60 research outputs found
First measurement of the helicity asymmetry E in eta photoproduction on the proton
Results are presented for the first measurement of the double-polarization helicity asymmetry E for the eta photoproduction reaction gamma p - \u3e eta p. Data were obtained using the FROzen Spin Target (FROST) with the CLAS spectrometer in Hall B at Jefferson Lab, covering a range of center-of-mass energy W from threshold to 2.15 GeV and a large range in center-of-mass polar angle. As an initial application of these data, the results have been incorporated into the Julich-Bonn model to examine the case for the existence of a narrow N* resonance between 1.66 and 1.70 GeV. The addition of these data to the world database results in marked changes in the predictions for the Eobservable from that model. Further comparison with several theoretical approaches indicates these data will significantly enhance our understanding of nucleon resonances. (C) 2016 Published by Elsevier B.V
Predictive Process Monitoring Methods: Which One Suits Me Best?
Predictive process monitoring has recently gained traction in academia and is
maturing also in companies. However, with the growing body of research, it
might be daunting for companies to navigate in this domain in order to find,
provided certain data, what can be predicted and what methods to use. The main
objective of this paper is developing a value-driven framework for classifying
existing work on predictive process monitoring. This objective is achieved by
systematically identifying, categorizing, and analyzing existing approaches for
predictive process monitoring. The review is then used to develop a
value-driven framework that can support organizations to navigate in the
predictive process monitoring field and help them to find value and exploit the
opportunities enabled by these analysis techniques
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
Predictive business process monitoring (PBPM) is a class of techniques
designed to predict behaviour, such as next activities, in running traces. PBPM
techniques aim to improve process performance by providing predictions to
process analysts, supporting them in their decision making. However, the PBPM
techniques` limited predictive quality was considered as the essential obstacle
for establishing such techniques in practice. With the use of deep neural
networks (DNNs), the techniques` predictive quality could be improved for tasks
like the next activity prediction. While DNNs achieve a promising predictive
quality, they still lack comprehensibility due to their hierarchical approach
of learning representations. Nevertheless, process analysts need to comprehend
the cause of a prediction to identify intervention mechanisms that might affect
the decision making to secure process performance. In this paper, we propose
XNAP, the first explainable, DNN-based PBPM technique for the next activity
prediction. XNAP integrates a layer-wise relevance propagation method from the
field of explainable artificial intelligence to make predictions of a long
short-term memory DNN explainable by providing relevance values for activities.
We show the benefit of our approach through two real-life event logs
First Results from The GlueX Experiment
The GlueX experiment at Jefferson Lab ran with its first commissioning beam
in late 2014 and the spring of 2015. Data were collected on both plastic and
liquid hydrogen targets, and much of the detector has been commissioned. All of
the detector systems are now performing at or near design specifications and
events are being fully reconstructed, including exclusive production of
, and mesons. Linearly-polarized photons were
successfully produced through coherent bremsstrahlung and polarization transfer
to the has been observed.Comment: 8 pages, 6 figures, Invited contribution to the Hadron 2015
Conference, Newport News VA, September 201
ΠΡΡΠΎΠΊΠΎΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π³Π΅Π½Π½ΡΡ ΠΌΡΡΠ°ΡΠΈΠΉ: Π·ΠΎΠ½Π΄Ρ TaqMan ΠΊΠ°ΠΊ Π±Π»ΠΎΠΊΠΈΡΡΡΡΠΈΠ΅ Π°Π³Π΅Π½ΡΡ
DNA Melting Analysis is very effective in clinical DNA diagnostics: it is simple to perform, high throughput, labor-, time- and cost-effectiveΒ and is implemented in the βclosed tubeβ format minimizing the risk of samples cross-contamination. Although more sensitive than sequencingΒ by Sanger (mutant allele detection limit is ~5 and ~15 % respectively), it, however, is inferior in this respect to some other, more laboriousΒ and expensive methods (in particular, ddPCR (digital droplet PCR)). Using the BRAF gene as a prototype, we developed the original versionΒ of the DNA melting analysis, based on the ability of TaqMan probes to hamper the primer extension reaction by Taq-polymerase. It is foundΒ that the weaker blocking effect on the mutant template, which is due to the mismatch in the probe-DNA heteroduplex, permits enriched amplificationΒ of the mutant allele and provides a significant (10-fold or more) increase in sensitivity of mutation scanning.ΠΠ΅ΡΠΎΠ΄ ΠΏΠ»Π°Π²Π»Π΅Π½ΠΈΡ ΠΠΠ Π²Π΅ΡΡΠΌΠ° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π΅Π½ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π³Π΅Π½ΠΎΠ΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, ΠΏΡΠΎΡΡ Π² ΠΈΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»Π΅Π½, ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅Π½ ΠΈ,Β ΠΊΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΡΠ΅Π°Π»ΠΈΠ·ΡΠ΅ΡΡΡ Π² Β«Π·Π°ΠΊΡΡΡΠΎΠΌ ΡΠΎΡΠΌΠ°ΡΠ΅Β», ΡΠ²ΠΎΠ΄ΡΡΠ΅ΠΌ ΠΊ ΠΌΠΈΠ½ΠΈΠΌΡΠΌΡ Π·Π°ΡΡΠ°ΡΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ, ΡΡΡΠ΄Π° ΠΈ ΡΠΈΡΠΊ ΠΏΠ΅ΡΠ΅ΠΊΡΠ΅ΡΡΠ½ΠΎΠ³ΠΎ Π·Π°Π³ΡΡΠ·Π½Π΅Π½ΠΈΡ ΠΎΠ±ΡΠ°Π·ΡΠΎΠ². ΠΠ°Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π±ΠΎΠ»Π΅Π΅ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ, ΡΠ΅ΠΌ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎ Π‘ΡΠ½Π³Π΅ΡΡ (ΠΏΡΠ΅Π΄Π΅Π» ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΌΡΡΠ°Π½ΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉΒ ~5 ΠΈ ~15 % ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ), ΠΎΠ΄Π½Π°ΠΊΠΎ ΡΡΡΡΠΏΠ°Π΅Ρ Π² ΡΡΠΎΠΌ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ Π΄ΡΡΠ³ΠΈΠΌ, Π±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΈΠΌ ΠΈ Π΄ΠΎΡΠΎΠ³ΠΈΠΌ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌ (Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ,Β ΠΊΠ°ΠΏΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠ°Π·Π½ΠΎΠΉ ΡΠ΅ΠΏΠ½ΠΎΠΉ ΡΠ΅Π°ΠΊΡΠΈΠΈ (digital droplet PCR)). ΠΠ° Π³Π΅Π½Π΅ BRAF (ΠΊΠ°ΠΊ ΠΏΡΠΎΡΠΎΡΠΈΠΏΠ΅) ΠΌΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π»ΠΈ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΡΠΉ Π²Π°ΡΠΈΠ°Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΏΠ»Π°Π²Π»Π΅Π½ΠΈΡ ΠΠΠ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ Π·ΠΎΠ½Π΄ΠΎΠ² TaqMan Π·Π°ΡΡΡΠ΄Π½ΡΡΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠ΅ Taq-ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠ°Π·ΡΒ ΠΏΠΎ ΠΌΠ°ΡΡΠΈΡΠ΅. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ ΡΡΡΠ΅ΠΊΡ Π±Π»ΠΎΠΊΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π°Π±Π΅Π΅ Π²ΡΡΠ°ΠΆΠ΅Π½ Π½Π° ΠΌΡΡΠ°Π½ΡΠ½ΠΎΠΉ ΠΌΠ°ΡΡΠΈΡΠ΅ ΠΈΠ·-Π·Π° ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΡ Π² Π΄ΡΠΏΠ»Π΅ΠΊΡΠ΅ Π·ΠΎΠ½Π΄-ΠΠΠ Π½Π΅ΡΠΏΠ°ΡΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΡΠΎΡΠΎΠΊΠΎΠ» ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠ°Π·Π½ΠΎΠΉ ΡΠ΅ΠΏΠ½ΠΎΠΉ ΡΠ΅Π°ΠΊΡΠΈΠΈ, Π΄ΠΈΡΠΊΡΠΈΠΌΠΈΠ½ΠΈΡΡΡΡΠΈΠΉ Π°ΠΌΠΏΠ»ΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΌΡΡΠ°Π½ΡΠ½ΡΡ
Β ΠΈ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠΉ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ (10-ΠΊΡΠ°ΡΠ½ΠΎΠ΅ ΠΈ Π±ΠΎΠ»Π΅Π΅) ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΌΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
ΠΡΡΠ°ΡΠΈΠΈ ΠΈΠ·ΠΎΡΠΈΡΡΠ°ΡΠ΄Π΅Π³ΠΈΠ΄ΡΠΎΠ³Π΅Π½Π°Π· 1 ΠΈ 2 ΠΈ ΠΌΠ΅ ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π³Π΅Π½Π° MGMT Π² Π³Π»ΠΈΠΎΠΌΠ°Ρ
Gliomas are the most common brain tumors. It is difficult to detect them at early stages of disease and there is a few available therapies providingΒ significant improvement in survival. Mutations of isocitrate dehydrogenase 1 and 2 genes (IDH1 and IDH2) play significant role in gliomogenesis,Β diagnostics and selection of patient therapy. We tested the distribution of IDH1 and IDH2 mutations in gliomas of different histologicalΒ types and grades of malignancy by DNA melting analysis using our protocol with a sensitivity of 5 %. The results of this assay wereΒ confirmed by conventional Sanger sequencing. IDH1/2 mutations were detected in 74 % of lower grade gliomas (II and III, World HealthΒ Organization) and in 14 % of glioblastomas (IV, World Health Organization). Mutation rate in gliomas with oligodendroglioma componentΒ were significantly higher then in other glioma types (Ρ = 0.014). The IDH1 mutations was the most common (79 % of general mutation number).Β IDH1/2 mutations can induce aberrant gene methylation. Detection of methylation rate of the gene encoding for O6-methylguanine-DNA-methyltransferase (MGMT), predictive biomarker for treatment of gliomas with the alkylating agents, has demonstrated a partial associationΒ with IDH1/2 mutations. In 73 % of IDH1/2-mutant tumors MGMT promoter methylation were observed. At the same time IDH1/2Β mutations were not revealed in 67 % tumors with MGMT promoter methylation. These results indicate existence of another mechanismΒ of MGMT methylation in gliomas. Our data strong support for necessity of both markers testing when patient therapy is selected.ΠΠ»ΠΈΠΎΠΌΡ β Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, ΡΡΡΠ΄Π½ΠΎ ΠΏΠΎΠ΄Π΄Π°ΡΡΠΈΠ΅ΡΡ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ. ΠΡΡΠ°ΡΠΈΠΈΒ Π² Π³Π΅Π½Π°Ρ
ΠΈΠ·ΠΎΡΠΈΡΡΠ°ΡΠ΄Π΅Π³ΠΈΠ΄ΡΠΎΠ³Π΅Π½Π°Π· 1 ΠΈ 2 (IDH1 ΠΈ IDH2) ΠΈΠ³ΡΠ°ΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΡΠΎΠ»Ρ Π² Π³Π»ΠΈΠΎΠΌΠΎΠ³Π΅Π½Π΅Π·Π΅, Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΈ Π²ΡΠ±ΠΎΡΠ΅ ΡΠ΅ΡΠ°ΠΏΠΈΠΈΒ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². ΠΡΠ»ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΌΡΡΠ°ΡΠΈΠΉ IDH1 / 2 Π² Π³Π»ΠΈΠΎΠΌΠ°Ρ
ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π³ΠΈΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠΏΠΎΠ² ΠΈ ΡΡΠ΅ΠΏΠ΅Π½Π΅ΠΉ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΡΠΈΠ²ΡΡ
ΠΏΠ»Π°Π²Π»Π΅Π½ΠΈΡ ΠΠΠ Ρ Π·ΠΎΠ½Π΄Π°ΠΌΠΈ TaqMan ΠΏΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΌΡ Π½Π°ΠΌΠΈ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Ρ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅ΠΌΡΒ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ ΠΌΡΡΠ°ΡΠΈΠΈ Ρ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡΡ 5 %. Π‘ΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΌΡΡΠ°ΡΠΈΠΉ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½Π° ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠΎ Π‘ΡΠ½Π³Π΅ΡΡ. Π Π³Π»ΠΈΠΎΠΌΠ°Ρ
II ΠΈ III ΡΡΠ΅ΠΏΠ΅Π½Π΅ΠΉ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΠΎ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΡΠ΅ΠΌΠΈΡΠ½ΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π·Π΄ΡΠ°Π²ΠΎΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΡ ΡΠ°ΡΡΠΎΡΠ° ΠΌΡΡΠ°ΡΠΈΠΉ IDH1 / 2 ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 74 %, Π² Π³Π»ΠΈΠΎΠ±Π»Π°ΡΡΠΎΠΌΠ°Ρ
(IV ΡΡΠ΅ΠΏΠ΅Π½Ρ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ) β 14 %. ΠΠ»ΠΈΠΎΠΌΡ, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠ΅ ΠΊΠ»Π΅ΡΠΊΠΈ Ρ ΠΎΠ»ΠΈΠ³ΠΎΠ΄Π΅Π½Π΄ΡΠΎΡΠΈΡΠ°ΡΠ½ΡΠΌ ΡΠΈΠΏΠΎΠΌ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΡΠΎΠ²ΠΊΠΈ, Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ ΡΠ°ΡΠ΅ ΠΈΠΌΠ΅Π»ΠΈ ΠΌΡΡΠ°ΡΠΈΠΈ IDH1 / 2, ΡΠ΅ΠΌ Π΄ΡΡΠ³ΠΈΠ΅ ΡΠΈΠΏΡ Π³Π»ΠΈΠΎΠΌ (Ρ = 0,014).Β ΠΡΠ΅ΠΎΠ±Π»Π°Π΄Π°ΡΡΠΈΠΌ ΡΠΈΠΏΠΎΠΌ ΠΌΡΡΠ°ΡΠΈΠΉ ΡΠ²Π»ΡΡΡΡΡ ΠΌΡΡΠ°ΡΠΈΠΈ IDH1 (79 % ΠΎΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΡΠΈΡΠ»Π° ΠΌΡΡΠ°ΡΠΈΠΉ). ΠΠ΄Π½ΠΎ ΠΈΠ· ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΉ ΠΌΡΡΠ°ΡΠΈΠΉΒ IDH1 / 2 β ΠΈΠ½Π΄ΡΠΊΡΠΈΡ Π°Π±Π΅ΡΡΠ°Π½ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³Π΅Π½ΠΎΠ². ΠΠ½Π°Π»ΠΈΠ· ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠΌΠΎΡΠΎΡΠ° Π³Π΅Π½Π° Π6βΠΌΠ΅ΡΠΈΠ»Π³ΡΠ°Π½ΠΈΠ½-ΠΠΠ-ΠΌΠ΅ΡΠΈΠ»-ΡΡΠ°Π½ΡΡΠ΅ΡΠ°Π·Ρ (MGMT, O6βmethylguanine-DNA-methyltransferase), ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠΊΠ΅ΡΠ° ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π³Π»ΠΈΠΎΠΌ ΠΊ ΡΠ΅ΡΠ°ΠΏΠΈΠΈΒ Π°Π»ΠΊΠΈΠ»ΠΈΡΡΡΡΠΈΠΌΠΈ Π°Π³Π΅Π½ΡΠ°ΠΌΠΈ Ρ ΡΠ΅Ρ
ΠΆΠ΅ Π±ΠΎΠ»ΡΠ½ΡΡ
, ΠΏΠΎΠΊΠ°Π·Π°Π» ΡΠ°ΡΡΠΈΡΠ½ΡΡ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΡ Ρ ΠΌΡΡΠ°ΡΠΈΡΠΌΠΈ IDH1 / 2. Π 73 % ΡΠ»ΡΡΠ°Π΅Π² Ρ ΠΌΡΡΠ°ΡΠΈΡΠΌΠΈΒ IDH1 / 2 Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΎΡΡ ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ MGMT. Π ΡΠΎ ΠΆΠ΅ Π²ΡΠ΅ΠΌΡ Π² 67 % ΡΠ»ΡΡΠ°Π΅Π² Ρ ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ MGMT ΠΎΡΡΡΡΡΡΠ²ΠΎΠ²Π°Π»ΠΈ ΠΌΡΡΠ°ΡΠΈΠΈΒ IDH1 / 2, ΡΡΠΎ ΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π½Π° ΡΡΡΠ΅ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΡΡΠ³ΠΈΡ
ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ MGMT Π² Π³Π»ΠΈΠΎΠΌΠ°Ρ
. ΠΠ°Π½Π½ΡΠ΅ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ Π² ΠΏΠΎΠ»ΡΠ·Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΠ΄Π½ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ 2 Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΏΡΠΈ Π²ΡΠ±ΠΎΡΠ΅ ΠΏΠΎΡΠ»Π΅ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ²
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