Charged mobile complexes in magnetic fields: A novel selection rule for magneto-optical transitions

The implications of magnetic translations for internal optical transitions of charged mobile electron-hole ($e$--$h$) complexes and ions in a uniform magnetic field $B$ are discussed. It is shown that transitions of such complexes are governed by a novel exact selection rule. Internal intraband transitions of two-dimensional (2D) charged excitons $X^-$ in strong magnetic fields are considered as an illustrative example.Comment: 4 pages, 2 figure

Braggoriton--Excitation in Photonic Crystal Infiltrated with Polarizable Medium

Light propagation in a photonic crystal infiltrated with polarizable molecules is considered. We demonstrate that the interplay between the spatial dispersion caused by Bragg diffraction and polaritonic frequency dispersion gives rise to novel propagating excitations, or braggoritons, with intragap frequencies. We derive the braggoriton dispersion relation and show that it is governed by two parameters, namely, the strength of light-matter interaction and detuning between the Bragg frequency and that of the infiltrated molecules. We also study defect-induced states when the photonic band gap is divided into two subgaps by the braggoritonic branches and find that each defect creates two intragap localized states inside each subgap.Comment: LaTeX, 8 pages, 5 figure

Метод приближенного анализа взаимодействия материала с валками в вибровалковом измельчителе

The article presents the results of a study of the process of material grinding in roller aggregates with various kinematic features. As the object of research, the design of a vibroroller unit is selected, which has great prospects for use in production. A characteristic feature of this unit is a significant influence on the grinding process of inertia forces. As the main method of research in relation to the movement of the working bodies of the roller and vibroroller shredder and the crushed material, a method of modeling is adopted. It is presented an approximate analysis of the interaction of the crushed material in roll units with rolls. The crushed material is modeled by a set of horizontal elementary layers. At the first stage, the material is crushed in rolls with constant kinematic parameters. Analytical dependencies of the roll pressure on the material are established. At the second stage, the grinding of materials in a vibroroller shredder is considered. A distinctive feature of the vibroroller shredder is the presence of an eccentrically installed roll. The variant is presented when the eccentric performs a curvilinear translational motion, and the roll performs harmonic fluctuation (vibrations) along the coordinate axes with an amplitude of e. The resulting inertia forces and oscillatory motions of the roll are considered. The analysis of the total force in the unit under consideration, which makes it possible to implement crushing-shear and vibration effects on the crushed material, is carried out. The force interaction of the roll with the material is described by two systems of forces: the elastic forces resulting from the contraction of the model layers according to Hooke’s law, and the forces caused by the vibration of the roll (inertia forces). The results obtained are of practical importance in the design of roller units and vibration equipment, as well as for the analysis of the operation of such designs of grinders.Представлены результаты исследования процесса измельчения материала в валковых агрегатах с различными кинематическими особенностями. В качестве объекта исследования выбрана конструкция вибровалкового агрегата, имеющего большие перспективы использования в производстве. Характерной особенностью такого агрегата является значительное влияние на процесс измельчения сил инерции. В качестве основного метода исследования принят метод моделирования, причем применительно к движению рабочих органов валкового и вибровалкового измельчителя и измельчаемого материала. Представлен приближенный анализ взаимодействия измельчаемого материала в валковых агрегатах с валками. Измельчаемый материал моделируется совокупностью горизонтальных элементарных слоев. На первом этапе представлено измельчение материала в валках с постоянными кинематическими параметрами. Установлены аналитические зависимости давления валков на материал. На втором этапе рассмотрено измельчение материалов в вибровалковом измельчителе. Отличительной особенностью вибровалкового измельчителя является наличие эксцентрично установленного валка. Представлен вариант, когда эксцентрик выполняет криволинейно-поступательное движение, а валок совершает гармонические колебания (вибрации) вдоль осей координат с амплитудой е. Рассмотрены возникающие при этом силы инерции и колебательные движения валка. Проведен анализ суммарного усилия в рассматриваемом агрегате, позволяющем реализовать раздавливающе-сдвиговое и вибрационное воздействия на измельчаемый материал. Силовое взаимодействие валка с материалом описано двумя системами сил: силами упругости, возникающими в результате сокращения модельных слоев согласно закону Гука, и силами, вызванными вибрацией валка (силами инерции). Полученные результаты имеют практическую значимость при проектировании валковых агрегатов и вибрационной техники, а также для анализа работы подобных конструкций измельчителей

Whole-exome sequencing and clinical interpretation of FFPE tumor samples to guide precision cancer medicine

Translating whole exome sequencing (WES) for prospective clinical use may impact the care of cancer patients; however, multiple innovations are necessary for clinical implementation. These include: (1) rapid and robust WES from formalin-fixed paraffin embedded (FFPE) tumor tissue, (2) analytical output similar to data from frozen samples, and (3) clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival FFPE tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a “long tail” of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15/16 patients. In one patient, previously undetected findings guided clinical trial enrollment leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine

Finding consistent disease subnetworks across microarray datasets

<p>Abstract</p> <p>Background</p> <p>While contemporary methods of microarray analysis are excellent tools for studying individual microarray datasets, they have a tendency to produce different results from different datasets of the same disease. We aim to solve this reproducibility problem by introducing a technique (SNet). SNet provides both quantitative and descriptive analysis of microarray datasets by identifying specific connected portions of pathways that are significant. We term such portions within pathways as “subnetworks”.</p> <p>Results</p> <p>We tested SNet on independent datasets of several diseases, including childhood ALL, DMD and lung cancer. For each of these diseases, we obtained two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produced almost the same list of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks was between 51.18% to 93.01%. In contrast, when the same pairs of microarray datasets were analysed using GSEA, t-test and SAM, this percentage fell between 2.38% to 28.90% for GSEA, 49.60% tp 73.01% for t-test, and 49.96% to 81.25% for SAM. Furthermore, the genes selected using these existing methods did not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway actually affected by the disease.</p> <p>Conclusions</p> <p>These results clearly demonstrate that our technique generates significant subnetworks and genes that are more consistent and reproducible across datasets compared to the other popular methods available (GSEA, t-test and SAM). The large size of subnetworks which we generate indicates that they are generally more biologically significant (less likely to be spurious). In addition, we have chosen two sample subnetworks and validated them with references from biological literature. This shows that our algorithm is capable of generating descriptive biologically conclusions.</p

Comparative analysis of RNA sequencing methods for degraded or low-input samples

available in PMC 2014 January 01RNA-seq is an effective method for studying the transcriptome, but it can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations or cadavers. Recent studies have proposed several methods for RNA-seq of low-quality and/or low-quantity samples, but the relative merits of these methods have not been systematically analyzed. Here we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and compared them against two control libraries. We found that the RNase H method performed best for chemically fragmented, low-quality RNA, and we confirmed this through analysis of actual degraded samples. RNase H can even effectively replace oligo(dT)-based methods for standard RNA-seq. SMART and NuGEN had distinct strengths for measuring low-quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.National Institutes of Health (U.S.) (Pioneer Award DP1-OD003958-01)National Human Genome Research Institute (U.S.) (NHGRI) 1P01HG005062-01)National Human Genome Research Institute (U.S.) (NHGRI Center of Excellence in Genome Science Award 1P50HG006193-01)Howard Hughes Medical Institute (Investigator)Merkin Family Foundation for Stem Cell ResearchBroad Institute of MIT and Harvard (Klarman Cell Observatory)National Human Genome Research Institute (U.S.) (NHGRI grant HG03067)Fonds voor Wetenschappelijk Onderzoek--Vlaandere

Medulloblastoma Exome Sequencing Uncovers Subtype-Specific Somatic Mutations

Medulloblastomas are the most common malignant brain tumors in children1. Identifying and understanding the genetic events that drive these tumors is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. Recently, our group and others described distinct molecular subtypes of medulloblastoma based on transcriptional and copy number profiles2–5. Here, we utilized whole exome hybrid capture and deep sequencing to identify somatic mutations across the coding regions of 92 primary medulloblastoma/normal pairs. Overall, medulloblastomas exhibit low mutation rates consistent with other pediatric tumors, with a median of 0.35 non-silent mutations per megabase. We identified twelve genes mutated at statistically significant frequencies, including previously known mutated genes in medulloblastoma such as CTNNB1, PTCH1, MLL2, SMARCA4 and TP53. Recurrent somatic mutations were identified in an RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR, and LDB1, novel findings in medulloblastoma. We show that mutant DDX3X potentiates transactivation of a TCF promoter and enhances cell viability in combination with mutant but not wild type beta-catenin. Together, our study reveals the alteration of Wnt, Hedgehog, histone methyltransferase and now N-CoR pathways across medulloblastomas and within specific subtypes of this disease, and nominates the RNA helicase DDX3X as a component of pathogenic beta-catenin signaling in medulloblastoma

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work

Mutational heterogeneity in cancer and the search for new cancer genes

Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention