Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201

Review of Artifact Rejection Methods for Electroencephalographic Systems

Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms

Comparison of muscle synergies elicited from transcranial meganetic stimulation (tms) and voluntary movements

A key question in motor control is the redundancy of musculoskeletal elements involved. This problem refers to as the degree of freedom problem. The Muscle Synergy Hypothesis is one of the hypotheses that aim to resolve the problem which defines that a muscle synergy is a combination of a small set of muscles activated at different levels, serving as a building block that constructs motor behaviors. A recent study (Overduin et al. 2012) demonstrated that muscle synergies decomposed by Nonnegative Matrix Factorization (NMF) from EMG patterns evoked by intra-cortical microsimulation (ICMS) in the monkey remarkably matched ones observed in naturalistic reach-and-grasp behaviors. Another study (Ajiboye et al. 2009) showed that synergies elicited from a small number of hand postures can allow prediction of hand postures in general. Inspired by aforementioned studies, the aim of this study was to investigate whether Transcranial Magnetic Stimulation (TMS) can elicit muscle synergies matching ones observed in voluntary movements in healthy human subjects and whether these synergies can serve as frameworks to predict EMG patterns evoked by either TMS or voluntary movements. Five healthy right-handed subjects participated in the study. 8 hand muscles were recorded to capture either TMS-evoked motor evoked potential (MEP) and electromyography (EMG) resulted from subjects’ shaping American Sign Language (ASL) letters and numbers. NMF was utilized to extract synergies from both MEP and EMG data. We observed 5 or 6 synergies can capture 90% of variance of original and matched synergies of two classes. The reconstructions of the original datasets (VTMS: MEP data; Vvol: EMG data; Vrand: Random data as control) from synergies (Hvol synergies elicited from ASL tasks; HTMS synergies elicited from TMS) was done by the nonnegative least-square algorithm, and Proportion of Variance Accounted for (PAV) served as a measure to quantify the quality of the estimation, giving results Hvol -\u3e Vvol: 0.92±0.02; HTMS -\u3e VTMS: 0.94±0.02; Hvol -\u3e Vrand: 0.53±0.03; HTMS -\u3e Vrand: 0.53±0.07; HTMS -\u3e Vvol: 0.70±0.06; Hvol -\u3e VTMS: 0.79±0.06. In conclusion, we argue that cortical components may involve in encoding synergies and we also demonstrate the possibility of synergies serving as frameworks in predicting and explaining human hand postures in general

Age-dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury

Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n = 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q \u3c 0.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age-dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography.

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported

Neuronal and Hemodynamic Functional Connectivity in the Awake Mouse

Resting State functional Magnetic Resonance Imaging (rs-fMRI) has revealed brain-wide correlation patterns throughout the human brain, interpreted as Functional Connectivity. Dynamic Functional Connectivity (DFC) has recently expanded on this technique via sliding window correlation analysis, revealing moment-to-moment changes in functional connectivity across an imaging session. However, the meaning of these transitions in terms of neural activity and behavior are not well understood.In this work, I utilized Dynamic Functional Connectivity analytical techniques in conjunction with Wide Field Optical Mapping (WFOM) in the awake, freely behaving mouse. I hypothesized that neural and hemodynamic activity observed with WFOM would exhibit similar transitions between functional connectivity states as reported by fMRI DFC studies. I also explored whether changes in functional connectivity would correspond to changes in behavior. Simultaneous neural and hemodynamic activity was collected using WFOM from five freely behaving head-fixed Thy1-jRGECO1a mice. Behavioral metrics of movement, whisking and pupillometry were acquired simultaneously. Raw neuroimaging data were dimensionally reduced to representative time courses across the dorsal surface of the cortex for each subject utilizing a semi-supervised clustering technique. Functional Connectivity analysis revealed rich spatiotemporal structures within neural and hemodynamic activity, which were consistent across imaging sessions and subjects. I observed broad changes in Functional Connectivity metrics during rest, locomotion, and transitional epochs between the two by directly comparing windows captured during these epochs. It was also observed that Functional Connectivity metrics immediately following locomotion offset could be distinguished from periods of sustained rest. Similar to human fMRI studies, a distinct increase in bilateral connectivity of anterior lateral prefrontal cortex was observed, which became significantly less synchronized with posterior brain regions during sustained periods of rest. I next used an unsupervised clustering technique on the same data to test if these properties could be observed in an indirect manner. This approach has been previously used in numerous human fMRI studies, and contextualized this work to human fMRI studies. A sliding window was used to calculate moment-to-moment Functional Connectivity maps across each imaging session. These dynamic correlation maps were clustered into multiple states, which could then be used to calculate the most representative state for any given epoch. Unsupervised clustering revealed strikingly similar dynamic states to our previous observations. These dynamic states also exhibited independent distributions of behavioral activity both in neural and hemodynamic models, leading us to conclude that there is not only a meaningful link between Functional Connectivity in neural and hemodynamic activity, but that behavioral shifts largely drive these changes. My findings provide strong evidence that Dynamic Functional Connectivity has neural origins, and hemodynamic responses are able to depict correlation patterns that tracks rapid changes in behavior and internal brain states such as the level of arousal or alertness. Future studies are necessary to further investigate this speculation, but this offers an excellent framework to better understand the rich, dynamic properties of brain activity

A comparison of edge-preserving approaches for differential interference contrast microscopy

In this paper we address the problem of estimating the phase from color images acquired with differential-interference-contrast microscopy. In particular, we consider the nonlinear and nonconvex optimization problem obtained by regularizing a least-squares-like discrepancy term with an edge-preserving functional, given by either the hypersurface potential or the total variation one. We investigate the analytical properties of the resulting objective functions, proving the existence of minimum points, and we propose effective optimization tools able to obtain in both the smooth and the nonsmooth case accurate reconstructions with a reduced computational demand

Physical limits to sensing material properties

Constitutive relations describe how materials respond to external stimuli such as forces. All materials respond heterogeneously at small scales, which limits what a localized sensor can discern about the global constitution of a material. In this paper, we quantify the limits of such constitutional sensing by determining the optimal measurement protocols for sensors embedded in disordered media. For an elastic medium, we find that the least fractional uncertainty with which a sensor can determine a material constant $\lambda_0$ is approximately \begin{equation*} \frac{\delta \lambda_0}{\lambda_0 } \sim \left( \frac{\Delta_{\lambda} }{ \lambda_0^2} \right)^{1/2} \left( \frac{ d }{ a } \right)^{D/2} \left( \frac{ \xi }{ a } \right)^{D/2} \end{equation*} for $a \gg d \gg \xi$, $\lambda_0 \gg \Delta_{\lambda}^{1/2}$, and $D>1$, where $a$ is the size of the sensor, $d$ is its spatial resolution, $\xi$ is the correlation length of fluctuations in the material constant, $\Delta_{\lambda}$ is the local variability of the material constant, and $D$ is the dimension of the medium. Our results reveal how one can construct microscopic devices capable of sensing near these physical limits, e.g. for medical diagnostics. We show how our theoretical framework can be applied to an experimental system by estimating a bound on the precision of cellular mechanosensing in a biopolymer network.Comment: 33 pages, 3 figure

Scanning Electron Microscopy for Nano-morphology Characterisation of Complex Hierarchical Polymer Structures

This thesis presents novel and innovative ways of imaging and analysing natural hierarchical polymers using low-voltage scanning electron microscopy and secondary electron energies. Materials such as plant fibres, feathers and silk, have received increased societal and scientific interest recently, while the plastic industry is faced with growing public concerns over its generation of waste, and use of petrochemical precursors. In nature, materials are produced sustainably and they furthermore exhibit inspiring mechanical performance. One such material is spider silk, which is spun at room temperature from a water-based protein gel to form a thread, which even with diameters as small as 5 μm easily suspends the weight of a palm-sized spider. It is known that the secret to spider silk’s remarkable properties lies within its nanoscale structures. However, the direct observation of these nanostructures has remained difficult due to their small size and their sensitivity to chemical and mechanical alteration. This work presents novel sample preparation protocols and demonstrates their use in accessing size and location information of key nanostructures within spider silk through nanoscale observation in the scanning electron microscope. As the secondary electron spectroscopy technique employed here is relatively new, new workflows from sample preparation, over optimal imaging and spectral acquisition and novel multivariate data analysis techniques are innovated and described in detail. The rigorous consideration of the material and method are exemplified on a feather section, to show that the secondary electron energy signal in the scanning electron microscope may generate molecular composition maps on a proteinaceous structural polymer. This work lays out all requirements for unlocking the vast potential for nanoscale chemical mapping which lies in the nanoscale secondary electron signal, to further inspire ground-breaking studies into the nanostructures of complex hierarchical polymers