THE PREVENTION OF OBESITY-ASSOCIATED COLORECTAL CANCER VIA DIETARY SUPPRESSION OF INFLAMMATION-DRIVEN WNT-SIGNALING

Colorectal cancer (CRC) is the third leading cause of cancer deaths in the United States. A number of population studies have established that modifiable lifestyle factors such as obesity plays an important role in colorectal carcinogenesis. In the United States, more than one-third of adults are obese and obesity prevalence rates have no sign of decrease. Therefore, the development of effective strategies to prevent obesity-induced CRC is a public health priority. This study aimed to investigate whether genetic or dietary strategies can prevent obesity-induced CRC and determine the potential molecular mechanisms underlying the prevention effects of these strategies. We used Apc1638N mice, germline heterozygous mutation in the Apc gene, and Caco-2 cell line to study intestinal tumorigenesis. Hematoxylin and eosin stain and QuickPlex SQ 120, a chemiluminescence assay, were used to measure the inflammatory status. Real-time PCR, Western blot assay, and immunohistochemical analysis were used to further examine the signaling pathway status. We found that loss of Tumor necrosis factor alpha (TNF-α) decreased obesity associated intestinal tumorigenesis by decreasing the inflammation, and manipulating the β-catenin pathway and NF-kB signaling. In addition, IKK, a component of the NF-kB signaling, was involved in the regulation of β-catenin pathway. The administration of Vitamin D (VD), at 5000 IU level, exerted an anti-inflammatory property and led to suppressed intestinal Wnt-signaling and tumorigenesis in obese mice. The molecular function of sulforaphane (SFN) on a high dose of VD supplementation, although displayed on the inhibition of HDAC and the activation of autophagy, needs further investigation. Butyrate can increase the activity of Wnt/β-catenin pathway. Knocking down FFAR2 by siRNA decreased the expression of cleaved caspase 3 and the expression of phospho-GSK3β (Ser9) and active β-catenin in Caco-2 cells, subsequently mitigated the anticancer effect of butyrate

Fully discrete semi-Lagrangian methods for advection of differential forms

We study the discretization of linear transient transport problems for differential forms on bounded domains. The focus is on unconditionally stable semi-Lagrangian methods that employ finite element approximation on fixed meshes combined with tracking of the flow map. We derive these methods as finite element Galerkin approach to discrete material derivatives and discuss further approximations leading to fully discrete schemes. We establish comprehensive a priori error estimates, in particular a new asymptotic estimate of order $O(h^{r+1}\tau^{-\frac{1}{2}})$ for the L 2-error of semi-Lagrangian schemes with exact L 2-projection. Here, h is the spatial meshwidth, τ denotes the timestep, and r is the (full) polynomial degree of the piecewise polynomial discrete differential forms used as trial functions. Yet, numerical experiments hint that the estimates may still be sub-optimal for spatial discretization with lowest order discrete differential form

A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding vocal bursts is attracting increasing research attention. In this work, we propose a hierarchical framework, based on chain regression models, for affective recognition from VBs, that explicitly considers multiple relationships: (i) between emotional states and diverse cultures; (ii) between low-dimensional (arousal & valence) and high-dimensional (10 emotion classes) emotion spaces; and (iii) between various emotion classes within the high-dimensional space. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE'' tasks. Experimental results based on the ACII Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.Comment: 5 pages, 3 figures, 5 table