Rechargeable lithium-ion batteries with high energy density that can be safely charged and discharged at high rates are desirable for electrified transportation and other applications 1-3. However, the sub-optimal intercalation potentials of current anodes result in a trade-off between energy density, power and safety. Here we report that disordered rock salt 4,5 Li3+xV2O5 can be used as a fast-charging anode that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li + reference electrode. The increased potential compared to graphite 6,7 reduces the likelihood of lithium metal plating if proper charging controls are used, alleviating a major safety concern (short-circuiting related to Li dendrite growth). In addition, a lithium-ion battery with a disordered rock salt Li3V2O5 anode yields a cell voltage much higher than does a battery using a commercial fastcharging lithium titanate anode or other intercalation anode candidates (Li3VO4 and LiV0.5Ti0.5S2) 8,9. Further, disordered rock salt Li3V2O5 can perform over 1,000 charge-discharge cycles with negligible capacity decay and exhibits exceptional rate capability, delivering over 40 per cent of its capacity in 20 seconds. We attribute the low voltage and high rate capability of disordered rock salt Li3V2O5 to a redistributive lithium intercalation mechanism with low energy barriers revealed via ab initio calculations. This low-potential, high-rate intercalation reaction can be used to identify other metal oxide anodes for fast-charging, long-life lithium-ion batteries.

BACKGROUND & AIMS: Approximately 10% of children on the liver transplant wait-list in the United States die every year. We examined deceased donor liver offer acceptance patterns and their contribution to pediatric wait-list mortality. METHODS: We performed a retrospective cohort study of children on the US liver transplant wait-list from 2007 through 2014 using national transplant registry databases. We determined the frequency, patterns of acceptance, and donor and recipient characteristics associated with deceased donor liver organ offers for children who died or were delisted compared with those who underwent transplantation. Children who died or were delisted were classified by the number of donor liver offers (0 vs 1 or more), limiting analyses to offers of livers that were ultimately transplanted into pediatric recipients. The primary outcome was death or delisting on the wait-list. RESULTS: Among 3852 pediatric liver transplant candidates, children who died or were delisted received a median 1 pediatric liver offer (inter-quartile range, 0–2) and waited a median 33 days before removal from the wait-list. Of 11,328 donor livers offered to children, 2533 (12%) were transplanted into children; 1179 of these (47%) were immediately accepted and 1354 (53%) were initially refused and eventually accepted for another child. Of 27,831 adults, 1667 (6.0%; median, 55 years) received livers from donors younger than 18 years (median, 15 years), most (97%) allocated locally or regionally. Of children who died or were delisted, 173 (55%) received an offer of 1 or more liver that was subsequently transplanted into another pediatric recipient, and 143 (45%) died or were delisted with no offers. CONCLUSIONS: Among pediatric liver transplant candidates in the US, children who died or were delisted received a median 1 pediatric liver offer and waited a median of 33 days. Of livers transplanted into children, 47% were immediately accepted and 53% were initially refused and eventually accepted for another child. Of children who died or were delisted, 55% received an offer of 1 or more liver that was subsequently transplanted into another pediatric recipient, and 45% died or were delisted with no offers. Pediatric prioritization in the allocation and development of improved risk stratification systems is required to reduce wait-list mortality among children.

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In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor personalized health recommendations to each subgroup. It is tempting to cluster the participants using all of the data types and timepoints, in order to fully exploit the available information. However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views. If this assumption does not hold, then clustering the participants using multiple data views may lead to spurious results. In this paper, we seek to evaluate the assumption that there is some underlying relationship among the clusterings from the different data views, by asking the question: are the clusters within each data view dependent or independent? We develop a new test for answering this question, which we then apply to clinical, proteomic, and metabolomic data, across two distinct timepoints, from the P100 study. We find that while the subgroups of the participants defined with respect to any single data type seem to be dependent across time, the clustering among the participants based on one data type (e.g. proteomic data) appears not to be associated with the clustering based on another data type (e.g. clinical data). Data integration; Hypothesis testing; Model-based clustering; Multiple-view data. *

Summary In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell’s state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this article, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study and apply count splitting to a data set of pluripotent stem cells differentiating to cardiomyocytes.