Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections

Signal De-noising method based on particle swarm algorithm and Wavelet transform

Wavelet analiza je novi alat za analizu odnosa vrijeme-frekvencija, razvijen na temelju Fourierove analize s dobrim svojstvom lokaliziranja vremena i frekvencije i mogućnosti donošenja višestrukih rješenja. Koristi se u cijelom nizu primjena u području obrade signala. U ovom se radu analizira primjena wavelet transforma u filtriranju signala korištenjem poboljšane optimalizacije roja čestica i predlaže inteligentna metoda uklanjanja šuma iz signala zasnovana na wavelet analizi. Metoda koristi Center Based Particle Swarm Algorithm (CBPSO) za izbor optimalnog praga za svaki pod-pojas u različitim mjerilima, inteligentno razaznavajući vrstu šuma iz samog signala, što ne zahtijeva nikakvo prethodno poznavanje šuma. Poboljšani algoritam roja čestica koristi se da potakne optimalni izbor različitih mjerila praga wavelet domena, što je dovelo do uklanjanja šuma iz signala kod različitih tipova pozadinskog šuma, i povećane brzine wavelet transforma i wavelet konstrukcije te ima veću fleksibilnost. Eksperimentalni rezultati su pokazali da se CBPSO algoritmom može postići bolji učinak uklanjanja šuma.Wavelet analysis is a new time-frequency analysis tool developed on the basis of Fourier analysis with good time-frequency localization property and multi-resolution characteristics, which is in a wide range of applications in the field of signal processing. This paper studies the application of wavelet transform in signal filtering, by using an improved particle swarm optimization, proposes an intelligent signal de-noising method based on wavelet analysis. The method uses a Center Based Particle Swarm Algorithm (CBPSO) to select the optimal threshold for each sub-band in different scales, learning the type of noise from the signal itself intelligently, which does not require any prior knowledge of the noise. The improved particle swarm algorithm is used to enhance the optimal choice of the different scales of the wavelet domain threshold, which realized the signal De-noising under different types of noise background, and improved the speed of wavelet transform and wavelet construction, and has greater flexibility. The experimental results showed that CBPSO algorithm can get better De-noising effect

Nuevas candidatas para funciones trampa multivariadas

Presentamos un nuevo método de reducción que permite construirparejas de polinomios HFE de grado alto, tal que la función construida concada una de estas parejas de polinomios es fácil de invertir. Para invertir lapareja de polinomios usamos un polinomio de grado bajo y de peso de Ham-ming tres, el cual es derivado mediante un método especial de reducción queinvolucra polinomios de peso de Hamming tres producidos a partir de los dospolinomios HFE. Esto nos permite construir nuevas candidatas para funcionestrampa multivariadas usando la pareja de polinomios HFE para construir lafunción central. Realizamos un análisis de seguridad cuando el campo base esGF(2) y mostramos que estas nuevas funciones trampa multivariadas tienen grado de regularidad alto, y por lo tanto resisten el ataque algebraico. Ademásdamos argumentos teóricos para mostrar que estas nuevas funciones trampasobre GF(2) tambien resisten el ataque MinRank.We present a new method for building pairs of HFE polynomialsof high degree, such that the map constructed with one of these pairs is easyto invert. The inversion is accomplished using a low degree polynomial ofHamming weight three, which is derived from a special reduction via Hammingweight three polynomials produced by these two HFE polynomials. This allowsus to build new candidates for multivariate trapdoor functions in which weuse the pair of HFE polynomials to fabricate the core map. We performed thesecurity analysis for the case where the base eld is GF(2) and showed thatthese new trapdoor functions have high degrees of regularity, and thereforethey are secure against the direct algebraic attack. We also give theoreticalarguments to show that these new trapdoor functions over GF(2) are secureagainst the MinRank attack as well

DISCOVERING A NOVEL ANTIFUNGAL TARGET IN DOWNSTREAM STEROL BIOSYNTHESIS USING A SQUALENE SYNTHASE FUNCTIONAL MOTIF

The sterol biosynthetic pathway is essential for growth of all eukaryotic cells and the main target of antifungal agents. The emergence of resistance to these antifungals in an already ill patient population indicates a need to develop drugs that have a broad spectrum of activity among pathogenic fungi and have minimal patient toxicity. Squalene synthase is the first committed step in the sterol pathway and has been studied intensively for development of antifungal agents. While the overall architecture of this enzyme is identical throughout eukaryotes, it was shown that plant and animal genes cannot complement a squalene synthase knockout mutation in yeast unless the carboxy-terminal domain is swapped for one of fungal origin. This implies that there is a component of the fungal carboxy-terminal domain that is responsible for the complementation phenotype and that is unique to the fungal kingdom of life. To determine the role of the carboxy-terminal domain of squalene synthase in the sterol pathway, we used the yeast Saccharomyces cerevisiae with a squalene synthase knockout mutation and expressed squalene synthases originating from fungi, plants, and animals. In contrast to previous observations, all enzymes tested could partially complement the knockout mutation when the genes were weakly expressed. When induced, non-fungal squalene synthases could not complement the knockout mutation and instead led to the accumulation of carboxysterol intermediates, suggesting an interaction between squalene synthase and the downstream sterol C4-decarboxylase. Overexpression of a sterol C4-decarboxylase from any kingdom of life both decreased the accumulation of carboxysterol intermediates and allowed non-fungal squalene synthases to complement the squalene synthase knockout mutation. Using chimeric squalene synthases from each kingdom of life, the motif in the C-terminal domain responsible for preventing this toxicity was mapped to a kingdom-specific 26-amino acid hinge motif adjacent to the catalytic domain. Furthermore, over-expression of the carboxy-terminal domain alone containing a hinge motif from fungi, not from animals or plants, led to growth inhibition of wild-type yeast. Since this hinge region is unique to and highly conserved within each kingdom of life, this data provides evidence for the development of an antifungal therapeutic as well as for tools to develop an understanding of triterpene catalytic activity and identify similar motifs in other biosynthetic pathways

New candidates for multivariate trapdoor functions

We present a new method for building pairs of HFE polynomials of high degree, such that the map constructed with such a pair is easy to invert. The inversion is accomplished using a low degree polynomial of Hamming weight three, which is derived from a special reduction via Hamming weight three polynomials produced by these two HFE polynomials. This allows us to build new candidates for multivariate trapdoor functions in which we use the pair of HFE polynomials to fabricate the core map. We performed the security analysis for the case where the base field is $GF(2)$ and showed that these new trapdoor functions have high degrees of regularity, and therefore they are secure against the direct algebraic attack. We also give theoretical arguments to show that these new trapdoor functions over $GF(2)$ are secure against the MinRank attack as well

Expression patterns of cell wall-modifying genes from banana during fruit ripening and in relationship with finger drop

Few molecular studies have been devoted to the finger drop process that occurs during banana fruit ripening. Recent studies revealed the involvement of changes in the properties of cell wall polysaccharides in the pedicel rupture area. In this study, the expression of cell-wall modifying genes was monitored in peel tissue during post-harvest ripening of Cavendish banana fruit, at median area (control zone) and compared with that in the pedicel rupture area (drop zone). To this end, three pectin methylesterase (PME) and seven xyloglucan endotransglycosylase/hydrolase (XTH) genes were isolated. The accumulation of their mRNAs and those of polygalaturonase, expansin, and pectate lyase genes already isolated from banana were examined. During post-harvest ripening, transcripts of all genes were detected in both zones, but accumulated differentially. MaPME1, MaPG1, and MaXTH4 mRNA levels did not change in either zone. Levels of MaPME3 and MaPG3 mRNAs increased greatly only in the control zone and at the late ripening stages. For other genes, the main molecular changes occurred 1–4 d after ripening induction. MaPME2, MaPEL1, MaPEL2, MaPG4, MaXTH6, MaXTH8, MaXTH9, MaEXP1, MaEXP4, and MaEXP5 accumulated highly in the drop zone, contrary to MaXTH3 and MaXTH5, and MaEXP2 throughout ripening. For MaPG2, MaXET1, and MaXET2 genes, high accumulation in the drop zone was transient. The transcriptional data obtained from all genes examined suggested that finger drop and peel softening involved similar mechanisms. These findings also led to the proposal of a sequence of molecular events leading to finger drop and to suggest some candidates

Investigating bacteroidetes gliding motility

Bacteroidetes gliding motility is a type of surface motility in which rod-shaped bacteria move up to 2 µm s in a corkscrewing motion. Flavobacterium johnsoniae is the primary model organism for the study of Bacteroidetes gliding. SprB is the main adhesin in this organism and moves in a helix along the cell surface. This movement is guided by an underlying track that is anchored to the inner leaflet of the outer membrane. The essential gliding lipoprotein GldJ, which is helically arranged when visualised in fixed cells, is suggested to form this track. However, direct in vivo imaging of GldJ is yet to be achieved. Two currently outstanding questions about Bacteroidetes gliding motility are 1) how adhesion of SprB to the substratum is controlled so that binding only occurs when moving from the leading to the lagging cell pole and 2) how/if the cell discriminate between the poles. In this thesis, a fusion of the HaloTag domain to SprB enabled labelling of SprB with stable and bright dyes. The movement of SprB could then be visualised using single-particle tracking to reveal the underlying track topology. These tracking data suggest that the underlying track is not a single closed loop currently proposed, but rather a complex and potentially dynamic structure that can form multiple loops and cover most of the cell surface. SprB is encoded by the sprB operon that further encodes RemFG, Fjoh_0982, and SprCDF. In this thesis I show that all these components, except fjoh_0982, are required for gliding motility but only sprF are required for SprB helical movement. All the sprB operon components required for gliding are also required for SprB-mediated attachment to glass, indicating that they regulate adhesion of SprB. RemG and SprCD move in a helix reminiscent of the SprB movement pattern. The helical movement does not depend on SprF or SprB, but rather on the SprFhomologous N-terminal domain of SprD. Observations of gliding cells with fluorescently labelled SprC revealed accumulation of SprC near the leading cell pole. This polar accumulation correlated with the direction of movement and was not observed in cells that did not move. Furthermore, a mutant lacking the C-terminal 50 residues of SprD was unable to accumulate SprC at the leading pole. SprB did not show a similar asymmetric distribution in gliding cells. Fluorescence microscopy shows that helically moving sprB operon proteins accumulate at midcell in dividing cells in a GldJ dependent manner. Cross-linking mass spectrometry indicates that GldJ interacts with the sprB operon proteins as well as GldKNO, essential outer membrane components of the type 9 secretion system which is a pre-requisite for Bacteroidetes gliding motility