Survival of Migrating Salmon Smolts in Large Rivers With and Without Dams

The mortality of salmon smolts during their migration out of freshwater and into the ocean has been difficult to measure. In the Columbia River, which has an extensive network of hydroelectric dams, the decline in abundance of adult salmon returning from the ocean since the late 1970s has been ascribed in large measure to the presence of the dams, although the completion of the hydropower system occurred at the same time as large-scale shifts in ocean climate, as measured by climate indices such as the Pacific Decadal Oscillation. We measured the survival of salmon smolts during their migration to sea using elements of the large-scale acoustic telemetry system, the Pacific Ocean Shelf Tracking (POST) array. Survival measurements using acoustic tags were comparable to those obtained independently using the Passive Integrated Transponder (PIT) tag system, which is operational at Columbia and Snake River dams. Because the technology underlying the POST array works in both freshwater and the ocean, it is therefore possible to extend the measurement of survival to large rivers lacking dams, such as the Fraser, and to also extend the measurement of survival to the lower Columbia River and estuary, where there are no dams. Of particular note, survival during the downstream migration of at least some endangered Columbia and Snake River Chinook and steelhead stocks appears to be as high or higher than that of the same species migrating out of the Fraser River in Canada, which lacks dams. Equally surprising, smolt survival during migration through the hydrosystem, when scaled by either the time or distance migrated, is higher than in the lower Columbia River and estuary where dams are absent. Our results raise important questions regarding the factors that are preventing the recovery of salmon stocks in the Columbia and the future health of stocks in the Fraser River

Estimating the mean and variance from the median, range, and the size of a sample

BACKGROUND: Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. METHODS: In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. RESULTS: We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 <n ≤ 70), our simulations show that the formula range/4 is the best estimator for the standard deviation (variance). For large samples (n > 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. CONCLUSION: Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

Csnk1a1 inhibition has p53-dependent therapeutic efficacy in acute myeloid leukemia

Despite extensive insights into the underlying genetics and biology of acute myeloid leukemia (AML), overall survival remains poor and new therapies are needed. We found that casein kinase 1 α (Csnk1a1), a serine-threonine kinase, is essential for AML cell survival in vivo. Normal hematopoietic stem and progenitor cells (HSPCs) were relatively less affected by shRNA-mediated knockdown of Csnk1a1. To identify downstream mediators of Csnk1a1 critical for leukemia cells, we performed an in vivo pooled shRNA screen and gene expression profiling. We found that Csnk1a1 knockdown results in decreased Rps6 phosphorylation, increased p53 activity, and myeloid differentiation. Consistent with these observations, p53-null leukemias were insensitive to Csnk1a1 knockdown. We further evaluated whether D4476, a casein kinase 1 inhibitor, would exhibit selective antileukemic effects. Treatment of leukemia stem cells (LSCs) with D4476 showed highly selective killing of LSCs over normal HSPCs. In summary, these findings demonstrate that Csnk1a1 inhibition causes reduced Rps6 phosphorylation and activation of p53, resulting in selective elimination of leukemia cells, revealing Csnk1a1 as a potential therapeutic target for the treatment of AML