Can We `Feel' the Temperature of Knowledge? Modelling Scientific Popularity Dynamics via Thermodynamics

Just like everything in the nature, scientific topics flourish and perish. While existing literature well captures article's life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could `feel' topic's activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics' ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics' distinct evolutionary cycles

Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis

The demand of artificial intelligent adoption for condition-based maintenance strategy is astonishingly increased over the past few years. Intelligent fault diagnosis is one critical topic of maintenance solution for mechanical systems. Deep learning models, such as convolutional neural networks (CNNs), have been successfully applied to fault diagnosis tasks for mechanical systems and achieved promising results. However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis. As a novel idea, deep transfer learning (DTL) is created to perform learning in the target domain by leveraging information from the relevant source domain. Inspired by Wasserstein distance of optimal transport, in this paper, we propose a novel DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based Deep Transfer Learning (WD-DTL), to learn domain feature representations (generated by a CNN based feature extractor) and to minimize the distributions between the source and target domains through adversarial training. The effectiveness of the proposed WD-DTL is verified through 3 transfer scenarios and 16 transfer fault diagnosis experiments of both unsupervised and supervised (with insufficient labelled data) learning. We also provide a comprehensive analysis of the network visualization of those transfer tasks

Evaluation of the Bacterial Diversity in the Human Tongue Coating Based on Genus-Specific Primers for 16S rRNA Sequencing

The characteristics of tongue coating are very important symbols for disease diagnosis in traditional Chinese medicine (TCM) theory. As a habitat of oral microbiota, bacteria on the tongue dorsum have been proved to be the cause of many oral diseases. The high-throughput next-generation sequencing (NGS) platforms have been widely applied in the analysis of bacterial 16S rRNA gene. We developed a methodology based on genus-specific multiprimer amplification and ligation-based sequencing for microbiota analysis. In order to validate the efficiency of the approach, we thoroughly analyzed six tongue coating samples from lung cancer patients with different TCM types, and more than 600 genera of bacteria were detected by this platform. The results showed that ligation-based parallel sequencing combined with enzyme digestion and multiamplification could expand the effective length of sequencing reads and could be applied in the microbiota analysis

High Performance Matrix Multiplication on Many Cores

Abstract. Moore’s Law suggests that the number of processing cores on a single chip increases exponentially. The future performance in-creases will be mainly extracted from thread-level parallelism exploited by multi/many-core processors (MCP). Therefore, it is necessary to find out how to build the MCP hardware and how to program the paral-lelism on such MCP. In this work, we intend to identity the key archi-tecture mechanisms and software optimizations to guarantee high per-formance for multithreaded programs. To illustrate this, we customize a dense matrix multiplication algorithm on Godson-T MCP as a case study to demonstrate the efficient synergy and interaction between hard-ware and software. Experiments conducted on the cycle-accurate simu-lator show that the optimized matrix multiplication could obtain 97.1% (124.3GFLOPS) of the peak performance of Godson-T.

ICANet: a simple cascade linear convolution network for face recognition

Abstract Recently, deep convolutional networks have demonstrated their capability of improving the discriminative power compared with other machine learning method, but its feature learning mechanism is not very clear. In this paper, we present a cascaded linear convolutional network, based on independent component analysis (ICA) filters, named ICANet. ICANet consists of three parts: a convolutional layer, a binary hash, and a block histogram. It has the following advantages over other methods: (1) the network structure is simple and computationally efficient, (2) the ICA filter is trained with an unsupervised algorithm using unlabeled samples, which is practical, and (3) compared to deep learning models, each layer parameter in ICANet can be easily trained. Thus, ICANet can be used as a benchmark for the application of a deep learning framework for large-scale image classification. Finally, we test two public databases, AR and FERET, showing that ICANet performs well in facial recognition tasks

Transcription Factor AtOFP1 Involved in ABA-Mediated Seed Germination and Root Growth through Modulation of ROS Homeostasis in Arabidopsis

Ovate family proteins (OFPs) are valued as a family of transcription factors that are unique to plants, and they play a pluripotent regulatory role in plant growth and development, including secondary-cell-wall synthesis, DNA repair, gibberellin synthesis, and other biological processes, via their interaction with TALE family proteins. In this study, CHIP-SEQ was used to detect the potential target genes of AtOFP1 and its signal-regulation pathways. On the other hand, Y2H and BIFC were employed to prove that AtOFP1 can participate in ABA signal transduction by interacting with one of the TALE family protein called AtKNAT3. ABA response genes are not only significantly upregulated in the 35S::HAOFP1 OE line, but they also show hypersensitivity to ABA in terms of seed germination and early seedling root elongation. In addition, the AtOFP1-regulated target genes are mainly mitochondrial membranes that are involved in the oxidative–phosphorylation pathway. Further qRT-PCR results showed that the inefficient splicing of the respiratory complex I subunit genes NAD4 and NAD7 may lead to ROS accumulation in 35S::HA-AtOFP1 OE lines. In conclusion, we speculated that the overexpression of AtOFP1 may cause the ABA hypersensitivity response by increasing the intracellular ROS content generated from damage to the intima systems of mitochondria

Hardware Trojan Detection Based on Ordered Mixed Feature GEP

In the hardware Trojan detection field, destructive reverse engineering and bypass detection are both important methods. This paper proposed an evolutionary algorithm called Ordered Mixed Feature GEP (OMF-GEP), trying to restore the circuit structure only by using the bypass information. This algorithm was developed from the basic GEP through three sets of experiments at different stages. To solve the problem, this paper transformed the GEP by introducing mixed features, ordered genes, and superchromosomes. And the experiment results show that the algorithm is effective

