Recovering the state sequence of hidden Markov models using mean-field approximations

Inferring the sequence of states from observations is one of the most fundamental problems in Hidden Markov Models. In statistical physics language, this problem is equivalent to computing the marginals of a one-dimensional model with a random external field. While this task can be accomplished through transfer matrix methods, it becomes quickly intractable when the underlying state space is large. This paper develops several low-complexity approximate algorithms to address this inference problem when the state space becomes large. The new algorithms are based on various mean-field approximations of the transfer matrix. Their performances are studied in detail on a simple realistic model for DNA pyrosequencing.Comment: 43 pages, 41 figure

Degradable Terpolymers with Alkyl Side Chains Demonstrate Enhanced Gene Delivery Potency and Nanoparticle Stability

Degradable, cationic poly(β-amino ester)s (PBAEs) with alkyl side chains are developed for non-viral gene delivery. Nanoparticles formed from these PBAE terpolymers exhibit significantly enhanced DNA transfection potency and resistance to aggregation. These hydrophobic PBAE terpolymers, but not PBAEs lacking alkyl side chains, support interaction with PEG-lipid conjugates, facilitating their functionalization with shielding and targeting moieties and accelerating the in vivo translation of these materials.National Heart, Lung, and Blood InstituteNational Institutes of Health (U.S.) (Program of Excellence in Nanotechnology (PEN) Award, Contract #HHSN268201000045C)National Institutes of Health (U.S.) (NIH Grant R01-EB000244-27)National Institutes of Health (U.S.) (NIH Grant 5-R01-CA132091-04)National Institutes of Health (U.S.) (NIH Grant R01-DE016516-03)National Science Foundation (U.S.) (Graduate Research Fellowship)Juvenile Diabetes Research Foundation International (Grant 17–2007-1063

Effect of molecular weight of amine end-modified poly(β-amino ester)s on gene delivery efficiency and toxicity

Amine end-modified poly(β-amino ester)s (PBAEs) have generated interest as efficient, biodegradable polymeric carriers for plasmid DNA (pDNA). For cationic, non-degradable polymers, such as polyethylenimine (PEI), the polymer molecular weight (MW) and molecular weight distribution (MWD) significantly affect transfection activity and cytotoxicity. The effect of MW on DNA transfection activity for PBAEs has been less well studied. We applied two strategies to obtain amine end-modified PBAEs varying in MW. In one approach, we synthesized four amine end-modified PBAEs with each at 15 different monomer molar ratios, and observed that polymers of intermediate length mediated optimal DNA transfection in HeLa cells. Biophysical characterization of these feed ratio variants suggested that optimal performance was related to higher DNA complexation efficiency and smaller nanoparticle size, but not to nanoparticle charge. In a second approach, we used preparative size exclusion chromatography (SEC) to obtain well-defined, monodisperse polymer fractions. We observed that the transfection activities of size-fractionated PBAEs generally increased with MW, a trend that was weakly associated with an increase in DNA binding efficiency. Furthermore, this approach allowed for the isolation of polymer fractions with greater transfection potency than the starting material. For researchers working with gene delivery polymers synthesized by step-growth polymerization, our data highlight the potentially broad utility of preparative SEC to isolate monodisperse polymers with improved properties. Overall, these results help to elucidate the influence of polymer MWD on nucleic acid delivery and provide insight toward the rational design of next-generation materials for gene therapy.Alnylam Pharmaceuticals (Firm)National Institutes of Health (U.S.) (Grant R01-EB000244-27)National Institutes of Health (U.S.) (Grant 5-R01-CA132091-04)National Science Foundation (U.S.). Graduate Research FellowshipNational Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (F32-EB011867

Nucleic acid-mediated intracellular protein delivery by lipid-like nanoparticles

Intracellular protein delivery has potential biotechnological and therapeutic application, but remains technically challenging. In contrast, a plethora of nucleic acid carriers have been developed, with lipid-based nanoparticles (LNPs) among the most clinically advanced reagents for oligonucleotide delivery. Here, we validate the hypothesis that oligonucleotides can serve as packaging materials to facilitate protein entrapment within and intracellular delivery by LNPs. Using two distinct model proteins, horseradish peroxidase and NeutrAvidin, we demonstrate that LNPs can yield efficient intracellular protein delivery in vitro when one or more oligonucleotides have been conjugated to the protein cargo. Moreover, in experiments with NeutrAvidin in vivo, we show that oligonucleotide conjugation significantly enhances LNP-mediated protein uptake within various spleen cell populations, suggesting that this approach may be particularly suitable for improved delivery of protein-based vaccines to antigen-presenting cells.National Heart, Lung, and Blood Institute (Contract HHSN268201000045C)National Institutes of Health (U.S.) (Grant R01-EB000244-27)National Institutes of Health (U.S.) (Grant 5-R01-CA132091-04)National Science Foundation (U.S.)Juvenile Diabetes Research Foundation International (Grant 17–2007-1063)United States. Dept. of Defense. Congressionally Directed Medical Research Programs (Grant W81XWH-13-1-0215

Eternity Kills

Panel: The Calling: Writing with Responsibilit

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Alkane-modified short polyethyleneimine for siRNA delivery

RNA interference (RNAi) is a highly specific gene-silencing mechanism triggered by small interfering RNA (siRNA). Effective intracellular delivery requires the development of potent siRNA carriers. Here, we describe the synthesis and screening of a series of siRNA delivery materials. Short polyethyleneimine (PEI, Mw 600) was selected as a cationic backbone to which lipid tails were conjugated at various levels of saturation. In solution these polymer–lipid hybrids self-assemble to form nanoparticles capable of complexing siRNA. The complexes silence genes specifically and with low cytotoxicity. The efficiency of gene knockdown increased as the number of lipid tails conjugated to the PEI backbone increased. This is explained by reducing the binding affinity between the siRNA strands to the complex, thereby enabling siRNA release after cellular internalization. These results highlight the importance of complexation strength when designing siRNA delivery materials.Misrock FoundationAmerican Society for Engineering Education. National Defense Science and Engineering Graduate FellowshipNational Institutes of Health (U.S) (Grant EB000244)National Cancer Institute (U.S.) (MIT-Harvard Center of Cancer Nanotechnology Excellence. Grant CA151884)National Science Foundation (U.S.)Massachusetts Institute of Technology (Presidential Fellowships