Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Future studies are now needed to determine how the neurons are able to re-wire themselves during recovery sleep. is a ubiquitous phenomenon and most species, including humans, spend a significant time asleep. Although the function of sleep remains unknown, it is widely acknowledged that sleep is crucial for proper brain function. Indeed, learning and memory, particularly those types mediated by the hippocampus, are promoted by sleep and disrupted by sleep deprivation (Havekes et al. , 2012a; Abel et al. , 2013; Whitney and Hinson, 2010). Despite the general consensus that sleep deprivation impairs hippocampal function, the molecular signaling complexes and cellular circuits by which sleep deprivation leads to cognitive deficits remain to be defined. The alternation of wakefulness and sleep has a profound impact on synaptic function, with changes observed in synaptic plasticity and transmission (Havekes et al. , 2012a; Abel et al. , 2013; Tononi and Cirelli, 2014). This relationship has led to the development of influential theories on the function of sleep (Tononi and Cirelli, 2006; Pavlides and Winson, 1989). Recent imaging suggests that dendritic structure is dynamic, especially during development, with alterations in spine numbers correlating with changes in sleep/wake state (Maret et al. , 2011; Yang and Gan, 2012). However, the impact of sleep deprivation or sleep on synaptic structure in the hippocampus in the context of memory storage or synaptic plasticity has not been examined. This is an important issue, as such structural changes in ensembles of synapses have been shown to play a critical role in memory storage (Caroni et al. , 2012; Vogel-Ciernia et al. , 2013). The formation of associative memories increases the number of dendritic spines in area CA1 of the hippocampus (Leuner et al. , 2003). Also, the induction of long-term potentiation (LTP), a cellular correlate of memory storage (Mayford et al. , 2012), is associated with an increase in spine density in cultured hippocampal neurons (Oe et al. , 2013). In addition to a critical function during development (Gurniak et al. , 2005), cofilin plays an essential role in synapse structure by mediating both the enlargement and pruning of dendritic spines (Rust, 2015; Bamburg, 1999; Bosch et al. , 2014).

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: While there was variation in expert opinion of the aggregate value to biodiversity of each vegetation patch, rankings and aggregate scores were relatively consistent among the 29 experts (Fig 2A and 2E). A boosted regression tree of initial vegetation condition revealed that expert identity had the largest relative influence (a normalised relative influence, NRI, of 31) such that some experts tended to score all sites higher or lower than the average expert. Shrub cover (NRI = 22), grass cover (NRI = 20) and tree cover (NRI = 10) were also found to be relatively important in explaining expert judgements of vegetation condition. Expert estimates of vegetation condition tended to be positively correlated with all native vegetation attributes but negatively correlated with invasive alien plant cover (S4 File). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 2. Boxplots of expert estimates of vegetation condition (A, E), management gain (B, F), averted loss (C, G) and total benefit (D, H). A through D are the five common vegetation patches provided to all 29 experts. Each patch in E through H was assessed by 4–6 experts. In all graphs the sites are ranked according to panel E, i. e. by their median current expert vegetation condition score. Averted loss and total benefit are based on estimates from 16 experts. Future predictions of biodiversity value. Most experts predicted that MG would be relatively small (Fig 2B & 2F). Median expert estimates of MG across the 64 patches was 5 100, with lower 25th and upper 75th quartiles of 0 and 10 respectively. However, a single expert had median predictions of 25 100 and a maximum of 70100 (S5 File) while negative estimates of MG were also elicited from some experts (Fig 2B & 2F). The boosted regression tree models indicated that expert identity had the largest relative influence on management gain (NRI = 72), with only small contributions from initial vegetation condition (NRI = 15. 4), invasive alien plant cover (NRI = 2. 9) and landscape vegetation cover (NRI = 2. 5). The data (Fig 2B & 2F) and BRT fitted functions (S4 File) suggest a negative, yet non-linear correlation between current biodiversity value and MG, a weak positive effect of landscape vegetation cover and a negative effect of invasive alien plant cover.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Trenberth 1999: Observed and model simulated precipitation diurnal cycle over the contiguous United States. J. Geophys. Res. , 104, 6377– 6402. )| falseSearch 744. Dai, A. , X. Lin and K-L. Hsu 2007: The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes. Climate Dyn. , 29, 727– 744. )| falseSearch 507. Higgins, R. W. , Y. Yao E. S. Yarosh J. E. Janowiak and K. C. Mo 1997: Influence of the low-level jet on summertime precipitation and moisture transport over the central United States. J. Climate, 10, 481– 507. )| falseSearch 3105. doi:10. 1029/2005JD006156. Janowiak, J. E. , V. E. Kousky and R. J. Joyce 2005: Diurnal cycle of precipitation determined from the high spatial and temporal resolution global precipitation analyses. J. Geophys. Res. , 110, D23105. doi:10. 1029/2005JD006156. )| falseSearch 503. Joyce, R. J. , J. E. Janowiak P. A. Arkin and P. Xie 2004: : A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor. , 5, 487– 503. )| falseSearch 933. Koch, S. E. , F. Zhang M. L. Kaplan Y-L. Lin R. Weglarz and C. M. Trexler 2001: Numerical simulations of a gravity wave event over CCOPE. Part III: The role of a mountain–plains solenoid in the generation of the second wave episode. Mon. Wea. Rev. , 129, 909– 933. )| falseSearch 2415. Lin, Y. , and F. Zhang 2008: Tracking mesoscale gravity waves in baroclinic jet-front systems. J. Atmos. Sci. , 65, 2402– 2415. )| falseSearch 168. Qian, W. , H. Kang and D.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: The activity of cofilin is negatively regulated by phosphorylation. Specifically, phosphorylation of serine 3 of cofilin suppresses its depolymerizing and F-actin severing activity (Bamburg, 1999). Importantly, increased cofilin activity can lead to the depolymerization and severing of F-actin, which in turn results in the shrinkage and loss of spines (Rust, 2015; Zhou et al. , 2004; Davis et al. , 2011; Shankar et al. , 2007). Hippocampal cofilin phosphorylation levels are increased after the induction of long-term potentiation (LTP) (Rex et al. , 2010; Chen et al. , 2007; Briz et al. , 2015), and during memory consolidation (Fedulov et al. , 2007; Suzuki et al. , 2011). Additionally, elevated cofilin activity in the hippocampus was recently implicated in abnormal spine structure and function in mutant mice with altered chromatin remodeling (Vogel-Ciernia et al. , 2013). Here we show for the first time that 5 hr of sleep deprivation leads to the loss of dendritic spines of CA1, but not CA3, neurons in the dorsal hippocampus. The spine loss in CA1 neurons was accompanied by reductions in dendrite length. This process was readily reversed by sleep, with just 3 hr of recovery sleep normalizing this spine loss and dendrite length. The molecular mechanisms underlying these negative effects of sleep deprivation were shown to target cofilin, whose elevated activity could contribute to spine loss. Indeed, suppression of cofilin activity in hippocampal neurons prevented the structural, biochemical, and electrophysiological changes as well as the cognitive impairments associated with sleep loss. The elevated cofilin activity is caused by the activity of the cAMP degrading phosphodiesterase-4A5 isoform (PDE4A5), which suppresses activity of the cAMP-PKA-LIMK pathway. Genetic inhibition of the PDE4A5 isoform in hippocampal neurons restores LIMK and cofilin phosphorylation levels and prevents the cognitive impairments associated with sleep loss. Thus changes in the cAMP-PDE4-PKA-LIMK-cofilin signaling pathway in the adult hippocampus underlie the cognitive deficits associated with sleep loss.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Averted loss (AL), derived from expert estimates of the change in vegetation condition under a BAU scenario, was estimated by 16 experts (Fig 2C & 2G). The median estimate of AL was 5, with upper and lower quartiles of 0 and 9 respectively under a BAU scenario. A single expert predicted that vegetation condition would weakly increase with BAU. Experts again were consistently optimistic or pessimistic in their estimate of averted loss (NRI = 60. 3). Initial vegetation condition (NRI = 13. 0), invasive alien plant cover (NRI = 13. 2) and landscape vegetation cover (NRI = 4. 2) each had only small to moderate relative influence in the BRT. Data and fitted functions from the BRT indicate a weak positive non-linear relationship between current biodiversity value and expert estimates of AL. Averted loss was greatest at moderate to high initial vegetation condition (Fig 2C & 2G, S4 File). Averted loss was also weakly negatively correlated with landscape vegetation cover and positively with HT weed cover. Total benefit (TB) was estimated from values elicited from 16 experts. Median TB was 7. 5 with lower (25th) and upper (75th) quartiles of 5 and 15 respectively. There was large among-expert variation in TB, evident in the normalised relative influence from the BRT (NRI = 66. 3). Moderate relative influence was also associated with initial vegetation condition (NRI = 21. 9) and a slight influence of invasive alien plant cover (NRI = 4. 9). Data (Fig 2D & 2H) and the fitted functions from the BRT suggested that TB tended to be highest when initial vegetation condition was low to moderate (S4 File). There was little support for any correlation with either invasive alien plant cover or landscape vegetation cover (S4 File). The lack of effect of invasive alien plant cover or landscape vegetation cover was presumably because the magnitudes of AL and MG were similar and each was associated with opposing effects (ie. MG = +ve landscape vegetation cover, -ve invasive alien plant cover; AL = -ve landscape vegetation cover, +ve invasive alien plant cover). Individual attribute predictions from the trial roulette method Averted loss, management gain and total benefit for a WSGW.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Lee 2002: Distribution of seasonal rainfall in Monsoon region. Theor. Appl. Climatol. , 73, 151– 168. )| falseSearch 2461. Trier, S. B. , C. A. Davis D. A. Ahijevych M. L. Weisman and G. H. Bryan 2006: Mechanisms supporting long-lived episodes of propagating nocturnal convection within a 7-day WRF model simulation. J. Atmos. Sci. , 63, 2437– 2461. )| falseSearch 328. Tripoli, G. J. , and W. R. Cotton 1989: Numerical study of an observed orogenic mesoscale convective system. Part II: Analysis of governing dynamics. Mon. Wea. Rev. , 117, 305– 328. )| falseSearch 419. Wallace, J. M. , 1975: Diurnal variations in precipitation and thunderstorm frequency over the conterminous United States. Mon. Wea. Rev. , 103, 406– 419. )| falseSearch 1629. Wang, C. C. , G. T. J. Chen and R. E. Carbone 2004: A climatology of warm-season cloud patterns over east Asia based on GMS infrared brightness temperature observations. Mon. Wea. Rev. , 132, 1606– 1629. )| falseSearch 1508. Wolyn, P. G. , and T. B. McKee 1994: The mountain–plains circulation east of a 2-km-high north–south barrier. Mon. Wea. Rev. , 122, 1490– 1508. )| falseSearch 1170. Yin, S. , D. Chen and Y. Xie 2009: Diurnal variations of precipitation during the warm season over China. Int. J. Climatol. , 29, 1154– 1170. )| falseSearch 1704. doi:10. 1029/2006GL028129. Yu, R. , T. Zhou and A. Xiong 2007: Diurnal variation of summer precipitation over contiguous China. Geophys. Res. Lett. , 34, L01704. doi:10. 1029/2006GL028129. )| falseSearch 2796. Zhang, F. , and S. E.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: These observations provide a molecular model for the notion that prolonged wakefulness reduces structural signaling and negatively impacts dendritic structure, which is then restored with sleep. deprivation causes a robust reduction in apical and basal CA1 spine numbers and dendrite length To determine whether short periods of sleep loss affect dendritic structure in the hippocampus, we used Golgi staining to examine the length of dendrites and number of dendritic spines in the mouse hippocampus following 5 hr of sleep deprivation, a period of sleep loss that is known to impair selectively hippocampus-dependent memory consolidation and synaptic plasticity (Havekes et al. , 2012a; Abel et al. , 2013; Graves et al. , 2003; Vecsey et al. , 2009; Havekes et al. , 2014). Analyses of Golgi-impregnated CA1 neurons (Figure 1A) indicated that sleep deprivation significantly reduced the apical/basal spine density (Figure 1B; spine numbers per dendrite, NSD: 1. 42 ± 0. 03, SD: 1. 17 ± 0. 02; Student’s t-test, p=0. 0002) and dendrite length (Figure 1C; NSD: 1198. 4 ± 31. 6, SD: 984. 2 ± 29. 8 µm; Student’s t-test, p=0. 0012). This decrease in spine density and dendrite length was observed in both apical and basal dendrites (Figure 1—figure supplement 1A,B). To complement our Golgi studies, we conducted an additional experiment in which individual CA1 neurons in hippocampal slices from sleep deprived and non-sleep deprived mice were labeled using the DiI method as described (Seabold et al. , 2010). In line with our Golgi studies, we found that sleep deprivation significantly reduced the total number of spines on apical dendrites of CA1 neurons (Figure 1D; NSD: 1. 0 ± 0. 03, SD: 0. 84 ± 0. 04 Student’s t-test, p=0. 033; Figure 1E; NSD: 1. 0 ± 0. 06, SD: 0. 86 ± 0. 02 Student’s t-test, p=0. 03).

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: For each vegetation condition attribute, we estimated the distribution of AL, MG and TB by randomly sampling from each expert’s reference, future under BAU and future with offset probability distributions. These random samples were pooled (2100 random samples for each attribute) and the resultant distributions of AL, MG and TB reflect both variation within and among experts (Fig 3, S6 File). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 3. Boxplots of averted loss, management gain and total benefit after 20 years for individual vegetation attributes within a with moderate starting values. For each attribute, data are derived by pooling 2100 random draws from the future and reference probability distributions of 25 experts. Total benefit is the sum of averted loss and management gain. Averted loss is estimated as the difference between the counterfactual and the initial attribute condition and therefore takes a positive value when the attribute is expected to decline relative to the reference under a business as usual scenario. Ltrees = Large Trees. lower quartile (25th percentile) of AL estimates were >0 for the richness of all growth forms, the foliage cover of grasses and trees and the number of large trees (Fig 3). This suggests a tendency towards a consensus that these vegetation condition attributes will decline under a BAU scenario. Predictions of AL were less consistent for forb, shrub, length of fallen logs and litter cover (Fig 3). Positive estimates of MG indicate an expert opinion that with an offset the attribute is expected to improve within 20 years, relative to the starting value. Both litter cover and the length of fallen logs were generally expected to increase following adoption of an offset (lower quartile is >0) while declines, or at best no change, in large trees numbers was thought to be most likely (Fig 3). Although some other attributes tended to have a median MG > 0 (e. g. tree and forb foliage cover and grass richness), predictions were uncertain owing to both inter- and intra-variation among experts (S6 File) and both declines and increases were predicted. In all cases, median predictions of absolute changes with management were never more than one species or 5% foliage cover. The distribution of TB indicated that the sum of MG and AL were predicted to be mostly positive, except for large trees and tree richness (Fig 3). In most cases TB was dominated by AL.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Koch 2000: Numerical simulation of a gravity wave event observed during CCOPE. Part II: Wave generation by an orographic density current. Mon. Wea. Rev. , 128, 2777– 2796. )| falseSearch 4010. Zhou, T. , R. Yu H. Chen A. Dai and Y. Pan 2008: Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations. J. Climate, 21, 3997– 4010. )| falseSearch 1. (a) Study domain map and terrain distribution (shaded every 200 m) over northern China with the focus area highlighted in box ABCD, which includes the cities of Beijing (denoted as BJ) and Tianjin (TJ) and the provinces of Shanxi (SX), Inner-Mongolia (NM), Hebei (HB), Shandong (SD), Henan (HN), Anhui (AH), and Jiangsu (JS). (b) Distribution of mean warm-season hourly rainfall rate (mm h−1) averaged over all hours from May to August during 2003–09 estimated with the dataset. (c) Averaged terrain elevation and hourly precipitation rate along the northwest–southeast direction averaged over the boxed area in (a). The starting point of the cross section is an average over line AB, and the ending point is an average over line CD; the same averaging method will be used for Figs. 4 and 6 along the same cross section. View in gallery Fig. 2. (a) Percentage of the diurnal contributions of the total precipitation (calculated as the sum of the absolute differences between the mean precipitation rate at each hour and the mean hourly precipitation rate divided by the mean hourly precipitation rate). The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. (b) Isochrones (phase fronts) of the local diurnal precipitation peaks at different times of Fig. 3 with terrain elevations shaded every 200 m. The time label of the isochrones in (b) is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View in gallery Fig. 3.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Figure 1 with 2 supplements see all Download asset Open asset Sleep deprivation reduces spine numbers and dendrite length in CA1 neurons of the hippocampus. (A) Representative images of Golgi-impregnated dendritic spines of CA1 pyramidal neurons from sleep deprived (SD) and non-sleep deprived (NSD) mice. Scale bar, 5 µm. (B) Sleep deprivation reduces the spine density of apical/basal dendrites of CA1 neurons (n = 5–6, Student’s t-test, p=0. 0002). (C) Sleep deprivation decreases apical/basal dendrite length of CA1 neurons (n = 5–6, Student’s t-test, p=0. 0012). (D, E) Comparative analyses of spine numbers in the second-third branch of apical dendrites of CA1 neurons reveal a significant reduction as a result of sleep deprivation using either the DiI labeling method (n = 3–4, Student’s t-test, p=0. 03) or Golgi analyses (n = 5, Student’s t-test, p=0. 03). Importantly, for the comparison of the two methods we focused on the second and third branch of the apical dendrites. See also the Materials and methods section. (F) Sleep deprivation reduces the number of all spine types in apical/basal dendrites of CA1 neurons (n = 5–6, Student’s t-test, p<0. 005). (G) Sleep deprivation reduces spine density of apical/basal dendrites between 60 and 150 µm away from the soma of CA1 neurons (n = 5–6, Student’s t-test, p<0. 005). (H) Sleep deprivation reduces apical/basal spine density in branch 3–9 of CA1 neurons (n = 5–6, Student’s t-test, p<0. 005). NSD: non-sleep deprived, SD: sleep deprived, Values represent the mean ± SEM. *p<0. 05, ***p<0. 005, by Student’s t test. See also Figure 1—figure supplement 1 and 2 for separate Golgi analyses of apical and basal spine numbers.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: However, the results also indicated that TB can also often be negative, indicative of an expectation that even with an offset the attribute might decline relative to the counterfactual. Across all experts and all attributes, 25% of all randomly drawn estimates of TB were negative and a further 15% were 0, indicating no expected benefit. Variation among individual experts was large, although most experts tended to be pessimistic about the future trends for large trees and optimistic about trends for logs and litter (Fig 4), regardless of the management scenario. There was some tendency to suggest that those experts who were pessimistic about BAU trends also tended to be pessimistic with an offset (Fig 4). This pattern was evident as a negative correlation between expert average estimates of averted loss and management gain, and was most apparent for logs, shrub foliage cover and tree species richness. Although some experts demonstrated a systematic bias (either consistently optimistic or pessimistic), most were not and their opinions differed among vegetation attributes (S7 File). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 4. Individual expert average estimates of averted loss and management gain for each of 11 vegetation attributes, with relationships indicated by simple linear fits. Each point represents the mean opinion of an individual expert, derived from their elicited probability distributions. Estimates in the top left quadrant of each panel indicate an optimistic opinion about future trends under both BAU and an offset (i. e. low AL, high MG). Estimates in the bottom right quadrat indicate those where experts were pessimistic about future trends, regardless of management scenario (i. e. high AL, low MG). Those in the top right were pessimistic about trends under BAU but optimistic about trends with an offset (i. e. high AL, high MG). Do management gains vary according to attribute and site starting conditions? The distribution of management gains for each attribute were explored across three WSGW scenarios. The three scenarios had different initial attribute values, and were generally thought to represent good-, moderate- and poor-quality sites (Table 2). These broad site quality descriptions were supported by predictions of aggregate vegetation condition, out of 100, derived from an ensemble of expert BRT models (see S8 File). We examined how MG varied depending on the initial condition (BCstart) of each vegetation attribute (initial value relative to the reference value) and whether consistent patterns were evident in each of the three WSGW vegetation patches.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Map distribution of normalized diurnal precipitation deviations at (a) 0600, (b) 0900, (c) 1200, (d) 1500, (e) 1800, and (f) 0000 UTC estimated as the mean precipitation rate at this hour minus the mean precipitation rate throughout the date, normalized by the standard deviation of hourly precipitation at each location. Scales smaller than 200 km are truncated by a 2D spectral decomposition. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima). View in gallery Fig. 4. Distance–time Hovmöller diagram of the normalized hourly precipitation deviation without filtering averaged along the cross section from northwest to southeast (as in Fig. 1c). The dashed arrows show the southeastward phase propagation of the diurnal precipitation peaks. The time axis is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View in gallery Fig. 5. Spatial distribution of the 900-hPa vertical motions (colored) and perturbation wind vectors diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima) as in Fig. 3. View in gallery Fig. 6. Vertical profiles of the averaged vertical motion (colored; cm s−1), the perturbation vertical circulation vectors (horizontal wind component along the cross section and 100 times the vertical velocity), and the horizontal wind component perpendicular to the cross section (contoured in blue every 0. 5 m s−1, where solid indicates southwesterlies and dashed indicates northwesterlies) along the direction from northwest to southeast averaged as in Figs. 1c and 4 diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The red solid curves show the averaged normalized diurnal precipitation deviation, with the red dashed straight line as the zero value.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Subtype-specific apical/basal spine analyses of the Golgi impregnated neurons revealed a significant decrease for all spine subtypes in sleep-deprived mice (Figure 1F, for all spine types, Student’s t-tests p<0. 005, for separate apical and basal spine analyses see 1C,D). Sleep deprivation causes the greatest reduction in apical/basal spine density between 60 µm and 150 µm from the soma (Figure 1G, for separate apical and basal spine analyses see Figure 1—figure supplement 1E,F). This region corresponds to the middle range of the dendritic branch (third to ninth branch orders, Figure 1H) where the primary input from CA3 is located (Neves et al. , 2008), suggesting that the hippocampal Schaffer collateral pathway is particularly vulnerable to sleep loss. We next assessed whether sleep deprivation also impacted spine numbers and dendrite length of CA3 neurons. Surprisingly, in contrast to CA1 neurons, CA3 neurons were unaffected by sleep deprivation. We did not observe reductions in spine density or dendrite length of either basal or apical dendrites of any type (Figure 1—figure supplement 2). Together, these data suggest that CA1 neurons at the level of dendritic structure seem particularly vulnerable to sleep deprivation. To determine whether recovery sleep would reverse spine loss in CA1 neurons, we repeated the sleep deprivation experiment but then left the sleep-deprived mice undisturbed for three hours afterwards. This period was chosen as our previous work indicated that three hours of recovery sleep is sufficient to restore deficits in LTP caused by sleep deprivation (Vecsey et al. , 2009). In line with the electrophysiological studies, recovery sleep restored apical/basal spine numbers and dendrite length in CA1 neurons to those observed in non-sleep deprived mice (Figure 2A, spine density of apical/basal dendrites, NSD: 1. 23 ± 0. 02, RS: 1. 29 ± 0. 02; Student’s t-test, p>0. 05; Figure 2B, dendrite length in μm, NSD: 1817. 0 ± 64. 6, RS: 1741. 6 ± 55. 57; Student’s t-test, p=0.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: There was generally a negative relationship between the median expert estimate of MG and the attribute’s starting value relative to the reference value (Fig 5) and attributes whose starting condition were <0. 3 tended to increase with adoption of an offset. This same trend was observed in eli5. Median expert predictions of management gain and current condition for each of 11 vegetation attributes in poor-, moderate- and good-quality WSGW. Management gains are changes in the individual attributes, relative to a reference value, predicted to occur as a result of 20 years of offset management. Points are medians and lines are upper and lower quartiles derived from 2100 randomisations across 25 expert probability distributions. The broad qualitative site quality is supported by numeric estimates of aggregate vegetation condition, out of 100, derived from expert BRT models (condition score in legend in brackets). the good-quality scenario the median management gain was 0 for all attributes. With the exception of logs and litter cover, median initial condition was 1, indicating that 50% of the random samples were less than the starting attribute values. In such circumstances standardisation to a reference can obscure changes on the original measurement scale, especially if experts predicted an increase following adoption of an offset. However, median estimates of absolute changes on the original scale were small and never positive (S9 File). DiscussionBiodiversity offsetting has been widely adopted but we often lack adequate empirical data and models upon which to make predictions of future biodiversity outcomes. Predicting the likely ecological changes with and without offsets is fundamental to offsetting, and these predictions can have significant and very real biodiversity impacts. Despite this, making such predictions remains a major technical challenge [2] and a better understanding of the uncertainty around future conservation benefits is vitally important. Here, 29 ecological professionals were asked, using two separate approaches, to make predictions about future changes in vegetation composition and structure, as a surrogate for biodiversity, with and without the adoption of a biodiversity offset. The results revealed that experts on average expected averted loss, management gain and total benefits to be modest. Individual expert predictions were often highly uncertain, and this was compounded by variation among experts. Strikingly, our analyses uncovered the perception that declines in some vegetation condition attributes were also likely at offset sites, even with the adoption of conservation management. This finding necessitates approaches that accept that some offsets will fail to achieve estimated gains, and some will deliver beyond expectation. Reasonable outcomes can be achieved overall (i.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Analysis fields below the ground are masked with light-gray shadings. raw image Fig. 1. (a) Study domain map and terrain distribution (shaded every 200 m) over northern China with the focus area highlighted in box ABCD, which includes the cities of Beijing (denoted as BJ) and Tianjin (TJ) and the provinces of Shanxi (SX), Inner-Mongolia (NM), Hebei (HB), Shandong (SD), Henan (HN), Anhui (AH), and Jiangsu (JS). (b) Distribution of mean warm-season hourly rainfall rate (mm h−1) averaged over all hours from May to August during 2003–09 estimated with the dataset. (c) Averaged terrain elevation and hourly precipitation rate along the northwest–southeast direction averaged over the boxed area in (a). The starting point of the cross section is an average over line AB, and the ending point is an average over line CD; the same averaging method will be used for Figs. 4 and 6 along the same cross section. View raw image Fig. 2. (a) Percentage of the diurnal contributions of the total precipitation (calculated as the sum of the absolute differences between the mean precipitation rate at each hour and the mean hourly precipitation rate divided by the mean hourly precipitation rate). The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. (b) Isochrones (phase fronts) of the local diurnal precipitation peaks at different times of Fig. 3 with terrain elevations shaded every 200 m. The time label of the isochrones in (b) is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View raw image Fig. 3. Map distribution of normalized diurnal precipitation deviations at (a) 0600, (b) 0900, (c) 1200, (d) 1500, (e) 1800, and (f) 0000 UTC estimated as the mean precipitation rate at this hour minus the mean precipitation rate throughout the date, normalized by the standard deviation of hourly precipitation at each location. Scales smaller than 200 km are truncated by a 2D spectral decomposition. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: 1721; Figure 2C, Student’s t-test, p>0. 05 for each distance from soma; Figure 2D, Student’s t-test, p>0. 05 for each branch number; for separate apical and basal spine analyses see Figure 2—figure supplement 1) with the exception of branched spines in the basal dendrites (Figure 2—figure supplement 1C). Recovery sleep slightly but significantly elevated the number of filopodia spines of the apical CA1 dendrites and total spine numbers of the seventh and eighth branch of the apical and basal dendrites respectively (Figure 2—figure supplement 1). Figure 2 with 1 supplement see all Download asset Open asset Three hours of recovery sleep restores spine numbers and dendrite length of CA1 neurons in the hippocampus. (A) Golgi analyses indicated that three hours of recovery sleep after 5 hr of sleep deprivation restores the total number of spines per apical/basal dendrite of CA1 neurons (n = 6, Student’s t-test, p>0. 05). (B) Three hours of recovery sleep after 5 hr of sleep deprivation restores apical/basal dendrite length of CA1 neurons (n = 6, Student’s t-test, p=0. 173). (C, D) Three hours of recovery sleep restores apical/basal spine numbers at all distances from the soma (Student’s t-test, p>0. 05 for each distance from soma, C) and at each branch number (Student’s t-test, p>0. 05 for each branch number, C). NSD: non-sleep deprived, RS: Sleep deprivation + recovery sleep. Values represent the mean ± SEM. See also Figure 2—figure supplement 1 for separate Golgi analyses of apical and basal spine numbers. Sleep deprivation increases hippocampal cofilin activity and suppression of cofilin function prevents spine loss in CA1 neurons associated with the loss of sleep We hypothesized that the structural changes in the hippocampus following sleep deprivation might be related to increased activity of the actin-binding protein cofilin because increased cofilin activity can cause shrinkage and loss of dendritic spines through the depolymerization and severing of actin filaments (Zhou et al. , 2004; Davis et al.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: e. across many offset scenarios), but only if risks are managed appropriately. The opportunity therefore exists to minimise risk and maximise biodiversity outcomes through the identification and avoidance of high-risk offset sites. The modest estimates of total benefits suggest that offset schemes should adopt a precautionary approach and avoid optimistic estimates of either averted loss or management gain. Several authors have argued that offset schemes often rely on unrealistically high estimates of background rates of loss (i. e. averted loss) to meet NNL requirements [8, 17]. If actual background rates of loss are indeed less than predicted, then to meet NNL these offset schemes would need to increase their reliance on management gains, achieved through conservation actions that improve on current biodiversity values. This could shift emphasis in offset site selection from high quality sites where there is much to lose to low quality sites where the potential for improvement is greater. Prediction of management gains at offset sites is, however, also inherently risky, as revealed by the substantial variation within and among experts. One approach to managing the uncertainty of future conservation benefits is to limit trading to only those offsets that have already generated benefits [5]. The range of opinion and uncertainty described in this study lends support to the pursuit of policy and governance mechanisms that favour early or prior conservation benefits. However, the creation of prior benefits requires significant planning and investment and in practice even the often-cited wetland mitigation banks in the USA rarely provide full prior compensation [43]. In most cases, offsetting is likely to continue to rely on future benefits in which case it is imperative that monitoring and research focus on improving empirical data and models to better predict outcomes and reduce uncertainty around these predictions. When and where do experts believe conservation management will produce benefits? Although predicted benefits were small, the results from both elicitation methods suggest that experts believe that management actions to address threats and pressures can often be sufficient to negate counterfactual losses. However, the study highlights that expert predicted benefits are not likely to be equivalent among all vegetation attributes. This offers opportunities for policy makers to refine offset schemes but also highlights important considerations for monitoring and evaluating current offset outcomes. Point estimates of future aggregate biodiversity value suggested that experts believed management gains (MG) were most likely to balance counterfactual averted losses (AL) when sites were initially of moderate to low value, relative to a reference condition.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima). View raw image Fig. 4. Distance–time Hovmöller diagram of the normalized hourly precipitation deviation without filtering averaged along the cross section from northwest to southeast (as in Fig. 1c). The dashed arrows show the southeastward phase propagation of the diurnal precipitation peaks. The time axis is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View raw image Fig. 5. Spatial distribution of the 900-hPa vertical motions (colored) and perturbation wind vectors diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima) as in Fig. 3. View raw image Fig. 6. Vertical profiles of the averaged vertical motion (colored; cm s−1), the perturbation vertical circulation vectors (horizontal wind component along the cross section and 100 times the vertical velocity), and the horizontal wind component perpendicular to the cross section (contoured in blue every 0. 5 m s−1, where solid indicates southwesterlies and dashed indicates northwesterlies) along the direction from northwest to southeast averaged as in Figs. 1c and 4 diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The red solid curves show the averaged normalized diurnal precipitation deviation, with the red dashed straight line as the zero value. Analysis fields below the ground are masked with light-gray shadings. 30 Views 0 0 0 Views 1290 365 19 PDF Downloads 697 210 11 of Variability on the in Storage over the Tigris–Basin Authors: Li-and Guo-Evaluation of Performance of to Authors:. Yang, Ardeshir Ebtehaj, Sajad Vahedizade, and A 440-of in California from Authors: Ian M. Howard, David W. Stahle, Michael D. Dettinger, Cody Poulsen, F. Martin Ralph, Max C.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: (D–F) CofilinS3D expression was excluded from astrocytes in area CA1 as indicated by a lack of co-labeling (F) between viral cofilin (D) and GFAP expression (E). Scale bar, 100 µM. (G) Virally delivered cofilinS3D protein levels were approximately 75% (blue bar) of wild-type cofilin levels (green bar). Wild-type cofilin levels were not significantly affected by expression of cofilinS3D. An HA-tag antibody was used to detect the mutant inactive form of cofilin. (n = 4). (H) Hippocampal cofilinS3D expression prevents spine loss in apical/basal dendrites of CA1 neurons that is associated with sleep deprivation (n = 6, Student’s t-test, p>0. 05). (I) Hippocampal cofilinS3D expression prevents the decrease in apical/basal dendritic spine length in neurons of hippocampal that is caused by sleep deprivation (n = 6, Student’s t-test, p>0. 05). (J) Sleep deprivation does not alter the number of any spine type in apical/basal dendrites of CA1 neurons in the hippocampus of mice expressing cofilinS3D (n = 6, Student’s t-test, p>0. 05). (K) Sleep deprivation does not attenuate apical/basal spine density at any distance from the soma in mice expressing cofilinS3D (n = 6, Student’s t-test, p>0. 05). NSD: non-sleep deprived, SD: sleep deprived. Values represent the mean ± SEM. **p=0. 0090. Student’s t test. See also Figure 3—figure supplement 1. For separate analyses of apical and basal spine numbers see Figure 3—figure supplement 2. Figure 3—source data 1 Sleep deprivation reduces cofilin phosphorylation in the hippocampus. The data source file contains the relative optical density values (in arbitrary units) of the pcofilin and cofilin western blots for each individual animal of the non-sleep deprived (NSD) and sleep deprived (SD) group.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Likewise, the future probability distributions provided by experts of individual attributes suggested that positive MGs would most likely arise when attributes were initially low relative to their reference. Where attributes are already at or near reference values, for example in good-quality remnant vegetation, conservation benefits are only likely if AL is relatively large and management can maintain current values. If those sites support attributes that are expected to decline, even with conservation management in place (e. g. large trees), then total benefits will inevitably be zero (or less). This would suggest that, if management gain is the objective, then offsets should be established in areas of low to moderate condition with some potential to improve. Offsets are usually framed around the concept of NNL or Net Gain. The uncertainty surrounding the potential for gains, highlighted in the results we present here, emphasises the risks associated with loss of good-quality habitats. In most jurisdictions offsets are applied to residual impacts as part of the mitigation hierarchy, with priority given to avoidance and minimisation of impact, especially in the case of rare or high-quality habitats [44]. Despite this, situations do arise when high-quality habitat are impacted by human activities. If low to moderate condition habitats are selected as offsets because of their greater capacity for generating gains, this could lead to trading good-quality high-value habitat for larger areas of low-quality habitat, albeit habitat that has considerable potential to improve. However, this assumes that the loss of biodiversity in good-quality habitat is equivalent to a numerically similar improvement in low- or moderate-quality habitat, which need not be the case. While this can be partly addressed by nuances in the metrics (e. g. multipliers applied to rare habitats) or policy settings (e. g. regulations to prohibit clearing of rare or high-value habitats), ultimately, these decisions come down to human p10. 1371/journal. pone. 0216703. s001(PDF) S2 File. Management gains, do they vary according to vegetation type? . S3 File. Elicitation material and scenarios. S4 File. Fitted boosted regression tree functions for initial aggregate vegetation condition, management gain, averted loss and total benefit. S5 File. Boxplots of individual expert estimates of aggregate management gain. S6 File. Boxplots of individual expert probability distributions of MG, AL and TB. S7 File. Are individual experts consistently optimistic or pessimistic?

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: for the Bulletin of the of of Atmospheric and Journal of of of Journal of the and Forecasting Weather, Climate, and in Sign up Sign in Sign up for the Bulletin of the of of Atmospheric and Journal of of of Journal of the and Forecasting Weather, Climate, and 1. Introduction2. Methodology3. Results4. Concluding remarks 3555. Horel, J. D. , A. N. Hahmann, and J. E. Geisler, 1989: An investigation of the annual cycle of convective activity over the tropical Americas. J. Climate,2, 1388–1403. Jones, C. , 1990: An investigation of low-frequency variability of the large-scale circulation over South America. M. S. thesis, Department of Meteorology, University of Utah, 108 pp. ——, and J. D. Horel, 1991: High circulation and convective activity over (in Portuguese). Rev. Brasileira de Meteorologia,5-1, 379–387. ——, D. E. Waliser, J. E. Schemm, and W. K. Lau, 2000: Prediction skill of the Madden and in dynamical extended range weather forecasts. Climate Dyn. , in press. Kalnay, E. , and Coauthors, 1996: NCEP/NCAR 40-Project. Bull. Amer. Meteor. Soc. ,77, 437–471. Lenters, J. D. , and K. H. Cook, 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev. ,127, 409–431. Liebmann, B. , G. N. Kiladis, J. A. Marengo, T. Ambrizzi, J. D. Glick, 1999: Submonthly convective variability over and the Convergence Zone. J. Climate,12, 1877–1891. Madden, R. A. , and P. R. Julian, 1994: Observations of the 40–50 day tropical oscillation: A review. Mon. Wea. Rev. ,122, 814–837. Nogues-Paegle J. , K. Mo, and J.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: We subsequently determined whether expression of the inactive cofilinS3D prevented the loss of dendritic spines in hippocampal area CA1 caused by sleep deprivation. Analyses of Golgi-impregnated hippocampal neurons in area CA1 indicated that in cofilinS3D expressing mice sleep deprivation no longer reduced the spine density of apical/basal dendrites (NSD: 1. 42 ± 0. 03; SD: 1. 34 ± 0. 03; Student’s t-test, p>0. 05 Figure 3H,J,K; for separate apical and basal spine analyses see Figure 3—figure supplement 2) with the exception of a small but statistically significant reduction in branched spines of apical and basal dendrites (Figure 3, C, D) and a decrease in number of spines on apical dendrites about 180 µm away from the soma (Figure 3—figure supplement 2E,F). Likewise, sleep deprivation no longer affected dendrite length (NSD: 1283. 0 ± 35. 95 µm, SD: 1250. 1 ± 41. 19 µm; Student’s t-test, p=0. 5612; Figure 3I, for separate apical and basal dendrite length analyses see Figure 3—figure supplement 2B). Together these data suggest that suppressing cofilin function in hippocampal neurons prevents the negative impact of sleep deprivation on spine loss and dendrite length of CA1 neurons. Suppressing cofilin function in hippocampal neurons prevents the impairments in memory and synaptic plasticity caused by brief periods of sleep deprivation As a next step, we sought to determine whether prevention of the increase in cofilin activity in sleep-deprived mice would not only protect against the reduction in spine numbers on CA1 dendrites but also the functional impairment at the behavioral level. The consolidation of object-place memory requires the hippocampus (Oliveira et al. , 2010; Florian et al. , 2011) and is sensitive to sleep deprivation (Havekes et al. , 2014; Florian et al. , 2011; Prince et al. , 2014). Therefore, we assessed whether cofilinS3D expression would prevent cognitive deficits caused by sleep deprivation in this task.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Individual expert mean estimates of averted loss and management gain. S8 File. Boosted regression tree for predicting vegetation condition to new sites. S9 File. Predicted change in individual attribute values with offset on original measurement scale. 1. Biodiversity offsets and the challenge of achieving no net loss. Conserv Biol. 2013; 27: 1254–64. pmid:24033441 View 2. Taming a wicked problem: Resolving controversies in biodiversity offsetting. Bioscience. 2016; 66: 489–98. View 3. Spash CL. Bulldozing biodiversity: The economics of offsets and trading-in Nature. Biol Conserv. 2015; 192: 541–51. View 4. . Biodiversity offsetting in dynamic landscapes: Influence of regulatory context and counterfactual assumptions on achievement of no net loss. Biol Conserv. 2017; 206: 314–9. View 5. The biodiversity bank cannot be a lending bank. Conserv Lett. 2010; 3: 151–8. View 6. von Hase A, et al. A loss-gain calculator for biodiversity offsets and the circumstances in which no net loss is feasible. Conserv Lett. 2016; 9: 252–9. View 7. . Calculating the benefit of conservation actions. Conserv Lett. 2013; 6: 359–67. View 8. . Locking in loss: Baselines of decline in Australian biodiversity offset policies. Biol Conserv. 2015; 192: 504–12. View 9. -Gulland EJ. Importance of baseline specification in evaluating conservation interventions and achieving no net loss of biodiversity. Conserv Biol. 2014; 28: 799–809. pmid:24945031 View 10. -Gulland EJ. Biodiversity offsets in theory and practice. Oryx. 2013; 47: 369–80. View 11. Faustian bargains? Restoration realities in the context of biodiversity offset policies. Biol Conserv. 2012; 155: 141–8. View 12. . Is there any empirical support for biodiversity offset policy? Ecol Appl. 2014; 24: 617–32. pmid:24988764 View 13. Commonwealth of Australia. Act 1999 Policy. Canberra: Department of Sustainability, Environment, Water, Population and Communities, 2012. 14. .

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Paegle, 1998: Predictability of the NCEP/NCAR reanalysis model during austral summer. Mon. Wea. Rev. ,126, 3135–3152. Schemm, J. E. , H. Van den Dool, and S. Saha, 1996: A Multi-at NCEP. Preprints 11th Conf. on. Meteor. Soc. , 47–49. in gallery Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View in gallery Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View in gallery Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View in gallery Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). View in gallery Fig. 5.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Mice virally expressing eGFP or cofilinS3D were trained in this task 3 weeks after viral infection and sleep deprived for 5 hr immediately after training or left undisturbed in the home cage. Upon testing for memory the next day, sleep-deprived mice expressing eGFP showed no preference for the relocated object indicating that brief sleep deprivation impaired the consolidation of object-place memory. In contrast, mice expressing cofilinS3Dshowed a strong preference for the displaced object despite sleep deprivation (e: 45. 2 ± 6. 4%, eGFP SD: 33. 4 ± 2. 0%, cofilinS3D NSD: 51. 9 ± 2. 9%, cofilinS3D SD: 53. 2 ± 4. 6%; Figure 4A). Figure 4 with 2 supplements see all Download asset Open asset Increased cofilin activity in the hippocampus mediates the memory and synaptic plasticity deficits associated with sleep deprivation. (A) Mice expressing eGFP or cofilinS3D were trained in the hippocampus-dependent object-place recognition task. Half of the groups were sleep deprived for 5 hr and all mice were tested 24 hr later. Hippocampal cofilinS3D expression prevents memory deficits caused by sleep deprivation (n = 9–10, two-way ANOVA, effect of virus F1,35 = 18. 567, p=0. 0001; effect of sleep deprivation F1,35 = 2. 975, p=0. 093; interaction effect F1,35 = 4. 567, p=0. 040; eGFP SD group versus other groups, p<0. 05). The dotted line indicates chance performance (33. 3%). (B, C) Following 5 hr of sleep deprivation, long-lasting LTP was induced in hippocampal slices by application of four 100 Hz trains, 1 s each, spaced 5 min apart to the Schaffer collateral pathway. Five hours of sleep deprivation impairs long-lasting LTP in slices from mice expressing eGFP (n = 6–7, two-way ANOVA, effect of virus F1,10 = 21. 685, p<0. 001). In contrast, virally delivered cofilinS3D prevents sleep deprivation-induced deficits (n = 5, two-way ANOVA, effect of virus F1,8 = 0.

Output: corroborate


Input: Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: A disaggregated biodiversity offset accounting model to improve estimation of ecological equivalency and no net loss. Biol Conserv. 2016; 204: 322–32. View 15. The development of the Australian environmental offsets policy: from theory to practice. Environ Conserv. 2015; 42: 306–14. View 16. Government Queensland. offsets Policy (Version 1. 6). Conservation and Biodiversity Programs, Department of Environment and Science, 2018. View 17. . Perverse incentives risk undermining biodiversity offset policies. J Appl Ecol. 2015; 52: 532–7. View 18. Burgman MA. Trusting judgements. How to get the best out of experts. Cambridge, United Kingdom: Press; 2016. 203 p. 19. Keith D. Ocean shores to desert dunes: the native vegetation of Wales and the ACT. Department of Environment and Conservation. 2004. View 20. Thackway R, Cresswell I. for Australia: a framework for establishing the national system of reserves, Version 4. 0. 1995; 88. View 21. Commonwealth of Australia. Protection and Act 1999, Act No. 91 of 1999. 1999. 22. . Coal and coal seam gas resource assessment for the Namoi subregion. Product 1. 2 for the Namoi subregion from the Assessment. Australia: Department of the Environment, Bureau of and Geoscience Australia, 2014. 23. Prober SM. Conservation of the grassy white box woodlands: Rangewide floristic variation and implications for reserve design. Bot. 1996; 44: 57–77. View 24. . Conservation of the grassy white box woodlands: relative contributions of size and disturbance to floristic composition and diversity of remnants. Bot. 1995; 43: 349–66. View 25. Moreno-. The true loss caused by biodiversity offsets. Biol Conserv. 2015; 192: 552–9. View 26. . Ecological integrity assessment as a metric of biodiversity: are we measuring what we say we are? Biodivers Conserv. 2016; 25: 1011–35. View 27. Quetier F, Lavorel S. Assessing ecological equivalence in biodiversity offset schemes: Key issues and solutions. Biol Conserv. 2011; 144: 2991–9. View 28. . Assessing the quality of native vegetation: the ‘habitat hectares’ approach.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View in gallery Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View in gallery Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. raw image Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View raw image Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View raw image Fig. 3. Anomaly correlation between Z500 forecasts and verification fields.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: 016, p>0. 902). NSD: non-sleep deprived, SD: sleep deprived. Values represent the mean ± SEM. *p<0. 05 by posthoc Dunnet’s test, **p<0. 01 by Student’s t test. See also Figure 4—figure supplement 1. Figure 4—source data 1 CofilinS3D expression prevents memory deficits in the object-location memory task caused by sleep deprivation. The data source file contains the object exploration times for the displaced (DO) and non-displaced objects (NDO1, NDO2) for all individual animals of each group. Download elife-13424-fig4-data1-v3. xlsx Expression of the mutant form of cofilin did not affect object exploration during training, exploration of an open field or zero maze indicating that anxiety levels were unaffected by expression of cofilinS3D in the hippocampus (Figure 4—figure supplement 1A–C). Moreover, using a behaviorally naïve cohort of mice, we found that cofilinS3D expression did not alter short-term object-place memory in the same task (Figure 4—figure supplement 1D). Together, these findings demonstrate that cofilinS3D expression specifically prevents the cognitive deficits caused by sleep deprivation. Although we can not rule out the possibility of off-target effects of the cofilinS3D mutant, we think that these are unlikely as expression of this mutant form of cofilin reversed the effects of sleep deprivation, restoring spine loss and memory to non-sleep deprived levels while not having an effect in non-sleep deprived mice. To further define the role of cofilin in impairments in hippocampal function caused by sleep deprivation, we next determined if suppression of cofilin activity would prevent the deficits in hippocampal LTP caused by brief periods of sleep deprivation (Havekes et al. , 2012a; Abel et al. , 2013; Vecsey et al. , 2009; Prince et al. , 2014). Five hours of sleep deprivation significantly impaired long-lasting LTP induced by 4 high-frequency trains of electrical stimuli applied at 5-minute intervals (spaced 4-train stimulation) in hippocampal slices from mice expressing eGFP (Figure 4B), confirming our previously published findings with non-injected wild-type mice (Vecsey et al. , 2009).

Output: corroborate


Input: Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: Biodiversity offsetting and conservation: reframing nature to save it Published online by Press: 06 October 2015 andWilliam M. author details Evangelia Apostolopoulou* Affiliation: Department of Geography, University of Cambridge, CB2 3. Adams Affiliation: Department of Geography, University of Cambridge, CB2 3EN, Cambridge, UK * (Corresponding author) E-mail ea367@cam. ac. uk 0. 19 mb) View PDF [Opens in a new window] Save to to Save to & Permissions [Opens in a new window] offsetting involves the balancing of biodiversity loss in one place (and at one time) by an equivalent biodiversity gain elsewhere (an outcome referred to as Loss). The conservation science literature has chiefly addressed the extent to which biodiversity offsets can serve as a conservation tool, focusing on the technical challenges of its implementation. However, offsetting has more profound implications than this technical approach suggests. In this paper we introduce the concept of policy frames, and use it to identify four ways in which non-human nature and its conservation are reframed by offsetting. Firstly, offsetting reframes nature in terms of isolated biodiversity units that can be simply defined, measured and exchanged across time and space to achieve equivalence between ecological losses and gains. Secondly, it reframes biodiversity as lacking locational specificity, ignoring broader dimensions of place and deepening a nature–culture and nature–society divide. Thirdly, it reframes conservation as an exchange of credits implying that the value of non-human nature can be set by price. Fourthly, it ties conservation to land development and economic growth, foreshadowing and bypassing an oppositional position. We conclude that by presenting offsetting as a technical issue, the problem of biodiversity loss due to development is depoliticized. As a result the possibility of opposing and challenging environmental destruction is foreclosed, and a dystopian future of continued biodiversity loss is presented as the only alternative. unitsconservation bankingconservation creditsmetricsmitigation hierarchyoffsetsoffsetting Issue 1 January 2017 10. 1017/S0030605315000782 [Opens in a new window] This is an 4. 0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View raw image Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). View raw image Fig. 5. Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View raw image Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Expression of PDE4A5catnull did not alter PDE4 activity in the hippocampus, prefrontal cortex or cerebellum (Figure 5—figure supplement 1A–C). Next, we sleep deprived mice for 5 hr and assessed whether the phosphorylation of LIMK and cofilin was altered in the hippocampus. In agreement with our hypothesis, we observed that 5 hr of sleep deprivation reduced both LIMK and cofilin phosphorylation in hippocampal lysates from eGFP mice (Figure 5I,J). PDE4A5catnull expression prevented the sleep deprivation-induced decreases in LIMK and cofilin phosphorylation (Figure 5I,J). While expression of PDE4A5catnull fully restored the pcofilin/cofilin ratio in the hippocampus of sleep deprived mice to the levels observed under non-sleep deprivation conditions, it should be noted that phosphatases such as slingshot (Sarmiere and Bamburg, 2004) may also contribute to the reduction in cofilin phosphorylation levels under conditions of sleep deprivation. Three hours of recovery sleep was sufficient to restore both LIMK and cofilin phosphorylation levels in the hippocampus (Figure 5K,L). The latter observation is in line with our previous observations that a few hours of recovery sleep is sufficient to restore hippocampal synaptic plasticity (Vecsey et al. , 2009). Figure 5 with 1 supplement see all Download asset Open asset Expression of catalytically inactive PDE4A5 in hippocampal neurons prevents memory deficits and alterations in the cAMP-PKA-LIMK-cofilin signaling pathway associated with sleep deprivation. (A) Mice were injected with pAAV9-CaMKIIα0. 4-eGFP or pAAV9-CaMKIIα0. 4-PDE4A5catnull-VSV into the hippocampus to drive neuronal expression of eGFP or catalytically inactive full-length PDE4A5 (PDE4A5catnull). (B) Robust PDE4A5catnull expression was observed at the expected molecular weight, 108 kDa, in hippocampal lysates. (C–E) PDE4A5catnullexpression was observed in all 3 subregions of the hippocampus.

Output: corroborate


Input: Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: © Fauna & 2015 is rapidly expanding as a promising policy for allowing development and economic growth while achieving a Loss of biodiversity. This expansion is international. One of its key moments was the establishment of the Business and Programme (BBOP) in 2004 by a partnership of companies, financial institutions, government agencies, business and non-governmental organizations (BBOP, 2015). By 2011, at least 72 countries had either passed or were developing laws or policies related to biodiversity offsets or Loss (Madsen et al. , Reference Madsen, Carroll, Kandy and Bennett2011), and the EU has held a public consultation on a Loss policy (European Commission, 2015). In 2014 BBOP co-organized a conference in London with the inspired title of Biodiversity and Beyond. This was pitched as ‘the first global conference on approaches to avoid, minimize, restore, and offset biodiversity loss’, and brought together various corporations, governments and non-governmental organizations (BBOP, 2014). Biodiversity offsets are defined as ‘measurable conservation outcomes designed to compensate for significant residual adverse biodiversity impacts arising from project development after appropriate prevention and mitigation measures have been taken’ (BBOP, 2012a, p. 13). In effect, offsetting seeks to compensate for losses to biodiversity in one place (and at one time) by creating equivalent gains elsewhere. Thus forest cleared to make way for a development project might be compensated through the restoration of forest (or prevention of forest loss) somewhere else and in some cases at a later time. The appeal of offsetting to conservationists is its potential, when taken as part of the so-called mitigation hierarchy (BBOP, 2009), to deliver a Loss (or net gain) conservation outcome (BBOP, 2012a) by keeping a balance between nature destroyed by development and nature for conservation. The attraction of offsetting to developers is that it provides a practical, cost-effective and predictable process to address the environmental impacts of development while enabling the relocation of environmental compensation across space and time. The conservation science literature has so far focused on the technical challenges of implementing offsetting. There has been discussion of ways to equate ecological losses and gains in development and offset sites, to select appropriate biodiversity currencies, accounting systems and exchange rules, to address practical challenges for adequate monitoring, compliance and post-implementation evaluation, and to deal with perverse incentives (e. g. Bull et al.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View raw image Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. 30 Views 0 0 0 Views 235 105 4 PDF Downloads 104 28 4 of Variability on the in Storage over the Tigris–Basin Authors: Li-and Guo-Evaluation of Performance of to Authors:. Yang, Ardeshir Ebtehaj, Sajad Vahedizade, and A 440-of in California from Authors: Ian M. Howard, David W. Stahle, Michael D. Dettinger, Cody Poulsen, F. Martin Ralph, Max C. A. Torbenson, and Quantifying the Role of Variability and from to at in India Authors: Divya Upadhyay, Sudhanshu Dixit, and Rainfall and Flooding from in the Mid-of the Authors: Yibing Su, James A. Smith, and Previous 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2 Page(s): 486–494 3060. Email: cjones@icess. ucsb. edu author address: Dr. Charles Jones, Institute for (ICESS), University of 93106-3060. Email: cjones@icess. ucsb. edu 1. IntroductionThe climatology of the large-scale circulation over shows intense convective activity during the austral summer, especially over the Amazon basin. Many previous studies have demonstrated that associated with the region of deep cumulus convection and intense precipitation, there is an upper-level anticyclone, the Bolivian high, and a downstream trough over northeast Brazil (Horel et al. 1989; Jones 1990; Lenters and Cook 1999).

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: (F–H) PDE4A5catnullwas not expressed in astrocytes reflected by a lack of co-labeling between PDE4A5catnull and GFAP expression. (I) Sleep deprivation causes a reduction in LIMK serine 596 phosphorylation in the hippocampus that is prevented by PDE4A5catnull expression (n = 7–8; two-way ANOVA, effect of virus F1,27 = 3. 299, p=0. 08; effect of sleep deprivation F1,27 = 6. 124, p=0. 02; interaction effect F1,27 = 11. 336, p=0. 002; eGFP SD group versus other groups p<0. 05). (J) Sleep deprivation causes a reduction in cofilin phosphorylation in the hippocampus that is prevented by PDE4A5catnull expression (n = 9–10; two-way ANOVA, effect of virus F1,35 = 4. 122, p=0. 05; effect of sleep deprivation F1,35 = 2. 885, p=0. 1; interaction effect F1,35 = 9. 416, p=0. 004; eGFP SD group versus other groups p<0. 05). (K, L) Three hours of recovery sleep after five hours of sleep deprivation restores hippocampal LIMK phosphorylation at serine 596 and cofilin phosphorylation at serine 3 to those observed in non-sleep deprived controls (p>0. 45 for both comparisons). (M) Mice expressing eGFP or PDE4A5catnull were trained in the hippocampus-dependent object-place recognition task and immediately sleep deprived for 5 hr after training (SD) or left undisturbed (NSD). Hippocampal PDE4A5catnull expression prevents memory deficits caused by sleep deprivation (n = 8–10; two-way ANOVA, effect of virus F1,33 = 2. 626, p=0. 115; effect of sleep deprivation F1,33 = 2. 311, p=0. 138; interaction effect F1,33 = 7. 485, p=0. 01; posthoc Dunnet test eGFP SD group versus other groups p<0. 05). In all blots, each lane represents one individual animal.

