High-resolution structures have not been reported for replicative helicases at a replication fork at atomic resolution, a prerequisite to understanding the unwinding mechanism. The eukaryotic ...replicative CMG (Cdc45, Mcm2-7, GINS) helicase contains a Mcm2-7 motor ring, with the N-tier ring in front and the C-tier motor ring behind. The N-tier ring is structurally divided into a zinc finger (ZF) sub-ring followed by the oligosaccharide/oligonucleotide-binding (OB) fold ring. Here we report the cryo-EM structure of CMG on forked DNA at 3.9 Å, revealing that parental DNA enters the ZF sub-ring and strand separation occurs at the bottom of the ZF sub-ring, where the lagging strand is blocked and diverted sideways by OB hairpin-loops of Mcm3, Mcm4, Mcm6, and Mcm7. Thus, instead of employing a specific steric exclusion process, or even a separation pin, unwinding is achieved via a "dam-and-diversion tunnel" mechanism that does not require specific protein-DNA interaction. The C-tier motor ring contains spirally configured PS1 and H2I loops of Mcms 2, 3, 5, 6 that translocate on the spirally-configured leading strand, and thereby pull the preceding DNA segment through the diversion tunnel for strand separation.
The promised land de b'Beri, Boulou; Reid-Maroney, Nina; Wright, Handel K
The promised land,
2014., 20140606, 2014, 2014-06-06, 2014-06-09
eBook
"Eschewing the often romanticized Underground Railroad narrative that portrays southern Ontario as the welcoming destination of Blacks fleeing from slavery, The Promised Land reveals the Chatham-Kent ...area as a crucial settlement site for an early Black presence in Canada. The contributors present the everyday lives and professional activities of individuals and families in these communities and highlight early cross-border activism to end slavery in the United States and to promote civil rights in the United States and Canada. Essays also reflect on the frequent intermingling of local Black, White, and First Nations people. Using a cultural studies framework for their collective investigations, the authors trace physical and intellectual trajectories of Blackness that have radiated from southern Ontario to other parts of Canada, the United States, the Caribbean, and Africa. The result is a collection that represents the presence and diffusion of Blackness and inventively challenges the grand narrative of history."--Publisher's website.
Principal component analysis (PCA) and independent component analysis (ICA) have been widely used for process monitoring in process industry. Since the operation data of blast furnace (BF) ironmaking ...process contain both non-Gaussian distribution data and Gaussian distribution data, the above single PCA or ICA method hardly describes the data distribution information of the BF process completely, which makes the monitoring and diagnosis of abnormal working-conditions only with a single method prone to false positives and false negatives. In this article, a novel integrated PCA-ICA method is proposed for monitoring and diagnosing the abnormal furnace conditions in BF ironmaking by comprehensively considering and combining the characteristics of PCA and ICA. First, the process monitoring models of PCA and ICA are, respectively, established using the actual industrial BF data, while both them are using T 2 and squared prediction error statistics to monitor whether the process is abnormal. Based on this, in order to fully reveal the internal structure of actual BF ironmaking data, an integrated PCA-ICA strategy and algorithm is proposed for comprehensively monitoring and diagnosing the abnormal furnace conditions. The corresponding unified contribution charts indices and control limits for fault identification were also presented. Finally, data experiments using actual industrial BF data show that the proposed method can obtain good results in both monitoring and diagnosing the abnormal furnace conditions of BF ironmaking.
Low-rank and sparse representation (LRaSR)-based approaches have been widely used for anomaly detection (AD). Their central ideas are to minimize the rank of the low-rank space constrained to ...predetermined values, while using various regularization parameters to control the sparse representation. Three key issues arise from LRaSR. The first is how to determine the constrained rank. The second is an appropriate selection of regularization parameters. The third one is the detector used for AD. This article presents a new but rather simple competing model, called component decomposition analysis (CDA) which represents a data space X as a linear orthogonal decomposition of three components, X = PC<inline-formula> <tex-math notation="LaTeX">^{{m}} + </tex-math></inline-formula> IC<inline-formula> <tex-math notation="LaTeX">^{{j}} + </tex-math></inline-formula> N with <inline-formula> <tex-math notation="LaTeX">{m} </tex-math></inline-formula> principal components, PC<inline-formula> <tex-math notation="LaTeX">^{{m}} </tex-math></inline-formula>, generated by principal component analysis (PCA) and <inline-formula> <tex-math notation="LaTeX">{j} </tex-math></inline-formula> independent components, IC<inline-formula> <tex-math notation="LaTeX">^{{j}} </tex-math></inline-formula>, generated by independent component analysis (ICA) plus a noise component N. CDA offers several advantages over LRaSR. First, CDA uses well-known component analysis techniques to decompose the dataset without solving constrained optimization problems. Second, the values of <inline-formula> <tex-math notation="LaTeX">{m} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">{j} </tex-math></inline-formula> can be automatically determined by virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD). To better extract anomalies from the IC<inline-formula> <tex-math notation="LaTeX">^{{j}} </tex-math></inline-formula> component space, the concept of sparsity cardinality (SC) is further incorporated into CDA to derive a CDASC anomaly detector (CDASC-AD). The experimental results demonstrate that CDASC-AD is very competitive against the LRaSR-based models and performs well in hyperspectral AD.
Component-based development (CBD) is an important emerging topic in software engineering, promising long-sought-after benefits like increased reuse, reduced time to market, and, hence, reduced ...software production cost. The cornerstone of a CBD technology is its underlying software component model, which defines components and their composition mechanisms. Current models use objects or architectural units as components. These are not ideal for component reuse or systematic composition. In this paper, we survey and analyze current component models and classify them into a taxonomy based on commonly accepted desiderata for CBD. For each category in the taxonomy, we describe its key characteristics and evaluate them with respect to these desiderata.
This brief proposes an independent component analysis-principal component analysis (ICA-PCA) integrating with relevance vector machine (RVM) for multivariate process monitoring. Given the fact that ...the distribution of industrial process variables is mostly non-Gaussian and PCA cannot well deal with the non-Gaussian part. A hybrid ICA-PCA method is proposed to simultaneously extract the non-Gaussian and Gaussian information of multivariate processes. ICA is first used to monitor the non-Gaussian part of the process and then the Gaussian part of the residual process can be extracted using PCA. After feature extraction, a Bayesian-based classifier named RVM is established to make fault detection for the sake of both preventing the chosen of threshold as in traditional method and compensating for the single statistic. The performance of the proposed approach is validated using the Tennessee Eastman process. Simulation results verified the effectiveness of the proposed method.
Complement pathway proteins are reported to be increased in polycystic ovary syndrome (PCOS) and may be affected by obesity and insulin resistance. To investigate this, a proteomic analysis of the ...complement system was undertaken, including inhibitory proteins. In this cohort study, plasma was collected from 234 women (137 with PCOS and 97 controls). SOMALogic proteomic analysis was undertaken for the following complement system proteins: C1q, C1r, C2, C3, C3a, iC3b, C3b, C3d, C3adesArg, C4, C4a, C4b, C5, C5a, C5b-6 complex, C8, properdin, factor B, factor D, factor H, factor I, mannose-binding protein C (MBL), complement decay-accelerating factor (DAF) and complement factor H-related protein 5 (CFHR5). The alternative pathway of the complement system was primarily overexpressed in PCOS, with increased C3 (p < 0.05), properdin and factor B (p < 0.01). In addition, inhibition of this pathway was also seen in PCOS, with an increase in CFHR5, factor H and factor I (p < 0.01). Downstream complement factors iC3b and C3d, associated with an enhanced B cell response, and C5a, associated with an inflammatory cytokine release, were increased (p < 0.01). Hyperandrogenemia correlated positively with properdin and iC3b, whilst insulin resistance (HOMA-IR) correlated with iC3b and factor H (p < 0.05) in PCOS. BMI correlated positively with C3d, factor B, factor D, factor I, CFHR5 and C5a (p < 0.05). This comprehensive evaluation of the complement system in PCOS revealed the upregulation of components of the complement system, which appears to be offset by the concurrent upregulation of its inhibitors, with these changes accounted for in part by BMI, hyperandrogenemia and insulin resistance.
