PREDICTION METHODS IN LARGE-SCALE NETWORK ANALYSIS FOR NEUROIMAGING DATA

Brain functional connectivity data are critical for understanding human brain structure and cognitive disease diagnostics. The underlying genetic architecture behind brain functional connectivity is a critical topic in medical studies, which helps unveil the linkages between genetic variants and brain activity and further understand cognitive diseases and brain disorders. The rapid emergence of large scale imaging studies provides researchers with more opportunities to discover the connections between brain system and genes. However, existing methods in imaging genetics are not sufficient in dealing with the high-dimensional data with complex structure, thus limiting the discovery of biological foundation of neuro-development. Therefore, we developed novel statistical approaches for efficient analysis of imaging genetic data.In the first project, we developed a matrix decomposition based method for denoising and recovering the structure of the subject-wise network based on the assumption of factor model. We decompose the subject networks into two parts: a common low-rank basis and subject-specific loadings on the basis. A matrix L0 penalty problem was formulated to accelerate the algorithm. Meanwhile, to avoid iterative computation of high dimensional matrix, we will select a relatively lower dimension basis in the first step, which is a coarse estimator, and then do a fine-tuning in the second step based on the results in step one. In the simulation study, it showed that our approach outperformed other existing approaches in terms of recovering accuracy and computing speed. We also proved that under mild conditions, the algorithm converges fast in an exponential rate. In the second project, we proposed a matrix regression approach for imaging genetic studies. The proposed regression model includes two steps. In the first step, a marginal screening procedure was used to study the univariate associations between genetic variants (SNPs) and imaging phenotype. The theoretical p-value for the marginal screening step was derived using random matrix theories, and important SNPs were selected based on the univariate associations using knock-off. In the second step, a multivariate regression model with all the important SNPs selected as covariates were fitted, and a penalized optimization problem was solved using Nestrov methods. We studied the theoretical properties of the proposed two-stage algorithm thoroughly and simulation studies supported the efficiency and consistency of the proposed method.In the third project, we established a missing data imputation framework to address the issue of missing image modality in real data. The missingness of some imaging modality is common in real imaging data, which may undermine the statistical power in the prediction and inference. However, inaccurate imputation of the missing modality may lead to bias in prediction. Therefore, we thoroughly studied the performance of imputation approaches, including LASSO and ridge models, under different conditions, and concluded the optimal choice of imputation options under the different settings.Doctor of Philosoph

Atomically phase-matched second-harmonic generation in a 2D crystal.

Second-harmonic generation (SHG) has found extensive applications from hand-held laser pointers to spectroscopic and microscopic techniques. Recently, some cleavable van der Waals (vdW) crystals have shown SHG arising from a single atomic layer, where the SH light elucidated important information such as the grain boundaries and electronic structure in these ultra-thin materials. However, despite the inversion asymmetry of the single layer, the typical crystal stacking restores inversion symmetry for even numbers of layers leading to an oscillatory SH response, drastically reducing the applicability of vdW crystals such as molybdenum disulfide (MoS2). Here, we probe the SHG generated from the noncentrosymmetric 3R crystal phase of MoS2. We experimentally observed quadratic dependence of second-harmonic intensity on layer number as a result of atomically phase-matched nonlinear dipoles in layers of the 3R crystal that constructively interfere. By studying the layer evolution of the A and B excitonic transitions in 3R-MoS2 using SHG spectroscopy, we also found distinct electronic structure differences arising from the crystal structure and the dramatic effect of symmetry and layer stacking on the nonlinear properties of these atomic crystals. The constructive nature of the SHG in this 2D crystal provides a platform to reliably develop atomically flat and controllably thin nonlinear media

The combination approach of SVM and ECOC for powerful identification and classification of transcription factor

<p>Abstract</p> <p>Background</p> <p>Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.</p> <p>Results</p> <p>The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL).</p> <p>Conclusion</p> <p>The SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.</p

Simulation and experimental on the quick-freezing of diced mango by dry ice spray

In order to improve the quality of quick-frozen diced mango, a cylindrical quick-frozen container with dry ice spray is designed, the temperature field and velocity field of diced mango sprayed by dry ice in quick-freezing tank are simulated by COMSOL Multiphysics. The effects of different inlet velocities (0.15, 0.20, 0.25, 0.30, 0.35 and 0.40m/s), on the quick-freezing process of diced mango are studied. The results show that with the increase of the inlet velocity of dry ice, the time for diced mango to meet the requirements of quick freezing is continuously shortened, and the outlet solid fraction fluctuates within a certain range. When the inlet velocity is 0.25m/s, the inlet radius is 15mm and the size of diced mango is 10mm, the quick-freezing effect is the best. By the experimental verification, the average errors of surface temperature and core temperature of diced mango to meet the requirements of quick freezing are 3.9% and 3.8% respectively. The results lay a foundation for the popularization and application of dry ice spray quick frozen diced mango

ZeroWaste Dataset: Towards Deformable Object Segmentation in Extreme Clutter

Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Automated waste detection has great potential to enable more efficient, reliable, and safe waste sorting practices, but it requires label-efficient detection of deformable objects in extremely cluttered scenes. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over 1800 fully segmented video frames collected from a real waste sorting plant along with waste material labels for training and evaluation of the segmentation methods, as well as over 6000 unlabeled frames that can be further used for semi-supervised and self-supervised learning techniques, as well as frames of the conveyor belt before and after the sorting process, comprising a novel setup that can be used for weakly-supervised segmentation. Our experimental results demonstrate that state-of-the-art segmentation methods struggle to correctly detect and classify target objects which suggests the challenging nature of our proposed real-world task of fine-grained object detection in cluttered scenes. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu.edu/zerowaste/