Characterization of Uterine Cervix Cancers in Women from Appalachian Kentucky

Uterine cervix cancer (UCCx) is clinically and socioeconomically diverse among women in the United States (US), which obscures the discovery of effective radiochemotherapy approaches for this disease. UCCx afflicts 7.5 per 100,000 American women nationally but 11.7 per 100,000 women in Appalachian Kentucky (AppKY), when age-adjusted to the 2000 US standard population. Epidemiological chart review was performed on 212 women with UCCx treated at the University of Kentucky (UKY) between January 2001 and July 2021. Demographics, tumor characteristics, and relative radiochemotherapy dose and schedule intensity were compared among AppKY and non-AppKY cohorts as well as Surveillance, Epidemiology, and End Results (SEER) data. One hundred thirty-eight (65%) of 212 women seeking radiochemotherapy treatment for UCCx resided in AppKY. Most (80%) sought external-beam radiochemotherapy close to their AppKY residence. Brachytherapy was then most frequently (96%) conducted at UKY. Cancer stage at diagnosis was significantly more advanced in AppKY residents. Women residing in AppKY had a median 10-week radiochemotherapy course, longer than an 8-week guideline. Estimated survival in women residing in AppKY was 8% lower than US national averages. In summary, this study identified an increased percentage of advanced-stage UCCx cancer at diagnosis arising in AppKY residents, with a confounding population-specific delay in radiochemotherapy schedule intensity lowering survival.

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.

Using On-Farm Monitoring of Ergovaline and Tall Fescue Composition for Horse Pasture Management

Central Kentucky horse pastures contain significant populations of tall fescue (Schedonorus arundinacea (Schreb.) Dumort) infected with an endophyte (Epichloë coenophialum (Morgan-Jones and Gams) Bacon and Schardl) known to produce several ergot alkaloids, with ergovaline in the highest concentration. While most classes of horses are not adversely affected by average levels of ergovaline in pastures, late term pregnant mares have a low tolerance to ergovaline and the related ergot alkaloids. Endophyte-infected tall fescue has been known to cause prolonged gestation, thickened placenta, dystocia, agalactia, and foal and mare mortality. The University of Kentucky Horse Pasture Evaluation Program utilizes ergovaline and endophyte testing, as well as pasture species composition, to calculate ergovaline in the total diet in broodmare pastures. This data is used to develop detailed management recommendations for individual pastures. Application of these recommendations has led to reduced tall fescue toxicity symptoms on these farms, as well as improved pasture management and improved forage quality and quantity.

Incorporating Redundant Systems to Capture the Kentucky Money Shot

The Kentucky Eclipse Ballooning Project began in early 2015 when students and faculty from The University of Kentucky attended the NASA Marshall Space Flight Center BalloonSat Workshop in Huntsville, Alabama. The students accelerated their preparations after the Eclipse Ballooning Project Workshop hosted in Bozeman, Montana where they built and learned systems designed by Montana Space Grant. In 2016, the students began a sequence of 10 balloon launches in preparation for the total solar eclipse on August 21, 2017. In the early stages of this project, University of Kentucky students set the goal to capture footage of a separate high-altitude weather balloon in front of the solar eclipse, an image dubbed “The Kentucky Money Shot.” After establishing that goal, students began working on approaches and designs to capture this picture with one overarching theme: redundancy. Every aspect of the project from the number of balloons and imaging systems to tracking systems and launch procedures were designed with redundant aspects and through collaboration among the payload, ground station, launch, and mission control teams. The short time window of eclipse totality, 2 minutes 28 seconds, motivated design iterations throughout the progressive practice launches and ground tests including launching two balloons simultaneously, streaming and storing footage of the flight from multiple cameras, and using SPOT Trackers and Iridium systems as multiple tracking approaches. All of these practices and tests led to flying the final redundant designs on August 21st, 2017 to successfully capture “The Kentucky Money Shot”.

Enhancing Research Data Infrastructure to Address the Opioid Epidemic: The Opioid Overdose Network (02-Net)

Objective: Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support the design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina (MUSC), Dartmouth Medical School (DMS), University of Kentucky (UK), and University of California San Diego (UCSD)) worked to adapt ACT network. This paper reports their progress. Materials and Methods: The approach that was taken to enhancing ACT network focused on four activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing (NLP) tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Results: All four institutions were able to replicate their i2b2 and Shared Health Research Information Network (SHRINE) infrastructure. A five-category e-phenotype model based on ICD-10 coding was developed from prior published work. Ongoing work is refining this via machine learning and artificial intelligence methods. Portable NLP tools, focused on the sentence level, were also developed to identify uncoded opioid overdose-related concepts in provider notes. Optimal performance was seen in NLP tools that combined rule-based with deep learning methods (F score, 0.94). A template for ED overdose documentation was developed to improve primary data capture. Interactive prompts to physicians inside ED progress notes were effective in promoting the use of the template. The template had good system usability and net promoter scores (0.72 and 0.75, respectively, n=13). Discussion: Results suggested that tailoring of existing multipurpose research networks to a specific task is feasible: however, substantial efforts were required for coordination of the subnetwork and development of new tools for extension of available data.

LATE Neuropathologic Changes with Little or No Alzheimer Disease is Common and is Associated with Cognitive Impairment but Not Frontotemporal Dementia

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic changes (LATE-NC) often occur in aged brains that also contain appreciable Alzheimer disease neuropathologic changes (ADNC). Question has arisen as to whether LATE-NC can occur independently of ADNC. We evaluated data from the University of Kentucky Alzheimer’s Disease Research Center autopsy cohort (383 included subjects) to address 2 questions: (i) Is LATE-NC seen in the absence of ADNC, outside of persons who had the frontotemporal dementia (FTD) clinical syndrome? and (ii) is LATE-NC associated with cognitive impairment across the full spectrum of ADNC severity? In the present study, the pathologic combination of LATE-NC (Stage >1) and low/no ADNC was common: 8.9% (34/383) of all subjects (including demented and non-demented individuals) showed this combination. There were no FTLD-TDP cases to be included from the community-based cohort. Across a broad range of ADNC severity, the presence of LATE-NC was associated with impaired cognition but was never associated with a FTD clinical syndrome.

