d by DSS-induced colitis.These data together suggest that CSD can effectively mitigate intestinal inflammation, promote phenotypic change in macrophage phenotype and enhance colonic mucosal barrier function by, at least in part, regulating notch signaling in mice affected by DSS-induced colitis.Scutellaria baicalensis Georgi., a plant used in traditional Chinese medicine, has multiple biological activities, including anti-inflammatory, antiviral, antitumor, antioxidant, and antibacterial effects, and can be used to treat respiratory tract infections, pneumonia, colitis, hepatitis, and allergic diseases. The main active substances of S. baicalensis, baicalein, baicalin, wogonin, wogonoside, and oroxylin A, can act directly on immune cells such as lymphocytes, macrophages, mast cells, dendritic cells, monocytes, and neutrophils, and inhibit the production of the inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α, and other inflammatory mediators such as nitric oxide, prostaglandins, leukotrienes, and reactive oxygen species. The molecular mechanisms underlying the immunomodulatory and anti-inflammatory effects of the active compounds of S. baicalensis include downregulation of toll-like receptors, activation of the Nrf2 and PPAR signaling pathways, and inhibition of the nuclear thioredoxin system and inflammation-associated pathways such as those of MAPK, Akt, NFκB, and JAK-STAT. Given that in addition to the downregulation of cytokine production, the active constituents of S. baicalensis also have antiviral and antibacterial effects, they may be more promising candidate therapeutics for the prevention of infection-related cytokine storms than are drugs having only antimicrobial or anti-inflammatory activities. This study identified patterns of tobacco marketing exposures among youth and examined their associations with substance use and tobacco prevention strategies. In Fall 2018, 2,058 middle and high school students (ages 11-18) in an Appalachian county completed a substance use and behavioral health surveillance survey. We conducted latent class analysis (LCA) to identify exposure classes based on responses to 14 tobacco marketing exposures. Multinomial logistic regression was then performed to determine associations between the latent classes with past 30-day substance use and tobacco prevention strategies (e.g., school policies, parental rules, prevention messages). Four latent classes of marketing exposure were identified among middle school students low exposure, television, social media, and high exposure. Multinomial logistic regression found significant associations between e-cigarette use with the social media and high exposure classes, while prescription drug use was associated with the social meds from pro-tobacco communications.This study demonstrates the need for stricter tobacco marketing regulations and multi-level interventions beginning in early adolescence that focus on increasing media-based literacy for youth to better discern tobacco prevention messages from pro-tobacco communications. Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management. To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI are accurate, and whether they can supplant reference standard manual measurements from contrast-enhanced CT angiography (CTA). Thirty AAA patients (mean age, 71.9 ± 7.9 years) were recruited between 2014 and 2017. Participants underwent both non-contrast black blood MRI and CTA within 3 months of each other. Semi-automatic (computer-aided) MRI and CTA segmentations utilizing deformable registration methods were compared against manual segmentations of the same modality using the Dice similarity coefficient (DSC). AAA lumen and total aneurysm volumes and AAA maximum diameter, quantified automatically from these segmentations, were compared against manual measurements using Pearson mum AAA diameter (lumen volume 0.73, [-6.47 7.93] cm ; outer wall volume 0.98, [-10.54 12.5] cm ; maximal diameter 0.08, [-3.67 3.83] mm). Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.Mixed sample augmentation (MSA) has witnessed great success in the research area of semi-supervised learning (SSL) and is performed by mixing two training samples as an augmentation strategy to effectively smooth the training space. Following the insights on the efficacy of cut-mix in particular, we propose FMixCut, an MSA that combines Fourier space-based data mixing (FMix) and the proposed Fourier space-based data cutting (FCut) for labeled and unlabeled data augmentation. Specifically, for the SSL task, our approach first generates soft pseudo-labels using the model's previous predictions. The model is then trained to penalize the outputs of the FMix-generated samples so that they are consistent with their mixed soft pseudo-labels. In addition, we propose to use FCut, a new Cutout-based data augmentation strategy that adopts the two masked sample pairs from FMix for weighted cross-entropy minimization. Furthermore, by implementing two regularization techniques, namely, batch label distribution entropy maximization and sample confidence entropy minimization, we further boost the training efficiency. read more Finally, we introduce a dynamic labeled-unlabeled data mixing (DDM) strategy to further accelerate the convergence of the model. Combining the above process, we finally call our SSL approach as "FMixCutMatch", in short FMCmatch. As a result, the proposed FMCmatch achieves state-of-the-art performance on CIFAR-10/100, SVHN and Mini-Imagenet across a variety of SSL conditions with the CNN-13, WRN-28-2 and ResNet-18 networks. In particular, our method achieves a 4.54% test error on CIFAR-10 with 4K labels under the CNN-13 and a 41.25% Top-1 test error on Mini-Imagenet with 10K labels under the ResNet-18. Our codes for reproducing these results are publicly available at https//github.com/biuyq/FMixCutMatch.