Data Mining Mycobacterium tuberculosis

Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. In Mycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening of M. tuberculosis targets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined with t-tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct the M. tuberculosis pathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection with M. tuberculosis

Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers

Imaging through diffusive media is a challenging problem, where the existing solutions heavily rely on digital computers to reconstruct distorted images. We provide a detailed analysis of a computer-free, all-optical imaging method for seeing through random, unknown phase diffusers using diffractive neural networks, covering different deep learning-based training strategies. By analyzing various diffractive networks designed to image through random diffusers with different correlation lengths, a trade-off between the image reconstruction fidelity and distortion reduction capability of the diffractive network was observed. During its training, random diffusers with a range of correlation lengths were used to improve the diffractive network's generalization performance. Increasing the number of random diffusers used in each epoch reduced the overfitting of the diffractive network's imaging performance to known diffusers. We also demonstrated that the use of additional diffractive layers improved the generalization capability to see through new, random diffusers. Finally, we introduced deliberate misalignments in training to 'vaccinate' the network against random layer-to-layer shifts that might arise due to the imperfect assembly of the diffractive networks. These analyses provide a comprehensive guide in designing diffractive networks to see through random diffusers, which might profoundly impact many fields, such as biomedical imaging, atmospheric physics, and autonomous driving.Comment: 42 Pages, 9 Figure

The complexity of spanning tree problems involving graphical indices

We consider the computational complexity of spanning tree problems involving the graphical function-index. This index was recently introduced by Li and Peng as a unification of a long list of chemical and topological indices. We present a number of unified approaches to determine the NP-completeness and APX-completeness of maximum and minimum spanning tree problems involving this index. We give many examples of well-studied topological indices for which the associated complexity questions are covered by our results.</p

Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network

Quantitative phase imaging (QPI) is a label-free computational imaging technique used in various fields, including biology and medical research. Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction. Here, we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane, all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown, random phase diffusers. This QPI diffractive network is composed of successive diffractive layers, axially spanning in total ~70 wavelengths; unlike existing digital image reconstruction and phase retrieval methods, it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation. This all-optical diffractive processor can provide a low-power, high frame rate and compact alternative for quantitative imaging of phase objects through random, unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing. The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope, performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.Comment: 27 Pages, 7 Figure

Adaptive feature selection based on the most informative graph-based features

In this paper, we propose a novel method to adaptively select the most informative and least redundant feature subset, which has strong discriminating power with respect to the target label. Unlike most traditional methods using vectorial features, our proposed approach is based on graph-based features and thus incorporates the relationships between feature samples into the feature selection process. To efficiently encapsulate the main characteristics of the graph-based features, we probe each graph structure using the steady state random walk and compute a probability distribution of the walk visiting the vertices. Furthermore, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature, through the Jensen-Shannon divergence measure between the probability distributions from the random walk on different graphs. By solving a quadratic programming problem, we use the new measure to automatically locate the subset of the most informative features, that have both low redundancy and strong discriminating power. Unlike most existing state-of-the-art feature selection methods, the proposed information theoretic feature selection method can accommodate both continuous and discrete target features. Experiments on the problem of P2P lending platforms in China demonstrate the effectiveness of the proposed method

Cyanidin-3-O-Glucoside Supplement Improves Sperm Quality and Spermatogenesis in a Mice Model of Ulcerative Colitis

Impaired fertility and low sperm quality are the global health problem with high attention. It has been noted that inflammation may impact fertility by affecting testicular spermatogenesis. Cyanidin-3-O-glucoside is a natural functional pigment with various health benefits. Nevertheless, studies on the mechanism by which C3G protects male reproduction in mice with ulcerative colitis remain scarce. The purpose of this study is to illustrate the potential mechanism of C3G for improving impaired fertility caused by colitis. A DSS-induced colitis model was applied to assess the effects of sperm quality with colitis and the health benefit role of C3G. Results indicated that C3G-treated mice exhibited higher body weight, longer colon length, less crypt damage and focal inflammation infiltration. Being consistent with that, low sperm count, low testis weight, high inflammation levels and abnormal thickness of seminiferous epithelium also observed in the DSS group were significantly recovered upon C3G treatment. These findings suggested that colitis has a close link to impaired fertility. Further analysis found that C3G could significantly suppress the inflammatory mediators in serum. Results conjointly indicated that C3G might improve the impaired fertility of mice with colitis by inhibiting inflammatory cytokines through the blood–testis barrier. C3G could be a promising daily supplement for ameliorating impaired fertility caused by colitis

RTN: Reparameterized Ternary Network

To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed-up, memory saving with quantized activation and weights. We first bring up three omitted issues in extremely low-bit networks: the squashing range of quantized values; the gradient vanishing during backpropagation and the unexploited hardware acceleration of ternary networks. By reparameterizing quantized activation and weights vector with full precision scale and offset for fixed ternary vector, we decouple the range and magnitude from the direction to extenuate the three issues. Learnable scale and offset can automatically adjust the range of quantized values and sparsity without gradient vanishing. A novel encoding and computation pat-tern are designed to support efficient computing for our reparameterized ternary network (RTN). Experiments on ResNet-18 for ImageNet demonstrate that the proposed RTN finds a much better efficiency between bitwidth and accuracy, and achieves up to 26.76% relative accuracy improvement compared with state-of-the-art methods. Moreover, we validate the proposed computation pattern on Field Programmable Gate Arrays (FPGA), and it brings 46.46x and 89.17x savings on power and area respectively compared with the full precision convolution.Comment: To appear at AAAI-2