Assessment of extreme and metocean conditions in the Maldives for OTEC applications

The Maldives is a group of tropical atolls, considered globally to be one of the most desirable holiday destinations. There is an urgent requirement to decrease their dependency on fossil fuels that are currently the main source of energy, and a number of renewable energy alternatives are being evaluated. Among these, due to the favorable oceanographic and bathymetric conditions, ocean thermal energy conversion (OTEC) systems represent a viable opportunity for clean and reliable power. However, the stresses the OTEC platform will need to endure during adverse environmental conditions are not well defined. The magnitude of these stresses will then have a direct influence on the design of the OTEC device. In order to overcome this uncertainty, this paper uses hindcast data sets from global weather and ocean models to assess the metocean conditions of the Maldives, with particular reference to extreme conditions. After selecting a suitable location for the deployment of the devices, return values calculated using the peaks‐over‐threshold (POT) methodology are estimated for wind, waves, and currents. The 100‐year return value for the significant wave height is found to be 4.5 m, with a joint occurrence of energy periods between 7.5 and 8.5 seconds, whereas the 100‐year return wind has a velocity of 17.8 m/s and the 100‐year return current of 1.9 m/s. The directionality of these extreme events is also considered, showing the southern and western sub‐quadrants as the prevailing sources, which provides essential information for positioning of the platform. Additional evaluations of tropical revolving storms (TRS) and variations in temperature and salinity patterns are also provided over a 1500‐m water column; temperature varies by approximately 24°C, and salinity by around 2 ppt, showing the suitability of OTEC platforms in the Maldives. This work is therefore of interest to offshore renewable energy stakeholders interested in developing a project in the Maldives or those conducting an analogous analysis in other locations.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Optical and electrical characterization of carbon nanoparticles produced in laminar premixed flames

Optical and electrical properties of carbonaceous particles produced in laboratory scale, premixed ethylene/air flames are obtained. Light absorption and Raman spectroscopy show that the change in particle nanostructure follows a graphitization trajectory as the flame richness increases. The optical band gap decreases and the size of the aromatic network in the particle increases, while the interlayer spacing between parallel layers decreases. The electrical conductivity of the materials increases by increasing flame richness in agreement to the graphitization trajectory. A non-ohmic behavior has been found and explained in terms of electron tunneling in a percolative network. Our results show that the electrical properties of flame formed carbon nanoparticles are strongly dependent on their nanostructure, and hence they have to be used carefully for the determination of particle concentration with conductometric sensors. Moreover, the dependence of the electrical properties of combustion formed particles might be useful for the development of cheap sensors for the selective detection of different classes of combustion aerosols.     

Relationship between Non-Alcoholic Fatty Liver Disease and Psoriasis: A Novel Hepato-Dermal Axis?

Over the past 10 years, it has become increasingly evident that nonalcoholic fatty liver disease (NAFLD) is a multisystem disease that affects multiple extra-hepatic organ systems and interacts with the regulation of several metabolic and immunological pathways. In this review we discuss the rapidly expanding body of clinical and epidemiological evidence supporting a strong association between NAFLD and chronic plaque psoriasis. We also briefly discuss the possible biological mechanisms underlying this association, and discuss treatment options for psoriasis that may influence NAFLD development and progression. Recent observational studies have shown that the prevalence of NAFLD (as diagnosed either by imaging or by histology) is remarkably higher in psoriatic patients (occurring in up to 50% of these patients) than in matched control subjects. Notably, psoriasis is associated with NAFLD even after adjusting for metabolic syndrome traits and other potential confounding factors. Some studies have also suggested that psoriatic patients are more likely to have the more advanced forms of NAFLD than non-psoriatic controls, and that psoriatic patients with NAFLD have more severe psoriasis than those without NAFLD. In conclusion, the published evidence argues for more careful evaluation and surveillance of NAFLD among patients with psoriasis.     

Discordance Rate of HER2 Status in Primary Gastric Carcinomas and Synchronous Lymph Node Metastases: A Multicenter Retrospective Analysis

Background: The assessment of human epidermal growth factor receptor 2 (HER2) gene amplification is essential in order to identify those patients affected by advanced gastric cancer who may benefit from Trastuzumab targeted therapy. Materials and Methods: With the aim to investigate the concordance rate in HER2 status between primary gastric carcinoma (GC) and synchronous lymphnode metastases, we investigated HER2 status in a cohort of 108 surgical formalin-fixed paraffin-embedded specimens of GC and matched synchronous metastatic lymph nodes collected from three different units of Anatomic Pathology in southern of Italy. Fleiss-Cohen weighted k statistics were used to assess the concordance rate of HER2 status. Results: HER2 amplification was observed in 17% of primary GCs and the overall concordance rate with corresponding nodal metastases was 90.74%. Changes in HER2 status between primary GC and matched synchronous metastases were evidenced in 10 (9.26%) cases. Of these, 6 cases were HER2 amplified in the primary GC and not amplified in the metastases, while 4 were HER2 not amplified in the primary tumour and amplified in the lymph node metastases. Conclusions: Although at present the simultaneous determination of HER2 in advanced gastric cancer and corresponding metastatic lymph nodes is not mandatory, the possibility that the synchronous metastases of GC have a different HER2 status from that of the primary tumour is of remarkable significance; Indeed this may have influence on the therapeutic management and prognosis of the patients.