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Purpose: Deep learning is an emerging reconstruction method for positron emission tomography (PET), which can tackle complex PET corrections in an integrated procedure. This paper optimizes the direct PET reconstruction from sinogram on a long axial field of view (LAFOV) PET. Methods: This paper proposes a novel deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network, where the perceptual loss is used with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra long axial FOV (LAFOV) PET/CT. The patients are randomly split into a training dataset of 60 patients, a validation dataset of 10 patients, and a test dataset of 10 patients. The 3D sinograms are converted into 2D sinogram slices and used as input to the network. In addition, the vendor reconstructed images are considered as ground truths. Finally, the proposed method is compared with DeepPET, a benchmark deep learning method for PET reconstruction. Results: Compared with DeepPET, the proposed network significantly reduces the root-mean-squared error (NRMSE) from 0.63 to 0.6 (p < 0.01) and increases the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) from 0.93 to 0.95 (p < 0.01) and from 82.02 to 82.36 (p < 0.01), respectively. The reconstruction time is approximately 10 s per patient, which is shortened by 23 times compared with the conventional method. The errors of mean standardized uptake values (SUVmean) for lesions between ground truth and the predicted result are reduced from 33.5 to 18.7% (p = 0.03). In addition, the error of max SUV is reduced from 32.7 to 21.8% (p = 0.02). Conclusion: The results demonstrate the feasibility of using deep learning to reconstruct images with acceptable image quality and short reconstruction time. It is shown that the proposed method can improve the quality of deep learning-based reconstructed images without additional CT images for attenuation and scattering corrections. This study demonstrated the feasibility of deep learning to rapidly reconstruct images without additional CT images for complex corrections from actual clinical measurements on LAFOV PET. Despite improving the current development, AI-based reconstruction does not work appropriately for untrained scenarios due to limited extrapolation capability and cannot completely replace conventional reconstruction currently. [ABSTRACT FROM AUTHOR]

Graphene lateral spin valves (LSVs) are promising devices for future memory and magnetic field sensing applications. In this article, we study the dependence of the nonlocal spin resistance, $\Delta {R}_{{\text {NL}}}$ , and the baseline resistance, ${R}_{{\text {BS}}}$ , as a function of the graphene channel width, ${W}$. The scaling trend is quantitatively assessed by using graphene deposited by chemical vapor deposition, which provides a large number of devices with consistent performance. As ${W}$ is scaled from 10 to $0.5~\mu \text{m}$ , the change in $\Delta {R}_{{\text {NL}}}$ matches the theory of contact-induced spin relaxation with a current spin polarization of 3%–5% and a spin diffusion length of $\lambda _{s} = 1.5$ – $2.5~\mu \text{m}$. We also observe a systematic and dramatic decrease in ${R}_{{\text {BS}}}$ , which we attribute to the reduction in charge current spreading. However, we find in the narrowest devices that a small ${R}_{{\text {BS}}}$ remains that arises due to thermoelectric effects, and this trend is confirmed using gate voltage- and charge current-dependent analyses. Finally, we introduce a nonideality factor, ${m} = \vert {R}_{{\text {BS}}} / \Delta {R}_{{\text {NL}}}\vert $ , as a figure of merit to describe the suppression of the baseline relative to the spin signal. In an LSV with ${L} = {1.5}\,\,\mu \text{m}$ , ${W} = {0.5}\,\,\mu \text{m}$ , and n-type conduction, the nonideality factor is as low as ${m} = {0.0252}\,\,\pm \,\,0.0202$ at room temperature showing that nearly ideal bipolar and symmetric spin signals can be achieved in graphene LSVs. [ABSTRACT FROM AUTHOR]

