Clustering of Diverse Multiplex Networks

This dissertation introduces the DIverse MultiPLEx Generalized Dot Product Graph (DIMPLE-GDPG) network model where all layers of the network have the same collection of nodes and follow the Generalized Dot Product Graph (GDPG) model. In addition, all layers can be partitioned into groups such that the layers in the same group are embedded in the same ambient subspace but otherwise all matrices of connection probabilities can be different. In common particular cases, where layers of the network follow the Stochastic Block Model (SBM) and Degree Corrected Block Model (DCBM), this setting implies that the groups of layers have common community structures but all matrices of block connection probabilities can be different. For DCBM, each group can also equip with nodes\u27 specific weights. We refer to this two versions as the DIMPLE model and the DIMPLE-DECOR model. While the DIMPLE-GDPG model generalizes the COmmon Subspace Independent Edge (COSIE) random graph model, the DIMPLE model generalizes a multitude of papers that study multilayer networks with the same community structures in all layers (which include the tensor block model, the checker-board model as well as the Mixture Multilayer Stochastic Block Model (MMLSBM) as particular cases). This dissertation introduces novel algorithms for the recovery of similar groups of layers, for the estimation of the ambient subspaces in the groups of layers in the DIMPLE-GDPG setting, and for the within-layer clustering in the case of the DIMPLE model. We also consider applications of the DIMPLE models to real-life data, and its comparison with the MMLSBM. And the DIMPLE model with its SBM-imposed structures provided better descriptions of the organization of layers than the ones obtained on the basis of the MMLSBM setting

Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function. We also presented a scalable state representation method to model the prediction problem as a Markov decision process (MDP) and use reinforcement learning (RL) to solve the problem. Additionally, we built a dataset called SmallCity based on the crowdsourcing method to conduct the research. As far as we know, this is the first time the IRL approach has been introduced to the urban safety perception and planning field to help experts quantitatively analyze perceptual features. Our results showed that IRL has promising prospects in this field. We will later open-source the crowdsourcing data collection site and the model proposed in this paper.Comment: ACML2022 Camera-ready Versio

Mineralization of 4-chlorophenol and analysis of bacterial community in microbial fuel cells

Abstract4-Chlorophenol (4-CP) was co-metabolically degraded and mineralized with the presence of glucose in microbial fuel cells (MFCs), achieving a degradation rate of 0.58 ± 0.036mg/L-h (7.2 ± 0.5mg/g VSS-h) with an electricity generation of 5.4 ± 0.4W/m3 at an initial 4-CP concentration of 25mg/L. Compared to the open circuit controls, current generation accelerated the removal of 4-CP. Coulombic efficiency decreased from 30.3 ± 2.9% at an initial 4- CP concentration of 5mg/L to 6.3 ± 0.9% at 40mg/L. 4-CP was degraded via the formation of phenol, which was further mineralized. Dominant bacteria most similar to both the exoelectrogenic and electrotrophic uncultured Desulfovibrio, the exoelectrogenic and recalcitrant degrader of uncultured Desulfobulbus, and the exoelectrogenic uncultured Microbacterium were identified in the biofilms. These results demonstrate that 4-CP mineralization using MFCs may be a promising process for remediation of water contaminated with 4-CP as well as for power generation

The similar and different evolutionary trends of MATE family occurred between rice and Arabidopsis thaliana

Expression profiles of Arabidopsis MATE genes under various stress. (TIFF 5235 kb

Genome-scale identification of Soybean BURP domain-containing genes and their expression under stress treatments

