Dimensionality reduction using parallel ICA and its implementation on FPGA in hyperspectral image analysis

Hyperspectral images, although providing abundant information of the object, also bring high computational burden to data processing. This thesis studies the challenging problem of dimensionality reduction in Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension: band selection and feature extraction. This thesis presents a band selection technique based on Independent Component Analysis (ICA), an unsupervised signal separation algorithm. Given only the observations of hyperspectral images, the ICA –based band selection picks the independent bands which contain most of the spectral information of the original images. Due to the high volume of hyperspectral images, ICA -based band selection is a time consuming process. This thesis develops a parallel ICA algorithm which divides the decorrelation process into internal decorrelation and external decorrelation such that computation burden can be distributed from single processor to multiple processors, and the ICA process can be run in a parallel mode. Hardware implementation is always a faster and real -time solution to HSI analysis. Until now, there are few hardware designs for ICA -related processes. This thesis synthesizes the parallel ICA -based band selection on Field Programmable Gate Array (FPGA), which is the best choice for moderate designs and fast implementations. Compared to other design syntheses, the synthesis present in this thesis develops three ICA re-configurable components for the purpose of reusability. In addition, this thesis demonstrates the relationship between the design and the capacity utilization of a single FPGA, then discusses the features of High Performance Reconfigurable Computing (HPRC) to accomodate large capacity and design requirements. Experiments are conducted on three data sets obtained from different sources. Experimental results show the effectiveness of the proposed ICA -based band selection, parallel ICA and its synthesis on FPGA

A Pre-trained Data Deduplication Model based on Active Learning

In the era of big data, the issue of data quality has become increasingly prominent. One of the main challenges is the problem of duplicate data, which can arise from repeated entry or the merging of multiple data sources. These "dirty data" problems can significantly limit the effective application of big data. To address the issue of data deduplication, we propose a pre-trained deduplication model based on active learning, which is the first work that utilizes active learning to address the problem of deduplication at the semantic level. The model is built on a pre-trained Transformer and fine-tuned to solve the deduplication problem as a sequence to classification task, which firstly integrate the transformer with active learning into an end-to-end architecture to select the most valuable data for deduplication model training, and also firstly employ the R-Drop method to perform data augmentation on each round of labeled data, which can reduce the cost of manual labeling and improve the model's performance. Experimental results demonstrate that our proposed model outperforms previous state-of-the-art (SOTA) for deduplicated data identification, achieving up to a 28% improvement in Recall score on benchmark datasets

New Broad-Spectrum Viral Fusion Inhibitors Act by Deleterious Effect on the Viral Membrane through the Production Singlet Oxygen Molecules

Chitosan oligosaccharides (COS), the degraded products of chitosan, have been demonstrated to have versatile biological functions. In primary studies, it has displayed significant adjuvant effects when mixed with other vaccines. In this study, chitosan oligosaccharides with different deacetylation degrees were prepared and conjugated to porcine circovirus type 2 (PCV2) subunit vaccine to enhance its immunogenicity. The vaccine conjugates were designed by the covalent linkage of COSs to PCV2 molecules and administered to BALB/c mice three times at two-week intervals. The results indicate that, as compared to the PCV2 group, COS-PCV2 conjugates remarkably enhanced both humoral and cellular immunity against PCV2 by promoting lymphocyte proliferation and initiating a mixed T-helper 1 (Th1)/T-helper 2 (Th2) response, including raised levels of PCV2-specific antibodies and an increased production of inflammatory cytokines. Noticeably, with the increasing deacetylation degree, the stronger immune responses to PCV2 were observed in the groups with COS-PCV2 vaccination. In comparison with NACOS (chitin oligosaccharides)-PCV2 and LCOS (chitosan oligosaccharides with low deacetylation degree)-PCV2, HCOS (chitosan oligosaccharides with high deacetylation degree)-PCV2 showed the highest adjuvant effect, even comparable to that of PCV2/ISA206 (a commercialized adjuvant) group. In summary, COS conjugation might be a viable strategy to enhance the immune response to PCV2 subunit vaccine, and the adjuvant effect was positively correlated with the deacetylation degree of COS.</p

Substrate-blind photonic integration based on high-index glass materials

Conventional photonic integration technologies are inevitably substrate-dependent, as different substrate platforms stipulate vastly different device fabrication methods and processing compatibility requirements. Here we capitalize on the unique monolithic integration capacity of composition-engineered non-silicate glass materials (amorphous chalcogenides and transition metal oxides) to enable multifunctional, multi-layer photonic integration on virtually any technically important substrate platforms. We show that high-index glass film deposition and device fabrication can be performed at low temperatures ( < 250 °C) without compromising their low loss characteristics, and is thus fully compatible with monolithic integration on a broad range of substrates including semiconductors, plastics, textiles, and metals. Application of the technology is highlighted through three examples: demonstration of high-performance mid-IR photonic sensors on fluoride crystals, direct fabrication of photonic structures on graphene, and 3-D photonic integration on flexible plastic substrates.National Science Foundation (U.S.) (Award 1200406

Auto-Ubiquitination-Induced Degradation of MALT1-API2 Prevents BCL10 Destabilization in t(11;18)(q21;q21)-Positive MALT Lymphoma

BACKGROUND: The translocation t(11;18)(q21;q21) is the most frequent chromosomal aberration associated with MALT lymphoma and results in constitutive NF-kappaB activity via the expression of an API2-MALT1 fusion protein. The properties of the reciprocal MALT1-API2 were never investigated as it was reported to be rarely transcribed. PRINCIPAL FINDINGS: Our data indicate the presence of MALT1-API2 transcripts in the majority of t(11;18)(q21;q21)-positive MALT lymphomas. Based on the breakpoints in the MALT1 and API2 gene, the MALT1-API2 protein contains the death domain and one or both immunoglobulin-like domains of MALT1 (approximately 90% of cases)--mediating the possible interaction with BCL10--fused to the RING domain of API2. Here we show that this RING domain enables MALT1-API2 to function as an E3 ubiquitin ligase for BCL10, inducing its ubiquitination and proteasomal degradation in vitro. Expression of MALT1-API2 transcripts in t(11;18)(q21;q21)-positive MALT lymphomas was however not associated with a reduction of BCL10 protein levels. CONCLUSION: As we observed MALT1-API2 to be an efficient target of its own E3 ubiquitin ligase activity, our data suggest that this inherent instability of MALT1-API2 prevents its accumulation and renders a potential effect on MALT lymphoma development via destabilization of BCL10 unlikely