Topic-based analysis for technology intelligence

University of Technology Sydney. Faculty of Engineering and Information Technology.Since the past several decades, scientific literature, patents and other semi-structured technology indicators have been generating and accumulating at a very rapid rate. Their growth provides a wealth of information regarding technology development in both the public and private domain. However, it has also caused increasingly severe information overload problems whereby researchers, analysts and decision makers are not able to read, summarize and understand massive technical documents and records manually. The concept and tools of technology intelligence aims to handle this issue. In the current technology intelligence research, one of the big challenges is that, the frameworks and applications of existing technology intelligence conducted semantic content analysis and temporal trend estimation separately, lacking a comprehensive perspective on trend analysis of the detailed content within an area. In addition, existing research of technology intelligence is mainly constructed on the fundamentals of semantic properties of the semi-structured technology indicators; however, single keywords and their ranking alone, are too general or ambiguous to represent complex concepts and their corresponding temporal patterns. Thirdly, systematic post-processing, forecasting and evaluation on both content analysis and trend identification outputs are still in great demand, for diverse and flexible technological decision support and opportunity discovery. This research aims to handle these three challenges in both theoretical and practical aspects. It first quantitatively defines and presents temporal characteristics and semantic properties of typical semi-structured technology indicators. Then this thesis proposes a framework of topic-based technology intelligence, with three main functionalities, including data-driven trend identification, topic discovery and comprehensive topic evaluation, to synthetically process and analyse technological publication count sequence, textual data and metadata of target technology indicators. To achieve the three functionalities, this research proposes an empirical technology trend analysis method to extract temporal trend turning points and trend segments, which help with producing a more reasonable time-based measure; a topic-based technological forecasting method to first discover and characterize the semantic knowledge underlying in massive textual data of technology indicators, meanwhile estimating the future trends of the discovered topics; a comprehensive topic evaluation method that links metadata and discovered topics, to provide integrated landscape and technological insight in depth. In order to demonstrate the proposed topic-based technology intelligence framework and all the related methods, this research presents case studies with both patents and scientific literature. Experimental results on Australian patents, United States patents and scientific papers from Web of Science database, showed that the proposed framework and methods are well-suited in dealing with semi-structured technology indicators analysis, and can provide valuable topic-based knowledge to facilitate further technological decision making or opportunity discovery with good performance

How Embeddedness Affects the Evolution of Collaboration: The Role of Knowledge Stock and Social Interactions

Science and technology are becoming increasingly collaborative. This paper aims to explore the factors and mechanisms that impact the dynamic changes of collaborative innovation networks. We consider both collaborative interactions of organizations and their knowledge element exchanges to reveal how social and knowledge network embeddedness affects the collaboration dynamics. Knowledge elements are extracted to present the core concepts of scientific and technical information, overcoming the limitations of using predefined categorizations such as IPC when representing the content. Based on multiple collaboration and knowledge networks, we then conduct a longitudinal analysis and apply a stochastic actor-oriented model (SAOM) to model network dynamics over different periods. The influence of network features and structures, individual node characteristics, and various dimensions of proximity on collaboration dynamics is tested and analyzed.Comment: 2 pages, 1 figure. Conference presentatio

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method

Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: (1) data availability: real-time OD flow is not available during the prediction; (2) data dimensionality: the dimension of the OD flow is much higher than the cardinality of transportation networks; (3) data sparsity: URT OD flow is spatiotemporally sparse. There is a great need to develop novel OD flow forecasting method that explicitly considers the unique characteristics of the URT system. To this end, a channel-wise attentive split-convolutional neural network (CAS-CNN) is proposed. The proposed model consists of many novel components such as the channel-wise attention mechanism and split CNN. In particular, an inflow/outflow-gated mechanism is innovatively introduced to address the data availability issue. We further originally propose a masked loss function to solve the data dimensionality and data sparsity issues. The model interpretability is also discussed in detail. The CAS-CNN model is tested on two large-scale real-world datasets from Beijing Subway, and it outperforms the rest of benchmarking methods. The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.Comment: This paper has been accepted by the Transportation Research Part C: Emerging Technologies as a regular pape

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202

Neuroprotective Effects and Mechanism of β

Emerging evidence suggests that activated astrocytes play important roles in AD, and β-asarone, a major component of Acorus tatarinowii Schott, was shown to be a potential therapeutic candidate for AD. While our previous study found that β-asarone could improve the cognitive function of rats hippocampally injected with Aβ, the effects of β-asarone on astrocytes remain unclear, and this study aimed to investigate these effects. A rat model of Aβ1–42 (10 μg) was established, and the rats were intragastrically treated with β-asarone at doses of 10, 20, and 30 mg/kg or donepezil at a dose of 0.75 mg/kg. The sham and model groups were intragastrically injected with an equal volume of saline. Animals were sacrificed on the 28th day after administration of the drugs. In addition, a cellular model of Aβ1–42 (1.1 μM, 6 h) was established, and cells were treated with β-asarone at doses of 0, 2.06, 6.17, 18.5, 55.6, and 166.7 μg/mL. β-Asarone improved cognitive impairment, alleviated Aβ deposition and hippocampal damage, and inhibited GFAP, AQP4, IL-1β, and TNF-α expression. These results suggested that β-asarone could alleviate the symptoms of AD by protecting astrocytes, possibly by inhibiting TNF-α and IL-1β secretion and then downregulating AQP4 expression

Molecular characterization, expression pattern and immunologic function of CD82a in large yellow croaker (Larimichthys crocea)

Visceral white spot disease (VWND) caused by Pseudomonas plecoglossicida poses a major threat to the sustainable development of large yellow croaker (Larimichthys crocea) aquaculture. Genome-wide association analysis (GWAS) and RNA-seq research indicated that LcCD82a play an important role in resistance to visceral white spot disease in L. crocea, but the molecular mechanism of LcCD82a response to P. plecoglossicida infection is still unclear. In this study, we cloned and validated the Open Reading Frame (ORF) sequence of LcCD82a and explored the expression profile of LcCD82a in various tissues of L.crocea. In addition, two different transcript variants (LcCD82a-L and LcCD82a-S) of LcCD82a were identified that exhibit alternative splicing patterns after P. plecoglossicida infection, which may be closely related to the immune regulation during pathogenetic process of VWND. In order to explore the function of LcCD82a, we purified the recombinant protein of LcCD82a-L and LcCD82a-S. The bacterial agglutination and apoptosis function analysis showed that LcCD82a may involve in extracellular bacterial recognition, agglutination, and at the same time participate in the process of antigen presentation and induction of cell apoptosis. Collectively, our studies demonstrate that LcCD82a plays a crucial role in regulating apoptosis and antimicrobial immunity

Phylogenomic analysis of the chloroplast genome of the green-tide forming macroalga Ulva intestinalis Linnaeus (Ulvophyceae, Chlorophyta)

Ulva intestinalis Linnaeus 1753 (Ulvophyceae, Chlorophyta) is a marine green macroalga that is distributed on coasts of the Yellow Sea and the Bohai Sea in China. Here, the complete chloroplast genome of U. intestinalis was constructed and analyzed comparatively. The chloroplast genome of U. intestinalis is a 99,041-bp circular molecule that harbors a total of 112 genes including 71 protein-coding genes (PCGs), 26 transfer RNA genes (tRNAs), three ribosomal RNA genes (rRNAs), three free-standing open reading frames (orfs) and nine intronic orfs, and ten introns in seven genes (atpA, infA, psbB, psbC, petB, rrnL, and rrnS). The maximum likelihood (ML) phylogenomic analysis shows that U. intestinalis firstly groups with Ulva compressa, and then these two species together with the Ulva australis–Ulva fenestrata–Ulva rotundata subclade form a monophyletic clade, Ulva lineage II. U. intestinalis chloroplast genome is the only one in Ulva lineage II where the reversal of a collinear block of two genes (psbD–psbC) did not occur, and its genome structure is consistent with that of most chloroplast genomes in Ulva lineage I, indicating that the similarity of genome structure is not completely related to the genetic relationship of Ulva species. Our genomic data will facilitate the development of specific high-resolution chloroplast molecular markers for rapid identification of U. intestinalis, and help us understand its population diversity and genetic characteristics on a global scale

Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response

Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs) between GSCs with high and low GSScores were used to cluster lower-grade glioma (LGG) samples into three stemness subtypes. Differences in clinicopathological characteristics, including survival, copy number variations, mutations, tumor microenvironment, and immune and chemotherapeutic responses, among the three LGG stemness-associated subtypes were identified. Using machine learning methods, we further identified genes as subtype predictors and validated their performance using the CGGA datasets. In the current study, we identified a GSScore that correlated with LGG chemotherapeutic response. Through the score, we also identified a novel classification of the LGG subtype and associated subtype predictors, which might facilitate the development of precision therapy

Formal versus Informal Institutional Distance Impact on Strategic Assets Seeking Foreign M and A

This study intends to probe into the influence of different specific dimensions under formal versus informal institutional distances on sub-motives of emerging-market (EM) multinational enterprises (MNEs) strategic-asset seeking (SAS), in order to gain insights into the understanding of new trend of outward foreign direct investment (OFDI) from emerging economies like China. By adopting multinomial logistic regression through accessing to both macro and firm level data, we find that EMNEs would focus more on formal rather than informal institutional distance for seeking all types of strategic assets. Simultaneous results show that with higher formal institutional distance between home and host countries, the possibility of acquiring both patents and trademarks has significantly increased. While the informal distance is solely correlated with the motive of seeking trademarks only