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In recent years, the demand for indoor positioning systems has grown rapidly with regard to location-based services. As a cost-effective choice, WiFi-based indoor positioning has attracted great increasing research attentions because it does not require external devices installed in the target environment. Although extensive research has been conducted on WiFi fingerprint matching, the problem of automatically adapting radio maps to fresh signal space environment still exists. The traditional methods often conduct site surveys regularly to update the outdated radio maps, which is time consuming and laborious. In this work, we propose an indoor positioning system AAIMSS to automatically update the radio maps based on an enhanced transfer learning (TL) approach with altered access points (APs) identification and mapping space searching. In the system, the proposed TL approach removes the outlier features and then searches a more accurate mapping space between the original radio map and crowdsourcing data. Our lightweight solution does not rely on additional devices and inertial sensors with high-power consumption. A set of experiments has been conducted in a teaching building to evaluate AAIMSS. The results show that this AAIMSS is robust to locate users in a dynamic environment. The average positioning accuracy achieves 2.5m, which significantly outperforms the positioning strategies with the original radio map by 65.3%, the radio map by directly removing the altered APs by 19.4%, and the radio map by the traditional TL by 85.2%.

The Li dendrite issue is the major barrier that limits the implement of Li metal anode practically, especially at high current density. From the perspective of the nucleation and growth mechanism of the Li dendrite, we rationally develop a novel Prussian blue analogues (PBA)-derived separator, where tuning the metal ions bestows the PBAs with open metal site to confine anion movement and thereby afford a high Li

Organic contamination has been recognized as a matter of concern, and simultaneous monitoring of various organic contaminants in the complex environment has always been a part of the efforts of scientists. In the present study, settled house dust (SHD) was selected to establish HPLC fingerprint method for simultaneously evaluating organic matters in an indoor environment. The relative standard deviations (RSDs) of the relative retention time (RRT) and relative peak area (RPA) for precision were 2.46% and 0.47%, respectively, while the RSDs of RRT and RPA for repeatability were 2.51% and 0.50%, respectively, and for stability within 24 h were 2.51% and 0.49%, respectively, which indicated good reliability of the method. Then, the method was used to evaluate the trend of indoor organic matters of Yunyan district (urban) and Gangou village (rural) from 2012 to 2014. The results showed the overall organic matters of Yunyan did not change significantly within 2 years, while the organic matters of Gangou village were a little aggravated by the appearance of some suspicious matters. Our study suggests a method with the capability of simultaneously evaluating various organic matters and a possibility for detecting those that are not currently on the list of priority organic contaminants.

In recent years, Wi-Fi-based indoor positioning has attracted increasing research attention due to its ubiquitous deployment. Although extensive research has been conducted on Wi-Fi fingerprint-based positioning, especially, in complex environments and long-term deployments, the automatic adaptation of radio map has not been fully studied and the problems remain open. When the positions of some Access Points (APs) change, the traditional approach regularly conducts site surveying which is time-consuming and labor-intensive. In this paper, we propose a crowdsourcing indoor positioning approach based on ensemble learning for automatic Altered APs Identification and Fingerprints Updating, namely AAIFU. We propose an algorithm to detect and identify the altered APs in crowdsourcing data. After getting the altered APs, we use the relationship between the received signal strength values of the altered APs and the unaltered APs in the crowdsourcing data to train a prediction model used to update the radio map. We also handle the device diversity on all the processes of AAIFU. Our proposed solution is light-weight which does not rely on additional infrastructure and inertial sensors with high power consumption. The comprehensive experiments have been carried out in our teaching building to evaluate the effectiveness of AAIFU. The results show that our proposed AAIFU can effectively adapt the radio map to the movement of APs and improve positioning accuracy. Correspondingly, we achieve an average positioning accuracy of 2.6m which outperforms the fingerprinting approach with the original radio map by 63.9%.

Motivation Prediction of cancer patient’s response to therapeutic agent is important for personalized treatment. Because experimental verification of reactions between large cohort of patients and drugs is time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines. Results In this study, we present a novel approach named NRL2DRP, which integrates protein–protein interactions and captures similarity of cell lines’ functional contexts, to predict drug responses. Through integrating genomic aberrations and drug responses information with protein–protein interactions, we construct a large response-related network, where the neighborhood structure of cell line provides a functional context to its therapeutic responses. Representation vectors of cell lines are extracted through network representation learning method, which could preserve vertices’ neighborhood similarity and serve as features to build predictor for drug responses. The predictive performance of NRL2DRP is verified by cross-validation on GDSC dataset and methods comparison, where NRL2DRP achieves AUC > 79% for half drugs and outperforms previous methods. The validity of NRL2DRP is also supported by its effectiveness on uncovering accurate novel relationships between cell lines and drugs. Lots of newly predicted drug responses are confirmed by reported experimental evidences. Availability and implementation The code and documentation are available on https://github.com/USTC-HIlab/NRL2DRP. Supplementary information Supplementary data are available at Bioinformatics online.

This paper investigates the problem of clutter cancellation in airborne passive radar. To avoid the cancellation of targets falling in the clutter Doppler band, a robust space-time cancellation algorithm is proposed for the sidelooking airborne passive radar. Making use of the spatial-Doppler dependence of clutter, this algorithm cancels clutter along the clutter ridge and thus avoids the cancellation of targets. In addition, to ensure the performance in the existence of sensor errors, the adaptive estimation of sensor errors is introduced, making the proposed algorithm robust in practice. Simulation results with the experimental data show that clutter can be cancelled effectively with the proposed algorithm and signal-to-noise ratio (SNR) losses of targets in clutter Doppler band are slight, for example the losses are within 3dB for targets with radial velocity greater than 5m/s in the existence of the sensor errors, of which the standard deviation is 0.05.

An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP.

A simple and innovative processing method for fabricating anti-static ceramics has been developed based on Fe-infiltration in sintered tetragonal polycrystalline zirconia doped with 3 mol% yttria (3Y-TZP). The 3Y-TZP samples infiltrated by Fe in a reducing atmosphere showed both excellent anti-static and mechanical properties. Effects of infiltration temperature and time on the surface resistivity and Vickers hardness of the prepared samples were investigated. The surface resistivity and hardness were found to decrease with increases in the infiltration time and temperature. For a typical sample infiltrated at 1000 °C for 4 h, the surface resistivity decreased to 10 7 Ω/□, and the Vickers hardness was 11.2 GPa. The microstructure, chemical composition, and anti-static mechanism were investigated by XRD, SEM and XPS. The results indicated that there was a phase transformation from t -ZrO 2 to m -ZrO 2 during the infiltration process. It was also found that Fe, Fe 3 O 4 and FeO were located in the infiltration layer, conferring an antistatic property to the ZrO 2 ceramics.

An innovative processing method for regulating the surface resistivity of zirconia ceramic has been developed based on solution infiltration in porous preform body. In this study, effects of infiltration solution concentration on the surface resistivity and Vickers hardness were investigated. It was shown that the surface resistivity decreased with the increase of infiltration concentration, while the surface hardness was not significantly influenced. For a typical sample infiltrated in 3 mol/L Fe(NO 3 ) 3 ·9H 2 O solution for 2 h, the surface resistivity is reduced to 10 4 Ω/□, while the Vickers hardness was kept at 13 GPa. The content of element Fe decreased gradually from the surface to the core of the samples. The status of element Fe was verified as FeO and Fe 2 O 3 . About 49.4% of the element Fe was in the status of FeO, which was the conductive composition for anti-static purpose.

lncRNA plays important roles in many biological and pathological processes. lncRNAprotein interaction is the most common way of lncRNA performing their functions. Thus, predicting lncRNAprotein interaction is very significant to understand the nature of lncRNA. Earlier methods to predict RNAprotein interaction always adopt classification algorithms using features extracted from RNA and protein themselves. But their performance is not good enough in lncRNAprotein interaction prediction because of lncRNAs low conservation. In this paper, we try to use information implicit in the topologies of biological network associated with lncRNA to solve this problem. Firstly we construct a heterogeneous lncRNAprotein network which incorporate protein interaction information. We use an algorithm called HeteSim which can evaluate relevance between heterogeneous objects to perform relevance search in the lncRNA and protein heterogeneous network. The relevance search results are used to help the identification of lncRNAprotein interactions. Proteinprotein information is introduced to help in solving lncRNAprotein interaction prediction problem.The heterogeneous lncRNAprotein network is constructed.An algorithm which can measure relevance between heterogeneous objects is used to predict lncRNAprotein interaction.

The purpose of this study was to investigate polycyclic aromatic hydrocarbons (PAHs) accumulation in leaves of different plant species growing in the neighborhood of coal-fired power plant (CPP) and to identify potential bioindicators for PAHs pollution monitoring. The study was performed in 8 sites in the surrounding areas of CPP. PAHs concentrations in leaves of 21 plant species growing within 1 km of CPP ranged from 0.043 to 4.52 µg g−1. A higher mean concentration of PAHs was found in leaves of perennial herbs and shrubs compared with annual herbs and trees. Herbaceous plants had the highest concentrations of 5–6 rings PAHs, and 4-ring PAHs mainly existed in shrubs. For 2- to 3-rings PAHs, there was no significant difference among herbaceous plants trees and shrubs. Then, four representative plants were further chosen for investigating the effect of CPP on the spatial distribution patterns of PAH compounds. No distinct difference in the level of 2- to 3-rings PAHs was observed on Broussonetia kaempferi Sieb, whereas 4 rings, 5–6 rings, and Σ16PAHs had regional statistical differences. PAHs in Kalimeris indica (L.) Sch.-Bip had significant regional statistical differences. With the change of distance, the concentration of PAHs showed a significant decrease. Taraxacum mongolicum tended to capture the largest amount of both total PAHs and 5- to 6-ring PAHs, especially to BaP. These results could improve scientific evidence for the screening of bioindicators, in particular, T. mongolicum could be a priority.

The ceramic injection molding technique was used in the gas-pressure sintering of ultra-fine Si3N4 powder. The feedstock’s flowability, debinding rate, defect evolution, and microstructural evolution during production were explored. The results show that the solid volume loading of less than 50vol% and the surfactant mass fraction of 6wt% result in a perfect flowability of feedstock; this feedstock is suitable for injection molding. When the debinding time is 8 h at 40°C, approximately 50% of the wax can be solvent debinded. Defects detected during the preparation are traced to improper injection parameters, mold design, debinding parameters, residual stress, or inhomogeneous composition distribution in the green body. The bulk density, Vickers hardness, and fracture toughness of the gas-pressure-sintered Si3N4 ceramic reach 3.2 g/cm3, 16.5 GPa, and 7.2 MPa·m1/2, respectively.

Cognitive radio networks (CRNs) are applied to solve spectrum scarcity. In this study, the authors propose an efficient power control game to improve its performance based on outage probability of primary user in a spectrum-underlay CRN. The interference threshold deduced from outage probability and normalised signal to interference plus noise ratio are used to develop a novel non-linear pricing function, which is a key element of obtaining Pareto improvement in non-cooperative power control game. In addition, an efficient swarm intelligent algorithm originated from eco-group activities is designed in detail to accelerate the convergence speed and improve the energy-efficiency. Theoretical analysis and simulation results are presented to prove the effectiveness and superiority of the proposed power control game.

LncRNA plays important roles in many biological and pathological processes. LncRNA-protein interaction is the most significant way of lncRNA performing function. Thus, identifying lncRNA-protein interaction has huge significance. The previous methods of lncRNA-protein interaction prediction focus on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological network associated with lncRNA. In this paper, we adopt Hetesim algorithm which can evaluate the relevance between lncRNA and protein based on the heterogeneous lncRNA-protein network to identify lncRNA-protein interacitons.

Chitin has been subjected to regiospecific oxidation at C-6 with NaOCl in the presence of 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) and NaBr at room temperature in aqueous solution to yield fully soluble 6-carboxychitin. Several physical and chemical pretreatments of the original chitin changed its crystal structure from α to β. After this pretreatment of the chitin the oxidation was easier to effect and the yield was greatly increased from 36% to 97% and the molecular weight was about 4 × 10 4 which was ca. 8 times that from the unpretreated chitin. Infrared spectroscopy (IR), X-ray diffraction, 13 C NMR and solid-state NMR measurements, and thermal analysis techniques were used to characterize their molecular structures. The moisture absorption and retention abilities of these types of compounds were compared with those of sodium hyaluronan and carboxymethyl chitosan (CMCS) and were found to be superior. They therefore have the potential to substitute for hyaluronan for use in cosmetics and clinical medicine fields.

The Mediator complex plays an essential role in the regulation of eukaryotic transcription. The Saccharomyces cerevisiae core Mediator comprises 21 subunits, which are organized into Head, Middle and Tail modules. Previously, the Head module was assigned to a distinct dense domain at the base, and the Middle and Tail modules were identified to form a tight structure above the Head module, which apparently contradicted findings from many biochemical and functional studies. Here, we compared the structures of the core Mediator and its subcomplexes, especially the first 3D structure of the Head + Middle modules, which permitted an unambiguous assignment of the three modules. Furthermore, nanogold labeling pinpointing four Mediator subunits from different modules conclusively validated the modular assignment, in which the Head and Middle modules fold back on one another and form the upper portion of the core Mediator, while the Tail module forms a distinct dense domain at the base. The new modular model of the core Mediator has reconciled the previous inconsistencies between the structurally and functionally defined Mediator modules. Collectively, these analyses completely redefine the modular organization of the core Mediator, which allow us to integrate the structural and functional information into a coherent mechanism for the Mediator's modularity and regulation in transcription initiation.