A mathematical musculoskeletal shoulder model for proactive ergonomic analysis

Occupational shoulder musculoskeletal injuries and disorders are common. Generally available shoulder work analysis tools do not offer insight into specific muscle load magnitudes that may indicate increased risk, nor do they address many concerns germane to job analysis. To address these issues, a biomechanical model of the shoulder was developed to include several critical components: the systematic inclusion of kinematic and kinetic effects, population scalability, geometric realism, an empirical glenohumeral constraint, and integration with digital ergonomics analysis software tools. This unique combination of features in a single model was explored through examination of both experimental and simulated data with the developed analysis tool. The utility of the model is discussed together with a review of its specific strengths and weaknesses, and the potential for its future use in proactive ergonomic analyses and workplace simulations.     

An entropy-assisted musculoskeletal shoulder model

Optimization combined with a musculoskeletal shoulder model has been used to estimate mechanical loading of musculoskeletal elements around the shoulder. Traditionally, the objective function is to minimize the summation of the total activities of the muscles with forces, moments, and stability constraints. Such an objective function, however, tends to neglect the antagonist muscle co-contraction. In this study, an objective function including an entropy term is proposed to address muscle co-contractions. A musculoskeletal shoulder model is developed to apply the proposed objective function. To find the optimal weight for the entropy term, an experiment was conducted. In the experiment, participants generated various 3-D shoulder moments in six shoulder postures. The surface EMG of 8 shoulder muscles was measured and compared with the predicted muscle activities based on the proposed objective function using Bhattacharyya distance and concordance ratio under different weight of the entropy term. The results show that a small weight of the entropy term can improve the predictability of the model in terms of muscle activities. Such a result suggests that the concept of entropy could be helpful for further understanding the mechanism of muscle co-contractions as well as developing a shoulder biomechanical model with greater validity.     

A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems

Upper extremity neuroprostheses use functional electrical stimulation (FES) to restore arm motor function to individuals with cervical level spinal cord injury. For the design and testing of these systems, a biomechanical model of the shoulder and elbow has been developed, to be used as a substitute for the human arm. It can be used to design and evaluate specific implementations of FES systems, as well as FES controllers. The model can be customized to simulate a variety of pathological conditions. For example, by adjusting the maximum force the muscles can produce, the model can be used to simulate an individual with tetraplegia and to explore the effects of FES of different muscle sets. The model comprises six bones, five joints, nine degrees of freedom, and 29 shoulder and arm muscles. It was developed using commercial, graphics-based modeling and simulation packages that are easily accessible to other researchers and can be readily interfaced to other analysis packages. It can be used for both forward-dynamic (inputs: muscle activation and external load; outputs: motions) and inverse-dynamic (inputs: motions and external load; outputs: muscle activation) simulations. Our model was verified by comparing the model calculated muscle activations to electromyographic signals recorded from shoulder and arm muscles of five subjects. As an example of its application to neuroprosthesis design, the model was used to demonstrate the importance of rotator cuff muscle stimulation when aiming to restore humeral elevation. It is concluded that this model is a useful tool in the development and implementation of upper extremity neuroprosthetic systems.     

A novel modeling approach for the “end-to-end” analysis of marine ecosystems

There is a growing demand for “end-to-end” models, which are modeling tools used to analyze and understand the fundamental complexities of marine ecosystems and processes emerging from the interaction of individuals from different trophic groups with respect to the physical environment and, even, human activity. These models are valuable quantitative tools for ecosystem-based management. To explore potential answers to complex questions regarding ecosystems using these models, it is necessary to incorporate classical ontogenic changes through the life cycle of target individuals, in addition to inherited behavioral strategies, as an additional differentiating aspect, particularly when the behavior has a direct impact on the ecosystem phenomena under study. However, it is difficult to combine different fine scale time and spatial granularities to infer animal behavior and ontogenic development. This complexity has kept these two levels of analysis separated, because most current tools do not have the required computational resources and advanced software architecture. To address this issue, we propose an individual-based modeling framework that is capable of handling and unifying the two experimental categories with a comprehensive biological and behavioral model that strictly adheres to the physiological functions of ingestion, growth, and metabolism of organisms. In addition, this model incorporates the exchange and transfer of mass and energy through local interactions at all trophic levels (lower to higher), the physical environment, and anthropogenic activity. For the framework to model short time events, such as classical predator–prey interactions, while also generating long-term ecosystem emergent properties, a special interleaving scheduling engine and physical space computer model was devised, which optimizes memory and processing resources. The framework was tested through several experiments with a three-population ecosystem containing up to 40 thousand organisms evolving inside a 200,000 m² simulation environment during 12,000 model-hours; yet, requiring only a few hours of program execution on a regular personal computer. The model included various environmental physical elements, such as several hundred shelters, the number of which can be easily modified in each experiment to simulate substrate degradation and its impact on populations. With the aid of the quantitative and qualitative tools provided by the model, it was possible to observe a coupling between prey and predator population dynamics. In conclusion, we confirmed that the end-to-end model developed here could successfully generate detailed specific hypotheses about fish behavior and quantify impacts on population dynamics.     