<p>Abstract</p> <p>Background</p> <p>Multiple proteins containing BURP domain have been identified in many different plant species, but not in any other organisms. To date, the molecular function of the BURP domain is still unknown, and no systematic analysis and expression profiling of the gene family in soybean (<it>Glycine max</it>) has been reported.</p> <p>Results</p> <p>In this study, multiple bioinformatics approaches were employed to identify all the members of BURP family genes in soybean. A total of 23 BURP gene types were identified. These genes had diverse structures and were distributed on chromosome 1, 2, 4, 6, 7, 8, 11, 12, 13, 14, and 18. Phylogenetic analysis suggested that these BURP family genes could be classified into 5 subfamilies, and one of which defines a new subfamily, BURPV. Quantitative real-time PCR (qRT-PCR) analysis of transcript levels showed that 15 of the 23 genes had no expression specificity; 7 of them were specifically expressed in some of the tissues; and one of them was not expressed in any of the tissues or organs studied. The results of stress treatments showed that 17 of the 23 identified BURP family genes responded to at least one of the three stress treatments; 6 of them were not influenced by stress treatments even though a stress related <it>cis</it>-element was identified in the promoter region. No stress related <it>cis</it>-elements were found in promoter region of any BURPV member. However, qRT-PCR results indicated that all members from BURPV responded to at least one of the three stress treatments. More significantly, the members from the RD22-like subfamily showed no tissue-specific expression and they all responded to each of the three stress treatments.</p> <p>Conclusions</p> <p>We have identified and classified all the BURP domain-containing genes in soybean. Their expression patterns in different tissues and under different stress treatments were detected using qRT-PCR. 15 out of 23 BURP genes in soybean had no tissue-specific expression, while 17 out of them were stress-responsive. The data provided an insight into the evolution of the gene family and suggested that many BURP family genes may be important for plants responding to stress conditions.</p

Effects of Water Content and Particle Size on Yield and Reactivity of Lignite Chars Derived from Pyrolysis and Gasification

Water inside coal particles could potentially enhance the interior char–steam reactions during pyrolysis and gasification. This study aims to examine the effects of water contents on the char conversion during the pyrolysis and gasification of Shengli lignite. The ex-situ reactivities of chars were further analyzed by a thermo gravimetric analyzer (TGA). Under the pyrolysis condition, the increase in water contents has monotonically decreased the char yields only when the coal particles were small (\u3c75 μm). In contrast, the water in only large coal particles (0.9–2.0 mm) has clearly favored the increase in char conversion during the gasification condition where 50% steam in argon was used as external reaction atmosphere. The waved reactivity curves for the subsequent char–air reactions were resulted from the nature of heterogeneity of char structure. Compared to the large particles, the less interior char–steam reactions for the small particles have created more differential char structure which showed two different stages when reacting with air at the low temperature in TGA

Comprehensive analysis of PRPF19 immune infiltrates, DNA methylation, senescence-associated secretory phenotype and ceRNA network in bladder cancer

BackgroundPre-mRNA processing factor 19 (PRPF19) is an E3 ligase that plays a crucial role in repairing tumor-damaged cells and promoting cell survival. However, the predictive value and biological function of PRPF19 in bladder urothelial carcinoma (BLCA) require further investigation.MethodsIn this study, we utilized transcriptomic data and bladder cancer tissue microarrays to identify the high expression of PRPF19 in BLCA, suggesting its potential as a prognostic biomarker. To gain a better understanding of the role of PRPF19 in the immune microenvironment of BLCA, we performed single cell analysis and employed the LASSO method. Additionally, we examined the methylation profiles of PRPF19 using the SMART website. Our investigation confirmed the correlation between PRPF19 and BLCA cell senescence and stemness. Furthermore, we constructed a PRPF19-miR-125a-5p-LINC02693-MIR4435-2HG ceRNA network using the ENCORI and miRWALK databases.ResultsOur comprehensive analysis reveals that PRPF19 can serve as a prognostic marker for BLCA and is significantly associated with various immune-infiltrating cells in BLCA. Moreover, our findings suggest that PRPF19 influences cellular senescence through the regulation of stemness. Finally, we developed a ceRNA network that has the potential to predict the prognosis of BLCA patients.ConclusionWe confirmed the prognostic value and multiple biological functions of PRPF19 in BLCA. Furthermore, the specific ceRNA network can be used as a potential therapeutic target for BLCA

Simulation study of BESIII with stitched CMOS pixel detector using ACTS

Reconstruction of tracks of charged particles with high precision is very crucial for HEP experiments to achieve their physics goals. As the tracking detector of BESIII experiment, the BESIII drift chamber has suffered from aging effects resulting in degraded tracking performance after operation for about 15 years. To preserve and enhance the tracking performance of BESIII, one of the proposals is to add one layer of thin CMOS pixel sensor in cylindrical shape based on the state-of-the-art stitching technology, between the beam pipe and the drift chamber. The improvement of tracking performance of BESIII with such an additional pixel detector compared to that with only the existing drift chamber is studied using the modern common tracking software ACTS, which provides a set of detector-agnostic and highly performant tracking algorithms that have demonstrated promising performance for a few high energy physics and nuclear physics experiments