Individual Limb Muscle Bundles Are Formed through Progressive Steps Orchestrated by Adjacent Connective Tissue Cells during Primary Myogenesis

Although the factors regulating muscle cell differentiation are well described, we know very little about how differentiating muscle fibers are organized into individual muscle tissue bundles. Disruption of these processes leads to muscle hypoplasia or dysplasia, and replicating these events is vital in tissue engineering approaches. We describe the progressive cellular events that orchestrate the formation of individual limb muscle bundles and directly demonstrate the role of the connective tissue cells that surround muscle precursors in controlling these events. We show how disruption of gene activity within or genetic ablation of connective tissue cells impacts muscle precursors causing disruption of muscle bundle formation and subsequent muscle dysplasia and hypoplasia. We identify several markers of the populations of connective tissue cells that surround muscle precursors and provide a model for how matrix-modifying proteoglycans secreted by these cells may influence muscle bundle formation by effects on the local extracellular matrix (ECM) environment.

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Characterization of the events that prefigure the formation of individual muscle bundles

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Direct demonstration of the role of connective tissue cells in muscle morphogenesis

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Identification of markers of limb irregular connective tissue (ICT)

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Demonstration of molecularly distinct ICT subdomains in the limb

A novel hybrid approach for automated detection of retinal detachment using ultrasound images

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.

The Effect of Caffeine on Calcitriol-Inducible Vitamin D Receptor-Controlled Gene Expression in Intestinal and Osteoblastic Cells

Some epidemiological studies suggested caffeine consumption as the cause for bone mineral density loss. Certain genes involved in this process are regulated by vitamin D receptor (VDR). Therefore, we investigated if caffeine can affect inducible expression of VDR-regulated genes, some of them being involved in bone mineralization process. By employing reporter gene assay, polymerase chain reaction, and western blotting, we monitored the VDR activity and expression in cell cultures of intestinal (LS180), osteosarcoma (HOS), and normal human osteoblasts in vitro. While caffeine stimulated calcitriol-inducible VDR-dependent nanoluciferase activity in stable reporter cell line IZ-VDRE (derived from LS180), it rather modulated mRNA levels of target genes, like CYP24A1, BGLAP, SPP1, and TNSF11 in LS180 and HOS cells. However, caffeine significantly decreased calcitriol-inducible CYP24A1, TNSF11, and SPP1 transcripts in osteoblasts. This decrease had non-linear U-shaped profile. Our in vitro data demonstrate biphasic action of caffeine on the expression of certain calcitriol-inducible VDR-regulated genes in normal human osteoblasts.

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.

The use of aniline blue chromatin condensation test on prediction of pregnancy in mild male factor and unexplained male infertility

The aim of this study was to investigate the possibility of using sperm function tests (hypoosmotic swelling test [HOS], aniline blue [AB] staining test, and sperm chromatin dispersion [SCD]) to predict intrauterine insemination [IUI] success rate. A total of 243 couples with mild male factor or unexplained male infertility who underwent IUI were evaluated prospectively. The results of basic sperm analysis and sperm function tests were compared between pregnant or nonpregnant groups. The HOS (11.9 ± 9.6% vs. 10.1 ± 8.5%, p = 0.35) and SCD tests (32.9 ± 21.0% vs. 29.9 ± 19.0%, p = 0.48) were not significantly different between pregnant (n = 22) and nonpregnant (n = 221) groups. However, the AB staining negativity rate was significantly higher in the pregnant group compared to the nonpregnant group (35.2 ± 20.8% and 24.4 ± 18.0%, p = 0.008). On ROC analysis, a cut-off value of 24% for AB negativity showed a sensitivity and a specificity value of 82.35% and 51.38% (AUC) = 0.653; 95% confidence interval: 0.571-0.72 P (Area = 0.5) = 0.0267, respectively, for prediction of pregnancy. Our study showed that the sperm chromatin maturity, assessed by AB stain, may predict the pregnancy in couples with unexplained female infertility plus mild male factor or unexplained male infertility. The HOS and SCD failed to predict the pregnancy in this group of couples.

Measured and estimated data of non-linear BRAN channels using HOS in 4G wireless communications

The aim of this research is to develop a non-linear blind estimator able to represents a Broadband Radio Access Networks (BRAN) channels. In the one hand, we have used Higher Order Statistics (HOS) theory to build our algorithm. Indeed, we develop a non-linear method based only on fourth order cumulants for identifying the diagonal parameters of quadratic systems. In the other hand, the developed approach is applied to estimate the experimental channels, BRAN A, C and E data normalized for MC-CDMA, in non-linear case. However, the estimated data will be used in the blind equalization. The simulation results in noisy environment and for different signal to noise ratio (SNR) show the accuracy of develop estimator blindly (i.e., without any information about the input) with non-Gaussian signal input. Furthermore, in part of blind equalization problem the obtained results, using Zero forcing (ZF) and Minimum Mean Square Error (MMSE) equalizers, demonstrate that the proposed algorithm is very adequate to correct channel distortion in term the Bit Error Rate (BER). Finally, these estimated data present a necessary asset for conducting validation experiments, and can be also used as a baseline.

A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.

Mathematical modelling suggests a differential impact of β-transducin repeat-containing protein paralogues on Wnt/β-catenin signalling dynamics

The Wnt/β-catenin signalling pathway is involved in the regulation of a multitude of cellular processes by controlling the concentration of the transcriptional regulator β-catenin. Proteasomal degradation of β-catenin is mediated by two β-transducin repeat-containing protein paralogues, homologous to Slimb protein (HOS) and F-box/WD repeat-containing protein 1A (FWD1), which are functionally interchangeable and thereby considered to function redundantly in the pathway. HOS and FWD1 are both regulated by Wnt/β-catenin signalling, albeit in opposite directions, thus establishing interlocked negative and positive feedback loops. The functional relevance of the opposite regulation of HOS and FWD1 by Wnt/β-catenin signalling in conjunction with their redundant activities in proteasomal degradation of β-catenin remains unresolved. Using a detailed ordinary differential equation model, we investigated the specific influence of each individual feedback mechanism and their combination on Wnt/β-catenin signal transduction under wild-type and cancerous conditions. We found that, under wild-type conditions, the signalling dynamics are predominantly affected by the HOS feedback as a result of a higher concentration of HOS than FWD1. Transcriptional up-regulation of FWD1 by other signalling pathways reduced the impact of the HOS feedback. The opposite regulation of HOS and FWD1 expression by Wnt/β-catenin signalling allows the FWD1 feedback to be employed as a compensation mechanism against aberrant pathway activation as a result of a reduced HOS concentration. By contrast, the FWD1 feedback provides no protection against aberrant activation in adenomatous polyposis coli protein mutant cancer cells.

Psychometric Evaluation of the Lower Extremity Computerized Adaptive Test, the Modified Harris Hip Score, and the Hip Outcome Score

Background:

The applicability and validity of many patient-reported outcome measures in the high-functioning population are not well understood.

Purpose:

To compare the psychometric properties of the modified Harris Hip Score (mHHS), the Hip Outcome Score activities of daily living subscale (HOS-ADL) and sports (HOS-sports), and the Lower Extremity Computerized Adaptive Test (LE CAT). The hypotheses was that all instruments would perform well but that the LE CAT would show superiority psychometrically because a combination of CAT and a large item bank allows for a high degree of measurement precision.

Study Design:

Cohort study (diagnosis); Level of evidence, 2.

Methods:

Data were collected from 472 advanced-age, active participants from the Huntsman World Senior Games in 2012. Validity evidences were examined through item fit, dimensionality, monotonicity, local independence, differential item functioning, person raw score to measure correlation, and instrument coverage (ie, ceiling and floor effects), and reliability evidences were examined through Cronbach alpha and person separation index.

Results:

All instruments demonstrated good item fit, unidimensionality, monotonicity, local independence, and person raw score to measure correlations. The HOS-ADL had high ceiling effects of 36.02%, and the mHHS had ceiling effects of 27.54%. The LE CAT had ceiling effects of 8.47%, and the HOS-sports had no ceiling effects. None of the instruments had any floor effects. The mHHS had a very low Cronbach alpha of 0.41 and an extremely low person separation index of 0.08. Reliabilities for the LE CAT were excellent and for the HOS-ADL and HOS-sports were good.

Conclusion:

The LE CAT showed better psychometric properties overall than the HOS-ADL, HOS-sports, and mHHS for the senior population. The mHHS demonstrated pronounced ceiling effects and poor reliabilities that should be of concern. The high ceiling effects for the HOS-ADL were also of concern. The LE CAT was superior in all psychometric aspects examined in this study. Future research should investigate the LE CAT for wider use in different populations.

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.