1 (CCNB1) and promoted apoptosis via ERβ in both mouse and human melanoma cell lines, and inhibited melanoma cell growth in vivo. Our study suggested that C3G elicits an agonistic effect toward ERβ signaling enhancement, which may serve as a potential novel therapeutic and preventive approach for melanoma

English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

This paper combines domestic and international research results to analyze and study the difference between the attribute features of English phrase speech and noise to enhance the short-time energy, which is used to improve the threshold judgment sensitivity; noise addition to the discrepancy data set is used to enhance the recognition robustness. The backpropagation algorithm is improved to constrain the range of weight variation, avoid oscillation phenomenon, and shorten the training time. In the real English phrase sound recognition system, there are problems such as massive training data and low training efficiency caused by the super large-scale model parameters of the convolutional neural network. To address these problems, the NWBP algorithm is based on the oscillation phenomenon that tends to occur when searching for the minimum error value in the late training period of the network parameters, using the K-MEANS algorithm to obtain the seed nodes that approach the minimal error value, and using the boundary value rule to reduce the range of weight change to reduce the oscillation phenomenon so that the network error converges as soon as possible and improve the training efficiency. Through simulation experiments, the NWBP algorithm improves the degree of fitting and convergence speed in the training of complex convolutional neural networks compared with other algorithms, reduces the redundant computation, and shortens the training time to a certain extent, and the algorithm has the advantage of accelerating the convergence of the network compared with simple networks. The word tree constraint and its efficient storage structure are introduced, which improves the storage efficiency of the word tree constraint and the retrieval efficiency in the English phrase recognition search

Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China

The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) has become a focus of research in the post-epidemic era. Based on China’s experience in preventing and controlling COVID-19, this paper first analyzes the characteristics and process of ERA in public health emergencies, and then synthesizes the relevant Chinese studies in recent years to identify the intelligent technologies affecting ERA in China using word frequency analysis technology. We also construct an intelligent emergency resource allocation mechanism in four areas: medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence. Finally, we use the entropy-TOPSIS method to evaluate the impact of intelligent technologies on ERA, and we rank the criticality of intelligent technologies. The experimental results show that (i.) medical intelligence and management intelligence are the keys to developing intelligent ERA, and (ii.) among the identified essential intelligent technologies, artificial intelligence (AI), and big data technology have a more significant and critical role in emergency resource intelligence allocation

Cross-motif Matching and Hierarchical Contrastive Learning for Recommendation

Recently, leveraging different channels to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be a very promising work. However, how to deeply dig out the relationship between different channels and make full use of it while maintaining the uniqueness of each channel is a problem that has not been well studied and resolved in this field. Under such circumstances, this paper explores and verifies the deficiency of directly constructing contrastive learning tasks on different channels with practical experiments and proposes the scheme of interactive modeling and matching representation across different channels. This is the first attempt in the field of recommender systems, we believe the insight of this paper is inspirational to future self-supervised learning research based on multi-channel information. To solve this problem, we propose a cross-channel matching representation model based on attentive interaction, which realizes efficient modeling of the relationship between cross-channel information. Based on this, we also propose a hierarchical self-supervised learning model, which realizes two levels of self-supervised learning within and between channels, which improves the ability of self-supervised tasks to autonomously mine different levels of potential information. We have conducted abundant experiments, and various metrics on multiple public datasets show that the method proposed in this paper has a significant improvement compared with the state-of-the-art methods, no matter in the general or cold-start scenario. And in the experiment of model variant analysis, the benefits of the cross-channel matching representation model and the hierarchical self-supervised model proposed in this paper are also fully verified.Comment: Rename the paper,the full-text language was polished and part of the experiment content was revise

Probabilistic Evaluation of Tibetan Plateau Mesoscale Vortex on 18 July 2013

Tibetan Plateau (TP) mesoscale vortex (TPMV) was regarded as one of the most important rain bearing systems in China. Previous studies focused on the mechanisms of the TPMV in the viewpoint of deterministic forecast; however, few studies investigate the predictability of the TPMV using the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) from the European Center for Medium Range Weather Forecasts (ECWMF). This paper investigates the location and the intensity of the larger-scale synoptic systems that influenced the development of the TPMV and its associated heavy rainfall by correlation and composite analysis. The case study on 18 July 2013 shows that stronger Balkhash Lake ridge, weaker Baikal Lake trough, and weaker western Pacific subtropical high (WPSH) are favorable to formation of TPMV over the Sichuan basin (SCB); otherwise, weaker Balkhash Lake ridge, stronger Baikal Lake trough, and stronger WPSH result in formation of TPMV to west of the SCB slightly. After the initial time, forecast for next 48 h of the geopotential height over the SCB can be viewed as a precursor of the subsequent time-averaged 90–108 h forecast of TPMV. TPMV had critical contributions to the heavy rainfall over the SCB on 18 July 2013