Log-transformed Model Research Articles

Impact of Milk pH and Fat Content on the Prediction of Milk-to-Plasma Ratio: Knowledge Gap and Considerations for Lactation Study Design and Interpretation.

Different empirical lactation models have been published to predict the milk-to-plasma (M/P) ratio of drugs to gain knowledge on the extent of drug distribution to the breastmilk. M/P ratios will likely vary across the lactation period due to differences in physiological milk pH and fat content, which are not routinely reported in clinical lactation pharmacokinetic studies. This work aims to evaluate the sensitivity of two (a theory-based phase distribution and a log-transformed regression) lactation models for M/P prediction at different physiological milk pH and fat content. A literature search was conducted to collate reported M/P ratios for different drugs and their physicochemical parameters required for the prediction of the M/P ratio. Two distribution models were used for M/P ratio predictions. The M/P ratio of drugs was predicted under the physiological milk pHs of 6.8, 7.0, 7.2, and 7.4 and at of 1%, 3%, and 6% fat content. Calculated M/P ratios were compared with the observed M/P ratios. A total of 200 M/P ratios for 130 compounds (40 acids and 90 bases) were collected from clinical studies and included in the analysis. For both model, precision decreases and bias increases outside the milk pH range 7.0-7.2 and fat contents more than 3%. Significant variability exists in the observed M/P ratios. Both milk pH and fat content are important parameters for model prediction. Calculated M/P ratios are influenced by multiple covariates, including milk pH and fat content. The phase distribution model is less sensitive to these covariates than the log-transformed model, especially for acidic compounds. For complex matrices such as breastmilk, the actual physiological parameters of the sampled milk, at least milk fat and pH, and their distributions are required covariates to improve the prediction outcomes, design lactation pharmacokinetic studies, and inform the potential breastfed infant dose.

Efficacy of Interleukin-1β Inhibitor on Dry Eye Disease in Patients with Degenerative Arthritis.

To evaluate diacerein (interleukin-1β inhibitor) efficacy on ocular surface disease (OSD). This prospective observational study included patients who received diacerein for osteoarthritis and had dry eye (DE). The primary outcome was corneal staining score. Secondary outcomes were ocular surface disease index (OSDI) score, tear breakup time (TBUT), tear osmolarity (Osm), Schirmer's test results, interleukin-1α (IL-1α), interleukin-1β (IL-1β), and interleukin-1 receptor antagonist (IL-1Ra) levels in tears. All measurements were done at baseline and 2-month follow-up visits. Linear mixed models were used to examine the effect of all parameters, and log-transformed models were used for IL-1α, IL-1β, and IL-1Ra analyses. Thirty-four patients (31 females and 3 males) were enrolled. The corneal staining score improved by 1.29 points (P=0.022, 95% confidence interval [95% CI] 0.19 to 2.40) after 2 months, and the OSDI score improved by 17.2 points (P<0.001, 95% CI 10.82 to 23.58) but TUBT decreased by 0.66 seconds (P=0.021, 95% CI 0.10 to 1.22). No significant differences were observed in the tear Osm and Schirmer's test. IL-1Ra demonstrated no statistical difference, IL-1α was significantly increased by 80% (P=0.260), and IL-1β was significantly decreased by 99.21% (P<0.001). Diacerein can improve corneal staining and decrease IL-1β levels in tears, which reflects better DE symptoms. Diacerein may be a promising alternative treatment for patients with OSD and osteoarthritis.