Bacterial genetics: past achievements, present state of the field, and future challenges

Genetic tools are required to take full advantage of the wealth of information generated by genome sequencing efforts and ensuing global gene and protein expression analyses. Bacterial genetics was originally developed and refined in Escherichia coli. As a consequence, elegant plasmid, cloning, expression, and mutagenesis systems were developed over the years and a good number of them are commercially available. This is not true for other bacteria. Although the development of genetic tools has generally not kept up with the sequencing pace, substantial progress has been made in this arena with many bacterial species. This short review highlights selected topics and achievements in the field over the past 25 years and presents some strategies that may help address future challenges. BioTechniques has played an integral part in the publication of important technological advances in the field over the first 25 years of its existence and it can be anticipated that it will continue to do so in the future.     

A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments

Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r² of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r² among all 16 muscles was 0.645, and the five muscles with the greatest r² were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment.     

城市生态系统灵敏度模型评述

系统复杂性是困扰城市生态系统研究和管理的因素之一,成熟的系统分析模型可以帮助研究者及管理者应对这种挑战。德国系统思想大师Vester教授开发的灵敏度模型就是致力于解决城市规划管理中的复杂性问题,并在一系列的实践应用中取得了显著成效。网状思维(Networked Thinking)与生物控制论观点是Vester教授的核心思想,也是灵敏度模型的思想基础。模型主要分为三大层次:最底层是以数据的收集与筛选过程为代表的信息组织层次;随后是系统解释层次,主要是对系统关系网进行控制论解释;最后是系统综合评价层次,采用生物控制论观点对系统结构、行为等进行检验与评价。为方便用户使用,模型被分解为9个标准步骤,每一部分都包含数个实用的数学分析工具。模型还明确提出了4个等级的系统控制论指标(或特征)体系,以帮助使用者更好地进行系统思考。灵敏度模型本身具有众多的原创性贡献,但同时也有一定的局限性。这些经验与教训为未来的城市生态系统建模工作提供了宝贵启示。     

Directed indices for exploring gene expression data

MOTIVATION: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection. RESULTS: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).     

Highly improved homopolymer aware nucleotide-protein alignments with 454 data

ABSTRACT: BACKGROUND: Roche 454 sequencing is the leading sequencing technology for producing long read high throughput sequence data. Unlike most methods where sequencing errors translate to base uncertainties, 454 sequencing inaccuracies create nucleotide gaps. These gaps are particularly troublesome for translated search tools such as BLASTx where they introduce frame-shifts and result in regions of decreased identity and/or terminated alignments, which affect further analysis. RESULTS: To address this issue, the Homopolymer Aware Cross Alignment Tool (HAXAT) was developed. HAXAT uses a novel dynamic programming algorithm for solving the optimal local alignment between a 454 nucleotide and a protein sequence by allowing frame-shifts, guided by 454 flowpeak values. The algorithm is an efficient minimal extension of the Smith-Waterman-Gotoh algorithm that easily fits in into other tools.Experiments using HAXAT demonstrate, through the introduction of 454 specific frame-shift penalties, significantly increased accuracy of alignments spanning homopolymer sequence errors. The full effect of the new parameters introduced with this novel alignment model is explored. Experimental results evaluating homopolymer inaccuracy through alignments show a two to five-fold increase in Matthews Correlation Coefficient over previous algorithms, for 454-derived data. CONCLUSIONS: This increased accuracy provided by HAXAT does not only result in improved homologue estimations, but also provides un-interrupted reading-frames, which greatly facilitate further analysis of protein space, for example phylogenetic analysis.The alignment tool is available at http://bioinfo.ifm.liu.se/454tools/haxat.     

A Failure Reveals Success

Although environmental education and education for sustainable development have become well‐established areas of scholarship and practice, there has not been a similar development focused on “industrial ecology education.” A review of the historical context and guiding philosophies for each of these areas finds many similarities, as well as key differences. Environmental education traces its modern roots to the idealism of the 1960s and 1970s. It has focused mostly on improving environmental conditions. Education for sustainable development arose along with international concerns about social justice. It has emphasized general education as well as education about sustainability as necessary to ensure human prosperity. Industrial ecology, in its contemporary form, evolved as an applied approach to address environmental concerns and to meet sustainability goals. It has developed into a diverse, multifaceted approach to address the complexity inherent in industrial society. Education focused on industrial ecology remains decentralized, with core principles and tools being integrated into existing disciplinary programs as well as development of industrial‐ecology–specific curricula. These efforts have not coalesced into a formalized, industrial ecology education. Rather than reflecting a shortcoming, this potentially offers a more robust method for applying industrial ecology principles and tools widely.     

Applying QTL analysis to conservation genetics

Both analytical and molecular tools currently exist that can be used to prolifically apply quantitative trait loci (QTL) analysis to the study of natural populations. In this communication, we review and exemplify the use of QTL mapping tools and genetic modeling for conservation geneticists. We simulate populations inspired by relevant cases that can be encountered in the field and analyze them using the recently developed flexible intercross analysis (FIA) method. We then reanalyze these results with the also recently developed natural and orthogonal interactions (NOIA) model of genetic effects. Next, we further exemplify the potential of genetic modeling for the interpretation of the output of QTL analyses by reviewing studies on hybrids between wild individuals and their domesticated relatives. Based on the results here presented we emphasize several points that are pertinent in conservation genetics including (i) the advantages of FIA as a powerful tool to be applied to line crosses in which the parental lines are not inbred, (ii) the importance of obtaining estimates of genetic effects that are adequate to address the research issue under consideration, (iii) the versatility of genetic modeling, particularly NOIA, to dissect complex genetic architectures and (iv) the possibility of using currently available methods to address non-equilibrium multiallelic systems.     

Pattern recognition software and techniques for biological image analysis

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.     

Long-latency reflexes of the human arm reflect an internal model of limb dynamics

A key feature of successful motor control is the ability to counter unexpected perturbations. This process is complicated in multijoint systems, like the human arm, by the fact that loads applied at one joint will create motion at other joints [1-3]. Here, we test whether our most rapid corrections, i.e., reflexes, address this complexity through an internal model of the limb's mechanical properties. By selectively applying torque perturbations to the subject's shoulder and/or elbow, we revealed a qualitative difference between the arm's short-latency/spinal reflexes and long-latency/cortical reflexes. Short-latency reflexes of shoulder muscles were linked exclusively to shoulder motion, whereas its long-latency reflexes were sensitive to both shoulder and elbow motion, i.e., matching the underlying shoulder torque. In fact, a long-latency reflex could be evoked without even stretching or lengthening the shoulder muscle but by displacing just the elbow joint. Further, the shoulder's long-latency reflexes were appropriately modified across the workspace to account for limb-geometry changes that affect the transformation between joint torque and joint motion. These results provide clear evidence that long-latency reflexes possess an internal model of limb dynamics, a degree of motor intelligence previously reserved for voluntary motor control [3-5]. The use of internal models for both voluntary and reflex control is consistent with substantial overlap in their neural substrates and current notions of intelligent feedback control [6-8].     

Partial life cycle analysis: a model for pre-breeding census data

Matrix population models have become popular tools in research areas as diverse as population dynamics, life history theory, wildlife management, and conservation biology. Two classes of matrix models are commonly used for demographic analysis of age‐structured populations: age‐structured (Leslie) matrix models, which require age‐specific demographic data, and partial life cycle models, which can be parameterized with partial demographic data. Partial life cycle models are easier to parameterize because data needed to estimate parameters for these models are collected much more easily than those needed to estimate age‐specific demographic parameters. Partial life cycle models also allow evaluation of the sensitivity of population growth rate to changes in ages at first and last reproduction, which cannot be done with age‐structured models. Timing of censuses relative to the birth‐pulse is an important consideration in discrete‐time population models but most existing partial life cycle models do not address this issue, nor do they allow fractional values of variables such as ages at first and last reproduction. Here, we fully develop a partial life cycle model appropriate for situations in which demographic data are collected immediately before the birth‐pulse (pre‐breeding census). Our pre‐breeding census partial life cycle model can be fully parameterized with five variables (age at maturity, age at last reproduction, juvenile survival rate, adult survival rate, and fertility), and it has some important applications even when age‐specific demographic data are available (e.g., perturbation analysis involving ages at first and last reproduction). We have extended the model to allow non‐integer values of ages at first and last reproduction, derived formulae for sensitivity analyses, and presented methods for estimating parameters for our pre‐breeding census partial life cycle model. We applied the age‐structured Leslie matrix model and our pre‐breeding census partial life cycle model to demographic data for several species of mammals. Our results suggest that dynamical properties of the age‐structured model are generally retained in our partial life cycle model, and that our pre‐breeding census partial life cycle model is an excellent proxy for the age‐structured Leslie matrix model.     

Molecular screens for inner ear genes

Identification of the genes that encode proteins that are important for proper function of specific inner ear cell types is central to our understanding of the molecular basis of hearing and balance. Whereas the combination of electrophysiology and biophysics has resulted in an exquisite understanding of inner ear function, little is known about the proteins that confer these properties at the cellular level. Furthermore, the genes that control inner ear development, susceptibility to wear and tear, regeneration from damage, and age-related degeneration, are largely unknown. This review discusses tools that have been developed during the past few years to address this imbalance between a thorough physiologic characterization of inner ear function and a detailed understanding at a molecular level of the proteins involved in these functions. Creation of inner ear cDNA libraries has laid the foundation for the discovery of genes that are specifically expressed by cell types of the inner ear and that encode proteins that are important for molecular processes in these cells. In conjunction with expressed sequence tag database analysis, cDNA subtraction, and DNA arrays, functionally important genes, whose specific expression patterns are usually verified by gene expression analysis, can be identified. Discussion of these techniques takes into account the specific characteristics of the inner ear in relation to its study using molecular biological approaches.     

Using a multiple-delivery-mode training approach to develop local capacity and infrastructure for advanced bioinformatics in Africa

With more microbiome studies being conducted by African-based research groups, there is an increasing demand for knowledge and skills in the design and analysis of microbiome studies and data. However, high-quality bioinformatics courses are often impeded by differences in computational environments, complicated software stacks, numerous dependencies, and versions of bioinformatics tools along with a lack of local computational infrastructure and expertise. To address this, H3ABioNet developed a 16S rRNA Microbiome Intermediate Bioinformatics Training course, extending its remote classroom model. The course was developed alongside experienced microbiome researchers, bioinformaticians, and systems administrators, who identified key topics to address. Development of containerised workflows has previously been undertaken by H3ABioNet, and Singularity containers were used here to enable the deployment of a standard replicable software stack across different hosting sites. The pilot ran successfully in 2019 across 23 sites registered in 11 African countries, with more than 200 participants formally enrolled and 106 volunteer staff for onsite support. The pulling, running, and testing of the containers, software, and analyses on various clusters were performed prior to the start of the course by hosting classrooms. The containers allowed the replication of analyses and results across all participating classrooms running a cluster and remained available posttraining ensuring analyses could be repeated on real data. Participants thus received the opportunity to analyse their own data, while local staff were trained and supported by experienced experts, increasing local capacity for ongoing research support. This provides a model for delivering topic-specific bioinformatics courses across Africa and other remote/low-resourced regions which overcomes barriers such as inadequate infrastructures, geographical distance, and access to expertise and educational materials.