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The protection of earthen sites plays an important role in the context of preservation of cultural heritage, especially in the inheritance and promotion of history and culture. The aim of the paper is to present the essential results of an ongoing research on a reinforced rammed earthen wall in Suoyang City (Guazhou, China). The wall vibrations caused by ambient actions were analyzed using the stochastic subspace algorithm to estimate the modal parameters of the wall. The frequencies of the first three orders are 3.566 Hz, 5.003 Hz, and 6.250 Hz, and the corresponding modes are first-order transverse bending, second-order left and right torsion, and third-order vertical bending, respectively. Then, according to the data of elastic modulus obtained in the lab, the finite element calculation is carried out, and referring to the results of field measurement, the revised elastic modulus value is 205.90 MPa. It is worth mentioning that the revised value is significantly improved from the original laboratory value, and it is also indicated that the seismic performance of the reinforced wall has been significantly improved. The present work is expected to provide a theoretical basis for reinforcement, protection, and seismic control of earthen ruins.

Three-dimensional (3D) tracking of surface-tethered single particles reveals the dynamics of the molecular tether. However, most 3D tracking techniques lack precision, especially in the axial direction, for measuring the dynamics of biomolecules with a spatial scale of several nanometers. Here, we present a plasmonic imaging technique that can track the motion of ∼100 tethered particles in 3D simultaneously with sub-nanometer axial precision and single-digit nanometer lateral precision at millisecond time resolution. By tracking the 3D coordinates of a tethered particle with high spatial resolution, we are able to determine the dynamics of single short DNA and study its interaction with enzymes. We further show that the particle motion pattern can be used to identify specific and nonspecific interactions in immunoassays. We anticipate that our 3D tracking technique can contribute to the understanding of molecular dynamics and interactions at the single-molecule level.

Global change exposes forest ecosystems to many risks including novel climatic conditions, increased frequency of climatic extremes and sudden emergence and spread of pests and pathogens. At the same time, forest landscape restoration has regained global attention as an integral strategy for climate change mitigation. Owing to unpredictable future risks and the need for new forests that provide multiple ecosystem services, mixed-species forests have been advocated for this purpose. However, the successful establishment of mixed forests requires intrinsic knowledge of biodiversity's role for forest ecosystem functioning. In this respect, a better understanding of tree-tree interactions and how they contribute to observed positive tree species richness effects on key ecosystem functions is critical. Here, we review the current knowledge of the underlying mechanisms of tree-tree interactions and argue that positive net biodiversity effects at the community scale may emerge from the dominance of positive over negative interactions at the local neighbourhood scale. In a second step, we demonstrate how tree-tree interactions and the immediate tree neighbourhood's role can be systematically assessed in a tree diversity experiment. The expected results will improve predictions about the effects of tree interactions on ecosystem functioning based on general principles. We argue that this knowledge is urgently required to guide the design of tree species mixtures for the successful establishment of newly planted forests.

Quantification of molecular interactions on a surface is typically achieved via label-free techniques such as surface plasmon resonance (SPR). The sensitivity of SPR originates from the characteristic that the SPR angle is sensitive to the surface refractive index change. Analogously, in another interfacial optical phenomenon, total internal reflection, the critical angle is also refractive index dependent. Therefore, surface refractive index change can also be quantified by measuring the reflectivity near the critical angle. Based on this concept, we develop a method called critical angle reflection (CAR) imaging to quantify molecular interactions on glass surface. CAR imaging can be performed on SPR imaging setups. Through a side-by-side comparison, we show that CAR is capable of most molecular interaction measurements that SPR performs, including proteins, nucleic acids and cell-based detections. In addition, we show that CAR can detect small molecule bindings and intracellular signals beyond SPR sensing range. CAR exhibits several distinct characteristics, including tunable sensitivity and dynamic range, deeper vertical sensing range, fluorescence compatibility, broader wavelength and polarization of light selection, and glass surface chemistry. We anticipate CAR can expand SPR′s capability in small molecule detection, whole cell-based detection, simultaneous fluorescence imaging, and broader conjugation chemistry., Here, the authors present a method for quantifying molecular interactions on a glass surface, based on measuring surface refractive index changes via the reflectivity near the critical angle. They demonstrate tunable sensitivity and dynamic range, deep vertical sensing range, also for intracellular signals.

To combat the ongoing public health threat of antibiotic-resistant infections, a technology that can quickly identify infecting bacterial pathogens and concurrently perform antimicrobial susceptibility testing (AST) in point-of-care settings is needed. Here, we develop a technology for point-of-care AST with a low-magnification solution scattering imaging system and a real-time video-based object scattering intensity detection method. The low magnification (1-2×) optics provides sufficient volume for direct imaging of bacteria in urine samples, avoiding the time-consuming process of culture-based bacterial isolation and enrichment. Scattering intensity from moving bacteria and particles in the sample is obtained by subtracting both spatial and temporal background from a short video. The time profile of scattering intensity is correlated with the bacterial growth rate and bacterial response to antibiotic exposure. Compared to the image-based bacterial tracking and counting method we previously developed, this simple image processing algorithm accommodates a wider range of bacterial concentrations, simplifies sample preparation, and greatly reduces the computational cost of signal processing. Furthermore, development of this simplified processing algorithm eases implementation of multiplexed detection and allows real-time signal readout, which are essential for point-of-care AST applications. To establish the method, 130 clinical urine samples were tested, and the results demonstrated an accuracy of ∼92% within 60-90 min for UTI diagnosis. Rapid AST of 55 positive clinical samples revealed 98% categorical agreement with both the clinical culture results and the on-site parallel AST validation results. This technology provides opportunities for prompt infection diagnosis and accurate antibiotic prescriptions in point-of-care settings.

Yttria-stabilized zirconia (YSZ)-based potentiometric gas sensor has been widely utilized for detecting non-methane hydrocarbons (NMHCs) that derived from exhaust gases. Nevertheless, poor selectivity and sensitivity still remained a challenging issue. Herein, we reported an efficient strategy to sense NMHCs (e.g., C3H6) at high temperature through the gas phase reaction combined light-regulated sensing technique. When the YSZ-based sensor that was attached with ZnO sensing electrode and Mn-based reference electrode was operated without illumination, significant mutual interference that derived from CO was witnessed for sensing C3H6. On the contrary, enhanced sensitivity and selectivity are observed by simply illuminating the sensor. The low detection limit of the sensor to C3H6 extends to 0.768 ppm with the response/recovery rate of 27 s/30 s. These pilot results clearly indicate the validity of employing gas phase reaction combined light-regulated sensing technique in tailoring the response selectivity and sensitivity for future exhaust gas sensing.

© 2002-2011 IEEE. The detent force caused by the cogging effect and end effect will seriously affect the operation of a tubular flux-switching permanent magnet motor (TFSPMM). This paper proposes a method to weaken the end effect and minimize the detent force and force ripple of a TFSPMM using un-equal width stator slots. To overcome the problem of excessive calculation, the Taguchi method is performed to find the optimal combination of stators with unequal slot width. The proposed method can significantly decrease the detent force and force ripple and keep the thrust force basically unchanged. The permanent magnet flux linkage, detent force and electromagnetic force are calculated by the two dimensional (2D) finite element method (FEM).

Uncovering biodiversity as an inherent feature of ecosystems and understanding its effects on ecosystem processes is one of the most central goals of ecology. Studying organisms’ occurrence and biodiversity patterns in natural ecosystems has spurred the discovery of foundational ecological rules, such as the species–area relationship, and is of general scientific interest. Recent global changes add relevance and urgency to understanding the occurrence and diversity of organisms, and their respective roles in ecosystem processes. While information on ecosystem properties and abiotic environmental conditions are now available at unprecedented, highly‐resolved spatial and temporal scales, the most fundamental variable – biodiversity itself – is still often studied in a local perspective, and generally not available at a wide taxonomic breadth, high temporal scale and spatial coverage. This is limiting the capacity and impact of ecology as a field of science. In this forum article, we propose that complete biodiversity assessments should be inclusive across taxonomic and functional groups, across space, and across time to better understand emergent properties, such as ecosystem functioning. We use riverine ecosystems as a case example because they are among the most biodiverse ecosystems worldwide, but are also highly threatened, such that an in‐depth understanding of these systems is critically needed. Furthermore, their inherent spatial structure requires a multiscale perspective and consideration of spatial autocorrelation structures commonly ignored in biodiversity–ecosystem functioning studies. We show how recent methodological advances in environmental DNA (eDNA) provide novel opportunities to uncover broad biodiversity and link it to ecosystem processes, with the potential to revolutionize ecology and biodiversity sciences. We then outline a roadmap for using this technique to assess biodiversity in a complete and inclusive manner. Our proposed approach will help to get an understanding of biodiversity and associated ecosystem processes at spatial scales relevant for landscape ecology and environmental managers.

Charge sensitive optical detection (CSOD) technique is a label-free method for real-time measurement of molecular interactions. Traditional label-free optical detection techniques mostly measure the mass of a molecule, and they are less sensitive to small molecules. In contrast, CSOD detects the charge of a molecule, where the signal does not diminish with the size of the molecule, thus capable for studying small molecules. In addition, CSOD is compatible with the standard microplate platform, making it suitable for high-throughput screening of drug candidates. In CSOD, an optical fiber functionalized with the probe molecule is dipped into a well of a microplate where an alternate perpendicular electrical field is applied to the fiber, which drives the fiber into oscillation because of the presence of surface charge on the fiber. The binding of the target molecules changes the charge of the fiber, and thus the amplitude and phase of the oscillating fiber, which are precisely measured through tracking of the optical images of the fiber tip.