The potential of bamboo forests as a carbon sink and allometric equations for estimating their aboveground biomass

Bamboo forests in Colombia and the Andean region of South America represent high-value ecosystems that provide ecological and economic benefits with local and global impacts. One of the ecosystem services provided by these forests is associated with their capacity to store carbon. In this study, data collected from monitoring plots were used to estimate the carbon content in different pools. Bamboo biomass (Bba), tree biomass (Btree), litter (Cli) and soil organic carbon (SOC) were assessed. The approximate total ecosystem carbon stock (TECaprox) ranged from 198.4 Mg C ha−1 to 330.9 Mg C ha−1 and bamboo carbon Cba represents approximately 50%. In addition, considering the relevance of developing tools to facilitate bamboo inventory and biomass estimates, allometric equations (AE) to estimate bamboo aboveground biomass (AGB) were fitted using the diameter of culms at breast height (dbh) and the total culm length (l) as predictor variables. The fitted AEs included the weighted linear, weighted log-transformed and weighted nonlinear fixed effect models. To compliance the additivity of biomass components a simultaneous systems of biomass equations (seemingly unrelated regressions) were also fitted. The precision and accuracy were assessed considering the residual diagnostic plots and statistics, such as the root-mean-square error (RMSE), RMSE percentage error (RMSEPE) and the Furnival’s index (Fln) for weighted log-transformed models and cross-validation. The performance of the models was similar with an RMSE of approximately 10 kg and 26% of RMSEPE, with slightly lower error for the weighted log-transformed model for the fitting and validation phases. A proper performance was also evidenced for the simultaneous approach for predicting AGB. Bamboo forests showed high relevance as carbon sinks and therefore might be considered strategic tropical ecosystems for climate change mitigation. On the other hand, the fitted AE exhibited proper performance and therefore provided reliable possibilities for estimating the AGB of bamboo during inventories. For practical reasons, the use of models with dbh as a predictor variable is recommended.

Commutability assessment of human urine certified reference materials for albumin and creatinine on multiple clinical analyzers using different statistical models.

Urine albumin concentration and albumin-creatinine ratio are important for the screening of early-stage kidney damage. Commutable urine certified reference materials (CRMs) for albumin and creatinine are necessary for standardization of urine albumin and accurate measurement of albumin-urine ratio. Two urine CRMs for albumin and creatinine with certified values determined using higher-order reference measurement procedures were evaluated for their commutability on five brands/models of clinical analyzers where different reagent kits were used, including Roche Cobas c702, Roche Cobas c311, Siemens Atellica CH, Beckman Coulter AU5800, and Abbott Architect c16000. The commutability study was conducted by measuring at least 26 authentic patient urine samples and the human urine CRMs using both reference measurement procedures and the routine methods. Boththe linear regression model suggested by the Clinical and Laboratory Standard Institute (CLSI) guidelines and log-transformed model recommended by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Commutability Working Group were used to evaluate the commutability of the human urine CRMs. The commutability of the human urine CRMs was found to be generally satisfactory on all five clinical analyzers for both albumin and creatinine, suggesting that they are suitable to be used routinely by clinical laboratories as quality control or for method validation of urine albumin and creatinine measurements.

Equivalence tests before end of follow-up under the class of log transformation model

ABSTRACT One important topic in clinical trials is to show that the effects of new and standard treatments are equivalent in terms of clinical relevance. In literature, many equivalence tests based on the maximal difference between two survival functions for the two treatments over the whole time axis have been proposed. However, since survival times can only be observed until the end of follow-up, an equivalence test should be based on a comparison only in the observed time-window dictated by the end of follow-up. In this article, under the class of log transformation model, we propose an asymptotical -level equivalence test for the difference between two survival functions that only addresses equivalence until the end of follow-up. We demonstrate that the hypothesis of equivalence of two survival functions before the end of follow-up can be formulated as interval-based hypothesis testing which involves the treatment effect parameter. Simulation results indicate that when sample size is sufficiently large the proposed test controls the type I error effectively and performs well at detecting the equivalence. The proposed test is applied to a dataset from veteran’s administration lung cancer trial.

Allometric equations for estimating on-farm fuel production of Gliricidia sepium (Gliricidia) shrubs and Cajanus cajan (pigeon pea) plants in semi-arid Tanzania

BackgroundFuelwood is considered to be the primary source of cooking energy in Tanzania and, due to ongoing deforestation, access to fuelwood is becoming more cumbersome. On-farm agroforestry systems can reduce dependency on off-farm fuel; however, the output of on-farm produced fuel is typically uncertain as production potentials are often not known. In this paper, we have developed allometric equations to model the above-ground woody biomass (AGWB) production from intercropped Gliricidia sepium (Jacq.) Kunth ex Walp (Gliricidia) shrubs and Cajanus cajan (L.) Millsp. (pigeon pea) plants.MethodsWe used a destructive sampling approach, for measuring the dendrometric characteristics, such as the root collar diameter at a 20 cm stem height (RCD20) and the stem height to estimate the AGWB production. The models are based on 112 Gliricidia and 80 pigeon pea observations from annually pruned plants. Seven allometric equations were fitted to derive the best-fit models for the AGWB production.ResultsWe found that using a natural log-transformed linear model with RCD20 as a single predictor variable provides the highest explanatory value to estimate the AGWB production (Gliricidia: R2 = 95.7%, pigeon pea: R2 = 91.4%) while meeting Ordinary Least Square (OLS) estimator requirements. Adding stem height as an additional variable to predict the AGWB production does not improve model accuracy enough to justify the extra work for including it.ConclusionsWhile on-farm pigeon pea plants produced a stable amount of woody biomass per annum, annual fuelwood production from Gliricidia shrubs increased over the years. Compared to the annual fuelwood consumption data from the literature, our results show that on-farm produced fuelwood can substantially offset the demand for off-farm fuel, potentially resulting in household fuelwood autarky.

Abstract LB079: Circulating plasma phospholipid fatty acid levels &amp; breast cancer risk in the CPS-II Nutrition Cohort

Abstract Circulating plasma phospholipid fatty acid levels and breast cancer risk in the CPS-II Nutrition Cohort Background The association between dietary fat intake and breast cancer risk has been a source of controversy with conflicting results reported in past decades. Additional prospective studies that objectively measure circulating levels of fatty acids are now needed to better understand the associations between dietary fat and breast cancer development. MethodsWe assessed the relation between breast cancer risk and plasma levels of 60 phospholipid fatty acids in a nested case-control study of 2,718 postmenopausal women (905 breast cancer cases and 1,813 matched controls) enrolled in the Cancer Prevention Study II (CPS-II) Nutrition Cohort. Blood samples were collected at baseline (1997-1998). Multivariable-adjusted conditional logistic regression models that included established breast cancer risk factors (alcohol use, postmenopausal hormone use, smoking status, waist circumference, body mass index, and weight change from age 18 years to blood draw) were used to compute odds ratios (OR) and 95% confidence intervals (CI). The false discovery rate (FDR; q-value) was computed to control for multiple comparisons. ResultsAfter adjustment for multiple comparisons and in continuous log-transformed multivariable models, plasma levels of myristic acid (a saturated fatty acid) were positively associated with breast cancer risk (OR per log-value, 1.17, 95% CI: 1.07-1.28; q-value = 0.03), with similar magnitude associations found for estrogen receptor (ER) positive and ER negative breast cancer (p-interaction = 0.39). Positive associations were also found for plasma levels of monounsaturated fatty acid (OR per log-value, 1.09, 95% CI: 1.00-1.18; q-value = 0.24), palmitoleic acid (OR per log-value, 1.14, 95% CI: 1.04-1.24; q-value = 0.09), palmitic acid (OR per log-value, 1.12, 95% CI: 1.01-1.24; q-value = 0.17), dihomo-γ-linolenic acid (OR per log-value, 1.14, 95% CI: 1.04-1.25; q-value = 0.09), and adrenic acid (OR per log-value, 1.13, 95% CI: 1.03-1.24; q-value = 0.11) with breast cancer risk; however, after adjustment for multiple comparisons these associations did not reach the threshold of statistical significance. Similarly, inverse associations between plasma levels of trans α-linolenic acid (OR per log-value, 0.85, 95% CI: 0.76-0.96; q-value = 0.09), heptadecanoic acid (OR per log-value, 0.90, 95% CI: 0.82-0.99; q-value = 0.17), and vaccenic acid (OR per log-value, 0.88, 95% CI: 0.80-0.97; q-value = 0.11) with breast cancer risk were not statistically significant after adjustment for multiple comparisons. ConclusionThese findings suggest that higher circulating levels of myristic acid, that can be sourced from dietary intake of dairy foods or via de novo synthesis, may increase breast cancer risk. Additional studies are needed to replicate this finding and provide mechanistic insights. Citation Format: Michele Matta, Emily Deubler, Veronique Chajès, Marc Gunter, Neil Murphy, Mia M. Gaudet. Circulating plasma phospholipid fatty acid levels &amp; breast cancer risk in the CPS-II Nutrition Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB079.

