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The charge transport properties of conjugated polymers are commonly limited by the energetic disorder. Recently, several amorphous conjugated polymers with planar backbone conformations and low energetic disorder have been investigated for applications in field-effect transistors and thermoelectrics. However, there is a lack of strategy to finely tune the interchain π-π contacts of these polymers that severely restricts the energetic disorder of interchain charge transport. Here, we demonstrate that it is feasible to achieve excellent conductivity and thermoelectric performance in polymers based on thiophene-fused benzodifurandione oligo( p -phenylenevinylene) through reducing the crystallization rate of side chains and, in this way, carefully controlling the degree of interchain π-π contacts. N-type (p-type) conductivities of more than 100 S cm −1 (400 S cm −1 ) and power factors of more than 200 μW m −1 K −2 (100 μW m −1 K −2 ) were achieved within a single polymer doped by different dopants. It further demonstrated the state-of-the-art power output of the first flexible single-polymer thermoelectric generator.

Hepatocellular carcinoma (HCC) remains a crucial public health problem around the world, and the outlook remains bleak. More accurate prediction models are urgently needed because of the great heterogeneity of HCC. The S100 protein family contains over 20 differentially expressed members, which are commonly dysregulated in cancers. In this study, we analyzed the expression profile of S100 family members in patients with HCC based on the TCGA database. A novel prognostic risk score model, based on S100 family members, was developed using the LASSO regression algorithm, to analyze the clinical outcome. Our prediction model showed a powerful predictive value (1-year AUC: 0.738; 3-year AUC: 0.746; 5-year AUC: 0.813), while two former prediction models had less excellent performances than ours. And the S100 family members-based subtypes reveal the heterogeneity in many aspects, including gene mutations, phenotypic traits, tumor immune infiltration and predictive therapeutic efficacy. We further investigated the role of S100A9, one member with the highest coefficient in the risk score model, which was mainly expressed in para-tumoral tissues. Using the Single-Sample Gene Set Enrichment Analysis algorithm and immunofluorescence staining of tumor tissue sections, we found that S100A9 may be associated with macrophages. These findings provide a new potential risk score model for HCC and support further study of S100 family members in patients, especially S100A9.

PDF file - 556K, Biological process annotation with Gene Ontology analysis for 27 proteins.

PDF file - 41K, Comparison of tumor spheres metastatic potentials, invasive ability and EMT property.

PDF file - 83K, The 27 identified proteins with differential expression among Sph17, Sph40 and SW480 cells.

PDF file - 2897K, Stemness of cancer stem-like cells related to ERp29, CLIC4 and Smac.

Purpose: Cancer stem–like cells have been well accepted to be involved in recurrence and metastasis of cancers, but the prognostic potential of biomarkers integrating with metastasis and cancer stem–like cells for colorectal cancer is unclear.Experimental Design: We identified three proteins, CLIC4, ERp29, and Smac/DIABLO, from metastatic cancer stem–like cells of colorectal cancer and verified the proteins' role in metastatic behaviors. The proteins were detected by IHC in colorectal cancer tumors and matched colonic mucosa from patients with colorectal cancer who underwent radical surgery in the training cohort. The associations between proteins expression levels and five-year disease-specific survival (DSS) were evaluated to predict the survival probability in the training cohort of 421 cases and the validation cohort of 228 cases.Results: A three-protein panel including CLIC4, ERp29, and Smac/DIABLO, which was generated from multivariate analysis by excluding clinicopathologic characteristics from the training cohort, distinguished patients with colorectal cancer into very low-, low-, middle-, and high-risk groups with significant differences in five-year DSS probability (88.6%, 63.3%, 30.4%, 11.4%; P < 0.001). The panel is independent from tumor–node–metastasis staging system and histologic grading to predict prognosis, and also enables classification of validation cohort into four risk stratifications (five-year DSS probability is 98.2%, 80.2%, 25.6%, and 2.7%; P < 0.001).Conclusions: CLIC4, ERp29, and Smac/DIABLO integrated into a novel panel based on cancer stem–like cells in association with metastasis stratify the prognostic risks of colorectal cancer. Prediction of risks with molecular markers will benefit clinicians to make decisions of individual management with postoperative colorectal cancer patients. Clin Cancer Res; 20(14); 3809–17. ©2014 AACR.

PDF file - 45K, Representative images of IHC examination of CLIC4, ERp29 and Smac in CRC tumors and mucosa.

PDF file - 51K, Confirming the proteins associated with invasiveness and metastasis.

PDF file - 5616K, Generation of tumor spheres and determining the metastatic potentials of the tumor spheres from different metastatic single cell progenies (SCP).

PDF file - 191K

With the continuous development of low-level-light (LLL) night vision technology, the LLL night vision devices show the characteristics of many varieties, large performance differences, and wide application fields. How to sort out the main line of development from numerous LLL night vision devices has become a research hot spot for practitioners of low-light night vision devices. The development history of the zero-generation, first-generation, second-generation, third-generation, and ultra-second generation LLL night vision devices was reviewed. The industry-recognized methods for distinguishing the zero-generation, first-generation, second-generation, ultra-second generation, and third-generation low-light night vision devices were summarized. The concept of the fourth-generation low-light night vision device was put forward, and at the same time the position of several atypical low-light night vision devices in the technical field of low-light night vision devices was explained, and a tree diagram was drew, so that the technical characteristics of each low-light night vision device and their position in the field of low-light night vision device technology could be intuitively, accurately, and comprehensively understood. The proposed perspectives have a certain reference role for researchers engaged in low-light night vision device technology.

Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate statistical analysis methods including partial least squares regression (PLSR), support vector machine (SVM), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were carried out in developing full wavelength models. Good prediction was obtained by applying LS-SVM in the spectral range of 496–1000 nm with coefficients of determination (R2) of 0.94 and 0.94 as well as root-mean-squared errors (RMSEs) of 5.39% and 6.29% for calibration and prediction, respectively. To reduce the high dimensionality of hyperspectral data and to establish simplified models, a novel method named invasive weed optimization (IWO) was developed to select key wavelengths and it was compared with competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Among the four multivariate analysis models based on important wavelengths determined by IWO, the LS-SVM simplified model performed best where R2 of 0.97 and 0.95 as well as RMSEs of 4.74% and 5.67% were attained for calibration and prediction, respectively. The optimum simplified model was applied to hyperspectral images in pixel-wise to visualize the distribution of spoiled beef adulterant in fresh minced beef. The current study demonstrated that it was feasible to use Vis-NIR hyperspectral imaging to detect homologous adulterant in beef.

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs, meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 &deg, C, the minimum error at 0.124 &deg, C, and the MSE at 0.159 &deg, C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs&prime, internal body temperature.

Near-infrared hyperspectral imaging together with versatile chemometric algorithms including invasive weed optimization (IWO) were employed for optimizing fast classification of bacterial colonies on agar plates. Hyperspectral images of colonies from six strains of bacteria were collected, and classification models were established by applying partial least squares-discriminant analysis and support vector machine (SVM) on the original as well as difference spectra. The parameters of SVM models were optimized by comparing genetic algorithm, particle swarm optimization and the proposed IWO. The results showed that difference spectra amplified the variations among the spectra of the six strains thus potential for improving classification accuracy. The best full wavelength classification model was IWO-SVM model which produced overall correct classification rates (OCCRs) of 100.0% and 97.0% for calibration and prediction, respectively. Besides, competitive adaptive reweighted sampling (CARS), GA and successive projections algorithm (SPA) were utilized to select important wavelengths to establish simplified models. Among them, the simplified IWO-SVM model based on the feature wavelengths selected by CARS gave the best classification rates of 97.2% and 96.0% for calibration and prediction, respectively. The study demonstrated that IWO was a useful tool for optimizing calibration models thus potential for usage in many other applications.

The potential of near-infrared spectroscopy in classifying individual bacterial strains from different species was investigated in this study. Bacterial samples in liquid nutrient culture were collected periodically (0, 6 and 12 h) during incubation and their spectra were acquired in the near-infrared (NIR) range of 1000–2500 nm. Spectral transforms, including absorbance (A), transmittance (T) and Kubelka-Munk (KM) units were explored in order to enhance classification performance. Partial least squares discriminant analysis (PLS-DA), radial basis function neural network (RBF) and support vector machine (SVM) were used in classification model development. The results illustrated that nonlinear methods such as SVM and RBF neural network outperformed PLS-DA, where the overall correct classification rates (OCCRs) were both above 96%. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and random forest (RF) were employed to reduce spectral redundancy and to identify important wavelengths for simplifying models. The RF model yielded the best predictions as indicated by the shortest modeling time and the excellent OCCRs (100%) for both calibration and prediction. The overall results demonstrated the suitability of NIR spectroscopy with RF for the simultaneous classification of water-borne pathogenic strains from different species.

In this paper, we proposed artificial fish swarm algorithm (AF) for synchronous selection of wavelengths and pretreatment methods during quantification of beef adulteration with spoiled beef based on the analysis of ultraviolet (UV), visible and near-infrared (Vis-NIR), and UV-Vis-NIR spectra. The best partial least squares regression model was obtained when the wavelength-then-pretreatment scheme was adopted in the Vis-NIR range where 11 wavelengths were utilized to produce an RPD value of 2.68. The coefficients of determination R2 and root mean squared errors of the model were 0.91 and 0.08 as well as 0.87 and 0.11 for calibration and prediction, respectively. It was demonstrated that AF was a useful tool for model optimization as compared with genetic algorithm. Moreover, the sequence for selecting wavelengths and spectral pretreatment methods had great influence in model performance and could be decided by try-out approach since the performance due to selection sequence was sensitive to spectral range and optimization algorithm employed. Nevertheless, wavelength-then-pretreatment scheme was preferred due to simpler model structure and reduced computation loads. It was shown that Vis-NIR spectroscopy was feasible in quantifying beef adulteration, and AF was an advance method for optimizing model performance. Moreover, AF could be expended in more studies for optimizing food quality and safety analysis.

The impact of meteorological conditions and human factors on field spraying test of UAV was likely to cause problems such as chemical drift in the wind and uncontrollable droplet deposition areas. Thus research on droplets deposition distribution by UAV spraying was carried out in wind tunnel, in which the wind speed was controllable and adjustable. The influence mechanism of parameters such as airflow field parameters, droplet properties, and relative positions of nozzle-target on distribution of droplet deposition was discussed. The test results showed wind speed, flying height and droplet diameter were the most significant factors, rotor airflow was a significant factor, and nozzle spraying angle was not significant to affect deposition distribution. The influencing order of each factor on droplet deposition distribution was wind speed, flying height, droplet diameter, rotor airflow and nozzle spraying angle. The test results and obtained model could provide a theoretical basis for operating conditions, spraying parameters selecting and droplet deposition area predicting during UAV spraying in field.

The power generation device is developing rapidly, which focused on energy cleaning and inconvenient power supplying. This paper proposed a generation device which is mainly used to supply power for small power station such as plant factory lighting system. Firstly, fesibility analysis of piezoelectric power generation was made based on mathematic model. After that, installation mode of piezoelectric vibrator was determined. Secondly, shape of piezoelectric vibrator and suitable working thickness in the breeze were analyzed by ANSYS simulation. Finally, structural design was conducted by using SOLIDWORKS and 3D model of power supply device is established. Prototype making and test verification was carried out based on 3d model. The experimental results show that generation device has high stability and feasibility, which provide technical reference for optimization and improvement of piezoelectricity electric power facility.

The movement and deposition patterns of pesticide liquids in complex airflow field were difficult to determine, and thus led to some problems such as low utilization efficiency of pesticide and reduce quality of agricultural products. By using CFD technology, simulation test was carried out to study the droplet deposition characteristics within range of -1.5m to 4m in the downwind direction of UAV based on multi-feature fusion. The simulation boundary conditions and simulation parameters of 3-dimentional air field space were defined according to the spraying parameters measured from the UAV. The results showed that: droplet deposition was distributed in parabolic form. The deposition of droplets decreased with the increase of airflow velocity and spray height, and deposits scale was gradually moved away from the nozzle. When airflow velocity was greater than 3m/s, or spray height was more than 1.3m, it was not suitable for spraying. The deposition increased with the increase of droplet size, and deposits scale reduced. The change of spray angle caused a certain influence on distribution of droplet deposition, and it was a main factor that affects deposition characteristic. The result would provide a theoretical reference for spraying parameters determination and operating conditions selection.

Superplastic tensile behavior and deformation mechanism of TC18 titanium alloy were investigated by high temperature tensile test at 720-950℃ with initial strain rates of 6.7×10-5s-1-3.3×10-1s-1.The results show that under the optimal superplastic deformation condition (890℃ and 3.3×10-4s-1),the maximum elongation is 470%, the peak stress is 17.93MPa and with uniform grain size.Below the phase transus Tβ,the elongation firstly increases and then decreases.A maximum elongation of 373% is obtained at 830℃ and with initial strain rate of 3.3×10-4s-1 and the peak stress is 31.45MPa.The superplastic deformation mechanism of the TC18 titanium alloy in two-phase region is mainly grain rotation and boundary sliding,and the deformation coordination mechanism is dislocation slipping and climbing; the superplastic deformation mechanism in single phase region is intragranular dislocation motion and the deformation coordination mechanism is dynamic recovery and dynamic recrystallization.

Functional MRI (fMRI) of the resting-state utilizes spontaneous fluctuations in metabolic and hemodynamic signals to indirectly infer the underlying local changes in neuronal activity. For correct interpretation of spontaneous fluctuations and functional connectivity in the resting-state, it is important to characterize the neuronal mechanisms of fMRI in animal models. Animal studies of the evoked response and resting-state commonly use dexmedetomidine sedation. It has been demonstrated that dexmedetomidine combined with potent sensory stimuli is prone to inducing seizures in Sprague-Dawley (SD) rats. To characterize these seizures, here we combined optical imaging of intrinsic signals and cerebral blood flow with neurophysiological recordings. We characterize the susceptibility to seizures as a function of time from the beginning of dexmedetomidine administration. We show that these seizures are associated with spatially extensive high-amplitude cerebral blood flow and blood oxygenation responses, prone to be misinterpreted as normal responses in functional imaging studies that do not use neurophysiological recordings. We demonstrate that such seizures are generated not only in SD rats but also in Long-Evans rats. In contrast, we did not observe any seizures in C57BL6 mice that were similarly sedated with dexmedetomidine and stimulated with similar potent stimuli. We conclude that caution should be practiced in experiments that combine the administration of potent stimuli with dexmedetomidine sedation because high-amplitude hemodynamic responses evoked by peripheral stimulations are possibly due to the induction of epileptic activity in the cortical brain area. We further conclude that the susceptibility to dexmedetomidine-induced seizures is species dependent.

A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria&ndash, Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

Hyperspectral imaging was exploited for its potential in direct and fast determination of Pseudomonas loads in raw chicken breast fillets. A line-scan hyperspectral imaging system (900-1700 nm) was employed to obtain sample images, which were then further corrected, modified and processed. The prepared images were correlated with the true Pseudomonas counts of these samples using partial least squares (PLS) regression. To enhance model performance, different spectral extraction approaches, spectral preprocessing methods as well as wavelength selection schemes based on genetic algorithm were investigated. The results revealed that extraction of mean spectra is more efficient for representation of sample spectra than computation of median spectra. The best full wavelength model was attained based on spectral images preprocessed with standard normal variate, and the correlation coefficients (R) and root mean squared errors (RMSEs) for the model were above 0.81 and below 0.80 log10 CFU g(-1), respectively. In development of simplified models, wavelengths were selected by using a proposed two-step method based on genetic algorithm. The best model utilized only 14 bands in five segments and produced R and RMSEs of 0.91 and 0.55 log10 CFU g(-1), 0.87 and 0.65 log10 CFU g(-1) as well as 0.88 and 0.64 log10 CFU g(-1) for calibration, cross-validation and prediction, respectively. Moreover, the prediction maps offered a novel way for visualizing the gradient of Pseudomonas loads on meat surface. Hyperspectral imaging is demonstrated to be an effective tool for nondestructive measurement of Pseudomonas in raw chicken breast fillets.