Polarimetric radars make it possible to retrieve information on hydrometeors types, sizes and concentrations. Additional information on cloud electrification can be obtained from Lightning Mapping Arrays (LMAs). To study the development time and height of the hydrometeors and electrification require tracking their evolution within the lifecycle of convective cells. A new methodology for multi-cell identification and tracking (MCIT) is presented in this study. The algorithm in this study is different from traditional tracking methods; this new algorithm is applied to time series of radar volume scans. It tracks local maxima of vertically integrated liquid (VIL) water by identifying the two cells in consecutive radar scans that have maximum common VIL. The vertical profile of the polarimetric variables is used for constructing the time-height cross section of the cells' microphysical properties around the peak reflectivity as a function of height. The LMA sources that occur within the cell area are integrated as a function of height as well for each time step, as determined by the radar volume scans. The result of the tracking can provide insights on the evolution of storms, hydrometer types, precipitation initiation and cloud electrification under different thermodynamic and aerosol conditions. The details of the MCIT algorithm, its products and their performance for different type of storms are described in this paper. • Development of a novel cloud cell tracking algorithm. • Tracking cloud types include: convective and stratiform, isolated and clustered. • Tracking cloud cells through maturation into stratiform storm elements. • Use a synergy of polarimetric radar, satellite, lightning detection and model data. • Algorithm has flexible output options and possibilities for further studies. [ABSTRACT FROM AUTHOR]

Stochastic computing (SC) is a promising technology that can be used for low-cost hardware designs. However, SC suffers from its long latency. Although parallel processing can efficiently shorten the latency, duplicated stochastic number generators (SNGs) are necessary, which cause substantial hardware overhead. This paper proposes a scalable SNG based on the spin-Hall-effect (SHE), which is capable of generating multiple independent stochastic streams simultaneously. The design takes advantages of the efficient charge-to-spin conversion from the Spin-Hall material and the intrinsic stochasticity of nanomagnets. Compared to previous spintronic SNGs, the SHE-SNG can reduce the area by $1.6\times -7.8\times $ and the power by $4.9\times -13\times $ while increasing the degree of parallelism from 1 to 16. Compared to CMOS-based SNGs, the proposed SNG obtained $24\times -120\times $ and $53\times $ reduction in terms of area and power, respectively. Finally, three benchmarks were implemented, and the results indicate that SC implementations with the proposed SHE-SNG can achieve $1.2\times -29\times $ reduction of hardware resources compared to implementations with previous CMOS- and spintronic-based designs while scaling the degree of parallelism from 1 to 64. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • NiO, a new cataluminescence sensing material, was synthesized by a simple template-free hydrothermal method. • Camellia-like NiO with high specific surface area showed excellent sensing performance towards H 2 S. • The sensing mechanism was studied which proved the main emission peak is from SO 2 *. Abstract In this work, three different microstructures of NiO nanomaterials including tremella, camellia and hexagonal nanosheets were successfully synthesized by a simple template-free hydrothermal method. Among them, camellia-like NiO was reported and applied to cataluminescence (CTL) for the first time. SEM, TEM, XRD, XPS, BET and BJH were employed to characterize the as-prepared samples. As sensing materials, these NiO with various morphologies showed different CTL capabilities to detect H 2 S, and the camellia-like NiO exhibited the best catalytic performance with high sensitivity and selectivity, which may be attributed to the synergistic effect of its high specific surface area, porous nanostructure, as well as the surface adsorption oxygen. After optimization, a good linear relationship was obtained in the range of 0.8–30.8 ppm and the detection limit (S/N = 3, LOD) was 0.3 ppm. Sixteen common volatile organic compounds (VOCs), such as carbon disulfide, ethyl mercaptan and formaldehyde, were discussed as possible interferent components, and the sensing mechanism of the CTL sensing towards H 2 S was discussed in detail. Camellia-like NiO could be used as a new sensing material for H 2 S monitoring. [ABSTRACT FROM AUTHOR]

Cataluminescence (CTL), one kind of the chemiluminescence, generates on the surface of solid catalyst during a heterogeneous catalytic oxidation reaction with the advantages of high sensitivity, fast response, simple instrument and longtime stability. This review provides the advances in CTL, mainly discussed the developments of sensing materials, detection technologies and application of CTL in recent 3 years. Also, we proposed various prospects and expectations of CTL-based sensor system at the end of this paper. [ABSTRACT FROM AUTHOR]

An ac small signal model for lateral spin valves (LSVs) is developed, and is applied to two different types of devices: spin transmitters and magnetic field sensors. The small signal models explicitly decouple the charge- and spin-dependent components, which describe the intrinsic RC delay and the ac spin accumulation dynamics, separately. The analysis is applied to graphene LSVs as an example to illustrate the optimization strategies for LSV devices. By using the charge-spin circuit model, the scattering parameters and the reflection coefficients at the output terminals are quantitatively calculated as a function of frequency. This model can be used as a guide for future LSV design and to benchmark performance across different material systems. [ABSTRACT FROM AUTHOR]

During development, melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) become light sensitive much earlier than rods and cones. IpRGCs project to many subcortical areas, whereas physiological functions of these projections are yet to be fully elucidated. Here, we found that ipRGC-mediated light sensation promotes synaptogenesis of pyramidal neurons in various cortices and the hippocampus. This phenomenon depends on activation of ipRGCs and is mediated by the release of oxytocin from the supraoptic nucleus (SON) and the paraventricular nucleus (PVN) into cerebral-spinal fluid. We further characterized a direct connection between ipRGCs and oxytocin neurons in the SON and mutual projections between oxytocin neurons in the SON and PVN. Moreover, we showed that the lack of ipRGC-mediated, light-promoted early cortical synaptogenesis compromised learning ability in adult mice. Our results highlight the importance of light sensation early in life on the development of learning ability and therefore call attention to suitable light environment for infant care. [Display omitted] • Light sensation through ipRGCs early in life promotes cortical synaptogenesis • Light enhances CSF oxytocin level via an ipRGCs-SON-PVN oxytocin neuron circuit • Oxytocin is essential for light-promoted ipRGC-mediated synaptogenesis • Lack of light sensation in early development impairs learning ability in adult mice Light sensation by intrinsically photosensitive retinal ganglion cells early in life ensures proper learning ability in adult mice by activating oxytocin-releasing neurons, which in turn promotes cortical synaptogenesis. [ABSTRACT FROM AUTHOR]

In order to analyze the brain shift situation accurately, we need to register the medical image and analyze its deformation. In this paper, we introduce a framework with volume-preserving registration for brain shift analysis. First, a volume-preserving mapping is introduced for general manifolds supported by a rigorous continuous theory. The registration is then performed on the spherical tetrahedron mesh with MRI gray values. The registration can retain the equality of local volume elements while registering the manifold to a template at the same time. We use simulated brain shift data to test our method. The results show that our method can efficiently register the brain while preserving the volume of each vertex. [ABSTRACT FROM AUTHOR]

We propose a novel method to analyze a set of poses of 3D models that are represented with triangle meshes and unregistered. Different shapes of poses are transformed from the 3D spatial domain to a geometry spectrum domain that is defined by Laplace-Beltrami operator. During this space-spectrum transform, all near-isometric deformations, mesh triangulations and Euclidean transformations are filtered away. The different spatial poses from a 3D model are represented with near-isometric deformations; therefore, they have similar behaviors in the spectral domain. Semantic parts of that model are then determined based on the computed geometric properties of all the mapped vertices in the geometry spectrum domain. Semantic skeleton can be automatically built with joints detected as well. The Laplace-Beltrami operator is proved to be invariant to isometric deformations and Euclidean transformations such as translation and rotation. It also can be invariant to scaling with normalization. The discrete implementation also makes the Laplace-Beltrami operator straightforward to be applied on triangle meshes despite triangulations. Our method turns a rather difficult spatial problem into a spectral problem that is much easier to solve. The applications show that our 3D pose analysis method leads to a registration-free pose analysis and a high-level semantic part understanding of 3D shapes. [ABSTRACT FROM AUTHOR]

[Display omitted] • ZnMn 2 O 4 nanomaterial was firstly used in cataluminescence sensor. • The novel sensor exhibited excellent selectivity and high sensitivity toward H 2 S. • The cataluminescence sensing mechanism of H 2 S on ZnMn 2 O 4 -0.3 was further explored using density functional theory. Regarded as a highly toxic pollutants, H 2 S can severely threaten human health. Therefore, it is critical to develop an efficient sensing material to detect H 2 S gas. In this study, three nanomaterials—ZnMn 2 O 4 , ZnMn 2 O 4 -0.3, and ZnMn 2 O 4 -0.6—were successfully prepared via a simple hydrothermal method that is modified by the use of carbon spheres, and then applied to the field of cataluminescence (CTL). The nanostructure, surface composition, and morphology of the prepared catalysts were characterized via X-ray diffraction analysis, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy and transmission electron microscopy. Among the nanomaterials, ZnMn 2 O 4 -0.3 exhibited the highest CTL properties, including excellent H 2 S selectivity, because of its high content of surface-adsorbed O 2. Under optimized detection conditions, a good linear relationship (r = 0.997) of CTL intensity versus H 2 S concentration was obtained in the linear range of 0.810 – 26.3 μg mL−1, and the detection limit (S/N = 3, DL) was 0.405 μg mL−1. In addition, the CTL sensing mechanism for H 2 S on the ZnMn 2 O 4 -0.3 material was explored using the density functional theory. In comparison with previously reported various sensors based on CTL for H 2 S determination, ZnMn 2 O 4 -0.3 exhibited properties similar to or higher than other materials because of its low working temperature, fast response, and low DL, thereby confirming that ZnMn 2 O 4 can be used as a material for CTL detection of H 2 S. [ABSTRACT FROM AUTHOR]

This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement: Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting. [ABSTRACT FROM AUTHOR]

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. This study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain. • Proposed a geostationary satellite cloud top phase classification algorithm (MIA). • Cloud types classified by MIA include ice, supercooled water, and warm liquid water. • Performance decreases in multi-layer cloud scenario by using CALIOP as benchmark. • Overall hit rate of MIA outperforms current GOES-16 cloud top phase product. [ABSTRACT FROM AUTHOR]

To achieve large-scale development of triboelectric nanogenerators (TENGs) for water wave energy harvesting and powering the colossal sensors widely distributed in the ocean, facile and scalable TENGs with high output are urgently required. Here, an elastic self-recovering hybrid nanogenerator (ES-HNG) is proposed for water wave energy harvesting and marine environmental monitoring. The elastic skeletal support of the ES-HNG is manufactured using three-dimensional (3D) printing technology, which is more conducive to the large-scale integration of the ES-HNG. Moreover, the combination of a TENG and an electromagnetic generator (EMG) optimizes the utilization of device space, leading to enhanced energy harvesting efficiency. Experimental results demonstrate that the TENG achieves a peak power output of 42.68 mW, and the EMG reaches a peak power output of 4.40 mW. Furthermore, various marine environment monitoring sensors, such as a self-powered wireless meteorological monitoring system, a wireless alarm system, and a water quality monitoring pen, have been successfully powered by the sophisticated ES-HNG. This work introduces an ES-HNG for water wave energy harvesting, which demonstrates potential in marine environment monitoring and offers a new solution for the sustainable development of the marine internet of things. [ABSTRACT FROM AUTHOR]

• A novel methodology for rapidly detecting and identifying BTEX based on energy transfer ratiometric cataluminescence. • BTEX exhibit different energy transfer processes to Eu3+ and Tb3+ on the surface of Eu,Tb-MgO nanomaterial. • The synergistic effect of ozone induction and high active sensing material enhanced sensitivity of BTEX. The detection and discrimination of benzene, toluene, ethylbenzene and xylene (BTEX) are tricky and challenging issues no matter in environmental monitoring or process controlling in industrial fields. Herein, an ozone-inducted ratiometric cataluminescence strategy for fast quantifying and recognizing BTEX was developed. With ozone as oxidant and rare earth ions Eu3+, Tb3+ co-doped MgO nanocatalyst as sensing material, BTEX were found to produce strong cataluminescence (CTL) emission. And each analyte has unique ratio of the intensities under the characterization wavelength of Eu3+ and Tb3+ in a wide range of concentrations, which can be assumed as the qualitative parameter for BTEX recognition. On that basis, BTEX were supposed to be identified successfully with discrepant ratios for I Eu /I Tb of 0.73 ± 0.03, 1.34 ± 0.03, 4.47 ± 0.18, 2.39 ± 0.11, and measured simultaneously with detection limit of 0.13, 0.21, 0.092, 0.053 μg ml−1, respectively. Besides, the prepared catalysts were characterized and the possible CTL sensing recognition mechanism for BTEX were discussed in detail. The proposed methodology provides a new insight for monitoring and identifying low active compounds. [ABSTRACT FROM AUTHOR]

With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers' behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities. [ABSTRACT FROM AUTHOR]

Graphene is an ideal material for spintronic devices due to its low spin-orbit coupling and high mobility. One of the most important potential applications of graphene spintronics is for use in neuromorphic computing systems, where the tunable spin resistance of graphene can be used to apply analog weighting factors. A key capability needed to achieve spin-based neuromorphic computing systems is to achieve distinct regions of control, where injected and detected spin currents can be tuned independently. Here, we demonstrate the ability to achieve such independent control using a graphene spin valve geometry where the injector and detector regions are modulated by two separate bottom gate electrodes. The spin transport parameters and their dependence on each gate voltage are extracted from Hanle precession measurements. From this analysis, local spin transport parameters and their dependence on the local gate voltage are found, which provide a basis for a spatially-resolved spin resistance network that simulates the device. The data and model are used to calculate the spin currents flowing into, through, and out of the graphene channel. We show that the spin current flowing through the graphene channel can be modulated by 30% using one gate and that the spin current absorbed by the detector can be modulated by 50% using the other gate. This result demonstrates that spin currents can be controlled by locally tuning the spin resistance of graphene. The integration of chemical vapor deposition (CVD) grown graphene with local gates allows for the implementation of large-scale integrated spin-based circuits. [ABSTRACT FROM AUTHOR]

As natural medicines in complementary and alternative medicine, edible and medicinal resources are being gradually recognized throughout the world. According to statistics from the World Health Organization, about 80% of the worldwide population has used edible and medicinal resource products to prevent and treat diseases. Polysaccharides, one of the main effective components in edible and medicinal resources, are considered ideal regulators of various biological responses due to their high effectiveness and low toxicity, and they have a wide range of possible applications for the development of functional foods for the regulation of common, frequently occurring, chronic and severe diseases. Such applications include the development of polysaccharide products for the prevention and treatment of neurodegenerative diseases that are difficult to control by a single treatment, which is of great value to the aging population. Therefore, we evaluated the potential of polysaccharides to prevent neurodegeneration by their regulation of behavioral and major pathologies, including abnormal protein aggregation and neuronal damage caused by neuronal apoptosis, autophagy, oxidative damage, neuroinflammation, unbalanced neurotransmitters, and poor synaptic plasticity. This includes multi-target and multi-pathway regulation involving the mitochondrial pathway, MAPK pathway, NF-κB pathway, Nrf2 pathway, mTOR pathway, PI3K/AKT pathway, P53/P21 pathway, and BDNF/TrkB/CREB pathway. In this paper, research into edible and medicinal resource polysaccharides for neurodegenerative diseases was reviewed in order to provide a basis for the development and application of polysaccharide health products and promote the recognition of functional products of edible and medicinal resources. [ABSTRACT FROM AUTHOR]

An improved classical least-squares (CLS) method that can correct the detrimental effects of traditional spectroscopic data analysis CLS models has been developed. An additional noise (introduced by unmodeled concentrations) model is constructed, and the original mixed spectrum is purified using least squares to eliminate the additional noise signals. The extracted spectral signals are then used to augment the concentration predictive power. An application study is presented, which involves data recorded from an experiment of multicomponent concentration determination using Raman spectroscopy, and two designed strategies are exploited to test the predictive power of the proposed method. Results indicate that this method is an applicable spectral analysis tool for compound concentration determination against traditional CLS model ineffectiveness rendered by unmodeled concentrations, and the predictive power of the proposed method is comparable to existing methods. [ABSTRACT FROM AUTHOR]

We report that gastric cancer patients exhibiting high levels of heparanase 2 (Hpa2) survive longer. Similarly, mice administrated with gastric carcinoma cells engineered to overexpress Hpa2 produced smaller tumors and survived longer than mice administrated with control cells. These beneficial effects were found to associate with increased phosphorylation of AMP-activated protein kinase (AMPK) that play an instrumental role in cell metabolism and is situated at the center of a tumor suppressor network. We also found that MG132, an inhibitor of the proteasome that results in proteotoxic stress, prominently enhances Hpa2 expression. Notably, Hpa2 induction by MG132 appeared to be mediated by AMPK, thus establishing a loop that feeds itself where Hpa2 enhances AMPK phosphorylation that, in turn, induces Hpa2 expression, possibly leading to attenuation of gastric tumorigenesis. Heparanase is highly implicated in tumor metastasis due to its capacity to cleave heparan sulfate and, consequently, remodel the extracellular matrix underlying epithelial and endothelial cells. In striking contrast, only little attention was given to its close homolog, heparanase 2 (Hpa2), possibly because it lacks heparan sulfate-degrading activity typical of heparanase. We subjected sections of gastric carcinoma to immunostaining and correlated Hpa2 immunoreactivity with clinical records, including tumor grade, stage and patients' status. We over-expressed Hpa2 in gastric carcinoma cell lines and examined their tumorigenic properties in vitro and in vivo. We also evaluated the expression of Hpa2 by gastric carcinoma cells following inhibition of the proteasome, leading to proteotoxic stress, and the resulting signaling responsible for Hpa2 gene regulation. Here, we report that gastric cancer patients exhibiting high levels of Hpa2 survive longer. Similarly, mice administrated with gastric carcinoma cells engineered to over-express Hpa2 produced smaller tumors and survived longer than mice administrated with control cells. This was associated with increased phosphorylation of AMP-activated protein kinase (AMPK), a kinase that is situated at the center of a tumor suppressor network. We also found that MG132, an inhibitor of the proteasome that results in proteotoxic stress, prominently enhances Hpa2 expression. Notably, Hpa2 induction by MG132 appeared to be mediated by AMPK, and AMPK was found to induce the expression of Hpa2, thus establishing a loop that feeds itself where Hpa2 enhances AMPK phosphorylation that, in turn, induces Hpa2 expression, leading to attenuation of gastric tumorigenesis. These results indicate that high levels of Hpa2 in some tumors are due to stress conditions that tumors often experience due to their high rates of cell proliferation and high metabolic demands. This increase in Hpa2 levels by the stressed tumors appears critically important for patient outcomes. [ABSTRACT FROM AUTHOR]

We investigate theoretically the behavior of electron spin states near graphene edges at low temperature in the presence of an external electric field. The graphene Hamiltonian is solved directly in the low energy regime including the Rashba spin-orbit interaction. Spin propagation in graphene and spin reflection at the edge are explored by calculating the probability of spin flips between the final and initial states. The spin scattering at the edge is affected by the initial location and the propagating direction of the electron with respect to the edge. Results for an example device structure including the effects of the non-uniform electrostatic field and potential near the graphene edge are presented. [ABSTRACT FROM AUTHOR]

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models. [ABSTRACT FROM AUTHOR]

Purpose: To obtain the optimal simultaneous-multislice (SMS)—accelerated diffusion-weighted imaging (DWI) of the liver at 3.0 T MRI by systematically estimating the repeatability of apparent diffusion coefficient (ADC), signal-to-noise ratio (SNR) and image quality of different breathing schemes in comparison to standard DWI (STD) and other SMS sequences. Methods: In this institutional review board-approved prospective study, hepatic DWIs (b = 50, 300, 600 s/mm2) were performed in 23 volunteers on 3.0 T MRI using SMS and STD with breath-hold (BH-SMS, BH-STD), free-breathing (FB-SMS, FB-STD) and respiratory-triggered (RT-SMS, RT-STD). Reduction of scan time with SMS-acceleration was calculated. ADC and SNR were measured in nine anatomic locations and image quality was assessed on all SMS and STD sequences. An optimal SMS-DWI was decided by systematically comparing the ADC repeatability, SNR and image quality among above DWIs. Results: SMS-DWI reduced scan time significantly by comparison with corresponding STD-DWI (27 vs. 42 s for BH, 54 vs. 78 s for FB and 42 vs. 97 s for RT). In all DWIs, BH-SMS had the greatest intraobserver agreement (intraclass correlation coefficient (ICC): 0.920–0.944) and good interobserver agreement (ICC: 0.831–0.886) for ADC measurements, and had the best ADC repeatability (mean ADC absolute differences: 0.046–0.058 × 10−3mm2/s, limits of agreement (LOA): 0.010–0.013 × 10−3mm2/s) in nine locations. BH-SMS had the highest SNR in three representative sections except for RT-STD. There were no significant differences in image quality between BH-SMS and other DWI sequences (median BH-SMS: 4.75, other DWI: 4.5–5.0; P > 0.0.5). Conclusion: BH-SMS provides considerable scan time reduction with good image quality, sufficient SNR and highest ADC repeatability on 3.0 T MRI, which is thus recommended as the optimal hepatic DWI sequence for those subjects with adequate breath-holding capability. [ABSTRACT FROM AUTHOR]

• Protective autophagy induced by DDP decreases chemosensitivity in GC cells. • MGMT inhibited ATG4B to repress autophagy. • Inhibition of autophagy enhanced the chemosensitivity to DDP in GC xenograft model. • High MGMT expression and low ATG4B expression are significantly correlated with survival of GC. Cisplatin (DDP) is the first-line drug for the treatment of gastric cancer (GC). However, DDP resistance is common. Autophagy, which is closely related to chemoresistance, is a process of resolving and recycling proteins and damaged cellular organs. Additionally, O-6-methylguanine-DNA methyltransferase (MGMT) is responsible for alkylating drug resistance. However, the relationship between autophagy and MGMT in response to DDP in GC is still unknown. In the present study, we determined that autophagy induced by DDP decreases chemosensitivity in GC cell lines. DDP may have induced autophagy in GC by inhibiting MGMT to increase autophagy-related gene (ATG) 4B. Inhibition of MGMT-mediated ATG4B suppression resulted in autophagy induction and DDP resistance. In vivo , combined DDP and autophagy inhibitor chloroquine (CQ) enhanced the anti-tumor effect of DDP; additionally, the negative correlation of MGMT and ATG4B was confirmed. High expression of MGMT and low expression of ATG4B were significantly correlated with favorable five-year survival rate (P < 0.05) in 66 clinicopathologically characterized GC cases. Our study demonstrate that DDP inhibits MGMT-mediated autophagy suppression to decrease chemosensitivity in GC, which provides a novel therapeutic strategy to promote DDP chemosensitivity in GC. [ABSTRACT FROM AUTHOR]

Aerosol-cloud-radiation interactions represent one of the largest uncertainties in the current climate assessment. Much of the complexity arises from the non-monotonic responses of clouds, precipitation and radiative fluxes to aerosol perturbations under various meteorological conditions. In this study, an aerosol-aware WRF model is used to investigate the microphysical and radiative effects of aerosols in three weather systems during the March 2000 Cloud Intensive Observational Period campaign at the US Southern Great Plains. Three simulated cloud ensembles include a low-pressure deep convective cloud system, a collection of less-precipitating stratus and shallow cumulus, and a cold frontal passage. The WRF simulations are evaluated by several ground-based measurements. The microphysical properties of cloud hydrometeors, such as their mass and number concentrations, generally show monotonic trends as a function of cloud condensation nuclei concentrations. Aerosol radiative effects do not influence the trends of cloud microphysics, except for the stratus and shallow cumulus cases where aerosol semi-direct effects are identified. The precipitation changes by aerosols vary with the cloud types and their evolving stages, with a prominent aerosol invigoration effect and associated enhanced precipitation from the convective sources. The simulated aerosol direct effect suppresses precipitation in all three cases but does not overturn the aerosol indirect effect. Cloud fraction exhibits much smaller sensitivity (typically less than 2%) to aerosol perturbations, and the responses vary with aerosol concentrations and cloud regimes. The surface shortwave radiation shows a monotonic decrease by increasing aerosols, while the magnitude of the decrease depends on the cloud type. [ABSTRACT FROM AUTHOR]

Vacancy regulation is considered to be an effective means to improve the properties of materials, which has been applied in the fields of catalysis, electricity and optics. Herein, different contents of nitrogen vacancy in graphitic carbon nitride (g-C 3 N 4) could be modulated via changing temperature of secondary calcination. Results showed the introduction of nitrogen vacancy significantly promoted the cataluminescence (CTL) intensity towards ammonium sulfide ((NH 4) 2 S) and the CTL performance increased regularly with the increaseing vacancy content. The positive effect of nitrogen vacancy on CTL intensity of (NH 4) 2 S was unveiled in-depth by the experiment and density functional theory (DFT). Moreover, a cutting-edge CN-600-based sensor was established for the highly sensitive detection of (NH 4) 2 S. The sensor possessed excellent selectivity and stability, and a low detection limit (0.16 μg mL−1). Noteworthily, this study presents novel mentality for designing materials with improved catalytic activity. [Display omitted] • The nitrogen vacancy content in g-C 3 N 4 was successfully modulated by adjusting temperature of secondary calcination. • The mechanism of enhanced CTL performance of (NH 4) 2 S on g-C 3 N 4 surface modulated by nitrogen vacancy was revealed. • The constructed CN-600-based sensor exhibited excellent selectivity and stability for (NH 4) 2 S sensing. [ABSTRACT FROM AUTHOR]

A continental cloud complex, consisting of shallow cumuli, a deep convective cloud (DCC), and stratus, is simulated by a cloud-resolving Weather Research and Forecasting Model to investigate the aerosol microphysical effect (AME) and aerosol radiative effect (ARE) on the various cloud regimes and their transitions during the Department of Energy Routine Atmospheric Radiation Measurement Aerial Facility Clouds with Low Optical Water Depths Optical Radiative Observations (RACORO) campaign. Under an elevated aerosol loading with AME only, a reduced cloudiness for the shallow cumuli and stratus resulted from more droplet evaporation competing with suppressed precipitation, but an enhanced cloudiness for the DCC is attributed to more condensation. With the inclusion of ARE, the shallow cumuli are suppressed owing to the thermodynamic effects of light-absorbing aerosols. The responses of DCC and stratus to aerosols are monotonic with AME only but nonmonotonic with both AME and ARE. The DCC is invigorated because of favorable convection and moisture conditions at night induced by daytime ARE, via the so-called aerosol-enhanced conditional instability mechanism. The results reveal that the overall aerosol effects on the cloud complex are distinct from the individual cloud types, highlighting that the aerosol-cloud interactions for diverse cloud regimes and their transitions need to be evaluated to assess the regional and global climatic impacts. [ABSTRACT FROM AUTHOR]

The acrolein derived cyclic 1,N 2 -propanodeoxyguanosine adduct (Acr-dG), formed primarily from ω-3 polyunsaturated fatty acids such as docosahexaenoic acid (DHA) under oxidative conditions, while proven to be mutagenic, is potentially involved in DHA-induced apoptosis. The latter may contribute to the chemopreventive effects of DHA. Previous studies have shown that the levels of Acr-dG are correlated with apoptosis induction in HT29 cells treated with DHA. Because Acr-dG is shown to be repaired by the nucleotide excision repair (NER) pathway, to further investigate the role of Acr-dG in apoptosis, in this study, NER-deficient XPA and its isogenic NER-proficient XAN1 cells were treated with DHA. The Acr-dG levels and apoptosis were sharply increased in XPA cells, but not in XAN1 cells when treated with 125 μM of DHA. Because DHA can induce formation of various DNA damage, to specifically investigate the role of Acr-dG in apoptosis induction, we treated XPA knockdown HCT116 + ch3 cells with acrolein. The levels of both Acr-dG and apoptosis induction increased significantly in the XPA knockdown cells. These results clearly demonstrate that NER deficiency induces higher levels of Acr-dG in cells treated with DHA or acrolein and sensitizes cells to undergo apoptosis in a correlative manner. Collectively, these results support that Acr-dG, a ubiquitously formed mutagenic oxidative DNA adduct, plays a role in DHA-induced apoptosis and suggest that it could serve as a biomarker for the cancer preventive effects of DHA. [ABSTRACT FROM AUTHOR]

The MiT/TFE transcription factors play a pivotal role in the regulation of autophagy and lysosomal biogenesis. The subcellular localization and activity of MiT/TFE proteins are primarily regulated through phosphorylation. And the phosphorylated protein is retained in the cytoplasm and subsequently translocates to the nucleus upon dephosphorylation, where it stimulates the expression of hundreds of genes, leading to lysosomal biogenesis and autophagy induction. The transcription factor-mediated lysosome-to-nucleus signaling can be directly controlled by several signaling molecules involved in the mTORC1, PKC, and AKT pathways. MiT/TFE family members have attracted much attention owing to their intracellular clearance of pathogenic factors in numerous diseases. Recently, multiple studies have also revealed the MiT/TFE proteins as master regulators of cellular metabolic reprogramming, converging on autophagic and lysosomal function and playing a critical role in cancer, suggesting that novel therapeutic strategies could be based on the modulation of MiT/TFE family member activity. Here, we present an overview of the latest research on MiT/TFE transcriptional factors and their potential mechanisms in cancer. [ABSTRACT FROM AUTHOR]