What information theory can tell us about quantum reality

An investigation of Einstein's ``physical'' reality and the concept of quantum reality in terms of information theory suggests a solution to quantum paradoxes such as the Einstein-Podolsky-Rosen (EPR) and the Schroedinger-cat paradoxes. Quantum reality, the picture based on unitarily evolving wavefunctions, is complete, but appears incomplete from the observer's point of view for fundamental reasons arising from the quantum information theory of measurement. Physical reality, the picture based on classically accessible observables is, in the worst case of EPR experiments, unrelated to the quantum reality it purports to reflect. Thus, quantum information theory implies that only correlations, not the correlata, are physically accessible: the mantra of the Ithaca interpretation of quantum mechanics.Comment: LaTeX with llncs.cls, 11 pages, 6 postscript figures, Proc. of 1st NASA Workshop on Quantum Computation and Quantum Communication (QCQC 98

Novel highly potent CD4bs bNAb with restricted pathway to HIV-1 escape

Purpose: Broadly HIV-1 neutralizing antibodies (bNAbs) can suppress viremia in humans and represent a novel approach for effective immunotherapy. However, bNAb monotherapy selects for antibody-resistant viral variants. Thus, we focused on the identification of new antibody combinations and/or novel bNAbs that restrict pathways of HIV-1 escape. Methods: We screened HIV-1 positive patients for their neutralizing capacities. Following, we performed single cell sorting and PCR of HIV-1 Env-reactive mature B cells of identified elite neutralizers. Found antibodies were tested for neutralization and binding capacities in vitro. Further, their antiviral activity was tested in an HIV-1 infected humanized mouse model. Results: Here we report the isolation of antibody 1–18, a VH1–46-encoded CD4 binding site (CD4bs) bNAb identified in an individual ranking among the top 1% neutralizers of 2,274 HIV-1-infected subjects. Tested on a 119-virus panel, 1–18 showed to be exceptionally broad and potent with a coverage of 97% and a mean IC50 of 0.048 lg/mL, exceeding the activity of most potent CD4bs bNAbs described to-date. A 2.4 Å cryo-EM structure of 1–18 bound to a native-like Env trimer revealed that it interacts with HIV-1 env similar to other CD4bs bNAbs, but includes additional contacts to the V3 loop of the adjacent protomer. Notably, in vitro, 1–18 maintained activity against viruses carrying mutations associated with escape from VRC01-class bNAbs. Further, its HIV-1 env wide escape profile differed critically from other CD4bs bNAbs. In humanized mice, monotherapy with 1–18 was sufficient to prevent the development of viral escape variants that rapidly emerged during treatment with other CD4bs bNAbs. Finally, 1–18 overcame classical HIV-1 mutations that are driven by VRC01-like bNAbs in vivo. Conclusion: 1–18 is a highly potent and broad bNAb that restricts escape and overcomes frequent CD4bs escape pathways, providing new options for bNAb combinations to prevent and treat HIV-1 infection

AIDS

OBJECTIVE: Model trajectories of CD4 and CD8 cell counts after starting combination antiretroviral therapy (ART), and use the model to predict trends in these counts and the CD4:CD8 ratio. DESIGN: Cohort study of antiretroviral-naive HIV-positive adults who started ART after 1997 (ART Cohort Collaboration) with >6 months of follow-up data. METHODS: We jointly estimated CD4 and CD8 count trends and their correlation using a bivariate random effects model, with linear splines describing their population trends, and predicted the CD4:CD8 ratio trend from this model. We assessed whether CD4 and CD8 count trends and the CD4:CD8 ratio trend varied according to CD4 count at start of ART (baseline), and, whether these trends differed in patients with and without virological failure more than 6 months after starting ART. RESULTS: A total of 39,979 patients were included (median follow-up was 53 months). Among patients with baseline CD4 count >/=50 cells/mm, predicted mean CD8 counts continued to decrease between 3 and 15 years post-ART, partly driving increases in the predicted mean CD4:CD8 ratio. During 15 years of follow-up, normalisation of the predicted mean CD4:CD8 ratio (to >1) was only observed among patients with baseline CD4 count >/=200 cells/mm. A higher baseline CD4 count predicted a shorter time to normalisation. CONCLUSIONS: Declines in CD8 count and increases in CD4:CD8 ratio occurred up to 15 years after starting ART. The likelihood of normalisation of the CD4:CD8 ratio is strongly related to baseline CD4 count.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Chronic Hepatitis B and C Virus Infection and Risk for Non-Hodgkin Lymphoma in HIV-Infected Patients: A Cohort Study

Background: Non-Hodgkin lymphoma (NHL) is the most common AIDS-defining condition in the era of antiretroviral therapy (ART). Whether chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infection promote NHL in HIV-infected patients is unclear. Objective: To investigate whether chronic HBV and HCV infection are associated with increased incidence of NHL in HIV-infected patients. Design: Cohort study. Setting: 18 of 33 cohorts from the Collaboration of Observational HIV Epidemiological Research Europe (COHERE). Patients: HIV-infected patients with information on HBV surface antigen measurements and detectable HCV RNA, or a positive HCV antibody test result if HCV RNA measurements were not available. Measurements: Time-dependent Cox models to assess risk for NHL in treatment-naive patients and those initiating ART, with inverse probability weighting to control for informative censoring. Results: A total of 52 479 treatment-naive patients (1339 [2.6%] with chronic HBV infection and 7506 [14.3%] with HCV infection) were included, of whom 40 219 (77%) later started ART. The median follow-up was 13 months for treatment-naive patients and 50 months for those receiving ART. A total of 252 treatment-naive patients and 310 treated patients developed NHL, with incidence rates of 219 and 168 cases per 100 000 person-years, respectively. The hazard ratios for NHL with HBV and HCV infection were 1.33 (95% CI, 0.69 to 2.56) and 0.67 (CI, 0.40 to 1.12), respectively, in treatment-naive patients and 1.74 (CI, 1.08 to 2.82) and 1.73 (CI, 1.21 to 2.46), respectively, in treated patients. Limitation: Many treatment-naive patients later initiated ART, which limited the study of the associations of chronic HBV and HCV infection with NHL in this patient group. Conclusion: In HIV-infected patients receiving ART, chronic co-infection with HBV and HCV is associated with an increased risk for NHL. Primary Funding Source: European Union Seventh Framework Programme

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Feasibility-Studie zu einem Deutsch-Chinesischen Kooperationsprojekt: Grundlagen der Nanotechnik: Entwicklung von Methoden zur Strukturierung, Analyse und Modellierung im Nanometerbereich unter Anwendung der Rastersondenmikroskopie und der Molekulardynamik Schlussbericht

SIGLEAvailable from TIB Hannover: F00B766 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung (BMBF), Bonn (Germany)DEGerman