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| 2. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 5. |
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| 6. |
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| 7. |
- Menden, MP, et al.
(författare)
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- 2019
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
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Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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| 8. |
- Mindur, B, et al.
(författare)
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Gas gain stabilisation in the ATLAS TRT detector
- 2016
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- The ATLAS (one of two general purpose detectors at the LHC) Transition Radiation Tracker (TRT) is the outermost of the three tracking subsystems of the ATLAS Inner Detector. It is a large straw-based detector and contains about 350,000 electronics channels. The performance of the TRT as tracking and particularly particle identification detector strongly depends on stability of the operation parameters with most important parameter being the gas gain which must be kept constant across the detector volume. The gas gain in the straws can vary significantly with atmospheric pressure, temperature, and gas mixture composition changes. This paper presents a concept of the gas gain stabilisation in the TRT and describes in detail the Gas Gain Stabilisation System (GGSS) integrated into the Detector Control System (DCS). Operation stability of the GGSS during Run-1 is demonstrated.
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| 9. |
- Teo, K. B. K., et al.
(författare)
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Wafer-scale graphene synthesis, transfer and FETs
- 2013
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Ingår i: Proceedings of the IEEE Conference on Nanotechnology. - 1944-9399 .- 1944-9380. - 9781479906758 ; , s. 1200-1203
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Konferensbidrag (refereegranskat)abstract
- Growth and characterization of graphene grown using copper foils as well as copper films on silicon dioxide on silicon substrates were performed. Kinetics of growth and effective activation energy for the graphene synthesis will be discussed for the surface catalytic synthesis of graphene. Conditions for large-scale synthesis of monolayer graphene will be addressed in this talk. Wafer-scale graphene transfer and electrical results will be presented. Based on our preliminary results from capped 100mm wafer scale graphene transistors, we expect a mobility of 4-6 k cm2/Vs with symmetry hole/electron transport. Key considerations and challenges for scaling are discussed and results for graphene growth on the 300mm wafer scale will be discussed.
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| 10. |
- Moore, MR, et al.
(författare)
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Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
- 2021
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1, s. 2809-
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Tidskriftsartikel (refereegranskat)abstract
- Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan–Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51–11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.
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