Eastern Beringia and beyond: Late Wisconsinan and Holocene landscape dynamics along the Yukon Coastal Plain, Canada

Terrestrial permafrost archives along the Yukon Coastal Plain (northwest Canada) have recorded landscape development and environmental change since the Late Wisconsinan at the interface of unglaciated Beringia (i.e. Komakuk Beach) and the northwestern limit of the Laurentide Ice Sheet (i.e. Herschel Island). The objective of this paper is to compare the late glacial and Holocene landscape development on both sides of the former ice margin based on permafrost sequences and ground ice. Analyses at these sites involved a multi-proxy approach including: sedimentology, cryostratigraphy, palaeoecology of ostracods, stable water isotopes in ground ice, hydrochemistry, and AMS radiocarbon and infrared stimulated luminescence (IRSL) dating. AMS and IRSL age determinations yielded full glacial ages at Komakuk Beach that is the northeastern limit of ice-free Beringia. Herschel Island to the east marks the Late Wisconsinan limit of the northwest Laurentide Ice Sheet and is composed of ice-thrust sediments containing plant detritus as young as 16.2 cal ka BP that might provide a maximum age on ice arrival. Late Wisconsinan ice wedges with sediment-rich fillings on Herschel Island are depleted in heavy oxygen isotopes (mean δ18O of −29.1‰); this, together with low dexcess values, indicates colder-than-modern winter temperatures and probably reduced snow depths. Grain-size distribution and fossil ostracod assemblages indicate that deglaciation of the Herschel Island icethrust moraine was accompanied by alluvial, proluvial, and eolian sedimentation on the adjacent unglaciated Yukon Coastal Plain until ~11 cal ka BP during a period of low glacio-eustatic sea level. The late glacial–Holocene transition was marked by higher-than-modern summer temperatures leading to permafrost degradation that began no later than 11.2 cal ka BP and caused a regional thaw unconformity. Cryostructures and ice wedges were truncated while organic matter was incorporated and soluble ions were leached in the thaw zone. Thermokarst activity led to the formation of ice-wedge casts and deposition of thermokarst lake sediments. These were subsequently covered by rapidly accumulating peat during the early Holocene Thermal Maximum. A rising permafrost table, reduced peat accumulation, and extensive ice-wedge growth resulted from climate cooling starting in the middle Holocene until the late 20th century. The reconstruction of palaeolandscape dynamics on the Yukon Coastal Plain and the eastern Beringian edge contributes to unraveling the linkages between ice sheet, ocean, and permafrost that have existed since the Late Wisconsinan

The STONE curve: A ROC-derived model performance assessment tool

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier (model) data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve, plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth's inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.Comment: 19 pages, including 4 figures. Currently in second-round review with "Earth and Space Science": https://agupubs.onlinelibrary.wiley.com/journal/2333508

The STONE Curve: A ROC‐Derived Model Performance Assessment Tool

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.Plain Language SummaryScientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model’s ability to predict events within the data. The ROC curve is made by sliding the event‐definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and preclassified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous‐valued data set.Key PointsA new event‐detection‐based metric for model performance appraisal is given with sliding thresholds in both observational and model valuesThe new metric is like the relative operating characteristic curve but uses continuous observational values, not just categorical statusThe new metric is used on real‐time model predictions of common geomagnetic activity parameters, demonstrating its features and strengthsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156486/2/ess2610.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156486/1/ess2610_am.pd

Eating increases oxidative damage in a reptile

While eating has substantial benefits in terms of both nutrient and energy acquisition, there are physiological costs associated with digesting and metabolizing a meal. Frequently, these costs have been documented in the context of energy expenditure while other physiological costs have been relatively unexplored. Here, we tested whether the seemingly innocuous act of eating affects either systemic pro-oxidant (reactive oxygen metabolite, ROM) levels or antioxidant capacity of corn snakes (Pantherophis guttatus) by collecting plasma during absorptive (peak increase in metabolic rate due to digestion of a meal) and non-absorptive (baseline) states. When individuals were digesting a meal, there was a minimal increase in antioxidant capacity relative to baseline (4%), but a substantial increase in ROMs (nearly 155%), even when controlling for circulating nutrient levels. We report an oxidative cost of eating that is much greater than that due to long distance flight or mounting an immune response in other taxa. This result demonstrates the importance of investigating non-energetic costs associated with meal processing, and it begs future work to identify the mechanism(s) driving this increase in ROM levels. Because energetic costs associated with eating are taxonomically widespread, identifying the taxonomic breadth of eating-induced ROM increases may provide insights into the interplay between oxidative damage and life history theory

Calcium carbonate dissolution rates in hydrothermal vent fields of the Guaymas Basin

Analysis of bivalve shell fragments that were embedded in epoxy blocks, mounted on titanium stakes, and deployed by DSRV Alvin at 5 sites in the Southern Trough of the Guaymas Basin (27°00′N, 111°24.55′W; depth 2012 m) indicates significant variation of calcium carbonate dissolution in in situ exposures of more than 900 days. Arrays of shell fragments of six bivalve species (i.e., Bathymodiolus thermophilus, Calyptogena magnifica, Calyptogena sp., Corbicula fluminea, Crassostrea virginica and Mytilus edulis) were positioned −17 cm, −7 cm and −2.5 cm below the sediment-water interface and 2.5 cm, 7 cm and 17 cm above the sediment-water interface in hydrothermal vent fields of the basin. Maximum dissolution rates for both calcite (mean = 86 μm/yr) and aragonite (mean = 312 μm/yr) were found in epoxy blocks located at the deepest point sampled in the sediment column (depth = 17 cm). Minimum dissolution rates of calcite and aragonite were found 7 cm (mean = 26 μm/yr) and 2.5 cm (mean = 96 μm/yr) above the sediment-water interface, respectively. Intermediate rates of dissolution were recorded 17 cm above the sediment-water interface (mean = 40 μm/yr for calcite and 126 μm/yr for aragonite). Mean rates of aragonite dissolution ranged from 59 μm/yr (site 5; clam area) to 227 μm/yr (site 3; clam area), and those of calcite dissolution ranged from 13 μm/yr (site 3; clam area) to 94 μm/yr (site 4; bacterial mat area). Dissolution rates were consistently highest in the bacterial mat area (site 4; mean = 94 μm/yr for calcite and 223 μm/yr for aragonite). Rates of calcium carbonate dissolution reported here for hydrothermal vent fields of the Guaymas Basin compare favorably with those of Rose Garden (Galapagos Rift) and 21N (East Pacific Rise) hydrothermal vent sites. These results have important implications for assessing biological rate processes in deep-sea hydrothermal vent environments

Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects

Background Abnormalities in motor activity represent a central feature in major depressive disorder. However, measurement issues are poorly understood, limiting the use of objective measurement of motor activity for diagnostics and treatment monitoring. Methods To improve measurement issues, especially sensor placement, analytic strategies and diurnal effects, we assessed motor activity in depressed patients at the beginning (MD; n=27) and after anti-depressive treatment (MD-post; n=18) as well as in healthy controls (HC; n=16) using wrist- and chest-worn accelerometers. We performed multiple analyses regarding sensor placements, extracted features, diurnal variation, motion patterns and posture to clarify which parameters are most powerful in distinguishing patients from controls and monitoring treatment effects. Results Whereas most feature-placement combinations revealed significant differences between groups, acceleration (wrist) distinguished MD from HC (d=1.39) best. Frequency (vertical axis chest) additionally differentiated groups in a logistic regression model (R2=0.54). Accordingly, both amplitude (d=1.16) and frequency (d=1.04) showed alterations, indicating reduced and decelerated motor activity. Differences between MD and HC in gestures (d=0.97) and walking (d=1.53) were found by data analysis from the wrist sensor. Comparison of motor activity at the beginning and after MD-treatment largely confirms our findings. Limitations Sample size was small, but sufficient for the given effect sizes. Comparison of depressed in-patients with non-hospitalized controls might have limited motor activity differences between groups. Conclusions Measurement of wrist-acceleration can be recommended as a basic technique to capture motor activity in depressed patients as it records whole body movement and gestures. Detailed analyses showed differences in amplitude and frequency denoting that depressed patients walked less and slower

Restoring mitofusin balance prevents axonal degeneration in a Charcot-Marie-Tooth type 2A model

Mitofusin-2 (MFN2) is a mitochondrial outer-membrane protein that plays a pivotal role in mitochondrial dynamics in most tissues, yet mutations in MFN2, which cause Charcot-Marie-Tooth disease type 2A (CMT2A), primarily affect the nervous system. We generated a transgenic mouse model of CMT2A that developed severe early onset vision loss and neurological deficits, axonal degeneration without cell body loss, and cytoplasmic and axonal accumulations of fragmented mitochondria. While mitochondrial aggregates were labeled for mitophagy, mutant MFN2 did not inhibit Parkin-mediated degradation, but instead had a dominant negative effect on mitochondrial fusion only when MFN1 was at low levels, as occurs in neurons. Finally, using a transgenic approach, we found that augmenting the level of MFN1 in the nervous system in vivo rescued all phenotypes in mutant MFN2R94Q-expressing mice. These data demonstrate that the MFN1/MFN2 ratio is a key determinant of tissue specificity in CMT2A and indicate that augmentation of MFN1 in the nervous system is a viable therapeutic strategy for the disease

Stabilization of mineral-associated organic carbon in Pleistocene permafrost

Ice-rich Pleistocene-age permafrost is particularly vulnerable to rapid thaw, which may quickly expose a large pool of sedimentary organic matter (OM) to microbial degradation and lead to emissions of climate-sensitive greenhouse gases. Protective physico-chemical mechanisms may, however, restrict microbial accessibility and reduce OM decomposition; mechanisms that may be influenced by changing environmental conditions during sediment deposition. Here we study different OM fractions in Siberian permafrost deposited during colder and warmer periods of the past 55,000 years. Among known stabilization mechanisms, the occlusion of OMin aggregates is of minor importance, while 33-74% of the organic carbon is associated with small, <6.3 μm mineral particles. Preservation of carbon in mineral-associated OM is enhanced by reactive iron minerals particularly during cold and dry climate, reflected by low microbial CO2 production in incubation experiments. Warmer and wetter conditions reduce OM stabilization, shown by more decomposed mineral-associated OM and up to 30% higher CO2 production. This shows that considering the stability and bioavailability of Pleistocene-age permafrost carbon is important for predicting future climate-carbon feedback

Credimus

We believe that economic design and computational complexity---while already important to each other---should become even more important to each other with each passing year. But for that to happen, experts in on the one hand such areas as social choice, economics, and political science and on the other hand computational complexity will have to better understand each other's worldviews. This article, written by two complexity theorists who also work in computational social choice theory, focuses on one direction of that process by presenting a brief overview of how most computational complexity theorists view the world. Although our immediate motivation is to make the lens through which complexity theorists see the world be better understood by those in the social sciences, we also feel that even within computer science it is very important for nontheoreticians to understand how theoreticians think, just as it is equally important within computer science for theoreticians to understand how nontheoreticians think