Does goal ambiguity influence the program structure of nonprofit human service organizations?

This study presents an alternative to outcome management based on accountability by analyzing the ambiguity of nonprofit human service organizations’ program goals and program structures. A total of 761 programs were chosen from the 2010–2020 annual plans of P Rehabilitation Center for analysis. The program goals were analyzed according to quantitative accuracy and outcome attainability standards and the program structures were analyzed using the program theory model. Human service organizations need to have a logical program structure according to the program goal, and it is necessary to minimize logical errors in the program structure.

High spectral stability and quantitative accuracy of LIBS by optimization of combined atomic and ionic line algorithm

Large spectral fluctuations bring large errors in laser-induced breakdown spectroscopy (LIBS) analysis, which impedes the improvement of precision and accuracy, limiting the large-scale application and commercialization. In this work, we...

Self-assembly of photonic crystals by controlling the nucleation and growth of DNA-coated colloids

DNA-coated colloids can self-assemble into an incredible diversity of crystal structures, but their applications have been limited by poor understanding and control over the crystallization dynamics. To address this challenge, we use microfluidics to quantify the kinetics of DNA-programmed self-assembly along the entire crystallization pathway, from thermally activated nucleation through reaction-limited and diffusion-limited phases of crystal growth. Our detailed measurements of the temperature and concentration dependence of the kinetics at all stages of crystallization provide a stringent test of classical theories of nucleation and growth. After accounting for the finite rolling and sliding rates of micrometer-sized DNA-coated colloids, we show that modified versions of these classical theories predict the absolute nucleation and growth rates with quantitative accuracy. We conclude by applying our model to design and demonstrate protocols for assembling large single crystals with pronounced structural coloration, an essential step in creating next-generation optical metamaterials from colloids.

3D-Simulation Data-Making Trial to present and analyze Small-sized Farmlands Fields with Car-shaped Robot, ROS2, SLAM and Foxy for Real agricultural workers

In this study, we create various application systems focusing on agricultural (agri-) field data digitalization issues that will benefit traditional agri-researchers, workers, and their respective managers. We obtain three-dimensional (3D) information on agri-environments (e.g., rice fields, farmlands) via roaming robots with sensors. Robot-controlled middleware, such as robot operating systems (ROS), are often used for such robots. Thus, we selected car-shaped robot (NANO-RT1), ROS2, and the SLAM-based system. The car-shaped robot-based system operates sensor units uniformly. With this technology, we can recognize our location at an unknown place, and the robot can run. There are challenges in accurately presenting quantitative accuracy data for this type of study. We address this by providing average and standard deviation (SD) data for certain situations using five algorithms: (1) Hector-SLAM, (2) G-mapping, (3) Karto-SLAM, (4) Core-SLAM, and (5) Lago-SLAM. We believe the proposed holistic system has the potential to improve not only agri-businesses, but also agri-skills and overall security levels.

Comparison of PSF and Non PSF BLOB based reconstructed image in Time-of-Flight PET/CT

Hybrid PET/CT imaging with the use of 18F FDG is a widely used imaging technique with major indications in oncology for staging, re-staging and monitoring response to therapy. There is a major issue of partial volume effect in PET images which affects image quality as well as quantitative accuracy in small lesions. Multiple attempts have been made to resolve these issues. The aim of our study was to look into impact of Point‐spread-function (PSF) on reconstructed attenuation corrected (AC) images of PET/CT and to find out best combination of the number of PSF iterations with regularization level while applying PSF.

Harmonization of PET image reconstruction parameters in simultaneous PET/MRI

Objective Simultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems. Method The NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.7:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 min listmode data acquisition and on 6 realizations of 5 min from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3–7 mm in steps of 1 mm was applied. Attenuation correction was provided from a scaled computed tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuation corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. A hybrid metric combining the root-mean-squared discrepancy (RMSD) and the absolute CRC values was used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting toward higher resolution reconstructions. The image reconstruction parameter set was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction. Results The range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves was obtained at the lowest RMSD values with: for CRCmean, 2 iterations-7 mm filter on the GE Signa and 4 iterations-6 mm filter on the Siemens mMR, for CRCmax, 4 iterations-6 mm filter on the GE Signa, 4 iterations-5 mm filter on the Siemens mMR and for CRCpeak, 4 iterations-7 mm filter with PSF on the GE Signa and 4 iterations-7 mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs was 1.8%, 3.6% and 2.9% for CRC mean, max and peak, respectively. The solution of 2 iterations-3 mm on the GE Signa and 4 iterations-3 mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 2.8%, 5.8% and 3.2%, respectively. Conclusions For two commercially available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing and PSF modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass-spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. We aimed to increase the throughput of high-sensitivity proteomics while achieving high proteome coverage and quantitative accuracy. We developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. Specifically, plexDIA using 3-plex nonisobaric mass tags enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. Furthermore, plexDIA increases the consistency of protein quantification, resulting in over 2-fold reduction of missing data across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis.