TR Model Research Articles

Do Entangled Polymer Chains Reptate?

AbstractNeutron spin echo spectroscopy of entangled polymer melts [M. Zamponi, et al. J. Phys. Chem. B 2008, 112, 16220], and of tracer diffusion of short polymer chains in highly entangled polymer melt [M. Zamponi et al. Phys. Rev. Lett. 2021, 126, 187801.] and [M. Kruteva et al. Macromolecules 2021, 54, 11384] found the center‐of‐mass mean‐square displacements at shorter times are subdiffusive, heterogeneous, non‐Gaussian, and cooperative. These properties contradict the assumption of reptation within the tube in the tube‐reptation (TR) model, but are in accord with the predictions from the many‐chain cooperative dynamics in the theory of Guenza. The inadequacy of the TR model revealed by the microscopic experiments and theory motivates the author to reexamine previously published data of diffusion of entangled polymer chains from experiments and simulations used to test the TR model. The results reported in this study lead to the conclusion that the key predictions of the TR model are at variance with experimental and simulation data. The cause lies in the reptation hypothesis contradicting the cooperative nature of entangled chain diffusion proven by its dynamics being isomorphic to cooperative diffusion in other materials. The Coupling Model has predictions consistent with the cooperative diffusion properties in interacting materials [Prog. Mater. Sci., 2023, 139, 101130.]. Applied to the entangled polymers, the predictions successfully explain the data, especially those contradicting the TR model. Thus, diffusion of entangled polymer chains is a cooperative many‐chain process in having the universal properties of many‐body cooperative diffusion established in many other interacting materials, and the reptation hypothesis is unwarranted.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

BackgroundNoninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.MethodsA total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman’s rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.ResultsThe T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821–0.936) and 0.911 (95%CI, 0.825–0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854–0.958) and 0.918 (95%CI, 0.831–0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709–0.863) and 0.782 (95%CI, 0.660–0.902)] in the both cohorts and achieved good calibration capability and clinical utility.ConclusionsCE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Disentangling the independent and interacting impacts of biophysical factors on the transpiration of a black locust (Robinia pseudoacacia) plantation in the semiarid Loess Plateau, China

AbstractTranspiration (Tr) is the largest continental water flux. Tr is jointly affected by biophysical factors, including atmospheric demand, soil water supply, and plant physiological features, represented by potential evapotranspiration (PET), soil water content (SWC), and leaf area index (LAI) respectively. Disentangling the independent and interacting impacts of the biophysical factors on Tr provides a better understanding on the mechanism of ecohydrological controls on forest water use. In this study, taking the black locust plantation of the Loess Plateau as an example, independent contributions of the three factors and their interactions on Tr were quantified based on field observation, model development, and factorial analysis. First, sapflow density, meteorological variables, SWC, and LAI were observed during five growing seasons. Second, a coupled Tr model (R2 = 0.85, RMSE = 5.92) was established by the long‐term observed data of Tr, PET, SWC, and LAI. Finally, factorial experiment analysis showed that contributions of LAI, PET, and SWC and their interactions (PET×LAI, LAI × SWC, and PET×SWC) on Tr accounted for 50.82%, 9.77%, 9.30%, 13.88%, 13.75%, and 2.47%, respectively. The results indicated that LAI and PET×LAI had the largest independent and interacting impact on Tr of black locust. This study suggested that regulation of canopy leaf area through forest cultivation practices could reduce forest water use, which was beneficial for soil desiccation mitigation and meeting the soil water capacity for vegetation in this region. This study also provided a methodological reference for disentangling the impacts of biophysical factors on forest transpiration.

Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO2 and drought.

The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (Tleaf ) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and Tleaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO2 ]) and drought. Here we established an [CO2 ] enrichment experiment in which three wheat genotypes were grown at ambient (400 ppm) and elevated (550 ppm) [CO2 ] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (Tr ) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at 1.5 mfrom the plants to derive PRI and the temperature difference between the leaf and its surrounding air ( Tleaf-air ). We found significant PRI and RUE and Tleaf-air and Tr correlations, with no significant differences among the genotypes. A PRI-RUE decoupling was observed under drought at ambient [CO2 ] but not at elevated [CO2 ], likely due to changes in photorespiration. For a LE range of 350 W m-2 , the ΔTleaf-air range was 10°C at ambient [CO2 ] and only 4°C at elevated [CO2 ]. Thicker leaves in plants grown at elevated [CO2 ] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO2 ] conditions. In general, Tleaf was maintained closer to the ambient temperature at elevated [CO2 ], even under drought. PRI, RUE, ΔTleaf -air , and Tr decreased linearly with canopy depth, displaying a single PRI-RUE and ΔTleaf -air Tr model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

COVID-19 pandemic and preparedness of teachers for online synchronous classes

COVID-19 pandemic has forced educational institutes to shut down, and teachers are compelled to adopt technology ardently so that the teaching-learning process does not suffer. Gradually, it is being realised that synchronous online classes are required to enhance the teaching-learning experience. The major challenge in India is the lack of preparedness of the teachers, as most teachers have little experience with technology. Nevertheless, they have to adapt themselves quickly. However, to effectively use technology for synchronous online teaching, teachers have to be technology ready and proficient with utilising the platform used for online classes. This study attempts to understand the impact of teachers' preparedness on the use of online platforms for synchronous teaching during the COVID-19 pandemic. This paper integrates the technology readiness index (TRI) and technology acceptance model (TAM), also known as the TR and acceptance model (TRAM), to investigate the phenomenon mentioned above.

Comparison and implementation of machine learning models for predicting the combustion phases of hydrogen-enriched Wankel rotary engines

Combustion phases, such as the development period (CA0-10) and flame propagation period (CA10-90), are the critical parameters for hydrogen-enriched Wankel rotary engines. An accurate simulation model and a suitable engine management system are required to control combustion phases. In this paper, five machine learning (ML) models, including the linear regression (LR), regression tree (TR), ensembles of trees (EnTR), support vector machine (SVM), and Gaussian process regression (GPR), are initially applied to predict combustion phases. Experiments were performed with variations of the main fuel types (gasoline and n-butanol), loads (idle and part load), ignition timing, hydrogen volume fraction, and excess air ratio. The sample data were divided into training and testing data set, and the normalization method, 5-fold cross-validation, and Bayesian optimization algorithm were used for data processing and model optimization. Among five ML models, the training speed of the LR model was the fastest; the generalization ability of the TR model was the worst. The minimum leaf size of the TR model significantly influenced regression and generalization ability. On this basis, the EnTR model improved the regression ability, but required more training time. The GPR model showed the best generalization ability among the above model, while SVM performed well in a certain data set. For CA0-10, the coefficient of determination (R2) of the best LR, TR, EnTR, SVM and GPR models was 0.9910, 0.9912, 0.9985, 0.9984 and 0.9994, respectively; for CA10-90, the R2 was 0.9348, 0.8974, 0.9873, 0.9916 and 0.9975, respectively. It is highly recommended to apply the GPR model to the combustion phases prediction and control system modeling.

Assessment of Able-Bodied and Amputee Cyclists' Aerodynamics by Computational Fluid Dynamics.

The aim of this study was to compare the aerodynamics of able-bodied and amputee cyclists by computational fluid dynamics. The cyclists’ geometry was obtained by a 3D scanner. Three CAD models were created as able-bodied, transtibial (Tt), and transradial (Tr) amputees. Numerical simulations were conducted up to 13 m/s with increments of 1 m/s to assess drag force. The drag ranged between 0.36 and 39.25 N for the able-bodied model, 0.36–43.78 for the Tr model and 0.37–41.39 N for the Tt model. The pressure drag ranged between 0.20 and 22.94 N for the normal model, 0.21–28.61 for the Tr model and 0.23–28.02 N for the Tt model. The viscous drag ranged between 0.16 and 15.31 N for the normal model, 0.15–15.17 for the Tr model and 0.14–13.38 N for the Tt model. The rolling resistance (RR) was higher on the able-bodied (2.23 N), followed by the Tr (2.20 N) and Tt (2.17 N) models. As a conclusion, the able-bodied cyclist showed less drag, followed by the Tt and Tr models, respectively. The RR presented higher values in the able-bodied, followed by the Tr and Tt models.

Steady-state neutronic measurements and comprehensive numerical analysis for the BME training reactor

This paper describes steady-state reactor physics measurements and calculations that were performed for the Training Reactor of Budapest University of Technology and Economics (BME TR) with the purpose of benchmarking. Based on the available geometry specifications and material compositions a model of BME TR was created with the well-validated, general-purpose Serpent 2 Monte Carlo code. Uncertain parameters (such as fuel density and control rod positions) were adjusted to related measurements. The Serpent 2 model was used for the generation of group constants, examining several homogenization schemes. Models were created in the PARCS diffusion code, the SPNDYN diffusion and SP3 code and the PARTISN discrete ordinates code. Various Monte Carlo and deterministic calculations were performed with the adjusted models and the results were then compared with actual measured data. The calculations and measurements show good agreement, this way the Serpent model was successfully validated, while the deterministic models make a good basis for more complex benchmarks in the future, such as transients with thermal–hydraulic feedback.

