Test-retest reliability of the nociceptive withdrawal reflex and electrical pain thresholds after single and repeated stimulation in patients with chronic low back pain

Recent studies have shown that the nociceptive withdrawal reflex threshold (NWR-T) and the electrical pain threshold (EP-T) are reliable measures in pain-free populations. However, it is necessary to investigate the reliability of these measures in patients with chronic pain in order to translate these techniques from laboratory to clinic. The aims of this study were to determine the test-retest reliability of the NWR-T and EP-T after single and repeated (temporal summation) electrical stimulation in a group of patients with chronic low back pain, and to investigate the association between the NWR-T and the EP-T. To this end, 25 patients with chronic pain participated in three identical sessions, separated by 1 week in average, in which the NWR-T and the EP-T to single and repeated stimulation were measured. Test-retest reliability was assessed using intra-class correlation coefficient (ICC), coefficient of variation (CV), and Bland-Altman analysis. The association between the thresholds was assessed using the coefficient of determination (r (2)). The results showed good-to-excellent reliability for both NWR-T and EP-T in all cases, with average ICC values ranging 0.76-0.90 and average CV values ranging 12.0-17.7%. The association between thresholds was better after repeated stimulation than after single stimulation, with average r (2) values of 0.83 and 0.56, respectively. In conclusion, the NWR-T and the EP-T are reliable assessment tools for assessing the sensitivity of spinal nociceptive pathways in patients with chronic pain.

Poor sleep is associated with higher plasma proinflammatory cytokine interleukin-6 and procoagulant marker fibrin D-dimer in older caregivers of people with Alzheimer's disease

OBJECTIVES: To determine whether objective measures of sleep correlate with plasma levels of the proinflammatory cytokine interleukin (IL)-6 and the procoagulant marker fibrin D-dimer in caregivers of patients with dementia. DESIGN: Cross-sectional study. SETTING: Subjects' homes. PARTICIPANTS: Sixty-four community-dwelling spousal caregivers (69% women, mean age+/-standard deviation 72+/-9) and 36 sex-matched noncaregiving controls. MEASUREMENTS: All participants underwent in-home full-night polysomnography. Demographic and lifestyle factors, depression, diseases, and medication that could affect inflammation, coagulation, and sleep were controlled for in analyses regressing sleep variables and caregiver status and their interaction on plasma levels of IL-6 and D-dimer. RESULTS: Caregivers had higher levels of D-dimer (781+/-591 vs 463+/-214 ng/mL, P=.001) and IL-6 (1.42+/-1.52 vs 0.99+/-0.86 pg/mL, P<.06) and lower levels of total sleep time (369+/-70 vs 393+/-51 minutes, P=.049) and sleep efficiency (77+/-11 vs 82+/-9%, P=.04) than controls. After controlling for age and body mass index, longer wake time after sleep onset (change in coefficient of determination (DeltaR2)=0.039, P=.04) and the interaction between caregiver status and higher apnea-hypopnea index (DeltaR2=0.054, P=.01) were predictors of IL-6. Controlling for age, caregiver status independently predicted D-dimer levels (DeltaR2=0.047, P=.01). Controlling for age and caregiver status, lower sleep efficiency (DeltaR2=0.032, P=.03) and the interaction between caregiver status and more Stage 2 sleep (DeltaR2=0.037, P=.02) independently predicted plasma D-dimer levels. CONCLUSION: Poor sleep was associated with higher plasma IL-6 and D-dimer levels. These effects were most pronounced in caregivers of subjects with Alzheimer's disease. The findings suggest a mechanism that may explain how disturbed sleep might be associated downstream with cardiovascular risk, particularly in older people under chronic stress.

Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used.The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9),Maybar (84. 0.57, 2.5),Megech (85, 0.15, 2.6), andWondoGenet (86, 0.52, 2.7) indicating that themodels were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

A chrysophyte-based quantitative reconstruction of winter severity from varved lake sediments in NE Poland during the past millennium and its relationship to natural climate variability

Chrysophyte cysts are recognized as powerful proxies of cold-season temperatures. In this paper we use the relationship between chrysophyte assemblages and the number of days below 4 °C (DB4 °C) in the epilimnion of a lake in northern Poland to develop a transfer function and to reconstruct winter severity in Poland for the last millennium. DB4 °C is a climate variable related to the length of the winter. Multivariate ordination techniques were used to study the distribution of chrysophytes from sediment traps of 37 low-land lakes distributed along a variety of environmental and climatic gradients in northern Poland. Of all the environmental variables measured, stepwise variable selection and individual Redundancy analyses (RDA) identified DB4 °C as the most important variable for chrysophytes, explaining a portion of variance independent of variables related to water chemistry (conductivity, chlorides, K, sulfates), which were also important. A quantitative transfer function was created to estimate DB4 °C from sedimentary assemblages using partial least square regression (PLS). The two-component model (PLS-2) had a coefficient of determination of View the MathML sourceRcross2 = 0.58, with root mean squared error of prediction (RMSEP, based on leave-one-out) of 3.41 days. The resulting transfer function was applied to an annually-varved sediment core from Lake Żabińskie, providing a new sub-decadal quantitative reconstruction of DB4 °C with high chronological accuracy for the period AD 1000–2010. During Medieval Times (AD 1180–1440) winters were generally shorter (warmer) except for a decade with very long and severe winters around AD 1260–1270 (following the AD 1258 volcanic eruption). The 16th and 17th centuries and the beginning of the 19th century experienced very long severe winters. Comparison with other European cold-season reconstructions and atmospheric indices for this region indicates that large parts of the winter variability (reconstructed DB4 °C) is due to the interplay between the oscillations of the zonal flow controlled by the North Atlantic Oscillation (NAO) and the influence of continental anticyclonic systems (Siberian High, East Atlantic/Western Russia pattern). Differences with other European records are attributed to geographic climatological differences between Poland and Western Europe (Low Countries, Alps). Striking correspondence between the combined volcanic and solar forcing and the DB4 °C reconstruction prior to the 20th century suggests that winter climate in Poland responds mostly to natural forced variability (volcanic and solar) and the influence of unforced variability is low.

Estimating Annual CO2 Flux for Lutjewad Station Using Three Different Gap-Filling Techniques

Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.

Biodiversity effects on nitrate concentrations in soil solution: a Bayesian model

Ecosystems are faced with high rates of species loss which has consequences for their functions and services. To assess the effects of plant species diversity on the nitrogen (N) cycle, we developed a model for monthly mean nitrate (NO3-N) concentrations in soil solution in 0-30 cm mineral soil depth using plant species and functional group richness and functional composition as drivers and assessing the effects of conversion of arable land to grassland, spatially heterogeneous soil properties, and climate. We used monthly mean NO3-N concentrations from 62 plots of a grassland plant diversity experiment from 2003 to 2006. Plant species richness (1-60) and functional group composition (1-4 functional groups: legumes, grasses, non-leguminous tall herbs, non-leguminous small herbs) were manipulated in a factorial design. Plant community composition, time since conversion from arable land to grassland, soil texture, and climate data (precipitation, soil moisture, air and soil temperature) were used to develop one general Bayesian multiple regression model for the 62 plots to allow an in-depth evaluation using the experimental design. The model simulated NO3-N concentrations with an overall Bayesian coefficient of determination of 0.48. The temporal course of NO3-N concentrations was simulated differently well for the individual plots with a maximum plot-specific Nash-Sutcliffe Efficiency of 0.57. The model shows that NO3-N concentrations decrease with species richness, but this relation reverses if more than approx. 25 % of legume species are included in the mixture. Presence of legumes increases and presence of grasses decreases NO3-N concentrations compared to mixtures containing only small and tall herbs. Altogether, our model shows that there is a strong influence of plant community composition on NO3-N concentrations.

Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia

Over the past few decades, the advantages of the visible-near infra-red (VisNIR) diffuse reflectance spectrometer (DRS) method have enabled prediction of soil organic carbon (SOC). In this study, SOC was predicted using regression models for samples taken from three sites (Gununo, Maybar and Anjeni) in Ethiopia. SOC was characterized in laboratory using conventional wet chemistry and VisNIR-DRS methods. Principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS) models were developed using Unscrambler X 10.2. PCA results show that the first two components accounted for a minimum of 96% variation which increased for individual sites and with data treatments. Correlation (r), coefficient of determination (R2) and residual prediction deviation (RPD) were used to rate four models built. PLS model (r, R2, RPD) values for Anjeni were 0.9, 0.9 and 3.6; for Gununo values 0.6, 0.3 and 1.2; for Maybar values 0.6, 0.3 and 0.9, and for the three sites values 0.7, 0.6 and 1.5, respectively. PCR model values (r, R2, RPD) for Anjeni were 0.9, 0.8 and 2.7; for Gununo values 0.5, 0.3 and 1; for Maybar values 0.5, 0.1 and 0.7, and for the three sites values 0.7, 0.5 and 1.2, respectively. Comparison and testing of models shows superior performance of PLS to PCR. Models were rated as very poor (Maybar), poor (Gununo and three sites) and excellent (Anjeni). A robust model, Anjeni, is recommended for prediction of SOC in Ethiopia.

Determination of free and total valproic acid in human plasma by capillary electrophoresis with contactless conductivity detection

A new approach for the determination of free and total valproic acid in small samples of 140 μL human plasma based on capillary electrophoresis with contactless conductivity detection is proposed. A dispersive liquid-liquid microextraction technique was employed in order to remove biological matrices prior to instrumental analysis. The free valproic acid was determined by isolating free valproic acid from protein-bound valproic acid by ultrafiltration under centrifugation of 100 μL sample. The filtrate was acidified to turn valproic acid into its protonated neutral form and then extracted. The determination of total valproic acid was carried out by acidifying 40 μL untreated plasma to release the protein-bound valproic acid prior to extraction. A solution consisting of 10 mM histidine, 10 mM 3-(N-morpholino)propanesulfonic acid and 10 μM hexadecyltrimethylammonium bromide of pH 6.5 was used as background electrolyte for the electrophoretic separation. The method showed good linearity in the range of 0.4-300 μg/mL with a correlation coefficient of 0.9996. The limit of detection was 0.08 μg/mL, and the reproducibility of the peak area was excellent (RSD=0.7-3.5%, n=3, for the concentration range from 1 to 150 μg/mL). The results for the free and total valproic acid concentration in human plasma were found to be comparable to those obtained with a standard immunoassay. The corresponding correlation coefficients were 0.9847 for free and 0.9521 for total valproic acid.

Nonperturbative estimate of the heavy quark momentum diffusion coefficient

We estimate the momentum diffusion coefficient of a heavy quark within a pure SU(3) plasma at a temperature of about 1.5Tc. Large-scale Monte Carlo simulations on a series of lattices extending up to 1923×48 permit us to carry out a continuum extrapolation of the so-called color-electric imaginary-time correlator. The extrapolated correlator is analyzed with the help of theoretically motivated models for the corresponding spectral function. Evidence for a nonzero transport coefficient is found and, incorporating systematic uncertainties reflecting model assumptions, we obtain κ=(1.8–3.4)T3. This implies that the “drag coefficient,” characterizing the time scale at which heavy quarks adjust to hydrodynamic flow, is η−1D=(1.8–3.4)(Tc/T)2(M/1.5  GeV)  fm/c, where M is the heavy quark kinetic mass. The results apply to bottom and, with somewhat larger systematic uncertainties, to charm quarks.