Measurements of underwater piling noise during nearshore windfarm construction in the UK potential impact on marine mammals in compliance with German UBA limit

Offshore construction work, such as pile and conductor driving, can potentially cause acoustic disturbance to marine mammals, such as cetaceans (whales, dolphins and por-poises), the odontocetes (toothed cetaceans) of which rely on the underwater sound field for spatial orientation, navigation, prey capture, communication, and predator avoidance. Disturbance ranges from behavioural changes, masking of communication signals, and temporary or even permanent hearing loss. There is currently no specific legal noise threshold in UK waters, but the Marine Management Organisation (MMO) has stipulated the requirement for noise monitoring during pile-driving operations when some windfarms are constructed. Measurements presented in this paper were taken during nearshore pile driving in the UK from a support vessel located 750 m from each pile (wind-turbine foun-dation). Results were compared with a threshold issued by the German Federal Environ-ment Agency (UBA). Noise level beyond the measurement location was predicted using a numerical model. Comparing results with the Southall criteria (Southall, B. L., et al., Ma-rine Mammal Noise Exposure Criteria: Initial Scientific Recommendations. Aquatic Mam-mals, 33 (4), 2007), the Joint Nature Conservation Committee (JNCC) 500 m exclusion zone offered protection for most of marine mammals during pile driving events in this particular case. Keywords: Underwater piling noise, wind-farm, marine mammals, UBA limi

A Case Study of Emerging Challenges and Reflections on Internationalization of Higher Education

Purpose – The purpose of this research is to examine challenges and issues of higher education (HE) internationalization in the quest to reflect on HE internationalization. Design/methodology/approach – A qualitative research is conducted in a UK university. The total of 20 interviewees from the case study university participate in this research. Content analysis, critical discourse analysis and categorization of meaning are adopted as data analysis strategies. Findings – This study identifies critical issues that challenge HE internationalization within an institutional context, and provides reflection of the development of HE internationalization. These critical issues include resource and investment, workload, agent and partnership management, communication, integration and cooperation, motivation and incentives, programmes contextualization, and staff attitude and development. Research limitation/implications – This research contributes to rich understands of issues and challenges stem from the present case study. Therefore, further research in this area is encouraged to test the generalizability of these highlighted challenges through quantitative research. Practical implications – Research findings provide different understanding of critical challenges and issues of HE internationalization at the present university. These issues are empirical and creditable to international operation at the case study. This study encourages an internal cohesion and reflection of internationalization across different key departments. Originality/value – This research suggests that prior attention should be given to these practical issues and challenges that stem from the empirical investigation of HE internationalization. Compared to the extent discussion of risks and challenges, these factors are more operational and relevant to an institution’s daily function of internationalization. Research findings can guide institutions to precisely address and resolve these issues. These issues are also transferrable and applicable to other similar cases. Keywords -- Internationalization, higher education, issues, challenges, risks and reflection of higher education internationalization

Actinomyces Infection Leading to Pseudoepitheliomatous Hyperplasia Within a Tattoo

A Caucasian woman in her 40s presented with a one-year history of raised, dry, pruritic papules on the tattoo on the left medial lower leg she received six months prior. Examination revealed multiple open comedones and pustules coalescing into an edematous plaque, limited to the red portions of the tattoo. Histological examination revealed pseudoepitheliomatous hyperplasia, tattoo ink, and brisk lymphohistiocytic inflammation, suggestive of an infectious process. A wound culture grew Actinomyces neuii, and she was subsequently started on amoxicillin 500 mg TID for six months. Topically, she applied mupirocin ointment daily. Subsequent clinic visits demonstrated flattening and resolution of the papules and comedones on this regimen. Tattoos have risen in popularity since the 1970s, and some estimates have found that 10-20% of people of Western cultures have at least one tattoo. Tattoo complications may occur with a broad spectrum of clinical findings. Allergies, pigment foreign body granulomatous reactions, and infections are the most common complications in tattoo. Tattoo infections are commonly due to endogenous bacteria such as Streptococci and Staphylococci species or exogenous agents, leading to viral hepatitis or HIV. This report describes a case of an Actinomyces infection involving the red pigment of a tattoo. Red pigment within tattoos is the most common cause of cutaneous reactions to tattoos. The most common reaction patterns include allergic dermatitis, photosensitivity, granulomatous, lichenoid, and pseudolymphomatous reactions. We describe a case of PEH secondary to Actinomyces neuii infection limited to the red portions of a tattoo. To our knowledge, this is the first case in which Actinomyces species has been implicated in a tattoo infection. Actinomyces species are naturally found in mucous membranes of the mouth, gastrointestinal, and genitourinary tract. Potential niduses for infection in this case could include the use of dirtied instruments, contaminated pigments, or lack of sterility during the procedure. While Actinomyces rarely cause infections in humans, cutaneous infections typically manifest as a soft tissue infection often located on the head or neck, requiring treatment with antibiotics and incision and drainage. Primary cutaneous Actinomycosis is rare; they are typically chronic, recur after short courses of antibiotic treatment and lead to the formation of granules. Actinomyces neuii infection has only been reported in approximately one hundred cases, most commonly associated with abscesses, infected atheromas and diabetic ulcers. One case of A. neuii has been reported as a superinfection of hidradenitis suppurativa. Bacterial infections of tattoos are most commonly associated with Staphylococci or Streptococci infections. Mycobacteria infections of tattoos have also been reported; M. haemophilum is thought to have a predilection for tattoos and extremities as it requires low incubation temperature and iron supplementation for growth. While reactions within red tattoos and bacterial infections of tattoos may be relatively common, infection of the red component of a tattoo with Actinomyces has not yet been described. Biopsy and evaluation for bacterial infections such as Actinomyces should be considered within the differential of a red tattoo reaction.https://scholarlycommons.henryford.com/merf2020caserpt/1059/thumbnail.jp

Metabolic engineering of yeast for increased production of cyclopropane fatty acids

Biological production of chemicals and fuels using whole cells is an important and growing segment of manufacturing and among the various forms, microorganisms are the most successfully utilized. In particular, yeasts such as Saccharomyces cerevisiae are both widely used production organisms and metabolic models for oleaginous yeasts. Fatty acid-containing lipids are one example of moderate value, highly versatile chemicals produced by yeasts that are used in a broad range of industries for lubrication, cosmetics, fuels and polymers. Production levels of standard fatty acids by yeasts has increased enormously over the past 10 years through the application of metabolic pathway engineering, flux analysis, computational approaches and to a lesser extent, bioprocessing improvements. Combined, these advances have brought yeast-based fatty acid production close to commercial reality. Functionalized fatty acids such as those containing hydroxyl or cyclopropyl groups are more valuable as chemical feedstocks and are an attractive target for yeast production as commercial supply is limited. Cyclopropane fatty acids, possessing a strained 3-membered ring and having a saturated chain, are especially attractive as they have application in cosmetics and specialty lubrication. However, cyclopropyl fatty acids present greater challenges for metabolic engineering as they are not produced naturally by yeast. Please click Additional Files below to see the full abstract

Semi-Supervised Adversarial Domain Adaptation for Seagrass Detection Using Multispectral Images in Coastal Areas

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods

Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances

We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with “neighbouring” sensors. The multi-target tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an “offspring” of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithm’s convergence are illustrated by numerical simulations

Quantifying Seagrass Distribution in Coastal Water With Deep Learning Models

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations