Dietary Patterns And Thyroid Cancer Risk: A Population-Based Case-Control Study

Thyroid cancer (TC) incidence has increased greatly during the past decades with a few established risk factors. The relationship between diet factors and TC remains unclear. Limited literature has investigated the association and with inconsistent findings. We examined the association between dietary pattern and risk of TC in a population based case-control study conducted in Connecticut (2010-2011). 390 historically confirmed incident TC cases and 436 population-based controls that completed baseline dietary history questionnaires were included in the analyses. We identified 3 distinct dietary patterns (“Starchy Foods and Desserts”, “Fruits and Vegetables”, “High Protein and Fat”) through principal components analysis. Multivariate unconditional logistic regression models were used to investigate the association between dietary pattern and risk of TC, controlling for potential confounders. A diet rich in fruits and vegetables was significantly associated with a reduced risk of overall TC (OR=0.59, 95%CI: 0.39-0.90, Ptrend=0.023). Stronger protective effect with significant dose-response relationship was observed among women ≥ 50 years of age in risk of both overall TC (OR=0.45, 95%CI: 0.24-0.86, Ptrend=0.036) and papillary TC (OR=0.42, 95%CI: 0.21-0.84, Ptrend=0.031). A diet rich in starchy foods and dessert is positively associated with overall TC risk among men, and negatively associated with risk of TC among women. No significant associations were found between high protein and fat intake with risk of TC

A Two-Stage Real-time Prediction Method for Multiplayer Shooting E-Sports

E-sports is an industry with a huge base and the number of people who pay attention to it continues to rise. The research results of E-sports prediction play an important role in many aspects. In the past game prediction algorithms, there are mainly three kinds: neural network algorithm, AdaBoost algorithm based on Naïve Bayesian (NB) classifier and decision tree algorithm. These three algorithms have their own advantages and disadvantages, but they cannot predict the match ranking in real time. Therefore, we propose a real-time prediction algorithm based on random forest model. This method is divided into two stages. In the first stage, the weights are trained to obtain the optimal model for the second stage. In the second stage, each influencing factor in the data set is corresponded to and transformed with the data items in the competition log. The accuracy of the prediction results and its change trend with time are observed. Finally, the model is evaluated. The results show that the accuracy of real-time prediction reaches 92.29%, which makes up for the shortage of real-time in traditional prediction algorithm

The influence of online Danmu on users\u27 reward behavior: Based on the data of Douyu live broadcast

In live streaming, the Danmu is a crucial technique of interaction, and the reward is the interaction\u27s feedback. The audience receives more input through the reward the more frequently they interact. The effect of the bullet screen in the live broadcast on the audience\u27s reward behavior was investigated by gathering data from the live broadcast room 5720533 on Douyu, a domestic Danmu live-streaming website, from February 14 to February 24, 2021. Based on empirical research, the following conclusions can be drawn: the number of user Danmu, the proportion of fan Danmu, the number of user entry Danmu, and the number of super Danmu will all significantly improve users\u27 reward, while personal experience attenuates the positive impact of the number of user access Danmu and the number of super Danmu on the impact of user reward. The study\u27s findings will offer theoretical justification for the creation of live broadcast platforms, the upkeep of anchors\u27 notoriety, and users\u27 rational consumption

The Role of CDK8 in Metastatic Growth of Colon Cancer

Unresectable hepatic metastases of colon cancer poorly respond to existing therapies and are a major cause of colon cancer lethality. Transcription- regulating Mediator kinase CDK8, an early clinical stage drug target, is amplified or overexpressed in many colon cancers and CDK8 expression correlates with shorter patient survival. Here we show that CDK8 inhibition does not generally suppress proliferation of CDK8-overexpressing colon cancer cells but nevertheless CDK8 knockdown by shRNA or CDK8 kinase inhibition by a selective small-molecule drug candidate suppresses metastatic growth of mouse and human colon cancer cells in the liver. This effect is due at least in part to the inhibition of already established hepatic metastases, indicating therapeutic potential of CDK8 inhibitors in the metastatic setting. In contrast, CDK8 knockdown or inhibition has no significant effect on the growth of tumors implanted subcutaneously, intrasplenically or orthotopically in the cecum. Mechanistically, the site-specific effect of CDK8 on colon cancer growth in the liver is mediated through the downregulation of matrix metalloproteinase (MMP) inhibitor TIMP3 via the TGFβ/SMAD-driven expression of a TIMP3-targeting microRNA, miR-181b, along with the induction of MMP3 in murine or MMP9 in human colon cancer cells via Wnt/β-catenin-driven transcription. These findings reveal a new mechanism for negative regulation of gene expression by CDK8 and a site-specific role for CDK8 in colon cancer hepatic metastasis. Our results indicate potential utility of CDK8 inhibitors for the treatment of colon cancers that metastasized into the liver and suggest that CDK8 inhibitors may be considered in other therapeutic settings involving TGFβ/SMAD or Wnt/β-catenin pathway activation

Minimizing Age of Collection for Multiple Access in Wireless Industrial Internet of Things

This paper investigates the information freshness of Industrial Internet of Things (IIoT) systems, where each IoT device makes a partial observation of a common target and transmits the information update to a central receiver to recover the complete observation. We consider the age of collection (AoC) performance as a measure of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when all cooperative packets for a common observation are successfully received. Hence, effectively allocating wireless time-frequency resources among IoT devices to achieve a low average AoC at the central receiver is paramount. Three multiple access schemes are considered in this paper: time-division multiple access (TDMA) without retransmission, TDMA with retransmission, and frequency-division multiple access (FDMA). First, our theoretical analysis indicates that TDMA with retransmission outperforms the other two schemes in terms of average AoC. Subsequently, we implement information update systems based on the three schemes on software-defined radios. Experimental results demonstrate that considering the medium access control (MAC) overhead in practice, FDMA achieves a lower average AoC than TDMA with or without retransmission in the high signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission provides a stable and relatively low average AoC over a wide SNR range, which is favorable for IIoT applications. Overall, we present a theoretical-plus-experimental investigation of AoC in IIoT information update systems

LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem

The Maximum Satisfiability (MaxSAT), an important optimization problem, has a range of applications, including network routing, planning and scheduling, and combinatorial auctions. Among these applications, one usually benefits from having not just one single solution, but k diverse solutions. Motivated by this, we study an extension of MaxSAT, named Diversified Top-k MaxSAT (DTKMS) problem, which is to find k feasible assignments of a given formula such that each assignment satisfies all hard clauses and all of them together satisfy the maximum number of soft clauses. This paper presents a local search algorithm, LS-DTKMS, for DTKMS problem, which exploits novel scoring functions to select variables and assignments. Experiments demonstrate that LS-DTKMS outperforms the top-k MaxSAT based DTKMS solvers and state-of-the-art solvers for diversified top-k clique problem