Using Experimentally Calibrated Regularized Stokeslets to Assess Bacterial Flagellar Motility Near a Surface

The presence of a nearby boundary is likely to be important in the life cycle and evolution of motile flagellate bacteria. This has led many authors to employ numerical simulations to model near-surface bacterial motion and compute hydrodynamic boundary effects. A common choice has been the method of images for regularized Stokeslets (MIRS); however, the method requires discretization sizes and regularization parameters that are not specified by any theory. To determine appropriate regularization parameters for given discretization choices in MIRS, we conducted dynamically similar macroscopic experiments and fit the simulations to the data. In the experiments, we measured the torque on cylinders and helices of different wavelengths as they rotated in a viscous fluid at various distances to a boundary. We found that differences between experiments and optimized simulations were less than 5% when using surface discretizations for cylinders and centerline discretizations for helices. Having determined optimal regularization parameters, we used MIRS to simulate an idealized free-swimming bacterium constructed of a cylindrical cell body and a helical flagellum moving near a boundary. We assessed the swimming performance of many bacterial morphologies by computing swimming speed, motor rotation rate, Purcell’s propulsive efficiency, energy cost per swimming distance, and a new metabolic energy cost defined to be the energy cost per body mass per swimming distance. All five measures predicted that the optimal flagellar wavelength is eight times the helical radius independently of body size and surface proximity. Although the measures disagreed on the optimal body size, they all predicted that body size is an important factor in the energy cost of bacterial motility near and far from a surface

Effects of combined rice flour and molasses use on the growth performance of Pacific white shrimp (<em>Litopenaeus vannamei</em> Boone, 1931) applied biofloc technology

A 63-day completely random experiment with three replications was carried out to compare the effects of five different combination ratios of rice flour (R) and molasses (M) on the growth and survival rates of Pacific white shrimp (Litopenaeus vannamei Boone, 1931) postlarvae applied biofloc technology. Five biofloc (BF) treatments, including R90-M10, R70-M30, R50-M50, R30-M70, and R10-M90, formed with the addition of different combination ratios of rice flour and molasses, i.e., 90% R+10% M, 70% R+30% M, 50% R+50% M, 30% R+70% M, and 10% R+90% M, respectively, with C/N ratios of 15:1, and a control (neither rice flour nor molasses applied) was randomly arranged into the 18 plastic tanks of 1.0 m3 volume (with 0.5 m3 of water) each tank and salinity of 15‰. The postlarvae (0.095 g) were stocked into the tanks at a 150 ind. m−3 density and fed pelleted feed (40% protein). There was an improvement in growth (FMW, WG, DWG, and SGR) for all treatments. Besides, treatments with more than or equal to 30% molasses have improved SR, FCR, and FB. Especially the highest SR (94.2%) was obtained at the R70-M30, which perhaps created the highest FB (1.435 kg m−3) in this treatment. The lowest FCR (1.28) was also observed in the R70-M30 and significantly differed from the control and other treatments. Besides, water quality parameters were within the ranges recommended for Pacific white shrimp health during the experimental period. Our findings indicated the benefits of shrimp culture using the BF system when different combined ratios of rice flour and molasses were applied, of which a ratio of 70% rice flour and 30% molasses was considered as the best

On the fixed-effects vector decomposition

This paper analyses the properties of the fixed-effects vector decomposition estimator, an emerging and popular technique for estimating time-invariant variables in panel data models with unit effects. This estimator was initially motivated on heuristic grounds, and advocated on the strength of favorable Monte Carlo results, but with no formal analysis. We show that the three-stage procedure of this decomposition is equivalent to a standard instrumental variables approach, for a specific set of instruments. The instrumental variables representation facilitates the present formal analysis which finds: (1) The estimator reproduces exactly classical fixed-effects estimates for time-varying variables. (2) The standard errors recommended for this estimator are too small for both time-varying and time-invariant variables. (3) The estimator is inconsistent when the time-invariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence are incorrect. (5) We recommend an alternative shrinkage estimator that has superior risk properties to the decomposition estimator, unless the endogeneity problem is known to be small or no relevant instruments exist

Human resource requirements for quality-assured electronic data capture of the tuberculosis case register

<p>Abstract</p> <p>Background</p> <p>The tuberculosis case register is the data source for the reports submitted by basic management units to the national tuberculosis program. Our objective was to measure the data entry time required to complete and double-enter one record, and to estimate the time for the correction of errors in the captured information from tuberculosis case registers in Cambodia and Viet Nam. This should assist in quantifying the additional requirements in human resources for national programs moving towards electronic recording and reporting.</p> <p>Methods</p> <p>Data from a representative sample of tuberculosis case registers from Cambodia and Viet Nam were double-entered and discordances resolved by rechecking the original case register. Computer-generated data entry time recorded the time elapsed between opening of a new record and saving it to disk.</p> <p>Results</p> <p>The dataset comprised 22,732 double-entered records of 11,366 patients (37.1% from Cambodia and 62.9% from Viet Nam). The mean data entry times per record were 97.5 (95% CI: 96.2-98.8) and 66.2 (95% CI: 59.5-73.0) seconds with medians of 90 and 31 s respectively in Cambodia and in Viet Nam. The percentage of records with an error was 6.0% and 39.0% respectively in Cambodia and Viet Nam. Data entry time was inversely associated with error frequency. We estimate that approximately 118-person-hours were required to produce 1,000 validated records.</p> <p>Conclusions</p> <p>This study quantifies differences between two countries for data entry time for the tuberculosis case register and frequencies of data entry errors and suggests that higher data entry speed is partially offset by requiring revisiting more records for corrections.</p

Implementation of Medication Event Reminder Monitors among patients diagnosed with drug susceptible tuberculosis in rural Viet Nam: A qualitative study.

BACKGROUND: Despite the criticality of adherence to tuberculosis treatment, there is paucity of rigorous experimental research exploring the efficacy of interventions to promote adherence and a greater lack of inquiry addressing the integral role of adherence behaviour. The aim of this formative study was to examine the way in which the Wisepill evriMED Medication Event Reminder Monitor (MERM) was used among outpatients with drug susceptible pulmonary tuberculosis. METHODS: In depth interviews were conducted with 20 outpatients receiving treatment from two public healthcare facilities in Thanh Hoa, a rural province in northern Viet Nam. Patients had been enrolled in a randomized controlled trial evaluating the effect of using the MERM device upon adherence for between 1-3 months. The control group used the device without an alert, while the intervention group used the device with a daily alert and scheduled dosing history review. FINDINGS: All 20 patients interviewed were supportive of using the MERM device. Those able to be at home at the time that their treatment was due (50%) used the device as intended. Patients who worked all reported separating the time when the box was opened from the time at which they ingested their medication. Patients expressed fidelity to the prescribed medication taking time and concerns regarding the portability of the device. Limitations of the study surround the inclusion of a small sample population that did not experience factors that further compromise adherence. CONCLUSIONS: Data recorded by the box did not always accurately reflect usage patterns. The alert in the intervention arm was able to support adherence only in patients who did not work while completing their treatment. MERM implementation can be improved by better aligning prescriber instructions with patients' daily routines, and increasing the use of adherence data to guide adherence support practices. Healthcare staff need to be aware of potential barriers to optimal use of MERM devices. A rigorous qualitative approach to formative assessment is essential to inform the scale up of new digital technologies

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outper-formed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability