808 research outputs found
Interpretation of UV Absorption Lines in SN1006
We present a theoretical interpretation of the broad silicon and iron UV
absorption features observed with the Hubble Space Telescope in the spectrum of
the Schweizer-Middleditch star behind the remnant of Supernova 1006. These
features are caused by supernova ejecta in SN1006. We propose that the
redshifted SiII2 1260 A feature consists of both unshocked and shocked SiII.
The sharp red edge of the line at 7070 km/s indicates the position of the
reverse shock, while its Gaussian blue edge reveals shocked Si with a mean
velocity of 5050 km/s and a dispersion of 1240 km/s, implying a reverse shock
velocity of 2860 km/s. The measured velocities satisfy the energy jump
condition for a strong shock, provided that all the shock energy goes into
ions, with little or no collisionless heating of electrons. The line profiles
of the SiIII and SiIV absorption features indicate that they arise mostly from
shocked Si. The total mass of shocked and unshocked Si inferred from the SiII,
SiIII and SiIV profiles is M_Si = 0.25 \pm 0.01 Msun on the assumption of
spherical symmetry. Unshocked Si extends upwards from 5600 km/s. Although there
appears to be some Fe mixed with the Si at lower velocities < 7070 km/s, the
absence of FeII absorption with the same profile as the shocked SiII suggests
little Fe mixed with Si at higher (before being shocked) velocities. The column
density of shocked SiII is close to that expected for SiII undergoing steady
state collisional ionization behind the reverse shock, provided that the
electron to SiII ratio is low, from which we infer that most of the shocked Si
is likely to be of a fairly high degree of purity, unmixed with other elements.
We propose that the ambient interstellar density on the far side of SN1006 is
anomalously low compared to the density around the rest of the remnant. ThisComment: 24 pages, with 8 figures included. Accepted for publication in the
Astrophysical Journa
RCytoscape: Tools for Exploratory Network Analysis
Background: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.
Results: RCytoscape integrates R (an open-ended programming environment rich in statistical power and datahandling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape\u27s functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance.
Conclusions: Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression
Π‘ΠΠ¦ΠΠΠΠ¬ΠΠ-ΠΠΠΠΠΠΠΠ§ΠΠ‘ΠΠΠ Π ΠΠΠΠΠ’ΠΠΠ-ΠΠ ΠΠΠΠΠ«Π ΠΠ‘ΠΠΠΠ’Π« ΠΠΠ‘Π’ΠΠ ΠΠΠΠ‘ΠΠΠ ΠΠΠΠ ΠΠ¦ΠΠ
The paper deals with the socio-economic and political-legal aspects of the post-crisis migration over time between 2012 and the first half of 2016 based on the data of the Automated Analytical Reporting System (AARS) of the Federal Migration Service, the State Statistical Records of the Federal State Statistics Service, statistical information of the General Directorate for Migration of the RF Interior Ministry. The purpose of the study was to analyze the factors of the migration processes development in problem regions of the post-Soviet space as well as the aftershock problems of the secondary migration from European countries that have to solve the problems of the mass flow of migrants from regions of armed and political conflicts. To achieve the goal, the author posed the following tasks: 1) the review of labor, capital, financial and other resources of the migration donor regions in the context of optimizing management decisions on the regulation of migration processes over the territory of the Russian Federation with a focus on individual economic sectors and occupational skill characteristics; 2) the study of migration processes in the labor market in accordance with indices established by the Russian Rules for Labor Market Monitoring; 3) the study of the migration activity in the DPRK and the PRC compared with political and legal decisions of local and central authorities of the Russian Federation in demographically unstable regions of the Far East and Siberia; 4) assessing the prospects for Russian investments in migration donor countries to level migration flows on financial and economic conditions favorable for the recipient country; 5) systematization of mechanisms for managing the goal setting for migration flows and attracting foreign workers in priority occupational skill groups in line with the Russian economy demands and the public consent interests. Based on the task solution results, it is intended to develop a mid-term forecast of external migration risks for the Russian Federation and propose a system of measures to prevent the migration threats of the post-crisis migration.Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎ-ΠΏΡΠ°Π²ΠΎΠ²ΡΠ΅ Π°ΡΠΏΠ΅ΠΊΡΡ ΠΏΠΎΡΡΠΊΡΠΈΠ·ΠΈΡΠ½ΠΎΠΉ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΈ Π² Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ Ρ 2012 Π³. Π΄ΠΎ ΠΏΠ΅ΡΠ²ΠΎΠΉ ΠΏΠΎΠ»ΠΎΠ²ΠΈΠ½Ρ 2016 Π³. Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΡΡΠ΅ΡΠ½ΠΎΡΡΠΈ Π€Π΅Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ»ΡΠΆΠ±Ρ (ΠΠ‘ΠΠ), ΠΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΡΡΠ΅ΡΠ½ΠΎΡΡΠΈ Π ΠΎΡΡΡΠ°ΡΠ°, ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΠ»Π°Π²Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΈ ΠΠΈΠ½ΠΈΡΡΠ΅ΡΡΡΠ²Π° Π²Π½ΡΡΡΠ΅Π½Π½ΠΈΡ
Π΄Π΅Π» Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - Π°Π½Π°Π»ΠΈΠ· ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π² ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΠΏΠΎΡΡΡΠΎΠ²Π΅ΡΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π°, ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ Π°ΡΡΠ΅ΡΡΠΎΠΊΠ° Π²ΡΠΎΡΠΈΡΠ½ΠΎΠΉ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΈ ΠΈΠ· ΡΡΡΠ°Π½ ΠΠ²ΡΠΎΠΏΡ, Π²ΡΠ½ΡΠΆΠ΄Π΅Π½Π½ΡΡ
ΡΠ΅ΡΠ°ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠΎΠΊΠ° ΠΌΠΈΠ³ΡΠ°Π½ΡΠΎΠ² ΠΈΠ· ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² Π²ΠΎΠΎΡΡΠΆΠ΅Π½Π½ΡΡ
ΠΈ ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΠΎΠ². ΠΠ»Ρ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΡΠ΅Π»ΠΈ Π°Π²ΡΠΎΡ ΠΏΠΎΡΡΠ°Π²ΠΈΠ» ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ: 1) ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΡΡΡΠ΄ΠΎΠ²ΡΡ
, ΠΊΠ°ΠΏΠΈΡΠ°Π»ΡΠ½ΡΡ
, ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
, Π΄ΡΡΠ³ΠΈΡ
ΡΠ΅ΡΡΡΡΠΎΠ² ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² - ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
Π΄ΠΎΠ½ΠΎΡΠΎΠ² Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π½Π° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π² ΡΠ°Π·ΡΠ΅Π·Π΅ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΠΎΡΡΠ°ΡΠ»Π΅ΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ, ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ-ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ; 2) ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π½Π° ΡΡΠ½ΠΊΠ΅ ΡΡΡΠ΄Π° ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌ, ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π½ΡΠΌ ΠΡΠ°Π²ΠΈΠ»Π°ΠΌΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠΈΡΡΠ°ΡΠΈΠΈ Π½Π° ΡΡΠ½ΠΊΠ΅ ΡΡΡΠ΄Π° Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ; 3) ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΠΠΠ , ΠΠΠ Π² ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠΈ Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎ-ΠΏΡΠ°Π²ΠΎΠ²ΡΠΌΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΌΠ΅ΡΡΠ½ΠΎΠΉ ΠΈ ΡΠ΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π²Π»Π°ΡΡΠΈ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π² Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ΡΡΡΠΎΠΉΡΠΈΠ²ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΠΠ°Π»ΡΠ½Π΅Π³ΠΎ ΠΠΎΡΡΠΎΠΊΠ°, Π‘ΠΈΠ±ΠΈΡΠΈ; 4) Π²ΡΡΡΠ½Π΅Π½ΠΈΠ΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π² ΡΡΡΠ°Π½Ρ, ΡΠ²Π»ΡΡΡΠΈΠ΅ΡΡ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠΌΠΈ Π΄ΠΎΠ½ΠΎΡΠ°ΠΌΠΈ Π΄Π»Ρ Π½ΠΈΠ²Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ² Π½Π° Π²ΡΠ³ΠΎΠ΄Π½ΡΡ
Π΄Π»Ρ ΡΡΡΠ°Π½Ρ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠ° ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
; 5) ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ΅Π»Π΅ΠΏΠΎΠ»Π°Π³Π°Π½ΠΈΠ΅ΠΌ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ² ΠΈ ΠΏΡΠΈΠ²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΠΈΠ½ΠΎΡΡΡΠ°Π½Π½ΡΡ
ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΏΠΎ ΠΏΡΠΈΠΎΡΠΈΡΠ΅ΡΠ½ΡΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ-ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ Π³ΡΡΠΏΠΏΠ°ΠΌ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ ΡΠΎ ΡΠΏΡΠΎΡΠΎΠΌ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΈ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°ΠΌΠΈ ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΎΠ³Π»Π°ΡΠΈΡ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π·Π°Π΄Π°Ρ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π΅ΡΡΡ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°ΡΡ ΡΡΠ΅Π΄Π½Π΅ΡΡΠΎΡΠ½ΡΠΉ ΠΏΡΠΎΠ³Π½ΠΎΠ· Π²Π½Π΅ΡΠ½ΠΈΡ
ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΡΠΊΠΎΠ² Π΄Π»Ρ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ ΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠΈΡΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠ΅Ρ ΠΏΠΎ ΠΏΡΠ΅Π²Π΅Π½ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ³ΡΠΎΠ· ΠΏΠΎΡΡΠΊΡΠΈΠ·ΠΈΡΠ½ΠΎΠΉ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΈ
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