With the continuous improvement of magnetic and mechanical properties of soft magnetic composite (SMC) material, there is a trend to develop novel electrical machines with SMC cores for some special applications. Among these electrical machines, permanent magnet claw pole machine (CPM) has been extensively studied over the past few decades. As linear global winding has been used in this machine, it can be regarded as a linear winding CPM (LWCPM). To improve the performance of LWCPM, a new S-shape winding CPM (SWCPM) is proposed in this article. The main stator structures of the LWCPM and SWCPM are optimized to achieve maximum torque ability. Compared with LWCPM, SWCPM provides higher average torque, power factor, and higher efficiency. The main disadvantage of the proposed SWCPM is its lower flux weakening ability. 3-D finite element model is used to evaluate the performance of the proposed LWCPM and SWCPM. The accuracy of the 3-D finite element model is verified by using a previous prototype.

Livestock grazing strongly affects biodiversity and ecosystem functioning in grasslands. However, it remains unclear how different grazing impact multiple biodiversity, ecosystem multifunctionality (EMF), and their relationship with the interactions of grazing duration, livestock type and climatic factors. Here, we conducted a global synthesis from 104 published studies. Our results showed that light and moderate grazing improved multi-diversity, but heavy grazing significantly decreased multi-diversity and EMF. The grazing-induced decrease of EMF intensified with grazing duration, and the reduction of multi-diversity and EMF under intensive grazing was stronger in more arid climates. The response of EMF increased linearly with that of multi-diversity under all grazing intensities. Moreover, grazing intensity reduced EMF largely via decreasing multi-diversity, whereas a shift of livestock type from small to large size promoted EMF by increasing multi-diversity. This study provides first empirical evidence and new insights into the relationship between multi-diversity and EMF under grazing in global grasslands.

Antibiotic resistance is an increasing public health threat. To combat it, a fast method to determine the antibiotic susceptibility of infecting pathogens is required. Here we present an optical imaging-based method to track the motion of single bacterial cells and generate a model to classify active and inactive cells based on the motion patterns of the individual cells. The model includes an image-processing algorithm to segment individual bacterial cells and track the motion of the cells over time, and a deep learning algorithm (Long Short-Term Memory network) to learn and determine if a bacterial cell is active or inactive. By applying the model to human urine specimens spiked with an Escherichia coli lab strain, we show that the method can accurately perform antibiotic susceptibility testing as fast as 30 minutes for five commonly used antibiotics.

Hierarchical clustering with data field can find clusters with various shape and filter the noises in data set without input parameters. However, its clustering process is complex and cannot effectively deal with complex and high dimensional data. In this paper, a novel clustering algorithm is proposed by differencing potential (DP) of data field. The potential difference specifies the nearest object which has high potential as the aggregation direction, and the data distance is used to divide the global data set into local multiple clusters. Simultaneously, noises are identified effectively in the light of the potential of data field. Experimental results on eight popular data sets and a facial image data set indicate that the proposed method outperforms existing clustering algorithms for dealing with data set with high dimensions and distribution in complex shape, as well as noise identification.

Microwave interferometry is an effective and reliable way to measure line integrated plasma electron density. An extremely low noise heterodyne interferometer has been developed for routine operation in Sino-UNIted Spherical Tokamak (SUNIST) and acts as the prototype design of a microwave inteferometer for Experimental Advanced Superconducting Tokamak (EAST) in the near future. The system has been designed with optimum frequency deduced from detailed calculation in real geometry and discharge parameters. In contrary to traditional heterodyne interferometers, the application of a single sideband modulator (SSBM) eliminates the necessity of the second millimeter wave (MMW) oscillator, which averts the intermediate frequency (IF) stability problem aroused by the two high frequency oscillators in the traditional heterodyne configuration. A pair of specially designed spot focusing antenna is also applied to boost the signal to noise ratio (SNR), whose near field scan data displays excellent focusability. The bench test data and plasma electron density measurement results from SUNIST discharges consistently show excellent performance of the interferometer, which is expected to be suitable for steady-state plasma experiments due to the high stability.

User activity analysis (UAA) is a promising technology for network management and network security via network traffic. Recently, deep learning (DL) has been applied into network traffic analysis for outstanding performance. These previously proposed network traffic analysis methods generally requires huge amounts of data from network users. In detail, the common methods are to collect these traffic data collected from users into cloud server for processing and analysis, which generally have great performance and are denoted as centralized methods. However, one of the biggest drawbacks of these methods is the risk of data privacy disclosure. Thus, we proposed a federated learning-based UAA method (which is named as FedeUAA) for reducing the risk of data leakage in mobile wireless networks. FedeUAA method has no requirement to upload data to cloud server, while it directly trains the DL models in local devices, and only needs to upload the knowledge (model weight or model gradient) rather than data. Simulation results demonstrated that the FedeUAA method can effectively reduce the risk of data privacy disclosure with slight performance loss.

A method for optical imaging of single protein molecules including tethering single protein molecules via a flexible polymer linker to a glass slide having a surface coated with an indium tin oxide (ITO) so that the single protein molecules are tethered to the coated surface. The single protein molecules are driven into oscillation by applying an alternating electric field to the coated surface and the glass slide is located in the field of view of an objective lens. Incident light is directed onto the coated surface from an angle to generate an evanescent field and produce scattered light. The scattered light is collected and imaged by a CMOS imager to record a sequence of images of the scattered light. A Fast Fourier Transform (FFT) filter is applied to each pixel of the recorded image sequence to produce an oscillation amplitude image from which size, charge, and mobility of the plurality of single protein molecules can be determined.

