Development and Multi-Site Assessment of a Novel Shoulder Motion Joint Simulator

Background: Multiple biomechanical shoulder simulators have been described in the literature, with a trend toward increasing complexity to better simulate clinical scenarios. Our objective was to develop an advanced, novel shoulder joint simulator and compare outcomes at two separate institutions, for a typical shoulder joint motion simulation. Methods: Identical shoulder simulators were developed & deployed at both institutions. Eight cadaveric upper extremities were tested by simulating actively controlled, arm elevation in the plane of the scapula for two sequential test conditions (intact and non-destructive simulated cuff-tear), each repeated for a total of five trials. Muscle forces and joint translations were recorded for both conditions. The intact condition was repeated following simulated cuff-tear to assess effect of testing order. Statistical analyses were aimed at assessing repeatability and reproducibility of results within specimens, between specimens, and between institutions. Results: The highest average forces were observed for the middle deltoid (233N or 32.5% body weight), followed by infraspinatus (99.0N), and posterior deltoid (93.7N) muscles. Differentiation between test conditions was unhindered by variability between repeated trials. Data from testing repeated over time, and between the two institutions were not significantly different. Conclusions: The novel shoulder simulator produced repeatable results with low trial-to-trial variation and outcomes were comparable between the two institutions. The results demonstrated a consistent response in muscle forces and humeral translation for the simulated rotator cuff tear condition. Such advanced shoulder simulators could thus be used for evaluating and optimizing surgical interventions and implant strategies.

Trichoscopy for the Hair Transplant Surgeon—Assessing for Mimickers of Androgenetic Alopecia and Preoperative Evaluation of Donor Site Area

Preoperative diagnostic confidence and donor site assessment are important for all hair transplant surgery patients. While the majority of patients seek hair transplantation for male or female pattern hair loss (androgenetic alopecia [AGA]), there are mimickers that must be differentiated from patterned hair loss, as they alter the candidacy of the patient for transplantation. They are termed mimickers as they also can present with patterned hair loss. The use of trichoscopy has become increasingly popular for such use. Patterned hair loss mimickers, which include the underappreciated alopecia areata incognita (AAI) and fibrosing alopecia in patterned distribution (FAPD), can be identified clinically with key trichoscopic findings such as yellow dots and peripilar casts, respectively, that correlate with their histologic diagnosis. Donor hair density and putative hair pathology of the safe donor area can also by assessed via trichoscopy. This article discusses the use of trichoscopy, particularly for diagnosing mimickers of patterned hair loss as well as preoperative donor site assessment.

Potential Human Exposure to Mercury (Hg) in a Chlor-Alkali Plant Impacted Zone: Risk Characterization Using Updated Site Assessment Data

Industrial activities have resulted in severe environmental contamination that may expose rural and urban populations to unacceptable health risks. For example, chlor-alkali plants (CAPs) have historically contributed mercury (Hg) contamination in different environmental compartments. One such site (a burden from the Soviet Union) is located in an industrial complex in Pavlodar, Kazakhstan. Earlier studies showed the CAP operating in the second half of the twentieth century caused elevated Hg levels in soil, water, air, and biota. However, follow-up studies with thorough risk characterization are missing. The present study aims to provide a detailed risk characterization based on the data from a recent site assessment around the former CAP. The ⅀HI (hazard index) ranged from 9.30 × 10−4 to 0.125 (deterministic method) and from 5.19 × 10−4 to 2.54 × 10−2 (probabilistic method). The results indicate acceptable excess human health risks from exposure to Hg contamination in the region, i.e., exposure to other Hg sources not considered. Air inhalation and soil ingestion pathways contributed to the highest ⅀HI values (up to 99.9% and 92.0%, respectively). The residential exposure scenario (among four) presented the greatest human health risks, with ⅀HI values ranging from 1.23 × 10−2 to 0.125. Although the local urban and rural population is exposed to acceptable risks coming from exposure to Hg-contaminated environmental media, an assessment of contamination directly on the former CAP site on the industrial complex could not be performed due to access prohibition. Furthermore, the risks from ingesting contaminated fish were not covered as methyl-Hg was not targeted. An additional assessment may be needed for the scenarios of exposure of workers on the industrial complex and of the local population consuming fish from contaminated Lake Balkyldak. Studies on the fate and transport of Hg in the contaminated ecosystem are also recommended considering Hg methylation and subsequent bioaccumulation in the food chain.

