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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]

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]

[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]

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]

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]

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]