A Study of Nektonic and Benthic Faunas of the Shallow Gulf of Mexico Off the State of Mississippi As Related to Some Physical, Chemical, and Geological Factors

A seasonal study of the nektonic and benthic faunas of the shallow Gulf of Mexico off Mississippi was conducted from January 1967 through May 1969. It was planned to sample monthly six fixed offshore stations at depths ranging from 5 to 50 fathoms in the open Gulf. In general this was carried out fairly well. Water samples were taken from surface, midwater, and bottom levels each time a station was occupied, and temperatures and salinities were recorded for each of these. Samples were tested for the presence of nitrates, nitrites, ortho-phosphates and total phosphates. Secchi disc extinction points were recorded. Grab samples were taken for the determination of bottom composition. Plankton samples were taken from surface, midwater and bottom levels. Copepods, brachyuran zoea and megalops, stomatopod larvae, Lucifer faxoni, Acetes a. carolinae, Penilia avirostris, Doliolum sp. and fish eggs and larvae were present in greatest abundance. Surface and benthic nekton samples were obtained. Dredge samples were made quarterly and twelve invertebrate species and three species of fishes were collected. Renilla mülleri was the most abundant species taken, and the fish catch consisted of Centropristes ocyurus, Citharichthys spilopterus and Etropus crossotus. Accounts of 50 invertebrate species (24,679 specimens) and 129 fishes (93,563 specimens) taken in trawl hauls is presented. Temperature and salinity data are given for all species. Relative abundance, seasonal bathymetric distributions and movements, apparent growth patterns, catch per unit of effort and various biological data are noted for the most abundant species. Station 5 (40 fathoms) produced the largest percentage of trawl catches (22.7). Renilla mülleri was the most abundant invertebrate taken in trawling. The brown shrimp, Penaeus aztecus, was second in abundance (10.92%). The five most abundant species comprising 80.57% numerically of the catch were croaker, longspine porgy, butterfish, spot, and seatrout. The species comprising 91.89% of the catch by weight were the croaker, longspine porgy, spot, seatrout, lizardfish, butterfish, pinfish, bank sea bass, sea catfish and black fin sea robin. The families Sciaenidae, Sparidae and Stromateidae were represented by the greatest numbers and comprised 82.9% of the total catch. Families considered to be of commercial importance contributed 92.9% to the total fish catch

Discovering hidden geothermal signatures using non-negative matrix factorization with customized \u3ci\u3ek\u3c/i\u3e-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (\u3c90◦C) and medium (90-150◦C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTens ors/GeoThermalCloud.jl

Additions to Philippine slender skinks of the Brachymeles bonitae complex (Reptilia: Squamata: Scincidae) IV: Resurrection and redescription of Brachymeles burksi

The diversity of Philippine amphibians and reptiles has increased over the last few decades, in part due to re-evaluation of species formerly believed to be widespread. Many of these investigations of widespread species have uncovered multiple closely related cryptic lineages comprising species complexes, each restricted to individual Pleistocene Aggregate Island Complexes (PAICs). One group in particular for which widespread cryptic diversity has been common is the clade of Philippine skinks of the genus Brachymeles. Recent phylogenetic studies of the formerly recognized widespread species Brachymeles bonitae have indicated that this species is actually a complex distributed across several major PAICs and smaller island groups in the central and northern Philippines, with numerous species that exhibit an array of digit loss and limb reduction patterns. Despite the recent revisions to the B. bonitae species complex, studies suggest that unique cryptic lineages still exist within this group. In this paper, we resurrect the species Brachymeles burksi Taylor 1917, for a lineage of non-pentadactyl, semi-fossorial skink from Mindoro and Marinduque islands. First described in 1917, B. burksi was synonymized with B. bonitae in 1956, and has rarely been reconsidered since. Evaluation of genetic and morphological data (qualitative traits, meristic counts, and mensural measurements), and comparison of recently-obtained specimens to Taylor’s original description support this species’ recognition, as does its insular distribution on isolated islands in the central portions of the archipelago. Morphologically, B. burksi is differentiated from other members of the genus based on a suite of unique phenotypic characteristics, including a small body size, digitless limbs, a high number of presacral vertebrae, the absence of auricular openings, and discrete (non-overlapping) meristic scale counts. The recognition of this central Philippine species further increases the diversity of non-pentadactyl members of the B. bonitae complex, and reinforces the biogeographic uniqueness of the Mindoro faunal region

A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers

Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of the true positive and false positive rate of the inferences of such models has been performed. Findings indicate a superior inferential capacity of the designed argument-based models

ERS statement on standardisation of cardiopulmonary exercise testing in chronic lung diseases

The objective of this document was to standardise published cardiopulmonary exercise testing (CPET) protocols for improved interpretation in clinical settings and multicentre research projects. This document: 1) summarises the protocols and procedures used in published studies focusing on incremental CPET in chronic lung conditions; 2) presents standard incremental protocols for CPET on a stationary cycle ergometer and a treadmill; and 3) provides patients’ perspectives on CPET obtained through an online survey supported by the European Lung Foundation. We systematically reviewed published studies obtained from EMBASE, Medline, Scopus, Web of Science and the Cochrane Library from inception to January 2017. Of 7914 identified studies, 595 studies with 26 523 subjects were included. The literature supports a test protocol with a resting phase lasting at least 3 min, a 3-min unloaded phase, and an 8- to 12-min incremental phase with work rate increased linearly at least every minute, followed by a recovery phase of at least 2–3 min. Patients responding to the survey (n=295) perceived CPET as highly beneficial for their diagnostic assessment and informed the Task Force consensus. Future research should focus on the individualised estimation of optimal work rate increments across different lung diseases, and the collection of robust normative data.The document facilitates standardisation of conducting, reporting and interpreting cardiopulmonary exercise tests in chronic lung diseases for comparison of reference data, multi-centre studies and assessment of interventional efficacy. http://bit.ly/31SXeB

3D visualization processes for recreating and studying organismal form

The study of biological form is a vital goal of evolutionary biology and functional morphology. We review an emerging set of methods that allow scientists to create and study accurate 3D models of living organisms and animate those models for biomechanical and fluid dynamic analyses. The methods for creating such models include 3D photogrammetry, laser and CT-scanning, and 3D software. New multi-camera devices can be used to create accurate 3D models of living animals in the wild and captivity. New websites and virtual reality/augmented reality devices now enable the visualization and sharing of these data. We provide examples of these approaches for animals ranging from large whales to lizards and show applications for several areas: Natural history collections; body condition/scaling, bioinspired robotics, computational fluids dynamics (CFD), machine learning, and education. We provide two data sets to demonstrate the efficacy of CFD and machine learning approaches and conclude with a prospectus