Units of rotational information

Entanglement in angular momentum degrees of freedom is a precious resource for quantum metrology and control. Here we study the conversions of this resource, focusing on Bell pairs of spin-J particles, where one particle is used to probe unknown rotations and the other particle is used as reference. When a large number of pairs are given, we show that every rotated spin-J Bell state can be reversibly converted into an equivalent number of rotated spin one-half Bell states, at a rate determined by the quantum Fisher information. This result provides the foundation for the definition of an elementary unit of information about rotations in space, which we call the Cartesian refbit. In the finite copy scenario, we design machines that approximately break down Bell states of higher spins into Cartesian refbits, as well as machines that approximately implement the inverse process. In addition, we establish a quantitative link between the conversion of Bell states and the simulation of unitary gates, showing that the fidelity of probabilistic state conversion provides upper and lower bounds on the fidelity of deterministic gate simulation. The result holds not only for rotation gates, but also to all sets of gates that form finite-dimensional representations of compact groups. For rotation gates, we show how rotations on a system of given spin can simulate rotations on a system of different spin.Comment: 25 pages + appendix, 7 figures, new results adde

Development and test of a mini-Data Acquisition system for the High-Luminosity LHC upgrade of the ATLAS Monitored Drift Tube detector

New front-end electronics including ASICs and FPGA boards are under development for the ATLAS Monitored Drift Tube (MDT) detector to handle the large data rates and harsh environment expected at high-luminosity LHC runs. A mobile Data Acquisition (miniDAQ) system is designed to perform integration tests of these front-end electronics. In addition, it will be used for surface commissioning of 96 small-radius MDT (sMDT) chambers and for integration and commissioning of new front-end electronics on the present ATLAS MDT chambers. Details of the miniDAQ hardware and firmware are described in this article. The miniDAQ system is also used to read out new front-end electronics on an sMDT prototype chamber using cosmic muons and results obtained are shown.Comment: 10 pages, 12 figure

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

Strategies for Improving Postpartum Contraception Compared With Routine Maternal Care: A Systematic Review and Meta-Analysis

Objectives: This study aimed to systematically review the effectiveness of service interventions for improving postpartum contraception, including contraceptive use, prevention of repeat pregnancies and induced abortions.Methods: A systematic literature search was conducted in three databases until June 2022 (PROSPERO registration CRD42022328349). Estimates of intervention effects from meta-analyses were represented as odds ratios (OR) with 95% confidence intervals (CI).Results: 16 studies with 14,289 participants were included, with four kinds of interventions recognized. Interventions effect in increasing use of contraceptives and decreasing rates of repeated pregnancy for up to 6 months postpartum (OR = 2.24, 0.06, 95% CI = 1.46–3.44, 0.02–0.22, respectively), with no significant associations with contraceptive use at 12 months postpartum, prevention of postpartum repeat pregnancies and induced abortions during 1 year after childbirth.Conclusion: We concluded that interventions impact the initiation of postpartum contraceptive use and prevention of repeat pregnancy with an overall certainty from low to moderate. These findings highlight the need for additional studies to integrate the beneficial effect of several interventions and then design more feasible strategies, which is important for the maternal and child healthcare systems

Elevating the potential of CAR-T cell therapy in solid tumors: exploiting biomaterials-based delivery techniques

Chimeric antigen receptor (CAR) T cells exhibit promising progress in addressing hematologic malignancies. However, CAR-T therapy for solid tumors remains limited, with no FDA-approved CAR-T products available for clinical use at present. Primary reasons include insufficient infiltration, accumulation, tumor immunosuppression of the microenvironment, and related side effects. Single utilization of CAR-T cannot effectively overcome these unfavorable obstacles. A probable effective pathway to achieve a better CAR-T therapy effect would be to combine the benefits of biomaterials-based technology. In this article, comprehensive biomaterials strategies to break through these obstacles of CAR-T cell therapy at the tumor sites are summarized, encompassing the following aspects: 1) generating orthotopic CAR-T cells; 2) facilitating CAR-T cell trafficking; 3) stimulating CAR-T cell expansion and infiltration; 4) improving CAR-T cell activity and persistence; 5) reprogramming the immunosuppressive microenvironments. Additionally, future requirements for the development of this field, with a specific emphasis on promoting innovation and facilitating clinical translation, are thoroughly discussed

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity between augmented views of the same graph and is widely used for GSSL. However, MAE and CL are considered separately in existing works for GSSL. We observe that the MAE and CL paradigms are complementary and propose the graph contrastive masked autoencoder (GCMAE) framework to unify them. Specifically, by focusing on local edges or node features, MAE cannot capture global information of the graph and is sensitive to particular edges and features. On the contrary, CL excels in extracting global information because it considers the relation between graphs. As such, we equip GCMAE with an MAE branch and a CL branch, and the two branches share a common encoder, which allows the MAE branch to exploit the global information extracted by the CL branch. To force GCMAE to capture global graph structures, we train it to reconstruct the entire adjacency matrix instead of only the masked edges as in existing works. Moreover, a discrimination loss is proposed for feature reconstruction, which improves the disparity between node embeddings rather than reducing the reconstruction error to tackle the feature smoothing problem of MAE. We evaluate GCMAE on four popular graph tasks (i.e., node classification, node clustering, link prediction, and graph classification) and compare with 14 state-of-the-art baselines. The results show that GCMAE consistently provides good accuracy across these tasks, and the maximum accuracy improvement is up to 3.2% compared with the best-performing baseline