Near infrared (NIR) hyperspectral imaging (HSI) and different spectroscopic transforms were investigated for their potential in detecting total viable counts in raw chicken fillets. A laboratory-based pushbroom hyperspectral imaging system was utilized to acquire images of raw chicken breast fillets and the resulting reflectance images were corrected and transformed into hypercubes in absorbance and Kubelka-Munck (K-M) units. Full wavelength partial least regression models were established to correlate the three spectral profiles with measured bacterial counts, and the best calibration model was based on absorbance spectra, where the correlation coefficients (R) were 0.97 and 0.93, and the root mean squared errors (RMSEs) were 0.37 and 0.57 log10 colony forming units (CFU) per gram for calibration and cross validation, respectively. To simplify the models, several wavelengths were selected by stepwise regression. More robustness was found in the resulting simplified models and the model based on K-M spectra was found to be excellent with an indicative high ratio of performance to deviation (RPD) value of 3.02. The correlation coefficients and RMSEs for this model were 0.96 and 0.40 log10 CFU per gram as well as 0.94 and 0.50 log10 CFU per gram for calibration and cross validation, respectively. Visualization maps produced by applying the developed models to the images could be an alternative to test the adaptability of a calibration model. Moreover, multi-spectral imaging systems were suggested to be developed for online applications.

In this paper,the electromagnetic responses and potential applications of THz metamaterials are reviewed through the focus on fabrication,unit structure design,and material selection,respectively.It describes different kinds of fabrication technologies for obtaining metamaterials with special electromagnetic responses such as magnetic resonance and reconfigurable tunability,which is helpful for further understanding of electromagnetic resonances in metamaterials.The paper analyzes the electromagnetic response characteristics in detail and points out that the unit structure design can be used to obtain desired electromagnetic characteristics,such as anisotropy,bianisotropy,polarization modulation,multiband response,broadband response,asymmetric transmission,optical activity,and perfect absorption,etc.The dependence of electromagnetic responses upon surrounding dielectrics can be used not only to control resonant frequency by a proper substrate selection,but also for sensing applications.Furthermore,the introduction of functional materials with controllable dielectric properties by external optical field,electrical field,magnetic field and temperature has the potential to achieve tunable metamaterials,which is highly desirable for THz functional devices.Finally,the opportunities and challenges for further developments of THz metamaterials are briefly introduced.

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

The overall qualities of chestnuts are greatly affected by worm-eaten chestnuts, as they lead to a reduction of profits. Hence a fast, accurate and non-destructive method for sorting chestnuts is in great demand. In this study, the technology of machine vision was employed to grade chestnuts. A recognition method to identify worm-eaten chestnuts is presented based on the edge image of the wormhole. First, by applying a Sobel operator, binary images were gained through extracting the edges of the gray images, which were preprocessed with the denoising method of a Wiener filter. The binary image contained both the edge of the contour and the wormhole. The wormhole edges were obtained through separating the wormhole edge in light of the character that the gray degree of pixels in the neighborhood of the wormhole edge is lower than the threshold value set. Second, through morphological dilating and eroding, the denoised wormhole edge images were obtained. The connected component of the binary images of the wormhole edge were labeled, and the first three longest components were considered as feature values of the worm channel, which were then normalized. Third, the normalized data were input to a back-propagation (BP) neural network for training, where the hidden layer was 7. And only three steps were needed for iteration. When the model was utilized for prediction, the recognition rate was as high as 100%. The results showed that the proposed worm-eaten chestnut recognition method is accurate and fast, and it can provide a basis for on-line detection. Since the gray degree of the wormhole region is close to the normal region, this study used an enhanced boundary detection method to extract the edge of the worm channel solely, rather than the normally used region segmentation.