With the continuous development of computer technology, traditional large-scale business systems are increasingly unable to meet the needs. Business systems based on workflow, small and light, and scalable are gradually becoming the mainstream. This paper takes the solution to the dilemma faced by the printing and publishing industry as the starting point, based on the workflow technology, and utilizes the unique organizational structure and data management technology advantages of workflow technology. The application of streaming technology in the traditional printing and publishing industry has been analyzed and researched, according to the requirements analysis, model abstraction, business process design, and authority configuration steps. The modeling methods and business process design schemes of workflow technology applied in the printing and publishing industry have been proposed. The key technologies required for system implementation are analyzed. The paper also provides ideas and methods in order to solve the current problems in the printing and publishing industry: the data is numerous, the management efficiency is low and other issues.

For widely used semiconductor-based chemoresistive sensors, low sensitivity and poor anti-interference in the complex environment (humidity resistance, etc.) are two main constraints of application. To enhance the sensitivity, many effective strategies have been developed such as constructing heterostructures with synergistic effects, introducing abundant oxygen vacancies as active sites, designing hollow/cavity structure to increase surface area to facilitate target gas adsorption, and so on. Besides, the interference caused by water vapor can be blocked.via MOF or molecular sieve membranes but is accompanied with the shielding of some target molecules. The above methods are faced with the problems of complex process, large workload of material screening and failure to maintain the device stability. Hence, an effective vapor-phase method derived N-C@SnO2-Co3O4 complex that combined the hydrophobicity, acetone selectivity, p-n heterostructure with mesoporous hollow characteristics was proposed, named mesoporous N-C@SnO2-Co3O4 hollow nanoboxes (HNBs). Particularly, the chemoresistive sensor based on N-C@SnO2-Co3O4 HNBs (2.0%) showed satisfactory selectivity to acetone vapor at relatively low working temperature (160 °C), and the response remained stable even under high humidity (with the R.H. of 90%). Impressively, a three-fold enhancement in response signal was observed for the sensor based on N-C@SnO2-Co3O4 HNBs when compared with its counterpart of SnO2-Co3O4, which can be ascribed to the consequent unpaired electrons from oxygen vacancies and hollow mesoporous structures. Additionally, a sensing prototype based on the N-C@SnO2-Co3O4 HNBs was practically fabricated. The satisfactory sensing response and stability further verified the potential applications in industrial acetone detection. This facile vapor-phase approach sheds light on designing sensing materials with enhanced sensitivity and humidity resistance, as well as device stability simultaneously.

A novel gas sensor composed of Pt nanoparticles-decorated hierarchical SnO2 nanostructures (Pt–SnO2) was synthesized via a facile dipping-precipitation strategy. Pt nanoparticles with small sizes (avg. 4 nm) were successfully dispersed onto our pre-synthesized 3D hierarchical SnO2 nanoflowers supports by using lysine as both capping and linking agents. The morphology, structure and composition of the as-prepared samples were characterized by means of field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). Comparisons of the gas sensing performance between pure SnO2 and Pt–SnO2 nanocomposites revealed that after supporting Pt nanoparticles, the decorated sensor not only got an optimal working temperature of as low as 100 °C, but also exhibited faster response and recovery speeds and higher response than the pristine one at such low temperature. Moreover, good selectivity and excellent stability were also shown for the hybrid sensor. Sensing mechanisms were illustrated to help explain the strong spillover effect of the Pt nanoparticles and the Schottky barriers at the interface of metal and semiconductor, both of which facilitated the low temperature sensing performance. The Pt–SnO2 sensors are considered to be a promising candidate for trace environmental gas detections in practical use.

The significant role of telomeres in cells has attracted much attention since they were discovered. Fluorescence imaging is an effective method to study subcellular structures like telomeres. However, the diffraction limit of traditional optical microscope hampers further investigation on them. Recent progress on superresolution fluorescence microscopy has broken this limit. In this work, we used stimulated emission depletion (STED) microscope to observe fluorescence-labeled telomeres in interphase cell nuclei. The results showed that the size of fluorescent puncta representing telomeres under the STED microscope was much smaller than that under the confocal microscope. Two adjacent telomeres were clearly separated via STED imaging, which could hardly be discriminated by confocal microscopy due to the diffraction limit. We conclude that STED microscope is a more powerful tool that enable us to obtain detailed information about telomeres.

