Network inference with statistical guarantees

Networks arise in a huge variety of real data scenarios: starting from social networks like Facebook or user product networks in recommendation systems to protein-protein interaction networks in biological systems, etc. In this thesis, we focus on developing fast and provable algorithms for some network inference problems. In the first part, we are interested in overlapping community detection problem under the popular Mixed Membership Stochastic Blockmodel (MMSB). We firstly establish sufficient conditions for the symmetric non-negative matrix factorization optimization to have a unique solution under MMSB, and propose a computationally efficient algorithm called GeoNMF that is provably optimal and hence consistent for a broad parameter regime. Then using the inherent geometry of MMSB, we link the inference of overlapping communities to the problem of finding corners in a noisy rotated and scaled simplex, for which consistent algorithms exist. We use this as a building block for our algorithm to infer the community memberships of each node, and provide uniform rates of convergence for the inferred community membership vector of each node in the network. As a byproduct of our analysis, we derive sharp row-wise eigenvector deviation bounds, and provide a cleaning step that improves the performance drastically for sparse networks. Our results hold over a broad parameter regime where the average degree only grows poly-logarithmically with the number of nodes. Using experiments with simulated and real datasets, we show that our method achieves better error with lower variability over competing methods, and processes real world networks of up to 100,000 nodes within tens of seconds. For the second part, we go beyond MMSB for overlapping community detection. Notice that many existing overlapping clustering methods model each person (or word, or book) as a non-negative weighted combination of "exemplars" who belong solely to one community, with some small noise. Geometrically, each person is a point on a cone whose corners are these exemplars. This basic form encompasses the widely used MMSB of networks and its degree corrected variants, as well as topic models such as LDA. We show that a simple one-class SVM yields provably consistent parameter inference for all such models, and scales to large datasets. Experimental results on several simulated and real datasets show our algorithm (called SVM-cone) is both accurate and scalable. The final contribution of this thesis is novel nonparametric methods for network covariate estimation. Networks with node covariates are commonplace: for example, people in a social network have interests, or product preferences, etc. If we know the covariates for some nodes, can we infer them for the remaining nodes? We provide two provably consistent methods to solve this problem. For "low-rank" latent variable models, we develop SVD-RBF, which uses the top principal components of the network in a non-parametric regression. For general models, we present CN-VEC, which constructs a similarity measure between two nodes, based on the patterns of their 2-hop neighborhoods. CN-VEC then predicts node covariates by averaging the covariates of the top-k most similar nodes using this measure. SVD-RBF is consistent for low-rank models when the average degree grows with Õ(log n), while CN-VEC is consistent for a wide range of models when the degree grows with Õ(n¹ [superscript /] ³). To our knowledge, CN-VEC is the first provably consistent method for this problem under general models. Both methods are fast, and CN-VEC is also parameter-free. Experiments on 4 simulated network models and 3 real-world datasets show the effectiveness of our algorithms compared to the state of the art.Computer Science

Preconditioned MSCs Alleviate Cerebral Ischemia-Reperfusion Injury in Rats by Improving the Neurological Function and the Inhibition of Apoptosis

Mesenchymal stem cells (MSCs) have great application prospects in the treatment of ischemic injury. However, their long-time cultivation before transplantation and poor survival after transplantation greatly limit the therapeutic effect and applications. This study aimed to investigate whether MSCs under the ischemic microenvironment could improve their survival and better alleviate cerebral ischemic injury. Firstly, we used ischemic brain tissue to culture MSCs and evaluated the functional changes of MSCs. Then a middle cerebral artery occlusion (MCAO) model was induced in rats, and the pretreated MSCs were injected via the tail vein. The adhesive removal test, rotarod test, modified neurological severity score, and pathological analyses were applied to assess the rats’ neurological function. Then the expression of neuron and apoptosis related markers was detected. The results indicated that ischemic brain tissue pretreated MSCs promoted the proliferation and the release of the growth factors of MSCs. Meanwhile, in MCAO model rats, transplantation of pretreated MSCs enhanced the neurogenesis, attenuated behavioral changes, reduced infarct size, and inhibited apoptosis. The expression of B-cell lymphoma-2 (Bcl-2), brain-derived neurotrophic factor (BDNF), glial fibrillary acidic protein (GFAP), NF-L, and NeuN were increased, while BCL2-Associated X (Bax) and Caspase-3 decreased. Our results suggest that MSCs pretreatment with stroke brain tissue could be an effective strategy in treating cerebral ischemic injury

