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BackgroundSynovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.MethodsThe OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein–protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.ResultsOA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.ConclusionsWe developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy.

A lubricant is a substance that lessens friction and wear between mechanical parts. A good quality lubricating oil is required for protecting the mechanical equipment and reducing energy consumption. Its performance index (friction coefficient) could be measured by the four-ball wear test. For the morphological analysis of testing data from the four-ball wear test, determination of wear angle is very important for measuring the wear spot diameter, validity of the four-ball wear test and recognition of abnormal wear scar. On the basis of character analysis and image processing technology, a novel wear angle determination algorithm for wear scar is presented in this article. The direction of the wear scar (wear angle) is determined based on angle transformation and grey consistency (the smallest change of grey level). Simulation results show that the algorithm has high precision, good robustness and less time consuming. The average absolute error of our method is 0.8° and the absolute error of 95% of samples is

Road segment representation is important for evaluating travel time, route recovery and traffic anomaly detection. Recent works mainly consider topology information of road network based on graph neural network, while dynamic character of topology relationship is usually ignored. Especially, the relationship between road segments is evolving with time elapsing. To obtain road segment representation based on dynamic spatial information, we propose a model named temporal and spatial deep graph infomax network (ST-DGI). It not only captures road topology relationship, but also denotes road segment representation under different time intervals. Meanwhile, the global traffic status/flow will also affect local road segments’ traffic situation. Our model would learn the mutual relationship between them, with maximizing mutual information between road segment (local) representation and traffic status/flow (global) representation. Furthermore, it would make road segment representation more distinguishable by this kind of unsupervised learning, and be helpful for downstream application. Extensive experiments are conducted on two important traffic datasets. Compared with the state-of-the-arts models, the experiment results demonstrate the superior effectiveness of our model.

The gas spindle is a key component in wafer thinning equipment. The performance of the gas spindle directly affects the wafer processing quality. By establishing the mathematical model of the gas spindle thrust bearing flow field, the gas flow process in the orifice and gas film is studied, and the bearing capacity distribution of the thrust bearing is obtained. The variation of the bearing characteristics of the thrust bearing with the thickness of the gas film is analyzed in detail. Between 1μm ~ 11μm, the bearing capacity declines gently, and the capacity changes by only 2%. Between 12μm and 30μm, the bearing capacity has a significant downward trend, from 6800N to 1800N, a decrease of 73%. After 31μm, the change in bearing capacity is attributed to a gradual change. Before 18μm, the stiffness value increases with the increase of the thickness of the gas film, and increases rapidly. The maximum stiffness value appears at 18μm, which is 376N/μm. In addition, compared with the increasing trend of stiffness value, the downward trend is relatively gentle. Through the above research, it provides a theoretical reference for the development of thinning equipment.

From Descartes to Spinoza, Western philosophers have attempted to propose an axiomatic systemization of ethics. However, without consensus on the contents and objects of ethics, the system remains incomplete. This four-volume set presents a model that highlights a Chinese philosopher’s insights on ethics after a 22 year study. Three essential components of ethics are examined: metaethics, normative ethics, and virtue ethics. This volume is the second part of the discussion on normative ethics. The author analyzes humanity, liberty, justice, happiness, and systems of moral rules. He puts forward 26 value standards that construct a system of measuring state instruction; reveals the relationship between humanity, liberty and justice; puts forward three objective laws of happiness; and discusses the goodness of important moral rules, such as honesty, self-respect and courage. This set is an essential read for students and scholars of ethics and philosophy in general.

Amphiphilic Janus gold nanoparticles (GNPs) are prospected to encapsulate drug molecules in cancer therapy and to serve as heterogeneous catalysts at oil/water interfaces, where Janus GNPs with different sizes are required. In this work, multiple-sized precursor GNPs were synthesized by seeded growth method protected with tris(hydroxymethyl)phosphine oxide (THPO) ligand molecule, and a ligand exchange reaction with triphenylphosphine (PPh3) at the toluene/water interface was employed to prepare amphiphilic Janus GNPs. UV-vis and transmission electron microscopy (TEM) analyses indicate that the as-prepared GNPs are nanocrystals with average diameters of 2.3 nm, 9.5 nm, 16.1 nm and 18.8 nm, respectively. Contact angle, Raman and X-ray photonic spectroscopy (XPS) analyses reveal that the self-assembled GNP films exhibit hydrophilic on one side and hydrophobic on another, owing to the adsorption of hydrophilic ligands (THPO and THP) and a similar amount of hydrophobic ligands (PPh3 and PPh3O). Angle-resolved XPS analysis further demonstrates that the individual GNPs actually possess hydrophilic and hydrophobic compartments on the surface, which regularly packed by supramolecular interactions at toluene/water interface to form the self-assembled GNP films.

In the point-to-point high-speed positioning process of the IC packaging mechanism, the nonlinear characteristics of the servo system influence the performance of the servo system to a different extent, which may easily cause resonance of the system and reduce die attach efficiency of the packaging equipment. Based on the above problems, this paper proposes a cross-global artificial bee PID algorithm to suppress mechanical resonance at the load end. Firstly, the reasons of resonance formation and the influence on the servo system are analyzed. Secondly, the modal of the swing arm mechanism in the IC packaging mechanism is analyzed. Finally, the experimental platform is built. The results show that the resonance of the load end is effectively controlled and the positioning efficiency is improved by 9.1%.

Based on bidirectional Erbium-doped fiber amplifier (B-EDFA), which can amplify not only forward pulse light but also backward Rayleigh scattering light, a long distance (123 km) Φ-OTDR system is demonstrate. In this system, four B-EDFAs are employed to extend sensing length. Meanwhile, by using the difference value of upper-envelope and lower-envelope of Rayleigh scattering trace, the intrusion with SNR of 5.45 dB、3.64 dB and 4.51 dB at three test points of sensing fiber is extracted.

(2004). Come Back from Overseas! The 'Talent Reflux' Era Has Arrived. Chinese Education & Society: Vol. 37, No. 2, pp. 7-11.

According to the 31th statistical reports of China Internet network information center (CNNIC), by the end of December 2012, the number of Chinese netizens has reached 564 million, and the scale of mobile Internet users also reached 420 million. But when the network brings great convenience to people's life, it also brings huge threat in the life of people. So through collecting and analyzing the information in the computer system or network we can detect any possible behaviors that can damage the availability, integrity and confidentiality of the computer resource, and make timely treatment to these behaviors which have important research significance to improve the operation environment of network and network service. At present, the Neural Network, Support Vector machine (SVM) and Hidden Markov Model, Fuzzy inference and Genetic Algorithms are introduced into the research of network intrusion detection, trying to build a healthy and secure network operation environment. But most of these algorithms are based on the total sample and it also hypothesizes that the number of the sample is infinity. But in the field of network intrusion the collected data often cannot meet the above requirements. It often shows high latitudes, variability and small sample characteristics. For these data using traditional machine learning methods are hard to get ideal results. In view of this, this paper proposed a Generalized Multi-Kernel Learning method to applied to network intrusion detection. The Generalized Multi-Kernel Learning method can be well applied to large scale sample data, dimension complex, containing a large number of heterogeneous information and so on. The experimental results show that applying GMKL to network attack detection has high classification precision and low abnormal practical precision.

