A High-Throughput Macromolecule Characterization System

The size and complexity in structure of biopharmaceutical products makes them more susceptible to chemical or structural changes leading to lower potency or altered immunogenicity. Sustaining the stability of macromolecules becomes one of the greatest challenges in the development of biopharmaceutical products. The biophysical characterization of macromolecules is an essential step in stable formulation development. Structural changes of macromolecules in response to various environmental stresses or solution additives are measured using various techniques, and can then be analyzed using the empirical phase diagram (EPD). The empirical phase diagram (EPD) is a colored representation of overall structural integrity and conformational stability of macromolecules in response to various environmental perturbations. Numerous proteins and macromolecular complexes have been analyzed by EPDs to summarize results from large data sets from multiple biophysical techniques. The current EPD method suffers from a number of deficiencies including lack of a meaningful relationship between color and actual molecular features, difficulties in identifying contributions from individual techniques, and a limited ability to be interpreted by color blind individuals. Three improved data visualization approaches are proposed as techniques complementary to the EPD. Experimental data sets can be visualized as (1) RGB colors using three-index empirical phase diagrams, (2) equiangular polygons using radar charts, and (3) human facial features using Chernoff face diagrams. Recent development of high-throughput and multimodal spectrophotometers help rapidly collect the large volume of data that is required to create EPDs. Incompatible data formats of various instruments and heterogeneous analysis software are, however, standing in the way of quickly organizing and analyzing such large volumes of data. It is essential to develop dedicated analysis software for such biophysical data to achieve high-throughput systems, in terms of both hardware and software, for biophysical characterization of macromolecules. For this purpose, a web-based software framework called MiddaughSuite was developed in this work. The software was designed to easily handle data from various instruments, quickly analyze data using multiple mathematical functions, visualize data in the forms of graphs and diagrams including EPDs, radar chars and Chernoff face diagrams, and share data with other researchers

Association of Diet Quality with Fall Risk among Community-Dwelling Older Adults in a Fall-Prevention Program

Falls are a serious threat to older adults\u27 quality of life. Evidence is lacking regarding the influence of diet on fall risk factors. This study aims to assess the relationship between diet, functional measures, and fall risk among older adults participating in a fall-prevention intervention. Cross-sectional analysis of baseline data from 192 participants with an average age of 70.9 years was conducted using Chi-square tests, t-test, Wilcoxon test, and nominal logistic analysis. Based on Dietary Screening Tool (DST) scores, 39.5% of participants were classified as nutritionally being “at-risk,” 46.1% were at “possible-risk,” and 14.4% were “not-at-risk.” Fall risk was assessed using the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) classifications. There were no significant associations between the “not at fall risk” group and “at fall risk” groups in terms of DST total score (p=0.97), protein score (p=0.27), multivitamin use (p=0.73) and DST risk categories (p=0.64). In the correlation analysis, the DST total scores had a positive correlation with total physical activities (r=0.1648, p=0.029), and a negative correlation with body mass index (BMI) (r=-0.1496, p=0.04) and depression (r=-0.1433, p=0.048). In the nominal logistic analysis, neither of the primary predictors, total DST score or DST protein score, showed significance with STEADI fall risk categories. In each model, the Four-Square Step Test (FSST), an indicator of greater risk of future falls, had the closet likelihood ratio test to the statistical trend as the major component associated with STEADI risk categories. A significant relationship between diet, functional measures, and fall risk was not detected

A follow-up study of phonological development in bilingual children: implications for clinical assessment

A lack of information about typical phonological development in bilingual children presents as a challenge to many speech-language pathologists assessing bilingual children with suspected speech sound disorder. The purpose of the current study was to investigate age-related changes in speech accuracy (percentage of consonants correct) and error production in Korean-English bilingual children, drawn from a larger study conducted in New Zealand. Sixteen Korean-English bilingual children were followed up at a six-month interval, totalling three time points of data collection. They were aged between 3;1 and 5;11 at the first point of data collection. The Diagnostic Evaluation of Articulation and Phonology was used to obtain single-word samples in English and the Assessment of Phonology and Articulation for Children was used for Korean. We found considerable individual variations in the longitudinal data. Age-related changes in speech accuracy were not sensitive to those changes in error production. Significantly, we found some error patterns emerged during the course of development, instead of being progressively resolved with age. Unlike previous findings in the literature with monolingual children, the findings related to re-emergence of error patterns were not limited to young children. Our findings suggest that speech-language pathologists should take a considered approach to identifying bilingual children with speech sound disorder solely based on the information provided in cross-sectional studies. We suggest that a follow-up session may provide valuable information facilitating the clinical assessment procedure to identify bilingual children with speech sound disorder

Four-probe thermal and thermoelectric transport measurements of bismuth antimony telluride, silicon, and boron arsenide nanostructures

Thermal management in electronic devices has become a significant challenge because of the high power density in nanoelectronic devices. This challenge calls for a better understanding of thermal transport processes in nanostructures and devices, as well as new thermal management approaches such as high thermal conductivity materials and efficient on-chip thermoelectric coolers. While several experimental methods have been developed to investigate size-dependent thermal and thermoelectric properties, there are a number of limitations in the current experimental capability in probing nanoscale thermal and thermoelectric transport properties. Among these limitations is the difficulty in determining and eliminating the contact thermal resistance error so as to obtain the intrinsic thermal and thermoelectric properties of nanostructures. This dissertation presents an effort to develop new experimental methods for uncovering the intrinsic thermal and thermoelectric properties of nanostructures, and the applications of these methods for investigating the thermal and thermoelectric transport phenomena in three materials systems. The intrinsic thermoelectric properties of bismuth antimony telluride nanostructures, which are synthesized by two different methods, are characterized with a four-probe thermoelectric measurement method based on a suspended device. The obtained thermoelectric property reveals a transition from n-type to p-type electronic transport as the antimony to bismuth ratio is increased to about 0.25. The peak zT was found when this ratio is close to 0.5. A new four-probe thermal transport measurement method is established in this work to probe both the contact thermal resistance and intrinsic thermal resistance of a nanostructure, which can be either an electrical conductor or insulator. The effectiveness of this method is demonstrated with its use to reveal size-dependent thermal conductivity of patterned silicon nanowires. The new four-probe measurement method is employed to obtain both the intrinsic thermal and thermoelectric properties of nanostructures of boron arsenide (BAs) with potentially record high thermal conductivity. The measurement results suggest that the thermal conductivity of one of such sample with an equivalent diameter of about 1.1 μm is higher than that of bulk silicon, despite pronounced phonon scattering by surface roughness and point defects associated with arsenic vacancies. In addition, high thermoelectric power factor was measured on the BAs sample.Mechanical Engineerin

A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop