Independent Asymmetric Embedding for Cascade Prediction on Social Networks

The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. Cascade prediction aims to predict the individuals who will potentially repost the message on the social network. One kind of methods either exploit demographical, structural, and temporal features for prediction, or explicitly rely on particular information diffusion models. The other kind of models are fully data-driven and do not require a global network structure. Thus massive diffusion prediction models based on network embedding are proposed. These models embed the users into the latent space using their cascade information, but are lack of consideration for the intervene among users when embedding. In this paper, we propose an independent asymmetric embedding method to learn social embedding for cascade prediction. Different from existing methods, our method embeds each individual into one latent influence space and multiple latent susceptibility spaces. Furthermore, our method captures the co-occurrence regulation of user combination in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and cost-effectiveness of our approach

Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives

Name ambiguity is common in academic digital libraries, such as multiple authors having the same name. This creates challenges for academic data management and analysis, thus name disambiguation becomes necessary. The procedure of name disambiguation is to divide publications with the same name into different groups, each group belonging to a unique author. A large amount of attribute information in publications makes traditional methods fall into the quagmire of feature selection. These methods always select attributes artificially and equally, which usually causes a negative impact on accuracy. The proposed method is mainly based on representation learning for heterogeneous networks and clustering and exploits the self-attention technology to solve the problem. The presentation of publications is a synthesis of structural and semantic representations. The structural representation is obtained by meta-path-based sampling and a skip-gram-based embedding method, and meta-path level attention is introduced to automatically learn the weight of each feature. The semantic representation is generated using NLP tools. Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method. The experimental results show the superiority of our new method for capturing the most attributes from publications and reducing the impact of redundant information

Direct contact membrane distillation of refining waste stream from precious metal recovery:Chemistry of silica and chromium (III) in membrane scaling

Precious metals, such as platinum group metals (PGMs) with distinct catalytic activity, are widely used as active components in various industrial catalysts. It is, therefore, highly desirable to recover these valuable components from the end-of-life products. We explored treatment of refining wastewater from precious metals recovery using direct contact membrane distillation (DCMD). The role of various initial pH of refining wastewater on DCMD performance was assessed. Results suggested that hydrochloride acid (HCl) and high-quality water can be reclaimed from the real refining wastewater by adjusting initial pH. Furthermore, DCMD water flux decline was mainly caused by silica and chromium (III) scaling, which was dependent on initial pH of refining wastewater. Silica scaling was responsible for the decrease of DCMD performance when the initial pH of refining wastewater increased from original 0.03 to 5 and 7. Silica oligomers in the concentrated feed with various initial pH were identified using mass spectra. Dichlorotetraaquochromiun was identified by X-ray photoelectron spectroscopy and ultraviolet and visible absorbance spectra as the main species contributing to the green colour and scaling on the PTFE membrane surface. Our results suggest that DCMD can be used as a promising and feasible solution for resource recovery from acidic refining waste stream.</p

Circulating plasma and exosome levels of the miR-320 family as a non-invasive biomarker for methamphetamine use disorder

The neurobiological mechanism underlying methamphetamine (MA) use disorder was still unclear, and no specific biomarker exists for clinical diagnosis of this disorder. Recent studies have demonstrated that microRNAs (miRNAs) are involved in the pathological process of MA addiction. The purpose of this study was to identify novel miRNAs for the diagnosis biomarkers of MA user disorder. First, members of the miR-320 family, including miR-320a-3p, miR-320b, and miR-320c, were screened and analyzed in the circulating plasma and exosomes by microarray and sequencing. Secondly, plasma miR-320 was quantified by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) in eighty-two MA patients and fifty age-gender-matched healthy controls. Meanwhile, we also analyzed exosomal miR-320 expression in thirty-nine MA patients and twenty-one age-matched healthy controls. Furthermore, the diagnostic power was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The expression of miR-320 significantly increased in plasma and exosomes of MA patients compared with healthy controls. The AUC of the ROC curves of miR-320 in plasma and exosomes of MA patients were 0.751 and 0.962, respectively. And the sensitivities of miR-320 were 0.900 and 0.846, respectively, whereas the specificities of miR-320 were 0.537 and 0.952, respectively, in plasma and exosomes in MA patients. And the increased plasma miR-320 was positively correlated with cigarette smoking, age of onset, and daily use of MA in MA patients. Finally, cardiovascular disease, synaptic plasticity, and neuroinflammation were predicted to be the target pathways related to miR-320. Taken together, our findings indicated that plasma and exosomal miR-320 might be used as a potential blood-based biomarker for diagnosing MA use disorder

Global Infectious Diseases in July 2023: Monthly Analysis

Many infectious diseases are ubiquitous and pose persistent adverse effects on public health. Infectious diseases have also been leading causes of high mortality in different periods of history. Real-time monitoring and analysis of global infectious disease transmission can provide a comprehensive understanding of critical information regarding the transmission routes, scope, velocity, and effects of viruses or bacteria. Here, using Shusi Tech’s Global Epidemic Information Monitoring System, we analyzed the prevalence of infectious diseases worldwide. We describe types of infectious diseases with relatively low incidence from 24 June 2023 to 23 July 2023 as comprehensibly as possible

100 essential questions for the future of agriculture

Publication history: Accepted - 8 March 2023; Published online - 11 April 2023.The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture. Key points Growing demand and resource limitations pose a critical challenge for agriculture, necessitating innovative and sustainable approaches. The paper identifies 100 priority questions for the future of agriculture, indicating current and future research directions. Sustainable agriculture depends on interdisciplinary and multidisciplinary research that harmonises basic and applied sciences and fosters collaboration among different stakeholders