Additional Data Points Research Articles

Candidemia in Cancer Patients

Introduction While the global epidemiology of candidemia has been studied in great detail, there are relatively few data available examining the effects of recent changes in antifungal treatment patterns on the susceptibility and species distribution patterns of Candida species. Likewise, there are relatively few data documenting improved outcomes in patients with candidemia. Materials and Methods We reviewed the charts of all patients at our institution with one or more positive blood cultures for Candida species between January 1, 2001, and June 18, 2014. We first looked at the remaining set of 247 patients who met these criteria, followed by an isolated subset of 115 patients with hematologic malignancies (HMs). All patients were selected from the H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, based on positive Candida culture reports obtained from Moffitt's microbiology laboratories. From these reports, we obtained the patient name and medical record number, positive sample collection dates, number of positive culture days, species of Candida, and patient location in the hospital. We included positive samples taken from peripheral and central lines but excluded samples taken from catheter tips. Blood cultures were initially analyzed using either the Bactec or the BacT/Alert system. Cultures that were positive for yeasts were Gram-stained, and yeasts were cultured on Sabouraud dextrose agar and chocolate agar. Yeast species were tentatively classified by visual examination and conclusively identified using either the RapID YeastOne system or the Vitek 2 system. Drug susceptibilities were determined via serial dilutions using the TREK Sensititre YeastOne system. After data collection from the laboratory reports, we examined the patient's medical records and collected additional data points. Susceptibility data on fluconazole, amphotericin B, voriconazole, micafungin, 5-flucytosine, posaconazole, caspofungin, itraconazole, and anidulafungin were collected if available. We compared frequency of each Candida species overall as well as over different time intervals for all patients, those with only non-HMs, and those with only HMs. Results The most prevalent infecting species overall was Candida albicans followed closely by Candida glabrata. The frequency of infection with other species, beginning with Candida krusei, was much lower. The most prevalent infecting species was C. albicans, which by itself represented a little less than half of all non-HM patients. Candida glabrata was once again the next most frequent species. The most prevalent infecting species for patients with HMs was C. glabrata followed by C. krusei, which together combined for more than half of infections. Candida albicans was not nearly as frequent in this group, and in fact was the least prevalent of the major species. When comparing the first 5 years with the last 5 years, the most prevalent species during both periods was C. glabrata, C. albicans, and Candida tropicalis with incidences falling substantially, whereas Candida parapsilosis incidences rose substantially. The prevalence of C. krusei also showed a moderate increase. There were no other Candida species between 2001 and 2006, further illustrating the shift that has occurred. Eleven deaths were deemed attributable to candidemia in comparison to 40 deaths deemed attributable to the underlying disease (malignancy, other infections, organ failure due to graft-versus-host disease, etc). Conclusions Our data regarding breakthrough infections while on prophylactic antifungal treatment illustrate how most candidemias that breakthrough while on micafungin are C. parapsilosis and C. krusei. Further information is needed to determine how these trends might impact treatment recommendations for candidemia and to ensure optimal effectiveness in preventing and treating candidemia in patients with cancer.

On the drag of freely falling non-spherical particles

We present a new general model for the prediction of the drag coefficient of non-spherical solid particles of regular and irregular shapes falling in gas or liquid valid for sub-critical particle Reynolds numbers (i.e. Re<3×105). Results are obtained from experimental measurements on 300 regular and irregular particles in the air and analytical solutions for ellipsoids. Depending on their size, irregular particles are accurately characterized with a 3D laser scanner or SEM micro-CT method. The experiments are carried out in settling columns with height of 0.45 to 3.60m and in a 4m-high vertical wind tunnel. In addition, 881 additional experimental data points are also considered that are compiled from the literature for particles of regular shapes falling in liquids. New correlation is based on the particle Reynolds number and two new shape descriptors defined as a function of particle flatness, elongation and diameter. New shape descriptors are easy-to-measure and can be more easily characterized than sphericity. The new correlation has an average error of ~10%, which is significantly lower than errors associated with existing correlations. Additional aspects of particle sedimentation are also investigated. First, it is found that particles falling in dense liquids, in particular at Re>1000, tend to fall with their maximum projection area perpendicular to their falling direction, whereas in gases their orientation is random. Second, effects of small-scale surface vesicularity and roughness on the drag coefficient of non-spherical particles found to be <10%. Finally, the effect of particle orientation on the drag coefficient is discussed and additional correlations are presented to predict the end members of drag coefficient due to change in the particle orientation.

Structural, dielectric and electrocaloric properties in lead-free Zr-doped Ba0.8Ca0.2TiO3 solid solution

Abstract We investigate in the present work the additional data points ( x =0.02, 0.06 and 0.08) to the previous reported paper (Asbani et al., 2015) [21] that sufficiently enrich to understand the EC properties and underlying phase diagram of the lead-free Ba 0.8 Ca 0.2 Ti (1− x ) Zr x O 3 ( x BCTZ) system. X-ray diffraction analysis performed at room temperature, confirms a continuous tetragonal solid solution with P 4 mm (No. 99) space group that evolved to pseudo-cubic symmetry for the high Zr-content compounds. Ferroelectric and paraelectric behaviors were highlighted using P–E hysteresis data versus temperature. Phase transition was confirmed by dielectric permittivity measurements versus temperature showing a decrease of the Curie temperature when Zr-content increases. From P–E hysteresis recording the electrocaloric temperature change (Δ T ) was calculated that ranged in between 0.12 and 0.27 K and the electrocaloric responsivity ( ξ ) in 0.15 to 0.34×10 −6 K·m/V under 7.95 kV/cm applied electric field. The zero-field entropy is compared to electrocaloric isothermal entropy to estimate the extent the EC is being under-driven.

A Decade of GRB Follow-Up by BOOTES in Spain (2003–2013)

This article covers ten years of GRB follow-ups by the Spanish BOOTES stations: 71 follow-ups providing 23 detections. Follow-ups by BOOTES-1B from 2005 to 2008 were given in a previous article and are here reviewed and updated, and additional detection data points are included as the former article merely stated their existence. The all-sky cameras CASSANDRA have not yet detected any GRB optical afterglows, but limits are reported where available.

Measures of Reliability in the Kinetics of British Army Marching Drill

Marching drill is considered an essential component of basic military training. Assessing the magnitude, and understanding the implications of forces experienced when performing drill movements requires the determination of reliable measures for each of the drill movements. Little is currently known regarding relative effects of systematic bias (SB), within-subject (WS) variation, or test-retest reliability to the overall reliability of kinetic variables of marching drill. PURPOSE: Statistically analyse measures of reliability (SB, WS variation and test-retest reliability) in the kinetics of marching drill. METHODS: 15 male participants performed 10 trials of; stand at ease (SaE), stand at attention (SaA), quick march (QM), halt; normal walk was included for comparison. Vertical ground reaction forces (vGRF) were sampled at 1000Hz via Kistler force plate. Rate of force development (RFD) was determined from the vGRF data. SB was calculated from a one-way Anova. WS variation and test-retest reliability were calculated as the coefficient of variation of the typical error (CVte%) and intra-class correlation coefficients (ICC), using an iterative process; adding additional data points and recalculating until acceptable values were achieved. RESULTS: There was no SB (sig. difference between sessions) for Halt, QM and Walk (p> 0.05). Statistically sig. differences were observed between session (S) 1 and S2 (p=.044) and S1 and S3 (p=.004) for SaA and between S1 and S2 (p=.011) and S1 and S3 (p=.014) for SaE. Due to SB, the initial 10 trials of SaA and SaE were removed, with the remaining 20 trials used for further reliability calculations. The CVte% and ICC values were: vGRF - SaA (CVte%=7.6, ICC=0.75), SaE (CVte%=7.0, ICC=0.82), QM (CVte%=8.4, 0.91), halt, (CVte%=7.2, ICC=0.79), walk (CVte%=2.4, ICC=0.88); and RFD - SaA (CVte%=13.8, ICC=0.75), SaE (CVte%=10.4, ICC=0.79), QM (CVte%=41.4, ICC=0.79), halt (CVte%=14.9, ICC=0.65), and walk (CVte%=49.9, ICC=0.75). CONCLUSION: Differences between S1 and the remaining 2 sessions for SaA and SaE indicate that a familiarization session was required. Beyond 8 trials diminishing returns were observed in CVte% with the addition of extra data. At 8 trials ICC values of ≥0.75 were achieved for all tasks, except the Halt drill indicating reasonable/good levels of test-retest reliability. Therefore to accurately represent the kinetics of marching drill performance 8 trials are required.

A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments

Complex real-world systems can accurately be modeled by simulations. Evaluating high-fidelity simulators can take several days, making them impractical for use in optimization, design space exploration, and analysis. Often, these simulators are approximated by relatively simple math known as a surrogate model. The data points to construct this model are simulator evaluations meaning the choice of these points is crucial: each additional data point can be very expensive in terms of computing time. Sequential design strategies offer a huge advantage over one-shot experimental design because information gathered from previous data points can be used in the process of determining new data points. Previously, LOLA-Voronoi was presented as a hybrid sequential design method which balances exploration and exploitation: the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in interesting regions which were previously discovered. Although this approach is very successful in terms of the required number of data points to build an accurate surrogate model, it is computationally intensive. This paper presents a new approach to the exploitation component of the algorithm based on fuzzy logic. The new approach has the same desirable properties as the old method but is less complex, especially when applied to high-dimensional problems. Experiments on several test problems show the new approach is a lot faster, without losing robustness or requiring additional samples to obtain similar model accuracy.

Error Metrics and the Sequential Refinement of Kriging Metamodels

Metamodels, or surrogate models, have been proposed in the literature to reduce the resources (time/cost) invested in the design and optimization of engineering systems whose behavior is modeled using complex computer codes, in an area commonly known as simulation-based design optimization. Following the seminal paper of Sacks et al. (1989, “Design and Analysis of Computer Experiments,” Stat. Sci., 4(4), pp. 409–435), researchers have developed the field of design and analysis of computer experiments (DACE), focusing on different aspects of the problem such as experimental design, approximation methods, model fitting, model validation, and metamodeling-based optimization methods. Among these, model validation remains a key issue, as the reliability and trustworthiness of the results depend greatly on the quality of approximation of the metamodel. Typically, model validation involves calculating prediction errors of the metamodel using a data set different from the one used to build the model. Due to the high cost associated with computer experiments with simulation codes, validation approaches that do not require additional data points (samples) are preferable. However, it is documented that methods based on resampling, e.g., cross validation (CV), can exhibit oscillatory behavior during sequential/adaptive sampling and model refinement, thus making it difficult to quantify the approximation capabilities of the metamodels and/or to define rational stopping criteria for the metamodel refinement process. In this work, we present the results of a simulation experiment conducted to study the evolution of several error metrics during sequential model refinement, to estimate prediction errors, and to define proper stopping criteria without requiring additional samples beyond those used to build the metamodels. Our results show that it is possible to accurately estimate the predictive performance of Kriging metamodels without additional samples, and that leave-one-out CV errors perform poorly in this context. Based on our findings, we propose guidelines for choosing the sample size of computer experiments that use sequential/adaptive model refinement paradigm. We also propose a stopping criterion for sequential model refinement that does not require additional samples.

Application of Artificial Intelligent Modeling for Predicting Activated Carbons Properties Used for Methane Storage

Performance characterization and optimization of activated carbons are extensively studied using artificial intelligence modeling. In this study, the effect of several parameters on the preparation of activated carbon by chemical activation is investigated. Various preliminary parameters have been considered. The study has resulted in finding four parameters, which are of higher importance compared to the others. These parameters include chemical agent type, chemical agent to precursor ratio, activation temperature, and activation time. In our previous study, 36 activated carbon (AC) samples were prepared using the aforementioned parameters at various levels. In the present investigation, these experimental results have been used for the modeling.As a novel approach, an adaptive neuro-fuzzy inference system (ANFIS) is also applied to the experimental data presented in this study. ANFIS is established by combining artificial neural network (ANN) with fuzzy inference system. After determining the model parameters, some additional data points are used to validate the models. Finally, the outcomes are compared with the experimental results. The normalized mean square error (NMSE) has been obtained as 0.00327, which is very satisfactory for the model validation.These attempts to simulate the preparation stage of activated carbons would provide a simple and flexible route with various AC preparations. Such an effort is essential to develop the adsorbed natural gas (ANG) technology.

Impact of Transportation Convenience, Housing Affordability, Location, and Schooling in Residence Choice Decisions

AbstractThis paper aims to investigate the impacts of housing affordability, which can be denoted as the ratio of housing price (HP) and monthly expense per person (EXP), travel time to the city center, distance to a subway station, and schooling on residents’ apartments purchasing behavior in Dalian, China. The paper employed a logit model based on stated preference (SP) data. Results show that travel time to the city center (by subway) and housing affordability have negative effects on individual utility. This reveals that residents tend to buy apartments near the city center based on their income. Considering a series of negative external factors, distance to a subway station is positive to utility. Moreover, the willingness for residents to pay for different attributes varies according to their levels of EXP. At last the elasticity of different variables is analyzed and 180 additional data points are used to test the model. The testing result shows that the model can satisfy the basic requirement.

The Great Salt Lake Ecosystem (Utah, USA): long term data and a structural equation approach: Comment

The Great Salt Lake Ecosystem (Utah, USA): long term data and a structural equation approach: Comment

Computational Thinking in High School Science Classrooms

[ILLUSTRATION OMITTED] Computational thinking is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability (Wing 2006, p. 33). A Framework for K-12 Science Education identified eight practices as essential elements of the K-12 science and engineering curriculum (NRC 2012, p. 49). These practices are deeply embedded within the Next Generation Science Standards (NGSS Lead States 2013a), where they are wedded closely to core ideas in the science disciplines. Most of the practices, such as Developing and Using Models, Planning and Carrying Out Investigations, and Analyzing and Interpreting Data, are well known among science educators. In contrast, the practice of Using Mathematics and Computational Thinking raises questions in the minds of many educators. As mathematics has long been integral to science teaching, the questions tend to revolve around the meaning of computational thinking. The Framework envisions computational thinking as a powerful intellectual tool: Since the mid-20th century, computational theories, information and computer technologies, and algorithms have revolutionized virtually all scientific and engineering fields. These tools and strategies allow scientists and engineers to collect and analyze large data sets, search for distinctive patterns, and identify relationships and significant features in ways that were previously impossible. They also provide powerful new techniques for employing mathematics to model complex phenomena---for example, the circulation of carbon dioxide in the atmosphere and ocean (NRC 2012, p. 64). and computational thinking We (the authors) asked ourselves how the different forms of computational thinking suggested in this definition differed from mathematical thinking. Mathematics, it's important to note, is not just a skill but also a way of thinking about the world. The Framework distinguishes mathematics from data analysis in the use of symbols to express relationships. So, for example, a student might graph data from an experiment and notice a pattern. Mathematics comes in when the student expresses the pattern as an equation that can predict additional data points. Students develop mathematical thinking when they approach a new situation with a range of mathematical skills in mind. Similarly, they develop computational thinking when they approach a new situation with an awareness of the many ways that computers can help them visualize systems and solve problems. The Venn diagram in Figure 1 shows how we see the relationship between mathematical and computational thinking. The diagram (itself a mathematical tool) shows which capabilities can be considered part of mathematical thinking, which are part of computational thinking, and which are part of both. As the diagram illustrates, analyzing and interpreting data is common to both mathematical and computational thinking, as are problem solving, mathematical modeling, and statistics and probability. Figure 1 also lists capabilities unique to computational thinking. In the remainder of this article we will illustrate how simulation, data mining, and automated data collection are important in today's science classroom. As you read these examples, keep in mind that simply using computers is not enough; students must be encouraged to re-orient and deepen their understanding about science using computational thinking. Your goal should be to help students build learning skills by recognizing the ways they can use computers to carry out investigations and solve practical problems. [FIGURE 1 OMITTED] The NGSS describes the practice of mathematics and computational thinking for high school as follows: Mathematical and computational thinking in 9-12 builds on K-8 experiences and progresses to using algebraic thinking and analysis, a range of linear and nonlinear functions including trigonometric functions, exponentials and logarithms, and computational tools for statistical analysis to analyze, represent, and model data. …

Spatial variability of soil phosphorus in the Fribourg canton, Switzerland

Phosphorus (P) is the second essential nutrient for plant growth but can become an ecological and economical concern in case of over-fertilization. Soil P dynamic is influenced by many parameters like soil physical–chemical properties and farming practices. A better understanding of the factors controlling its distribution is required to achieve best management of P in cropping systems. In Switzerland, the FRIBO network was launched in 1987 and consists of 250 sites covering a wide diversity of soils (Cambisols, Gleysols, Rendzinas, Lithosols, Luvisols, Fluvisols) and three different land uses (cropland, grassland and mountain pasture) across the Fribourg canton. A spatial investigation of the different P forms (total, organic and available) for the FRIBO network led to the following main conclusions:(i)The P status in agricultural soils was significantly different among the three land uses encountered, with the highest mean values of available P found in croplands, from 2.12 (CO2 saturated water extraction) to 81.3mg.kg−1 (acetate ammonium+EDTA extraction); whereas total P was more abundant in permanent grasslands (1186mg.kg−1), followed by mountain pastures (1039mg.kg−1) and croplands (935mg.kg−1). This full characterization of the soil P status provides important data on P distribution related to soil properties and land use.(ii)Environmental variables such as altitude, slope, wetness index or plan curvature, derived from the digital elevation model (DEM) only explained a small part of the spatial variation of the different P forms (20 to 25%). Thus, the geostatistical analyses revealed that land use plays a significant role in soil P distribution. Improved predictions of the spatial distribution of P-related forms at landscape scales are needed and would require additional data points and variables such as parent material, soil types and terrain attributes.

Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries

Incremental open-circuit voltage (OCV) curves and low-current charge/discharge voltage profiles of a lithium-ion (Li-ion) battery are compared and evaluated for optimizing measurement time and resolution. Since these curves are often used for further analysis, minimizing kinetic contributions is crucial for approximating battery OCV behavior. In this context, an incremental OCV measurement is characterized by state of charge (SOC) intervals and relaxation times. Various constant low C-rates, SOC intervals, and relaxation times are tested for approximating OCV behavior. Differential capacity and voltage analysis is used to check whether the main electrode features can be resolved satisfactorily. An interpolation method yields additional data points for the differential analysis of incremental OCV curves. It is shown that incremental OCV measurements are suitable for an approximation of battery OCV behavior, rather than low current-voltage profiles. Furthermore, extrapolation of voltage relaxation enables the estimation of fully relaxed OCV.

In Their Own Words? Methodological Considerations in the Analysis of Terrorist Autobiographies

Despite the growth of terrorism literature in the aftermath of the 9/11 attacks, there remain several methodological challenges to studying certain aspects of terrorism. This is perhaps most evident in attempts to uncover the attitudes, motivations, and intentions of individuals engaged in violent extremism and how they are sometimes expressed in problematic behavior. Such challenges invariably stem from the fact that terrorists and the organizations to which they belong represent clandestine populations engaged in illegal activity. Unsurprisingly, these qualities make it difficult for the researcher to identify and locate willing subjects of study—let alone a representative sample. In this research note, we suggest the systematic analysis of terrorist autobiographies offers a promising means of investigating difficult-to-study areas of terrorism-related phenomena. Investigation of autobiographical accounts not only offers additional data points for the study of individual psychological issues, but also provides valuable perspectives on the internal structures, processes, and dynamics of terrorist organizations more broadly. Moreover, given most autobiographies cover critical events and personal experiences across the life course, they provide a unique lens into how terrorists perceive their world and insight into their decision-making processes. We support our advocacy of this approach by highlighting its methodological strengths and shortcomings.

Whitepapers on Imaging Infrastructure for Research Part Three: Security and Privacy

This is the third part of three describing an extension of the process for developing a clinical research project including the use of in vivo imaging data. The high-level process diagram for development of a research project including images is shown in Fig. 1. In part two of this series, data management requirements and practices for including images were described. This part describes the security and privacy requirements for supporting images used in research. Fig. 1 Process diagram including the workflow steps required for imaging infrastructure for research An important point that the authors wish to make is that imaging information is nearly always not simply an additional data point. The inclusion of imaging data results in many additional complexities that can lead to unintended or unrecognized biases and errors. For that reason, it is critical that imaging experts be involved in studies that rely on imaging data. That involvement is required during the conception and design of the experiment, the data collection phase, and the data analysis phase. Some of those challenges that are present when imaging data is used in research will be further defined in this paper. The proper collection of imaging data for research use demands monitoring that can be made better and more efficient than current manual methods. How measurements are extracted from the raw images and represented is a critical step that entails its own set of challenges. Finally, there are some unique and common security issues when images are used for research. Security and Privacy Issues Research consortiums and multi-site trials are increasingly a reality in the healthcare and research enterprise. In addition, there is increasing incentive and/or requirement for federally funded research projects to share the data collected and generated as part of the research plan. As the amount of data being collected increases in volume and complexity, the necessity of data-sharing infrastructure becomes obvious. With the implementation of formal data-sharing mechanisms comes the requirement of secure data sharing, both to protect subject privacy, as well as to provide investigator interests. Research image sharing scenarios face the same challenges as those for the generic data-sharing scenario. At the current time, however, there exists a gap in a researcher, an institution, or a research project’s ability to provide common infrastructure to support secure image sharing in a research scenario. In this part, the authors investigate the security requirements of the research imaging workflow outlined in part 1. While a significant emphasis will be placed on the security requirements of image sharing, it should be noted that data acquisition as well as analysis and result management also have significant security requirements and implications. The objective of summarizing these security requirements is to provide general guidance in the design and implementation of a secure image sharing system and to motivate research organizations and funding agencies in their consideration of data security issues in collaborative research. The paper is organized as follows. The first section reviews the research workflow outlined in part 1 and identifies the specific component of the research workflow whose security concerns this paper focuses on. The next section outlines relevant regulations and policies that impacts secure image data sharing. Next, a high-level overview of the specific capabilities required to support secure data sharing is presented. The next section discusses the security requirements of different general data-sharing scenarios. The final section lists the current available standards and technologies and gaps that might be applied to address some of the requirements we outline.

Service life prediction of residential interior finishes for life cycle assessment

Service life of building products has an important influence on life cycle assessment (LCA) results of buildings. The goal of this study was to propose a systematic approach to estimate service life of building products by including both technical and social factors. A hybrid service life prediction method, combining the statistical approach described in American Society for Testing and Materials standard G166 with the Factor Method adopted by International Organization for Standardization standard 15686, was proposed. In their current forms, the two methods are not suitable to provide reliable lifetime estimates for the wide variety of products that are used in buildings. Statistical analysis was preferred over a deterministic approach. Regression analysis was used to define Weibull distribution parameters for each product. These distributions were then used to calculate the mean estimated service life of products with an 80% confidence interval. Using actual lifetime observed from practice instead of design lifetime for reference service life was preferred. This enables the use of a smaller range of coefficients for each factor affecting service life, which decreases subjectivity and increases reliability of results. Example median service life estimates were demonstrated for common residential interior finishes that are replaced more frequently, and therefore require more maintenance planning and potentially have significant environmental impacts. Probability of renovation distributions was also presented for interior finishes. The proposed method inherently includes social factors in the dataset used to define lifetime distributions, which could be as important as durability for some building product categories. Another advantage is that choosing reference service life based on real-life conditions decreases the range of coefficients necessary for modifying factors in comparison to when design lifetime is used, thus decreasing the subjectivity of results due to variations in assigned values by different users. The Factor Method is the most promising method available to estimate service life of products. Unless additional data points were gathered for investigated products, the presented lifetime distribution results can be directly applied to LCA studies. The proposed hybrid method can also be applied to other products that are studied within the Factor Method. Products whose lifetimes are influenced by social factors are prime candidates to apply this method.

A quality by design (QbD) case study on liposomes containing hydrophilic API: II. Screening of critical variables, and establishment of design space at laboratory scale

Two statistical designs were used in this case study as part of an investigation into the feasibility and the advantages of applying QbD concepts to liposome-based complex parenteral controlled release systems containing a hydrophilic active pharmaceutical ingredient (API). The anti-viral drug Tenofovir was used as a model compound. First design (Plackett–Burman) was used to screen eight high-risk variables obtained from risk analysis and assess their impact on liposome characteristics (drug encapsulation efficiency, particle size, and physical stability). It was discovered that out of eight high-risk variables only lipid and drug concentration had significant effects on the drug encapsulation efficiency. This allowed the use of a central composite design (CCD) (with more predictive capability) to fully elucidate the relationship between lipid concentration, drug concentration and encapsulation efficiency. On comparing the CCD model generated response surface with additional data points, the accuracy and robustness of the model was confirmed. Using this developed model, the design space for Tenofovir liposomes preparation has been established in a laboratory setting, within which the preparation variability is minimized. With regard to sample storage stability, it was shown that at 4°C the prepared Tenofovir liposomes, dispersed in aqueous phase, achieved stability for at least 2 years. These principles can be applied to liposomes containing other hydrophilic APIs, and can provide time and cost saving to industrial formulation scientists, and result in a more robust liposome preparation process.

Rainfall erosivity in Central Chile

Summary One of the most widely used indicators of potential water erosion risk is the rainfall-runoff erosivity factor (R) of the Revised Universal Soil Loss Equation (RUSLE). R is traditionally determined by calculating a long-term average of the annual sum of the product of a storm’s kinetic energy (E) and its maximum 30-min intensity (I30), known as the EI30. The original method used to calculate EI30 requires pluviograph records for at most 30-min time intervals. Such high resolution data is difficult to obtain in many parts of the world, and processing it is laborious and time-consuming. In Chile, even though there is a well-distributed rain gauge network, there is no systematic characterization of the territory in terms of rainfall erosivity. This study presents a rainfall erosivity map for most of the cultivated land in the country. R values were calculated by the prescribed method for 16 stations with continuous graphical record rain gauges in Central Chile. The stations were distributed along 800 km (north–south), and spanned a precipitation gradient of 140–2200 mm yr−1. More than 270 years of data were used, and 5400 storms were analyzed. Additionally, 241 spatially distributed R values were generated by using an empirical procedure based on annual rainfall. Point estimates generated by both methods were interpolated by using kriging to create a map of rainfall erosivity for Central Chile. The results show that the empirical procedure used in this study predicted the annual rainfall erosivity well (model efficiency = 0.88). Also, an increment in the rainfall erosivities was found as a result of the rainfall depths, a regional feature determined by elevation and increasing with latitude from north to south. R values in the study area range from 90 MJ mm ha−1 h−1 yr−1 in the north up to 7375 MJ mm ha−1 h−1 yr−1 in the southern area, at the foothills of the Andes Mountains. Although the map and the estimates could be improved in the future by generating additional data points, the erosivity map should prove to be a good tool for land-use planners in Chile and other areas with similar rainfall characteristics.

Identification of Reading Problems in First Grade Within a Response-to-Intervention Framework

Models of Response to Intervention (RTI) include parameters of assessment and instruction. This study focuses on assessment with the purpose of developing a screening battery that validly and efficiently identifies first-grade children at risk for reading problems. In an RTI model, these children would be candidates for early intervention. We examined accuracy, fluency, growth, and teacher rating measures as predictors of child status (at risk, not at risk) at the end of the school year based on an unselected sample of 243 children. The prediction model that best fit our selection criteria included 2-word fluency measures and a teacher rating of reading problems. Word-fluency growth was an equally plausible choice statistically, but, because the measure would require an additional data point, it was not the most efficient choice. The receiver-operator characteristic curve analysis yielded an area-under-the-curve index of .96, which indicates the selected 3-variable model is highly accurate.

Conducting a Journal Assessment Using an Enhanced MS Access Database

An existing Microsoft Access journals-specific database providing aggregated subject and other department-specific title information was enhanced to support the ease and efficacy of conducting a survey of School of Medicine faculty to ensure the journal collection reflected their educational and professional needs. Additional evaluative data points were recorded in the database. MS Access then facilitated the creation of the surveys, recording results from returned surveys and creation of results summaries for departments. The surveys expanded the knowledge of full-time faculty about departmental holdings at the title level, while enabling the serials team to obtain better information to support collection development.