Analytical and numerical analyses of the micromechanics of soft fibrous connective tissues

State of the art research and treatment of biological tissues require accurate and efficient methods for describing their mechanical properties. Indeed, micromechanics motivated approaches provide a systematic method for elevating relevant data from the microscopic level to the macroscopic one. In this work the mechanical responses of hyperelastic tissues with one and two families of collagen fibers are analyzed by application of a new variational estimate accounting for their histology and the behaviors of their constituents. The resulting, close form expressions, are used to determine the overall response of the wall of a healthy human coronary artery. To demonstrate the accuracy of the proposed method these predictions are compared with corresponding 3-D finite element simulations of a periodic unit cell of the tissue with two families of fibers. Throughout, the analytical predictions for the highly nonlinear and anisotropic tissue are in agreement with the numerical simulations

Genetic structure of the high dispersal Atlanto-Mediterreanean sea star Astropecten aranciacus revealed by mitochondrial DNA sequences and microsatellite loci

To investigate the impact of potential marine barriers on gene-flow in high dispersal marine invertebrates, we assessed the population genetic structure of the sea star Astropecten aranciacus. Samples were obtained from nine locations within the Atlantic and the Mediterranean Sea including populations east of the Siculo-Tunisian Strait. We obtained both DNA sequence data of the mitochondrial control region and genotype data at four microsatellite loci. Both markers were highly polymorphic and showed a great level of genetic diversity. Genetic differentiation between populations (F (ST)) was in general low, particularly for nuclear data, as is often the case in high dispersal marine invertebrates. Nevertheless, both marker sets indicated a significant genetic differentiation of the population from the island of Madeira to most other populations. Our results also demonstrate a clear pattern of isolation-by-distance supported by both mitochondrial and nuclear markers. Therefore, we conclude that larval dispersal of A. aranciacus is somewhat limited even within the basins of the Atlantic, the west Mediterranean and the east Mediterranean. Microsatellite loci further revealed genetic differentiation between the three basins; however, it is not clear whether this is truly caused by marine barriers. Genetic differentiation between basins might also be a result of isolation-by-distance allowing for any grouping to be significant as long as geographical neighbors are clustered together. Although levels of genetic differentiation were less pronounced in mirosatellite data, both datasets were coherent and revealed similar patterns of genetic structure in A. aranciacus

The Seafood Supply Chain from a Fraudulent Perspective

Food fraud is an intentional act for economic gain. It poses a risk to food integrity, the economy, public health and consumers’ ethics. Seafood is one commodity which has endured extensive fraudulent activity owing to its increasing consumer demand, resource limitations, high value and complex supply chains. It is essential that these fraudulent opportunities are revealed, the risk is evaluated and countermeasures for mitigation are assigned. This can be achieved through mapping of the seafood supply chains and identifying the vulnerability analysis critical control points (VACCP), which can be exposed, infiltrated and exploited for fraudulent activity. This research systematically maps the seafood supply chain for three key commodities: finfish, shellfish and crustaceans in the United Kingdom. Each chain is comprised of multiple stakeholders across numerous countries producing a diverse range of products distributed globally. For each supply chain the prospect of fraud, with reference to species substitution, fishery substitution, illegal, unreported and unregulated substitution, species adulteration, chain of custody abuse, catch method fraud, undeclared product extension, modern day slavery and animal welfare, has been identified and evaluated. This mapping of the fraudulent opportunities within the supply chains provides a foundation to rank known and emerging risks and to develop a proactive mitigation plan which assigns control measures and responsibility where vulnerabilities exist. Further intelligence gathering and management of VACCPs of the seafood supply chains may deter currently unknown or unexposed fraudulent opportunities

Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning.

BACKGROUND: Allogeneic hematopoietic stem transplantation (allo-HSCT) constitutes a curative treatment for various hematological malignancies. However, various complications limit the therapeutic efficacy of this approach, increasing the morbidity and decreasing the overall survival of allo-HSCT recipients. In everyday clinical practice, various laboratory and clinical biomarkers and scorning systems have been developed and implemented focusing on the recognition of high-risk patients for organ dysfunction-related complications and those who might experience low overall survival. However, the predictive accuracy of developed scores has been reported deficient in some studies. The aim of the current retrospective study is to develop a machine learning (ML) model to predict the long-term survivorship of patients who receive allo-HSCT based on clinical pre- and post-allo-HSCT variables, and on transplantation-related characteristics. METHODS: For this purpose, a database of 564 allo-HSCT recipients incorporating 16 clinical and laboratory variables and the survivorship status of the patients during follow-up (Alive, Dead, Alive but follow-up less than 24 months) was used. An ML model was developed and tested, based on the previously published Data Ensemble Refinement Greedy Algorithm (DEGRA) algorithm. RESULTS: A predictive ML model was built with 92.02 % accuracy. The eight parameters included in the algorithm were the following: CD34+ cells infused, patients' age and gender, conditioning regimen toxicity, disease risk index (DRI), graft source, and platelet and neutrophil engraftment. CONCLUSION: To our knowledge, this is the first AI model incorporating post-HSCT variables for the prediction of mortality in adult HSCT recipients. In the era of precision medicine, the recognition of patients who undergo allo-HSCT and face a great risk for mortality and morbidity, with high-accuracy algorithms is crucial