Morphometrics of 39 fishes from the Seychelles artisanal fisheries

AbstractA total of 5478 fishes were sampled between 2009 and 2020 to assess length–weight, length–length and weight–weight relationships in 39 marine species from 10 families caught in the Seychelles waters by the artisanal fishery. Two types of length (total length TL, fork length FL) and three types of weight (whole weight WT, gutted weight GW and gilled-gutted weight GGW) were measured. The parameters of the relationships were estimated using the log-transformed allometric model with bias correction. Our results include length–weight, length–length and weight–weight relationships for 39, 20 and 18 species, respectively. Our length–weight data and resulting relationships were compared against FishBase database for 36 species and were in the Bayesian 95% confidence interval of the relationships available for 33 species and above for Gnathanodon speciosus, Lutjanus gibbus and Variola louti. Finally, for five abundant and widely dispersed species we tested for spatial differences in morphometric relationships between the Mahé Plateau and three southern atoll groups. Significant differences were found for two species only, but their magnitude was small. We thus argue for the regression relationships based on pooled data to be used for most types of population and community analyses. The availability of these morphometric relationships will support the application of accurate size-based analyses for Seychelles fisheries survey data, and so enhance understanding of the ecology of the reef-associated fish component of marine ecosystems and food webs, and improve fisheries research management.

Tests for equivalence of two survival functions: alternatives to the PH and PO models

ABSTRACT For the equivalence trial with survivor outcomes from two treatment groups, the most popular testing procedure is the extension of log-rank test under proportional hazards (PH) model. In literature, an alternative test has been proposed under the proportional odds (PO) survival model. In practice, both PH and PO hazards assumptions can be violated. One alternative is the log transformation model other than the PH or PO models. Another alternative model is the partly Aalen’s additive risk model which allows the influence of some covariates to vary over time. In this article, we propose equivalence tests for the difference of two survival functions under the class of log transformation model and that of two cumulative hazard functions under the partly Aalen’s model. Simulation studies demonstrate that the proposed tests perform well in finite samples.

Factors associated with disparities in out-of-pocket expenditure on dental care: results from two cross-sectional national surveys

BackgroundSocioeconomic differences in oral health and dental care utilization are a persistent problem in many high-income countries. We evaluated demographic, geographic and socioeconomic factors associated with disparities in households’ out-of-pocket expenditure (OOPE) on dental care, and the effect of ongoing dental health reform on these disparities.MethodsThis cross-sectional analysis used data collected in two Israeli Household Expenditure Surveys conducted in 2014 and 2018. OOPE for dental care was estimated using a two-part multivariable model. A logistic regression was used to examine the likelihood of reporting any OOPE, and a log-transformed linear regression model examined the level of expenditure among those who reported any OOPE.ResultsIn 2018, OOPE on dental care accounted for 22% of total health expenditure for all households, whereas among those who reported dental OOPE it reached 43%. Households with children up to age 14 years reported lower OOPE, regardless of ownership of supplementary health insurance. Owning supplementary health insurance had a heterogeneous effect on the level of OOPE, with a significant increase among those with 0–8 years of education, compared to households without such insurance, but not among those of higher educational level. In 2014, Arab ethnic minority and residence in the country periphery were associated with a greater likelihood for any OOPE and higher amounts of OOPE on dental care. While the gaps between Jewish and Arab households persisted into 2018, those between peripheral and non-peripheral localities seem to have narrowed.ConclusionsThe burden of dental OOPE on Israeli households remains heavy and some disparities still exist, even after the implementation of the dental health reform. Expanding the dental health reform and addressing barriers to preventive dental care, especially among Arabs and those of lower educational level, may help in reducing households’ private expenses on dental care.

Testing the non-random hypothesis of medicinal plant selection using the woody flora of the Mpumalanga Province, South Africa

Medicinal plants have been used by local communities to treat all sorts of diseases, and this unique indigenous knowledge has been documented in various studies. However, using this vast knowledge to formulate and test hypothesis in ethnobotany is not yet a common practice in the discipline despite recent calls for more hypothesis-driven ethnobotanical researches. Here, we collected ethnobotanical data on 811 woody plant species in the Mpumalanga Province of South Africa to test the non-random hypothesis of medicinal plant selection, which predicts a positive correlation between the size of plant families and the number of medicinal plants in the families. We tested this hypothesis by fitting the commonly used simple linear regression model and the negative binomial model. Our analysis confirmed the hypothesis and revealed that some plant families are over-utilised—i.e. contain more medicinal plants than expected. The identification of over-utilised families is the first step towards the prioritisation of research efforts for drug discovery. The proportion of over-utilised families ranges from 50% (linear regression with untransformed data) and 55% (linear regression after log–log transformation) to 34% (negative binomial model). With the simple linear model and untransformed data, the top over-utilised families are Fabaceae (residual = + 34.44), Apocynaceae (+ 5.82) and Phyllanthaceae (+ 5.53). The log-transformed model confirms these three families as the top over-utilised families but in a slightly different sequence: Fabaceae (+ 1.55), Phyllanthaceae (+ 0.83) and Apocynaceae (+ 0.79). However, using the negative binomial model, Fabaceae is no longer even part of the top 10 over-utilised families, which are now Phyllanthaceae (+ 2.09), Apocynaceae (+ 1.51), Loganiaceae (+ 1.48), Rhamnaceae (+ 1.48), Sapotaceae (+ 1.48), Oleaceae (+ 1.39), Salicaceae (+ 1.39), Clusiaceae (+ 1.30), Boraginaceae (+ 1.28) and Lamiaceae (+ 1.18). This suggests that the relative medicinal value of some families may have been over-estimated in comparison with others. Our study is an illustration of the need to apply appropriate model while testing ethnobotanical hypotheses to inform priority setting for drug discovery.

Influence of slope, aspect and competition index on the height-diameter relationship of Cyclobalanopsis glauca trees for improving prediction of height in mixed forests

Diameter at breast height (DBH) and height (H) of trees are two important variables used in forest management plans. However, collecting the measurements of H is time-consuming and costly. Instead, the H-DBH relationship is modeled and used to estimate H. But, ignoring the effects of slope, aspect and tree competition on the H-DBH relationship often impedes the improvement of H predictions. In this study, to improve predictions of (Thunb.) Oerst. tree H in mixed forests, we compared eleven H-DBH models and examined the influence of slope and aspect on the H-DBH relationship using 426 trees. We then improved Hegyi competition index and explored its effect on the H predictions by including it in the selected models. Results showed 1) There were statistically significant effects of slope and aspect on the H-DBH relationship; 2) The log transformation and exponential model performed best for sunny- and shady-steep, respectively, and the Gompertz’s model was optimal for both sunny- and shady-gentle; 3) Compared with the whole dataset, the division of the data into the slope and aspect sub-datasets significantly reduced the RMSE of H predictions; 4) Compared with the selected models without competition index, adding the original Hegyi and improved Hegyi_I into the models improved the H predictions but only the models containing the improved Hegyi_I significantly increased the prediction accuracy at the significant level of 0.1. This study implied that modeling the H-DBH relationship under different slopes and aspects and including the improved Hegyi_I provided the great potential to improve the H predictions.Cyclobalanopsis glauca

Log-Transformed Electromyography Amplitude-Power Output Relationship: Single-Leg Knee-Extensor Versus Single-Leg Cycle Ergometry.

