Areca catechu-From farm to food and biomedical applications.

The family Arecaceae includes 181 genera and 2,600 species with a high diversity in physical characteristics. Areca plants, commonly palms, which are able to grow in nearly every type of habitat, prefer tropical and subtropical climates. The most studied species Areca catechu L. contains phytochemicals as phenolics and alkaloids with biological properties. The phenolics are mainly distributed in roots followed by fresh unripe fruits, leaves, spikes, and veins, while the contents of alkaloids are in the order of roots, fresh unripe fruits, spikes, leaves, and veins. This species has been reputed to provide health effects on the cardiovascular, respiratory, nervous, metabolic, gastrointestinal, and reproductive systems. However, in many developing countries, quid from this species has been associated with side effects, which include the destruction of the teeth, impairment of oral hygiene, bronchial asthma, or oral cancer. Despite these side effects, which are also mentioned in this work, the present review collects the main results of biological properties of the phytochemicals in A. catechu. This study emphasizes the in vitro and in vivo antioxidant, antimicrobial, anticancer, and clinical effectiveness in humans. In this sense, A. catechu have demonstrated effectiveness in several reports through in vitro and in vivo experiments on disorders such as antimicrobial, antioxidant, or anticancer. Moreover, our findings demonstrate that this species presents clinical effectiveness on neurological disorders. Hence, A. catechu extracts could be used as a bioactive ingredient for functional food, nutraceuticals, or cosmeceuticals. However, further studies, especially extensive and comprehensive clinical trials, are recommended for the use of Areca in the treatment of diseases.

Finding the mean in a partition distribution.

BACKGROUND: Bayesian clustering algorithms, in particular those utilizing Dirichlet Processes (DP), return a sample of the posterior distribution of partitions of a set. However, in many applied cases a single clustering solution is desired, requiring a 'best' partition to be created from the posterior sample. It is an open research question which solution should be recommended in which situation. However, one such candidate is the sample mean, defined as the clustering with minimal squared distance to all partitions in the posterior sample, weighted by their probability. In this article, we review an algorithm that approximates this sample mean by using the Hungarian Method to compute the distance between partitions. This algorithm leaves room for further processing acceleration. RESULTS: We highlight a faster variant of the partition distance reduction that leads to a runtime complexity that is up to two orders of magnitude lower than the standard variant. We suggest two further improvements: The first is deterministic and based on an adapted dynamical version of the Hungarian Algorithm, which achieves another runtime decrease of at least one order of magnitude. The second improvement is theoretical and uses Monte Carlo techniques and the dynamic matrix inverse. Thereby we further reduce the runtime complexity by nearly the square root of one order of magnitude. CONCLUSIONS: Overall this results in a new mean partition algorithm with an acceleration factor reaching beyond that of the present algorithm by the size of the partitions. The new algorithm is implemented in Java and available on GitHub (Glassen, Mean Partition, 2018).

Accelerating segmentation of fossil CT scans through Deep Learning.

Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably extract complex skeletal structures. Here we present a method for automated Deep Learning segmentation to obtain high-fidelity 3D models of fossils digitally extracted from the surrounding rock, training the model with less than 1%-2% of the total CT dataset. This workflow has the capacity to revolutionise the use of Deep Learning to significantly reduce the processing time of such data and boost the availability of segmented CT-scanned fossil material for future research outputs. Our final Unet segmentation model achieved a validation Dice similarity of 0.96.

The effect of neuter status on longevity in the Rottweiler dog.

Surgical sterilization or neutering of dogs is a commonly performed procedure in veterinary practices in many countries. In recent decades, concerns have been raised regarding possible side effects of neutering, including increased risk of certain neoplastic, musculoskeletal and endocrinological conditions. Considering that age serves as a significant confounding factor for some of these conditions, evaluating longevity statistics could provide valuable insights into the impact of neutering. The aim of this study was to compare longevity between neutered and sexually intact male and female Rottweilers, using electronic patient records collected by the VetCompass Australia database. Male and female Rottweilers neutered before 1 year of age (n = 207) demonstrated an expected lifespan 1.5 years and 1 year shorter, respectively, than their intact counterparts (n = 3085; p < 0.05). Broadening this analysis to include animals neutered before the age of 4.5 years (n = 357) produced similar results.

Environmental conditions constrain nursery habitat value in Australian sub-tropical estuaries.