API Process Safety Site Assessment Program PSSAP®: Significant Program Updates and Future Focus

The American Petroleum Institute (API) and the U.S. oil and natural gas industry have long been committed to protecting the health and safety of our workers, contractors and neighbors. For more the 75 years, API has led the development of industry standards, sharing lessons learned as well as the establishment of training and certification programs. In recent years, despite safety improvements by the refining industry, incidents have increased attention on process safety by industry, governments, non-government organizations (NGOs), and the media. Recognizing these concerns, API and our memebrs are working collectively to improve or develop new programs improve process safety performance. As part of the industry's ongoing commitment, API, in collaboration with industry partners, has developed a Process Safety Site Assessment Program (PSSAP®), an assessment program focused on evaluating higher risk activities in a refining, petrochemical, or chemical facility. This program is intended to: Promote process safety performance improvement industry wide; Promote learnings from industry practices; Provide benchmarking through the consistent use of industry-developed good practice protocols; Serve as a feedback mechanism for an analysis of industry performance; and, Encourage safety collaboration among participating sites and industry experts. PSSAP benchmarking, a key aspect of the program, allows sites to judge their performance against that of their peers in a blinded fashion. In addition to this benchmarking, the consistent use of our good-practice protocols enables API to analyze where companies may still be working to improve. Taking that information, API has implemented other programs to assist industry in those areas. Further, it has allowed API to quantify PSSAP protocol scoring improvements across the industry, seeing positive momentum in benchmarking scoring across the life of the program. PSSAP® is also a primary resource to support API Energy Excellence® implementation. API Energy Excellence is another critical API program in which all API members commit to enhance the integrity of operations across the industry by applying standards, implementing workforce training programs, and participating in performance initiatives. Downstream and petrochemical operators can use these PSSAP protocols to help demonstrate conformance to their API Energy Excellence requirements. PSSAP® is flexible so that sites can tailor assessments to specific needs and operations. It provides options for smaller sites that do not have on-site internal assessment capabilities or do not think a full PSSAP General Assessment is warranted. It is intended that assessments focus on higher risk activities and includes an evaluation of both the quality of written programs at a site and the effectiveness of field implementation of those programs.

The Future of English for Specific Purposes Classes Beyond the Pandemic Context as Seen by the Students of Agricultural Sciences

While English for Specific Purposes has been dealt with by researchers all over the world, the insertion of educational technology and online environment is a relatively new concept. This article explores the main aspects of an effective online ESP learning process during the 2019-2020 academic year within the split frame generated by the pandemic context. It also aims at setting the educational frame for the future of ESP classes from the standpoint of the freshmen of Agricultural Sciences. An online questionnaire was answered, its structure being underlined by aspects such as content and skill-related tasks, accessibility, motivation, engagement, educational environment, media and assessment. The use of specialization-related materials is positively adjusted towards the online ESP classes, whereas frequency usage of skill-related tasks shows more balanced values between on-site and online ESP classes. In terms of schedule flexibility, the online medium is the more manageable of the two. If the combination of online exercises, specialized sites, online apps and platforms is clearly the norm for the online ESP classes, on-site assessment is evaluated as being more accurate. Although the students’ answers are relatively balanced, the majority agreed that the online environment is the most suitable frame for the future ESP classes.

A site assessment tool for inpatient controlled human infection models for enteric disease pathogens

The use of the controlled human infection model to facilitate product development and to advance understanding of host-pathogen interactions is of increasing interest. While administering a virulent (or infective) organism to a susceptible host necessitates an ongoing evaluation of safety and ethical considerations, a central theme in conducting these studies in a safe and ethical manner that yields actionable data is their conduct in facilities well-suited to address their unique attributes. To that end, we have developed a framework for evaluating potential sites in which to conduct inpatient enteric controlled human infection model to ensure consistency and increase the likelihood of success.

Assessing the Accuracy of Fault Interpretation using Machine Learning Techniques when Risking Faults for CO2 Storage Site Assessment

Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, utilizing methods such as Deep Learning. These Deep Learning techniques use knowledge from Neural Networks to allow end-users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare Deep Learning fault interpretation versus manual fault interpretation, and can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when Deep Learning methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults show a close similarity between the resulting fault surfaces when both Deep Learning and manual fault interpretation methods are employed, and hence we also observe a close similarity between any attributes and fault analyses made.