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- Adam, J., et al.
(författare)
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Precision measurement of the mass difference between light nuclei and anti-nuclei
- 2015
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Ingår i: Nature Physics. - 1745-2473. ; 11:10, s. 120-811
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Tidskriftsartikel (refereegranskat)abstract
- The measurement of the mass differences for systems bound by the strong force has reached a very high precision with protons and anti-protons(1,2). The extension of such measurement from (anti-)baryons to (anti-) nuclei allows one to probe any difference in the interactions between nucleons and anti-nucleons encoded in the (anti-) nuclei masses. This force is a remnant of the underlying strong interaction among quarks and gluons and can be described by effective theories(3), but cannot yet be directly derived from quantum chromodynamics. Here we report a measurement of the difference between the ratios of the mass and charge of deuterons (d) and anti-deuterons ((d) over bar), and He-3 and (3)(He) over bar nuclei carried out with the ALICE (A Large Ion Collider Experiment)(4) detector in Pb-Pb collisions at a centre-of-mass energy per nucleon pair of 2.76 TeV. Our direct measurement of the mass-over-charge differences confirms CPT invariance to an unprecedented precision in the sector of light nuclei(5,6). This fundamental symmetry of nature, which exchanges particles with anti-particles, implies that all physics laws are the same under the simultaneous reversal of charge(s) (charge conjugation C), reflection of spatial coordinates (parity transformation P) and time inversion (T).
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| 2. |
- Toro-Dominguez, D, et al.
(författare)
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Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 15502-
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Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.
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