R2 Index Research Articles

Predictive accuracy of novel risk factors and markers: A simulation study of the sensitivity of different performance measures for the Cox proportional hazards regression model.

Predicting outcomes that occur over time is important in clinical, population health, and health services research. We compared changes in different measures of performance when a novel risk factor or marker was added to an existing Cox proportional hazards regression model. We performed Monte Carlo simulations for common measures of performance: concordance indices (c, including various extensions to survival outcomes), Royston’s D index, R2-type measures, and Chambless’ adaptation of the integrated discrimination improvement to survival outcomes. We found that the increase in performance due to the inclusion of a risk factor tended to decrease as the performance of the reference model increased. Moreover, the increase in performance increased as the hazard ratio or the prevalence of a binary risk factor increased. Finally, for the concordance indices and R2-type measures, the absolute increase in predictive accuracy due to the inclusion of a risk factor was greater when the observed event rate was higher (low censoring). Amongst the different concordance indices, Chambless and Diao’s c-statistic exhibited the greatest increase in predictive accuracy when a novel risk factor was added to an existing model. Amongst the different R2-type measures, O’Quigley et al.’s modification of Nagelkerke’s R2 index and Kent and O’Quigley’s displayed the greatest sensitivity to the addition of a novel risk factor or marker. These methods were then applied to a cohort of 8635 patients hospitalized with heart failure to examine the added benefit of a point-based scoring system for predicting mortality after initial adjustment with patient age alone.

Haplotyping strategy highlights the specificity of FTO gene association with polycystic ovary syndrome in Tunisian women population

The FTO (fat mass and obesity associated) gene was associated with different metabolic disorders in populations from different origins but with great difference between African and non-African populations. North-African populations combine many genetic backgrounds, among which African, Berber and Caucasian components, which makes North-Africans a good model for studying the genetic association of FTO. In the present investigation we explored the association of FTO gene with polycystic ovary syndrome (PCOS) in a population from Tunisia (n=278). Single nucleotide polymorphisms (SNPs) used in this study were previously associated in non-African populations: rs8050136 (A/C), rs9939609 (A/T), rs9930506 (G/A), or in both African and non-African populations: rs8057044 (A/G). Genotyping was performed by allelic discrimination method on StepOne real-time PCR system or KASPar technology. Linkage disequilibrium (LD) pattern was assessed by HAPLOVIEW and reconstruction of haplotypes was performed by PHASE, while statistical analyses were performed using StatView and GoldenHelix programs. Among the 13 haplotypes in the population, three (h1, h7 and h13) were strongly associated with PCOS notably h13 (P<0.0001, OR95%CI=0.040 [0.005–0.294]) while SNPs display weaker association. Moreover the LD pattern in FTO in the Tunisian population (r2 index) was intermediary between those of Caucasian and Africans. This highlights the need for studying the genetics of complex disorders in the North-African populations taking into-account the haplotype structure of candidate loci more than SNPs taken alone.

An Intelligence Approach to Predict Fire Flame Length under Tunnel Ceiling

Various analytical models have developed to determine fire flame length under tunnel ceilings during fire emergency based on the regression and dimensional analysis. Artificial intelligence techniques are now being used as an alternate to statistical techniques. In this study, the artificial neural network (ANN) is applied to forecast fire flame length in tunnels. Moreover, particle swarm optimization algorithms were used for ANN training in order to overcome very slow convergence and easy entrapment in a local minimum of back propagation training algorithms. The model predicts flame length using Fire Heat Release Rate, Air velocity, Tunnel Width, Tunnel Height and Tunnel Cross Section. The predictive PSO-ANN model was implemented on the MATLAB and developed based on a database including 44 data sets from large scale fire test programs. The coefficient of determination (R2), the variance account for (VAF) and the root mean square error (RMSE) were calculated to check the prediction performance of the model. The R2, VAF and RMSE indices were obtained as95.884, 99.86% and 1.05.These indices revealed that the developed model is suitable for practical use in tunnels.

Heart Rate Variability Responses in Vertical Jump Performance of Basketball Players

Our objectives were to investigate the acute responses of the heart rate variability (HRV) before, during and after the execution of different Vertical Jump (VJ) protocols and correlate the levels of sympathetic and parasympathetic modulation after the VJ execution with the performance variables in VJ. Eight male athletes (29±4.9 years; 186±8.6 cm; 24.8±2.1 kg.m-2; 12±1.2% body fat) of a team participating in the first division of the National Basketball League (NBL), from Brazil, were evaluated. For the HRV analysis, each subject was instructed to stand in a orthostatic position on the jump platform System Optical (Cefise®, Sao Paulo) for a period of 10 minutes of rest before (R1) and after the VJ executions (R2). Right after the R1 period, athletes performed 5VJ with five seconds intervals (total time ~ 30 seconds) between VJ (5VJ) and then underwent 60 seconds of maximum continuous VJ (60SVJ). The HRV were automatically transferred and analyzed by the softwares Polar ProTrainer5™ (version 5.41.002., Kempele, Finland) and HRV analisys (version 2.1., Kuopio, Finland), respectively. The HFnu was used to represent the parasympathetic modulation, whereas LFnu represented the sympathetic modulation. The ratio LF:HF was used to represent the balance between sympathetic and parasympathetic modulations. The one-way Analysis of Variance (ANOVA) with post hoc Tukey test was used to compare differences in HRV during the execution of the experimental protocol. Linear regression and Pearson's correlation coefficient were used between the VJ performance variables (mean of 5VJ and 60SVJ, jumpsnumber and fatigue index) and the sympathetic and parasympathetic modulation LF:HF R2 (p< 0.05). The following comparisons of the HRV were found between the different protocols: 5VJ (LF 93.9±5.4 nu) versus R1 (LF 64.2±19.9 nu); 5VJ (HF 6.0±5,4 nu) versus R1 (HF 35.6±19,9 nu); 5VJ (LF:HF 40.1±37.4) versus R1 (LF:HF 2.6±1.9 nu) (p < 0.001); 60SVJ (LF:HF 8.3±13.8 ms²) versus 5VJ (LF:HF 40.1±7.7 ms²); R2 (LF:HF 6.5±5.7 ms²) versus 5VJ (LF:HF 40.1±7.7 ms²) (p < 0.001). A significant correlation was found between the average height of 5VJ with the sympathetic and parasympathetic modulation LF:HF R2 (r = 0.7942 , p = 0.01 ) and fatigue index 60SVJ with the sympathetic and parasympathetic modulation LF:HF R2 (r = 0.7206 , p = 0.04). It is concluded that the 5VJ test characterized by intermittent high intensity loads, showed high significant responses of sympathetic modulation (LF; LF:HF) compared to the 60SVJ protocol. The 5 min recovery was not sufficient for the onset of vagal tone (parasympathetic modulation), which was accompanied by higher fatigue index presented by basketball athletes.

Comparisons of Patient and Physician Assessment of Pain-Related Domains in Cancer Pain Classification: Results From a Large International Multicenter Study

The aim of the present study is to compare physician clinical assessment with patient-rated evaluations in the classification of cancer pain patients into groups with different pain levels, according to the presence of incident/breakthrough pain, neuropathic pain, and psychological distress. Average pain in the previous 24 hours was used as the dependent variable in multivariate linear regression models, and incident/breakthrough pain, neuropathic pain, and psychological distress were tested as regressors; in the assessment of regressors, physicians used the Edmonton Classification System for Cancer Pain, whereas patients used structured self-assessment questionnaires. The amount of variability in pain intensity scores explained by the 2 sets of regressors, physician and patient rated, was compared using R2 values. When tested in 2 separate models, patient ratings explained 20.3% of variability (95% confidence interval [CI] = 15.2–25.3%), whereas physician ratings explained 6.1% (95% CI = 2.2–9.8%). The higher discriminative capability of patient ratings was still maintained when both regressor sets were introduced in the same model, with R2 indices of 17.6% (95% CI = 13.0–22.2%) for patient ratings vs 3.4% (95% CI = .9–5.9%) for physician ratings. Patients' self-assessment of subjective symptoms should be integrated in future cancer pain classification systems. PerspectiveOur results indicate that patient-structured assessment of incident/breakthrough pain, neuropathic pain, and psychological distress significantly contributes to the discrimination of cancer patients with different pain levels. The integration of patient self-assessment tools with more objective clinician assessments can improve the classification of cancer pain.

Mixed Species Allometric Models for Estimating above-Ground Liana Biomass in Tropical Primary and Secondary Forests, Ghana

The study developed allometric models for estimating liana stem and total above-ground (TAGB) biomass in primary and secondary forests in the Asenanyo Forest Reserve, Ghana. Liana biomass was determined for 50 individuals for each forest using destructive sampling. Various predictors involving liana diameter and length were run against liana biomass in regression analysis, and R2, RMSE, and Furnival's index of fit (FI) were used for model comparison. The equations comprised models fitted to untransformed and log-transformed data. Forest type had a significant influence (P&lt;0.05) on liana allometric models in the current study, resulting in the development of forest-type-specific equations. There were significant and strong linear relationships between liana biomass and the predictors in both forests (R2&gt;0.970). Liana diameter was a better predictor of biomass than liana length. Generally, the models which were based on log-transformed data showed better fit (higher FI values) than those fitted to untransformed data. Comparison of the site specific models in the current study with previously published models indicated that the models of the current study differed from the previous ones. This indicates the need for forest specific equations to be used for accurate determination of above-ground liana biomass.

Structural Modeling of HCV NS3/4A Serine Protease Drug-Resistance Mutations Using End-Point Continuum Solvation and Side-Chain Flexibility Calculations

Computational methods of modeling protein-ligand interactions have gained widespread application in modern drug discovery. In continuum solvation-based methods of binding affinity estimation, limited description of solvent environment and protein flexibility is traded for a time scale that fits medicinal chemistry test cycles. The results of this speed-accuracy trade-off have been promising in terms of modeling structure-activity relationships of ligand series against protein targets. The potential of these approaches in recapitulating structural and energetic effects of resistance mutations, which involve large changes in binding affinity, remains relatively unexplored. We used continuum solvation binding affinity predictions and graph theory-based flexibility calculations to model thirteen drug resistance mutations in HCV NS3/4A serine protease, against three small-molecule inhibitors, with a 2-fold objective: quantitative assessment of binding energy predictions against experimental data and elucidation of structural/energetic determinants of resistance. The results show statistically significant correlation between predicted and experimental binding affinities, with R(2) and predictive index of up to 0.83 and 0.91, respectively. The level of accuracy was consistent with what has been reported for the inverse problem of binding affinity estimation of congeneric ligands against the same target. The quality of predictions was poor for mutations involving induced-fit effects, primarily, because of the lack of entropy terms. Flexibility analysis explained this discrepancy by indicating characteristic changes in side-chain mobility of a key binding site residue. The combined results from two approaches provide novel insights regarding the molecular mechanism of resistance. NS3/4A inhibitors, with large P2 substituents, derive high affinity with optimal van der Waals interactions in the S2 subsite, in order to overcome unfavorable desolvation and entropic cost of induced-fit effects. High-level resistance mutations tend to increase the desolvation and/or entropic barrier to ligand binding. The lead optimization strategies should, therefore, address the balance of these opposing energetic contributions in both the wild-type and mutant target.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Abstract Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

Abstract. Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and R2 of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and R2 of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and R2 indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

The blink reflex recovery cycle differs between essential and presumed psychogenic blepharospasm

Psychogenic blepharospasm is difficult to distinguish clinically from benign essential blepharospasm (BEB). The blink reflex recovery cycle measures the excitability of human brainstem interneurons and is abnormal in BEB. We wished to study the blink reflex recovery cycle in patients with atypical (presumed psychogenic) blepharospasm (AB). This was a prospective data collection study investigating the R2 blink reflex recovery cycle at interstimulus intervals (ISI) of 200, 300, 500, 1,000, and 3,000 msec in 10 patients with BEB, 9 patients with AB, and 9 healthy controls. All patients had spasm of the orbicularis oculi muscles. To compare individual patients, an R2 recovery index was calculated as average of the recovery values at ISIs of 200, 300, and 500 msec, with the upper limit of normal defined as mean (control group) + 2 SD. The R2 recovery cycle was significantly disinhibited in patients with BEB, whereas patients with AB did not differ from controls on a group level. The upper limit of normal for the R2 recovery index was 61%. The R2 index was abnormal in 9 out of 10 patients with BEB and in none of the patients with AB. A normal blink reflex recovery cycle indicates normal brainstem interneuron excitability. Assessment of the R2 recovery cycle may provide a useful diagnostic tool to distinguish patients with psychogenic blepharospasm from BEB and is worthy of further study.

Predicting carcass energy content and composition in broilers using the group method of data handling-type neural networks

SUMMARYThe success of poultry meat production has been strongly related to improvements in growth and carcass yield, mainly by increasing breast proportion and reducing carcass fat. Conventional laboratory techniques for determining carcass composition are expensive, cumbersome and time consuming. These disadvantages have prompted a search for alternative methods. In this respect, the potential benefits from modelling growth are considerable. Neural networks (NNs) are a relatively new option for modelling growth in animal production systems. One self-organizing sub-model of artificial NN is the group method of data handling-type NN (GMDH-type NN). The present study aimed at applying the GMDH-type NNs to data from two studies with broilers in order to predict carcass energy (CEn, MJ/g) content and relative growth (g/g of body weight) of carcass components (carcass protein, breast muscle, leg and thigh muscles, carcass fat, abdominal fat, skin fat and visceral fat). The effective input variables involved in the prediction of CEn and carcass fat content using data from the first study were dietary metabolizable energy (ME, kJ/kg), crude protein (CP, g/kg of diet), fat (g/kg of diet) and crude fibre (CF, g/kg of diet). For data from the second study, the effective input variables involved in the prediction of carcass components were dietary ME (MJ/kg), CP (g/kg of diet), methionine (g/kg of diet), lysine (g/kg of diet) and body weight (kg). Quantitative examination of the goodness of fit, using R2 and error measurement indices, for the predictive models proposed by the GMDH-type NN revealed close agreement between observed and predicted values of CEn and carcass components.

Predicting caloric and feed efficiency in turkeys using the group method of data handling-type neural networks

Neural networks (NN) are a relatively new option to model growth in animal production systems. One self-organizing submodel of artificial NN is the group method of data handling (GMDH)-type NN. The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. The present study aimed to apply the GMDH-type NN to predict caloric efficiency (CE, g of gain/kcal of caloric intake) and feed efficiency (FE, kg of gain/kg of feed intake) in tom and hen turkeys fed diets containing different energy and amino acid levels. Involved effective input parameters in prediction of CE and FE were age, dietary ME, CP, Met, and Lys. Quantitative examination of the goodness of fit for the predictive models was made using R2 and error measurement indices commonly used to evaluate forecasting models. Statistical performance of the developed GMDH-type NN models revealed close agreement between observed and predicted values of CE and FE. In conclusion, using such powerful models can enhance our ability to predict economic traits, make precise prediction of nutrition requirements, and achieve optimal performance in poultry production.

Effects of Various Soap Elements on Skin

109 Objective: To elucidate the effects of sea salt and various other soap ingredients on skin in humans. Methods: Forty healthy women (age 30.4 ± 6.0 years) were assigned to one of four test-soap groups (sea salt, SS; collagen added, SS+C; humic added, SS+H; or, fucoidan added, SS+F) or a control group. Subjects washed their face with the soap twice daily for eight weeks. Before and after the study, subjects undertook the Anti-Aging QOL Common Questionnaire (AAQol), skin image analysis (VISIAII), elasticity tests by a Cutometer (MPA580), and moisture tests by a Corneometer (CM825). Results: At 8 weeks, significant improvements were seen in symptoms scores for “noticeable blackheads in pores” and “oily face” in the SS group, the score for “concerned about spots or freckles” in the SS+H group, and the scores for “noticeable pores”, “concerned about pore opening”, “dry skin”, “rough skin”, “concerned about rough skin”, and “corner of eyes sagging” in the SS+F group. There was no significant change in skin moisture. Compared to controls, the R2 index of skin elasticity showed improvement in the SS+C group by inter-group analysis (p=0.009). On skin image analysis, the number of face wrinkles bilaterally (– 26.6%, p=0.008) in the SS+F group and the number of wrinkles on the right side of the face (– 45.5%, p＝0.005) in the SS group) showed significant improvement. Although an increase in ultraviolet (UV) spots was seen in the SS and control groups, this increase was significantly attenuated in the SS+C group (inter-group analysis: p<0.05). Further, compared to controls, there was a significant reduction in the number of red spots in the SS+H group (number：– 7.9%, p＝0.022, % area：– 11.7%, p=0.039). No severe adverse effects were observed during this study. Conclusion: An improvement in wrinkles was seen in the SS group, with different additional elements displaying benefits in different areas of skin appearance and health. The SS+C group showed beneficial results with regard to elasticity and UV spots, the SS+H group showed beneficial results in red spots, and the SS+F group showed beneficial results in moisture levels and wrinkles.

A Multipurpose Portable Instrument for Determining Ripeness in Wine Grapes Using NIR Spectroscopy

This paper describes the development of a noninvasive instrument that is designed to measure three parameters of ripeness in wine grapes, i.e., sugar (Brix), pH, and anthocyanin concentration. The instrument is based on near-infrared spectroscopy, and it comes in contact with the berry in the cluster without altering its ripening process. A thorough description of the calibration process for the instrument is done for the different grape varieties, e.g., Cabernet Sauvignon, Carmenere, Merlot, Pinot Noir, and Chardonnay. Samples from vineyards located in the Maipo Valley (Chile) taken during the 2003 season were processed to develop calibration models using partial least squares techniques. The models were validated in terms of root mean square error of validation and R2 indices. The results show the great potential of this technique regarding Brix and pH measurements. For the anthocyanin concentration measurements, the results are promising but require an accurate procedure to obtain reference values for model calibration. The instrument can be useful for sampling strategies that look for optimum harvest schedules according to grape maturity in terms of not only sugar content but also pH and anthocyanin concentration.

Neural network-based prediction techniques for global modeling of M(3000)F2 ionospheric parameter

In recent times neural networks (NNs) have been employed to solve many problems in ionospheric predictions. This paper illustrates a new application of NNs in developing a global model of the ionospheric propagation factor M(3000)F2. NNs were trained with daily hourly values of M(3000)F2 from various ionospheric stations spanning the period 1964–1986 with the following temporal and spatial input parameters: Universal Time, geographic latitude, magnetic inclination, magnetic declination, solar zenith angle, day of the year, A16 index (a 2-day running mean of the 3-h planetary magnetic ap index), R2 index (a 2-month running mean of sunspot number), and the angle of meridian relative to the subsolar point. The performance of the NNs was verified by comparing the predicted values of M(3000)F2 with observed values from a few selected ionospheric stations and the IRI (International Reference Ionosphere) model (CCIR M(3000)F2 model) predicted values. The results obtained compared favourably with the IRI model. Based on the error differences, the result obtained justifies the potential of the NN technique for the predictions of M(3000)F2 values on a global scale.

Comparison of hydrologic calibration of HSPF using automatic and manual methods

The automatic calibration software Parameter Estimation (PEST) was used in the hydrologic calibration of Hydrological Simulation Program–Fortran (HSPF), and the results were compared with a manual calibration assisted by the Expert System for the Calibration of HSPF (HSPEXP). In this study, multiobjective functions based on the HSPEXP model performance criteria were developed for use in PEST, which allowed for the comparison of the calibration results of the two methods. The calibrated results of both methods were compared in terms of HSPEXP model performance criteria, goodness‐of‐fit measures (R2, E, and RMSE), and base flow index. The automatic calibration results satisfied most of the HSPEXP model performance criteria and performed better with respect to R2, E, RMSE, and base flow index than manual calibration results. The results of the comparison with the manual calibration suggest that the automatic method using PEST may be a suitable alternative to manual method assisted by HSPEXP for calibration of hydrologic parameters for HSPF. However, further research of the weights used in the objective functions is necessary to provide guidance when applying PEST to surface water modeling.

Ratios of involved nodes in early breast cancer

IntroductionThe number of lymph nodes found to be involved in an axillary dissection is among the most powerful prognostic factors in breast cancer, but it is confounded by the number of lymph nodes that have been examined. We investigate an idea that has surfaced recently in the literature (since 1999), namely that the proportion of node-positive lymph nodes (or a function thereof) is a much better predictor of survival than the number of excised and node-positive lymph nodes, alone or together.MethodsThe data were abstracted from 83,686 cases registered in the Surveillance, Epidemiology, and End Results (SEER) program of women diagnosed with nonmetastatic T1–T2 primary breast carcinoma between 1988 and 1997, in whom axillary node dissection was performed. The end-point was death from breast cancer. Cox models based on different expressions of nodal involvement were compared using the Nagelkerke R2 index (R2N). Ratios were modeled as percentage and as log odds of involved nodes. Log odds were estimated in a way that avoids singularities (zero values) by using the empirical logistic transform.ResultsIn node-negative cases both the number of nodes excised and the log odds were significant, with hazard ratios of 0.991 (95% confidence interval 0.986–0.997) and 1.150 (1.058–1.249), respectively, but without improving R2N. In node-positive cases the hazard ratios were 1.003–1.088 for the number of involved nodes, 0.966–1.005 for the number of excised nodes, 1.015–1.017 for the percentage, and 1.344–1.381 for the log odds. R2N improved from 0.067 (no nodal covariate) to 0.102 (models based on counts only) and to 0.108 (models based on ratios).DiscussionRatios are simple optimal predictors, in that they provide at least the same prognostic value as the more traditional staging based on counting of involved nodes, without replacing them with a needlessly complicated alternative. They can be viewed as a per patient standardization in which the number of involved nodes is standardized to the number of nodes excised. In an extension to the study, ratios were validated in a comparison with categorized staging measures using blinded data from the San Jose–Monterey cancer registry. A ratio based prognostic index was also derived. It improved the Nottingham Prognostic Index without compromising on simplicity.

Testing an Antispasticity Drug (Tetrazepam) with the Pendulum Test: A Monocentric Pilot Study

The improved pendulum test of Wartenberg provides reliable and reproducible data in spastic patients. The R2 index calculated from the pendulum curve is the most useful single value to quantify spasticity of the legs. During a drug trial with the 1,4- benzodiazepine tetrazepam, the R2 indicates faithfully the changes in spasticity. It is also useful in deciding when a dose increase is needed in individual cases. Compared with the Ashworth Scale, it is independent of the raters' impression and expressed in digital values. Its current limitation is that it can be used only for the lower extremi ties. Its decisive advantage over other methods is that it expresses the degree of spas ticity in a single value. Tetrazepam was applied in increasing doses to nine patients and proved to be an effective antispastic drug with good tolerability and only minor side effects. The changes in the R2 index during a 5-day trial were statistically significant.

Frozen strawberries with improved thaw texture

It is necessary to apply some preservatives for tomato paste since in the harvest season, a high load of tomatoes need to be processed as they are among highly perishable products. Application of antimicrobial extracts or essential oils, as natural preservatives, in their raw forms might reduce their efficiency when they are exposed to environmental conditions. However, microencapsulation is a well-known method to solve this problem. Our main goal was to restrict fungal growth rate in stored tomato paste and increase its storage stability by incorporating encapsulated olive leaf phenolic-rich extract. Total Soluble Solids (TSS), consistency, pH, color indices and diametrical growth rate of Aspergillus flavus were measured for different samples. The treatments designed in terms of considering two levels of non-encapsulated olive phenolics extract with 500 and 1000 ppm (NE500 and NE1000), the same levels with encapsulated extract; i.e., 500 and 1000 ppm (ME500 and ME1000), and similar levels of the common preservative of sodium benzoate with 500 and 1000 ppm (B500 and B1000). Antifungal properties of NE samples were higher than ME ones during storage although ME samples could maintain diametrical growth rate of the fungus more stable than NE ones. NE samples justified lower maximum growth rate than ME samples while ME samples could extend lag phase of microbial growth compared with NE one and delay their internal deteriorative reactions. Among Baranyi, modified Baranyi, Modified Gompertz, and Logistic models, Modified Gompertz model represented the best model and could fit the growth factors of A. flavus on tomato paste with higher R2 index as well as lower RMSE and SSE indices. Based on the results obtained, it could be concluded that usage of encapsulated olive leaf extract in tomato paste is an effective, natural and sustainable approach to improve the shelf life of tomato paste since this natural compound could perform as favourable as preservatives; also it could maintain physicochemical as well as microbial properties of tomato paste for a long term. Thus, it is strongly recommended that application of encapsulated olive leaf extract to be considered seriously by the tomato paste industry as it can effectively reduce the mold and fungal contaminations which are very common and prevalent in the plants. The future work in this regard should focus on sensory evaluations when incorporating encapsulated olive leaf extract into tomato paste.

Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors.

Inhibition of aromatase, a cytochrome P450 that converts androgens to estrogens, is relevant in the therapeutic control of breast cancer. We investigate this inhibition using a three-dimensional quantitative structure-activity relationship (3D QSAR) method known as Comparative Molecular Field Analysis, CoMFA [Cramer III, R.D. et al., J. Am. Chem. Soc., 110 (1988) 5959]. We analyzed the data for 50 steroid inhibitors [Numazawa, M. et al., J. Med. Chem., 37 (1994) 2198, and references cited therein] assayed against androstenedione on human placental microsomes. An initial CoMFA resulted in a three-component model for log(l/Ki), with an explained variance r2 of 0.885, and a cross-validated q2 of 0.673. Chemometric studies were performed using GOLPE [Baroni, M. et al., Quant. Struct.-Act. Relatsh., 12 (1993) 9]. The CoMFA/GOLPE model is discussed in terms of robustness, predictivity, explanatory power and simplicity. After randomized exclusion of 25 or 10 compounds (repeated 25 times), the q2 for one component was 0.62 and 0.61, respectively, while r2 was 0.674. We demonstrate that the predictive r2 based on the mean activity (Ym) of the training set is misleading, while the test set Ym-based predictive r2 index gives a more accurate estimate of external predictivity. Using CoMFA, the observed differences in aromatase inhibition among C6-substituted steroids are rationalized at the atomic level. The CoMFA fields are consistent with known, potent inhibitors of aromatase, not included in the model. When positioned in the same alignment, these compounds have distinct features that overlap with the steric and electrostatic fields obtained in the CoMFA model. The presence of two hydrophobic binding pockets near the aromatase active site is discussed: a steric bulk tolerant one, common for C4, C6-alpha and C7-alpha substituents, and a smaller one at the C6-beta region.