Strong Association Between Two Polymorphisms on 15q25.1 and Lung Cancer Risk: A Meta-Analysis

Background: The association between polymorphisms on 15q25.1 and lung cancer has been widely evaluated; however, the studies have yielded contradictory results. We sought to investigate this inconsistency by performing a comprehensive meta-analysis on two polymorphisms (CHRNA3 gene: rs1051730 and AGPHD1 gene: rs8034191) on 15q25.1. Methods: Data were extracted from 15 and 14 studies on polymorphisms rs1051730 and rs8034191 involving 12301/14000 and 14075/12873 lung cancer cases/controls, respectively. The random-effects model was applied, addressing heterogeneity and publication bias. Results: The two polymorphisms followed Hardy-Weinberg equilibrium for all studies (P\u3e0.05). For rs1051730-G/A, carriers of A allele had a 36% increased risk for lung cancer (95% confidence interval [CI]: 1.27–1.46; P\u3c0.0005), without heterogeneity (P = 0.258) or publication bias (PEgger = 0.462). For rs8034191-T/C, the allelic contrast indicated that C allele conferred a 23% increased risk for lung cancer (95% CI: 1.08–1.4; P = 0.002), with significant heterogeneity (P\u3c0.0005), without publication bias (PEgger = 0.682). Subgroup analyses suggested that the between-study heterogeneity was derived from ethnicity, study design, matched information, and lung cancer subtypes. For example, the association of polymorphisms rs1051730 and rs8034191 with lung cancer was heterogeneous between Caucasians (OR = 1.32 and 1.22; 95% CI: 1.25–1.44 and 1.05–1.42; PP = 0.237 and 0.934, respectively) under the allelic model, and this association was relatively strengthened under the dominant model. There was no observable publication bias for both polymorphisms. Conclusions: Our findings demonstrated that CHRNA3 gene rs1051730-A allele and AGPHD1 gene rs8034191-T allele might be risk-conferring factors for the development of lung cancer in Caucasians, but not in East-Asians

GRASP-1 A Neuronal RasGEF Associated with the AMPA Receptor/GRIP Complex

AbstractThe PDZ domain–containing proteins, such as PSD-95 and GRIP, have been suggested to be involved in the targeting of glutamate receptors, a process that plays a critical role in the efficiency of synaptic transmission and plasticity. To address the molecular mechanisms underlying AMPA receptor synaptic localization, we have identified several GRIP-associated proteins (GRASPs) that bind to distinct PDZ domains within GRIP. GRASP-1 is a neuronal rasGEF associated with GRIP and AMPA receptors in vivo. Overexpression of GRASP-1 in cultured neurons specifically reduced the synaptic targeting of AMPA receptors. In addition, the subcellular distribution of both AMPA receptors and GRASP-1 was rapidly regulated by the activation of NMDA receptors. These results suggest that GRASP-1 may regulate neuronal ras signaling and contribute to the regulation of AMPA receptor distribution by NMDA receptor activity

Tea polyphenols induced apoptosis of breast cancer cells by suppressing the expression of Survivin

To study the mechanism of tea polyphenols (TP)-induced apoptosis of breast cancer cells. Proliferation of MCF-7 and SK-BR-3 cells was evaluated by MTT assays. Cellular ultrastructure was examined by electron microscopy. Apoptosis was detected by TUNEL. PCNA, Cyclin D1, Cyclin E and Survivin expression was measured by Western blot. Cell proliferation was significantly inhibited by TP. Spindle and round cells were loosely distributed with increased particles after TP treatment. Increased cell size, frequent nuclear atypia and a collapse of apoptosis were observed. The nucleus was pushed towards one side, while the cytoplasm was rich in free ribosome. The membrane of mitochondria was thickening, and the cell apoptotic body was observed. TP treated cells experienced significantly enhanced apoptosis compared with 5-Fu treated or control groups. The expression of survivin was downregulated by TP. To conclude, TP can inhibit cell growth and induce apoptosis through downregulating the expression of survivin in breast cancer

ABCC3 as a marker for multidrug resistance in non-small cell lung cancer

Multidrug resistance (MDR) contributes to the failure of chemotherapy and high mortality in non-small cell lung cancer (NSCLC). We aim to identify MDR genes that predict tumor response to chemotherapy. 199 NSCLC fresh tissue samples were tested for chemosensitivity by MTT assay. cDNA microarray was done with 5 samples with highest resistance and 6 samples with highest sensitivity. Expression of ABCC3 mRNA and protein was detected by real-time PCR and immunohistochemisty, respectively. The association between gene expression and overall survival (OS) was examined using Cox proportional hazard regression. 44 genes were upregulated and 168 downregulated in the chemotherapy-resistant group. ABCC3 was one of the most up-regulated genes in the resistant group. ABCC3-positive expression correlated with lymph node involvement, advanced TNM stage, more malignant histological type, multiple-resistance to anti-cancer drugs, and reduced OS. ABCC3 expression may serve as a marker for MDR and predictor for poor clinical outcome of NSCLC

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community

Phase shift and magnetic anisotropy induced field splitting of impurity states in (Li1-xFex)OHFeSe superconductor

Revealing the energy and spatial characteristics of impurity induced states in superconductors is essential for understanding their mechanism and fabricating new quantum state by manipulating impurities. Here by using high-resolution scanning tunneling microscopy/spectroscopy, we investigated the spatial distribution and magnetic field response of the impurity states in (Li1-xFex)OHFeSe. We detected two pairs of strong in-gap states on the "dumbbell" shaped defects. They display clear damped oscillations with different phase shifts and a direct phase-energy correlation. These features have long been predicted for classical Yu-Shiba-Rusinov (YSR) state, which are demonstrated here with unprecedented resolution for the first time. Moreover, upon applying magnetic field, all the in-gap state peaks remarkably split into two rather than shift, and the splitting strength is field orientation dependent. Via detailed numerical model calculations, we found such anisotropic splitting behavior can be naturally induced by a high-spin impurity coupled to anisotropic environment, highlighting how magnetic anisotropy affects the behavior of YSR states.Comment: Main text with supplementary (accepted by Phys. Rev. Lett.

A hybrid data assimilation system based on machine learning

In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems