Multisystem inflammatory syndrome in children (MIS-C) is a post-infectious immune-mediated condition, seen 3–5 weeks after COVID-19. Maternal SARS-CoV-2 may potentially cause a similar hyperinflammatory syndrome in neonates due to transplacental transfer of antibodies. We reviewed the perinatal history, clinical features, and outcomes of 20 neonates with features consistent with MIS-C related to maternal SARS-CoV-2 in Kolhapur, India, from 1 September 2020 to 30 April 2021. Anti-SARS-CoV-2 IgG and IgM antibodies were tested in all neonates. Fifteen singletons and five twins born to eighteen mothers with a history of COVID-19 disease or exposure during pregnancy presented with features consistent with MIS-C during the first 5 days after birth. Nineteen were positive for anti-SARS-CoV-2 IgG and all were negative for IgM antibodies. All mothers were asymptomatic and therefore not tested by RTPCR-SARS-CoV-2 at delivery. Eighteen neonates (90%) had cardiac involvement with prolonged QTc, 2:1 AV block, cardiogenic shock, or coronary dilatation. Other findings included respiratory failure (40%), fever (10%), feeding intolerance (30%), melena (10%), and renal failure (5%). All infants had elevated inflammatory biomarkers and received steroids and IVIG. Two infants died. We speculate that maternal SARS-CoV-2 and transplacental antibodies cause multisystem inflammatory syndrome in neonates (MIS-N). Immunomodulation may be beneficial in some cases, but further studies are needed.

We consider two fundamental problems in stochastic optimization: approximation algorithms for stochastic matching, and sampling bounds in the black-box model. For the former, we improve the current-best bound of 3.709 due to Adamczyk, Grandoni, and Mukherjee [1], to 3.224; we also present improvements on Bansal, Gupta, Li, Mestre, Nagarajan, and Rudra [2] for hypergraph matching and for relaxed versions of the problem. In the context of stochastic optimization, we improve upon the sampling bounds of Charikar, Chekuri, and Pál [3].

Disk-shaped specimens were prepared from additively (NX and DT), subtractively (MZ), and conventionally manufactured denture base resins (CV). Surface roughness and color coordinates were measured after polishing, simulated brushing, and coffee thermocycling, while surface roughness was also measured before polishing. Polishing reduced the surface roughness of all materials. Brushing and coffee thermocycling increased the surface roughness of only DT. CV had the highest susceptibility to consecutive brushing and coffee thermocycling as it had the highest surface roughness, which was above the clinically acceptable threshold. All materials had similar stainability; only MZ had perceptible color change after brushing. Even though stainability of tested denture base resins was similar, additively or subtractively manufactured computer-aided design and computer-aided manufacturing (CAD-CAM) resins had smoother surfaces after brushing and coffee thermocycling, regardless of the material. Therefore, complete dentures made out of these CAD-CAM resins may have favorable surface properties in the long term. Graphical abstract

The relative ease of collaborative data science and analysis has led to a proliferation of many thousands or millions of versions of the same datasets in many scientific and commercial domains, acquired or constructed at various stages of data analysis across many users, and often over long periods of time. Managing, storing, and recreating these dataset versions is a non-trivial task. The fundamental challenge here is the storage-recreation trade-off: the more storage we use, the faster it is to recreate or retrieve versions, while the less storage we use, the slower it is to recreate or retrieve versions. Despite the fundamental nature of this problem, there has been a surprisingly little amount of work on it. In this paper, we study this trade-off in a principled manner: we formulate six problems under various settings, trading off these quantities in various ways, demonstrate that most of the problems are intractable, and propose a suite of inexpensive heuristics drawing from techniques in delay-constrained scheduling, and spanning tree literature, to solve these problems. We have built a prototype version management system, that aims to serve as a foundation to our DataHub system for facilitating collaborative data science. We demonstrate, via extensive experiments, that our proposed heuristics provide efficient solutions in practical dataset versioning scenarios.

As data-driven methods are becoming pervasive in a wide variety of disciplines, there is an urgent need to develop scalable and sustainable tools to simplify the process of data science, to make it easier for the users to keep track of the analyses being performed and datasets being generated, and to enable the users to understand and analyze the work ows. In this paper, we describe our vision of a uni ed provenance and metadata management system to support lifecycle management of complex collaborative data science workows. We argue that the information about the analysis processes and data artifacts can, and should be, captured in a semi-passive manner; and we show that querying and analyzing this information can not only simplify bookkeeping and debugging tasks but also enable a rich new set of capabilities like identifying aws in the data science process itself. It can also signi cantly reduce the user time spent in xing post-deployment problems through automated analysis and monitoring. We have implemented a prototype system, P DB, on top of git and Neo4j, and we describe its key features and capabilities.