Professional Human Resources to Create Consumer Satisfaction and The Impact on Purchasing Intention.

Knowledge and skills possessed by individuals will lead to behavior. Meanwhile, the behavior will produce performance. A person's ability, both knowledge or skills possessed in carrying out work will encourage them to perform well. Individual aspects related to individual abilities and work professionalism, group aspects related to the environment in which they work; such as discipline, job compensation, and job satisfaction as well as aspects of the organizational system related to the extent to which the employee adheres to a commitment to growing the company. The responsibility of human resource management in carrying out their duties is to be able to provide suitable substrates for talented and capable personnel in the organization such as; individual ability, work professionalism, organizational commitment, work discipline, work compensation, and job satisfaction so that they can carry out their duties with good quality. Thus, according to the results and objectives of this study, professional human resources can directly create consumer satisfaction and its impact on purchasing intention. This type of research is explanatory research with a quantitative approach. To test the developed hypothesis using path analysis. Data were obtained from the results of distributing questionnaires to culinary tourism actors using a survey method. The sampling technique used the entire population as a sample (census) with a Likert scale. Primary data analysis was carried out after testing the validity and reliability and normality of the data.   Keyword :  Customer Satisfaction, Purchasing Intention, Professional Human Resources

In silico design of crop ideotypes under a wide range of water availability

Given the changing climate and increasing impact of agriculture on global resources, it is important to identify phenotypes which are global and sustainable optima. Here, an in silico framework is constructed by coupling evolutionary optimization with thermodynamically sound crop physiology, and its ability to rationally design phenotypes with maximum productivity is demonstrated, within well‐defined limits on water availability. Results reveal that in mesic environments, such as the North American Midwest, and semi‐arid environments, such as Colorado, phenotypes optimized for maximum productivity and survival under drought are similar to those with maximum productivity under irrigated conditions. In hot and dry environments like California, phenotypes adapted to drought produce 40% lower yields when irrigated compared to those optimized for irrigation. In all three representative environments, the trade‐off between productivity under drought versus that under irrigation was shallow, justifying a successful strategy of breeding crops combining best productivity under irrigation and close to best productivity under drought

Computer vision and machine learning enabled soybean root phenotyping pipeline

Background Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. Results This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. Conclusions This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use

Shared genetic control of root system architecture between Zea mays and Sorghum bicolor

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed CREAMD (Core Root Excavation using Compressed-air), a high-throughput pipeline for the cleaning of field-grown roots, and COFE (Core Root Feature Extraction), a semi-automated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from \u3e3,300 maize roots and \u3e1,470 sorghum roots. SNP-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via eRD-GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are co-regulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves trans-eQTL for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa

Inertial focusing of cancer cell lines in curvilinear microchannels

Circulating tumor cells (CTCs) are rare cancer cells, which originate from the primary tumors and migrate to the bloodstream. Separation of CTCs from blood is critical because metastatic CTCs might hold different genomic and phenotypic properties compared to primary tumor cells. In this regard, accurate prognosis and effective treatment methods are necessary. For this purpose, focusing biological particles and cells using microfluidic systems have been implemented as an efficient CTCs enumeration and enrichment method. Passive, continuous, label-free and parallelizable size-dependent focusing based on hydrodynamic forces is preferred in this study to sort cancer cells while avoiding cell death and achieving high throughput. The focusing behavior of MDA-MB-231 (11–22 μm), Jurkat (8–17 μm), K562 (8–22 μm), and HeLa (16–29 μm) was examined with respect to different Reynolds numbers and Dean numbers. The effect of curvature on cell focusing was carefully assessed. The focusing positions of the cells clearly indicated that isolations of MDA cells from MDA-Jurkat cell mixtures as well as of HeLa cells from HeLa-Jurkat cell mixtures were possible by using the curvilinear channels with a curvature angle of 280° at the Reynolds number of 121. © 2019 The Author(s