Research Progress of DNA Methylation in Thyroid Cancer

We have summarized increasing data from all kinds of experiment results of papers in recent years, which are associated with tumor suppressor genes, oncogenes, and thyroid-specific genes and attempt to elucidate the importance of epigenetic modifications and the mechanisms of aberrant DNA methylation in thyroid cancer in this review. The results showed that current articles have revealed the importance of epigenetic modifications and the different types of mechanisms in thyroid cancer. The mechanisms of DNA methylation related to thyroid cancer demonstrate that acquired epigenetic abnormalities together with genetic changes play an important role in alteration of gene expression patterns. Aberrant DNA methylation has been well known in the CpG regions. Among the genes identified, we have shown the status of DNA promoter methylation in papillary, follicular, medullary, and anaplastic thyroid cancer. It suggested that thyroid cancer subtypes present differential promoter methylation signatures, which will encourage potential thyroid cancer detection in its early stages, assessment of prognosis, and targeted cancer treatment

Food emergency dispatching method based on optimized fireworks algorithm

In order to solve the problem of food emergency dispatching under emergencies, a food emergency dispatching method based on the optimal fireworks algorithm was proposed. The fitness function was used to measure the individual merits of fireworks, the tabu table was set to avoid the fireworks algorithm falling into the local optimal, and the tournament strategy was adopted as the iterative strategy of fireworks population. The goal of the fitness function is to maximize the satisfaction of demand points and minimize the vehicle travel time.In order to accurately predict the amount of food required at the point of demand, an infectious disease model (SEIR) was used.By comparing with the basic fireworks algorithm and genetic algorithm, the simulation results show that the proposed algorithm has higher computational efficiency and can be used in food emergency dispatching

Electrochemical Characterization of Li 4

Li4Ti5O12/C composite was synthesized by starch-sol-assisted rheological phase method using inexpensive raw material starch as carbon coating precursor. The Li4Ti5O12/C powder was characterized using XRD, SEM, and TG techniques. The synthesized Li4Ti5O12 crystallites are cohesively covered by conductive carbon from starch sol which leads to increased conductivity, and the particle size of Li4Ti5O12/C is about 500 nm. The electrochemical performance of Li4Ti5O12/C was characterized by galvanostatic charge/discharge and EIS methods, and the results show that the Li4Ti5O12/C presents a high discharge capacity, high rate capability, and long cycle life. The capacity retention was at 87% (500 cycles at 1C) and 73.0% (2000 cycles at 20C) indicating promising high rate performance of Li4Ti5O12/C as anode material for lithium ion battery

Efficient Methods for Non-stationary Online Learning

Non-stationary online learning has drawn much attention in recent years. In particular, dynamic regret and adaptive regret are proposed as two principled performance measures for online convex optimization in non-stationary environments. To optimize them, a two-layer online ensemble is usually deployed due to the inherent uncertainty of the non-stationarity, in which a group of base-learners are maintained and a meta-algorithm is employed to track the best one on the fly. However, the two-layer structure raises the concern about the computational complexity -- those methods typically maintain $\mathcal{O}(\log T)$ base-learners simultaneously for a $T$-round online game and thus perform multiple projections onto the feasible domain per round, which becomes the computational bottleneck when the domain is complicated. In this paper, we present efficient methods for optimizing dynamic regret and adaptive regret, which reduce the number of projections per round from $\mathcal{O}(\log T)$ to $1$. Moreover, our obtained algorithms require only one gradient query and one function evaluation at each round. Our technique hinges on the reduction mechanism developed in parameter-free online learning and requires non-trivial twists on non-stationary online methods. Empirical studies verify our theoretical findings.Comment: preliminary conference version appeared at NeurIPS 2022; this extended version improves the paper presentation, further investigates the interval dynamic regret, and adds two applications (online non-stochastic control and online PCA

Quantum Interference of Stored Coherent Spin-wave Excitations in a Two-channel Memory

Quantum memories are essential elements in long-distance quantum networks and quantum computation. Significant advances have been achieved in demonstrating relative long-lived single-channel memory at single-photon level in cold atomic media. However, the qubit memory corresponding to store two-channel spin-wave excitations (SWEs) still faces challenges, including the limitations resulting from Larmor procession, fluctuating ambient magnetic field, and manipulation/measurement of the relative phase between the two channels. Here, we demonstrate a two-channel memory scheme in an ideal tripod atomic system, in which the total readout signal exhibits either constructive or destructive interference when the two-channel SWEs are retrieved by two reading beams with a controllable relative phase. Experimental result indicates quantum coherence between the stored SWEs. Based on such phase-sensitive storage/retrieval scheme, measurements of the relative phase between the two SWEs and Rabi oscillation, as well as elimination of the collapse and revival of the readout signal, are experimentally demonstrated