Noble, EB, Pilarski, JM, Vora, HK, Zuniga, JM, and Malek, MH. Log-transformed electromyography amplitude-power output relationship: single-leg knee-extensor versus single-leg cycle ergometry. J Strength Cond Res 33(5): 1311-1319, 2019-Comparing and contrasting motor unit recruitment and activation for the same muscles for multiple versus single-joint exercise may provide a better understanding of neuromuscular fatigue. The purpose of this study, therefore, is to compare the slope and y-intercept terms for the 3 superficial quadriceps femoris (QF) (vastus lateralis, rectus femoris, and vastus medialis) muscles derived from the log-transformed electromyography (EMG) amplitude-power output relationship between the single-leg knee-extensor ergometry (KE) and the single-leg cycle ergometry (CE). Ten healthy college-aged men who engaged in regular physical activity visited the laboratory on 2 occasions separated by 7 days to perform either single-leg CE or single-leg KE in a randomized order. For each visit, subjects performed incremental exercise until voluntary fatigue. Electromyography electrodes were placed on the superficial QF muscles. The slope and y-intercept terms, for each muscle, for the EMG amplitude versus power output relationship was examined using the log-transformed model for each subject's data. The results indicated no significant (p > 0.05) mean differences for either slope or y-intercept terms between exercise modes and across muscles. In addition, separate repeated-measures analyses of variance (ANOVAs) were used to determine mean differences for the slope and y-intercept values between the 3 muscles. In addition, separate 2 (mode: CE or KE) × intensity (intensity: 30, 60, and 90% of maximal workload) repeated-measures ANOVAs were conducted for each muscle. There was a significant (p < 0.05) mode × exercise intensity interaction for each muscle. Follow-up testing indicated that in most cases, the normalized EMG amplitude was significantly higher for single-leg KE than single-leg CE. These results indicated that incremental single-leg KE activates the superficial QF muscles significantly greater than single-leg CE.

Renewable integration through transmission network expansion planning under uncertainty

In this paper we bring together a stochastic mixed integer programming model for transmission network expansion planning, incorporating portfolios of real options to address the evolution in time of uncertain parameters, with the adjusted generalized log-transformed model, to expand the number of correlated parameters that can be modeled. We apply these methods to evaluate the potential contribution of underwater transmission investments to increase renewables penetration in the Azores archipelago. The approach also includes expansion lead times, due to the large timespans involved in the construction of new transmission lines. Our analysis focuses on a set of the four closest islands in the archipelago, Pico, Faial, S. Jorge and Terceira, and shows that even though investments are delayed and the future network configuration varies according to the evolution of renewable generation scenarios, an investment in underwater transmission, if technically feasible, within the assumptions of our model, could in fact contribute to increase renewables penetration, by enabling islands with an excess in generation from renewable sources to supply other islands in a deficit situation.

Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China.