Evaluation of Ceftolozane/Tazobactam, Ceftazidime/Avibactam and Meropenem/Vaborbactam Against Multidrug-Resistant (MDR) Pseudomonas

Background: Data on the patient outcomes for newer β-lactam–β-lactamase inhibitor (BLBI) drugs compared to carbapenem-containing combination antibiotics for multidrug-resistant (MDR)–Pseudomonas aeruginosa infections are limited. Methods: This retrospective, case–control observational study was based on chart review of the patients managed at the University of Kentucky. Results: In total, 143 patients with MDRO Pseudomonas aeruginosa infections were identified and divided into 2 groups: 1 group received newer BLBI combinations with or without aminoglycosides or polymyxins, for at least 72 hours, and the control group received carbapenem containing combination antibiotics or other antibiotics. Baseline characteristics and patient outcomes are shown in Table 1. Discussion: The newer BLBI combinations group consisted of 60.8% MDR Pseudomonas bacteremia, whereas the control group had 68.4% of MDR Pseudomonas respiratory cultures. Overall, the use of newer BLBI combinations such as ceftazidime/avibactam, ceftolozane/tazobactam, and meropenem/vaborbactam was associated with lower rates of acute kidney injury (AKI), shorter LOS, and lower mortality rates compared to the control group, and these differences were statistically significant. Because the 2 populations of patient differed significantly based on the site of infection (sepsis vs pneumonia), the data were reanalyzed to evaluate the impact of therapy on the occurrence of AKI, LOS, and mortality based on the site of infection. Only those patients with sepsis who received the newer combination drugs had significantly better rates of AKI, lower LOS, and had lower rates of mortality. The 2 treatment arms were not statistically different when comparing patients with pneumonia. Additionally, the use of these new combination therapies did not make a difference regarding readmission rates or duration of bacteremia for the patients included in the study.Funding: NoDisclosures: None

Scholarly Editing and AI: Machine Predicted Text and Herculaneum Papyri

In 2016 the Digital Restoration Initiative (DRI) at the University of Kentucky, under the direction of Professor Brent Seales, virtually unrolled a carbonized parchment scroll from Ein Gedi, revealing a copy of Leviticus written in iron gall ink. In 2019 the DRI applied a new machine learning method to reveal a Greek character written in carbon ink from an actual Herculaneum papyrus fragment. Virtual unwrapping of cultural heritage objects is a reality. The application of machine and deep learning methods to enhance difficult-to-detect ink signals in tomography will continue to evolve. This raises an important question. How will the process of editing texts that are ‘true-born virtual’ (the object can never be opened to verify the results) change to reflect the presence and dependency on AI? This paper produces a theoretical model for how a critical edition of a virtually unwrapped papyrus text must document the role of the machine. It also engages the possible requirements, in terms of Data Science, that this new type of text compels in order to ensure transparency at the level of its ‘birth’. Put simply, a new virtual edition model that is a fusion of humanities and science is needed.

Reviewer Acknowledgements

Journal of Education and Training Studies (JETS) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether JETS publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 9, Number 6Francisca Serrano, University of Granada, SpainJohn Mark Asio, Gordon College, PhilippinesKeyla Ferrari Lopes, UNICAMP, BrazilLucy Lugo Mawang, Kenyatta University, KenyaMatt Varacallo, University of Kentucky, USAMaurizio Sajeva, Pellervo Economic Research PTT, FinlandMehmet Inan, Marmara University, TurkeyMeral Seker, Alanya Alaaddin Keykubat University, TurkeyMichail Kalogiannakis, University of Crete, GreeceNiveen M. Zayed, MENA College of Management, JordanRichard H. Martin, Mercer University, USARichard Penny, University of Washington Bothell, USASadia Batool, Preston University Islamabad, PakistanSamah El-Sakka, Suez University, EgyptShu-wen Lin, Sojo University, JapanVeronica Velasco Gonzalez, University of Valladolid, Spain Robert SmithEditorial AssistantOn behalf of,The Editorial Board of Journal of Education and Training StudiesRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAE-mail 1: [email protected] 2: [email protected]: http://jets.redfame.com

Cancer Prevention Behaviors and Knowledge of Cancer Screening Among Food Insecure College Students

Objectives The objective of the current study were to examine cancer prevention knowledge and risky behaviors by diet and food insecurity. Methods A cross-sectional examination of college students examined FI status and cancer prevention behavior (fruit and vegetable intake (FVI), alcohol intake frequency (AIF), and tobacco use (TU)) and screening knowledge (age to receive breast, cervical, lung, prostate, and colon screening for “normal risk”). Results Students were 34.13% food insecure. Body mass index was significant by food insecurity status (P < .01; FI = 26.41 kg/m2, Food Secure (FS) = 23.42 kg/m2). FVI was not significant by group however average intake of the whole population was 3.04 + 1.84 servings. Likewise, AIF by group was not significant (P > .05). A higher percentage of FI used tobacco (P = .01). Lung and pap smear screening age knowledge by FI status was significantly different (P = .027 and .047, respectively). Conclusions As FI is correlated with negative health outcomes, upstream interventions should be proposed to target long term impacts such as risky behaviors and cancer prevention knowledge. Funding Sources University of Kentucky Start-up funds.