Movement of sturgeons (Acipenser oxyrinchus oxyrinchus and A. fulvescens) in the St. Lawrence Estuary: A 35-year tagging study

Movement of Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) and lake sturgeon (A. fulvescens) in the St. Lawrence Estuary (Québec, Canada) are not fully understood. To assess the movement extent of both species, a mark–recapture study was conducted in collaboration with commercial fishermen operating in the St. Lawrence Estuary. Between 1981 and 2015, 3,367 Atlantic sturgeon (fork length 21.8–199.5 cm) and 3,180 lake sturgeon (fork length 17.8–190.8 cm) were tagged and released. Of these, 673 Atlantic sturgeon and 42 lake sturgeon were recaptured. The maximum distances traveled between capture and recapture locations were 1,307 km for Atlantic sturgeon (8 years after initial capture) and 252 km for lake sturgeon (less than 1 year after initial capture). Statistical analyses identified differences in the dispersal distance of both species as revealed by a first component characterized by individuals with short dispersal distances (98% and <35 km for Atlantic sturgeon; 58% and <1 km for lake sturgeon) and a second component characterized by individuals with longer dispersal distances (2% and >600 km for Atlantic sturgeon; 42% and >190 km for lake sturgeon). We suggest that the short dispersal distances detected in the vast majority of Atlantic sturgeon recaptures likely reflect strong site fidelity, highlighting the importance of the St. Lawrence Estuary as a preferred habitat for juveniles and subadults. Although recaptures were low for lake sturgeon because this species is only marginally targeted by commercial fishermen in the St. Lawrence Estuary, our results also showed that this species uses estuarine habitats and that half of the population seems to exhibit strong site fidelity (67% of individuals were recaptured within 2 km).     

Toward insights on antimicrobial selectivity of host defense peptides via machine learning model interpretation

Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question).     

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Capture‐recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture‐recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large‐scale simulation experiment based on the scheme discussed by Pledger.     

Evolutionary profile of the family Calliphoridae,with notes on the origin of myiasis

The family Calliphoridae is a group of heterogenous calyptrate flies with a worldwide distribution including species of ecological, veterinary, medical, and forensic importance. Notorious for their parasitic habits, the larvae of many blowflies are characterised – like some other dipteran larvae – by their ability to develop in animal flesh. When parasitism affects a living host, it is termed “myiasis”. This has led the Calliphoridae to be considered as a pivotal family in its relationship with a man. Nevertheless, even after more than 50 years of research, the phylogenetic relationships among calliphorid subfamilies together with the evolutionary origin of myiasis remain unclear. In order to elucidate these problems, we constructed three phylogenetic trees by using nucleotide sequence data from cytochrome oxidase subunit one (COI), representing a mitochondrial conservative gene, and nuclear 28S subunit of ribosomal RNA gene (28S rRNA) in order to interpret the evolutionary profile of myiasis in the family Calliphoridae. The sequenced data represented species associated with ectoparasitic life-styles, either saprophagy or facultative and obligate parasitism. A total number of 50 accessions were collected for 28S rRNA, 56 for COI, and 38 for combined sequences phylogeny. Molecular Evolutionary Genetics Analysis (MEGA) software was used to align 2197 nucleotide positions of 28S rRNA and 1500 nucleotide positions of COI with a gap opening penalties and gap extension penalties equalling 20 and 0.1 respectively. The results reveal the non-monophyly of the family Calliphoridae despite the stable monophyletic status of the Chrysomyinae, Luciliinae, and Auchmeromyiinae. Also, our findings recommend ranking the Toxotarsinae as a separate family. Furthermore, comparative analysis of the phylogenetic trees shows that the habit of obligatory myiasis originated independently more than five times. This strengthens our hypothesis that the origin of eating fresh meat is a case of convergent evolution that has taken place after speciation events millions of years ago. Finally, estimating the divergence dates between lineages from molecular sequences provides a better chance of understanding their evolutionary biology.     

Recognition of brain tumors in MRI images using texture analysis

ObjectivesBrain neoplasms or intracranial tumors, which are more common in older adults, can affect individuals of any age including pediatric and children. Exposure to carcinogenic agents including ionizing radiation and family history is one of the main causes of the disease. Early diagnosis is crucial to avoid prolonged. patients' suffering. The aim of the study was to efficiently recognize the brain tumors from the other brain tissues which include grey and white matter as well as cerebrospinal fluid (CSF).Materials and methodsThis study was performed using axial, sagittal and coronal views for fifty brain tumor patients randomly selected from a set of 200 patients, with a “control” set consisting of images showing no sign of disease; and the “test” brain MRI images for patients diagnosed with brain tumor. The study includes both genders with age ranging from 18 years to 83 years old, (56.5 ± 17.2). The brain images were acquired using a standard head coil Philips Intera 1.5 Tesla machine (USA). The thickness of each section in the entire sequence was 8 mm. Acquisition of T2-weighted and T1-weighted were performed. Interactive Data Language software was used to analyze the data.ResultsThe results of this study showed that: the overall accuracy of classification process was 94.8%, and for the tumor; the sensitivity was 97.3%. White matter and grey matter showed a classification accuracy of 95.7% and 89.7% and for CSF the accuracy was 94.3%.ConclusionThe results showed that brain tumor can be classified successfully and delineated using texture analysis with minimum efforts and with high accuracy for brain tumors.     

Detection of Breast Cancer Based on Fuzzy Frequent Itemsets Mining

Background: Breast cancer, a type of malignant tumor, affects women more than men. About one third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic based approaches help in the mining of association rules from quantitative transactions.Methods: In this study a novel fuzzy methodology IFFP (Improved Fuzzy Frequent Pattern Mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. This method consists of two phases. During the first phase, fuzzy frequent itemsets are mined using the proposed algorithm IFFP. Fuzzy association rules are formed during the second phase, indicating whether a person belongs to benign or malignant. This algorithm is applied on WBCD (Wisconsin Breast Cancer Database) to detect the presence of breast cancer.Results: It is determined that the factor, Mitoses has low range of values on both malignant and benign and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of Bare Nuclei shows more chances for the presence of breast cancer.Conclusion: Experimental evaluations on real datasets show that our proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.     

Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts

Clinical gait analysis has proven to reduce uncertainties in selecting the appropriate quantity and type of treatment for patients with neuromuscular disorders. However, gait analysis as a clinical tool is under-utilised due to the limitations and cost of acquiring and managing data. To overcome these obstacles, inertial motion capture (IMC) recently emerged to counter the limitations attributed to other methods. This paper investigates the use of IMC for training and testing a back-propagation artificial neural network (ANN) for the purpose of distinguishing between hemiparetic stroke and able-bodied ambulation. Routine gait analysis was performed on 30 able-bodied control subjects and 28 hemiparetic stroke patients using an IMC system. An ANN was optimised to classify the two groups, achieving a repeatable network accuracy of 99.4%. It is concluded that an IMC system and appropriate computer methods may be useful for the planning and monitoring of gait rehabilitation therapy of stroke victims.     

Kinematic analysis of over-determinate biomechanical systems

In this paper, we introduce a new general method for kinematic analysis of rigid multi body systems subject to holonomic constraints. The method extends the standard analysis of kinematically determinate rigid multi body systems to the over-determinate case. This is accomplished by introducing a constrained optimisation problem with the objective function given as a function of the set of system equations that are allowed to be violated while the remaining equations define the feasible set.

We show that exact velocity and acceleration analysis can also be performed by solving linear sets of equations, originating from differentiation of the Karush–Kuhn–Tucker optimality conditions.

The method is applied to the analysis of an 18 degrees-of-freedom gait model where the kinematical drivers are prescribed with data from a motion capture experiment.

The results show that significant differences are obtained between applying standard kinematic analysis or minimising the least-square errors on the two fully equivalent 3D gait models with only the way the experimental data is processed being different.