Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonill

Thermodynamic Modeling and Sensitivity Analysis of K_d Values for Radionuclide Migration in Sedimentary Host Rocks

ABSTRACTThe effects of key geochemical parameters on Kd values for radionuclides in the host rock (pumice, sandstone) of a LLW repository were elucidated through a sensitivity analysis, using a thermodynamic speciation/sorption model for the elements Sr and Ni. The complex mineral assemblage of the rock was approximated by a component-additivity approach. Using published ion exchange and surface complexation parameters, Kd for both Sr and Ni could be well explained by the same model mineralogy and surface chemistry. Model results suggest that pCO2 can have a significant effect on Kd, and that a correct approximation of groundwater chemistry is a critical component of sorption modeling.</jats:p

Systematic Trends and Empirical Modeling of Lead Uptake by Cements and Cement Minerals

ABSTRACTUptake of Pb was investigated for different hydrated fresh and leached Portland and high-alumina cements, different CSH phases including tobermorite, portlandite, as well as for several relevant cement Al-minerals. Except for highly degraded cement, CSH minerals are the most important phases for Pb uptake. A systematic analysis of our data and a comparison with recent literature data for CSH phases [1] shows that Pb uptake is characterized by three trends: 1) between pH 10–13, Pb uptake decreases with increasing pH, 2) Pb uptake increases with increasing Fe content in the clinker, and 3) Pb uptake is dependent on the Pb concentration in the solution. Following these findings, an empirical model was developed based on regression analyses of uptake vs. Pb concentration, pH, and Fe content in the clinker. The resulting relation can predict Kd values for a large range of fresh and leached cements, as well as for individual cement minerals under different experimental conditions. Model application to independent data on cement phases, whole cement, and mortar gave very good agreements.</jats:p

Supplemental Material, Online_Appendix - Use of Visual Decision Aids in Physician–Patient Communication: A Pilot Investigation

Supplemental Material, Online_Appendix for Use of Visual Decision Aids in Physician–Patient Communication: A Pilot Investigation by Mary Beth Mercer, Susannah L Rose, Cassandra Talerico, Brian J Wells, Mahesh Manne, Nirav Vakharia, Stacey Jolly, Alex Milinovich, Janine Bauman, and Michael W Kattan in Journal of Patient Experience </p

Use of Visual Decision Aids in Physician–Patient Communication

Introduction: A risk calculator paired with a personalized decision aid (RC&DA) may foster shared decision-making in primary care. We assessed the feasibility of using an RC&DA with patients in a primary care outpatient clinic and patients’ experiences regarding communication and decision-making. Methods: This pilot study was conducted with 15 patients of 3 primary care physicians at a clinic within a tertiary medical center. An atherosclerotic cardiovascular disease (ASCVD) risk calculator was used to generate a personalized RC&DA that displayed absolute 10-year risk information as an icon array graphic. Patient perceptions of utility of the RC&DA, preferences for decision-making, and uncertainty with risk reduction decisions were measured with a semi-structured interview. Results: Patients reported that the RC&DA was easy to understand and knowledge gained was useful to modify their ASCVD risk. Patients used the RC&DA to make decisions and reported low uncertainty with those decisions. Conclusions: Our findings demonstrate the feasibility of, and positive patient experiences related to using, an RC&DA to facilitate shared decision-making between physicians and patients in an outpatient primary care setting

Myosin binding protein C1: a novel gene for autosomal dominant distal arthrogryposis type 1

Distal arthrogryposis type I (DA1) is a disorder characterized by congenital contractures of the hands and feet for which few genes have been identified. Here we describe a five-generation family with DA1 segregating as an autosomal dominant disorder with complete penetrance. Genome-wide linkage analysis using Affymetrix GeneChip Mapping 10K data from 12 affected members of this family revealed a multipoint LODmax of 3.27 on chromosome 12q. Sequencing of the slow-twitch skeletal muscle myosin binding protein C1 (MYBPC1), located within the linkage interval, revealed a missense mutation (c.706T>C) that segregated with disease in this family and causes a W236R amino acid substitution. A second MYBPC1 missense mutation was identified (c.2566T>C)(Y856H) in another family with DA1, accounting for an MYBPC1 mutation frequency of 13% (two of 15). Skeletal muscle biopsies from affected patients showed type I (slow-twitch) fibers were smaller than type II fibers. Expression of a green fluorescent protein (GFP)-tagged MYBPC1 construct containing WT and DA1 mutations in mouse skeletal muscle revealed robust sarcomeric localization. In contrast, a more diffuse localization was seen when non-fused GFP and MYBPC1 proteins containing corresponding MYBPC3 amino acid substitutions (R326Q, E334K) that cause hypertrophic cardiomyopathy were expressed. These findings reveal that the MYBPC1 is a novel gene responsible for DA1, though the mechanism of disease may differ from how some cardiac MYBPC3 mutations cause hypertrophic cardiomyopathy