An automated program to find animals and crop photographs for individual recognition

Detailed data on individual animals are critical to ecological and evolutionary studies, but attaching identifying marks can alter individual fates and behavior leading to biases in parameter estimates and ethical issues. Individual-recognition software has been developed to assist in identifying many species from non-invasive photographic data. These programs utilize algorithms to find unique individual characteristics and compare images to a catalogue of known individuals. Currently, all applications for individual identification require manual processing to crop images so only the area of interest remains, or the area of interest must be manually delineated in each image. Thus, one of the main bottlenecks in processing data from photographic capture-recapture surveys is in cropping to an area of interest so that matching algorithms can identify the individual. Here, we describe the development and testing of an automated cropping program. The methods and techniques we describe are broadly applicable to any system where raw photos must be cropped down to a specific area of interest before pattern recognition software can be used for individual identification. We developed and tested the program for use with identification photos of wild giraffes.     

Finite-element-method (FEM) model generation of time-resolved 3D echocardiographic geometry data for mitral-valve volumetry

Introduction  

Mitral Valve (MV) 3D structural data can be easily obtained using standard transesophageal echocardiography (TEE) devices but quantitative pre- and intraoperative volume analysis of the MV is presently not feasible in the cardiac operation room (OR). Finite element method (FEM) modelling is necessary to carry out precise and individual volume analysis and in the future will form the basis for simulation of cardiac interventions.     

基于CT的股骨有限元模型的建立及髋关节中空多孔假体植入后受力分布的初步研究

利用有限元分析的方法,对髋关节中空多孔假体植入后的受力分布改变进行研究,为改进中空多孔人工关节假体的设计提供依据.建立了股骨和假体三维有限元模型,应用有限元分析软件Ansys5.7模拟单腿负重状态,考察应力分布并进行比较.使用SPSS Statistics 17.0软件进行分析处理,绘制折线图.结果表明:a.开孔后张力侧最大压应力有所减小,开孔部位的应力水平变化不明显.b.与周围骨质形成连接后孔缘局部应力增加,张力侧受力改变不明显.c.短柄假体受力模式仍为远端应力集中;张力侧应力减小,开孔部位应力明显增加.d.下孔受力均大于上孔,受力方向基本一致.     

Numerical simulation (FEM) of the human mandible: validation of the function of the masticatory muscles]

The article describes part of a research project aiming to develop a new modular software tool for the individual dynamic numerical simulation of the human mandible using the finite element method (FEM). Its planned use in the clinical setting makes it very important to validate the results of the simulations. Here, the function of the masticatory muscles is to be tested. On the basis of biomechanical data from the literature, standard movements, such as closing the mouth, forward movement, lateral movement or backward movement, were dynamically simulated. Apart from muscle activity, the movements of the mandible are defined by the temporomandibular joint. At present, translating the condylar dynamics to the simulation still poses problems. For this reason, therefore, simulations of the two extreme cases "fixed" and "force-free" condyles are compared. While in the case of fixed condyles, some of the movements could be reproduced either not at all or only weakly, in the case of force-free condyles, all standard movements were reproduced qualitatively, albeit without the guiding effect of the joint capsule or the articular disc.     

Testing dental implants with an in vivo finite element model]

The finite element method (FEM) makes it possible to simulate biomechanical situations on a computer. In the present study the so-called voxel method [9, 14, 17, 18, 19] was used for the construction of the mandible model. For this, the relationship between the biological tissue (e.g. bone) and the corresponding attenuation coefficient of CT data (Hounsfield units = HU) were utilized. The CT data were obtained from an edentulous patient provided with a prosthesis borne on two titanium implants. In a parallel study, the bite forces of the same individual were measured. These were recorded digitally in three dimensions (cranio-caudal, anterior-posterior and left-right) The forces determined by a special program were then transferred to the FEM model implants. We were able to show that a bar joining the implants had a far greater effect on maximum equivalent stress than was expected from the measuring data alone. The highest stress at maximum occlusive force was lowered by 704% on using the connecting bar. On chewing, a stress reduction of 59.9% was observed. The reduction in stress achieved by the bar could, we believe, prolong the life of the implant.     

Utilizing FEM-Software to quantify pre- and post-interventional cardiac reconstruction data based on modelling data sets from surgical ventricular repair therapy (SVRT) and cardiac resynchronisation therapy (CRT)

Background  

Left ventricle (LV) 3D structural data can be easily obtained using standard transesophageal echocardiography (TEE) devices but quantitative pre- and intraoperative volumetry and geometry analysis of the LV is presently not feasible in the cardiac operation room (OR). Finite element method (FEM) modelling is necessary to carry out precise and individual volume analysis and in the future will form the basis for simulation of cardiac interventions.     

create: a software to create input files from diploid genotypic data for 52 genetic software programs

create is a Windows program for the creation of new and conversion of existing data input files for 52 genetic data analysis software programs. Programs are grouped into areas of sibship reconstruction, parentage assignment, genetic data analysis, and specialized applications. create is able to read in data from text, Microsoft Excel and Access sources and allows the user to specify columns containing individual and population identifiers, birth and death data, sex data, relationship information, and spatial location data. create's only constraints on source data are that one individual is contained in one row, and the genotypic data is contiguous. create is available for download at http://www.lsc.usgs.gov/CAFL/Ecology/Software.html.     

ImageParser: a tool for finite element generation from three-dimensional medical images

Background

The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy.

Methods

A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements.

Results

The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues.

Conclusion

The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information.