Deformation characteristics of a large landslide reactivated by human activity in Wanyuan city, Sichuan Province, China

With the rapid urbanization, an increasing number of landslides have been induced by human activities. In this study, a typical human-induced landslide known as the Maobazi landslide, which was triggered by foundation pit excavation in Sichuan Province, China, was analyzed. An emergency investigation was carried out to detect the basic deformation characteristics, followed by implementations of multiple monitoring schemes and emergency control measures to monitor and control reactivated deposits. The reactivated deposits depicted rapid deformations with a maximum deformation exceeding 140 mm from July to September before the emergency control measures were completed. The reactivated deposits gradually settled and were finally controlled in 2019. The results showed that the 2019 Maobazi landslide was a large; reactivated landslide with a scale reached to 520 Mm3, which could result in catastrophic consequences if it slipped down to nearby residential areas

Facet joint degeneration—An initial procedure of the cervical spine degeneration

Abstract Objective This study aims to emphasize the initiating role of facet joint (FJ) degeneration in the process of cervical spine degeneration induced by tangential load, and we further validate it in a novel cervical spine degeneration animal model. Methods The characteristics of cervical degeneration in patients of different ages were summarized through case collection. In the rat models, Hematoxylin–Eosin, Safranin O staining, and micro‐computed tomography were used to show the histopathological changes and bone fiber structure of FJ and the height of intervertebral disc (IVD) space. The ingrowth of nociceptive sensory nerve fibers was observed by immunofluorescence staining. Results FJ degeneration without IVDs degeneration was more common in people with cervical spondylosis in young patients. The obvious degeneration phenotypes of the FJs preceded the IVDs at the same cervical segment in our animal model. The SP+ and CGRP+ sensory nerve fibers were observed in the articular subchondral bone of degenerated FJs and porous endplates of degenerated IVDs. Conclusion The FJ degeneration may act as the major contributor to cervical spine degeneration in young people. The dysfunction of functional unit of spine, not a certain part of IVD tissue, results in the occurrence of cervical degeneration and neck pain

Restoring the dampened expression of the core clock molecule BMAL1 protects against compression-induced intervertebral disc degeneration

The circadian clock participates in maintaining homeostasis in peripheral tissues, including intervertebral discs (IVDs). Abnormal mechanical loading is a known risk factor for intervertebral disc degeneration (IDD). Based on the rhythmic daily loading pattern of rest and activity, we hypothesized that abnormal mechanical loading could dampen the IVD clock, contributing to IDD. Here, we investigated the effects of abnormal loading on the IVD clock and aimed to inhibit compression-induced IDD by targeting the core clock molecule brain and muscle Arnt-like protein-1 (BMAL1). In this study, we showed that BMAL1 KO mice exhibit radiographic features similar to those of human IDD and that BMAL1 expression was negatively correlated with IDD severity by systematic analysis based on 149 human IVD samples. The intrinsic circadian clock in the IVD was dampened by excessive loading, and BMAL1 overexpression by lentivirus attenuated compression-induced IDD. Inhibition of the RhoA/ROCK pathway by Y-27632 or melatonin attenuated the compression-induced decrease in BMAL1 expression. Finally, the two drugs partially restored BMAL1 expression and alleviated IDD in a diurnal compression model. Our results first show that excessive loading dampens the circadian clock of nucleus pulposus tissues via the RhoA/ROCK pathway, the inhibition of which potentially protects against compression-induced IDD by preserving BMAL1 expression. These findings underline the importance of the circadian clock for IVD homeostasis and provide a potentially effective therapeutic strategy for IDD