The cohesin ring concatenates sister DNA molecules

Sister chromatid cohesion, which is essential for mitosis, is mediated by a multi-subunit protein complex called cohesin whose Scc1, Smc1, and Smc3 subunits form a tripartite ring structure. It has been proposed that cohesin holds sister DNAs together by trapping them inside its ring. To test this, we used site-specific cross-linking to create chemical connections at the three interfaces between the ring’s three constituent polypeptides, thereby creating covalently closed cohesin rings. As predicted by the ring entrapment model, this procedure produces dimeric DNA/cohesin structures that are resistant to protein denaturation. We conclude that cohesin rings concatenate individual sister minichromosome DNAs

Cosmic ray diffusion near the Bohm limit in the Cassiopeia A supernova remnant

Supernova remnants (SNRs) are believed to be the primary location of the acceleration of Galactic cosmic rays, via diffusive shock (Fermi) acceleration. Despite considerable theoretical work the precise details are still unknown, in part because of the difficulty in directly observing nucleons that are accelerated to TeV energies in, and affect the structure of, the SNR shocks. However, for the last ten years, X-ray observatories ASCA, and more recently Chandra, XMM-Newton, and Suzaku have made it possible to image the synchrotron emission at keV energies produced by cosmic-ray electrons accelerated in the SNR shocks. In this article, we describe a spatially-resolved spectroscopic analysis of Chandra observations of the Galactic SNR Cassiopeia A to map the cutoff frequencies of electrons accelerated in the forward shock. We set upper limits on the electron diffusion coefficient and find locations where particles appear to be accelerated nearly as fast as theoretically possible (the Bohm limit).Comment: 18 pages, 5 figures. Accepted for publication in Nature Physics (DOI below), final version available week of August 28, 2006 at http://www.nature.com/nphy

Electric Field Effects on Graphene Materials

Understanding the effect of electric fields on the physical and chemical properties of two-dimensional (2D) nanostructures is instrumental in the design of novel electronic and optoelectronic devices. Several of those properties are characterized in terms of the dielectric constant which play an important role on capacitance, conductivity, screening, dielectric losses and refractive index. Here we review our recent theoretical studies using density functional calculations including van der Waals interactions on two types of layered materials of similar two-dimensional molecular geometry but remarkably different electronic structures, that is, graphene and molybdenum disulphide (MoS$_2$). We focus on such two-dimensional crystals because of they complementary physical and chemical properties, and the appealing interest to incorporate them in the next generation of electronic and optoelectronic devices. We predict that the effective dielectric constant ($\varepsilon$) of few-layer graphene and MoS$_2$ is tunable by external electric fields ($E_{\rm ext}$). We show that at low fields ($E_{\rm ext}^{}<0.01$ V/\AA) $\varepsilon$ assumes a nearly constant value $\sim$4 for both materials, but increases at higher fields to values that depend on the layer thickness. The thicker the structure the stronger is the modulation of $\varepsilon$ with the electric field. Increasing of the external field perpendicular to the layer surface above a critical value can drive the systems to an unstable state where the layers are weakly coupled and can be easily separated. The observed dependence of $\varepsilon$ on the external field is due to charge polarization driven by the bias, which show several similar characteristics despite of the layer considered.Comment: Invited book chapter on Exotic Properties of Carbon Nanomatter: Advances in Physics and Chemistry, Springer Series on Carbon Materials. Editors: Mihai V. Putz and Ottorino Ori (11 pages, 4 figures, 30 references

Three applications of path integrals: equilibrium and kinetic isotope effects, and the temperature dependence of the rate constant of the [1,5] sigmatropic hydrogen shift in (Z)-1,3-pentadiene

Recent experiments have confirmed the importance of nuclear quantum effects even in large biomolecules at physiological temperature. Here we describe how the path integral formalism can be used to describe rigorously the nuclear quantum effects on equilibrium and kinetic properties of molecules. Specifically, we explain how path integrals can be employed to evaluate the equilibrium (EIE) and kinetic (KIE) isotope effects, and the temperature dependence of the rate constant. The methodology is applied to the [1,5] sigmatropic hydrogen shift in pentadiene. Both the KIE and the temperature dependence of the rate constant confirm the importance of tunneling and other nuclear quantum effects as well as of the anharmonicity of the potential energy surface. Moreover, previous results on the KIE were improved by using a combination of a high level electronic structure calculation within the harmonic approximation with a path integral anharmonicity correction using a lower level method.Comment: 9 pages, 4 figure

Quercetin prevents progression of disease in elastase/LPS-exposed mice by negatively regulating MMP expression

