In vivo functional immunoprotection correlates for vaccines against invasive bacteria.

Vaccination has significantly reduced the incidence of invasive infections caused by several bacterial pathogens, including Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis. However, no vaccines are available for many other invasive pathogens. A major hurdle in vaccine development is the lack of functional markers to quantify vaccine immunity in eliminating pathogens during the process of infection. Based on our recent discovery of the liver as the major organ of vaccine-induced clearance of blood-borne virulent bacteria, we here describe a new vaccine evaluation system that quantitatively characterizes the key features of effective vaccines in shuffling virulent bacteria from the blood circulation to the liver resident macrophage Kupffer cells (KCs) and sinusoidal endothelial cells (LSECs) in mouse septic infection model. This system consists of three related correlates or assays: pathogen clearance from the bloodstream, pathogen trapping in the liver, and pathogen capture by KCs/LSECs. These readouts were consistently associated with the serotype-specific immunoprotection levels of the 13-valent pneumococcal polysaccharide conjugate vaccine (PCV13) against lethal infection of S. pneumoniae, a major invasive Gram-positive pathogen of community-acquired infections in humans. Furthermore, the reliability and sensitivity of these correlates in reflecting vaccine efficacy were verified with whole cell vaccines of Klebsiella pneumoniae and Escherichia coli, two major Gram-negative pathogens in hospital-acquired invasive infections. This system may be used as effective readouts to evaluate the immunoprotective potential of vaccine candidates in the preclinical phase by filling the current technical gap in vaccine evaluation between the conventional in vitro approaches (e.g. antibody production and pathogen neutralization/opsonophagocytosis) and survival of immunized animals.

Methyl Parathion Exposure Induces Development Toxicity and Cardiotoxicity in Zebrafish Embryos.

Methyl parathion (MP) has been widely used as an organophosphorus pesticide for food preservation and pest management, resulting in its accumulation in the aquatic environment. However, the early developmental toxicity of MP to non-target species, especially aquatic vertebrates, has not been thoroughly investigated. In this study, zebrafish embryos were treated with 2.5, 5, or 10 mg/L of MP solution until 72 h post-fertilization (hpf). The results showed that MP exposure reduced spontaneous movement, hatching, and survival rates of zebrafish embryos and induced developmental abnormalities such as shortened body length, yolk edema, and spinal curvature. Notably, MP was found to induce cardiac abnormalities, including pericardial edema and decreased heart rate. Exposure to MP resulted in the accumulation of reactive oxygen species (ROS), decreased superoxide dismutase (SOD) activity, increased catalase (CAT) activity, elevated malondialdehyde (MDA) levels, and caused cardiac apoptosis in zebrafish embryos. Moreover, MP affected the transcription of cardiac development-related genes (vmhc, sox9b, nppa, tnnt2, bmp2b, bmp4) and apoptosis-related genes (p53, bax, bcl2). Astaxanthin could rescue MP-induced heart development defects by down-regulating oxidative stress. These findings suggest that MP induces cardiac developmental toxicity and provides additional evidence of MP toxicity to aquatic organisms.

CeO2 modified carbon nanotube electrified membrane for the removal of antibiotics.

Electrified carbon nanotube membranes (ECM) are used as electroactive porous materials for the degradation of micropollutants. It integrated design of both electrochemical processes and filtration functions. In this study, CeO2 modified carbon nanotube electrified membrane (CeO2@CNT membrane) was prepared and activate NaClO towards degradation of antibiotics. As CeO2 with face-centered cubic (Fcc) fluorite structure was loaded onto the CNT sidewalls, the CeO2@CNT membrane showed a higher over potential and a smaller equivalent polarization resistance compared to ECM. More reactive oxygen species (ROS) and reactive chlorine species (RCS) were generated by CeO2@CNT membrane due to faster electron transfer at the solid-liquid interface. Thus, the removal efficiencies of DCF, SMX, CIP, TC and CBZ were more than 91.2%, 91.3%, 94.4%, 99.3% and 89.4% by the CeO2@CNT membrane with NaClO, respetively. And the apparent reaction rate constant (k) of the CeO2@CNT membrane was 2.9 times of that of ECM. The selective capping experiments and density functional theory (DFT) calculation showed that the oxygen vacancies of CeO2 contributed to the generation of ‧OH, and the generation of ClO‧ and ‧O2- would mainly occur on Lewis acid sites of CeO2. In addition, the CeO2@CNT membrane showed a reasonable stability to treat actual water samples and reduced disinfection byproducts (DBPs) formation, suggesting that it can potentially be combined with the conventional chlorine disinfection to degrade antibiotics in water.

Observation of the density dependence of the closed-channel fraction of a 6Li superfluid.

Atomic Fermi gases provide an ideal platform for studying pairing and superfluid physics, using a Feshbach resonance between closed-channel molecular states and open-channel scattering states. Of particular interest is the strongly interacting regime. We show that the closed-channel fraction [Formula: see text] provides an effective probe for important many-body interacting effects, especially through its density dependence, which is absent from two-body theoretical predictions. Here we measure [Formula: see text] as a function of interaction strength and the Fermi temperature [Formula: see text] in a trapped 6Li superfluid throughout the entire Bardeen-Cooper-Schrieffer-Bose-Einstein-condensate crossover, in quantitative agreement with theory when important thermal contributions outside the superfluid core are taken into account. Away from the deep-BEC regime, the fraction [Formula: see text] is sensitive to [Formula: see text]. In particular, our data show [Formula: see text] with [Formula: see text] at unitarity, in quantitative agreement with calculations of a two-channel pairing fluctuation theory, and [Formula: see text] increases rapidly into the BCS regime, reflecting many-body interaction effects as predicted.

Linear Characteristics of the Differences in Phase Tangents of Triple-Coil Electromagnetic Sensors and Their Application in Nonmagnetic Metal Classification.

Metal sorting is the first step in scrap metal recycling. The traditional magnetic separation method can classify ferromagnetic metals, but it is not applicable to some nonmagnetic metals with higher value. To address this situation, we propose an eddy current testing (ECT) technology-based method for classifying nonmagnetic metals. In this study, a triple-coil electromagnetic sensor, which works as two coil pairs, is tested. By analyzing the physical model of the sensor, a feature related to the conductivity of the sample under test is obtained as the difference in the tangent of the impedance changes in the two coil pairs. Additionally, we derive a linear relationship between this feature and the lift-off height, which is verified experimentally and will help to solve the classification error caused by the variation in the lift-off height. In addition, we find that the excitation frequency does not affect this linear feature. Moreover, in this study, the spectrum scanning method is converted into a single-frequency measurement, and the time consumption is greatly reduced, which improves the efficiency of the real-time metal classification system.

Conductivity Classification of Multi-Shape Nonmagnetic Metal Considering Spatial Position Drift Effect with a Triple-Coil Electromagnetic Sensor.

The primary step in metal recovery is metal classification. During eddy current testing (ECT), the shape of the sample can have an impact on the measurement results. To classify nonmagnetic metals in three shapes-planar, cylindrical, and spherical-a triple-coil electromagnetic sensor that operates as two coil pairs is used, and the difference in the phase tangent of the impedance change of the two coil pairs is used as a feature for the classification. The effect of spatial position drift between the sensor and the sample divided into lift-off vertically and horizontal drift horizontally on this feature is considered. Experimental results prove that there is a linear relationship between the feature and lift-off regardless of the metal shape, whereas horizontal drift has no effect on this feature. In addition, the slope of the curve between the feature and the lift-off is different for different shapes. Finally, a classification method eliminating the effect of lift-off variation has been constructed, and the classification accuracy of Cu-Al-Zn-Ti metals reached 96.3%, 96.3%, 92.6%, and 100%, respectively, with an overall correct classification rate of 96.3%.

Defect Detection Method for CFRP Based on Line Laser Thermography.

A continuous line laser scanning inspection technique for tracing load-bearing structures was developed and applied to defect detection of unidirectional carbon-fiber-reinforced polymers for aero engines. The heat transfer model of the material was analyzed using the finite element software COMSOL. Meanwhile, a laser platform was built and an image algorithm was used to verify the feasibility of the method. The potential of this technique for detecting defects and providing information on the location of defects in carbon fiber composites was analyzed. Results indicate line laser thermal imaging can successfully determine the size, location, and crack angle of surface damage with extremely high accuracy. The positioning accuracy error for impact and fracture defects is less than 20%, and the detection rate can reach 100% if the defect is in the special position of just leaving the heating area. The angle detection of fracture cracks can be accurate within 10°.

Inhibitory Effects against Alpha-Amylase of an Enriched Polyphenol Extract from Pericarp of Mangosteen (Garcinia mangostana).

The pericarp of mangosteen, a by-product of the mangosteen, is rich in polyphenols. In this study, an efficient and environmentally friendly method for preparative enrichment of polyphenols from mangosteen pericarp (MPPs) was developed, and the inhibitory effects on starch digestion were also evaluated. It was found that the optimal extract method of MPPs was at a solid to solvent ratio of 1:50 g/mL, pH of 2, and at 80 °C for 2 h. The IC50 of MPPs for α-amylase was 0.28 mg/mL. Based on the fluorescence quenching results, we presumed that MPPs could alter the natural structure of α-amylase, resulting in inhibitory activity on α-amylase. In addition, MPPs significantly reduced the blood glucose peak and AUC of glucose responses in rats after ingestion of the starch solution. Taken together, MPPs may have the potential as a functional supplement for blood glucose control and diabetes prevention.

Catalytic Transfer Hydrogenation of 5-Hydroxymethylfurfural with Primary Alcohols over Skeletal CuZnAl Catalysts.

Catalytic transfer hydrogenation (CTH) with alcohols has been increasingly employed as effective tool for biomass upgrading, however, relying predominantly on secondary alcohols. Herein, for the first time skeletal CuZnAl catalysts were employed for the activation of a primary alcohol, ethanol, for the hydrogenation 5-hydroxymethylfurfual (HMF) to 2,5-bis(hydroxymethyl)furan (BHMF) under a mild condition. The catalysts were extensively characterized to reveal the structure characteristics and surface compositions. Over 90 % yield of BHMF were obtained over the optimal CuZnAl-0.5 catalyst at the reaction temperatures of 100-120 °C. Reaction kinetics indicated a competitive adsorption between HMF and ethanol on the catalyst surface, with the activation of ethanol being the rate-determining step (apparent activation energy Ea =70.9 kJ mol-1 ). Preliminary adsorption investigation using combined attenuated total reflectance infrared spectroscopy and density functional theory calculation proposed a η2 -(O,O)-aldehyde, furoxy perpendicular configuration of HMF on catalyst surface. The catalyst was further applied to the CTH of various aldehydes to the corresponding alcohols with high yields, demonstrating the broad applicability of the current system.

Efficient photodegradation of PFOA using spherical BiOBr modified TiO2 via hole-remained oxidation mechanism.

Photo-induced holes (h+) oxidation is an efficient approach for perfluorooctanoic acid (PFOA; C7F15COOH) removal. To maintain a high amount of h+ on the surface of photocatalysts participating in the PFOA photodegradation could be a critical issue. Herein, a highly efficient spherical BiOBr-modified nano-TiO2 (P25) was synthesised and used for PFOA photodegradation through direct oxidation with h+. A high number of h+ could be generated and remain on the surface of P25/BiOBr due to the appropriate position of the conduction band (CB) and valence band (VB) levels between P25 and BiOBr. Meanwhile, PFOA molecules were coordinated to the P25/BiOBr's surface via unidentate binding, being directly activated and oxidised by h+, resulting in a decomposition yield of 99.5% (100 mg/L) under simulated solar light irradiation within 100 min, at the initial pH condition (3.5). A stepwise photodegradation pathway was proposed due to the significant intermediates detected as the short-chain perfluorinated carboxylic acids (C2-C7). Reactive oxygen species (ROS) generation, scavenging and trapping analysis indicated that the direct oxidation on h+ followed PFOA degradation. In a real aqueous environment of Tangxun lake (adjusted pH 3.5), stable common anions and natural organic matter (NOM) would restrain the PFOA photodegradation. However, adding 10 mg/L of NO3- or HA could reduce the inhibition effect of PFOA photodegradation. These findings gave an alternative strategy to drive an h+ directly oxidation to treat PFOA contaminated water bodies.

Paper-based detection of Epstein-Barr virus using asymmetric polymerase chain reaction and gold silicon particles.

A new paper-based lateral flow nucleic acid (LFNA) test platform was established in this study using asymmetric polymerase chain ceaction (A-PCR) for signal amplification. This new method allowed a visual detection of Epstein-Barr virus (EBV) nucleic acids with high specificity and low cost. In addition, as part of our strategy we employed a sandwich system of capture probe (CP)/gold nanoparticles (AuNPS) and silicon dioxide (SiO2) (AuNPS@SiO2) nanospheres/target DNA/avidin complexes as the sensing platform. Biotin-labeled target DNA was obtained by A-PCR and later introduced in the LF device. The CP/target DNA/AuNPS@SiO2 complexes were captured on the test zone by the specific reaction between biotin and avidin, and the remaining CP/AuNPS@SiO2 particles were captured on the quality control zone by the hybridization between CP and a quality control probe. Au@SiO2 accumulation in the test and quality control zones of the device enabled a visual detection of the specific target sequences. The method detection limit was 50 nM of the target DNA, which was lower than that of the LFNA biosensor (LFNAB) without PCR amplification and Au@SiO2 particles. In conclusion, the novel paper-based platform described here is a low cost, efficient and fast visual detection method that offers high sensitivity and other benefits compared to alternative methods in use.

Prediction of dMRI signals with neural architecture search.

BACKGROUND: There is growing interest in the neuroscience community in estimating and mapping microscopic properties of brain tissue non-invasively using magnetic resonance measurements. Machine learning methods are actively investigated to predict the signals measured in diffusion magnetic resonance imaging (dMRI). NEW METHOD: We applied the neural architecture search (NAS) to train a recurrent neural network to generate a multilayer perceptron to predict the dMRI data of unknown signals based on the different acquisition parameters and training data. The search space of NAS is the number of neurons in each layer of the multilayer perceptron network. To our best knowledge, this is the first time to apply NAS to solve the dMRI signal prediction problem. RESULTS: The experimental results demonstrate that the proposed NAS method can achieve fast training and predict dMRI signals accurately. For dMRI signals with four acquisition strategies of double diffusion encoding (DDE), double oscillating diffusion encoding (DODE), multi-shell and DSI-like pulsed gradient spin-echo (PGSE), the mean squared errors of the multilayer perceptron network designed by NAS are 0.0043, 0.0034, 0.0147 and 0.0199, respectively. COMPARISON WITH EXISTING METHOD(S): We also compared NAS with other machine learning prediction methods, such as support vector regression (SVR), decision tree (DT) and random forest (RF), k-nearest neighbors (KNN), adaboost regressor (AR), gradient boosting regressor (GBR) and extra-trees regressor (ET). NAS achieved the better prediction performance in most cases. CONCLUSION: In this study, NAS was developed for the prediction of dMRI signals and could become an effective prediction tool.

Fabrication and Calibration of Pt-Rh10/Pt Thin-Film Thermocouple.

Aiming at the dynamic testing of the ignition temperature of micro-initiating explosives, a novel Pt-Rh10/Pt thin-film thermocouple was designed in this paper. The author carried out the preparation of the thermocouple by using a screen printing process on an Al2O3 ceramic substrate. The formed thermocouple was made of Pt-Rh10 wire and Pt wire as compensation wires, with a size of ≤ 1 mm and a thickness of about 6 µm. In the testing process, the static calibration of the thermocouple at 50~600 °C and 650~1500 °C was completed by a portable temperature verification furnace and a horizontal high temperature verification furnace, and the results showed that the Seebeck coefficient of the thermocouple was about 10.70 µV/°C, and its output voltage-temperature curve was similar to that of a standard S-type thermocouple, which achieved the effective temperature measurement up to 1500 °C. The dynamic response of Pt-Rh10/Pt thin-film thermocouple was then tested and studied using the pulsed laser method, and the results show that the time constant of the thermocouple prepared in this paper is about 535 µs, which has the characteristics of fast response and high precision high-temperature testing. Compared with the traditional thin-film thermocouple, the thermocouple has excellent electrical conductivity, more oxidation resistance, the surface layer is not easy to peel off and other advantages.

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure.

Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Hybrid neural network based on novel audio feature for vehicle type identification.

Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.

Groove-Assisted Global Spontaneous Alignment of Carbon Nanotubes in Vacuum Filtration.

Ever since the discovery of carbon nanotubes (CNTs), it has long been a challenging goal to create macroscopically ordered assemblies, or crystals, of CNTs that preserve the one-dimensional quantum properties of individual CNTs on a macroscopic scale. Recently, a simple and well-controlled method was reported for producing wafer-scale crystalline films of highly aligned and densely packed CNTs through spontaneous global alignment that occurs during vacuum filtration (Nat. Nanotechnol. 2016, 11, 633). However, a full understanding of the mechanism of such global alignment has not been achieved. Here, we report results of a series of systematic experiments that demonstrate that the CNT alignment direction can be controlled by the surface morphology of the filter membrane used in the vacuum filtration process. More specifically, we found that the direction of parallel grooves pre-existing on the surface of the filter membrane dictates the direction of the resulting CNT alignment. Furthermore, we intentionally imprinted periodically spaced parallel grooves on a filter membrane using a diffraction grating, which successfully defined the direction of the global alignment of CNTs in a precise and reproducible manner. These results are promising not only for developing novel devices based on macroscopically aligned CNTs but also for understanding the microscopic physical mechanism of the alignment process.

Oscillatory-like expansion of a Fermionic superfluid.

We study the expansion behaviors of a Fermionic superfluid in a cigar-shaped optical dipole trap for the whole BEC-BCS crossover and various temperatures. At low temperature (0.06(1)TF), the atom cloud undergoes an anisotropic hydrodynamic expansion over 30 ms, which behaves like oscillation in the horizontal plane. By analyzing the expansion dynamics according to the superfluid hydrodynamic equation, the effective polytropic index γ¯ of Equation-of-State (EoS) of Fermionic superfluid is extracted. The γ¯ values show a non-monotonic behavior over the BEC-BCS crossover, and have a good agreement with the theoretical results in the unitarity and BEC side. The normalized quasi-frequencies of the oscillatory expansion are measured, which drop significantly from the BEC side to the BCS side and reach a minimum value of 1.73 around 1/kFa=-0.25. Our work improves the understanding of the dynamic properties of strongly interacting Fermi gas.

Anthocyanins from black peanut skin protect against UV-B induced keratinocyte cell and skin oxidative damage through activating Nrf 2 signaling.

Excessive Ultraviolet (UV) irradiation induces skin damage. In the present study, the potential protective activity of anthocyanins (cyanidin-3-O-sophoroside and cyanidin-3-O-sambubioside) from black peanut against skin damage induced by UV-B was evaluated in vitro and in vivo. Treatment with anthocyanins significantly reversed UV-B induced oxidative damage and following apoptotic death in human HaCaT cells. Nuclear-factor-E2-related factor 2 (Nrf 2) was activated by anthocyanins through Nrf 2 protein stabilization and nuclear translocation, along with the expressions of antioxidant responsive element (ARE)- related genes (HO1, GCLC and NOQ1). Nrf 2 knockdown in HaCaT cells by targeted-shRNA plasmid markedly abolished the protective activity of anthocyanins against UV-B irradiation. Additionally, topical application of anthocyanins (5 mg cm-2) inhibited UV-B induced oxidative stress and cell apoptosis in BALB/c mouse skin tissues. The protective effect of anthocyanins can be explained by the regulation of oxidative-stress and the suppression of cell apoptosis through the activation of Nrf-2 by interaction with the MAPK and NF-κB signaling pathways. Our results suggested that anthocyanins from black peanut skin might be used as a potential photochemo-protective agent against UV-B induced skin damage.

TiO2 Facets Shaped by Concentration-Dependent Surface Diffusion of Dopamine.

Facet engineering highlights the fundamental understanding of kinetic growth of facets with capping agents. Here, we provide a roadmap for modulating TiO2 facets using dopamine as a capping agent inspired by density functional theory calculations and molecular dynamics simulations. Our calculations revealed that the surface diffusion of dopamine and their facet-specific affinity direct the kinetic growth of TiO2{100} and {101} facets into a nonequilibrium crystal shape. Our TiO2 synthesis agreed well with the theoretical predictions, suggesting that the concentration-dependent diffusion is central in accurately tuning a desirable ratio of mixed facets. Our findings shed new light on the diffusion-limited kinetically controlled facet growth mechanism, and this fine-tuning of mixed facets on a single crystal provides a general approach to design and fabricate facets on metal oxides.