The Role of Cell Cycle Regulators in Cell Survival—Dual Functions of Cyclin-Dependent Kinase 20 and p21Cip1/Waf1

The mammalian cell cycle is important in controlling normal cell proliferation and the development of various diseases. Cell cycle checkpoints are well regulated by both activators and inhibitors to avoid cell growth disorder and cancerogenesis. Cyclin dependent kinase 20 (CDK20) and p21^Cip1/Waf1 are widely recognized as key regulators of cell cycle checkpoints controlling cell proliferation/growth and involving in developing multiple cancers. Emerging evidence demonstrates that these two cell cycle regulators also play an essential role in promoting cell survival independent of the cell cycle, particularly in those cells with a limited capability of proliferation, such as cardiomyocytes. These findings bring new insights into understanding cytoprotection in these tissues. Here, we summarize the new progress of the studies on these two molecules in regulating cell cycle/growth, and their new roles in cell survival by inhibiting various cell death mechanisms. We also outline their potential implications in cancerogenesis and protection in heart diseases. This information renews the knowledge in molecular natures and cellular functions of these regulators, leading to a better understanding of the pathogenesis of the associated diseases and the discovery of new therapeutic strategies.     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

Sensitization of MCF7 Cells with High Notch1 Activity by Cisplatin and Histone Deacetylase Inhibitors Applied Together

Histone deacetylase inhibitors (HDIs) are promising anti-cancer agents that inhibit proliferation of many types of cancer cells including breast carcinoma (BC) cells. In the present study, we investigated the influence of the Notch1 activity level on the pharmacological interaction between cisplatin (CDDP) and two HDIs, valproic acid (VPA) and suberoylanilide hydroxamic acid (SAHA, vorinostat), in luminal-like BC cells. The type of drug–drug interaction between CDDP and HDIs was determined by isobolographic analysis. MCF7 cells were genetically modified to express differential levels of Notch1 activity. The cytotoxic effect of SAHA or VPA was higher on cells with decreased Notch1 activity and lower for cells with increased Notch1 activity than native BC cells. The isobolographic analysis demonstrated that combinations of CDDP with SAHA or VPA at a fixed ratio of 1:1 exerted additive or additive with tendency toward synergism interactions. Therefore, treatment of CDDP with HDIs could be used to optimize a combined therapy based on CDDP against Notch1-altered luminal BC. In conclusion, the combined therapy of HDIs and CDDP may be a promising therapeutic tool in the treatment of luminal-type BC with altered Notch1 activity.     

Migraine: Calcium Channels and Glia

Migraine is a common neurological disease that affects about 11% of the adult population. The disease is divided into two main clinical subtypes: migraine with aura and migraine without aura. According to the neurovascular theory of migraine, the activation of the trigeminovascular system (TGVS) and the release of numerous neuropeptides, including calcitonin gene-related peptide (CGRP) are involved in headache pathogenesis. TGVS can be activated by cortical spreading depression (CSD), a phenomenon responsible for the aura. The mechanism of CSD, stemming in part from aberrant interactions between neurons and glia have been studied in models of familial hemiplegic migraine (FHM), a rare monogenic form of migraine with aura. The present review focuses on those interactions, especially as seen in FHM type 1, a variant of the disease caused by a mutation in CACNA1A, which encodes the α1A subunit of the P/Q-type voltage-gated calcium channel.     

Salivary Proteome Changes in Response to Acute Psychological Stress Due to an Oral Exam Simulation in University Students: Effect of an Olfactory Stimulus

The autonomic nervous system (ANS) plays a crucial role both in acute and chronic psychological stress eliciting changes in many local and systemic physiological and biochemical processes. Salivary secretion is also regulated by ANS. In this study, we explored salivary proteome changes produced in thirty-eight University students by a test stress, which simulated an oral exam. Students underwent a relaxation phase followed by the stress test during which an electrocardiogram was recorded. To evaluate the effect of an olfactory stimulus, half of the students were exposed to a pleasant odor diffused in the room throughout the whole session. Saliva samples were collected after the relaxation phase (T0) and the stress test (T1). State anxiety was also evaluated at T0 and T1. Salivary proteins were separated by two-dimensional electrophoresis, and patterns at different times were compared. Spots differentially expressed were trypsin digested and identified by mass spectrometry. Western blot analysis was used to validate proteomic results. Anxiety scores and heart rate changes indicated that the fake exam induced anxiety. Significant changes of α-amylase, polymeric immunoglobulin receptor (PIGR), and immunoglobulin α chain (IGHA) secretion were observed after the stress test was performed in the two conditions. Moreover, the presence of pleasant odor reduced the acute social stress affecting salivary proteome changes. Therefore, saliva proteomic analysis was a useful approach to evaluate the rapid responses associated to an acute stress test also highlighting known biomarkers.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.