As an alternative energy of conventional resources, coalbed methane (CBM) has been studied globally. So far, the pressure distribution model for multi-stage fractured horizontal well (MFW) in stress sensitive coal seam is almost solved by numerical method. In this paper, a transient pressure behaviour model of MFW was established. Using perturbation transformation, Laplace transform, image theory and superposition principle the mathematical model was solved. According to the result of calculation, the flow process of MFW can be identified as six regimes. Stress-sensibility primarily influences the latter five stages. The well bore dimensionless pressure drop is several times larger comparing with the situation that does not take the stress sensitive into account. Accordingly, the influences of some of the critical parameters on the transient pressure behaviour were studied, including the fracture number, permeability modulus, storage ratio, and so on. [Received: August 25, 2016; Accepted: October 9, 2017]

Gated power supply is a key control circuit unit of image intensifier gated imaging. It has a great significance for the reduction of gated LLL image intensifier volume and the improvement of image intensifier imaging quality to carry out the research of small volume cathode high-voltage pulse circuit of gated power supply. By analyzing the photocathode gated operational principle of LLL image intensifier, this paper is to design a small volume cathode high-voltage pulse circuit for the gated image intensifier, and it gives the circuit block diagram and experimental results of gated power supply's cathode high-voltage pulse.

The image intensifier is special auto-gating power supply is an important component of the LLL image intensifier. It is the image intensifier tubes provide cathode, micro channel plate (MCP), anode the required voltage. It can realize automatic brightness control and image intensifier cathode protection function. The purpose of this paper is to design a cathode pulse control circuit which can protect the gating mode of auto-gating power supply and combining the independent cathode gating technology.

To solve the damping problem of water hammer wave in the modeling method of water diversion system of hydropower station, this paper introduces the feedback regulation technology from head to flow, that is: A fixed water head is taken out for flow feedback, and the following conclusions are obtained through modeling and simulation. Adjusting the feedback coefficient F of the water head to the flow rate can change the damping characteristic of the system, which can simulate the attenuation process of the water shock wave in the true water diversion pipeline. Even if a small feedback coefficient is introduced, the damping effect of the system is very obvious, but it has little effect on the amplitude of the first water shock wave after the transition process. Therefore, it is feasible and reasonable to introduce water head to flow rate feedback coefficient F in hydraulic turbine diversion system.

Proton radiography is used for advanced hydrotesting as a new type radiography technology due to its powerful penetration capability and high detection efficiency. A new proton radiography terminal will be developed to radiograph static samples at Institute of Modern Physics of Chinese Academy of Science (IMP-CAS). The proton beam with the maximum energy of 2.6 GeV will be produced by Heavy Ion Research Facility in Lanzhou-Cooling Storage Ring (HIRFL-CSR). The proton radiography terminal consists of the matching magnetic lens and the Zumbro lens system. In this paper, the design scheme and all optic parameters of this beam terminal for 2.6GeV proton energy are presented by simulating the beam optics using WINAGILE code. My-BOC code is used to test the particle tracking of proton radiography beam line. Geant4 code and G4beamline code are used for simulating the proton radiography system. The results show that the transmission efficiency of proton without target is 100%, and the effect of secondary particles can be neglected. In order to test this proton radiography system, the proton images for an aluminum plate sample with two rectangular orifices and a step brass plate sample are respectively simulated using Geant4 code. The results show that the best spatial resolution is about 36{\mu}m, and the differences of the thickness is not greater than 10%., Comment: 10 pages,8 figures

A proton recoil method for measuring D-T neutron energy spectra using polyethylene film and Si (Au) surface barrier detector was presented. An iteration algorithm for unfolding the recoil proton energy spectrum to the neutron energy spectrum was investigated. The response matrixes R of polyethylene film at 0 degree and 45 degree were obtained by simulating the recoil proton energy spectra from the mono-energetic neutron using MCNP code. Under an assumed D-T neutron spectrum, the recoil proton spectra from polyethylene film at 0 degree and 45 degree were also simulated using MCNP code. Based on the response matrixes R and the simulated recoil proton spectra at 0 degree and 45 degree, the unfolded neutron spectra were respectively obtained using the iteration algorithm, and compared with the assumed neutron spectrum. The results show that the iteration algorithm method can be applied to unfold the recoil proton energy spectrum to the neutron energy spectrum for D-T neutron energy spectra measurement using recoil proton method., 9 pages,7 figures,submitted to Chinese Physics C

Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed.