Output: corroborate


Input: Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: , Reference Bull, Suttle, Gordon, Singh and Milner-Gulland2013; Gardner et al. , Reference Gardner, von Hase, Brownlie, Ekstrom, Pilgrim and Savy2013; Pilgrim et al. , Reference Pilgrim, Brownlie, Ekstrom, Gardner, von Hase and ten Kate2013; Gordon et al. , Reference Gordon, Bull, Wilcox and Maron2015). Despite criticisms (e. g. Quétier & Lavorel, Reference Quétier and Lavorel2011; Maron et al. , and Gardner2012) this literature tends to approach offsetting as a neutral conservation tool and take for granted the role of offsetting as a solution to the problem of the environmental impacts of development or even as a means to achieve sustainable development (e. g. Bayon et al. , Reference Bayon, Fox and Carroll2008; Gordon et al. , Reference Gordon, Bull, Wilcox and Maron2015). The implications of offsetting are more substantial than these essentially technical discussions suggest. The underlying promise of offsetting, namely the production of equivalent natures (E. Apostolopoulou & W. M. Adams, unpubl. data), has the potential to bring about a profound change both to the conception of nature and the practice of conservation. Here, we consider these changes and their implications, using the concept of policy frames (Entman, Reference Entman1993). We define framing as the act of defining problems, diagnosing their causes, making moral judgements and suggesting remedies (Tuchman, Reference Tuchman1978; Apostolopoulou & Paloniemi, and Paloniemi2012). Focusing on policy frames can shed light on the implicit politics of presenting (and chiefly analysing) offsetting as neutral and free of ideology. The latter is important since framing is a critical and unavoidable element of policy-making and thus an understanding of the way a policy issue is framed is essential (Lakoff, Reference Lakoff2010) if the consequent policy options and underlying value struggles (Sullivan & Hannis, and Hannis2015) are to be unravelled and properly assessed. The way nature is discussed in debates about development (as when it is set within a frame of thinking about offsetting) affects the chances that it will survive or be destroyed.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: In contrast, the weakening of the SACZ is related to suppressed convection in the central Pacific but enhanced convective activity in the western and eastern Pacific. The different behavior in the predictability during strong versus weak SACZ events deserves additional investigation. The purpose of this note is to further motivate research on the influence of intraseasonal variations in the predictive skill over the SACZ. As is pointed out above, the SACZ exhibits a wide range of intraseasonal variability. Therefore, this study considers the errors in numerical weather forecasts in for two types of variations: 30–70 days and 10–30 days. The former is directly related to the MJO, while the latter is still intraseasonal but not necessarily linked to the 30–60-day oscillation. 2. MethodologyIn this study we use results from the recent (DERF) experiment concluded with the reanalysis version of the Forecast (MRF) model (Schemm et al. 1996; see also Jones et al. 1998). The present DERF experiment includes 50-day forecasts, initialized once a day (0000 UTC) with reanalyses fields, and covering the 5-yr period from 1 January 1985 to 31 December 1989. In the present version of the DERF forecast runs, the sea surface temperature (SST) is damped from its initial observed values to climatology using a 90-day e-folding time. The reanalysis version of the MRF model is identical to the NCEP global operational model of 1996, except the horizontal spectral resolution is reduced to T62 (equivalent to 210 km) (Kalnay et al. 1996). The NCEP–NCAR reanalysis fields at 0000 UTC are used for forecast verification and we limit our discussion to errors in 500-hPa geopotential height (Z500) in the austral summer (1 November–31 March; 1985–86, 1986–87, 1987–88, and 1988–89). Time filtering was applied to verification and forecast fields in order to compute anomalies from the annual cycle, which was determined by averaging the 5 yr of reanalysis and computing the first two harmonics. The annual cycle constructed with the mean and first two harmonics was then subtracted from the time series of Z500. No other filtering is applied to the forecasts and reanalysis.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: NSD: non-sleep deprived, SD: sleep deprived, SD+RS: sleep deprived plus recovery sleep. Scale bar, 100 µm. Values represent the mean ± SEM. *p<0. 05 by posthoc Dunnet’s posthoc test. See also Figure 5—figure supplement 1. Figure 5—source data 1 Recovery sleep following sleep deprivation restores LIMK and cofilin phosphorylation levels in the hippocampus, and expression of an inactive version of PDE4A5 in hippocampal neurons prevents memory deficits associated with sleep deprivation. (K) The data source file contains the relative optical density values (in arbitrary units) of the pLIMK and LIMK western blots for each individual animal of both the non-sleep deprived control group (NSD) and the group that underwent 5 hr of sleep deprivation followed by 3 hr of recovery sleep (SD + RS). (L) The data source file contains the relative optical density values (in arbitrary units) of the pcofilin and cofilin western blots for each individual animal of both the non-sleep deprived control group (NSD) and the group that underwent 5 hr of sleep deprivation followed by 3 hr of recovery sleep (SD + RS). (M) The data source file contains the object exploration times for the displaced (DO) and non-displaced objects (NDO1, NDO2) for each individual animal of each group. Download elife-13424-fig5-data1-v3. xlsx Blocking PDE4A5 function in hippocampal neurons prevents memory deficits caused by sleep deprivation Because PDE4A5catnull expression in hippocampal neurons prevents changes in the cAMP-PKA-LIMK-cofilin pathway caused by sleep deprivation, we hypothesized that expression of PDE4A5catnull in hippocampal excitatory neurons would also prevent the memory deficits induced by 5 hr of sleep deprivation. Mice expressing eGFP showed a clear preference for the displaced object 24 hr after training, which was lost in animals that were deprived of sleep for 5 hr immediately after training (Figure 5M). In contrast, mice expressing PDE4A5catnull showed a strong preference for the displaced object despite sleep deprivation (Figure 5M).

Output: corroborate


Input: Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: (Reference Monbiot2014) observes, offsetting reframes the issue of conservation: ‘those who believe they can protect nature by adopting this frame are stepping into a trap their opponents have set’. Here, we identify four ways in which offsetting achieves such a reframing of biodiversity and its conservation and we explore the ways it changes the options for conservation by bringing in and disallowing certain kinds of arguments. Reframing biodiversity conservation Offsetting reframes non-human nature as a score of isolated biodiversity units In offsetting, biodiversity and ecosystems are defined, valued and characterized in terms of quantitative units or credits that are used to represent selected ecosystem attributes and are considered exchangeable across space and time (e. g. DEC, 2006; Defra, 2013). The selection of units of measurement, currencies and rules for exchanges between different sites has proven inherently challenging (Tucker et al. , and Rayment2013). The attempt to design simple and practical offset metrics has often made them highly reductionist (e. g. the simple habitat area ratio based metrics widely used in Germany and the USA, and more sophisticated metrics such as the Hectares offsetting scheme initially developed in Victoria; Tucker et al. , and Rayment2013; IEEP, 2014). In the UK concerns have been raised that the offsetting process will resemble a fast ‘box-ticking exercise’ that is inadequate to assess a site's year-round biodiversity (Kinver, Reference Kinver2013). The creation of offset metrics to represent ecological losses and gains through numerical scores (Environment Bank, 2013a) involves a narrowing of focus to isolated parts of an ecosystem. This narrowing is fundamental to offsetting calculations and reproduces the reductionist myth of simplicity (Levins & Lewontin, and Lewontin1980). This is inherently unsatisfactory and impoverishes both the advance of theory and conservation practice because it traps ecology in reductionist strategies involving a continuous retreat from the study of intrinsically complex systems (Levins & Lewontin, and Lewontin1980). Ecosystems are dialectically composed, dynamic, multi-layered systems that do not form simple mappable units (Boitani et al. , Reference Boitani, Mace and Rondinini2015), and biodiversity is non-interchangeable in terms of type, space and time (Walker et al. , Reference Walker, Brower, Stephens and Lee2009).

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Intraseasonal convective activity over the SACZ is selected according to the magnitude of OLR anomalies. Pentads of OLR were obtained from NCEP from 1–5 January 1979 through 27–31 December 1996. To obtain intraseasonal anomalies, two Lanczos band-pass filters with 49 weights were applied to the OLR pentads [see Jones et al. (2000) for details on time filtering]. Figure 1 shows the frequency responses of the two band-pass filters: 30–70 days and 10–30 days. The first band is denoted here as intraseasonal type 1 (IS1), while the second is intraseasonal type 2 (IS2). 3. ResultsBefore examining the skill of the MRF model over the SACZ region for each band of intraseasonal variations, it is instructive to discuss the mean summertime Z500 and the standard deviation fields (Fig. 2). The mean Z500 (top) indicates a midtroposphere maximum associated with the Bolivian high and strong horizontal gradients in subtropical and middle latitudes of South America. The total standard deviation in Z500 (bottom) shows, as expected, small magnitudes near the equatorial region and increasing variability toward the midlatitudes of the Southern Hemisphere. Anomaly correlations between Z500 forecasts and verification fields for all days during the four summer seasons were computed for 1–20 days lead time (Fig. 3). The MRF model shows higher skill (correlations greater than 0. 7 for 1 day lead time) in a narrow band over subtropical South America. Note that near the equatorial region the skill is less and progressively breaks down toward higher latitudes as the lead time increases. These results can be compared to the errors in the forecasts for each IS1 and IS2 band discussed next. Figure 4a displays the mean OLR during the austral summer and Fig. 4b shows the total standard deviation (i. e. , variations greater than two pentads and shorter than the seasonal average). Although the lowest values of OLR, indicative of strong tropical convection, are observed over the Amazon basin, the largest variability is found over eastern South America. The standard deviations of OLR anomalies (OLRA) in the 30–70 days and 10–30 days are shown in Figs.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Brief sleep deprivation after training in the object-place recognition task resulted in a loss of preference for the displaced object in mice expressing PDE4A5catnullΔ4 (Figure 5—figure supplement 1I). The inability of PDE4A5catnullΔ4 to prevent the memory deficit caused by sleep deprivation was not a consequence of altered exploration levels during training (Figure 5—figure supplement 1H). This finding indicates that the memory rescue in the previous experiment was a result of the full length PDE4A5catnull being sequestered to specific signalosomes through the isoform-unique N-terminal region rather than a consequence of PDE4A5catnull being unable totarget the functionally relevant complexes sequestering full length PDE4A5. It also indicates that displacing sequestered, active endogenous PDE4A5 in hippocampal excitatory neurons is sufficient to prevent memory deficits induced by 5 hr of sleep deprivation. Overall, these data suggest that sleep deprivation negatively impacts spine numbers by targeting the PKA-LIMK-cofilin pathway through the alterations in activity of PDE4A5 (Figure 6). Figure 6 Download asset Open asset The impact of sleep deprivation on hippocampal spine dynamics. Sleep deprivation increases PDE4A5 protein levels that cause a reduction in cAMP levels and attenuation of the PKA-LIMK signaling pathway, which results in a reduction in the phosphorylation of cofilin. Dephosphorylated cofilin can lead to spine loss. Suppressing PDE4A5 function through viral expression of a catalytically inactive PDE4A5 prevents alterations in LIMK and cofilin signaling as well as the cognitive impairments caused by sleep deprivation. Likewise, attenuating cofilin activity through viral expression of a catalytically inactive form of cofilin prevents the loss of dendritic spines, impairments in synaptic plasticity, and memory deficits associated with sleep loss. Proteins whose function is reduced after sleep deprivation are shown in blue. Proteins whose function is promoted by sleep deprivation are shown in red. of the major challenges in sleep research is the elucidation of molecular mechanisms and cellular circuits underlying the adverse consequences of sleep loss. Here, we use in vivo rescue experiments to define a critical molecular mechanism by which brief sleep deprivation leads to cognitive impairments.

Output: corroborate


Input: Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: No single surrogate (or even a series of them) can entirely capture biodiversity, since not all biodiversity attributes are measurable, and therefore it is impossible to guarantee that no biodiversity is lost (and thus that Loss is actually achieved; BBOP, 2012b; Bull et al. , Reference Bull, Suttle, Gordon, Singh and Milner-Gulland2013; Gardner et al. , Reference Gardner, von Hase, Brownlie, Ekstrom, Pilgrim and Savy2013). The use of reductionist metrics is common in conservation but the way they are used in offsetting is distinctive in several critical ways. Firstly, offsetting metrics incorporate assumptions about future states of nature, both in terms of future rates of loss (which conservation action can be predicted to slow; Seagle, Reference Seagle2012) and in terms of the potential of ecological restoration. In reframing biodiversity as fully replaceable and re-creatable by human action, offsetting deliberately confuses the state of ecological restoration science and practice with its aspiration. Restoration ecology has advanced greatly in sophistication but it is ‘not a magic bullet that provides instant ecosystems of the desired type’ (Menz et al. , Reference Menz, Dixon and Hobbs2013). Techniques such as the relocation of soils from on the route of the HS2 high-speed train in the UK are at best experimental (HS2, 2013) and many studies have proven that the majority of offsets do not deliver what they promise (e. g. Kettlewell et al. , and Fay2008). Secondly, as a standard procedure, offsetting conflates the state of nature with other factors, for example applying crude multipliers to address issues such as time lags between biodiversity loss and future gain in an offset site (Eftec, IEEP et al. , 2010; HS2, 2013), or the distance between the development and offset sites (IEEP, 2014). Such calculations raise fundamental problems of incommensurability because such issues cannot possibly be adequately addressed by simply increasing the number of credits required to offset the damage caused in a development site. Thirdly, the crude reductionism of offsetting metrics is not a technical issue that more scientific data can resolve. On the contrary, it is a defining characteristic of the offsetting logic itself.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: 4c,d respectively, and indicate that intraseasonal variations account for a large part of the total variance (Liebmann et al. 1999). Based on the spatial pattern of the OLRA variability, we defined a base region (solid lines) in which the magnitudes of the OLRA were used to characterize periods of weak (strong) SACZ. The extent of the base region is chosen to represent the mean position of the SACZ. A time series of 30–70-day OLRA spatially averaged in the base region was constructed and periods above (below) 0. 8 (−0. 8) standard deviation during the austral summer were selected. The weak events are denoted IS1 positive (IS1 pos), while the strong are IS1 negative (IS1 neg). In addition, periods of very inactive SACZ were selected when the magnitudes of the 30–70-day OLRA values in the base region were between ±0. 3 standard deviation (IS1 null). Likewise, a time series of 10–30-day OLRA was constructed over the base region. Periods of weak IS2 variability were selected when OLRA are above 0. 8 standard deviation (IS2 pos); strong when OLRA are below −0. 8 standard deviation (IS2 neg); and null events when OLRA are between ±0. 3 standard deviation (IS2 null). The frequency distributions of the selected IS1 and IS2 events per season are plotted in Fig. 5. For the IS1 type (top) there are a total of 27 (IS1 pos), 29 (IS1 neg), and 32 (IS1 null) pentads, which represent 11, 11, and 20 independent events, respectively. Similarly, for IS2 type there are 23 (IS2 pos), 23 (IS2 neg), and 34 (IS2 null) pentads, so that 20, 19, and 22 are independent events. We consider independent events nonconsecutive pentads. The skill of the MRF model in forecasting Z500 was examined for each sample of intraseasonal variability defined above (IS1 and IS2). Two skill measures, anomaly correlation and root-mean-square (rms) error, were computed for samples IS1 pos, IS1 neg, IS1 null, IS2 pos, IS2 neg, and IS2 null.

Output: pass


Input: Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: First, we show that sleep deprivation dramatically reduces spine number and dendrite length of hippocampal CA1 neurons without affecting dendritic structure of CA3 neurons. Second, we demonstrate that sleep deprivation increases cofilin activity in the hippocampus, but not the prefrontal cortex, which is a likely explanation for the reductions in CA1 spine numbers and dendrite length. Third, we find that three hours of recovery sleep restores spine number, dendrite length and cofilin phosphorylation levels to those observed in non-sleep deprived mice. Fourth, we show that suppression of cofilin activity in hippocampal excitatory neurons is sufficient to prevent sleep-deprivation-induced decreases in dendritic spines number, LTP impairments, and memory. Fifth, we demonstrate that hippocampal expression of constitutively active cofilin is sufficient to cause long-term memory deficits but not LTP impairments. Sixth, we find that suppression of PDE4A5 function through overexpression of a catalytically inactive mutant version of PDE4A5 changes LIMK and cofilin phosphorylation levels caused by sleep deprivation. Finally, we show that hippocampal suppression of PDE4A5 function prevents the negative impact of sleep deprivation on memory consolidation. Thus, our studies demonstrate that changes in the cAMP/PKA/LIMK/cofilin pathway are necessary to cause memory deficits under conditions of sleep deprivation. In light of the fact that elevated cofilin activity can lead to spine shrinkage and spine loss (Zhou et al. , 2004; Pontrello et al. , 2012), our genetic manipulations of cofilin and PDE4A5 signaling independently link impairments in synaptic plasticity and memory caused by brief sleep deprivation with the loss of dendritic spines in the hippocampus. Deficits in synaptic plasticity and memory both represent read outs of the impact of sleep deprivation on hippocampal function, but our work does not directly examine the direct relationship between synaptic plasticity and memory, a topic that has been the subject of extensive study and discussion in the literature (Lynch, 2004; Sah et al. , 2008).

Output: corroborate


Input: Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: The promise of offsetting to resolve the longstanding conflicts between economic development and conservation (Bull & Brownlie, and Brownlie2016) reproduces the classic ‘win–win’ scenario of sustainable development and depends significantly on the reductionism of its metrics. Offsetting is useful precisely because the efficiency and cost-effectiveness of its methodologies make the quantification of losses and gains in planning more straightforward, less costly and less time consuming than alternative approaches (e. g. Defra, 2013). The purpose of offsetting is not conservation as such, but to ‘give greater certainty for businesses’ in their development planning (Commonwealth of Australia, 2012, p. 4). Current international best practice in offsetting (Gordon et al. , Reference Gordon, Bull, Wilcox and Maron2015) is represented by the Business and Programme launched by Forest Trends, an organization created to promote market-based approaches to conservation and bridge ‘traditional divides’ between industry, donors and environmental groups (Forest Trends, 2015). Ecosystems are sliced into biodiversity units precisely to simplify the measurement of development impacts, and these units are purposefully separated from their ecological context through functional and spatial abstraction (Robertson, Reference Robertson2004; Sullivan, Reference Sullivan2013a; see also Bumpus & Liverman, and Liverman2008, about carbon offsets) to become equivalent and thus to allow their exchange across space and time. Interestingly, Robertson (Reference Robertson2006, p. 384) notes that the ‘rapid assessment methods’ that are currently used in wetland mitigation banking in the USA are the descendants of much more complex rapid assessment methods developed in the early 1980s, when wetland banking was performed only non-commercially. Offsetting therefore champions mechanistic reductionism to quantify biodiversity in a way that makes it possible to speak about it ‘in business terms’ (Baker, Reference Baker2014) and make non-human nature something ‘that capital can see’ (Robertson, Reference Robertson2006). Offsetting removes the place specificity of nature In its representation of non-human nature as biodiversity units, offsetting explicitly reframes the links between biodiversity, conservation and place at the most fundamental level by reworking nature's specific relation to place. Location is critical to biodiversity both in biogeographical terms (biodiversity reflects the geophysical context; Comer et al.

Output: contrast