Src Is a Prime Target Inhibited by Celtis choseniana

Celtis choseniana is the traditional plant used at Korea as a herbal medicine to ameliorate inflammatory responses. Although Celtis choseniana has been traditionally used as a herbal medicine at Korea, no systemic research has been conducted on its anti-inflammatory activity. Therefore, the present study explored an anti-inflammatory effect and its underlying molecular mechanism using Celtis choseniana methanol extract (Cc-ME) in macrophage-mediated inflammatory responses. In vitro anti-inflammatory activity of Cc-ME was evaluated using RAW264.7 cells and peritoneal macrophages stimulated by lipopolysaccharide (LPS), pam3CSK4 (Pam3), or poly(I:C). In vivo anti-inflammatory activity of Cc-ME was investigated using acute inflammatory disease mouse models, such as LPS-induced peritonitis and HCl/EtOH-induced gastritis. The molecular mechanism of Cc-ME-mediated anti-inflammatory activity was examined by Western blot analysis and immunoprecipitation using whole cell and nuclear fraction prepared from the LPS-stimulated RAW264.7 cells and HEK293 cells. Cc-ME inhibited NO production and mRNA expression of inducible nitric oxide synthase (iNOS), cyclooxygenase (COX-2), and tumor necrosis factor-alpha (TNF-α) in the RAW264.7 cells and peritoneal macrophages induced by LPS, pam3, or poly(I:C) without cytotoxicity. High-performance liquid chromatography (HPLC) analysis showed that Cc-ME contained anti-inflammatory flavonoids quercetin, luteolin, and kaempferol. Among those, the content of luteolin, which showed an inhibitory effect on NO production, was highest. Cc-ME suppressed the NF-κB signaling pathway by targeting Src and interrupting molecular interactions between Src and p85, its downstream kinase. Moreover, Cc-ME ameliorated the morphological finding of peritonitis and gastritis in the mouse disease models. Therefore, these results suggest that Cc-ME exerted in vitro and in vivo anti-inflammatory activity in LPS-stimulated macrophages and mouse models of acute inflammatory diseases. This anti-inflammatory activity of Cc-ME was dominantly mediated by targeting Src in NF-κB signaling pathway during macrophage-mediated inflammatory responses

Dual-function transaminases with hybrid nanoflower for the production of value-added chemicals from biobased levulinic acid

The U.S. Department of Energy has listed levulinic acid (LA) as one of the top 12 compounds derived from biomass. LA has gained much attention owing to its conversion into enantiopure 4-aminopentanoic acid through an amination reaction. Herein, we developed a coupled-enzyme recyclable cascade employing two transaminases (TAs) for the synthesis of (S)-4-aminopentanoic acid. TAs were first utilized to convert LA into (S)-4-aminopentanoic acid using (S)-α-Methylbenzylamine [(S)-α-MBA] as an amino donor. The deaminated (S)-α-MBA i.e., acetophenone was recycled back using a second TAs while using isopropyl amine (IPA) amino donor to generate easily removable acetone. Enzymatic reactions were carried out using different systems, with conversions ranging from 30% to 80%. Furthermore, the hybrid nanoflowers (HNF) of the fusion protein were constructed which afforded complete biocatalytic conversion of LA to the desired (S)-4-aminopentanoic acid. The created HNF demonstrated storage stability for over a month and can be reused for up to 7 sequential cycles. A preparative scale reaction (100 mL) achieved the complete conversion with an isolated yield of 62%. Furthermore, the applicability of this recycling system was tested with different β-keto ester substrates, wherein 18%–48% of corresponding β-amino acids were synthesized. Finally, this recycling system was applied for the biosynthesis of pharmaceutical important drug sitagliptin intermediate ((R)-3-amino-4-(2,4,5-triflurophenyl) butanoic acid) with an excellent conversion 82%

Measurement Report: Long-Range Transport Patterns into the Tropical Northwest Pacific during the CAMP2Ex Aircraft Campaign: Chemical Composition, Size Distributions, and the Impact of Convection

The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT

Detailed molecular and isotopic characterization of carbonaceous aerosols to assess air quality issues in urban areas : the San Francisco Bay Area and the Houston metropolitan area.

The objective of this dissertation is to provide detailed characterization of carbonaceous organic aerosols to better understand major sources of particulate matter (PM) and their atmospheric formation in an oxidizing and highly complex urban atmosphere. For this dissertation, optimized radiocarbon (14C) and source characterization techniques were applied to PM samples from the Houston Metropolitan Area and the San Francisco Bay Area. The San Francisco Bay area study was focused on identifying seasonal trends (winter and non-winter) and sources of elemental carbon (EC). The study required isolation of EC for 14C-based source apportionment. Chemical mass balance model (CMB) of EC and 14C-based total organic carbon (TOC; OC + EC) were also included for comparison of source apportionment methods and different carbonaceous aerosol fractions, respectively. Sources of EC and TOC were similar at most of the sites while a few sites (e.g. San Francisco and Napa) were distinctively more impacted by fossil fuel or contemporary/biomass burning sources. The winter season had significantly larger TOC concentration due to meteorological conditions and changes in emissions (e.g. increased residential wood smoke). Relatively good agreement between the 14C-EC- and CMB-EC- was observed for both seasons. The first and second Houston studies focused on identifying diurnal and temporal trends of aerosols using both fine and coarse PM and contribution of secondary organic carbon during periods of poor air quality (i.e. high ozone and PM), respectively. The largest concentrations of fine EC and BC concentrations occured during the mornings while periods of enhanced TOC was driven by an increase in the fine PM. Interestingly, a relatively large contribution of coarse EC was measured in Houston. Based on the 14C and CMB analysis, Houston’s carbonaceous aerosols are largely from secondary biogenic sources while secondary fossil contribution was highly variable. Furthermore, the poor air quality period in the Houston metropolitan area was driven by favorable meteorological conditions (i.e. Bay Breeze) providing stagnant atmospheric conditions, allowing for accumulation and photooxidation of fossil fuel emissions. Overall, the study results provided up-to-date characterization and source apportionment of less studied carbonaceous aerosols fractions at two major U.S. urban coastal regions

Generative Adversarial Network-Based Signal Inpainting for Automatic Modulation Classification

Automatic modulation classification (AMC) aims to automatically identify the modulation type of a detected signal in an intelligent wireless receiver, such as software-defined radio (SDR). Recently, deep learning-based methods such as convolutional neural networks have been applied to AMC, showing high-accuracy performance. However, the earlier studies do not consider various signal degradations that can possibly occur during the transmission and reception of wireless signals. Particularly, the signal reception can be often unstable, and the signal can be partially received due to the dynamic spectrum sensing or signal sensing in the intelligent wireless systems. The corrupted signal with missing samples considerably degrades the accuracy of modulation classification of the deep learning-based models, because it is very different from the training datasets. To address this issue, the preprocessing process of restoring the corrupted signal, called signal inpainting, is essential. Although it is significant for the modulation classification, no studies have been performed to investigate the effect of signal inpainting on AMC. To that end, this study proposes a generative adversarial network(GAN)-based signal inpainting method that fills in the missing samples in a wireless signal. The proposed inpainting method can restore the time-domain signal with up to 50% missing samples while maintaining the global structure of each modulation type. The correct recovery of the global structure enables the extraction of distinctive features that play a key role in the modulation classification. To investigate this effect of signal inpainting on AMC, we perform intensive experiments on the RadioML dataset that has been widely used in the AMC studies. We compare the accuracy performance of the two state-of-the-art AMC models with and without the proposed signal inpainting, respectively. Through the analysis of the results, we show that the proposed GAN-based inpainting method significantly improves the accuracy of AMC

Interactions between the apolipoprotein E ɛ4 allele status and adverse childhood experiences on depressive symptoms in older adults

Background: The influence of childhood adversity on depression is modulated by genetic vulnerability. The apolipoprotein E ɛ4 (APOE-ɛ4) allele is a strong genetic risk factor for Alzheimer's disease (AD). Because late-life depressive symptoms could be a part of the preclinical course of AD, the APOE-ɛ4 allele may contribute to depression in old age. Objective: The aim of this study was to evaluate whether an APOE-ɛ4 carrier status was associated with depressive symptoms in older adults and to detect the gene–environment interaction between APOE-ɛ4 status and childhood adversity in relation to depressive symptoms in old age. Method: The participants consisted of 137 older adults (age range 50–70) without any psychiatric history or clinically significant cognitive impairment. APOE genotypes and measures of childhood adversity and depressive symptoms were obtained. Results: There was a significant positive association between adverse childhood experiences (ACE) scores and depressive symptoms (B=0.60; 95% CI=0.26, 0.93 for a 1 score increase in ACE scores; p=0.001). Although APOE-ɛ4 status per se was not associated with depressive symptoms, there was a significant interaction of the ACE scores with the APOE genotype in relation to depressive symptoms (B=0.78; 95% CI=0.02, 1.55; p=0.044). There was a significantly higher effect of childhood adversity on depressive symptoms in APOE-ɛ4 carriers than non-carriers (t=2.13, p=0.035). Conclusions: Our results suggest that the APOE-ɛ4 may modulate the association between childhood adversity and depressive symptoms in older adults. However, more research in a larger sample is needed to gain a better understanding of the relationship between the APOE-ɛ4, childhood adversity, and depression

Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks

Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip. This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its disposition. To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. The performance was compared by each root mean squared error (RMSE) and MFP incidence rate according to whether or not MFCN is applied to five conventional models. In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45%. The RMSE was improved by over 63% from an average of 26.85mm (17.16 to 35.80mm) to 9.72mm (9.37 to 10.98mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65\%. Therefore, by applying the proposed MFCN, we observed the significant/consistent detection performance improvement of PICC tip location compared to the existing model.Comment: Subin Park and Yoon Ki Cha have contributed equally to this work as the co-first author. Kyung-Su Kim ([email protected]) and Myung Jin Chung ([email protected]) have contributed equally to this work as the co-corresponding autho