Muscle LIM protein is upregulated in fast skeletal muscle during transition toward slower phenotypes

Muscle LIM protein (MLP) is constitutively expressed in slow, but undetectable in fast, muscles of the rat. Here we show that MLP was upregulated at both the mRNA and protein levels under experimental conditions leading to transitions from fast to slower phenotypes. Chronic low-frequency stimulation and mechanical overloading by synergist removal both induced fast-to-slow shifts in myosin heavy chain (MHC) isoforms and expression of MLP in fast muscles. High amounts of MLP mRNA and protein were also present in fast muscles of the myotonic, hyperactive ADR mouse. Hypothyroidism evoked shifts in myosin composition toward slower isoforms and increased the MLP protein content of soleus (SOL) muscle but failed to induce MLP in fast muscles. Unweighting by hindlimb suspension elicited slow-to-fast transitions in MHC expression without altering MLP levels in SOL muscle. Hyperthyroidism shifted the MHC pattern toward faster isoforms but did not affect MLP content in SOL muscle. We conclude that alterations in MLP expression are associated with transitions from fast to slower phenotypes but not with slow-to-fast muscle fiber transitions.     

Multiscale modeling of cancellous bone considering full coupling of mechanical,electric and magnetic effects

Modeling of cancellous bone has important applications in the detection and treatment of fatigue fractures and diseases like osteoporosis. In this paper, we present a fully coupled multiscale approach considering mechanical, electric and magnetic effects by using the multiscale finite element method and a two-phase material model on the microscale. We show numerical results for both scales, including calculations for a femur bone, comparing a healthy bone to ones affected by different stages of osteoporosis. Here, the magnetic field strength resulting from a small mechanical impact decreases drastically for later stages of the disease, confirming experimental research.

     

Transplantation of polymer encapsulated neurotransmitter secreting cells: effect of the encapsulation technique

Deficits associated with neurological diseases may be improved by the transplantation within the brain lesioned target structure of polymer encapsulated cells releasing the missing neurotransmitter. Surrounding cells with a permselective membrane of appropriate molecular weight cut-off allows inward diffusion of nutrients and outward diffusion of neurotransmitters, but prevents immunoglobulins or immune cells from reaching the transplant. This technique therefore allows transplantation of post-mitotic cells across species. It also permits neural grafting of transformed cell lines since the polymer capsule prevents the formation of tumors by physically sequestering the transplanted tissue. In the present study, we compared the ability of dopamine-secreting cells, encapsulated by 2 different methods, to reverse experimental Parkinson's disease, a neurodegenerative disease characterized by motor disturbances due to a lack of dopamine within the striatum following degeneration of the dopaminergic nigro-striatal pathway. PC12 cells were loaded in polyelectrolyte-based microcapsules or thermoplastic-based macrocapsules and maintained in vitro or transplanted in a rat experimental Parkinson model for 4 weeks. Chemically-induced depolarization increased the in vitro release of dopamine from macrocapsules over time, while no increase in release was observed from microcapsules. Encapsulated PC12 cells were able to reduce lesion-induced rotational asymmetry in rats for at least 4 weeks, regardless of the encapsulation technique used. With both encapsulation methods, PC12 cell viability was greater in vivo than in vitro which suggests that the striatum releases trophic factors for PC12 cells. More brain tissue damage was observed with microcapsules than macrocapsules, possibly the result of the difficulty of manipulating the more fragile microcapsules.(ABSTRACT TRUNCATED AT 250 WORDS)     

Multiaxial mechanical behavior of the porcine anterior lens capsule

The biomechanics of the lens capsule of the eye is important both in physiologic processes such as accommodation and clinical treatments such as cataract surgery. Although the lens capsule experiences multiaxial stresses in vivo, there have been no measurements of its multiaxial properties or possible regional heterogeneities. Rather all prior mechanical data have come from 1-D pressure–volume or uniaxial force-length tests. Here, we report a new experimental approach to study in situ the regional, multiaxial mechanical behavior of the lens capsule. Moreover, we report multiaxial data suggesting that the porcine anterior lens capsule exhibits a typical nonlinear pseudoelastic behavior over finite strains, that the in situ state is pre-stressed multiaxially, and that the meridional and circumferential directions are principal directions of strain, which is nearly equibiaxial at the pole but less so towards the equator. Such data are fundamental to much needed constitutive formulations.     

Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation

Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.     

Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network

In this paper, a new approach based on eigen-systems pseudo-spectral estimation methods, namely Eigenvector (EV) and MUSIC, and Multiple Layer Perceptron (MLP) neural network is introduced. In this approach, the calculated EEG (electroencephalogram) spectrum is divided into smaller frequency sub-bands. Then, a set of features, {maximum, entropy, average, standard deviation, mobility}, are extracted from these sub-bands. Next, incorporating a set of the EEG time domain features {standard deviation, complexity measure} with the spectral feature set, a feature vector is formed. The feature vector is then fetched into a MLP neural network to classify the signal into the following three states: normal (healthy), epileptic patient signal in a seizure-free interval (inter-ictal), and epileptic patient signal in a full seizure interval (ictal). The experimental results show that the classification of the EEG signals maybe achieved with approximately 97.5% accuracy and the variance of 0.095% using an available public EEG signals database. The results are among the best reported methods for classifying the three states aforementioned. This is a high speed with high accuracy as well as low misclassifying rate method so it can make the practical and real-time detection of this chronic disease feasible.     

A real time hyperelastic tissue model

Real-time soft tissue modeling has a potential application in medical training, procedure planning and image-guided therapy. This paper characterizes the mechanical properties of organ tissue using a hyperelastic material model, an approach which is then incorporated into a real-time finite element framework. While generalizable, in this paper we use the published mechanical properties of pig liver to characterize an example application. Specifically, we calibrate the parameters of an exponential model, with a least-squares method (LSM) using the assumption that the material is isotropic and incompressible in a uniaxial compression test. From the parameters obtained, the stress–strain curves generated from the LSM are compared to those from the corresponding computational model solved by ABAQUS and also to experimental data, resulting in mean errors of 1.9 and 4.8%, respectively, which are considerably better than those obtained when employing the Neo-Hookean model. We demonstrate our approach through the simulation of a biopsy procedure, employing a tetrahedral mesh representation of human liver generated from a CT image. Using the material properties along with the geometric model, we develop a nonlinear finite element framework to simulate the behaviour of liver during an interventional procedure with a real-time performance achieved through the use of an interpolation approach.     

Deformation analysis of microcapsules compressed by two rigid parallel plates

Alginate-poly(l)lysine-alginate microcapsules with a diameter of 200–300 μm were produced and subjected to compression experiments using two rigid parallel plates. The deformed shape was observed, and the relationship between the applied force and the displacement of the plate was measured. A practical analysis method is proposed for the prediction of mechanical properties of microcapsules, namely, the changes in force, transmural pressure, and deformed shape due to the change in the displacement of the plate. The analysis is based on a microcapsule model comprising an axi-symmetric elastic membrane with a constant capsule volume during deformation. Young’s modulus of the membrane, which is used in the analysis, was determined by applying the Hertz contact theory to the experimental results obtained by using an atomic force microscope. The bending stiffness and permeability of the membrane as well as the frictional force between the membrane and the plate were neglected in the analysis. A non-dimensional relationship between the applied force and the displacement of the plate was shown. The theoretically predicted compression force was smaller than the experimentally measured force in the small displacement region, whereas both forces were in good agreement in the large displacement region. The effects of change in Poisson’s ratio, which varied from 0 to 0.5, on the force, transmural pressure, and deformed shape were shown. Under the same displacement conditions, it was observed that the calculated deformed shape was almost coincident with the shape observed in the experiment.     

A real time hyperelastic tissue model

Real-time soft tissue modeling has a potential application in medical training, procedure planning and image-guided therapy. This paper characterizes the mechanical properties of organ tissue using a hyperelastic material model, an approach which is then incorporated into a real-time finite element framework. While generalizable, in this paper we use the published mechanical properties of pig liver to characterize an example application. Specifically, we calibrate the parameters of an exponential model, with a least-squares method (LSM) using the assumption that the material is isotropic and incompressible in a uniaxial compression test. From the parameters obtained, the stress-strain curves generated from the LSM are compared to those from the corresponding computational model solved by ABAQUS and also to experimental data, resulting in mean errors of 1.9 and 4.8%, respectively, which are considerably better than those obtained when employing the Neo-Hookean model. We demonstrate our approach through the simulation of a biopsy procedure, employing a tetrahedral mesh representation of human liver generated from a CT image. Using the material properties along with the geometric model, we develop a nonlinear finite element framework to simulate the behaviour of liver during an interventional procedure with a real-time performance achieved through the use of an interpolation approach.     

The cardiac mechanical stretch sensor machinery involves a Z disc complex that is defective in a subset of human dilated cardiomyopathy

Muscle cells respond to mechanical stretch stimuli by triggering downstream signals for myocyte growth and survival. The molecular components of the muscle stretch sensor are unknown, and their role in muscle disease is unclear. Here, we present biophysical/biochemical studies in muscle LIM protein (MLP) deficient cardiac muscle that support a selective role for this Z disc protein in mechanical stretch sensing. MLP interacts with and colocalizes with telethonin (T-cap), a titin interacting protein. Further, a human MLP mutation (W4R) associated with dilated cardiomyopathy (DCM) results in a marked defect in T-cap interaction/localization. We propose that a Z disc MLP/T-cap complex is a key component of the in vivo cardiomyocyte stretch sensor machinery, and that defects in the complex can lead to human DCM and associated heart failure.     

Real-Time Decision Fusion for Multimodal Neural Prosthetic Devices

Background

The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user''s intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device.

Methodology/Principal Findings

Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.

Conclusions

Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.     

Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (>0.95) magnitudes of the coefficient of correlation and low (<4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.     

Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction

Background

Researchers discover LncRNA–miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network.

Results

In this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations.

Conclusion

The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction.

     

A robust blind medical image watermarking approach for telemedicine applications

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

     

Deep architecture neural network-based real-time image processing for image-guided radiotherapy

IntroductionTo develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising.Methods & materialsSetup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training.ResultsMore than 6 convolutional layers with convolutional kernels >5 × 5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with >3 convolutions each and rCNNs with >12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality.ConclusionsUse of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer.     

Deep transformation models for functional outcome prediction after acute ischemic stroke

In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semistructured data like tabular and image data. We show how to apply deep transformation models (DTMs) for distributional regression that fulfill these requirements. DTMs allow the data analyst to specify (deep) neural networks for different input modalities making them applicable to various research questions. Like statistical models, DTMs can provide interpretable effect estimates while achieving the state-of-the-art prediction performance of deep neural networks. In addition, the construction of ensembles of DTMs that retain model structure and interpretability allows quantifying epistemic and aleatoric uncertainty. In this study, we compare several DTMs, including baseline-adjusted models, trained on a semistructured data set of 407 stroke patients with the aim to predict ordinal functional outcome three months after stroke. We follow statistical principles of model-building to achieve an adequate trade-off between interpretability and flexibility while assessing the relative importance of the involved data modalities. We evaluate the models for an ordinal and dichotomized version of the outcome as used in clinical practice. We show that both tabular clinical and brain imaging data are useful for functional outcome prediction, whereas models based on tabular data only outperform those based on imaging data only. There is no substantial evidence for improved prediction when combining both data modalities. Overall, we highlight that DTMs provide a powerful, interpretable approach to analyzing semistructured data and that they have the potential to support clinical decision-making.     

Improving mechanical stability and density distribution of hepatocyte microcapsules by fibrin clot and gold nano-particles

Bio-artificial livers (BAL) with microencapsulated hepatocytes have the typical limitations in maintaining hepatocyte functions, mechanical stability and uniform perfusion in packed or fluidized-bed bioreactors. We have previously developed microcapsules with enhanced hepatocyte functions. Here we have introduced a fibrin network inside microcapsules by (1) mixing collagen and fibrinogen with the encapsulated hepatocytes to support the cells; (2) submerging the microcapsules into a thrombin solution to induce the formation of an insoluble fibrin network inside the microcapsules. Fracture analysis on the microcapsules revealed significant improvement in mechanical stability. We have also introduced different amounts of gold nano-particles into microcapsules to achieve different densities for uniform bioreactor perfusion. These gold nano-particles also improved the mechanical stability of the microcapsules. Both the fibrin network and gold nano-particles exhibited the additional benefits of enhancing certain bio-functions of the encapsulated hepatocytes. The applications of these improved microcapsules in the development of bio-artificial livers are discussed.     

The role of the cortex in indentation experiments of animal cells

We present a model useful for interpretation of indentation experiments on animal cells. We use finite element modeling for a thorough representation of the complex structure of an animal cell. In our model, the crucial constituent is the cell cortex—a rigid layer of cytoplasmic proteins present on the inner side of the cell membrane. It plays a vital role in the mechanical interactions between cells. The cell cortex is modeled by a three-dimensional solid to reflect its bending stiffness. This approach allows us to interpret the results of the indentation measurements and extract the mechanical properties of the individual elements of the cell structure. During the simulations, we scan a broad range of parameters such as cortex thickness and Young’s modulus, cytoplasm Young’s modulus, and indenter radius, which define cell properties and experimental conditions. Finally, we propose a simple closed-form formula that approximates the simulated results with satisfactory accuracy. Our formula is as easy to use as Hertz's function to extract cell properties from the measurement, yet it considers the cell’s inner structure, including cell cortex, cytoplasm, and nucleus.

     

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

Machine vision-based monitoring of pig lying behaviour is a fast and non-intrusive approach that could be used to improve animal health and welfare. Four pens with 22 pigs in each were selected at a commercial pig farm and monitored for 15 days using top view cameras. Three thermal categories were selected relative to room setpoint temperature. An image processing technique based on Delaunay triangulation (DT) was utilized. Different lying patterns (close, normal and far) were defined regarding the perimeter of each DT triangle and the percentages of each lying pattern were obtained in each thermal category. A method using a multilayer perceptron (MLP) neural network, to automatically classify group lying behaviour of pigs into three thermal categories, was developed and tested for its feasibility. The DT features (mean value of perimeters, maximum and minimum length of sides of triangles) were calculated as inputs for the MLP classifier. The network was trained, validated and tested and the results revealed that MLP could classify lying features into the three thermal categories with high overall accuracy (95.6%). The technique indicates that a combination of image processing, MLP classification and mathematical modelling can be used as a precise method for quantifying pig lying behaviour in welfare investigations.     

Cell encapsulation with alginate and alpha-phenoxycinnamylidene-acetylated poly(allylamine)

In general, microcapsules prepared from alginate and polycations lack mechanical strength because the interaction between alginate and polycations is ionic instead of covalent, which represents a much stronger bond. To increase the mechanical strength of the capsule, we prepared photosensitive microcapsules that could form covalent bonds between polymers in the capsular membrane by light irradiation. Two types of photosensitive poly(allylamine), with 5% and 10% of amino groups modified by alpha-phenoxycinnamylidene acetylchloride, were synthesized. Both photopolymers exhibited an absorption maximum at 325 nm and were capable of crosslinking upon light exposure. These photosensitive polymers were used for the preparation of microcapsules. The capsules formed from this photosensitive poly(allylamine) and alginate were strengthened significantly by light irradiation. Only 28% of the microcapsules prepared from the 5%-modified photopolymer fractured after 48 h of shaking at 150 rpm. This fracture percentage is much lower when compared with the 60% of capsules fractured when prepared from the untreated poly(allylamine). By using poly(allylamine) at 10% modification, the mechanical strength was improved only slightly, with 26% of capsules fractured. Analysis of the permeability test indicated that the photo-crosslinked capsular membrane was freely permeable to cytochrome c and myoglobin, but less permeable to serum albumin. The encapsulation method was used to entrap and culture IW32 mouse leukemia cells. The cells proliferated to a density of about 1.1 x 10(7) cells/mL in the capsules after 7 days of cultivation. Concurrently, the concentration of erythropoietin in the microcapsules increased to 800 mU/mL. This new encapsulation technique has great potential in the application of a bioindustrial cell-culturing process.