     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

NMπ 2.0: Software update to minimize the risk of false positives among determinants of reproductive success

In plant populations, parentage analysis helps understand factors shaping individual reproductive success. However, estimating reproductive success determinants based on parentage counts requires decoupling the effects of individual fecundity and propagule dispersal. The neighbourhood model implemented in the NMπ software provides a standard solution for this problem based on the fixed-effects regression-like approach. Nonetheless, it has been recently shown that the method is prone to false discoveries when important fecundity determinants are omitted. To account for the unexplained variance in fecundity, the Bayesian approach was developed based on the new model (the hierarchical neighbourhood model; HNM). Here, I present the NMπ software update that allows the HNM approach to be used in the framework of a friendly interface. More importantly, the HNM approach is now made available for both dispersed (seedlings) and nondispersed (seeds with known mothers) progeny data. The Bayesian approach, among others, selects significant fecundity determinants, estimates the proportion of variance in reproductive potential explained by selected determinants (R²), and provides individual female and male fecundity values. Although the software was designed to handle microsatellite marker data, a solution is proposed for large sets of single nucleotide polymorphisms. The program can be run on Windows (using either a terminal or a graphical interface) as well as (using a terminal) on Linux, or macOS platforms. In any case, NMπ can utilize multicore processors to speed up the analysis. The updated package containing the code, the executable file, the user manual, and example data is available at https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/ .     

On-line data acquisition and evaluation of long-term measurements of circadian rhythms in individual cells of Acetabularia

A multitasking time-sharing computer system was implemented for studies of different circadian rhythms in individual cells of the unicellular green alga, Acetabularia. This fully automatized system allows simultaneous data acquisition and analysis. Graphical presentation of untreated and mathematically treated data is permanently available on three graphic displays and on a digital plotter. The sampling rate for the data acquisition in each of the 60 channels connected to the system is 720/24 h. Provisions have been made to guarantee uninterrupted data uptake for these long-term measurements by including an auto-restart module and by providing extremely reliable software for the experimenter using menu techniques.     

Bee Tracker—an open‐source machine learning‐based video analysis software for the assessment of nesting and foraging performance of cavity‐nesting solitary bees

The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time‐consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity‐nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning‐based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees’ thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual‐based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity‐nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi‐field, and field conditions.     

Understanding the impact of root morphology on overturning mechanisms: a modelling approach

BACKGROUND AND AIMS: The Finite Element Method (FEM) has been used in recent years to simulate overturning processes in trees. This study aimed at using FEM to determine the role of individual roots in tree anchorage with regard to different rooting patterns, and to estimate stress distribution in the soil and roots during overturning. METHODS: The FEM was used to carry out 2-D simulations of tree uprooting in saturated soft clay and loamy sand-like soil. The anchorage model consisted of a root system embedded in a soil block. Two root patterns were used and individual roots removed to determine their contribution to anchorage. KEY RESULTS: In clay-like soil the size of the root-soil plate formed during overturning was defined by the longest roots. Consequently, all other roots localized within this plate had no influence on anchorage strength. In sand-like soil, removing individual root elements altered anchorage resistance. This result was due to a modification of the shape and size of the root-soil plate, as well as the location of the rotation axis. The tap root and deeper roots had more influence on overturning resistance in sand-like soil compared with clay-like soil. Mechanical stresses were higher in the most superficial roots and also in leeward roots in sand-like soil. The relative difference in stresses between the upper and lower sides of lateral roots was sensitive to root insertion angle. Assuming that root eccentricity is a response to mechanical stresses, these results explain why eccentricity differs depending on root architecture. CONCLUSIONS: A simple 2-D Finite Element model was developed to better understand the mechanisms involved during tree overturning. It has been shown how root system morphology and soil mechanical properties can modify the shape of the root plate slip surface as well as the position of the rotation axis, which are major components of tree anchorage.     

On-line data acquisition and evaluation of long-term measurements of circadian rhythms in individual cells ofAcetabularia

A multitasking time-sharing computer system was implemented for studies of different circadian rhythms in individual cells of the unicellular green alga,Acetabularia. This fully automatized system allows simultaneous data acquisition and analysis. Graphical presentation of untreated and mathematically treated data is permanently available on three graphic displays and on a digital plotter. The sampling rate for the data acquisition in each of the 60 channels connected to the system is 720/24 h. Provisions have been made to guarantee uninterrupted data uptake for these long-term measurements by including an auto-restart module and by providing extremely reliable software for the experimenter using menu techniques.     

HTAPP: High‐throughput autonomous proteomic pipeline

Recent advances in the speed and sensitivity of mass spectrometers and in analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. The development of a lab‐based automated proteomic software platform for the automated collection, processing, storage, and visualization of expansive proteomic data sets is critically important. The high‐throughput autonomous proteomic pipeline described here is designed from the ground up to provide critically important flexibility for diverse proteomic workflows and to streamline the total analysis of a complex proteomic sample. This tool is composed of a software that controls the acquisition of mass spectral data along with automation of post‐acquisition tasks such as peptide quantification, clustered MS/MS spectral database searching, statistical validation, and data exploration within a user‐configurable lab‐based relational database. The software design of high‐throughput autonomous proteomic pipeline focuses on accommodating diverse workflows and providing missing software functionality to a wide range of proteomic researchers to accelerate the extraction of biological meaning from immense proteomic data sets. Although individual software modules in our integrated technology platform may have some similarities to existing tools, the true novelty of the approach described here is in the synergistic and flexible combination of these tools to provide an integrated and efficient analysis of proteomic samples.