Development and AFM study of porous scaffolds for wound healing applications

An engineering approach to the development of biomaterials for promotion of wound healing emphasises the importance of a well-controlled architecture and concentrates on optimisation of morphology and surface chemistry to stimulate guidance of the cells within the wound environment. A series of three-dimensional porous scaffolds with 80–90% bulk porosity and fully interconnected macropores were prepared from two biodegradable materials – cellulose acetate (CA) and poly (lacticco-glycolic acid) (PLGA) through the phase inversion mechanism of formation. Surface morphology of obtained scaffolds was determined using atomic force microscopy (AFM) in conjunction with optical microscopy. Scanning Electron Microscopy (SEM) was applied to characterise scaffolds bulk morphology. Biocompatibility and biofunctionality of the prepared materials were assessed through a systematic study of cell/material interactions using atomic force microscopy (AFM) methodologies together with in vitro cellular assays. Preliminary data with human fibroblasts demonstrated a positive influence of both scaffolds on cellular attachment and growth. The adhesion of cells on both biomaterials were quantified by AFM force measurements in conjunction with a cell probe technique since, for the first time, a fibroblast probe has been successfully developed and optimal conditions of immobilisation of the cells on the AFM cantilever have been experimentally determined

Public perspective towards social impact of chang e lunar probe program

The present article is based on the MA thesis of Hou Bowen (Ph.D candidate) and on the presentation made at the ISA World Congress of Sociology held in Yokohama (Japan) on July 2014 at the Session on “Assessing Technologies: Global Patterns of Trust and Distrust” of RC23-Sociology of Science and Technology.During the past decades, assessing the impact of technological project and related engineering has long been paid attention. The objective of this research is to investigate technological project and related engineering’s social impact through public perspective. The present article investigated the social impact of China’s Chang E Lunar Probe project by using Social Impact Assessment (SIA) methods, resulting from a research study conducted in 2012. SIA is a collective of the systematic methods used to investigate the influence of engineering, project or policy and to present their potential social impacts. A survey from public respondents indicated that public spoke highly of Chang E Probe on the whole. Furthermore, a factor analysis of the perspective of public perspective towards Chang E Lunar Probe project has discovered such impact were mainly assessed in four dimensions by public, these impacts were military impact, political impact, public support and educational impact. From the results obtained so far, it revealed that public perspective towards the political impact of the Chang E Probe varies from each other but unified when they assess Chang E’s military impact, meanwhile student’s perspective towards the educational impact of Chang E Probe was largely different from other publics.The MA thesis has the supervision of Prof. Yin Haijie (professor in Harbin Institute of Technology)

Improved Neural Relation Detection for Knowledge Base Question Answering

Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.Comment: Accepted by ACL 2017 (updated for camera-ready

A Unified and Optimal Multiple Testing Framework based on rho-values

Multiple testing is an important research direction that has gained major attention in recent years. Currently, most multiple testing procedures are designed with p-values or Local false discovery rate (Lfdr) statistics. However, p-values obtained by applying probability integral transform to some well-known test statistics often do not incorporate information from the alternatives, resulting in suboptimal procedures. On the other hand, Lfdr based procedures can be asymptotically optimal but their guarantee on false discovery rate (FDR) control relies on consistent estimation of Lfdr, which is often difficult in practice especially when the incorporation of side information is desirable. In this article, we propose a novel and flexibly constructed class of statistics, called rho-values, which combines the merits of both p-values and Lfdr while enjoys superiorities over methods based on these two types of statistics. Specifically, it unifies these two frameworks and operates in two steps, ranking and thresholding. The ranking produced by rho-values mimics that produced by Lfdr statistics, and the strategy for choosing the threshold is similar to that of p-value based procedures. Therefore, the proposed framework guarantees FDR control under weak assumptions; it maintains the integrity of the structural information encoded by the summary statistics and the auxiliary covariates and hence can be asymptotically optimal. We demonstrate the efficacy of the new framework through extensive simulations and two data applications

‘I could eat a horse’: The Impact of Hyperbole on Product Sales on Short Video Platforms

How to design video content to promote sales remains unclear, despite the fact that short video has become a popular tool for brand advertising. This study proposes a novel content strategy of utilizing hyperbole in short videos and investigates its potential for driving product sales. Hyperbole, which involves exaggerating text, audio, and video features, can stimulate interest and catch audience\u27s attention, but may also hurt credibility if perceived as misleading. This study employs machine learning algorithms to measure multimodal hyperbole, and assesses its impact on actual sales from short videos. Our findings indicate that hyperbole has a positive and significant impact on product sales. However, the effect of hyperbole is weaker for products and influencers with higher reputation. This study contributes to the literature on advertising content and video marketing by providing insights for effective content design strategies to promote purchase behaviors in the short video context

A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation

Cold-start problem is a fundamental challenge for recommendation tasks. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and has shown great potential for cold-start recommendation. However, due to the over-smoothing problem, PT-GNN can only capture up to 3-order relation, which can not provide much useful auxiliary information to depict the target cold-start user or item. Besides, the embedding reconstruction task only considers the intra-correlations within the subgraph of users and items, while ignoring the inter-correlations across different subgraphs. To solve the above challenges, we propose a multi-strategy based pre-training method for cold-start recommendation (MPT), which extends PT-GNN from the perspective of model architecture and pretext tasks to improve the cold-start recommendation performance. Specifically, in terms of the model architecture, in addition to the short-range dependencies of users and items captured by the GNN encoder, we introduce a Transformer encoder to capture long-range dependencies. In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items. We train the GNN and Transformer encoders on these pretext tasks under the meta-learning setting to simulate the real cold-start scenario, making the model easily and rapidly being adapted to new cold-start users and items. Experiments on three public recommendation datasets show the superiority of the proposed MPT model against the vanilla GNN models, the pre-training GNN model on user/item embedding inference and the recommendation task

Genome mining for anti-CRISPR operons using machine learning

Motivation: Encoded by (pro-)viruses, anti-CRISPR (Acr) proteins inhibit the CRISPR-Cas immune system of their prokaryotic hosts. As a result, Acr proteins can be employed to develop more controllable CRISPR-Cas genome editing tools. Recent studies revealed that known acr genes often coexist with other acr genes and with phage structural genes within the same operon. For example, we found that 47 of 98 known acr genes (or their homologs) co-exist in the same operons. None of the current Acr prediction tools have considered this important genomic context feature. We have developed a new software tool AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known acr genes and their homologs. Results: AOminer is the first machine learning based tool focused on the discovery of Acr operons (AOs). A two-state HMM (hidden Markov model) was trained to learn the conserved genomic context of operons that contain known acr genes or their homologs, and the learnt features could distinguish AOs and non-AOs. AOminer allows automated mining for potential AOs from query genomes or operons. AOminer outperformed all existing Acr prediction tools with an accuracy¼0.85. AOminer will facilitate the discovery of novel anti-CRISPR operons