This dissertation work is motivated by the challenges in high-speed precision output tracking and transition in emerging applications, particularly for nonminimum-phase systems. Although fundamental performance limits to achieving precision output tracking with feedback control alone have been studied and quantified, for nonminimum-phase systems, exact output tracking cannot be achieved by using feedback control alone, due to the limits imposed by the nonminimum-phase zeros of the system. On the feedforward control side, the stable inversion theory solved the challenging output tracking problem and achieved exact tracking of a given desired output trajectory for nonminimum-phase systems (linear and nonlinear). The obtained solution, however, is noncausal and requires the entire desired trajectory to be known a priori. This noncausality constraint has been alleviated through the development of the preview-based inversion approach. Therefore, the stable-inversion framework provides an effective approach to output tracking of nonminimum-phase systems. Challenges, however, still exist in the existing stable-inversion theory for continuously more stringent control requirements. For example, the control problem of nonperiodic tracking-transition switching with preview for nonminimum-phase systems cannot be satisfactorily addressed by using the existing techniques. Another challenge in the existing stable-inversion approach is that as a feedforward control technique, it can be sensitive to the system dynamics uncertainties. Finally, the demanding online computation involved in the preview-based stable-inversion technique hinders the application of this approach in the presence of limited computation power. Therefore, these challenges, as magnified in applications of high-speed nano-manipulation and nano-fabrication, motivate the research work of this dissertation. First, the problem of nonperiodic tracking-transition switching with preview is considered. In the proposed preview-based optimal output tracking and transition (POOTT) approach, the optimal desired output trajectory for the transition sections is designed through direct minimization of the output energy, and the needed control input is obtained to maintain the smoothness of system state across all tracking-transition switching instants by using a preview-based stable-inversion approach. The needed preview time is quantified, and the recently-developed optimal preview-based inversion approach is also incorporated to minimize the amount of preview time. Secondly, a B-spline-decomposition (BSD)-based approach to output tracking with preview is developed for nonminimum-phase systems, that not only substantially reduces the dynamics uncertainty effect on tracking performance, but also minimizes the online demanding computation. The BSD approach is illustrated through simulation study of a nanomanipulation application using a nonminimum-phase piezo actuator model, and then further demonstrated by a 2D nanomanipulation in experiments using AFM. Finally, a multi-axis inversion-based iterative control (MAIIC) approach is developed to compensate for the dynamics coupling in multi-axis motion during high-speed nanofabrication. By using this advanced control technique, precision position control of the probe with respect to the sample substrate can be achieved during highspeed, large-range multi-axis nanofabrication. Particularly, the cross-axis dynamics coupling effect on the output tracking can be compensated for during the iterative learning process with no additional steps to learn the cross-coupling effect separately. The MAIIC approach is illustrated through experiments by implementing it to fabricate two Chinese characters pattern via mechanical scratching on a gold-coated silicon sample surface at high speed. The research work of this dissertation addresses the limits and further extends the inversion-based control techniques for high-speed precision tracking/transition in emerging applications, particularly, the challenges involved in output tracking with non-periodic tracking-transition switching, accounting for dynamics uncertainty and demanding computation requirements for nonminimum-phase systems, and cross-axis coupling in high-speed multi-axis motion. The experimental part of the work demonstrates and illustrates the efficacy of the proposed control techniques.

Based on the difference between reconstruction and reconfiguration of distribution network, a mathematical model of distribution network reconstruction which sets minimal economic losses as its goal is presented. At last, three main options are made to achieve economic reconstruction of distribution network in general. Accordingly, the algorithm which regards minimize economic losses as heuristic information in the course of search on three major programs on specific examples is described, in order to find a distribution network of economic reconstruction programs and reduce distribution network reconstruction of capital investments.

Perkinsus marinus, a protozoan parasite of the eastern oyster Crassostrea virginica, has devastated natural and farmed oyster populations along the Atlantic and Gulf coasts of the United States. It is classified as a member of the Perkinsozoa, a recently established phylum considered close to the ancestor of ciliates, dinoflagellates, and apicomplexans, and a key taxon for understanding unique adaptations (e.g. parasitism) within the Alveolata. Despite intense parasite pressure, no disease-resistant oysters have been identified and no effective therapies have been developed to date. To gain insight into the biological basis of the parasite's virulence and pathogenesis mechanisms, and to identify genes encoding potential targets for intervention, we generated >31,000 5' expressed sequence tags (ESTs) derived from four trophozoite libraries generated from two P. marinus strains. Trimming and clustering of the sequence tags yielded 7,863 unique sequences, some of which carry a spliced leader. Similarity searches revealed that 55% of these had hits in protein sequence databases, of which 1,729 had their best hit with proteins from the chromalveolates (E-value ��� 1e-5). Some sequences are similar to those proven to be targets for effective intervention in other protozoan parasites, and include not only proteases, antioxidant enzymes, and heat shock proteins, but also those associated with relict plastids, such as acetyl-CoA carboxylase and methyl erythrithol phosphate pathway components, and those involved in glycan assembly, protein folding/secretion, and parasite-host interactions. Our transcriptome analysis of P. marinus, the first for any member of the Perkinsozoa, contributes new insight into its biology and taxonomic position. It provides a very informative, albeit preliminary, glimpse into the expression of genes encoding functionally relevant proteins as potential targets for chemotherapy, and evidence for the presence of a relict plastid. Further, although P. marinus sequences display significant similarity to those from both apicomplexans and dinoflagellates, the presence of trans-spliced transcripts confirms the previously established affinities with the latter. The EST analysis reported herein, together with the recently completed sequence of the P. marinus genome and the development of transfection methodology, should result in improved intervention strategies against dermo disease.