Development of a Hadal Microbial In Situ Multi-Stage Filtering and Preserving Device

The unique environment of the hadal zone has created material circulation patterns and biological gene characteristics. Microbes play an irreplaceable role in the ocean ecological environment and material circulation due to their pervasiveness, abundance, and metabolic diversity. In this paper, we designed and developed a microbial sampling device that can be used in a depth of 10,000 m, with its working parts suitable for the full-sea depth. The multi-stage membrane realized the in situ multi-stage filtrations. The samples were in situ fixedly preserved by RNAlater storage solution. At the same time, we modeled and calculated the multi-stage membrane separation and filtration process, simulated the interception phenomenon of particles with different sizes passing through the multi-stage membrane area, and explored the influence of varying inlet velocities. A multi-stage membrane separation and filtration test system was built. The operational characteristics of different filters were compared and analyzed, and the appropriate filter material was selected according to the flow capacity and physical properties. A 100 MPa high-pressure test was carried out to check the device’s performance under a high-pressure environment. The sampler prototype was constructed and tested in the Mariana Trench. The results indicated that the device could work at the deepest point of the Mariana trench

Revealing the Pharmacological Mechanism of Acorus tatarinowii in the Treatment of Ischemic Stroke Based on Network Pharmacology

Aim. Stroke is the second significant cause for death, with ischemic stroke (IS) being the main type threatening human being’s health. Acorus tatarinowii (AT) is widely used in the treatment of Alzheimer disease, epilepsy, depression, and stroke, which leads to disorders of consciousness disease. However, the systemic mechanism of AT treating IS is unexplicit. This article is supposed to explain why AT has an effect on the treatment of IS in a comprehensive and systematic way by network pharmacology. Methods and Materials. ADME (absorbed, distributed, metabolized, and excreted) is an important property for screening-related compounds in AT, which were screening out of TCMSP, TCMID, Chemistry Database, and literature from CNKI. Then, these targets related to screened compounds were predicted via Swiss Targets, when AT-related targets database was established. The gene targets related to IS were collected from DisGeNET and GeneCards. IS-AT is a common protein interactive network established by STRING Database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analysed by IS-AT common target genes. Cytoscape software was used to establish a visualized network for active compounds-core targets and core target proteins-proteins interactive network. Furthermore, we drew a signal pathway picture about its effect to reveal the basic mechanism of AT against IS systematically. Results. There were 53 active compounds screened from AT, inferring the main therapeutic substances as follows: bisasaricin, 3-cyclohexene-1-methanol-α,α,4-trimethyl,acetate, cis,cis,cis-7,10,13-hexadecatrienal, hydroxyacoronene, nerolidol, galgravin, veraguensin, 2′-o-methyl isoliquiritigenin, gamma-asarone, and alpha-asarone. We obtained 398 related targets, 63 of which were the same as the IS-related genes from targets prediction. Except for GRM2, remaining 62 target genes have an interactive relation, respectively. The top 10 degree core target genes were IL6, TNF, IL1B, TLR4, NOS3, MAPK1, PTGS2, VEGFA, JUN, and MMP9. There were more than 20 terms of biological process, 7 terms of cellular components, and 14 terms of molecular function through GO enrichment analysis and 13 terms of signal pathway from KEGG enrichment analysis based on P<0.05. Conclusion. AT had a therapeutic effect for ischemic via multicomponent, multitarget, and multisignal pathway, which provided a novel research aspect for AT against IS

Mechanisms of improved aortic stiffness by arotinolol in spontaneously hypertensive rats.

OBJECTIVES: This study investigates the effects on aortic stiffness and vasodilation by arotinolol and the underlying mechanisms in spontaneously hypertensive rats (SHR). METHODS: The vasodilations of rat aortas, renal and mesenteric arteries were evaluated by isometric force recording. Nitric oxide (NO) was measured in human aortic endothelial cells (HAECs) by fluorescent probes. Sixteen-week old SHRs were treated with metoprolol (200 mg·kg-1·d⁻¹), arotinolol (30 mg·kg-1·d⁻¹) for 8 weeks. Central arterial pressure (CAP) and pulse wave velocity (PWV) were evaluated via catheter pressure transducers. Collagen was assessed by immunohistochemistry and biochemistry assay, while endothelial nitric oxide synthase (eNOS) and eNOS phosphorylation (p-eNOS) of HAECs or aortas were analyzed by western blotting. RESULTS: Arotinolol relaxed vascular rings and the relaxations were attenuated by Nω-nitro-L-arginine methyl ester (L-NAME, NO synthase inhibitor) and the absence of endothelium. Furthermore, arotinolol-induced relaxations were attenuated by 4-aminopyridine (4-AP, Kv channels blocker). Arotinolol produced more nitric oxide compared to metoprolol and increased the expression of p-eNOS in HAECs. These results indicated that arotinolol-induced vasodilation involves endothelium-derived NO and Kv channels. The treatement with arotinolol in 8 weeks, but not metoprolol, markedly decreased CAP and PWV. Biochemistry assay and immunohistochemistry showed that aortic collagen depositions in the arotinolol groups were reduced compared with SHRs with metoprolol. Moreover, eNOS phosphorylation was significantly increased in aortinolol-treated SHR compared with SHRs with metoprolol. CONCLUSIONS: Arotinolol improves arterial stiffness in SHR, which involved in increasing NO and decreasing collagen contents in large arteries