Multi-omics integration to understand the immune system

To establish a better understanding of how inter-individual variability relates to the susceptibility to immune-mediated diseases, and thereby gain a better prioritization of new therapeutic targets, there is an urgent need to characterize the inter-individual variation of immune responses in the human population and to identify the impact of genetic and non-genetic factors on immune variation. To this end, this thesis is divided into two major sections with different focus. The first section investigates genetic, environmental, and metabolic determinants of immune function in both healthy and disease cohorts. In this section, we investigated immune dominators in health and diseases. We applied researches with the Human Functional Genomics Project (HFGP), which aims to systematically and comprehensively study inter-individual variation of human immune parameters and function by combining multi-omics data with deep immune phenotyping in health and diseases.The second section studies the transcriptional responses in immune-mediated diseases. In this section, we presents researches on transcriptome level in infectious disease and autoimmune diseases context. By performing transcriptional studies at both “bulk” and single-cell level, we improved our understanding of the genes and pathways altered in the infectious and autoimmune disease context

q-Analogues of π\pi-Series by Applying Carlitz Inversions to q-Pfaff-Saalschutz Theorem

By applying multiplicate forms of the Carlitz inverse series relations to the $q$-Pfaff-Saalschtz summation theorem, we establish twenty five nonterminating $q$-series identities with several of them serving as $q$-analogues of infinite series expressions for $\pi$ and $1/\pi$, including some typical ones discovered by Ramanujan (1914) and Guillera

Moments on Catalan numbers

AbstractBy combining inverse series relations with binomial convolutions and telescoping method, moments of Catalan numbers are evaluated, which resolves a problem recently proposed by Gutiérrez et al. [J.M. Gutiérrez, M.A. Hernández, P.J. Miana, N. Romero, New identities in the Catalan triangle, J. Math. Anal. Appl. 341 (1) (2008) 52–61]

Dixon’s F23(1)-series and identities involving harmonic numbers and the Riemann zeta function

AbstractDixon’s classical summation theorem on F23(1)-series is reformulated as an equation of formal power series in an appropriate variable x. Then by extracting the coefficients of xm, we establish a general formula involving harmonic numbers and the Riemann zeta function. Several interesting identities are exemplified as consequences, including one of the hardest challenging identities conjectured by Weideman (2003)

Coordinate-based Neural Network for Fourier Phase Retrieval

Fourier phase retrieval is essential for high-definition imaging of nanoscale structures across diverse fields, notably coherent diffraction imaging. This study presents the Single impliCit neurAl Network (SCAN), a tool built upon coordinate neural networks meticulously designed for enhanced phase retrieval performance. Remedying the drawbacks of conventional iterative methods which are easiliy trapped into local minimum solutions and sensitive to noise, SCAN adeptly connects object coordinates to their amplitude and phase within a unified network in an unsupervised manner. While many existing methods primarily use Fourier magnitude in their loss function, our approach incorporates both the predicted magnitude and phase, enhancing retrieval accuracy. Comprehensive tests validate SCAN's superiority over traditional and other deep learning models regarding accuracy and noise robustness. We also demonstrate that SCAN excels in the ptychography setting

High-Performance Multi-Mode Ptychography Reconstruction on Distributed GPUs

Ptychography is an emerging imaging technique that is able to provide wavelength-limited spatial resolution from specimen with extended lateral dimensions. As a scanning microscopy method, a typical two-dimensional image requires a number of data frames. As a diffraction-based imaging technique, the real-space image has to be recovered through iterative reconstruction algorithms. Due to these two inherent aspects, a ptychographic reconstruction is generally a computation-intensive and time-consuming process, which limits the throughput of this method. We report an accelerated version of the multi-mode difference map algorithm for ptychography reconstruction using multiple distributed GPUs. This approach leverages available scientific computing packages in Python, including mpi4py and PyCUDA, with the core computation functions implemented in CUDA C. We find that interestingly even with MPI collective communications, the weak scaling in the number of GPU nodes can still remain nearly constant. Most importantly, for realistic diffraction measurements, we observe a speedup ranging from a factor of $10$ to $10^3$ depending on the data size, which reduces the reconstruction time remarkably from hours to typically about 1 minute and is thus critical for real-time data processing and visualization.Comment: work presented in NYSDS 201

Multi-Omics Approaches in Immunological Research

The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases