Robust Velocity Dealiasing for Weather Radar Based on Convolutional Neural Networks

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. In weather radar, achieving a longer aliasing range (ra) is crucial for surveillance, and a higher aliasing velocity (va) is also important to obtain dynamical information of storms unambiguously. However, the desire for longer ra and higher va creates a conflict because these two parameters are inversely related to the pulse repetition time (PRT). This conflict is known as the "Doppler dilemma", as ra and va cannot be improved simultaneously using a single PRT. This phenomenon is more challenging at shorter wavelengths, which means it has a more significant impact on X-band, followed by C-band and S-band. There are two main approaches to mitigating this issue. The first approach to dealias the velocity is the post-processing method. This method checks for abrupt changes from one end of the va to another, and a fold is detected when such instances are encountered. The underlying assumption is that the velocity field should be spatially continuous. This approach performs well for wide and spatially continuous storms. However, it still suffers when the storms are isolated within the radar field of view. The second approach is the waveform design method, which utilizes two or more pulse repetition times (PRTs), and the aliased velocities are found by searching for disagreement between two or more velocities observed from different PRTs. Velocity dealiasing is performed by solving a least-common-multiple problem. However, this method still has the inherent limitation of ra. The post-processing method allows the system to operate everything else, such as ground clutter filter, continuous pulse-pair processing, etc., as waveform design methods require modifications to the existing filters. Therefore, in this study, the main focus will be on the post-processing method, and the key is to detect the aliased velocity accurately, leading to the correct velocity dealiasing. The detection of aliased velocity can be compared to classification. Raw aliased velocity can be regarded as the input image, and the aliased count can be regarded as label. With advancements in technology, machine learning can be applied to image classification. Convolutional neural networks (CNNs) are widely used for image segmentation, enabling the model to output the same size as the input image. Therefore, in this study, a CNN is utilized to tackle the velocity dealiasing issue. In the training process, the input data comprises aliased velocity and the aliased count (the sign and how many times they are aliased). The best weights and the biases are determined through a fit-and-adjust process. After the training process, the performance is evaluated using unseen test data. The aliased velocity is used as input, and the output is the aliasing count. Velocity dealiasing is performed by combining the input (aliased) velocity, the aliasing count, and the known va. For evaluation, the CNN method is compared to the traditional region-based method, which is also a post-processing method in Python ARM Radar Toolkit (Py-ART). Both methods are evaluated on mostly filled precipitation and sparsely filled precipitation. Sensitivity tests are conducted on template size and the va used to optimize the CNN model to cover the X-band range coverage. This model can be used regardless of va. Both methods demonstrate similar performance on mostly filled precipitation. However, the CNN method shows better performance on sparsely filled precipitation, as it processes the entire scan at once while the region-based method only processes the limited adjacent area. The overarching goal of this study is to exploit CNN for velocity dealiasing and to achieve human-level performance. Through this process, it is expected that the labor-intensive work could be automated

Delayed rupture of a pseudoaneurysm in the brachial artery of a burn reconstruction patient

A brachial artery pseudoaneurysm is a rare but serious condition that can be limb threatening. A number of reports have found that it may be the result of damage to the blood vessels around the brachial artery, either directly or indirectly, due to trauma or systemic diseases. We present our experience of delayed pseudoaneurysm rupture of the brachial artery in a rehabilitation patient with burns of the upper extremity who underwent fasciotomy and musculocutaneous flap coverage. We also provide a review of the brachial artery pseudoaneurysm

Calculation of Response Characteristics of Various Hydrocarbon Gas Sensors

Technologies for detecting leaks of gases and measuring gaseous concentrations have been widely developed with every sensor type. To manufacture excellent gas leak detector, an excellent gas sensors are necessary parts. In this research, the design of a system to simultaneously measure performances of five gas sensors is introduced. That system need the components of measuring appliances, sensing circuits, control firmware, and PC software to be operated. Also the performances to test gas sensors need response characteristics, accuracy, and repeatability according to output signals for injecting gas amounts into gas sensors. The firmware is implemented to operate sensors and to acquire output data against for input of sensors in real time. Acquired data were stored in the text file according to every sensor during a pre-set measurement interval. Software is coded to draw graphs of the voltage values measured by each sensor in real time. Using proposed a testing system we showed how to test response characteristics and induce better calibration equations of five sensors. This paper compared experimental data of five sensors and verified which gas sensor is the best

increased Igfbp2 Levels By Placenta-Derived Mesenchymal Stem Cells Enhance Glucose Metabolism in a Taa-injured Rat Model Via ampk Signaling Pathway