Abstract Background Chronic obstructive pulmonary disease (COPD) is characterized by chronic bronchitis, emphysema and irreversible airflow limitation. These changes are thought to be due to oxidative stress and an imbalance of proteases and antiproteases. Quercetin, a plant flavonoid, is a potent antioxidant and anti-inflammatory agent. We hypothesized that quercetin reduces lung inflammation and improves lung function in elastase/lipopolysaccharide (LPS)-exposed mice which show typical features of COPD, including airways inflammation, goblet cell metaplasia, and emphysema. Methods Mice treated with elastase and LPS once a week for 4 weeks were subsequently administered 0.5 mg of quercetin dihydrate or 50% propylene glycol (vehicle) by gavage for 10 days. Lungs were examined for elastance, oxidative stress, inflammation, and matrix metalloproteinase (MMP) activity. Effects of quercetin on MMP transcription and activity were examined in LPS-exposed murine macrophages. Results Quercetin-treated, elastase/LPS-exposed mice showed improved elastic recoil and decreased alveolar chord length compared to vehicle-treated controls. Quercetin-treated mice showed decreased levels of thiobarbituric acid reactive substances, a measure of lipid peroxidation caused by oxidative stress. Quercetin also reduced lung inflammation, goblet cell metaplasia, and mRNA expression of pro-inflammatory cytokines and muc5AC. Quercetin treatment decreased the expression and activity of MMP9 and MMP12 in vivo and in vitro, while increasing expression of the histone deacetylase Sirt-1 and suppressing MMP promoter H4 acetylation. Finally, co-treatment with the Sirt-1 inhibitor sirtinol blocked the effects of quercetin on the lung phenotype. Conclusions Quercetin prevents progression of emphysema in elastase/LPS-treated mice by reducing oxidative stress, lung inflammation and expression of MMP9 and MMP12.http://deepblue.lib.umich.edu/bitstream/2027.42/78260/1/1465-9921-11-131.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78260/2/1465-9921-11-131.pdfPeer Reviewe

Graphene plasmonics

Two rich and vibrant fields of investigation, graphene physics and plasmonics, strongly overlap. Not only does graphene possess intrinsic plasmons that are tunable and adjustable, but a combination of graphene with noble-metal nanostructures promises a variety of exciting applications for conventional plasmonics. The versatility of graphene means that graphene-based plasmonics may enable the manufacture of novel optical devices working in different frequency ranges, from terahertz to the visible, with extremely high speed, low driving voltage, low power consumption and compact sizes. Here we review the field emerging at the intersection of graphene physics and plasmonics.Comment: Review article; 12 pages, 6 figures, 99 references (final version available only at publisher's web site

Why do physicians prescribe dialysis? A prospective questionnaire study

Funding Information: This study was supported by an unrestricted grant 14CECPDEU1001 from Baxter Healthcare International. Baxter Novum is the result of a grant from Baxter Healthcare Corporation to Division of Renal Medicine and Baxter Novum, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, to support research activities at Karolinska Institutet to promote the understanding and treatment of renal disease. Bengt Lindholm is employed by Baxter Healthcare Corporation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Publisher Copyright: © 2017 Heaf et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.Introduction.The incidence of unplanned dialysis initiation (DI) with consequent increased comorbidity, mortality and reduced modality choice remains high, but the optimal timing of dialysis initiation (DI) remains controversial, and there is a lack of studies of specific reasons for DI. We investigated why and when physicians prescribe dialysis and hypothesized that physician motivation for DI is an independent factor which may have clinical consequences. Methods In the Peridialysis study, an ongoing multicenter prospective study assessing the causes and timing of DI and consequences of unplanned dialysis, physicians in 11 hospitals were asked to describe their primary, secondary and further reasons for prescribing DI. The stated reasons for DI were analyzed in relation to clinical and biochemical data at DI, and characteristics of physicians. Results In 446 patients (median age 67 years; 38% females; diabetes 25.6%), DI was prescribed by 84 doctors who stated 23 different primary reasons for DI. The primary indication was clinical in 63% and biochemical in 37%; 23% started for life-threatening conditions. Reduced renal function accounted for only 19% of primary reasons for DI but was a primary or contributing reason in 69%. The eGFR at DI was 7.2 ±3.4 ml/min/1.73 m2, but varied according to comorbidity and cause of DI. Patients with cachexia, anorexia and pulmonary stasis (34% with heart failure) had the highest eGFR (8.2–9.8 ml/min/1.73 m2), and those with edema, “low GFR”, and acidosis, the lowest (4.6–6.1 ml/min/1.73 m2). Patients with multiple comorbidity including diabetes started at a high eGFR (8.7 ml/min/1.73 m2). Physician experience played a role in dialysis prescription. Non-specialists were more likely to prescribe dialysis for life-threatening conditions, while older and more experienced physicians were more likely to start dialysis for clinical reasons, and at a lower eGFR. Female doctors started dialysis at a higher eGFR than males (8.0 vs. 7.1 ml/min/1.73 m2). Conclusions DI was prescribed mainly based on clinical reasons in accordance with current recommendations while low renal function accounted for only 19% of primary reasons for DI. There are considerable differences in physicians´ stated motivations for DI, related to their age, clinical experience and interpretation of biochemical variables. These differences may be an independent factor in the clinical treatment of patients, with consequences for the risk of unplanned DI.publishersversionPeer reviewe

Prognostic significance of a systemic inflammatory response in patients receiving first-line palliative chemotherapy for recurred or metastatic gastric cancer

<p>Abstract</p> <p>Background</p> <p>There is increasing evidence that the presence of an ongoing systemic inflammatory response is associated with poor prognosis in patients with advanced cancers. We evaluated the relationships between clinical status, laboratory factors and progression free survival (PFS), and overall survival (OS) in patients with recurrent or metastatic gastric cancer receiving first-line palliative chemotherapy.</p> <p>Methods</p> <p>We reviewed 402 patients with advanced gastric adenocarcinoma who received first-line palliative chemotherapy from June 2004 and December 2009. Various chemotherapy regimens were used. Eastern Cooperative Oncology Group performance status (ECOG PS), C-reactive protein (CRP), albumin, Glasgow prognostic score (GPS), and clinical factors were recorded immediately prior to first-line chemotherapy. Patients with both an elevated CRP (>1.0 mg/dL) and hypoalbuminemia (<3.5 mg/dL) were assigned a GPS of 2. Patients in whom only one of these biochemical abnormalities was present were assigned a GPS of 1, and patients with a normal CRP and albumin were assigned a score of 0. To evaluate the factors that affected PFS and OS, univariate and multivariate analyses were performed.</p> <p>Results</p> <p>According to multivariate analysis, the factors independently associated with PFS were ECOG PS (HR 1.37, 95% CI 1.02-1.84, <it>P </it>= 0.035), bone metastasis (HR 1.74, 95% CI 1.14-2.65, <it>P </it>= 0.009), and CRP elevation (HR 1.64, 95% CI 1.28-2.09, <it>P </it>= 0.001). The factors independently associated with OS were ECOG PS (HR 1.33, 95% CI 1.01-1.76, <it>P </it>= 0.037), bone metastasis (HR 1.61, 95% CI 1.08-2.39, <it>P </it>= 0.017), and GPS ≥ 1 (HR 1.76, 95% CI 1.41-2.19, <it>P </it>= 0.001).</p> <p>Conclusions</p> <p>The results of this study showed that the presence of a systemic inflammatory response as evidenced by the CRP, GPS was significantly associated with shorter PFS and OS in patients with recurrent or metastatic gastric cancer receiving first-line palliative chemotherapy. Bone metastasis and GPS were very useful indicator for survival in patients with recurrent or metastatic gastric cancer receiving palliative chemotherapy.</p

Dual-functioning transcription factors in the developmental gene network of Drosophila melanogaster

Quantitative models for transcriptional regulation have shown great promise for advancing our understanding of the biological mechanisms underlying gene regulation. However, all of the models to date assume a transcription factor (TF) to have either activating or repressing function towards all the genes it is regulating.In this paper we demonstrate, on the example of the developmental gene network in D. melanogaster, that the data-fit can be improved by up to 40% if the model is allowing certain TFs to have dual function, that is, acting as activator for some genes and as repressor for others. We demonstrate that the improvement is not due to additional flexibility in the model but rather derived from the data itself. We also found no evidence for the involvement of other known site-specific TFs in regulating this network. Finally, we propose SUMOylation as a candidate biological mechanism allowing TFs to switch their role when a small ubiquitin-like modifier (SUMO) is covalently attached to the TF. We strengthen this hypothesis by demonstrating that the TFs predicted to have dual function also contain the known SUMO consensus motif, while TFs predicted to have only one role lack this motif.We argue that a SUMOylation-dependent mechanism allowing TFs to have dual function represents a promising area for further research and might be another step towards uncovering the biological mechanisms underlying transcriptional regulation

Improving SNR and reducing training time of classifiers in large datasets via kernel averaging

Kernel methods are of growing importance in neuroscience research. As an elegant extension of linear methods, they are able to model complex non-linear relationships. However, since the kernel matrix grows with data size, the training of classifiers is computationally demanding in large datasets. Here, a technique developed for linear classifiers is extended to kernel methods: In linearly separable data, replacing sets of instances by their averages improves signal-to-noise ratio (SNR) and reduces data size. In kernel methods, data is linearly non-separable in input space, but linearly separable in the high-dimensional feature space that kernel methods implicitly operate in. It is shown that a classifier can be efficiently trained on instances averaged in feature space by averaging entries in the kernel matrix. Using artificial and publicly available data, it is shown that kernel averaging improves classification performance substantially and reduces training time, even in non-linearly separable data