Identifying and Evaluating the Perceptions of Near-Miss Reporting within the Kentucky Transportation Cabinet

In the field of construction, most safety data and practices focus on preventing and mitigating serious incidents resulting in injuries or fatalities. However, on construction sites, near-miss events occur more frequently than said serious incidents and, under marginally different conditions, could potentially lead to damages, injuries, or fatalities. Therefore, near-miss reporting can serve as a useful tool for managing safety as it allows for workers to identify and managers to address potential risk factors within construction sites. While most construction companies have implemented some method for reporting near-miss events, many organizations, such as the Kentucky Transportation Cabinet (KYTC), struggle with a lack of near-miss reporting from employees. Therefore, the purpose of this study is to address and improve upon this near-miss reporting deficiency. To accomplish this goal, potential factors that result in a lack of reporting are identified through the synthesis of existing literature, areas for improving existing near-miss reporting systems are discussed, and a survey study created by the author and fellow researchers is distributed to KYTC maintenance superintendents. Results and analysis of this study suggest that many of the barriers that lead to a lack of near-miss reporting at KYTC stem from the managerial level. Some of these main barriers include a lack of knowledge on how to report a near-miss, a lack of training on how to report a near-miss, a lack of awareness of KYTC’s web-based reporting tool and how to access it, and a lack of corrective actions from previous near-miss reports. Some initial recommendations to KYTC management in attempt to overcoming these barriers include providing and/or requiring more near-miss training, making the web-based reporting tool more well-known and accessible, and taking more visible action in correcting reported near-misses

Deep Archetypal Analysis

"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes. The proposed method is an extension of linear "Archetypal Analysis" (AA), an unsupervised method to represent multivariate data points as sparse convex combinations of extremal elements of the dataset. Unlike the original formulation of AA, "Deep AA" can also handle side information and provides the ability for data-driven representation learning which reduces the dependence on expert knowledge. Our method is motivated by studies of evolutionary trade-offs in biology where archetypes are species highly adapted to a single task. Along these lines, we demonstrate that "Deep AA" also lends itself to the supervised exploration of chemical space, marking a distinct starting point for de novo molecular design. In the unsupervised setting we show how "Deep AA" is used on CelebA to identify archetypal faces. These can then be superimposed in order to generate new faces which inherit dominant traits of the archetypes they are based on.Comment: Published at the German Conference on Pattern Recognition 2019 (GCPR

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma

We describe the landscape of somatic genomic alterations of 66 chromophobe renal cell carcinomas (ChRCCs) based on multidimensional and comprehensive characterization, including mitochondrial DNA (mtDNA) and whole genome sequencing. The result is consistent that ChRCC originates from the distal nephron compared to other kidney cancers with more proximal origins. Combined mtDNA and gene expression analysis implicates changes in mitochondrial function as a component of the disease biology, while suggesting alternative roles for mtDNA mutations in cancers relying on oxidative phosphorylation. Genomic rearrangements lead to recurrent structural breakpoints within TERT promoter region, which correlates with highly elevated TERT expression and manifestation of kataegis, representing a mechanism of TERT up-regulation in cancer distinct from previously-observed amplifications and point mutations