Testing the Efficacy and Synergistic Components of Sesamol and Black Rice Extract on Human Colon Cancer Cells

Purpose: Systemic treatment of colorectal cancer involves chemotherapeutic agents which elicit serious and negative side effects from the toxicity of the drug. To address this issue, we are testing dietary supplements for their efficacy against human colon cancer cell lines and also their potential synergistic effects when combined with conventional chemotherapy. Dietary supplements (specifically sesamol and black rice extract) exhibit anticancer, anti-inflammatory, and chemo-preventive properties. Meanwhile, one of the cancer resistance mechanisms is the upregulation of drug elimination mechanisms, leading to multi-drug resistance. We hypothesize that dietary compounds will act as chemo-enhancers, thus enhancing potency of the chemotherapy drug(s) on colon cancer cell lines, even in the presence of induced drug-resistance mechanisms. Methods: LS180 or HT29 human colonic adenocarcinoma cells were cultured in 96-well plates in standard media lacking or containing active vitamin D (250nM). Doxorubicin, oxaliplatin, irinotecan (chemotherapeutics), sesamol, and black rice extract (dietary supplements) were examined at varying concentrations to determine their antiproliferative potencies. Alamar blue activity was measured by fluorimetry to compare the growth rates in the presence of the treatments. Results: In the presence of vitamin D, the tested substances showed decreased potency against cell proliferation. Vitamin D treatments accelerated cell proliferation and decreased the potency of doxorubicin (IC50 values: 1.9nM (alone), 3.9nM (vitamin D)). However, a large degree of variability obscured some of the results. Conclusions: Futures studies will find ways to decrease the variability and determine the extent to which sesamol and black rice extract enhance chemotherapy and overcome drug resistance mechanisms.https://scholarscompass.vcu.edu/uresposters/1429/thumbnail.jp

Loss-tolerant quantum secure positioning with weak laser sources

Quantum position verification (QPV) is the art of verifying the geographical location of an untrusted party. Recently, it has been shown that the widely studied Bennett & Brassard 1984 (BB84) QPV protocol is insecure after the 3 dB loss point assuming local operations and classical communication (LOCC) adversaries. Here, we propose a time-reversed entanglement swapping QPV protocol (based on measurement-device-independent quantum cryptography) that is highly robust against quantum channel loss. First, assuming ideal qubit sources, we show that the protocol is secure against LOCC adversaries for any quantum channel loss, thereby overcoming the 3 dB loss limit. Then, we analyze the security of the protocol in a more practical setting involving weak laser sources and linear optics. In this setting, we find that the security only degrades by an additive constant and the protocol is able to verify positions up to 47 dB channel loss.Comment: 11 pages, 3 figures. Partially based on an earlier work in arXiv:1510.0489

Youth Smoking, Cigarette Prices, and Anti-Smoking Sentiment

In this paper, we develop a new direct measure of state anti-smoking sentiment and merge it with micro data on youth smoking in 1992 and 2000. The empirical results from the cross-sectional models show two consistent patterns: after controlling for differences in state anti-smoking sentiment, the price of cigarettes has a weak and statistically insignificant influence on smoking participation; and state anti-smoking sentiment appears to be a potentially important influence on youth smoking participation. The cross-sectional results are corroborated by results from discrete time hazard models of smoking initiation that include state fixed effects. However, there is evidence of price-responsiveness in the conditional cigarette demand by youth and young adult smokers.

Renewable energy policy and initiatives in Malaysia

Energy has always been an essential element for the generation of social and economic growth in a country. It is no longer viewed as a luxury as it used to be but it has become a necessity in our everyday life. Malaysia, a country located between 1° and 7° North of the Equator, has an abundance of renewable energy resources such as solar, wind, hydro and biomass but most of these renewable energy resources are not fully exploited. Presently, Malaysia is still very much dependent on fossil fuels as its primary source of energy. Due to the current upward trend of fuel prices, especially crude oil prices in the world market, the Malaysian Government is forced to look into other alternative energy sources with the emphasis on renewable energy. There are numerous efforts taken by the Malaysian Government in pursuit of the exploitation of renewable energy. This paper will discuss the five main initiatives taken by the Malaysian government on renewable energy, namely Renewable Energy as the 5th Fuel under the 8th and 9th Malaysian Plan, MS1525 or Energy Efficiency in Commercial Buildings, the Kyoto Protocol, the Malaysian Building Integrated Photovoltaic Programme, also known as MBIPV, and Biomass

Using mixup as a regularizer can surprisingly improve accuracy and out-of-distribution robustness

We show that the effectiveness of the well celebrated Mixup can be further improved if instead of using it as the sole learning objective, it is utilized as an additional regularizer to the standard cross-entropy loss. This simple change not only improves accuracy but also significantly improves the quality of the predictive uncertainty estimation of Mixup in most cases under various forms of covariate shifts and out-of-distribution detection experiments. In fact, we observe that Mixup otherwise yields much degraded performance on detecting out-of-distribution samples possibly, as we show empirically, due to its tendency to learn models exhibiting high-entropy throughout; making it difficult to differentiate in-distribution samples from out-of-distribution ones. To show the efficacy of our approach (RegMixup), we provide thorough analyses and experiments on vision datasets (ImageNet & CIFAR-10/100) and compare it with a suite of recent approaches for reliable uncertainty estimation

Graph Inductive Biases in Transformers without Message Passing

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.Comment: Published as a conference paper at ICML 2023; 17 page