Unsupervised Deep Cross-Language Entity Alignment

Cross-lingual entity alignment is the task of finding the same semantic entities from different language knowledge graphs. In this paper, we propose a simple and novel unsupervised method for cross-language entity alignment. We utilize the deep learning multi-language encoder combined with a machine translator to encode knowledge graph text, which reduces the reliance on label data. Unlike traditional methods that only emphasize global or local alignment, our method simultaneously considers both alignment strategies. We first view the alignment task as a bipartite matching problem and then adopt the re-exchanging idea to accomplish alignment. Compared with the traditional bipartite matching algorithm that only gives one optimal solution, our algorithm generates ranked matching results which enabled many potentials downstream tasks. Additionally, our method can adapt two different types of optimization (minimal and maximal) in the bipartite matching process, which provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990, and 0.996 Hits@1 rates on the DBP15K dataset in Chinese, Japanese, and French to English alignment tasks. We outperformed the state-of-the-art method in unsupervised and semi-supervised categories. Compared with the state-of-the-art supervised method, our method outperforms 2.6% and 0.4% in Ja-En and Fr-En alignment tasks while marginally lower by 0.2% in the Zh-En alignment task.Comment: 17 pages,5 figures, Accepted by ECML PKDD 2023(Research Track

Ananas comosus

In this study, we aimed to investigate the effect and action mechanisms of pineapple leaf phenols (PLPs) on liver fat metabolism in high-fat diet-fed mice. Results show that PLP significantly reduced abdominal fat and liver lipid accumulation in high-fat diet-fed mice. The effects of PLP were comparable with those of FB. Furthermore, at the protein level, PLP upregulated the expression of carnitine palmitoyltransferase 1 (CPT-1), whereas FB had no effects on CPT-1 compared with the HFD controls. Regarding mRNA expression, PLP mainly promoted the expression of CPT-1, PGC1a, UCP-1, and AMPK in the mitochondria, whereas FB mostly enhanced the expression of Ech1, Acox1, Acaa1, and Ehhadh in peroxisomes. PLP seemed to enhance fat metabolism in the mitochondria, whereas FB mainly exerted the effect in peroxisomes. In addition, p-coumaric acid (CA), one of the main components from PLP, significantly inhibited fat accumulation in oleic acid-induced HepG2 cells. CA also significantly upregulated CPT-1 mRNA and protein expressions in HepG2 cells. We, firstly, found that PLP enhanced liver fat metabolism by upregulating CPT-1 expression in the mitochondria and might be promising in treatment of fatty liver diseases as alternative natural products. CA may be one of the active components of PLP

Scorpion in Combination with Gypsum: Novel Antidiabetic Activities in Streptozotocin-Induced Diabetic Mice by Up-Regulating Pancreatic PPARγ and PDX-1 Expressions

The management of diabetes without any side effects remains a challenge in medicine. In this study, antidiabetic activity and the mechanism of action of scorpion combined with gypsum (SG) were investigated. Streptozotocin-induced diabetic mice were orally administrated with scorpion (200 mg kg−1 per day) in combination with gypsum (200 mg kg−1 per day) for 5 weeks. SG treatment resulted in decreased body weight, blood glucose and lipid levels, and increased serum and pancreatic insulin levels in diabetic mice. Furthermore, SG significantly increased the number and volume of beta cells in the Islets of Langerhans and promoted peroxisome proliferator-activated receptor gamma and pancreatic duodenal homeobox 1 expressions in pancreatic tissues. However, scorpion or gypsum alone had no significant effect in this animal model. Metformin showed a slight or moderate effect in this diabetic model, but this effect was weak compared with that of SG. Taken together, SG showed a new antidiabetic effect in streptozotocin-induced diabetic mice. This effect may possibly be involved in enhancing beta-cell regeneration and promoting insulin secretion by targeting PPARγ and PDX-1. Moreover, this new effect of SG offers a promising step toward the treatment of diabetic patients with beta-cell failure as a complementary and alternative medicine