The development of a nanoscale Coulter counter for rapid genetic sequence recognition

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 195-205).The goal of this thesis is the development of a nanoscale Coulter counter for the direct electrical detection of specific genetic sequences of deoxyribonucleic acid (DNA); the general approach used to accomplish sequence recognition is a refinement of the resistive pulse technique. Commercial Coulter counters fabricated with sub-micrometer apertures can size particles with roughly twenty nanometers of resolution. The characterization of DNA, which is more than an order of magnitude smaller than this resolution limit, requires the development of a detection system with a two nanometer limiting aperture. To help develop the techniques and instrumentation explored in this thesis, the biological toxin, alpha hemolysin, was implemented as "prototype" limiting aperture. With the practical knowledge gained from using a toxin channel, a general model for the nanopore as a low-noise sensor was developed. With this model, two broad goals were achieved. The first achievement was the development of novel genetic recognition strategies that exploit the properties of the nanopore within the limitations imposed by DNA structure and existing channel geometries. The second achievement was the design and prototyping of novel interface picoammeter for the measurement of the current fluctuations associated with DNA translocation through a nanopore. Although the instrumentation and methods developed in this thesis are limited to genetic sequence recognition, the hope is that elements of this work will be integrated with the development of silicon nanopores to achieve rapid de novo DNA sequencing in the future.by Timothy Allman Denison.Ph.D

Optical contrast between transparent materials through external modulation of the Faraday effect

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (leaves 99-101)./ Timothy Allman Denison.M.S

Numerical modelling of plasticity induced by Quadri-pulse stimulation

Quadri-pulse stimulation (QPS), a type of repetitive transcranial magnetic stimulation (rTMS), can induce a considerable aftereffect on cortical synapses. Human experiments have shown that the type of effect on synaptic efficiency (in terms of potentiation or depression) depends on the time interval between pulses. The maturation of biophysically-based models, which describe the physiological properties of plasticity mathematically, offers a beneficial framework to explore induced plasticity for new stimulation protocols. To model the QPS paradigm, a phenomenological model based on the knowledge of spike timing-dependent plasticity (STDP) mechanisms of synaptic plasticity was utilized where the cortex builds upon the platform of neuronal population modeling. Induced cortical plasticity was modeled for both conventional monophasic pulses and unidirectional pulses generated by the cTMS device, in a total of 117 different scenarios. For the conventional monophasic stimuli, the results of the predictive model broadly follow what is typically seen in human experiments. Unidirectional pulses can produce a similar range of plasticity. Additionally, changing the pulse width had a considerable effect on the plasticity (approximately 20% increase). As the width of the positive phase increases, the size of the potentiation will also increase. The proposed model can generate predictions to guide future plasticity experiments. Estimating the plasticity and optimizing the rTMS protocols might effectively improve the safety implications of TMS experiments by reducing the number of delivered pulses to participants. Finding the optimal stimulation protocol with the maximum potentiation/depression can lead to the design of a new TMS pulse generator device with targeted hardware and control algorithms

Energy rebound as a potential threat to a low-carbon future: findings from a new exergy-based national-level rebound approach

150 years ago, Stanley Jevons introduced the concept of energy rebound: that anticipated energy efficiency savings may be “taken back” by behavioural responses. This is an important issue today because, if energy rebound is significant, this would hamper the effectiveness of energy efficiency policies aimed at reducing energy use and associated carbon emissions. However, empirical studies which estimate national energy rebound are rare and, perhaps as a result, rebound is largely ignored in energy-economy models and associated policy. A significant difficulty lies in the components of energy rebound assessed in empirical studies: most examine direct and indirect rebound in the static economy, excluding potentially significant rebound of the longer term structural response of the national economy. In response, we develop a novel exergy-based approach to estimate national energy rebound for the UK and US (1980–2010) and China (1981–2010). Exergy—as “available energy”—allows a consistent, thermodynamic-based metric for national-level energy efficiency. We find large energy rebound in China, suggesting that improvements in China’s energy efficiency may be associated with increased energy consumption (“backfire”). Conversely, we find much lower (partial) energy rebound for the case of the UK and US. These findings support the hypothesis that producer-sided economies (such as China) may exhibit large energy rebound, reducing the effectiveness of energy efficiency, unless other policy measures (e.g., carbon taxes) are implemented. It also raises the prospect we need to deploy renewable energy sources faster than currently planned, if (due to rebound) energy efficiency policies cannot deliver the scale of energy reduction envisaged to meet climate targets

Stimulating at the right time: phase-specific deep brain stimulation.

SEE MOLL AND ENGEL DOI101093/AWW308 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Brain regions dynamically engage and disengage with one another to execute everyday actions from movement to decision making. Pathologies such as Parkinson's disease and tremor emerge when brain regions controlling movement cannot readily decouple, compromising motor function. Here, we propose a novel stimulation strategy that selectively regulates neural synchrony through phase-specific stimulation. We demonstrate for the first time the therapeutic potential of such a stimulation strategy for the treatment of patients with pathological tremor. Symptom suppression is achieved by delivering stimulation to the ventrolateral thalamus, timed according to the patient's tremor rhythm. Sustained locking of deep brain stimulation to a particular phase of tremor afforded clinically significant tremor relief (up to 87% tremor suppression) in selected patients with essential tremor despite delivering less than half the energy of conventional high frequency stimulation. Phase-specific stimulation efficacy depended on the resonant characteristics of the underlying tremor network. Selective regulation of neural synchrony through phase-locked stimulation has the potential to both increase the efficiency of therapy and to minimize stimulation-induced side effects

ANDES: Statistical tools for the ANalyses of DEep Sequencing

<p>Abstract</p> <p>Background</p> <p>The advancements in DNA sequencing technologies have allowed researchers to progress from the analyses of a single organism towards the deep sequencing of a sample of organisms. With sufficient sequencing depth, it is now possible to detect subtle variations between members of the same species, or between mixed species with shared biomarkers, such as the 16S rRNA gene. However, traditional sequencing analyses of samples from largely homogeneous populations are often still based on multiple sequence alignments (MSA), where each sequence is placed along a separate row and similarities between aligned bases can be followed down each column. While this visual format is intuitive for a small set of aligned sequences, the representation quickly becomes cumbersome as sequencing depths cover loci hundreds or thousands of reads deep.</p> <p>Findings</p> <p>We have developed ANDES, a software library and a suite of applications, written in Perl and R, for the statistical ANalyses of DEep Sequencing. The fundamental data structure underlying ANDES is the position profile, which contains the nucleotide distributions for each genomic position resultant from a multiple sequence alignment (MSA). Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.</p> <p>Conclusions</p> <p>As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common. We have provided a software package and demonstrated its application on various empirically-derived datasets. Investigators may download the software from Sourceforge at <url>https://sourceforge.net/projects/andestools</url>.</p

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces.

BACKGROUND Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. OBJECTIVES Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. METHODS Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. RESULTS The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. CONCLUSIONS Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration