Life Cycle Assessment of Cross-Laminated Timber Transportation from Three Origin Points

Cross-laminated timber (CLT) used in the U.S. is mainly imported from abroad. In the existing literature, however, there are data on domestic transportation, but little understanding exists about the environmental impacts from the CLT import. Most studies use travel distances to the site based on domestic supply origins. The new Adohi Hall building at the University of Arkansas campus, Fayetteville, AR, presents the opportunity to address the multimodal transportation with overseas origin, and to use real data gathered from transporters and manufacturers. The comparison targets the environmental impacts of CLT from an overseas transportation route (Austria-Fayetteville, AR) to two other local transportation lines. The global warming potential (GWP) impact, from various transportation systems, constitutes the assessment metric. The findings demonstrate that transportation by water results in the least greenhouse gas (GHG) emission compared with freight transportation by rail and road. Transportation by rail is the second most efficient, and by road the least environmentally efficient. On the other hand, the comparison of the life cycle assessment (LCA) tools, SimaPro (Ecoinvent database) and Tally (GaBi database), used in this research, indicate a remarkable difference in GWP characterization impact factors per tonne.km (tkm), primarily due to the different database used by each software.

Evaluation of Traits Associated With Seed Characteristics in Arkansas Restorer Lines

Rice Grain dimension and weight are two critical factors for marketing and increasing yield capacity. Seed shape is measured by its length, width, thickness, and ratio of length-width. In this study, an experiment was conducted in a controlled condition from fall 2017 to 2020 to identify QTL and candidate genes associated with seed dimension and weight using a bi-parental population resulting from two University of Arkansas developed genotypes: a restorer line 367R and an advanced breeding line RU1501139, in Stuttgart, Arkansas. Five seed dimension traits, including seed length, seed width, seed thickness, seed length-width ratio, and 100-seeds weight, were obtained for QTL detection. The study detected a total of 17 QTL. Four QTL associated with seed length were identified, in which two were positioned on chr. 3, one on chr. 7, and one on chr. 11. Two QTL related to seed length-width ratio were detected on chr. 3 and 7. Whereas a total of three QTL were identified for seed thickness, one each on chr. 5, 6, and 8. Eight QTL associated with seed weight were found, of which four QTL were detected on chr. 12, two each on chr. 1 and 10, and one on chr. 3. Of 17 QTL, four QTL originated from RU1501139, while the origin of the other 13 QTL was 367R. Since multiple genes could control the yield and seed physical characteristics, the detected QTL can play a role in introducing superior parental lines for developing conventional and hybrid rice production.

Developing probiotics, prebiotics, and organic acids to control Salmonella spp. in commercial turkeys at the University of Arkansas, USA

In the United States, non-typhoidal Salmonella causes over one million foodborne infections every year and turkey meat contaminated with Salmonella has been associated from the farm to the processing plant. These outbreaks emphasize efforts on decreasing and preventing human illness associated with live poultry contact through comprehensive interventions from \farm-to-fork" levels. This review article revises the role of the turkey upper respiratory tract, which is now known to play a crucial role in colonization and as a source of contamination, for this remarkable bacterium that has co-evolved to infect plants and animals. Because agriculture represents over 60% of the economy of the state of Arkansas, the mission of our laboratory over the last 21 years has been directed to evaluate and develop applied research to help reduce the incidence of Salmonella spp. from commercial turkey operations. A summary of the published research is presented.

Basics of SQL for Audit Data Retrieval and Analysis

The purpose of this teaching note is to explain how Structured Query Language (SQL) queries can help auditors to independently retrieve properly formatted data as audit evidence or for further analyses. The note demonstrates data extraction using Microsoft (MS) Access, one of the simplest SQL compliant database software applications. We use a dataset fragment extracted from the publicly available enterprise datasets provided by Walton College (University of Arkansas) to run SQL queries as a part of audit investigations. Data extraction is the first step of Extract, Transform, and Load (ETL) and may be time-consuming. We demonstrate how SQL queries can assist with this task, thereby allowing the auditor to begin analysis sooner. This teaching note can be used to prepare future auditors for the emerging data-rich and technology-driven business environment.

Effect of Harvest Time and Fruit Firmness on Red Drupelet Reversion in Blackberry

Red drupelet reversion (RDR) is a postharvest disorder of blackberries (Rubus L. subgenus Rubus Watson) in which fully black drupelets revert to red after harvest. This disorder can negatively impact consumer perception of fresh-market blackberries. The cause of RDR is hypothesized to be related to intracellular damage sustained because of mechanical and environmental stress during and after harvest. Cultivars differ in susceptibility to this disorder; and cultural factors, including nitrogen rate, harvest and shipping practices, and climate during harvest, influence RDR severity. In this 2-year study, seven genotypes (cultivars and advanced selections) developed in the University of Arkansas System Division of Agriculture (UA) blackberry breeding program, with a range of fruit textures, were evaluated to determine whether firmness was correlated with RDR. In addition, fruit was harvested at four different times (7:00 am, 10:00 am, 1:00 pm, and 4:00 pm) to investigate whether harvest time influences RDR. All seven genotypes were harvested at the four times on two harvest dates per year and evaluated for RDR and firmness after 1 week of cold storage (5 °C). Fruit harvested early in the day had less RDR, with 7:00 am harvests having the least RDR in both years. Significant genotypic differences in RDR and fruit firmness were found in each year. Firmness was negatively correlated with RDR in 2018 and 2019. These results indicate that growers may be able to reduce the prevalence of RDR by choosing cultivars with firm fruit texture and harvesting early in the day.

Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring

This work aimed to assist physicians by improving their speed and diagnostic accuracy when interpreting portable CXRs as well as monitoring the treatment process to see whether a patient is improving or deteriorating with treatment. These objectives are in especially high demand in the setting of the ongoing COVID-19 pandemic. With the recent progress in the development of artificial intelligence (AI), we introduce new deep learning frameworks to align and enhance the quality of portable CXRs to be more consistent, and to more closely match higher quality conventional CXRs. These enhanced portable CXRs can then help the doctors provide faster and more accurate diagnosis and treatment planning. The contributions of this work are four-fold. Firstly, a new database collection of subject-pair radiographs is introduced. For each subject, we collected a pair of samples from both portable and conventional machines. Secondly, a new deep learning approach is presented to align the subject-pairs dataset to obtain a pixel-pairs dataset. Thirdly, a new PairFlow approach is presented, an end-to-end invertible transfer deep learning method, to enhance the degraded quality of portable CXRs. Finally, the performance of the proposed system is evaluated by UAMS doctors in terms of both image quality and topological properties. This work was undertaken in collaboration with the Department of Radiology at the University of Arkansas for Medical Sciences (UAMS) to enhance portable/mobile COVID-19 CXRs, to improve the speed and accuracy of portable CXR images and aid in urgent COVID-19 diagnosis, monitoring and treatment.

Consolidated EHR Workflow for Endoscopy Quality Reporting

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice. The objective of this study was to build a Machine Learning (ML) model that predicts if a colonoscopy can be successfully completed with moderate sedation based on patients’ demographics, comorbidities, and prescribed medications. XGBoost model was trained and tested on 10,025 colonoscopies (70% – 30%) performed at University of Arkansas for Medical Sciences (UAMS). XGBoost achieved average area under receiver operating characteristic curve (AUC) of 0.762, F1-score to predict procedures that need moderate sedation was 0.85, and precision and recall were 0.81 and 0.89 respectively. The proposed model can be employed as a decision support tool for physicians to bolster their confidence while choosing between moderate sedation and anesthesia for a colonoscopy procedure.