International audience; Although diversity–stability relationships have been extensively studied in local ecosystems, the global biodiversity crisis calls for an improved understanding of these relationships in a spatial context. Here, we use a dynamical model of competitive metacommunities to study the relationships between species diversity and ecosystem variability across scales. We derive analytic relationships under a limiting case; these results are extended to more general cases with numerical simulations. Our model shows that, while alpha diversity decreases local ecosystem variability, beta diversity generally contributes to increasing spatial asynchrony among local ecosystems. Consequently, both alpha and beta diversity provide stabilising effects for regional ecosystems, through local and spatial insurance effects respectively. We further show that at the regional scale, the stabilising effect of biodiversity increases as spatial environmental correlation increases. Our findings have important implications for understanding the interactive effects of global environmental changes (e.g. environmental homogenisation) and biodiversity loss on ecosystem sustainability at large scales.

With the increasing prevalence of antibiotic resistance, the need to develop antimicrobial susceptibility testing (AST) technologies is urgent. The current challenge has been to perform the antibiotic susceptibility testing in short time, directly with clinical samples, and with antibiotics over a broad dynamic range of clinically relevant concentrations. Here we develop a technology for point-of-care diagnosis of antimicrobial-resistant bacteria in urinary tract infections (UTI), by imaging the clinical urine samples directly with an innovative large-image-volume solution scattering imaging (LVSi) system and analyzing the image sequences with a single-cell division tracking method. The high sensitivity of single-cell division tracking associated with large volume imaging enabled rapid antibiotic susceptibility testing directly on the clinical urine samples. The results demonstrated direct detection of bacterial infections in 60 clinical urine samples with a 60 min LVSi video, and digital AST of 30 positive clinical samples with 100% categorical agreement with both the clinical culture results and the on-site agar plating validation results. This technology provides opportunities for precise antibiotic prescription and prompt proper treatment of the patient within a single clinic visit.

Scleractinian corals provide an excellent archive of geochemical proxies that allow the reconstruction of the oceanographic and climatic changes in the oceans. However, limited knowledge regarding the distribution coefficients (D) of trace metals between coral lattices and seawater has complicated efforts to obtain accurate quantitative data for seawater chemistry. Here we estimated the D values of five trace metals (Cu, Zn, Pb, Cd and Cr) for three coral species (Favia palauensis, Porites lutea, and Pavona decussata) and in situ seawater around Weizhou Island in the northern South China Sea. Despite several variations that could not be explained solely by species, our results were within the approved ranges of the previous reported estimates, and provide the most accurate D values for the these species to date. We also focused on the possible effects of surface seawater temperature (SST) on D vaules, finding no significant relationship. Nevertheless, the effects of SST, which could impact the distribution behavior of trace metals (especially Cd) should be paid more attention in the context of global climate change. The more precise D values for corals provided here represent important basic data for future research on coral reefs and expanded our knowledge of chemical signatures in biogenic lattices with regard to ocean composition.

With the development of Internet technology, new publishing forms such as electronic reading and mobile publishing have been recognized and accepted by users. Users no longer merely the knowledge content of the readers, but also the content of the producers and publishers, the formats of the publication industry in nature has changed, the digital publishing industry get rapid development, followed by piracy. Block chain technology can effectively solve the infringement problem in digital copyright protection due to its distributed data storage structure, non-tamperability of content and easy to track and trace. Based on the analysis of block chain technology, the basic structure of block chain applied to digital copyright protection is studied, and the advantage of block chain technology in digital copyright protection is summarized.

Background: As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. Objective: In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. Method: Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. Results: An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. Conclusion: Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

Background: Accurately recognizing nitrated tyrosine residues from protein sequences would pave a way for understanding the mechanism of nitration and the screening of the tyrosine residues in sequences. Results: In this study, we proposed a prediction model that used the extreme learning machine (ELM) algorithm as the prediction engine to identify nitrated tyrosine residues. To encode each tyrosine residue, a sliding window technique was adopted to extract a peptide segment for each tyrosine residue, from which a number of features were extracted. These features were analyzed by a popular feature selection method, Minimum Redundancy Maximum Relevance (mRMR) method, producing a feature list, in which all features were ranked in a rigorous way. Then, the Incremental Feature Selection (IFS) method was utilized to discover the optimal features, on which the optimal ELM-based prediction model was built. This model produced satisfactory results on the training dataset with a Matthews correlation coefficient of 0.757. The model was also evaluated by an independent test dataset that contained only positive samples, yielding a sensitivity of 0.938. Conclusion: Compared to other prediction models that use classic machine learning algorithms as prediction engines on the same datasets with their own optimal features, the optimal ELM-based prediction model produced much better results, indicating the superiority of the proposed model for the identification of nitrated tyrosine residues from protein sequences.

The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein–protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity–disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity–disease co-occurrence and provide useful insights for disease prevention and intervention.

Traditional permission management based on Session has many problems, such as high memory cost, unsuitable for mobile application, and not convenient for cross domain resource sharing. The permission management based on Token can solve the above problems better. Through the analysis of Token authentication process and the research of Apache Shiro architecture, a set of universal and easy access control model based on RBAC (Role-Based Access Control) is designed. JWT (JSON Web Token) is integrated and a security authentication framework based on Token is implemented. The practical application shows that the security authentication framework is flexible, which is easy to expand and transplant.

The occurrence, distribution, bioconcentration and diet safety via seafood consumption of 19 antibiotics were investigated in eight closed mariculture ponds, four estuaries, two nearshore areas and one offshore area from the Beibu Gulf. Seventeen, 16, 15 and 7 antibiotics were detected at total concentrations of 43.2 − 885 ng L−1, 22.4 − 118 ng L−1, 22.7 − 24.5 ng L−1, and 1.81–3.23 ng L−1 in the water of the above different areas, respectively. This indicates that the mariculture ponds are important sources of antibiotic pollution on the coast of the Beibu Gulf. Ten antibiotics were detected in feed samples with concentrations ranging from 0.03 to 95.4 ng g−1, demonstrating the presence of antibiotics in the feed and/or residual antibiotics in the raw material of the feed. The field bioconcentration factors (BCFs) of the antibiotics calculated in different culture organisms ranged from 0.55 to 10,774 L kg−1. The estimated daily intakes (EDIs) of sulphonamides, fluoroquinolones, macrolides and chloramphenicols via aquatic products were 19.8–105, 33.7–178, 34.9–186 and 6.9–37.1 ng d−1, respectively. According to the acceptable daily intakes (ADIs) and maximum residue limits (MRLs) proposed by different organisations, these aquatic products (shrimp, crab and oyster) reached the standard of safe consumption and could not pose a health risk to humans. However, a potential elevated risk to humans may remain because of the occurrence of multiple antibiotics in the cultured organisms, particularly for sensitive populations, such as pregnant women, the elderly and children.

Studying electrical activities in cells, such as action potential and its propagation in neurons, requires a sensitive and non-invasive analytical tool that can image local electrical signals with high spatial and temporal resolutions. Here we report a plasmonics-based electrochemical impedance imaging technique to study transient electrical activities in single cells. The technique is based on the conversion of the electrical signal into a plasmonic signal, which is imaged optically without labels. We demonstrate imaging of the fast initiation and propagation of action potential within single neurons, and validate the imaging technique with the traditional patch clamp technique. We anticipate that the plasmonic imaging technique will contribute to the study of electrical activities in various cellular processes.

Surface plasmon resonance microscopy (SPRM) is a powerful platform for biomedical imaging and molecular binding kinetics analysis. However, the spatial resolution of SPRM along the plasmon propagation direction (longitudinal) is determined by the decaying length of the plasmonic wave, which can be as large as tens of microns. Different methods have been proposed to improve the spatial resolution, but each at the expense of decreased sensitivity or temporal resolution. Here we present a method to achieve high spatial resolution SPRM based on deconvolution of complex field. The method does not require additional optical setup and improves the spatial resolution in the longitudinal direction. We applied the method to image nanoparticles and achieved close-to-diffraction limit resolution in both longitudinal and transverse directions.

International audience; Ecological theory is essential to predict the effects of global changes such as habitat loss and fragmentation on biodiversity. Species-area relationships (SAR), metapopulation models (MEP) and species distribution models (SDM) are commonly used tools incorporating different ecological processes to explain biodiversity distribution and dynamics. Yet few studies have compared the outcomes of these disparate models and investigated their complementarity. Here we show that the processes underlying SAR (patch area), MEP (patch isolation) and SDM (environmental conditions) models can be compared with a common statistical framework. Our approach allows for species and community-level predictions under current and future landscape scenarios, facilitates multi-model comparison and provides the machinery for integrating multiple mechanisms into one model. We apply this framework to the distribution of eight focal vertebrate species in current and future projected landscapes where 10% of the landscape is lost to land-use change in southwestern, Quebec, Canada. Based on a model selection approach, we found that a model including patch area was the top ranked model for four of our focal species and models including patch isolation and environmental conditions were the top ranked models for two focal species each. Community-level predictions of models based on patch area, patch isolation and environmental conditions for both current and future landscapes showed high spatial overlap, however, patch area models always predicted a reduction of species richness per patch whereas both the patch isolation and environmental conditions models predicted an increase or decrease in species richness per patch following habitat loss and fragmentation. Our comparative tool will allow ecologists and conservation practitioners to relate structural uncertainty to key mechanisms underlying each model. Ultimately, this approach is one step in the direction of deriving robust predictions for the change and loss of biodiversity under global change, which is key for informing conservation plans.

The aim of the present study was to investigate the effect of a herbal medicine formula, Gan-fu-kang (GFK), on the treatment of liver fibrosis in rats and the mechanisms via which it exerts its effect. Liver fibrosis was induced in rats by subcutaneous injection of carbon tetrachloride (CCl4) at 0.5 mg/kg body weight, twice a week for 8 weeks. The rats were randomly selected to receive saline or GFK at 31.25, 312.5 or 3,125 mg/kg body weight/day between weeks 9 and 20. An additional group of rats without CCl4 injection was used as the baseline. In the liver fibrosis model rats, an increase in plasma liver enzymes, fibrotic markers in serum and liver fibrosis, production of α-smooth muscle actin, matrix metalloproteinase-2 and tissue inhibitor of metalloproteinase-1, synthesis of collagen and activation of the Wnt/β-catenin signaling pathway were observed. GFK administration was found to significantly reduce these changes. Results of this study demonstrate that GFK has a protective and therapeutic effect on liver fibrosis induced by CCl4, which may be associated with its inhibitory activity on HSC proliferation and collagen synthesis, effectively downregulating Wnt/β-catenin signaling.

Charge-based detection of small molecules is demonstrated by plasmonic-based electrochemical impedance microscopy (P-EIM). The dependence of surface plasmon resonance (SPR) on surface charge density is used to detect small molecules (60-120 Da) printed on a dextran-modified sensor surface. Local variations in charge density on an electrode surface are manifest in an optical SPR signal. The SPR response to an applied ac potential measures the sensor surface impedance which is a function of the surface charge density. This optical signal is comprised of a dc and an ac component, and is measured with high spatial resolution. The dc element of the SPR signal represents conventional SPR imaging information. The amplitude and phase of local surface impedance is provided by the ac component. The phase signal of the small molecules is a function of their charge status, which is manipulated by the pH of a solution. Small molecules with positive, neutral, and negative charge are detected by P-EIM. This technique is used to detect and distinguish small molecules based on their charge status, thereby circumventing the mass limitation (~100 Da) of conventional SPR measurement.

In this paper, component carrier selection and beamforming on carrier aggregated channels in Heterogeneous Networks are proposed. The scheme jointly selects the component carrier and precoding (i.e. beamforming) vectors with the cooperation of the other cells to deal with the interference between Macro cell and Pico cell. The component carrier selection and beamforming is achieved by optimizing the multi-cell downlink throughput. This optimization results in shutting down a subset of the component carrier in order to allow for a perfect interference removal at the receive side in the dense low power node deployment scenario. Additionally, algorithm based on Branch and Bound Method is used to reduce the search complexity of the algorithm. Simulation results show that the proposed scheme can achieve high cell-average and cell-edge throughput for the Pico cell in the Heterogeneous Networks.

One-dimensional (1D) SnO2-coated ZnO nanowire (SnO2/ZnO NW) N-N heterojunctions were successfully constructed by an effective solvothermal treatment followed with calcination at 400 °C. The obtained samples were characterized by means of XRD, SEM, TEM, Scanning TEM coupled with EDS and XPS analysis, which confirmed that the outer layers of N-type SnO2 nanoparticles (avg. 4 nm) were uniformly distributed onto our pre-synthesized n-type ZnO nanowire supports (diameter 80~100 nm, length 12~16 μm). Comparisons of the gas sensing performances among pure SnO2, pure ZnO NW and the as-fabricated SnO2/ZnO NW heterojunctions revealed that after modification, SnO2/ZnO NW based sensor exhibited remarkably improved response, fast response and recovery speeds, good selectivity and excellent reproducibility to n-butylamine gas, indicating it can be used as promising candidates for high-performance organic amine sensors. The enhanced gas-sensing behavior should be attributed to the unique 1D wire-like morphology of ZnO support, the small size effect of SnO2 nanoparticles, and the semiconductor depletion layer model induced by the strong interfacial interaction between SnO2 and ZnO of the heterojunctions. The as-prepared SnO2/ZnO NW heterojunctions may also supply other novel applications in the fields like photocatalysis, lithium-ion batteries, waste water purification, and so on.

We report on imaging of local electric field on an electrode surface with plasmonic electrochemical impedance microscopy (P-EIM). The local electric field is created by putting an electrode inside a micropipet positioned over the electrode and applying a voltage between the two electrodes. We show that the distribution of the surface charge as well as the local electric field at the electrode surface can be imaged with P-EIM. The spatial distribution and the dependence of the local charge density and electric field on the distance between the micropipet and the surface are measured, and the results are compared with the finite element calculations. The work also demonstrates the possibility of integrating plasmonic imaging with scanning ion conductance microscopy (SICM) and other scanning probe microscopies.

Most face hallucination methods are usually limited to frontal face with small pose variations. This letter presents a simple and efficient multiview face hallucination (MFH) method to generate high-resolution (HR) multiview faces from a single given low-resolution (LR) one. The problem is addressed in two steps. A simple face transformation method is proposed by defining a constrained least square problem for LR multiview face transformation and a position-patch based face hallucination method is extended to incorporate HR multiview face details. Experimental results show that our approach has some advantages over existing MFH methods.

An Electrochemical Landscape Electrochemical detection is an analytical method that has been used for a wide range of purposes, including trace chemical analysis, glucose and neurotransmitter monitoring, DNA and protein detection, and electrocatalysis. Scanning electrochemical microscopy maps changes in the local electrochemical current along a surface in a serial way, but serial probing can disrupt the process under study. Shan et al. (p. 1363 ) show that optical measurements of surface plasmon resonances can be used as a less disruptive way of determining the concentration of electrochemically active species on gold-coated glass slides and their current density. This method can be used for a wide range of applications from analyzing DNA and protein microarrays and enzyme-amplified biosensors to probing the activities of cells.