The insulin resistance caused by impaired glucose metabolism induces ovarian dysfunction due to the central importance of glucose as a source of energy. However, the research on glucose metabolism in the ovaries is still lacking. The objectives of this study were to analyze the effect of PD-MSCs on glucose metabolism through IGFBP2-AMPK signaling and to investigate the correlation between glucose metabolism and ovarian function. Thioacetamide (TAA) was used to construct a rat injury model. PD-MSCs were transplanted into the tail vein (2 × 1

Metabolite profiles of live or dead carp (Cyprinus carpio) exposed to endosulfan sulfate using a targeted GC–MS analysis

Endosulfan sulfate is a major oxidized metabolite of endosulfan, which is a broad-spectrum chlorinated cyclodiene insecticide. In this study, GC–MS-based metabolic profiles of dead or live carp (Cyprinus carpio) exposed to endosulfan sulfate were investigated to elucidate the molecular toxicological effects of endosulfan sulfate on carp. Three different extraction methods were compared, and a 50% methanol solution was chosen as an efficient extraction method. Carp was exposed to endosulfan sulfate at a concentration of 8 ppb for 2 days. After exposure, the whole body of the fish was homogenized with liquid N2, extracted with the 50% methanol solution and dried before TMS derivatization for GC–MS analyses of the dead and live carp. A SIM (selected ion monitoring)-library of 373 metabolites was applied after GC–MS analysis to detect 146 metabolites in carp. Based on the one-way ANOVA results (P  1.5 or < 0.667), 30 metabolites were identified as biomarkers that were significantly different in the metabolic profiles among the control, dead and live carp. A metabolic pathway analysis using MetaboAnalyst 4.0 revealed that those biomarkers were important for the living or death response to endosulfan sulfate. The pathways indicated by the metabolic pathway analysis included starch and sucrose metabolism, galactose metabolism, glycerolipid metabolism, the citrate cycle and linoleic acid metabolism. These results suggest that these pathways underwent significant perturbations over the exposure period

Cerebral microbleeds are associated with nocturnal reverse dipping in hypertensive patients with ischemic stroke

Background Abnormalities in nocturnal blood pressure dipping are well known for its relationship to cardiovascular diseases. Cerebral microbleeds are frequently observed in patients with hypertension and are known to be potent risk factors for stroke. However, there are scanty reports about the relationship between nocturnal dipping and cerebral microbleeds. Methods We recruited consecutive patients with both hypertension and ischemic stroke within 7 days after symptom onset, and those with cardioembolism were excluded. We applied 24-hour ambulatory blood pressure monitoring two weeks after stroke onset, and we used brain MRI to detect cerebral microbleeds. Various blood pressure parameters such as mean 24-hour blood pressure, awake/sleep blood pressure, and morning surge were compared between cerebral microbleeds (+) vs. (-) groups. Subjects were further classified according to nocturnal dipping status and were analyzed by logistic regression to determine its association with cerebral microbleeds with adjustment for age, gender, and cardiovascular risk factors. Results A total of 162 patients (100 males, age 65.33 ± 10.32 years) were included. Cerebral microbleeds were detected in 65 patients (40.1%). Most ambulatory blood pressure parameters except morning surge were significantly higher in those who had cerebral microbleeds. After adjusting for the confounding factors, the reverse dippers were prone to have cerebral microbleeds (odds ratio, 3.81; 95% confidential interval, 1.36-10.65; p-value = 0.01). Conclusion Cerebral microbleeds are independently associated with reverse dipping on ambulatory blood pressure monitoring in hypertensive stroke patients.This study was supported by a grant from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea. (A101311)Peer Reviewe

Perfusable micro-vascularized 3D tissue array for high-throughput vascular phenotypic screening

