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With an increasing number of web-based calculators designed to provide the probabilities of an individual achieving improvement after lumbar spine surgery, there is a need to determine the accuracy of these models. To perform an internal and external validation study of the reduced Quality Outcomes Database web-based Calculator (QOD-Calc). Observational longitudinal cohort. Patients enrolled study-wide in Quality Outcomes Database (QOD) and patients enrolled in DaneSpine at a single institution who had elective lumbar spine surgery with baseline data to complete QOD-Calc and 12-month postoperative data. Oswestry Disability Index (ODI), Numeric Rating Scales (NRS) for back and leg pain, EuroQOL-5D (EQ-5D). Baseline data elements were entered into QOD-Calc to determine the probability for each patient having Any Improvement and 30% Improvement in NRS leg pain, back pain, EQ-5D and ODI. These probabilities were compared with the actual 12-month postop data for each of the QOD and DaneSpine cases. Receiver-operating characteristics analyses were performed and calibration plots created to assess model performance. 24,755 QOD cases and 8,105 DaneSpine lumbar cases were included in the analysis. QOD-Calc had acceptable to outstanding ability (AUC: 0.694–0.874) to predict Any Improvement in the QOD cohort and moderate to acceptable ability (AUC: 0.658–0.747) to predict 30% Improvement. QOD-Calc had acceptable to exceptional ability (AUC: 0.669–0.734) to predict Any improvement and moderate to exceptional ability (AUC: 0.619–0.862) to predict 30% Improvement in the DaneSpine cohort. AUCs for the DaneSpine cohort was consistently lower that the AUCs for the QOD validation cohort. QOD-Calc performs well in predicting outcomes in a patient population that is similar to the patients that was used to develop it. Although still acceptable, model performance was slightly worse in a distinct population, despite the fact that the sample was more homogenous. Model performance may also be attributed to the low discrimination threshold, with close to 90% of cases reporting Any Improvement in outcome. Prediction models may need to be developed that are highly specific to the characteristics of the population. [ABSTRACT FROM AUTHOR]

Postoperative recovery after adult spinal deformity (ASD) operations is arduous, fraught with complications, and often requires extended hospital stays. A need exists for a method to rapidly predict patients at risk for extended length of stay (eLOS) in the preoperative setting. To develop a machine learning model to preoperatively estimate the likelihood of eLOS following elective multi-level lumbar/thoracolumbar spinal instrumented fusions (≥3 segments) for ASD. Retrospectively from a state-level inpatient database hosted by the Health care cost and Utilization Project. Of 8,866 patients of age ≥50 with ASD undergoing elective lumbar or thoracolumbar multilevel instrumented fusions. The primary outcome was eLOS (>7 days). Predictive variables consisted of demographics, comorbidities, and operative information. Significant variables from univariate and multivariate analyses were used to develop a logistic regression-based predictive model that use six predictors. Model accuracy was assessed through area under the curve (AUC), sensitivity, and specificity. Of 8,866 patients met inclusion criteria. A saturated logistic model with all significant variables from multivariate analysis was developed (AUC=0.77), followed by generation of a simplified logistic model through stepwise logistic regression (AUC=0.76). Peak AUC was reached with inclusion of six selected predictors (combined anterior and posterior approach, surgery to both lumbar and thoracic regions, ≥8 level fusion, malnutrition, congestive heart failure, and academic institution). A cutoff of 0.18 for eLOS yielded a sensitivity of 77% and specificity of 68%. This predictive model can facilitate identification of adults at risk for eLOS following elective multilevel lumbar/thoracolumbar spinal instrumented fusions for ASD. With a fair diagnostic accuracy, the predictive calculator will ideally enable clinicians to improve preoperative planning, guide patient expectations, enable optimization of modifiable risk factors, facilitate appropriate discharge planning, stratify financial risk, and accurately identify patients who may represent high-cost outliers. Future prospective studies that validate this risk assessment tool on external datasets would be valuable. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is a common complication after cardiac surgery. This study aims to develop and validate a risk model for predicting AKI after cardiac valve replacement surgery. Data from patients undergoing surgical valve replacement between January 2015 and December 2018 in our hospital were retrospectively analyzed. The subjects were randomly divided into a derivation cohort and a validation cohort at a ratio of 7:3. The primary outcome was defined as AKI within 7 days after surgery. Logistic regression analysis was conducted to select risk predictors for developing the prediction model. Receiver operator characteristic curve (ROC), calibration plot and clinical decision curve analysis (DCA) will be used to evaluate the discrimination, precision and clinical benefit of the prediction model. A total of 1159 patients were involved in this study. The prevalence of AKI following surgery was 37.0% (429/1159). Logistic regression analysis showed that age, hemoglobin, fibrinogen, serum uric acid, cystatin C, bicarbonate, and cardiopulmonary bypass time were independent risk factors associated with AKI after surgical valve replacement (all P < 0.05). The areas under the ROC curves (AUCs) in the derivation cohort and the validation cohort were 0.777 (95% CI 0.744–0.810) and 0.760 (95% CI 0.706–0.813), respectively. The calibration plots indicated excellent consistency between the prediction probability and actual probability. DCA demonstrated great clinical benefit of the prediction model. We developed a prediction model for predicting AKI after cardiac valve replacement surgery that was internally validated to have good discrimination, calibration, and clinical practicability. • Few studies have focused on the prediction of AKI following surgical valve replacement. • This study aimed to develop and validate a risk model for the early prediction of AKI after cardiac valve replacement surgery. • Age, hemoglobin, fibrinogen, serum uric acid, cystatin C, bicarbonate, and cardiopulmonary bypass time are independent risk factors associated with AKI after surgical valve replacement. • We developed a prediction model for predicting AKI after cardiac valve replacement surgery based on 7 risk factors that were internally validated to have good discrimination, calibration, and clinical practicability. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate (both internally and externally) a prediction model examining a combination of risk factors in order to predict postpartum haemorrhage (PPH) in a general obstetric Irish population of singleton pregnancies.Study Design: We used data from the National Maternal and Newborn Clinical Management System (MN-CMS), including all singleton deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019. We defined PPH as an estimated blood loss of ≥ 1000 ml following the birth of the baby. Multivariable logistic regression with backward stepwise selection was used to develop the prediction model. Candidate predictors included maternal age, maternal body mass index, parity, previous caesarean section, assisted fertility, gestational age, fetal macrosomia, mode of delivery and history of PPH. Discrimination was assessed using the area under the receiver operating characteristic curve (ROC) C-statistic. We used bootstrapping for internal validation to assess overfitting, and conducted a temporal external validation using data from all singleton deliveries at CUMH during 2020.Results: Out of 6,077 women, 5,807 with complete data were included in the analyses, and there were 270 (4.65%) cases of PPH. Four variables were considered the best combined predictors of PPH, including parity (specifically nulliparous), macrosomia, mode of delivery (specifically operative vaginal delivery, emergency caesarean section and prelabour caesarean section), and history of PPH. These predictors were used to develop a nomogram to provide individualised risk assessment for PPH. The original apparent C-statistic was 0.751 (95% CI: 0.721, 0.779) suggesting good discriminative performance. There was minimal optimism adjustment to the C-statistic after bootstrapping, indicating good internal performance (optimism adjusted C-statistic: 0.748). Results of external validation were comparable with the development model suggesting good reproducibility.Conclusions: Four routinely collected variables (parity, fetal macrosomia, mode of delivery and history of PPH) were identified when predicting PPH in a general obstetric Irish population of singleton pregnancies. Use of our nomogram could potentially assist with individualised risk assessment of PPH and inform clinical decision-making allowing those at highest risk of PPH be actively managed. [ABSTRACT FROM AUTHOR]

We performed an internal laboratory validation of the Precision ID GlobalFiler NGS STR panel v2 kit to assist the introduction of the technology into the routine forensic casework practice. The study was designed and evaluated based not only on the key validation standards like sensitivity, stability, reproducibility, repeatability, mixture, and concordance, but we also tested the effect of reduced input DNA, we measured and applied locus-specific analytical threshold values, tested two different PCR cycle conditions, sequence artifacts and stutters were also analysed. During the study we also tested the new method on real casework samples. The sensitivity study confirmed that adding 500 pg template DNA for library preparation can be optimal at base PCR cycle number (that was 24), because the measured average heterozygote balance was not lower than 0.82, and each allele was detected above the analytical threshold. However, contrary to previous communications, increasing the PCR cycle numbers up to 28 has not resulted the significant elevation of the heterozygote imbalance. According to our results, raised PCR cycle condition (i.e. 28) is appropriate at or below 150 pg total input DNA. For most loci, the calculated AT was lower than the manufacturer's recommended. Applying the newly established ATs with raised PCR cycle conditions the allele detection sensitivity and reliability increased. We observed allele dropouts only at the 15 pg template DNA experiments with 5 % frequency, that is better to previously published studies. This result indicates that this low amount of DNA (i.e. 15 pg) could be a minimum limit of template input for a potentially successful analysis. In the mixture study the minor contributor could be detected up to 1:19 mixture ratio. We detected minor alleles in all measurements and concentrations above the threshold if the template DNA were fixed and only SNP differences were observed between the same alleles of the contributors. To test concordance between the new method and traditional STR genotyping we analysed 58 Hungarian individual samples in parallel. Nearby the detected 248 different length-based alleles on the 31 loci in the sample pool we revealed additional 75 sequence variant alleles, that represent an approximately 23 % increase in the total number of observed alleles. The casework study confirmed that the Precision ID GlobalFiler NGS STR panel v2 kit is effective even in genotyping degraded samples with extremely low levels of DNA, if we apply elevated cycle number for library preparation and use locus-specific analytical thresholds. • GlobalFiler NGS STR Panel v2 kit applied with locus-specific analytical threshold. • 15 pg input DNA was the lower limit to generate a profile with elevated PCR-cycle. • Minor allelic components were recoverable with mixture ratio of 1:19. • Compatible profiles were obtained from degraded casework samples. • Obtained genotypes were >99 % in concordance with CE results. [ABSTRACT FROM AUTHOR]

The use of TDM in clinical practice to monitor the plasma levels of antibiotics administered to critically ill patients is a well-established approach that allows for optimization of the patient's response to drug therapy, considering the characteristics of the drug, the clinical and physiological status of the patient and any peculiar of the pathogen that caused the clinical picture. In our laboratory, we have developed a single LC-MS/MS analysis for dosing the serum concentration of an antibacterial panel composed of eight antibacterial and two selective inhibitors. The method presented used a certified material furnished by a commercial company and was internally validated using the EMA guidelines. The results have shown high sensitivity, precision, and accuracy, a lower matrix effect combined with simple sample preparation and a time-saving procedure. We have evaluated the recovery rate and matrix effect by testing serum samples without pathological index and serum pools obtained from haemolysed, icteric, or lipemic samples. The assay has shown a recovery range between 94% and 101%. • The tdm allows rapid adaptation of therapy in hospitalized subjects. • Personalized therapy is essential for the management of the critical patient. • Mass spectrometry is the pioneering technique in the correct determination of blood concentrations of drugs. • Validation of analytical methods is mandatory for the use of a method in diagnostics. [ABSTRACT FROM AUTHOR]

Abstract Background It is important to find ways to minimize errors when children self-report food consumption. Objective The objective of this study was to investigate whether assistance given to children completing a self-administered 24-hour dietary recall instrument called SACANA (Self-Administered Child, Adolescent and Adult Nutrition Assessment) increased the repeatability and plausibility of energy intake (EI) estimates. Participants/setting The study was conducted between October 2013 and March 2016 in a convenience sample of 395 children, aged 8 to 17 years, from eight European countries participating in the I.Family study. Design SACANA was used to recall the previous day's food intake, twice in a day, once with and once without assistance. Main outcome measures The difference in EI between the first and second recalls was the main repeatability measure; the ratio of EI to basal metabolic rate was the plausibility measure. Statistical methods Generalized linear mixed models, adjusted for sex, age, and body mass index z -score, were used to assess whether assistance during the first vs second recall influenced repeatability and plausibility. Results The difference in estimated EI (EI from second recall minus EI from first recall) was significantly lower (P <0.001) in those assisted at first (median=–76 kcal) than those assisted at second recall (median=282 kcal). Modeling showed that EI at assisted first recall was 19% higher (95% CI 1.13 to 1.24) than in assisted second recall. Overall, 60% of recalls had a plausible EI. Modeling to estimate the simultaneous effects of second vs first recall and assistance vs no assistance on plausibility showed that those assisted at first recall had significantly higher odds of a plausible recall than those unassisted (odds ratio 3.64, 95% CI 2.20 to 6.01), with no significant difference in plausibility of second recall compared to the first (odds ratio 1.48, 95% CI 0.92 to 2.35). Conclusions When children are assisted at first recall, the plausibility and repeatability of the later unassisted recall improve. This improvement was evident for all ages. A future, adequately powered study is required to investigate the age range for which assistance is advisable. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to compare nonlinear modeling methods for handling continuous predictors for reproducibility and transportability of prediction models. Study Design and Setting: We analyzed four cohorts of previously unscreened men who underwent prostate biopsy for diagnosing prostate cancer. Continuous predictors of prostate cancer included prostate-specific antigen and prostate volume. The logistic regression models included linear terms, logarithmic terms, fractional polynomials of degree one or two (FP1 and FP2), or restricted cubic splines (RCS) with three or five knots (RCS3 and RCS5). The resulting models were internally validated by bootstrap resampling and externally validated in the cohorts not used at model development. Performance was assessed with the area under the receiver operating characteristic curve (AUC) and the calibration component of the Brier score (CAL). Results: At internal validation models with FP2 or RCS5 showed slightly better performance than the other models (typically 0.004 difference in AUC and 0.001 in CAL). At external validation models containing logarithms, FP1, or RCS3 showed better performance (differences 0.01 and 0.002). Conclusion: Flexible nonlinear modeling methods led to better model performance at internal validation. However, when application of the model is intended across a wide range of settings, less flexible functions may be more appropriate to maximize external validity. [ABSTRACT FROM AUTHOR]

To internally and externally validate our diagnostic criteria of early stage knee osteoarthritis (OA) in the CHECK and OAI cohorts. We applied two previously developed diagnostic models to all knees in CHECK and OAI cohorts to calculate probabilities of early stage knee OA at baseline. Knees were categorized into three groups based on probability: 'no OA' (probability ≤ 30%), 'uncertain' (probability between 30% and 70%) and 'early stage OA' (probability ≥ 70%). To validate the diagnosis, we obtained OA related outcome measures at 10-year follow-up in the CHECK cohort, and at 8-9-year follow-up in the OAI cohort. We compared outcome measures between 'no OA' and 'early stage OA' knees, and between 'no OA' and 'uncertain' knees using generalized estimating equations. In CHECK (n = 1042 knees) both models showed 'early stage OA' knees presented with significant and clinically relevant higher WOMAC scores, higher Kellgren & Lawrence (KL) grade, and higher rates of joint space narrowing (JSN) progression after 10 years, compared to 'no OA' knees. In OAI (n = 2937 knees) both models showed 'early stage OA' knees presented with significant and clinically relevant higher WOMAC scores, higher KL grade, and higher rates of KL and JSN progression after 8-9 years, compared to 'no OA' knees. Smaller, but still significant differences between 'uncertain' and 'no OA' knees were observed in both cohorts. These results support internal and external validity of the two sets of diagnostic criteria for early stage knee OA. [ABSTRACT FROM AUTHOR]

Abstract: Background: Urinary prostate cancer antigen 3 (PCA3) assay in combination with established clinical risk factors improves the identification of men at risk of harboring prostate cancer (PCa) at initial biopsy (IBX). Objective: To develop and validate internally the first IBX-specific PCA3-based nomogram that allows an individual assessment of a man''s risk of harboring any PCa and high-grade PCa (HGPCa). Design, setting, and participants: Clinical and biopsy data including urinary PCA3 score of 692 referred IBX men at risk of PCa were collected within two prospective multi-institutional studies. Intervention: IBX (≥10 biopsy cores) with standard risk factor assessment including prebiopsy urinary PCA3 measurement. Outcome measurements and statistical analysis: PCA3 assay cut-off thresholds were investigated. Regression coefficients of logistic risk factor analyses were used to construct specific sets of PCA3-based nomograms to predict any PCa and HGPCa at IBX. Accuracy estimates for the presence of any PCa and HGPCa were quantified using area under the curve of the receiver operator characteristic analysis and compared with a clinical model. Bootstrap resamples were used for internal validation. Decision curve analyses quantified the clinical net benefit related to the novel PCA3-based IBX nomogram versus the clinical model. Results and limitations: Any PCa and HGPCa were diagnosed in 46% (n =318) and 20% (n =137), respectively. Age, prostate-specific antigen, digital rectal examination, prostate volume, and PCA3 were independent predictors of PCa at IBX (all p <0.001). The PCA3-based IBX nomograms significantly outperformed the clinical models without PCA3 (all p <0.001). Accuracy was increased by 4.5–7.1% related to PCA3 inclusion. When applying nomogram-derived PCa probability thresholds ≤30%, only a few patients with HGPCa (≤2%) will be missed while avoiding up to 55% of unnecessary biopsies. External validation of the PCA3-based IBX-specific nomogram is warranted. Conclusions: The internally validated PCA3-based IBX-specific nomogram outperforms a clinical prediction model without PCA3 for the prediction of any PCa, leading to the avoidance of unnecessary biopsies while missing only a few cases of HGPCa. Our findings support the concepts of a combination of novel markers with established clinical risk factors and the superiority of decision tools that are specific to a clinical scenario. [Copyright &y& Elsevier]

Abstract: The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008–2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R 2 =0.817, PRESS/SSY=0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001–2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (, PRESS/SSY=0.187) and with the maximal external prediction for the 2001–2002 campaign ( and PRESS/SSY=0.321) versus the 2001–2004 campaign (, PRESS/SSY=0.449). [Copyright &y& Elsevier]

• Physicochemical properties of crude oils have a structure suitable for clustering. • k-means with 3 clusters provides additional knowledge to better group crude oils. • Usage of internal validation indexes to measure clustering quality is relevant. The values of the physicochemical properties of crude oils vary significantly, depending on their geographical origins. A standard categorization of crude oils is grossly based on the density and sulfur content, not considering other properties that can have meaningful impacts on blending and in some refining processes. Cluster analysis is an unsupervised machine learning technique that categorizes observations based on their similarity. In this work, k-means clustering algorithm was applied to a wide range of physicochemical properties to identify groups of crudes oils with high affinity that possibly have similar behavior later on, in downstream operations. A data set from Galp SA refineries (located in Portugal) containing 454 observations, corresponding to values of 9 properties, from 45 different crude oil sources was used in the present analysis. After suitable preprocessing, k-means was applied using different cluster numbers, and their performance was evaluated through the internal validation metrics silhouette index and Local Cores-based Cluster Validity (LCCV) index. The recommend number of clusters was 3, which presented the best performance with a LCCV index of 0.39. Crude oils from the same source should be incorporated in the same cluster, and this was corroborated by external validation, with 1.8% of the observations were placed in a different cluster than the majority of same source crude oils. The proposed method was also able to identify observations with unusually high iron contents concerning the same source of crude oils when more clusters were considered. This work provides a methodology to obtain a better categorization of crude oils by using cluster analysis, allowing the refineries to know how similar crude oils and their sources are. This categorization is very useful for improving the formulation of crude blends and the crude oils quality control, with the goal to optimize further the refining operations. [ABSTRACT FROM AUTHOR]

Abstract: According to guidelines of the quality assurance standards for the forensic DNA testing laboratories, prior to introducing an existing DNA analysis procedure, reliability of the procedure has to be demonstrated by carrying out internal validation. In order to introduce a sensitive and accurate DNA quantification method in our laboratory, a validation study of the 7500 Real Time PCR System (Applied Biosystems) using the QUANTIFILER Human DNA Quantification Kit (Applied Biosystems) was performed. Here we report the results and the experience we have gained during this internal validation study. [Copyright &y& Elsevier]

Abstract: Commercial and research scale plantings of short-rotation woody crops require reliable and efficient estimations of biomass yield before time of harvest. Biomass equations currently exist but the accuracy and efficiency of estimation procedures at the level of specificity needs to be quantified for clones being used in North America. Diameter-based allometric equations for aboveground biomass for four clones of willow (Salix discolor, Salix alba, Salix dasyclados, and Salix sachalinensis), between two sites (Canastota and Tully, NY), and across four years (1998–2001), were developed using ordinary least-squares regression (OLSR) on log-transformed variables, weighted least squares regression (WLSR) on log-transformed variables, and nonlinear regression (NLR) methods and validated using independent data sets. Biomass estimations derived from clone, age, and site (Specific) using OLSR equations had highest R 2 and lowest percent bias (<2.3%) allowing for accurate estimations of standing biomass. Values for specific equations using WLSR were similar, but bias was higher for NLR (0.7–12.5%). However, the amount of time and effort required to develop specific equations, is large and in many situations prohibitive. Biomass estimates derived from clone and age, regardless of site (Intermediate), resulted in small increases in prediction error and a small increase in percent bias using OLSR (<0.4%) and WLSR (<1.7%). The increase in percent bias was larger (1.1–5.7%) for NLR equations. Intermediate models correspond to the loss of only a small amount of accuracy while gaining more efficiency in estimating standing biomass. Estimates of biomass derived from clone alone (general) equations, considering neither age nor site, had the weakest prediction abilities that may lead to large errors for biomass estimations using OLSR (7.0–9.5%), WLSR (1.1–21.7%) or NLR (31.9–143.4%). [Copyright &y& Elsevier]

: Background and objectivePrediction models tend to perform better on data on which the model was constructed than on new data. This difference in performance is an indication of the optimism in the apparent performance in the derivation set. For internal model validation, bootstrapping methods are recommended to provide biascorrected estimates of model performance. Results are often accepted without sufficient regard to the importance of external validation. This report illustrates the limitations of internal validation to determine generalizability of a diagnostic prediction model to future settings.: MethodsA prediction model for the presence of serious bacterial infections in children with fever without source was derived and validated internally using bootstrap resampling techniques. Subsequently, the model was validated externally.: ResultsIn the derivation set (n = 376), nine predictors were identified. The apparent area under the receiver operating characteristic curve (95% confidence interval) of the model was 0.83 (0.78–0.87) and 0.76 (0.67–0.85) after bootstrap correction. In the validation set (n = 179) the performance was 0.57 (0.47–0.67).: ConclusionFor relatively small data sets, internal validation of prediction models by bootstrap techniques may not be sufficient and indicative for the model''s performance in future patients. External validation is essential before implementing prediction models in clinical practice. [Copyright &y& Elsevier]

We performed a simulation study to investigate the accuracy of bootstrap estimates of optimism (internal validation) and the precision of performance estimates in independent validation samples (external validation). We combined two data sets containing children presenting with fever without source (n = 376 + 179 = 555; 120 bacterial infections). Random samples were drawn from this combined data set for the development (n = 376) and validation (n = 179) of logistic regression models. The models included statistically significant predictors for infection selected from a set of 57 candidate predictors. Model development, including the selection of predictors, and validation were repeated in a bootstrapping procedure. The resulting expected optimism estimate in the receiver operating characteristic (ROC) area was compared with the observed optimism according to independent validation samples. The average apparent ROC area was 0.74, which was expected (based on bootstrapping) to decrease by 0.07 to 0.67, whereas the observed decrease in the validation samples was 0.09 to 0.65. Omitting the selection of predictors from the bootstrap procedure led to a severe underestimation of the optimism (decrease 0.006). The standard error of the observed ROC area in the independent validation samples was large (0.05). We recommend bootstrapping for internal validation because it gives reasonably valid estimates of the expected optimism in predictive performance provided that any selection of predictors is taken into account. For external validation, substantial sample sizes should be used for sufficient power to detect clinically important changes in performance as compared with the internally validated estimate. [Copyright &y& Elsevier]

• The HYPE model reproduces well the spatiotemporal variability of NO 3 - -N and TP. • Multi-site calibration increases the spatial capability of nutrient catchment model. • Hydro-meteorological sub-catchment delineation is important for nutrient prediction. Semi-distributed hydrological and water quality models are increasingly used as innovative and scientific-based management tools. However, their application is usually restricted to the gauging stations where they are originally calibrated, limiting their spatial capability. In this study, the semi-distributed hydrological water quality model HYPE (HYdrological Predictions for the Environment) was tested spatially to represent nitrate-N (NO 3 - - N) and total phosphorus (TP) concentrations and loads of the nested and heterogeneous Selke catchment (463 km2) in central Germany. First, an automatic calibration procedure and uncertainty analysis were conducted using the DiffeRential Evolution Adaptive Metropolis (DREAM) tool to simulate discharge, NO 3 - -N and TP concentrations. A multi-site and multi-objective calibration approach was applied using three main gauging stations, covering the most important hydro-meteorological and physiographical characteristics of the whole catchment. Second, the model's capability was tested to represent further internal stations, which were not initially considered for calibration. Results showed that discharge was well represented by the model at all three main stations during both calibration (1994–1998) and validation (1999–2014) periods with lowest Nash-Sutcliffe Efficiency (NSE) of 0.71 and maximum Percentage BIAS (PBIAS) of 18.0%. The model was able to reproduce the seasonal dynamics of NO 3 - -N and TP concentrations with low predictive uncertainty at the three main stations, reflected by PBIAS values in the ranges from −16.1% to 6.4% and from −20.0% to 11.5% for NO 3 - -N and TP load simulations, respectively. At internal stations, the model could represent reasonably well the seasonal variation of nutrient concentrations with PBIAS values in the ranges from −9.0% to 14.2% for NO 3 - -N and from −25.3% to 34.3% for TP concentration simulations. Overall, results suggested that the spatial validation of a nutrient transport model can be better ensured when a multi-site and multi-objective calibration approach using archetypical gauging stations is implemented. Further, results revealed that the delineation of sub-catchments should put more focus on hydro-meteorological conditions than on land-use features. [ABSTRACT FROM AUTHOR]

• Conventional and reversed GC×GC column setups were compared for Baijiu analysis. • OPLS-DA for aroma and regional Baijiu classification was used and validated. • Conventional GC×GC-TOFMS column setup classified better Jian and Strong flavours. • Strong aroma Baijiu of 3 regions were 100% correctly classified using both setups.. Baijiu is a traditional Chinese spirit with an extraordinarily rich pattern of volatile compounds resulting from the microflora involved in fermentation, as well as the complexity of technological process. Comprehensive two-dimensional gas chromatography in the conventional and reversed column setups (non-polar – polar and polar – non-polar columns) was tested for its ability to differentiate Baijiu samples in terms of their aroma and origin (region). A total of 65 Baijiu samples were used for the study and volatile compounds were extracted by SPME with a subsequent analysis by GC×GC-TOFMS. Orthogonality of both setups was compared, so was the number of compounds identified using each setup. Repeatability of compound groups for the conventional and reversed column setups was compared; moreover, multiblock orthogonal component analysis (MOCA) was used to visualize data sets. OPLS-DA was used for Baijiu classification. Both column setups provided excellent discrimination of the Light, Soy sauce, Feng and Herbal aromas. A better classification result for the Strong and Jian aromas was recorded for the conventional column setup. Within the Strong aroma using OPLS-DA both column setups provided perfect abilities to discriminate Baijiu from the Sichuan, Heilongjiang and Jiangsu regions. Two validation methods were applied in the classification models - all the predictive abilities evaluated by the internal validation were further confirmed by the external validation. [ABSTRACT FROM AUTHOR]

Flash flooding is the natural hazard provoking the largest number of casualties, so adequately characterizing vulnerability is key to improve flood risk analysis and management. Developing composite indices is the most widely used methodology in vulnerability analysis. However, very few studies have so far assessed vulnerability in urban areas prone to flash flooding and the resulting research presents two main drawbacks: i) a fragmented approach is often pursued, i.e. without jointly considering the vulnerability components (exposure, sensitivity and resilience) and the two most influential dimensions in urban environments (social and economic); and ii) vulnerability indices are not usually validated because an ancillary dataset is not generally available and flash flooding events do not happen simultaneously in all urban areas of a particular region. Considering the above gaps, this paper describes the construction of an Integrated Socio-Economic Vulnerability Index (ISEVI) at the regional scale, which considers all vulnerability components and social and economic dimensions. ISEVI was subsequently validated through an uncertainty and sensitivity analysis using the Monte Carlo method. Further, regional spatial patterns of vulnerability were identified implementing a Latent Class Cluster Analysis. Uncertainty analysis reveals the high stability of vulnerability categories of the ISEVI and sensitivity analysis shows that the type and the conservation state of buildings are the vulnerability factors that cause a greater variability in ISEVI scores. The method deployed here may allow specific strategies for vulnerability reduction to be developed based on disaggregating the validated ISEVI into dimensions and components and using the regional spatial patterns characterized. Unlabelled Image • An Integrated Socio-Economic Vulnerability Index (ISEVI) has been constructed and validated. • ISEVI's construction followed an integrated approach, considering vulnerability components and dimensions. • Uncertainty analysis shows the stability of the different categories of the ISEVI. • Sensitivity analysis notes which vulnerability factors causes greater variability in ISEVI scores. • Spatial patterns of vulnerability are identified, which may help decision makers to develop tailored mitigation strategies. [ABSTRACT FROM AUTHOR]

Abstract: Seven elements which comprised sensitivity, precision, reproducibility, non-probative casework, stutter, heterozygous peak height ratio and mixture interpretations were studied by the Forensic DNA Laboratory at the Department of Chemistry, Malaysia to validate the use of the AmpFlSTR® Identifiler PCR™ Amplification Kit on the ABI Prism® 3100 Genetic Analyzer. The AmpFlSTR® Identifiler PCR™ multiplex co-amplifies fifteen STRs loci, D8S1179, D21S11, D7S820, CSF1PO, D3S1358, TH01, D13S317, D16S539, D2S1338, D19S433, vWA, TPOX, D18S51, D5S818, FGA and the Amelogenin, the gender marker. Based on the results obtained, guidelines for the interpretation of DNA STR profiles are drawn up for use in forensic casework at the Department of Chemistry, Malaysia. [Copyright &y& Elsevier]

ABO blood typing has been used for personal identification and paternity testing in forensic medicine and criminal investigations for many years, since the ABO blood types obtained from evidentiary samples are usually one of the initial pieces of information available that could lead to identification of the suspect. Because of this importance of the initial information, many different molecular typing methods have been reported to complement routine serological ABO blood typing in forensics. The method using a multiplex allele-specific primer set enables the detection of six SNP sites in the ABO gene (nt 261, 526 and 803) along with the determination of ABO genotypes from their combinations, which is applied to forensic science casework samples in this paper. An internal validation study has been conducted to show that this method successfully determined ABO genotypes of blood, blood stain, saliva stain, buccal swab, vaginal fluid, hair and low copy number sample with high sensitivity, accuracy, and reproducibility. These results suggest that the method using a multiplex allele-specific primer set is so reliable that it can be used as a rapid screen for forensic science casework samples before multi locus short tandem repeat (STR) profiling is performed. [ABSTRACT FROM AUTHOR]

Cabo Verde is located on the West African coast, was discovered uninhabited by Portuguese explorers, colonized by European and African slaves and is culturally characterized by a mixture of their settlers. Cabo Verde is one of the countries with the highest number of emigrants living in Portugal. In order to validate the genetic markers included in the Combined DNA Index System and in the European Standard Set, samples from 500 unrelated individuals from Cabo Verde, currently living in Lisboa, were studied with the PowerPlex ® Fusion System. To implement the genotyping method at the Serviço de Genética e Biologia Forenses (SGBF) we determined the minimum final volume of the PCR reaction, the proper number of PCR cycles and the minimum DNA concentration to obtain a complete genetic profile. Stutter identification and its proportion and allelic imbalance within markers were also determined. Results were compared with genetic profiles obtained by the current PCR kits used in SGBF routine testing verifying concordance between common loci. There were no off-ladder alleles detected and it was possible to type microvariants which were not present in the allelic ladders of other PCR kits. PowerPlex ® Fusion System allows for the simultaneous characterization of Combined DNA Index System and in the European Standard Set genetic markers, in an economically way, with greater sensitivity and solid results. [ABSTRACT FROM AUTHOR]

Summary: Background: The delay in diagnosis and treatment of tuberculous meningitis (TBM) is a major factor in the high mortality observed with this pathology. The distinction between bacterial meningitis (BM) and TBM by clinical features alone is often impossible, and the available biological resources remain inadequate or inaccessible, especially in developing countries. We attempted to develop a simple diagnostic algorithm on the basis of clinical and laboratory findings that could be used as an early predictor of TBM in adult patients in Morocco. Methods: We compared the clinical and laboratory features on admission of 508 adults in a medical intensive care unit in Morocco who satisfied diagnostic criteria for tuberculous (n =274) or bacterial (n =234) meningitis. Features independently predictive of TBM were modeled by multivariate logistic regression to create a diagnostic rule, and by a classification and regression tree (CART). Results: Six features were predictive of a diagnosis of TBM: female gender, duration of symptoms, the presence of localizing signs, white blood cell (WBC) count, the level of serum sodium, and the total cerebrospinal fluid WBC count. The sensitivity for CART was 87% and for a score >7 was 88%; specificity was 96% and 95%, respectively. The internal validation was excellent for both diagnostic methods, with a receiver operating characteristic (ROC) area of 0.906 bootstrap samples for a score >7 and 0.910 for CART. Conclusions: The clinical and laboratory parameters identified in this study may help the clinician with the empiric diagnosis of TBM and could be used in settings with limited microbiological diagnostic support. [Copyright &y& Elsevier]

Abstract: The AmpFLSTR NGM SElect™ is a next generation kit developed by Applied Biosystems that contains the 5 new loci specified in the recently expanded European Standard Set (ESS) together with the remaining markers from the SGM Plus® kit, plus the highly discriminating SE33. We performed a validation study of the NGM SElect Kit with evaluation of critical parameters as species specificity, sensitivity, degradation/inhibition study, performance on a wide variety of forensic samples, mixture sample analyses. Our feedback confirmed the multiplex shows a robust PCR chemistry and the improved performance requested by the forensic community for typing difficult samples and offers the option of a 29 or 30 cycle protocol for optimum data quality and sensitivity. [Copyright &y& Elsevier]

Abstract: Isolation of DNA from forensic samples is strongly conditioned by many factors such as wide variety of samples, degradation by environmental exposure, presence of PCR inhibitors or limited quantity of starting material. BTA performance in the extraction of genomic DNA has been evaluated from real casework powdered bones, teethes, carbonized tissues cigarettes, and tape adhesive lifts. DNA extracted from these samples was in sufficient quantities for downstream applications; the method was successfully able to remove inhibitors producing highly purified DNA; IPC values of Quantifiler® Human DNA Quantification Kit were consistent and within the normal range. The combination of BTA+AmpFℓSTR NGM PCR Amplification Kit provided a powerful tool for the typing of difficult forensic samples. [Copyright &y& Elsevier]