Integrated microring resonators fabricated on silicon wafers were used as signal transducers to detect alterations in physical traits of attached live mammalian cells. Cell adhesion and growth events could be monitored by the shift in resonance frequency of the microring resonator. Toxic chemical-induced changes in cell motility were rapidly detected based on variations in the fluctuation of resonance frequency. Microring resonators modified with an endothelial cell line (MS1) adhered onto its surface were used to detect the presence of two toxic chemicals, viz. sodium pentachlorophenate and Aldicarb at concentrations above the military exposure guideline levels within a duration of 1 h.

In this paper, a novel adaptive transmit-receive scheme is presented for indoor Direct Sequence Ultra-WideBand (DS-UWB) systems. In the proposed scheme, a simple switch module is introduced to improve the system throughput. Furthermore, adaptive detection is implemented via an improved Least Mean Squares (LMS) algorithm. The convergence behavior and the Bit Error Rate (BER) performance of the proposed scheme are examined under the realistic channel models. Simulation results show that, nearly without loss of the BER performance, the proposed scheme can obtain at least 2/3 improvement in training overhead compared with the conventional approach.

As the consumer demand for higher capacity, faster service, and more secure wireless connections increases, new enhanced technologies have to find their place in the overcrowded and scarce radio frequency (RF) spectrum. Ultra-wideband (UWB) technology can offer a promising solution to the RF spectrum drought by allowing new services to coexist with current narrowband systems. On the other hand, this coexistence brings the narrowband interference (NBI) problem for UWB systems due to their very low power spectral densities. To meet increasing requirements for low cost, low power, flexible, and reliable wireless multimedia networks, an adaptive NBI suppressing method employing space-time adaptive processing (STAP) with receive diversity is discussed and experimentally investigated for direct-sequence (DS) UWB systems. Analytical results show that, in wireless home environments, the investigated scheme is capable of significantly improving the performance in combating NBI, compared with the conventional temporal approach. The effect of receive diversity is also examined via the Monte-Carlo simulations

Recent developments in both the consumer electronics (CE) and PC industries make ultra-wideband (UWB) an almost perfect wireless solution to drive and benefit from the ongoing convergence of home entertainment and computing network. In order to meet increasing consumer demand for low cost, low power, flexible, and reliable wireless home entertainment networks, we present a high-speed DS-UWB link scheme employing space-time spreading (STS) technique combined with adaptive detection. Maximal path diversity at the receiver is achieved via our approach. In doing so, no extra spreading codes, transmit power, nor channel estimation is required and only minimal extra hardware complexity of the link. Both our analysis and simulation results show that, compared with the conventional single-antenna DS-UWB link, the proposed STS based DS-UWB link scheme is capable of obtaining at least 2.5 dB improvement in terms of the SNR, and therefore can be considered as a promising candidate to support multimedia wireless connections of good quality

New observations on the luminescence Of In2S3 and europium-doped In2S3 nanoparticles show a green (5 10 nm) emission from In2S3 and In1.8Eu0.2S3 nanoparticles while a blue (425 nm) emission is observed from ln(1.6)Eu(0.4)S(3) nanoparticles. Both the blue and green emissions have large Stokes shifts of 62 and 110 nm, respectively. Excitation with longer-wavelength photons causes the blue emission to shift to a longer wavelength while the green emission wavelength remains unchanged. The lifetimes of both the green and blue emissions are similar to reported values for excitonic recombination. When doped with Eu3+, in addition to the broad blue and green emissions, a red emission near 615 nm attributed to Eu3+ is observed. Temperature dependences on nanoparticle thin films indicate that with increasing temperature, the green emission wavelength remains constant, however, the blue emission shifts toward longer wavelengths. Based on these observations, the blue emission is attributed to exciton recombination and the green emission to Indium interstitial defects. These nanoparticles show full-color emission with high efficiency, fast lifetime decays, and good stability; they are also relatively simple to prepare, thus making them a new type of phosphor with potential applications in lighting, flat-panel displays, and communications.

Uveitis, defined as inflammation of the uveal tract, may cause blindness in both young and middle-aged people. Approximately 10–15% of blindness in the West is caused by uveitis. Therefore, a comprehensive investigation to determine the disease pathogenesis is urgent, as it will thus be possible to design effective treatments. Identification of the disease genes that cause uveitis is an important requirement to achieve this goal. To begin to answer this question, in this study, a computational method was proposed to identify novel uveitis-related genes. This method was executed on a large protein–protein interaction network and employed a popular ranking algorithm, the Random Walk with Restart (RWR) algorithm. To improve the utility of the method, a permutation test and a procedure for selecting core genes were added, which helped to exclude false discoveries and select the most important candidate genes. The five-fold cross-validation was adopted to evaluate the method, yielding the average F1-measure of 0.189. In addition, we compared our method with a classic GBA-based method to further indicate its utility. Based on our method, 56 putative genes were chosen for further assessment. We have determined that several of these genes (e.g., CCL4, Jun, and MMP9) are likely to be important for the pathogenesis of uveitis.