The primary aim of this study was to validate predictions of human knee-joint contact mechanics derived from finite-element models of the tibiofemoral and patellofemoral joints (specifically, contact pressure, contact area and contact force) against corresponding measurements obtained in vitro during simulated weight-bearing activity. A secondary aim was to perform sensitivity analyses of the model calculations to identify those parameters that most significantly affect model predictions of joint contact pressure, area and force. Joint pressures in the medial and lateral compartments of the tibiofemoral and patellofemoral joints were measured in vitro during two simulated weight-bearing activities: stair descent and squatting. Model-predicted joint contact pressure distribution maps were consistent with those obtained from experiment. Normalized root-mean-square errors between the measured and calculated contact variables were on the order of 15%. Pearson correlations between the time histories of model-predicted and measured contact variables were generally above 0.8. Mean errors in the calculated centre-of-pressure locations were 3.1 mm for the tibiofemoral joint and 2.1 mm for the patellofemoral joint. Model predictions of joint contact mechanics were most sensitive to changes in the material properties and geometry of the meniscus and cartilage, particularly estimates of peak contact pressure. The validated FE modelling framework offers a useful tool for non-invasive determination of knee-joint contact mechanics during dynamic activity under physiological loading conditions.

Soft robotic hands with monolithic structure have shown great potential to be used as prostheses due to their advantages to yield light weight and compact designs as well as its ease of manufacture. However, existing soft prosthetic hands design were often not geared towards addressing some of the practical requirements highlighted in prosthetics research. The gap between the existing designs and the practical requirements significantly hampers the potential to transfer these designs to real-world applications. This work addressed these requirements with the consideration of the trade-off between practicality and performance. These requirements were achieved through exploiting the monolithic 3D printing of soft materials which incorporates membrane enclosed flexure joints in the finger designs, synergy-based thumb motion and cable-driven actuation system in the proposed hand prosthesis. Our systematic design (tentatively named X-Limb) achieves a weight of 253gr, three grasps types (with capability of individual finger movement), power-grip force of 21.5N, finger flexion speed of 1.3sec, a minimum grasping cycles of 45,000 (while maintaining its original functionality) and a bill of material cost of 200 USD (excluding quick disconnect wrist but without factoring in the cost reduction through mass production). A standard Activities Measure for Upper-Limb Amputees benchmark test was carried out to evaluate the capability of X-Limb in performing grasping task required for activities of daily living. The results show that all the practical design requirements are satisfied, and the proposed soft prosthetic hand is able to perform all the real-world grasping tasks of the benchmark tests, showing great potential in improving life quality of individuals with upper limb loss.

The appropriate sensory information feedback is important for the success of an object grasping and manipulation task. In many scenarios, the need arises for multiple feedback information to be conveyed to a prosthetic hand user simultaneously. The multiple sets of information may either (1) directly contribute to the performance of the grasping or object manipulation task, such as the feedback of the grasping force, or (2) simply form additional independent set(s) of information. In this paper, the efficacy of simultaneously conveying two independent sets of sensor information (the grasp force and a secondary set of information) through a single channel of feedback stimulation (vibrotactile via bone conduction) to the human user in a prosthetic application is investigated. The performance of the grasping task is not dependent to the second set of information in this study. Subject performance in two tasks: regulating the grasp force and identifying the secondary information, were evaluated when provided with either one corresponding information or both sets of feedback information. Visual feedback is involved in the training stage. The proposed approach is validated on human-subject experiments using a vibrotactile transducer worn on the elbow bony landmark (to realize a non-invasive bone conduction interface) carried out in a virtual reality environment to perform a closed-loop object grasping task. The experimental results show that the performance of the human subjects on either task, whilst perceiving two sets of sensory information, is not inferior to that when receiving only one set of corresponding sensory information, demonstrating the potential of conveying a second set of information through a bone conduction interface in an upper limb prosthetic task.

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Virtual fractional flow reserve (vFFR) has been evaluated as an adjunct to invasive fractional flow reserve (FFR) in the light of its operational and economic benefits. The accuracy of vFFR and the complexity of hyperemic flow simulation are still not clearly understood. This study investigates the flow-pressure relation in an idealised multiple sequential stenoses coronary artery model via numerical and experimental approaches. Pressure drop is linearly correlated with flow rate irrespective of the number of stenosis. Computational fluid dynamics results are in good agreement with the experimental data, demonstrating reasonable accuracy of vFFR. It was also found that the difference between data obtained with steady and pulsatile flows is negligible, indicating the steady flow may be used instead of pulsatile flow conditions in vFFR computation. This study adds to the current understanding of vFFR and may improve its clinical applicability as an adjunct to invasively determined FFR.

Determining the mechanism of action (MOA) of novel or naturally occurring compounds mostly relies on assays tailored for individual target proteins. Here we explore an alternative approach based on pattern matching response profiles obtained using cultured neuronal networks. Conolidine and cannabidiol are plant-derivatives with known antinociceptive activity but unknown MOA. Application of conolidine/cannabidiol to cultured neuronal networks altered network firing in a highly reproducible manner and created similar impact on network properties suggesting engagement with a common biological target. We used principal component analysis (PCA) and multi-dimensional scaling (MDS) to compare network activity profiles of conolidine/cannabidiol to a series of well-studied compounds with known MOA. Network activity profiles evoked by conolidine and cannabidiol closely matched that of ω-conotoxin CVIE, a potent and selective Cav2.2 calcium channel blocker with proposed antinociceptive action suggesting that they too would block this channel. To verify this, Cav2.2 channels were heterologously expressed, recorded with whole-cell patch clamp and conolidine/cannabidiol was applied. Remarkably, conolidine and cannabidiol both inhibited Cav2.2, providing a glimpse into the MOA that could underlie their antinociceptive action. These data highlight the utility of cultured neuronal network-based workflows to efficiently identify MOA of drugs in a highly scalable assay.

BACKGROUND: Recent advances and automation in DNA sequencing technology has created a vast amount of DNA sequence data. This increasing growth of sequence data demands better and efficient analysis methods. Identifying genes in this newly accumulated data is an important issue in bioinformatics, and it requires the prediction of the complete gene structure. Accurate identification of splice sites in DNA sequences plays one of the central roles of gene structural prediction in eukaryotes. Effective detection of splice sites requires the knowledge of characteristics, dependencies, and relationship of nucleotides in the splice site surrounding region. A higher-order Markov model is generally regarded as a useful technique for modeling higher-order dependencies. However, their implementation requires estimating a large number of parameters, which is computationally expensive. RESULTS: The proposed method for splice site detection consists of two stages: a first order Markov model (MM1) is used in the first stage and a support vector machine (SVM) with polynomial kernel is used in the second stage. The MM1 serves as a pre-processing step for the SVM and takes DNA sequences as its input. It models the compositional features and dependencies of nucleotides in terms of probabilistic parameters around splice site regions. The probabilistic parameters are then fed into the SVM, which combines them nonlinearly to predict splice sites. When the proposed MM1-SVM model is compared with other existing standard splice site detection methods, it shows a superior performance in all the cases. CONCLUSION: We proposed an effective pre-processing scheme for the SVM and applied it for the identification of splice sites. This is a simple yet effective splice site detection method, which shows a better classification accuracy and computational speed than some other more complex methods.

BACKGROUND: Existing methods for whole-genome comparisons require prior knowledge of related species and provide little automation in the function prediction process. Bacteriophage genomes are an example that cannot be easily analyzed by these methods. This work addresses these shortcomings and aims to provide an automated prediction system of gene function. RESULTS: We have developed a novel system called SynFPS to perform gene function prediction over completed genomes. The prediction system is initialized by clustering a large collection of weakly related genomes into groups based on their resemblance in gene distribution. From each individual group, data are then extracted and used to train a Support Vector Machine that makes gene function predictions. Experiments were conducted with 9 different gene functions over 296 bacteriophage genomes. Cross validation results gave an average prediction accuracy of ~80%, which is comparable to other genomic-context based prediction methods. Functional predictions are also made on 3 uncharacterized genes and 12 genes that cannot be identified by sequence alignment. The software is publicly available at http://www.synteny.net/. CONCLUSION: The proposed system employs genomic context to predict gene function and detect gene correspondence in whole-genome comparisons. Although our experimental focus is on bacteriophages, the method may be extended to other microbial genomes as they share a number of similar characteristics with phage genomes such as gene order conservation.

BACKGROUND: Genomes of lower organisms have been observed with a large amount of horizontal gene transfers, which cause difficulties in their evolutionary study. Bacteriophage genomes are a typical example. One recent approach that addresses this problem is the unsupervised clustering of genomes based on gene order and genome position, which helps to reveal species relationships that may not be apparent from traditional phylogenetic methods. RESULTS: We propose the use of an overlapping subspace clustering algorithm for such genome classification problems. The advantage of subspace clustering over traditional clustering is that it can associate clusters with gene arrangement patterns, preserving genomic information in the clusters produced. Additionally, overlapping capability is desirable for the discovery of multiple conserved patterns within a single genome, such as those acquired from different species via horizontal gene transfers. The proposed method involves a novel strategy to vectorize genomes based on their gene distribution. A number of existing subspace clustering and biclustering algorithms were evaluated to identify the best framework upon which to develop our algorithm; we extended a generic subspace clustering algorithm called HARP to incorporate overlapping capability. The proposed algorithm was assessed and applied on bacteriophage genomes. The phage grouping results are consistent overall with the Phage Proteomic Tree and showed common genomic characteristics among the TP901-like, Sfi21-like and sk1-like phage groups. Among 441 phage genomes, we identified four significantly conserved distribution patterns structured by the terminase, portal, integrase, holin and lysin genes. We also observed a subgroup of Sfi21-like phages comprising a distinctive divergent genome organization and identified nine new phage members to the Sfi21-like genus: Staphylococcus 71, phiPVL108, Listeria A118, 2389, Lactobacillus phi AT3, A2, Clostridium phi3626, Geobacillus GBSV1, and Listeria monocytogenes PSA. CONCLUSION: The method described in this paper can assist evolutionary study through objectively classifying genomes based on their resemblance in gene order, gene content and gene positions. The method is suitable for application to genomes with high genetic exchange and various conserved gene arrangement, as demonstrated through our application on phages.

BACKGROUND: Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data. RESULTS: In this paper we propose a hybrid algorithm which combines several effective and informative input features with the state of the art support vector machine (SVM). To obtain the input features we employ information content method based on Shannon's information theory, Shapiro's score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data. CONCLUSION: In this study we propose a new feature based splice site detection method that shows improved acceptor and donor splice site detection in DNA sequences when the performance is compared with various state of the art and well known methods.

An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis.

Measurement losses adversely affect the performance of target tracking. The sensor network’s life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node’s path. First, we assume that the mobile sink node’s position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods’ performance.