Involvement of Nrf2 and mitochondrial apoptotic signaling in trehalose protection against cadmium-induced kidney injury.

Cadmium (Cd) poisoning is characterized by multiple organ dysfunction in organisms, and the kidney is the main target organ of Cd toxicity. Trehalose (Tr), a multifunctional bioactive disaccharide, possesses potential kidney protective properties. Nevertheless, the specific biological function of Tr in antagonizing kidney injury induced by Cd remains to be elucidated. Herein, an in vivo model of Tr antagonizing Cd nephrotoxicity was established and the indictors related to kidney function, oxidative stress, and apoptosis were detected to investigate the molecular mechanism underlying the Tr-protection against Cd-induced kidney injury of rats. Firstly, Tr significantly declined the levels of blood urea nitrogen (BUN) and serum creatinine, and partially restored renal pathological changes caused by Cd. Secondly, Cd exposure significantly increased the malondialdehyde (MDA) content, and decreased the levels of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT), and glutathione (GSH) in serum. However, Tr significantly ameliorated these abnormal alterations. Moreover, Tr regulated the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway to suppress the Cd-induced nuclear translocation of Nrf2 and the up-regulation of heme oxygenase-1 (HO-1) and NAD (P) H quinone reductase-1 (NQO1). Meanwhile, Tr significantly reversed the increased Sequestosome-1(SQSTM1/p62) and decreased Kelch-like ECH associated protein-1 (Keap1) protein levels induced by Cd. Thirdly, further mechanistic exploration suggested that Tr inhibited the mitochondrial apoptotic signaling pathway induced by Cd. Collectively, the results indicated that Tr exerts antioxidant and anti-apoptosis functions involving the Nrf2 and mitochondrial apoptotic signaling pathways to protect against Cd-induced kidney injury in rats.

Aminated Hydroximoyl Camelthorn Residues as a Novel Adsorbent for Extracting Hg(II) From Contaminated Water: Studies of Isotherm, Kinetics, and Mechanism

Camelthorn, a desert plant, has been utilized as an adsorbent material for the extraction of Hg(II) ions from aqueous solution after grafting with acrylonitrile followed by amination with hydroxylamine hydrochloride in basic medium to obtain aminated hydroximoyl camelthorn (AHCT). AHCT were found to exhibit excellent adsorption capacity over a wide range of Hg(II) concentration. The surface functional groups and morphology of AHCT were determined. The influences of time (0–60 min), pH (2–6), and dose (0.3–8 g/L) were also evaluated. The adsorption data were analyzed using the Langmuit, Freundlich and Temkin tr models at 30 °C using nonlinear regression analysis. The maximum adsorption capacity (qmax) of Hg(II) onto AHCT was 272.9 mg/g at an initial pH of 6 and a temperature of 30 °C and the Freundlich constants, KF and n, at 30 °C were found to be 25.47 mg/g and 3.2, respectively. The value of n (3.2), which being in the range 0–10 indicate that adsorption of Hg(II) ions onto AHCT is favorable. Various kinetics models including the pseudo-first-order, pseudo-second-order and intraparticle diffusion models have been applied to the experimental data to predict the adsorption kinetics. Kinetic study was carried out by varying initial concentration of Hg(II) at constant temperature and it was found that pseudo-second-order rate equation was better obeyed than pseudo-first-order and intraparticle diffusion supporting that chemisorption process was involved. The examination of R2 values and error analysis method (ARE) showed that the Langmuir model provide the best fit to experimental data than other isotherms and follow the following order: Langmuir > Freundlich > Temkin. The results revealed that the AHCT-Hg(II) ions adsorption system was promoted by the high density of active sites and the adsorption process is independent of the adsorbent surface area. Consequently, AHCT residues can offer an effective method of Hg (II) ion removal from aqueous solutions, demonstrating its potential role in water remediation processes.