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

Purpose: Cancer stem–like cells have been well accepted to be involved in recurrence and metastasis of cancers, but the prognostic potential of biomarkers integrating with metastasis and cancer stem–like cells for colorectal cancer is unclear. Experimental Design: We identified three proteins, CLIC4, ERp29, and Smac/DIABLO, from metastatic cancer stem–like cells of colorectal cancer and verified the proteins' role in metastatic behaviors. The proteins were detected by IHC in colorectal cancer tumors and matched colonic mucosa from patients with colorectal cancer who underwent radical surgery in the training cohort. The associations between proteins expression levels and five-year disease-specific survival (DSS) were evaluated to predict the survival probability in the training cohort of 421 cases and the validation cohort of 228 cases. Results: A three-protein panel including CLIC4, ERp29, and Smac/DIABLO, which was generated from multivariate analysis by excluding clinicopathologic characteristics from the training cohort, distinguished patients with colorectal cancer into very low-, low-, middle-, and high-risk groups with significant differences in five-year DSS probability (88.6%, 63.3%, 30.4%, 11.4%; P < 0.001). The panel is independent from tumor–node–metastasis staging system and histologic grading to predict prognosis, and also enables classification of validation cohort into four risk stratifications (five-year DSS probability is 98.2%, 80.2%, 25.6%, and 2.7%; P < 0.001). Conclusions: CLIC4, ERp29, and Smac/DIABLO integrated into a novel panel based on cancer stem–like cells in association with metastasis stratify the prognostic risks of colorectal cancer. Prediction of risks with molecular markers will benefit clinicians to make decisions of individual management with postoperative colorectal cancer patients. Clin Cancer Res; 20(14); 3809–17. ©2014 AACR.

Bacterial pathogens are the main culprits for outbreaks of food-borne illnesses. This study aimed to use the hyperspectral imaging technique as a non-destructive tool for quantitative and direct determination of Enterobacteriaceae loads on chicken fillets. Partial least squares regression (PLSR) models were established and the best model using full wavelengths was obtained in the spectral range 930–1450 nm with coefficients of determination R2 ⩾ 0.82 and root mean squared errors (RMSEs) ⩽0.47 log10 CFU g−1. In further development of simplified models, second derivative spectra and weighted PLS regression coefficients (BW) were utilised to select important wavelengths. However, the three wavelengths (930, 1121 and 1345 nm) selected from BW were competent and more preferred for predicting Enterobacteriaceae loads with R2 of 0.89, 0.86 and 0.87 and RMSEs of 0.33, 0.40 and 0.45 log10 CFU g−1 for calibration, cross-validation and prediction, respectively. Besides, the constructed prediction map provided the distribution of Enterobacteriaceae bacteria on chicken fillets, which cannot be achieved by conventional methods. It was demonstrated that hyperspectral imaging is a potential tool for determining food sanitation and detecting bacterial pathogens on food matrix without using complicated laboratory regimes.

To investigate risk factors associated with peritoneal metastasis in colorectal cancer.Clinicopathological data of 873 patients with colorectal cancer treated at the Nanfang Hospital of Southern Medical University from January 2003 to December 2008 were retrospectively analyzed. The associations between peritoneal metastasis and clinicopathological factors were evaluated by univariate analysis including independent-samples t test and χ(2) test, and multivariable logistic regression analysis.Univariate analysis showed that age(P=0.032), tumor size(P=0.001), tumor differentiation(P=0.008), depth of bowel wall invasion(P=0.000), lymph node metastasis(P=0.000), serum carcinoembryonic antigen (CEA) level(P=0.003), and serum carbohydrate antigen 19-9(CA19-9) level(P=0.009) were associated with peritoneal metastasis. On multivariable logistic regression analysis, depth of bowel wall invasion, lymph node metastasis, serum CEA level and CA19-9 level remained as significant risk factors(all P0.05).Depth of bowel wall invasion, lymph node metastasis, serum CEA level, and CA19-9 level are risk factors for peritoneal metastasis in colorectal cancer.

Measurement of soil total nitrogen (STN) is urgently important concerning requirement of precise and quantitative fertilizer application. To overcome the shortage of routine chemical detection, vis- short wavelength near infrared spectroscopy (Vis-SWNIRS) was employed as an accurate, cheap and timely alternative. Kennard-Stone algorithm was utilized for sample set partitioning, where 22 of the 32 samples were selected as calibration set, and the remaining 10 were included in validation set. No outliers were discovered under criterion based on spectral Mahalanobis distance and Dixon testing. Partial least squares regression (PLSR) was used to build STN detection model based on Vis-SWNIRS with cross validation by leave-one-out method. As a result, 5 principal components turned to be optimal considering model performance, where correlation coefficients for calibration and validation were 0.9724 and 0.8691, respectively, with PRESS (Prediction Residual Error Sum of Square) of 0.0684. It is feasible to detect STN by Vis-SWNIR spectroscopy.

To construct a clinical database for laparoscopic colorectal surgery in Chinese population using computerized technique.A clinical database system was constructed and used in multi-/single-center studies on laparoscopic colorectal surgery.The data of more than 1200 cases in the Nanfang Hospital during the past year were collected retrospectively. The database was used as the platform for "Southern China Laparoscopic Colorectal Surgery Study Group (SCLCSG)" and was used in the first stage of the clinical research of "Multicenter retrospective study of laparoscopic and open procedure for colorectal cancer" among 11 hospitals in Southern China. In order to test the system, the database was also used in "comparative study on oncologic results of laparoscopic versus open radical resection for rectal carcinoma".The evaluation system is reliable and efficient. This system has established a clinical database for laparoscopic colorectal cancer surgery and can be widely applied for the clinical research for colorectal cancer.

In recent years, the carbon nanotube (CNT) emitters used for ion sources or gas sensors have been investigated, and the progress of several approaches such as field ionization and field desorption sources has been reported. However, a major concern for these applications is possible loss of CNTs caused by field evaporation, which can shorten the lifetimes of CNT-based emitters used for high electric field ion sources. So in CNT-based field emitter technology, emitter lifetime and degradation will be key parameters to be controlled. However, up to now only very few investigations in this direction have been conducted. The reason for this might lie in the fact that one often considers that the threshold value of field evaporation for a kind of material ( 40 V/nm) is much higher than the field of ionization or desorption ( 10 V/nm) according to the metal material characteristics (such as the threshold values of field evaporation for tungsten and molybdenum are 54 V/nm and 45 V/nm, respectively). In this work, the carbon nanotube thin-film (the density of CNTs is about 2.5108/cm2) is fabricated by screen-printing method, and the field evaporation behavior of CNT thin-film is studied experimentally in an ultrahigh vacuum system typically operating at a pressure of lower than 10-9 Torr after a 4-hour bake-out at ~200℃. Unlike the vertically aligned CNT array having higher electric field around the edge of the array because of the shielding effect, the printed CNT thin-film has more uniform distribution of electric field and is very easy to relize the mass production. The results show that the prepared CNT thin-film has quite obvious field evaporation behavior (some contaminants have deposited on the surface of grid after field evaporation, and energy-dispersive X-ray spectroscopy elemental mapping result of the grid indicates that the contaminants consist mainly of carbon elements), with turn-on field in a range of 10.0-12.6 V/nm, ion current could reach up to hundreds of pA. Meanwhile, the results with scanning electron microscope analysis and field electron emission measurement indicate that the CNT distribution turns into more non-uniform distribution after field evaporation; even some CNTs are directly dragged away from the substrate by the strong field. The field evaporation of CNT thin-film also leads to field electron emission onset voltage increasing from 240 V to 300 V, field enhancement factor decreasing from 8300 to 4200, and threshold field of field evaporation rising from 10.0 V/nm to 12.6 V/nm. However, the repeatability of sample treated by the field evaporation brings about an improvement to a certain extent. It could be understood in this way: upon applying a positive voltage, the most protruding parts, which have the strongest emissive capability, are evaporated first, which leads to the declined field enhancement factor; the parts of CNTs which have relatively weak emissive capability are not evaporated. So the uniformity of electric field is improved through reducing the difference in field enhancement factor rather than surface morphology between carbon nanotubes. The field evaporation of CNT thin-film is also a process which improves the uniformity of electric field. Therefore, the stability and repeatability of the field electron emission for carbon nanotube thin-film are improved naturally.