Understanding forest carbon budget and dynamics for sustainable resource management and ecosystem functions requires quantification of above- and below-ground biomass at individual tree species and stand levels. In this study, a total of 122 trees (9–12 per species) were destructively sampled to determine above- and below-ground biomass of 12 tree species (Acer mandshuricum, Acer mono, Betula platyphylla, Carpinus cordata, Fraxinus mandshurica, Juglans mandshurica, Maackia amurensis, P. koraiensis, Populus ussuriensis, Quercus mongolica, Tilia amurensis and Ulmus japonica) in coniferous and broadleaved mixed forests of Northeastern China, an area of the largest natural forest in the country. Biomass allocation was examined and biomass models were developed using diameter as independent variable for individual tree species and all species combined. The results showed that the largest biomass allocation of all species combined was on stems (57.1%), followed by coarse root (21.3%), branch (18.7%), and foliage (2.9%). The log-transformed model was statistically significant for all biomass components, although predicting power was higher for species-specific models than for all species combined, general biomass models, and higher for stems, roots, above-ground biomass, and total tree biomass than for branch and foliage biomass. These findings supplement the previous studies on this forest type by additional sample trees, species and locations, and support biomass research on forest carbon budget and dynamics by management activities such as thinning and harvesting in the northeastern part of China.

The assessment of water resources in ungauged catchments in Rwanda

Study regionRwanda is a landlocked country in Africa with precipitation ranging from 800mmyr−1 in the east to 1500mmyr−1 in high-altitude regions in the north and west. Study focusStreamflow estimation is an important task that is required in water resource assessments due to its importance in planning, decision-making and economic development. In this study, streamflow characteristics of ungauged catchments in Rwanda were calculated using a regionalization approach based on climate similarity and stepwise multiple-regression analysis. One climatic homogeneous region was identified and datasets of nine gauged stations and general available catchment characteristics were used to develop non-transformed and log-transformed regression models. New hydrological insights for the regionResults of this study show that climate, physiography and land cover strongly influence the hydrology of catchments in Rwanda. Using leave-one-out cross-validation, the log-transformed models were found to predict the flow parameters more suitably. These models can be used for estimating the flow parameters in ungauged catchments in Rwanda and the methodology can be applied in any other region, as long as sufficient and good quality streamflow data is available.

Providing optimum models for predicting the water loss after harvesting and extracting trees aboveground biomass from the Hyrcanian mixed-beech forests

The ecological and biological attributes of trees stand as well as the water cycle in forests are substantially related to variations of water storage capacity in forest ecosystems. This study aimed to figure out a protocol for monitoring the water storage capacity variations in the Hyrcanian mixed-beech stands after harvesting and extracting trees form the forest. A total of 174 trees were felled and weighed, and destructive sampling following lines of exploitation was carried out for measuring the water content in aboveground biomass of trees. Curve estimation regression analyses including the tree biophysical variables (breast height diameter DBH, total height H, basic wood density $$\rho$$ ) were used for examining the prediction accuracy. Nonlinear models were log-transformed, and systematic bias was corrected by correction factor depending on standard error of estimate when back-transforming to the originally dependent value. The findings showed that the power-law models were the best functional form for predicting the dependent variables. Using only DBH in the simple power model explained 76% of total variance (Adj.R 2 = 0.76) with a low Akaike information criterion (AIC) and normalized root-mean-square error RMSE%, indicating a high accuracy of prediction ( $${\text{AIC}} \approx - 238$$ ; RMSE% = 11.7). Adding H and $$\rho$$ in the linear log-transformed power models with different interaction terms increased the certainty of prediction with the highest accuracy ( $${\text{Adj}}.R^{2} = 0.86,\;{\text{AIC}} = - 329,\;{\text{RMSE}}\% = 9$$ ). Considering diverse conditions for natural forest sites, the optimum models including the biophysical variables may have associated parameters in the other forests having different stand types and compositions.

Metabolic syndrome is associated with exposure to organochlorine pesticides in Anniston, AL, United States

The Anniston Community Health Survey, a cross-sectional study, was undertaken in 2005–2007 to study environmental exposure to polychlorinated biphenyl (PCB) and organochlorine (OC) pesticides and health outcomes among residents of Anniston, AL, United States. The examination of potential risks between these pollutants and metabolic syndrome, a cluster of cardiovascular risk factors (i.e., hypertension, central obesity, dyslipidemia and dysglycemia) was the focus of this analysis. Participants were 548 adults who completed the survey and a clinic visit, were free of diabetes, and had a serum sample for clinical laboratory parameters as well as PCB and OC pesticide concentrations. Associations between summed concentrations of 35 PCB congeners and 9 individual pesticides and metabolic syndrome were examined using generalized linear modeling and logistic regression; odds ratios (OR) and 95% confidence intervals (CI) are reported. Pollutants were evaluated as quintiles and as log transformations of continuous serum concentrations. Participants were mostly female (68%) with a mean age (SD) of 53.6 (16.2) years. The racial distribution was 56% white and 44% African American; 49% met the criteria for metabolic syndrome. In unadjusted logistic regression, statistically significant and positive associations across the majority of quintiles were noted for seven individually modeled pesticides (p,p′-DDT, p,p′-DDE, HCB, β-HCCH, oxychlor, tNONA, Mirex). Following adjustment for covariables (i.e., age, sex, race, education, marital status, current smoking, alcohol consumption, positive family history of diabetes or cardiovascular disease, liver disease, BMI), significant elevations in risk were noted for p,p′-DDT across multiple quintiles (range of ORs 1.61 to 2.36), for tNONA (range of ORs 1.62–2.80) and for p,p′-DDE [OR (95% CI)] of 2.73 (1.09–6.88) in the highest quintile relative to the first. Significant trends were observed in adjusted logistic models for log10 HCB [OR=6.15 (1.66–22.88)], log10 oxychlor [OR=2.09 (1.07–4.07)] and log10 tNONA [3.19 (1.45–7.00)]. Summed PCB concentrations were significantly and positively associated with metabolic syndrome only in unadjusted models; adjustment resulted in attenuation of the ORs in both the quintile and log-transformed models. In conclusion, several OC pesticides were found to have significant associations with metabolic syndrome in the Anniston study population while no association was observed for PCBs.

Aboveground tree additive biomass equations for two dominant deciduous tree species in Daxing’anling, northernmost China

ABSTRACTBiomass models for trees in Daxing’anling are imperative for carbon inventory, but available models are extremely scarce. The objective of this study was to formulate systems of additive equations to predict the aboveground biomass of two dominant deciduous species of Betula platyphylla and Populus davidiana in Daxing’anling using diameter at breast height (d) or both d and height (h) as independent variables. Best-fit models for individual fractions (stem wood, stem bark, branch, and foliage) were developed simultaneously on the basis of seemingly unrelated regression, forcing compatibility among different components. Model performance was validated by the jackknifing method. The results revealed that log-transformed models are favorable and that all models consistently fit well within R2 = 0.908 – 0.994, and = 0.898 – 0.994. Adding h into the additive models did not significantly improve the model fitting or performance, with branches and foliage yielding worse-fitting effects. Stem biomass contributed the largest proportion (approximately 60%) of aboveground biomass. In conclusion, the additive system of biomass equations using d as the single predictor could be easily and readily applied to further applications, providing a reference for biomass estimating and carbon accounting.

Factors and Predictions for Cadmium Transfer from Soils into Tomato Plants

ABSTRACTSoil-plant transfer models are needed to predict levels of cadmium (Cd) in vegetables when evaluating the food chain risks of Cd contamination in agricultural soils. In the present study, the transfer of Cd from a wide range of soils into tomato plants was investigated in order to identify the major factors and to develop predictive models. It was found that soil pH was the main factor controlling Cd uptake and lower pH was more favorable for Cd bioaccumulation. Furthermore, the hydrogen ion activity, other than pH, was correlated linearly with the ratio of plant Cd to soil Cd. Combining soil Cd, and the interaction between soil Cd and hydrogen ion activity greatly improved the model to predict the transfer of Cd from soil to plant. Compared with the previous log transformation model, the predicted model improved the regression coefficient from 0.799 to 0.973 and has important significance in practice.