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An MCM4 mutation detected in human cancer cells from endometrium was characterized. The mutation of G486D is located within MCM-box and the glycine at 486 in human MCM4 is conserved in Saccharomyces ...cerevisiae MCM4 and Sulfolobus solfataricus MCM. This MCM4 mutation affected human MCM4/6/7 complex formation, since the complex containing the mutant MCM4 protein is unstable and the mutant MCM4 protein is tend to be degraded. It is likely that the MCM4 mutation affects the interaction with MCM7 to destabilize the MCM4/6/7 complex. Cells with abnormal nuclear morphology were detected when the mutant MCM4 was expressed in HeLa cells, suggesting that DNA replication was perturbed in the presence of the mutant MCM4. Role of the conserved amino acid in MCM4 function is discussed.
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Abstract
The amino-terminal region of eukaryotic MCM4 is characteristic of the presence of a number of phosphorylation sites for CDK and DDK, suggesting that the region plays regulatory roles in the ...MCM2-7 helicase function. However, the roles are not fully understood. We analyzed the role of the amino-terminal region of human MCM4 by using MCM4/6/7 helicase as a model for MCM2-7 helicase. First we found that deletion of 35 amino acids at the amino-terminal end resulted in inhibition of DNA helicase activity of the MCM4/6/7 complex. Conversion of arginine at amino acid no. 10 and 11 to alanine had similar effect to the deletion mutant of Δ1-35, suggesting that these arginine play a role in the DNA helicase activity. The data suggest that expression of these mutant MCM4 in HeLa cells perturbed the progression of the S phase. Substitution of six CDK phosphorylation sites (3, 7, 19, 32, 54 and 110) in the amino-terminal region by phospho-mimetic glutamic acids affected the hexamer formation of the MCM4/6/7 complex. MCM4 phosphorylation by CDK may play a role in DNA replication licensing system, and the present results suggest that the phosphorylation interferes MCM function by lowering stability of MCM complex.
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Objective: Electrical impedance myography (EIM) is a relatively new technique to assess neuromuscular disorders (NMD). Although the application of EIM using surface electrodes (sEIM) has been adopted ...by the neurology community in recent years to evaluate NMD status, sEIM's sensitivity as a biomarker of skeletal muscle condition is impacted by subcutaneous fat (SF) tissue. Here, we develop a method that is able to remove the contribution of SF from sEIM data. Methods: We evaluate independent component analysis (ICA) and principal component analysis (PCA) for this purpose. Then, we introduce the so-called model component analysis (MCA). All methods are validated with numerical simulations using impedivity data from SF and muscle tissues. The methods are then tested with measurements performed in diseased individuals (n=3). Results: Simulations demonstrate that MCA is the most accurate method at separating the impedivity of SF and muscle tissues with the accuracy being 99.2%, followed by ICA with 51.4%, and finally PCA with 38.5%. Experimental results from sEIM data measured on the triceps brachii of patients are consistent with muscle grayscale level values obtained using ultrasound imaging. Conclusion: MCA can be used to separate the impedivity of SF and muscle tissues from sEIM data, thus increasing the sensitivity to detect changes in the muscle. Significance: MCA can make the sEIM technique a better diagnostic tool and biomarker of disease progression and response to therapy by removing the confounding effect of SF tissue in NMD patients with excess subcutaneous fat tissue for any reason.
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