Everyday characterizations of translational research: researchers\u2019 own use of terminology and models in medical research and practice

Biomedical literature and policy are highly concerned with encouraging and improving the clinical application and clinical benefit of new scientific knowledge. Debates, theorizing, and policy initiatives aiming to close the “bench-to-bedside gap” have led to the development of “Translational Research” (TR), an emerging set of research-related discourses and practices within biomedicine. Studies in social science and the humanities have explored and challenged the assumptions underpinning specific TR models and policy initiatives, as well as the socio-material transformations involved. However, only few studies have explored TR as a productive ongoing process of meaning-making taking place as part of the everyday practices of the actual researchers located at the very nexus of science and clinic. This article therefore asks the question of how the discourse and promise of translation is embedded and performed within the practices and perspective of the specific actors involved. The findings are based on material from ethnographic fieldwork among translational researchers situated in a Danish hospital research setting. The analysis draws on the analytical notion of performativity in order to approach statements and models of TR in the light of their performative dimension. This analytical approach thus helps to highlight how the characterizations of TR also contain prescriptions for how the world must change for these characterizations to become true. The analysis provides insights into four different characterizations of TR and reflects on the associated practices where performative success is achieved in practice. With the presentation of these four characterizations, this paper illustrates different uses of the term TR among the actual actors engaged in research-clinic activities and contributes insight into the complex processes of conceptual and material reorganization that form part of the emergence of TR in biomedicine.

Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area

Accurate information on soil moisture (SM) is critical in various applications including agriculture, climate, hydrology, soil and drought. In this paper, various predictive relationships including regression (Multiple Linear Regression, MLR), machine learning (Random Forest, RF; Triangular regression, Tr) and spatial modeling (Inverse Distance Weighing, IDW and Ordinary kriging, OK) approaches were compared to estimate SM in a semi-arid mountainous watershed. In developing predictive relationship, Remote Sensing datasets including Landsat 8 satellite imagery derived surface biophysical characteristic, ASTER digital elevation model (DEM) derived surface topographical characteristic, climatic data recorded at the synoptic station and in situ SM data measured at Landsat 8 overpass time were utilized, while in spatial modeling, point-based SM measurements were interpolated. While 70%(calibration set) of the measured SM data were used for modeling, 30%(validation set) were used to evaluate modeling accuracy. Finally, the SM uncertainty maps were created for different models based on a bootstrapping approach. Among the environmental parameter sets, land surface temperature (LST) showed the highest impact on the spatial distribution of SM in the region at all dates. Mean R2(RMSE) between measured and modeled SM on three dates obtained from the MLR, RF, IDW, OK, and Tr models were 0.70(1.97%), 0.72(1.92%), 0.59(2.38%), 0.59(2.27%) and 0.71(1.99%), respectively. The results showed that RF and IDW produced the highest and lowest performance in SM modeling, respectively. Generally, the performance of RS-based models was higher than interpolation models for estimating SM due to the influence from combination of topographic parameters and surface biophysical characteristics. Modeled SM uncertainty with different models varies in the study area. The highest uncertainty in SM modeling was observed at the north part of the study area where the surface heterogeneity is high. Using RS data increased the accuracy of SM modeling because they can capture the surface biophysical characteristics and topographical properties heterogeneity.

Scaling analysis of the viscoelastic response of linear polymers.

Viscoelastic response in terms of the complex shear modulus G*(ω) of the linear polymers poly(ethylene-alt-propylene), poly(isoprene), and poly(butadiene) is studied for molar masses (M) from 3k up to 1000k and over a wide temperature range starting from the glass transition temperature Tg (174 K-373 K). Master curves G'(ωτα) and G″(ωτα) are constructed for the polymer-specific relaxation. Segmental relaxation occurring close to Tg is independently addressed by single spectra. Altogether, viscoelastic response is effectively studied over 14 decades in frequency. The structural relaxation time τα used for scaling is taken from dielectric spectra. We suggest a derivative method for identifying the different power-law regimes and their exponents along G″(ωτα) ∝ ωε″. The exponent ε″ = ε″(ωτα) ≡ d ln G″(ωτα)/d ln(ωτα) reveals more details compared to conventional analyses and displays high similarity among the polymers. Within a simple scaling model, the original tube-reptation model is extended to include contour length fluctuations (CLFs). The model reproduces all signatures of the quantitative theory by Likhtman and McLeish. The characteristic times and power-law exponents are rediscovered in ε″(ωτα). The high-frequency flank of the terminal relaxation closely follows the prediction for CLF (ε″ = -0.25), i.e., G″(ω) ∝ ω-0.21±0.02. At lower frequencies, a second regime with lower exponent ε″ is observed signaling the crossover to coherent reptation. Application of the full Likhtman-McLeish calculation provides a quantitative interpolation of ε″(ωτα) at frequencies below those of the Rouse regime. The derivative method also allows identifying the entanglement time τe. However, as the exponent in the Rouse regime (ωτe > 1) varies along εeRouse = 0.66 ± 0.04 (off the Rouse prediction εRouse = 0.5) and that at ωτe < 1 is similar, only a weak manifestation of the crossover at τe is found at highest M. Yet, calculating τe/τα= (M/Mo)2, we find good agreement among the polymers when discussing ε″(ωτe). The terminal relaxation time τt is directly read off from ε″(ωτα). Plotting τt/τe as a function of Z = M/Me, we find universal behavior as predicted by the TR model. The M dependence crosses over from an exponent significantly larger than 3.0 at intermediate M to an exponent approaching 3.0 at highest M in agreement with previous reports. The frequency of the minimum in G″(ωτα) scales as τmin ∝ M1.0±0.1. An M-independent frequency marks the crossover to glassy relaxation at the highest frequencies. Independent of the amplitude of G″(ω), which may be related to sample-to-sample differences, the derivative method is a versatile tool to provide a detailed phenomenological analysis of the viscoelastic response of complex liquids.

Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran

Accurate estimation of the reference evapotranspiration (ETo) is needed in water resources planning and management, irrigation scheduling and efficient agricultural water management. The FAO56-PM combination model is usually applied as a benchmark model for calculating ETo and calibrating other ETo models. However, the need for large amount of meteorological variables is a major drawback of this model, especially in case of data scarcity. Therefore, application of ETo models relying on fewer meteorological parameters, as well as calculating ETo using estimated meteorological variables is recommended in literature. The present paper aims at assessing the performances of different ETo models using the recorded and estimated meteorological parameters and comparing the results with the corresponding gene expression programming (GEP) models (based on the same input parameters of the employed ETo models) in hyper-arid regions. Daily meteorological parameters from 5 hyper-arid locations of Iran (covering a period of 12 years) were used. The commonly used Hargreaves (HG), Priestley-Taylor (PT), Turc (Tr) and Kimberly-Penman (KP, for alfalfa reference crop) were established and calibrated using both the recorded and estimated solar radiation, relative humidity, and wind speed data. The obtained results revealed that the GEP models outperform the corresponding empirical and semi-empirical models in all three studied categorizes (temperature/humidity-, radiation-, and combination-based approaches). The results also showed that the calibrated PT (original) and Tr (with estimated relative humidity) models gave the most accurate results among the related groups.

Model selection for the North American Breeding Bird Survey: A comparison of methods

ABSTRACT The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population tr...

Evaluating and Calibrating Reference Evapotranspiration Models Using Water Balance under Hyper-Arid Environment

This research investigates five reference evapotranspiration models (one combined model, one temperature-based model, and three radiation-based models) under hyper-arid environmental conditions at the operational field level. These models were evaluated and calibrated using the weekly water balance of alfalfa by EnviroSCAN to calculate crop evapotranspiration (ETc). Calibration models were evaluated and validated using wheat and potatoes, respectively, on the basis of weekly water balance. Based on the results and discussion, the FAO-56 Penman-Monteith model proved to be superior in estimating ETc with a slight underestimation of 2 %. Meanwhile, the Hargreaves-Samani (HS) model (temperature-based) underestimated ETc by 20 % and the Priestley-Taylor (PT) and Makkink (MK) models (radiation-based) had similar performances underestimating by up to 35 % of the measured ETc. The Turc (TR) model had the lowest performance compared with other models, demonstrating values underestimated by up to 60 % of the measured ETc. Local calibration based on alfalfa evapotranspiration measurements was used to rectify these underestimations. The surprisingly good performance of the calibrated simple HS model, with a new coefficient 0.0029, demonstrated its favorable potential to improve irrigation scheduling. The MK and PT models were in third and fourth rank, respectively, reflecting minor differences between one another. The new coefficients obtained for the MK and PT models were 1.99 and 0.963, respectively. One important observation was that the calibrated TR model performed poorly, with an increase in its coefficient from 0.013 to 0.034 to account for hyper-arid environmental conditions; moreover, it required additional seasonal calibration to adequately improve its performance.

Yaratıcı Muhasebe Uygulamaları Açısından Borsa İstanbul da İşlem Gören Gıda Sektöründeki Şirketler Üzerine Bir Uygulama (A Study On Food Sector Firms Listed At Borsa Istanbul In Terms Of Creative Accounting Practices)

Public companies turn to creative accounting practices to obtain fund at a lower cost, pay less tax, distribute more dividend, and increase their firm and share values. Creative accounting practices are used for financial information manipulation, which reduces the confidence of information users in firms engaged in these activities. This study aims to determine whether or not the food sector firms that have been listed at Borsa Istanbul for at least 10 years turned to creative accounting practices and engaged in manipulation between 2013 and 2014 through the Beneish TR model.

A kinetic study of Trichoderma reesei Cel7B catalyzed cellulose hydrolysis

One prominent feature of Trichoderma reesei (Tr) endoglucanases catalyzed cellulose hydrolysis is that the reaction slows down quickly after it starts (within minutes). But the mechanism of the slowdown is not well understood. A structural model of Tr- Cel7B catalytic domain bound to cellulose was built computationally and the potentially important binding residues were identified and tested experimentally. The 13 tested mutants show different binding properties in the adsorption to phosphoric acid swollen cellulose and filter paper. Though the partitioning parameter to filter paper is about 10 times smaller than that to phosphoric acid swollen cellulose, a positive correlation is shown for two substrates. The kinetic studies show that the reactions slow down quickly for both substrates. This slowdown is not correlated to the binding constant but anticorrelated to the enzyme initial activity. The amount of reducing sugars released after 24h by Cel7B in phosphoric acid swollen cellulose, Avicel and filter paper cellulose hydrolysis is correlated with the enzyme activity against a soluble substrate p-nitrophenyl lactoside. Six of the 13 tested mutants, including N47A, N52D, S99A, N323D, S324A, and S346A, yield ∼15–35% more reducing sugars than the wild type (WT) Cel7B in phosphoric acid swollen cellulose and filter paper hydrolysis. This study reveals that the slowdown of the reaction is not due to the binding of the enzyme to cellulose. The activity of Tr- Cel7B against the insoluble substrate cellulose is determined by the enzyme’s capability in hydrolyzing the soluble substrate.

PHARMACOKINETIC STUDIES IN NEONATES: IS AN OPPORTUNISTIC SAMPLING STRATEGY SUFFICIENT FOR POPULATION PHARMACOKINETIC MODELLING AND DOSE PREDICTION?

Background and objectiveThe use of an opportunistic (also called scavenged) sampling strategy in a prospective pharmacokinetic study combined with population pharmacokinetic modelling has been proposed as an alternative strategy to conventional methods for accomplishing pharmacokinetic studies in neonates. However, the reliability of this approach in this particular paediatric population has not been evaluated. The objective of the present study was to evaluate the performance of an opportunistic sampling strategy for a population pharmacokinetic estimation as well as dose prediction, and compare this strategy to a pre-determined pharmacokinetic sampling approach.MethodsThree population pharmacokinetic models were derived for ciprofloxacin from opportunistic blood samples (SC model), pre-determined (i.e., scheduled) samples (TR model) and all samples (full model used to previously characterize ciprofloxacin pharmacokinetics), respectively, using NONMEM software. The predictive performance of developed models was evaluated in an independent group of patients.ResultsPharmacokinetic data from 60 newborns were obtained with a total of 430 samples available for analysis; 265 collected at pre-determined times and 165 that were scavenged from those obtained as part of clinical care. All data sets were fit using a two-compartment model with first order elimination. The SC model could identify the most significant covariates and provided reasonable estimates of population pharmacokinetic parameters (clearance and steady state volume of distribution) as compared to the TR and full models. Their predictive performances were further confirmed in an external validation by Bayesian estimation and showed similar results. Monte Carlo simulation based on AUC0–24/MIC using either the SC or the TR model gave similar dose prediction for ciprofloxacin.ConclusionBlood samples scavenged in the course of caring for neonates can be used to estimate ciprofloxacin pharmacokinetic parameters and therapeutic dose requirements.

SEPARABLE MODELS OF RANDOMIZATIONS

AbstractEvery complete first order theory has a corresponding complete theory in continuous logic, called the randomization theory. It has two sorts, a sort for random elements of models of the first order theory, and a sort for events. In this paper we establish connections between properties of countable models of a first order theory and corresponding properties of separable models of the randomization theory. We show that the randomization theory has a prime model if and only if the first order theory has a prime model. And the randomization theory has the same number of separable homogeneous models as the first order theory has countable homogeneous models. We also show that when T has at most countably many countable models, each separable model of TR is uniquely characterized by a probability density function on the set of isomorphism types of countable models of T. This yields an analogue for randomizations of the results of Baldwin and Lachlan on countable models of ω1-categorical first order theories.