Microfluidic organ-on-a-chip technologies have enabled construction of biomimetic physiologically and pathologically relevant models. This paper describes an injection molded microfluidic platform that utilizes a novel sequential edge-guided patterning method based on spontaneous capillary flow to realize three-dimensional co-culture models and form an array of micro-vascularized tissues (28 per 1 × 2-inch slide format). The MicroVascular Injection-Molded Plastic Array 3D Culture (MV-IMPACT) platform is fabricated by injection molding, resulting in devices that are reliable and easy to use. By patterning hydrogels containing human umbilical endothelial cells and fibroblasts in close proximity and allowing them to form vasculogenic networks, an array of perfusable vascularized micro-tissues can be formed in a highly efficient manner. The high-throughput generation of angiogenic sprouts was quantified and their uniformity was characterized. Due to its compact design (half the size of a 96-well microtiter plate), it requires small amount of reagents and cells per device. In addition, the device design is compatible with a high content imaging machine such as Yokogawa CQ-1. Furthermore, we demonstrated the potential of our platform for high-throughput phenotypic screening by testing the effect of DAPT, a chemical known to affect angiogenesis. The MV-IMPACT represent a significant improvement over our previous PDMS-based devices in terms of molding 3D co-culture conditions at much higher throughput with added reliability and robustness in obtaining vascular micro-tissues and will provide a platform for developing applications in drug screening and development.This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (No. 2021R1A3B1077481). This study was also supported by a grant of the Korean Health Technol‑ ogy R&D Project, Ministry of Health & Welfare, Republic of Korea (Grant No. HP20C0146010020), and by National Institutes of Health (R01HL141857 to YKH)

Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study

Background: Social anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. Objective: This study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. Methods: This study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naive Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. Results: The severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naive Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. Conclusions: This study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias

GSK3B induces autophagy by phosphorylating ULK1

Unc-51-like autophagy activating kinase 1 (ULK1), a mammalian homolog of the yeast kinase Atg1, has an essential role in autophagy induction. In nutrient and growth factor signaling, ULK1 activity is regulated by various posttranslational modifications, including phosphorylation, acetylation, and ubiquitination. We previously identified glycogen synthase kinase 3 beta (GSK3B) as an upstream regulator of insulin withdrawal-induced autophagy in adult hippocampal neural stem cells. Here, we report that following insulin withdrawal, GSK3B directly interacted with and activated ULK1 via phosphorylation of S405 and S415 within the GABARAP-interacting region. Phosphorylation of these residues facilitated the interaction of ULK1 with MAP1LC3B and GABARAPL1, while phosphorylation-defective mutants of ULK1 failed to do so and could not induce autophagy flux. Furthermore, high phosphorylation levels of ULK1 at S405 and S415 were observed in human pancreatic cancer cell lines, all of which are known to exhibit high levels of autophagy. Our results reveal the importance of GSK3B-mediated phosphorylation for ULK1 regulation and autophagy induction and potentially for tumorigenesis. © 2021, The Author(s).1

ATG16L1 down-regulates IL-1beta signaling via neddylated- Cullin-3 mediated p62 ubiquitination

The Genome-Wide Association Studies shows that ATG16L1 mutation such as single-mutation polymorphism or miss sense mutation makes a person susceptible to Crohn's Disease, a type of inflammatory bowel diseases. Then, we examined how the loss of ATG16L1 functions effects signal transduction pathways with present of pro-inflammatory stimulus. Using in vitro experiment with mouse embryonic fibroblast (MEF) cell-line, we have checked that deletion of ATG16L1 will significantly influence the amplification of IL-1[beta] signaling. Then, we have expected that influence of ATG16L1 occurs in the upstream of IL-1[beta] signaling cascade, and our result presents that increased p62 expression in ATG16L1 knockout cells result in stronger inflammatory response due to oligomerization of TRAF6. Also the data shows that ATG16L1 is required for ubiquitination and degradation of p62 and that ATG16L1 is the essential component to neddylate Cullin-3 (Cul-3), E3 ubiquitin ligase, to bring ubiquitin to p62. These findings implicate that ATG16L1 is important factor to suppress IL-1[beta] signaling through down-regulation of p62 level by Cul-3 neddylation. Importantly, future study may provide therapeutic solutions to pathological conditions due to increased p62 expression level. Also, this study might give a direction for treatment in Crohn's Disease patients with susceptible allele of ATG16L1 by specifically targeting Cullin-3 and p6