Validated estimation of growth and age in the New Zealand abalone Haliotis iris using stable oxygen isotopes

2007 ◽  
Vol 58 (4) ◽  
pp. 354 ◽  
Author(s):  
J. R. Naylor ◽  
B. M. Manighetti ◽  
H. L. Neil ◽  
S. W. Kim
Keyword(s):  
Carbon Isotopes ◽  
Stock Assessment ◽  
Growth Function ◽  
Growth Patterns ◽  
Age And Growth ◽  
Data Sets ◽  
Reproductive Patterns ◽  
Novel Approach ◽  
Isotopic Analyses ◽  
Rational Management

The growth and reproductive patterns of abalone are central to an understanding of the dynamics of their populations, and provide essential input into many of the stock assessment models currently used as the basis of assessing the sustainability of the fisheries. At present, most of this knowledge is obtained by tag-recapture methods, which are time consuming, often expensive and potentially confounding. The aim of the present study was to determine whether variations in the ratios of oxygen and carbon isotopes in the shells of Haliotis iris can be used to determine age, growth and reproductive patterns. Isotopic analyses of H. iris shells indicated that oxygen isotope profiles within the shells reflected ambient water temperature at the time of shell precipitation, and that these profiles could be used to determine age and growth patterns. To match the variation in isotopic ratios with ambient temperature cycles, we also adopted the novel approach of fitting a growth function to the data sets. The method should allow the collection of abalone growth information over the finer scales more appropriate for the rational management of abalone fisheries. Variations in the ratios of carbon isotopes showed no consistent patterns and, unlike some mollusc species, did not appear to be useful predictors of reproductive status at length.

scholarly journals Growth Modeling of the Giant Electric Ray Narcine entemedor in the Southern Gulf of California: Analyzing the Uncertainty of Three Data Sets

Animals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
Pablo Mora-Zamacona ◽  
Felipe N. Melo-Barrera ◽  
Víctor H. Cruz-Escalona ◽  
Andrés F. Navia ◽  
Enrique Morales-Bojórquez ◽  
...  
Keyword(s):  
Gulf Of California ◽  
Age Distribution ◽  
Growth Function ◽  
Age And Growth ◽  
Birth Date ◽  
Data Sets ◽  
Growth Band ◽  
Vertebral Centra ◽  
Elasmobranch Species ◽  
Back Calculation

The age and growth rate of the giant electric ray, Narcine entemedor, was estimated using growth bands deposited in the vertebral centra of 245 specimens. Differences in size and age distribution were found between the sexes, a pattern that suggests the annual deposition of band pairs, possibly occurring in April. Multimodel inference and back-calculation were performed to three age data sets of females considering their reproductive cycle and time of capture, among which the von Bertalanffy growth function was found to be the most appropriate (L∞ = 81.87 cm TL, k = 0.17 year−1). Our research supports the idea that age can be determined via biological features such as birth date and growth band periodicity. We concluded that N. entemedor is of a moderate body size, moderate longevity and is a fast-growing elasmobranch species.

Age and growth of school mackerel (Scomberomorus queenslandicus) and spotted mackerel (S. munroi) in Queensland east-coast waters with implications for stock structure

1998 ◽  
Vol 49 (2) ◽  
pp. 109 ◽  
Author(s):  
Gavin A. Begg ◽  
Michelle J. Sellin
Keyword(s):  
Fisheries Management ◽  
East Coast ◽  
Stock Assessment ◽  
Growth Patterns ◽  
Age And Growth ◽  
Stock Structure ◽  
Population Parameters ◽  
Geographic Regions

Age and growth of school mackerel (Scomberomorus queenslandicus) and spotted mackerel (S. munroi) in east-coast waters, Queensland, Australia (16˚S to 28˚S), were determined to provide population parameters required for stock assessment and fisheries management. Female school mackerel (L∞ = 651 mm, K = 0.59, t0 = –1.41) were estimated to grow to a greater asymptotic length, but at a slower rate, than males (L∞ = 628 mm, K = 0.71, t0 = –1.26). Growth patterns of school mackerel differed between geographic regions and suggested the existence of separate stocks throughout the east-coast distribution. In contrast, female spotted mackerel (L∞ = 849 mm, K = 0.46, t0 = –1.54) were estimated to reach a greater asymptotic length at a faster rate than males (L∞ = 768 mm, K = 0.23, t0 = –4.33). There was no difference in growth between spotted mackerel from different regions, suggesting that there is a single stock along the Queensland east coast. Identification of school and spotted mackerel populations in Queensland east-coast waters will enable the species to be managed on the basis of stock structure across this range.

Age and growth of bluespine unicornfish (Naso unicornis): a half-century life-span for a keystone browser, with a novel approach to bomb radiocarbon dating in the Hawaiian Islands

2016 ◽  
Vol 73 (10) ◽  
pp. 1575-1586 ◽  
Author(s):  
Allen H. Andrews ◽  
Edward E. DeMartini ◽  
Jeff A. Eble ◽  
Brett M. Taylor ◽  
Dong Chun Lou ◽  
...  
Keyword(s):  
Growth Curves ◽  
Growth Function ◽  
Age And Growth ◽  
Fish Length ◽  
Age Validation ◽  
Novel Approach ◽  
Naso Unicornis ◽  
The Pacific ◽  
Von Bertalanffy Growth Function ◽  
Bomb Radiocarbon

Bluespine unicornfish (Naso unicornis) from Hawaii were aged to >50 years using cross-sectioned sagittal otoliths. Fish length was a poor indicator of age because of rapid and variable early growth, exemplified by fish aged to be 4 years near maximum length. Growth was deterministic with adult ages decoupled from body length. Otolith mass and thickness were evaluated as proxies for age and both were encouraging; thickness explained more variance but mass was easier to measure. An age estimation protocol was validated through ontogeny using bomb radiocarbon (14C) dating. Use of the postbomb 14C decline period from a regional reference chronology enabled age validation of young fish — a novel approach for the Pacific Ocean. A probabilistic procedure for assigning bomb 14C dates (CALIBomb) was used for the first time to determine fish birth years. The age-reading protocol was generally validated, and it was possible to describe length-at-age despite difficulties in counting otolith annuli beyond 30–40 years. Growth curves differed between the sexes, and a four-parameter generalized von Bertalanffy growth function provided the best fit.

scholarly journals Integrated magnetotelluric and seismic investigation of Cenozoic graben structure near Obrzycko, Poland

2021 ◽  
Author(s):  
Adam Cygal ◽  
Michał Stefaniuk ◽  
Anna Kret
Keyword(s):  
Seismic Data ◽  
Geophysical Methods ◽  
Geological Structure ◽  
Data Sets ◽  
Low Velocity ◽  
Shallow Subsurface ◽  
Geophysical Model ◽  
Novel Approach ◽  
Loose Deposits ◽  
Static Corrections

AbstractThis article presents the results of an integrated interpretation of measurements made using Audio-Magnetotellurics and Seismic Reflection geophysical methods. The obtained results were used to build an integrated geophysical model of shallow subsurface cover consisting of Cenozoic deposits, which then formed the basis for a detailed lithological and tectonic interpretation of deeper Mesozoic sediments. Such shallow covers, consisting mainly of glacial Pleistocene deposits, are typical for central and northern Poland. This investigation concentrated on delineating the accurate geometry of Obrzycko Cenozoic graben structure filled with loose deposits, as it was of great importance to the acquisition, processing and interpretation of seismic data that was to reveal the tectonic structure of the Cretaceous and Jurassic sediments which underly the study area. Previously, some problems with estimation of seismic static corrections over similar grabens filled with more recent, low-velocity deposits were encountered. Therefore, a novel approach to estimating the exact thickness of such shallow cover consisting of low-velocity deposits was applied in the presented investigation. The study shows that some alternative geophysical data sets (such as magnetotellurics) can be used to significantly improve the imaging of geological structure in areas where seismic data are very distorted or too noisy to be used alone

scholarly journals Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonas Albers ◽  
Angelika Svetlove ◽  
Justus Alves ◽  
Alexander Kraupner ◽  
Francesca di Lillo ◽  
...  
Keyword(s):  
Three Dimensional ◽  
High Specificity ◽  
Histopathological Analysis ◽  
Specific Information ◽  
Data Sets ◽  
Virtual Histology ◽  
Novel Approach ◽  
Cell Tissue ◽  
Immuno Histochemistry ◽  
3D Information

AbstractAlthough X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

A Novel Approach for Crawling the Opinions from World Wide Web

2016 ◽  
Vol 6 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Surbhi Bhatia ◽  
Manisha Sharma ◽  
Komal Kumar Bhatia
Keyword(s):  
World Wide ◽  
Opinion Mining ◽  
Real Data ◽  
User Generated Content ◽  
Decision Making Process ◽  
Web Pages ◽  
Data Sets ◽  
Web Technologies ◽  
Design And Implementation ◽  
Novel Approach

Due to the sudden and explosive increase in web technologies, huge quantity of user generated content is available online. The experiences of people and their opinions play an important role in the decision making process. Although facts provide the ease of searching information on a topic but retrieving opinions is still a crucial task. Many studies on opinion mining have to be undertaken efficiently in order to extract constructive opinionated information from these reviews. The present work focuses on the design and implementation of an Opinion Crawler which downloads the opinions from various sites thereby, ignoring rest of the web. Besides, it also detects web pages which frequently undergo updation by calculating the timestamp for its revisit in order to extract relevant opinions. The performance of the Opinion Crawler is justified by taking real data sets that prove to be much more accurate in terms of precision and recall quality attributes.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals Clustering and enhanced classification using a hybrid quantum autoencoder

2021 ◽  
Author(s):  
Maiyuren Srikumar ◽  
Charles Daniel Hill ◽  
Lloyd Hollenberg
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Quantum Information Theory ◽  
Quantum States ◽  
Data Sets ◽  
New Paradigm ◽  
Novel Approach ◽  
Representational Space ◽  
Quantum Machine Learning ◽  
Pure States

Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify – and classically represent – their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states – which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

scholarly journals Optimising Daily Fantasy Sports Teams with Artificial Intelligence

2020 ◽  
Vol 19 (2) ◽  
pp. 21-35
Author(s):  
Ryan Beal ◽  
Timothy J. Norman ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Real World ◽  
National Football League ◽  
Mixed Integer ◽  
Data Sets ◽  
Fantasy Sports ◽  
Real World Data ◽  
Sports Teams ◽  
Novel Approach ◽  
Four Seasons ◽  
Daily Fantasy Sports

AbstractThis paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.

scholarly journals Discovering Latent Class Labels for Multi-Label Learning

2020 ◽  
Author(s):  
Jun Huang ◽  
Linchuan Xu ◽  
Jing Wang ◽  
Lei Feng ◽  
Kenji Yamanishi
Keyword(s):  
Large Scale ◽  
Latent Class ◽  
Training Data ◽  
Data Sets ◽  
Robust Learning ◽  
Large Scale Data ◽  
Novel Approach ◽  
Fixed Set ◽  
Class Labels ◽  
Scale Data

Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets. Discovering and exploring the latent labels hidden in the data may not only find interesting knowledge but also help us to build a more robust learning model. In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously. Extensive experiments show a competitive performance of DLCL against other state-of-the-art MLL approaches.

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