59 research outputs found

    First measurement of the helicity asymmetry E in eta photoproduction on the proton

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    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?

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    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

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    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

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    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 Ο€0\pi^{0}, Ξ·\eta and Ο‰\omega mesons. Linearly-polarized photons were successfully produced through coherent bremsstrahlung and polarization transfer to the ρ\rho has been observed.Comment: 8 pages, 6 figures, Invited contribution to the Hadron 2015 Conference, Newport News VA, September 201

    Π’Ρ‹ΡΠΎΠΊΠΎΡ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ΅ сканированиС Π³Π΅Π½Π½Ρ‹Ρ… ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉ: Π·ΠΎΠ½Π΄Ρ‹ TaqMan ΠΊΠ°ΠΊ Π±Π»ΠΎΠΊΠΈΡ€ΡƒΡŽΡ‰ΠΈΠ΅ Π°Π³Π΅Π½Ρ‚Ρ‹

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    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 β€œclosed tube” 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.ΠœΠ΅Ρ‚ΠΎΠ΄ плавлСния Π”ΠΠš вСсьма эффСктивСн Π² клиничСской гСнодиагностикС, прост Π² исполнСнии, ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚Π΅Π»Π΅Π½, экономичСн ΠΈ,Β ΠΊΡ€ΠΎΠΌΠ΅ Ρ‚ΠΎΠ³ΠΎ, рСализуСтся Π² Β«Π·Π°ΠΊΡ€Ρ‹Ρ‚ΠΎΠΌ Ρ„ΠΎΡ€ΠΌΠ°Ρ‚Π΅Β», сводящСм ΠΊ ΠΌΠΈΠ½ΠΈΠΌΡƒΠΌΡƒ Π·Π°Ρ‚Ρ€Π°Ρ‚Ρ‹ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ, Ρ‚Ρ€ΡƒΠ΄Π° ΠΈ риск пСрСкрСстного загрязнСния ΠΎΠ±Ρ€Π°Π·Ρ†ΠΎΠ². Π”Π°Π½Π½Ρ‹ΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ Π±ΠΎΠ»Π΅Π΅ Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΉ, Ρ‡Π΅ΠΌ сСквСнированиС ΠΏΠΎ БэнгСру (ΠΏΡ€Π΅Π΄Π΅Π» обнаруТСния ΠΌΡƒΡ‚Π°Π½Ρ‚Π½Ρ‹Ρ… Π°Π»Π»Π΅Π»Π΅ΠΉΒ ~5 ΠΈ ~15 % соотвСтствСнно), ΠΎΠ΄Π½Π°ΠΊΠΎ уступаСт Π² этом ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΈ Π΄Ρ€ΡƒΠ³ΠΈΠΌ, Π±ΠΎΠ»Π΅Π΅ Ρ‚Ρ€ΡƒΠ΄ΠΎΠ΅ΠΌΠΊΠΈΠΌ ΠΈ Π΄ΠΎΡ€ΠΎΠ³ΠΈΠΌ ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌ (Π² частности, капСльной Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ»ΠΈΠΌΠ΅Ρ€Π°Π·Π½ΠΎΠΉ Ρ†Π΅ΠΏΠ½ΠΎΠΉ Ρ€Π΅Π°ΠΊΡ†ΠΈΠΈ (digital droplet PCR)). На Π³Π΅Π½Π΅ BRAF (ΠΊΠ°ΠΊ ΠΏΡ€ΠΎΡ‚ΠΎΡ‚ΠΈΠΏΠ΅) ΠΌΡ‹ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π»ΠΈ ΠΎΡ€ΠΈΠ³ΠΈΠ½Π°Π»ΡŒΠ½Ρ‹ΠΉ Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° плавлСния Π”ΠΠš, основанный Π½Π° способности Π·ΠΎΠ½Π΄ΠΎΠ² TaqMan Π·Π°Ρ‚Ρ€ΡƒΠ΄Π½ΡΡ‚ΡŒ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠ΅ Taq-ΠΏΠΎΠ»ΠΈΠΌΠ΅Ρ€Π°Π·Ρ‹Β ΠΏΠΎ ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Π΅. УстановлСно, Ρ‡Ρ‚ΠΎ эффСкт блокирования слабСС Π²Ρ‹Ρ€Π°ΠΆΠ΅Π½ Π½Π° ΠΌΡƒΡ‚Π°Π½Ρ‚Π½ΠΎΠΉ ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Π΅ ΠΈΠ·-Π·Π° присутствия Π² дуплСксС Π·ΠΎΠ½Π΄-Π”ΠΠš нСспарСнного основания. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΡ€ΠΎΡ‚ΠΎΠΊΠΎΠ» ΠΏΠΎΠ»ΠΈΠΌΠ΅Ρ€Π°Π·Π½ΠΎΠΉ Ρ†Π΅ΠΏΠ½ΠΎΠΉ Ρ€Π΅Π°ΠΊΡ†ΠΈΠΈ, Π΄ΠΈΡΠΊΡ€ΠΈΠΌΠΈΠ½ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠΉ Π°ΠΌΠΏΠ»ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡŽ ΠΌΡƒΡ‚Π°Π½Ρ‚Π½Ρ‹Ρ…Β ΠΈ Π½ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½Ρ‹Ρ… Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΠΉ сущСствСнноС (10-ΠΊΡ€Π°Ρ‚Π½ΠΎΠ΅ ΠΈ Π±ΠΎΠ»Π΅Π΅) ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ сканирования

    ΠœΡƒΡ‚Π°Ρ†ΠΈΠΈ ΠΈΠ·ΠΎΡ†ΠΈΡ‚Ρ€Π°Ρ‚Π΄Π΅Π³ΠΈΠ΄Ρ€ΠΎΠ³Π΅Π½Π°Π· 1 ΠΈ 2 ΠΈ ΠΌΠ΅ Ρ‚ΠΈΠ»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ Π³Π΅Π½Π° MGMT Π² Π³Π»ΠΈΠΎΠΌΠ°Ρ…

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    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.Π“Π»ΠΈΠΎΠΌΡ‹ – Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ распространСнныС ΠΎΠΏΡƒΡ…ΠΎΠ»ΠΈ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, Ρ‚Ρ€ΡƒΠ΄Π½ΠΎ ΠΏΠΎΠ΄Π΄Π°ΡŽΡ‰ΠΈΠ΅ΡΡ Ρ€Π°Π½Π½Π΅ΠΉ диагностикС ΠΈ Π»Π΅Ρ‡Π΅Π½ΠΈΡŽ. ΠœΡƒΡ‚Π°Ρ†ΠΈΠΈΒ Π² Π³Π΅Π½Π°Ρ… ΠΈΠ·ΠΎΡ†ΠΈΡ‚Ρ€Π°Ρ‚Π΄Π΅Π³ΠΈΠ΄Ρ€ΠΎΠ³Π΅Π½Π°Π· 1 ΠΈ 2 (IDH1 ΠΈ IDH2) ΠΈΠ³Ρ€Π°ΡŽΡ‚ ΡΡƒΡ‰Π΅ΡΡ‚Π²Π΅Π½Π½ΡƒΡŽ Ρ€ΠΎΠ»ΡŒ Π² Π³Π»ΠΈΠΎΠΌΠΎΠ³Π΅Π½Π΅Π·Π΅, диагностикС ΠΈ Π²Ρ‹Π±ΠΎΡ€Π΅ Ρ‚Π΅Ρ€Π°ΠΏΠΈΠΈΒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ². Π‘Ρ‹Π»ΠΎ исслСдовано распрСдСлСниС ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉ IDH1 / 2 Π² Π³Π»ΠΈΠΎΠΌΠ°Ρ… Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… гистологичСских Ρ‚ΠΈΠΏΠΎΠ² ΠΈ стСпСнСй злокачСствСнности ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΡ€ΠΈΠ²Ρ‹Ρ… плавлСния Π”ΠΠš с Π·ΠΎΠ½Π΄Π°ΠΌΠΈ TaqMan ΠΏΠΎ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠΌΡƒ Π½Π°ΠΌΠΈ ΠΏΡ€ΠΎΡ‚ΠΎΠΊΠΎΠ»Ρƒ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰Π΅ΠΌΡƒΒ ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΡΡ‚ΡŒ ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΈ с Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΡŒΡŽ 5 %. Π‘ΠΏΠ΅Ρ†ΠΈΡ„ΠΈΡ‡Π½ΠΎΡΡ‚ΡŒ опрСдСлСния ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉ ΠΏΠΎΠ΄Ρ‚Π²Π΅Ρ€ΠΆΠ΄Π΅Π½Π° сСквСнированиСм ΠΏΠΎ БэнгСру. Π’ Π³Π»ΠΈΠΎΠΌΠ°Ρ… II ΠΈ III стСпСнСй злокачСствСнности ΠΏΠΎ классификации ВсСмирной ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΠΈ здравоохранСния частота ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉ IDH1 / 2 составила 74 %, Π² глиобластомах (IV ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ злокачСствСнности) – 14 %. Π“Π»ΠΈΠΎΠΌΡ‹, содСрТащиС ΠΊΠ»Π΅Ρ‚ΠΊΠΈ с ΠΎΠ»ΠΈΠ³ΠΎΠ΄Π΅Π½Π΄Ρ€ΠΎΡ†ΠΈΡ‚Π°Ρ€Π½Ρ‹ΠΌ Ρ‚ΠΈΠΏΠΎΠΌ Π΄ΠΈΡ„Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΡ€ΠΎΠ²ΠΊΠΈ, достовСрно Ρ‡Π°Ρ‰Π΅ ΠΈΠΌΠ΅Π»ΠΈ ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΈ IDH1 / 2, Ρ‡Π΅ΠΌ Π΄Ρ€ΡƒΠ³ΠΈΠ΅ Ρ‚ΠΈΠΏΡ‹ Π³Π»ΠΈΠΎΠΌ (Ρ€ = 0,014).Β ΠŸΡ€Π΅ΠΎΠ±Π»Π°Π΄Π°ΡŽΡ‰ΠΈΠΌ Ρ‚ΠΈΠΏΠΎΠΌ ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉ ΡΠ²Π»ΡΡŽΡ‚ΡΡ ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΈ IDH1 (79 % ΠΎΡ‚ ΠΎΠ±Ρ‰Π΅Π³ΠΎ числа ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉ). Одно ΠΈΠ· послСдствий ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΉΒ IDH1 / 2 – индукция Π°Π±Π΅Ρ€Ρ€Π°Π½Ρ‚Π½ΠΎΠ³ΠΎ мСтилирования Π³Π΅Π½ΠΎΠ². Анализ мСтилирования ΠΏΡ€ΠΎΠΌΠΎΡ‚ΠΎΡ€Π° Π³Π΅Π½Π° О6‑мСтилгуанин-Π”ΠΠš-ΠΌΠ΅Ρ‚ΠΈΠ»-трансфСразы (MGMT, O6‑methylguanine-DNA-methyltransferase), ΠΏΡ€Π΅Π΄ΡΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΌΠ°Ρ€ΠΊΠ΅Ρ€Π° Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ Π³Π»ΠΈΠΎΠΌ ΠΊ Ρ‚Π΅Ρ€Π°ΠΏΠΈΠΈΒ Π°Π»ΠΊΠΈΠ»ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠΌΠΈ Π°Π³Π΅Π½Ρ‚Π°ΠΌΠΈ Ρƒ Ρ‚Π΅Ρ… ΠΆΠ΅ Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ…, ΠΏΠΎΠΊΠ°Π·Π°Π» Ρ‡Π°ΡΡ‚ΠΈΡ‡Π½ΡƒΡŽ Π°ΡΡΠΎΡ†ΠΈΠ°Ρ†ΠΈΡŽ с мутациями IDH1 / 2. Π’ 73 % случаСв с мутациями IDH1 / 2 наблюдалось ΠΌΠ΅Ρ‚ΠΈΠ»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ MGMT. Π’ Ρ‚ΠΎ ΠΆΠ΅ врСмя Π² 67 % случаСв с ΠΌΠ΅Ρ‚ΠΈΠ»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ MGMT отсутствовали ΠΌΡƒΡ‚Π°Ρ†ΠΈΠΈΒ IDH1 / 2, Ρ‡Ρ‚ΠΎ ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ Π½Π° сущСствованиС Π΄Ρ€ΡƒΠ³ΠΈΡ… ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² мСтилирования MGMT Π² Π³Π»ΠΈΠΎΠΌΠ°Ρ…. Π”Π°Π½Π½Ρ‹Π΅ ΡΠ²ΠΈΠ΄Π΅Ρ‚Π΅Π»ΡŒΡΡ‚Π²ΡƒΡŽΡ‚ Π² ΠΏΠΎΠ»ΡŒΠ·Ρƒ нСобходимости ΠΎΠ΄Π½ΠΎΠ²Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ опрСдСлСния 2 Π±ΠΈΠΎΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ² ΠΏΡ€ΠΈ Π²Ρ‹Π±ΠΎΡ€Π΅ послСопСрационной Ρ‚Π΅Ρ€Π°ΠΏΠΈΠΈ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ²
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