High quality nursery grounds are important for species success and the long-term sustainability of fish stocks. However, even for important fisheries species, what constitutes nursery habitats is only coarsely defined, and details of specific requirements are often lacking. In this study we investigated upstream estuarine areas in central Queensland, Australia, to identify the environmental factors that constrain nursery ground utilization for important fisheries species. We used unbaited underwater video cameras to assess fish presence, and used a range of water quality sensors to record fluctuations in environmental conditions, likely to influence juveniles, over several months (e.g. tidal connection patterns, temperature, salinity and dissolved oxygen). We found that juveniles of three fisheries target species (Lutjanus argentimaculatus, Lutjanus russellii and Acanthopagrus australis) were common in the upstream sections of the estuaries. For each species, only a subset of the factors assessed were influential in determining nursery ground utilization, and their importance varied among species, even among the closely related L. argentimaculatus and L. russellii. Overall, tidal connectivity and the availability of complex structure, were the most influential factors. The reasons for the importance of connectivity are complex; as well as allowing access, tidal connectivity influences water levels, water temperature and dissolved oxygen - all important physiological requirements for successful occupation. The impact of variation in juvenile access to food and refuge in nursery habitat was not directly assessed. While crucial, these factors are likely to be subordinate to the suite of environmental characteristics necessary for the presence and persistence of juveniles in these locations. These results suggest that detailed environmental and biological knowledge is necessary to define the nuanced constraints of nursery ground value among species, and this detailed knowledge is vital for informed management of early life-history stages.

Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.

Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length2/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.

Age- and sex-dependent variation in relatedness corresponds to reproductive skew, territory inheritance, and workload in cooperatively breeding cichlids.

Kin selection plays a major role in the evolution of cooperative systems. However, many social species exhibit complex within-group relatedness structures, where kin selection alone cannot explain the occurrence of cooperative behavior. Understanding such social structures is crucial to elucidate the evolution and maintenance of multi-layered cooperative societies. In lamprologine cichlids, intragroup relatedness seems to correlate positively with reproductive skew, suggesting that in this clade dominants tend to provide reproductive concessions to unrelated subordinates to secure their participation in brood care. We investigate how patterns of within-group relatedness covary with direct and indirect fitness benefits of cooperation in a highly social vertebrate, the cooperatively breeding, polygynous lamprologine cichlid Neolamprologus savoryi. Behavioral and genetic data from 43 groups containing 578 individuals show that groups are socially and genetically structured into subgroups. About 17% of group members were unrelated immigrants, and average relatedness between breeders and brood care helpers declined with helper age due to group membership dynamics. Hence the relative importance of direct and indirect fitness benefits of cooperation depends on helper age. Our findings highlight how both direct and indirect fitness benefits of cooperation and group membership can select for cooperative behavior in societies comprising complex social and relatedness structures.

A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.

Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown great promise for scene analysis when trained on large-scale datasets. However, current datasets for fish analysis tend to focus on the classification task within constrained, plain environments which do not capture the complexity of underwater fish habitats. To address this limitation, we present DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine-environments of tropical Australia. The dataset originally contained only classification labels. Thus, we collected point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Our experiments provide an in-depth analysis of the dataset characteristics, and the performance evaluation of several state-of-the-art approaches based on our benchmark. Although models pre-trained on ImageNet have successfully performed on this benchmark, there is still room for improvement. Therefore, this benchmark serves as a testbed to motivate further development in this challenging domain of underwater computer vision.

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning.

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.

Genome-wide SNP validation and mantle tissue transcriptome analysis in the silver-lipped pearl oyster, Pinctada maxima.

Pearl oysters are not only farmed for their gemstone quality pearls worldwide, but they are also becoming important model organisms for investigating genetic mechanisms of biomineralisation. Despite their economic and scientific significance, limited genomic resources are available for this important group of bivalves, hampering investigations into identifying genes that regulate important pearl quality traits and unique biological characteristics (i.e. biomineralisation). The silver-lipped pearl oyster, Pinctada maxima, is one species where there is interest in understanding genes that regulate commercially important pearl traits, but presently, there is a dearth of genomic information. The objective of this study was to develop and validate a large number of type I genome-wide single nucleotide polymorphisms (SNPs) for P. maxima suitable for high-throughput genotyping. In addition, sequence annotations and Gene Ontology terms were assigned to a large mantle tissue 454 expressed sequence tag assembly (96,794 contigs) and information on known bivalve biomineralisation genes was incorporated into SNP discovery. The SNP discovery effort resulted in the de novo identification of 172,625 SNPs, of which 9,108 were identified as high value [minor allele frequency (MAF)≥ 0.15, read depth ≥ 8]. Validation of 2,782 of these SNPs using Illumina iSelect Infinium genotyping technology returned some of the highest assay conversion (86.6 %) and validation (59.9 %; mean MAF 0.28) rates observed in aquaculture species to date. Genomic resources presented here will be pivotal to future research investigating the biological mechanisms behind biomineralisation and will form a strong foundation for genetic selective breeding programs in the P. maxima pearling industry.

TECHNICAL ADVANCES: A maximum-likelihood relatedness estimator allowing for negative relatedness values.

Previously reported maximum-likelihood pairwise relatedness (r) estimator of Thompson and Milligan (M) was extended to allow for negative r estimates under the regression interpretation of r. This was achieved by establishing the equivalency of the likelihoods used in the kinship program and the likelihoods of Thompson. The new maximum-likelihood (ML) estimator was evaluated by Monte Carlo simulations. It was found that the new ML estimator became unbiased significantly faster compared to the original M estimator when the amount of genotype information was increased. The effects of allele frequency estimation errors on the new and existing relatedness estimators were also considered.

Robust cross-validation of linear regression QSAR models.

A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.

Statistical confidence for variable selection in QSAR models via Monte Carlo cross-validation.

A new variable selection wrapper method named the Monte Carlo variable selection (MCVS) method was developed utilizing the framework of the Monte Carlo cross-validation (MCCV) approach. The MCVS method reports the variable selection results in the most conventional and common measure of statistical hypothesis testing, the P-values, thus allowing for a clear and simple statistical interpretation of the results. The MCVS method is equally applicable to the multiple-linear-regression (MLR)-based or non-MLR-based quantitative structure-activity relationship (QSAR) models. The method was applied to blood-brain barrier (BBB) permeation and human intestinal absorption (HIA) QSAR problems using MLR to demonstrate the workings of the new approach. Starting from more than 1600 molecular descriptors, only two (TPSA(NO) and ALOGP) yielded acceptably low P-values for the BBB and HIA problems, respectively. The new method has been implemented in the QSAR-BENCH v2 program, which is freely available (including its Java source code) from www.dmitrykonovalov.org for academic use.

Benchmarking of QSAR models for blood-brain barrier permeation.

Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.

Partition-distance via the assignment problem.

MOTIVATION: Accuracy testing of various pedigree reconstruction methods requires an efficient algorithm for the calculation of distance between a known partition and its reconstruction. The currently used algorithm of Almudevar and Field takes a prohibitively long time for certain partitions and population sizes. RESULTS: We present an algorithm that very efficiently reduces the partition-distance calculation to the classic assignment problem of weighted bipartite graphs that has known polynomial-time solutions. The performance of the algorithm is tested against the Almudevar and Field partition-distance algorithm to verify the significant improvement in speed. AVAILABILITY: Computer code written in java is available upon request from the first author.

Modified SIMPSON O(n3) algorithm for the full sibship reconstruction problem.

MOTIVATION: The problem of reconstructing full sibling groups from DNA marker data remains a significant challenge for computational biology. A recently published heuristic algorithm based on Mendelian exclusion rules and the Simpson index was successfully applied to the full sibship reconstruction (FSR) problem. However, the so-called SIMPSON algorithm has an unknown complexity measure, questioning its applicability range. RESULTS: We present a modified version of the SIMPSON (MS) algorithm that behaves as O(n(3)) and achieves the same or better accuracy when compared with the original algorithm. Performance of the MS algorithm was tested on a variety of simulated diploid population samples to verify its complexity measure and the significant improvement in efficiency (e.g. 100 times faster than SIMPSON in some cases). It has been shown that, in theory, the SIMPSON algorithm runs in non-polynomial time, significantly limiting its usefulness. It has been also verified via simulation experiments that SIMPSON could run in O(n(a)), where a > 3. AVAILABILITY: Computer code written in Java is available upon request from the first author. CONTACT: Dmitry.Konovalov@jcu.edu.au.