Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs. Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R2 and RMSE values for most VIs do not vary very much. For percent VC, R2 values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R2 values for low-biomass areas (AGB < 800 g/m2) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m2 and 136.38 g/m2. The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m2), achieved R2 values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m2 to 259.20 g/m2. The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced. These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m2 can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.

Long Versus Short Feeding Period for Steer Calves of Three Frame Sizes Fed High Energy Finishing Diets

An experiment was conducted using Angus cross steer calves of three frame sizes (small, medium, and large) to compare performance of two different grow/finish feeding programs. Half of the cattle in each frame size group were fed a high energy ration through the growing period, similar to calves going directly into the feedlot. The other half was fed a low energy ration, similar to a backgrounding diet, for a period prior to the finishing phase. All cattle were fed a high energy ration through the finishing period. The data showed the cattle fed the low energy growing diet experienced some compensatory gains as shown by ultrasound backfat and average daily gains coupled with intakes greater than the increases seen in the high energy treatment. Carcass data and overall performance data showed no ill effects due to the low energy growing ration.

"Hot spot" in the PROP1 gene responsible for combined pituitary hormone deficiency.

As pituitary function depends on the integrity of the hypothalamic-pituitary axis, any defect in the development and organogenesis of this gland may account for a form of combined pituitary hormone deficiency (CPHD). Although pit-1 was 1 of the first factors identified as a cause of CPHD in mice, many other homeodomain and transcription factors have been characterized as being involved in different developmental stages of pituitary gland development, such as prophet of pit-1 (prop-1), P-Lim, ETS-1, and Brn 4. The aims of the present study were first to screen families and patients suffering from different forms of CPHD for PROP1 gene alterations, and second to define possible hot spots and the frequency of the different gene alterations found. Of 73 subjects (36 families) analyzed, we found 35 patients, belonging to 18 unrelated families, with CPHD caused by a PROP1 gene defect. The PROP1 gene alterations included 3 missense mutations, 2 frameshift mutations, and 1 splice site mutation. The 2 reported frameshift mutations could be caused by any 2-bp GA or AG deletion at either the 148-GGA-GGG-153 or 295-CGA-GAG-AGT-303 position. As any combination of a GA or AG deletion yields the same sequencing data, the frameshift mutations were called 149delGA and 296delGA, respectively. All but 1 mutation were located in the PROP1 gene encoding the homeodomain. Importantly, 3 tandem repeats of the dinucleotides GA at location 296-302 in the PROP1 gene represent a hot spot for CPHD. In conclusion, the PROP1 gene seems to be a major candidate gene for CPHD; however, further studies are needed to evaluate other genetic defects involved in pituitary development.

Om den af Keiser Julianus med forenede Jødernes og Hedningenes Kraefer til Spot for Christus og de Christne begyndte Tempelbygning i Jerusalem hvorledes samme ved Hvirvelvind, Lynild, Jordskielv og Ild-Udbrud af Jorden paa en mirakulos Maade blev standset og gandske forhindret : bedraget som et af de alleruimodsigeligste of kraftigste Beviser for den christelige Religions Sandhed...

af [Esaias] Fleischer

Are sample sizes clear and justified in RCTs published in dental journals?

Sample size calculations are advocated by the CONSORT group to justify sample sizes in randomized controlled trials (RCTs). The aim of this study was primarily to evaluate the reporting of sample size calculations, to establish the accuracy of these calculations in dental RCTs and to explore potential predictors associated with adequate reporting. Electronic searching was undertaken in eight leading specific and general dental journals. Replication of sample size calculations was undertaken where possible. Assumed variances or odds for control and intervention groups were also compared against those observed. The relationship between parameters including journal type, number of authors, trial design, involvement of methodologist, single-/multi-center study and region and year of publication, and the accuracy of sample size reporting was assessed using univariable and multivariable logistic regression. Of 413 RCTs identified, sufficient information to allow replication of sample size calculations was provided in only 121 studies (29.3%). Recalculations demonstrated an overall median overestimation of sample size of 15.2% after provisions for losses to follow-up. There was evidence that journal, methodologist involvement (OR = 1.97, CI: 1.10, 3.53), multi-center settings (OR = 1.86, CI: 1.01, 3.43) and time since publication (OR = 1.24, CI: 1.12, 1.38) were significant predictors of adequate description of sample size assumptions. Among journals JCP had the highest odds of adequately reporting sufficient data to permit sample size recalculation, followed by AJODO and JDR, with 61% (OR = 0.39, CI: 0.19, 0.80) and 66% (OR = 0.34, CI: 0.15, 0.75) lower odds, respectively. Both assumed variances and odds were found to underestimate the observed values. Presentation of sample size calculations in the dental literature is suboptimal; incorrect assumptions may have a bearing on the power of RCTs.

Persecution of the Jews at Damascus : the result of personal inquiry on the spot

statement of G. W. Pieritz

Identification of a new genomic hot spot of evolutionary diversification of protein function.

Establishment of phylogenetic relationships remains a challenging task because it is based on computational analysis of genomic hot spots that display species-specific sequence variations. Here, we identify a species-specific thymine-to-guanine sequence variation in the Glrb gene which gives rise to species-specific splice donor sites in the Glrb genes of mouse and bushbaby. The resulting splice insert in the receptor for the inhibitory neurotransmitter glycine (GlyR) conveys synaptic receptor clustering and specific association with a particular synaptic plasticity-related splice variant of the postsynaptic scaffold protein gephyrin. This study identifies a new genomic hot spot which contributes to phylogenetic diversification of protein function and advances our understanding of phylogenetic relationships.

Effect sizes and standardization in neighbourhood models of forest stands: potential biases and misinterpretations

Effects of conspecific neighbours on survival and growth of trees have been found to be related to species abundance. Both positive and negative relationships may explain observed abundance patterns. Surprisingly, it is rarely tested whether such relationships could be biased or even spurious due to transforming neighbourhood variables or influences of spatial aggregation, distance decay of neighbour effects and standardization of effect sizes. To investigate potential biases, communities of 20 identical species were simulated with log-series abundances but without species-specific interactions. No relationship of conspecific neighbour effects on survival or growth with species abundance was expected. Survival and growth of individuals was simulated in random and aggregated spatial patterns using no, linear, or squared distance decay of neighbour effects. Regression coefficients of statistical neighbourhood models were unbiased and unrelated to species abundance. However, variation in the number of conspecific neighbours was positively or negatively related to species abundance depending on transformations of neighbourhood variables, spatial pattern and distance decay. Consequently, effect sizes and standardized regression coefficients, often used in model fitting across large numbers of species, were also positively or negatively related to species abundance depending on transformation of neighbourhood variables, spatial pattern and distance decay. Tests using randomized tree positions and identities provide the best benchmarks by which to critically evaluate relationships of effect sizes or standardized regression coefficients with tree species abundance. This will better guard against potential misinterpretations.

Spinal Spot Sign.

INTRODUCTION A marker predictive of hematoma expansion in the central nervous system could aid the selection of patients for hemostatic or surgical treatment. CASE REPORT Here, we present a 83-year-old patient with acute spinal subdural hematoma with paraparesis progressing to paraplegia. A contrast extravasation within the intraspinal hematoma was visualized on spinal MR indicating active bleeding (spinal spot sign). A second acquisition of contrast-enhanced MR images showed progression of contrast extravasation helping to different active bleeding from spinal arteriovenous malformations/fistula. CONCLUSIONS A "spinal spot sign" may be important for treatment decisions, notably in patients with incomplete neurological deficits at the time of imaging.

Gestation-specific reference intervals for comprehensive spot urinary steroid hormone metabolite analysis in normal singleton pregnancy and 6 weeks postpartum

BACKGROUND: Normal pregnancy depends on pronounced adaptations in steroid hormone concentrations. Although in recent years, the understanding of these hormones in pregnancy has improved, the interpretation is hampered by insufficient reference values. Our aim was to establish gestation-specific reference intervals for spot urinary steroid hormone levels in normal singleton pregnancies and 6 weeks postpartum. METHODS: Cross-sectional multicentre observational study. Women recruited between 2008 and 2013 at 3 University Hospitals in Switzerland (Bern), Scotland (Glasgow) and Austria (Graz). Spot urine was collected from healthy women undergoing a normal pregnancy (age, 16-45 years; mean, 31 years) attending routine antenatal clinics at gestation weeks 11, 20, and 28 and approximately 6 weeks postpartum. Urine steroid hormone levels were analysed using gas-chromatography mass spectrometry. Creatinine was also measured by routine analysis and used for normalisation. RESULTS: From the results, a reference interval was calculated for each hormone metabolite at each trimester and 6 weeks postpartum. Changes in these concentrations between trimesters and postpartum were also observed for several steroid hormones and followed changes proposed for index steroid hormones. CONCLUSIONS: Normal gestation-specific reference values for spot urinary steroid hormones throughout pregnancy and early postpartum are now available to facilitate clinical management and research approaches to steroid hormone metabolism in pregnancy and the early postpartum period.

Evaluation of the T-SPOT(RTM).TB test in the diagnosis of latent tuberculosis infection in HIV-infected children in Uganda

Background. About a third of the world’s population is infected with tuberculosis (TB) with sub-Saharan Africa being the worst hit. Uganda is ranked 16th among the countries with the biggest TB burden. The burden in children however has not been determined. The burden of TB has been worsened by the advent of HIV and TB is the leading cause of mortality in HIV infected individuals. Development of TB disease can be prevented if TB is diagnosed during its latent stage and treated with isoniazid. For over a century, latent TB infection (LTBI) was diagnosed using the Tuberculin Skin Test (TST). New interferon gamma release assays (IGRA) have been approved by FDA for the diagnosis of LTBI and adult studies have shown that IGRAs are superior to the TST but there have been few studies in children especially in areas of high TB and HIV endemicity. ^ Objective. The objective of this study was to examine whether the IGRAs had a role in LTBI diagnosis in HIV infected children in Uganda. ^ Methods. Three hundred and eighty one (381) children were recruited at the Baylor College of Medicine-Bristol Meyers Squibb Children’s Clinical Center of Excellence at Mulago Hospital, Kampala, Uganda between March and August 2010. All the children were subjected to a TST and T-SPOT ®.TB test which was the IGRA chosen for this study. Sputum examination and chest x-rays were also done to rule out active TB. ^ Results. There was no statistically significant difference between the tests. The agreement between the two assays was 95.9% and the kappa statistic was 0.7 (95% CI: 0.55–0.85, p-value<0.05) indicating a substantial or good agreement. The TST was associated with older age and higher weight for age z-scores but the T-SPOT®. TB was not. Both tests were associated with history of taking anti-retroviral therapy (ART). ^ Conclusion. Before promoting use of IGRAs in children living in HIV/TB endemic countries, more research needs to be done. ^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^