Sparse Sample Detection Using Magnetic Bead Manipulation on a Digital Microfluidic Device

This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-­‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-­‐‑carrying wires embedded in the actuation electrode of an electrowetting-­‐‑on-­‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.

Analyse simultanée des hormones stéroïdiennes et leurs formes chimiques dans les matrices d'eau et d'urine par SPE-LC-MS/MS en ligne.

Dans les dernières années, les perturbateurs endocriniens ont été observés dans les rivières qui reçoivent des entrées importantes d’eaux usées. Parmi les perturbateurs endocriniens, les hormones stéroïdiennes naturelles et synthétiques sont des composés dont le potentiel d'imiter ou d'interférer avec les fonctions hormonales normales (développement, croissance et reproduction), est reconnu même au niveau ultra-traces (ng L-1). Bien que les hormones conjuguées soient moins actives que les hormones libres, elles peuvent être clivées et redevenir libres une fois exposées aux processus microbiens avant ou pendant le traitement des eaux usées. En raison de la nécessité d'identifier et de quantifier ces composés dans l'eau, une nouvelle méthode, entièrement automatisée, a été développée pour la détermination simultanée des deux formes de plusieurs hormones stéroïdiennes (conjuguées et libres) dans les matrices d'eau et dans l’urine des femmes. La méthode est basée sur l'extraction en phase solide couplée en ligne à la chromatographie liquide et la spectrométrie de masse en tandem (SPE-LC-MS/MS). Plusieurs paramètres ont été évalués dans le but d'optimiser l'efficacité de la méthode, tels que le type et le débit de la phase mobile, des différentes colonnes de SPE et de chromatographie, ainsi que différentes sources et modes d'ionisation des échantillons pour la MS. La méthode démontre une bonne linéarité (R2 > 0.993), ainsi qu'une précision avec un coefficient de variance inférieure à 10%. Les limites de quantification varient d’un minimum de 3 à 15 ng L-1 pour un volume d'injection entre 1 mL et 5 mL et le recouvrement des composés varie de 72 % à 117 %.

Elemental Analysis of Biological Matrices by Laser Ablation High Resolution Inductively Coupled Plasma Mass Spectrometry (LA-HR-ICP-MS) and High Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS)

The need for elemental analysis of biological matrices such as bone, teeth, and plant matter for sourcing purposes has emerged within the forensic and geochemical laboratories. Trace elemental analyses for the comparison of aterials such as glass by inductively coupled plasma mass spectrometry (ICP-MS) and laser ablation ICP-MS has been shown to offer a high degree of discrimination between different manufacturing sources. Unit resolution ICP-MS instruments may suffer from some polyatomic interferences including 40Ar16O+, 40Ar16O1H+, and 40Ca16O+ that affect iron measurement at trace levels. Iron is an important element in the analysis of glass and also of interest for the analysis of several biological matrices. A comparison of the nalytical performance of two different ICP-MS systems for iron analysis in glass for determining the method detection limits (MDLs), accuracy, and precision of the measurement is presented. Acid digestion and laser ablation methods are also compared. Iron polyatomic interferences were reduced or resolved by using dynamic reaction cell and high resolution ICP-MS. MDLs as low as 0.03 ìg g-1 and 0.14 ìg g-1 for laser ablation and solution based analyses respectively were achieved. The use of helium as a carrier gas demonstrated improvement in the detection limits of both iron isotopes (56Fe and 57Fe) in medium resolution for the HR-ICP-MS and with a dynamic reaction cell (DRC) coupled to a quadrupole ICP-MS system. The development and application of robust analytical methods for the quantification of trace elements in biological matrices has lead to a better understanding of the potential utility of these measurements in forensic chemical analyses. Standard reference materials (SRMs) were used in the development of an analytical method using HR-ICP-MS and LA-HR-ICP-MS that was subsequently applied on the analysis of real samples. Bone, teeth and ashed marijuana samples were analyzed with the developed method. Elemental analysis of bone samples from 12 different individuals provided discrimination between individuals, when femur and humerus bones were considered separately. Discrimination of 14 teeth samples based on elemental composition was achieved with the exception of one case where samples from the same individual were not associated with each other. The discrimination of 49 different ashed plant (cannabis)samples was achieved using the developed method.

Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.

Massive MIMO in sparse channels

Massive multi-user multiple-input multiple-output (MU-MIMO) systems are cellular networks where the base stations (BSs) are equipped with hundreds of antennas, N, and communicate with tens of mobile stations (MSs), K, such that, N ≫ K ≫ 1. Contrary to most prior works, in this paper, we consider the uplink of a single-cell massive MIMO system operating in sparse channels with limited scattering. This case is of particular importance in most propagation scenarios, where the prevalent Rayleigh fading assumption becomes idealistic. We derive analytical approximations for the achievable rates of maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Furthermore, we study the asymptotic behavior of the achievable rates for both MRC and ZF receivers, when N and K go to infinity under the condition that N/K → c ≥ 1. Our results indicate that the achievable rate of MRC receivers reaches an asymptotic saturation limit, whereas the achievable rate of ZF receivers grows logarithmically with the number of MSs.

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

Superregular matrices and applications to convolutional codes

The main results of this paper are twofold: the first one is a matrix theoretical result. We say that a matrix is superregular if all of its minors that are not trivially zero are nonzero. Given a a×b, a ≥ b, superregular matrix over a field, we show that if all of its rows are nonzero then any linear combination of its columns, with nonzero coefficients, has at least a−b + 1 nonzero entries. Secondly, we make use of this result to construct convolutional codes that attain the maximum possible distance for some fixed parameters of the code, namely, the rate and the Forney indices. These results answer some open questions on distances and constructions of convolutional codes posted in the literature.