Graph Regularized Tensor Sparse Coding for Image Representation

Sparse coding (SC) is an unsupervised learning scheme that has received an increasing amount of interests in recent years. However, conventional SC vectorizes the input images, which destructs the intrinsic spatial structures of the images. In this paper, we propose a novel graph regularized tensor sparse coding (GTSC) for image representation. GTSC preserves the local proximity of elementary structures in the image by adopting the newly proposed tubal-tensor representation. Simultaneously, it considers the intrinsic geometric properties by imposing graph regularization that has been successfully applied to uncover the geometric distribution for the image data. Moreover, the returned sparse representations by GTSC have better physical explanations as the key operation (i.e., circular convolution) in the tubal-tensor model preserves the shifting invariance property. Experimental results on image clustering demonstrate the effectiveness of the proposed scheme

Exploratory Research on an Affective e-Learning Model

Abstract. This paper explores how emotion evolves during the learning process with the longer term aim of developing learning systems that are able to recognize and respond appropriately to emotions exhibited by learners. We undertook this research by designing and building an experimental prototype of an emotion aware learning system conducting experiments and studying the relationship between emotion and learning. We report on our initial results which not only indicate there is a usable relationship between affect and learning, but by using the emotion states in Russell's affective model, we have been able to make some significant progress towards experimental validation of Kort's learning spiral model, which has not been empirically validated to-date

Multi-parameter comprehensive early warning of coal pillar rockburst risk based on DNN

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard

TGFβƒ1 Promotes Gemcitabine Resistance Through Regulating the LncRNA-LET/NF90/miR-145 Signaling Axis in Bladder Cancer

High tumor recurrence is frequently observed in patients with urinary bladder cancers (UBCs), with the need for biomarkers of prognosis and drug response. Chemoresistance and subsequent recurrence of cancers are driven by a subpopulation of tumor initiating cells, namely cancer stem-like cells (CSCs). However, the underlying molecular mechanism in chemotherapy-induced CSCs enrichment remains largely unclear. In this study, we found that during gemcitabine treatment lncRNA-Low Expression in Tumor (lncRNA-LET) was downregulated in chemoresistant UBC, accompanied with the enrichment of CSC population. Knockdown of lncRNA-LET increased UBC cell stemness, whereas forced expression of lncRNA-LET delayed gemcitabine-induced tumor recurrence. Furthermore, lncRNA-LET was directly repressed by gemcitabine treatment-induced overactivation of TGFβ/SMAD signaling through SMAD binding element (SBE) in the lncRNA-LET promoter. Consequently, reduced lncRNA-LET increased the NF90 protein stability, which in turn repressed biogenesis of miR-145 and subsequently resulted in accumulation of CSCs evidenced by the elevated levels of stemness markers HMGA2 and KLF4. Treatment of gemcitabine resistant xenografts with LY2157299, a clinically relevant specific inhibitor of TGFβRI, sensitized them to gemcitabine and significantly reduced tumorigenecity in vivo. Notably, overexpression of TGFβ1, combined with decreased levels of lncRNA-LET and miR-145 predicted poor prognosis in UBC patients. Collectively, we proved that the dysregulated lncRNA-LET/NF90/miR-145 axis by gemcitabine-induced TGFβ1 promotes UBC chemoresistance through enhancing cancer cell stemness. The combined changes in TGFβ1/lncRNA-LET/miR-145 provide novel molecular prognostic markers in UBC outcome. Therefore, targeting this axis could be a promising therapeutic approach in treating UBC patients

Study on diversity, nitrogen-fixing capacity, and heavy metal tolerance of culturable Pongamia pinnata rhizobia in the vanadium-titanium magnetite tailings

IntroductionThe diversity, nitrogen-fixing capacity and heavy metal tolerance of culturable rhizobia in symbiotic relationship with Pongamia pinnata surviving in vanadium (V) - titanium (Ti) magnetite (VTM) tailings is still unknown, and the rhizobia isolates from the extreme barren VTM tailings contaminated with a variety of metals would provide available rhizobia resources for bioremediation.MethodsP. pinnata plants were cultivated in pots containing the VTM tailings until root nodules formed, and then culturable rhizobia were isolated from root nodules. The diversity, nitrogen-fixing capacity and heavy metal tolerance of rhizobia were performed.ResultsAmong 57 rhizobia isolated from these nodules, only twenty strains showed different levels of tolerance to copper (Cu), nickel (Ni), manganese (Mn) and zinc (Zn), especially strains PP1 and PP76 showing high tolerance against these four heavy metals. Based on the phylogenetic analysis of 16S rRNA and four house-keeping genes (atpD, recA, rpoB, glnII), twelve isolates were identified as Bradyrhizobium pachyrhizi, four as Ochrobactrum anthropic, three as Rhizobium selenitireducens and one as Rhizobium pisi. Some rhizobia isolates showed a high nitrogen-fixing capacity and promoted P. pinnata growth by increasing nitrogen content by 10%-145% in aboveground plant part and 13%-79% in the root. R. pachyrhizi PP1 showed the strongest capacity of nitrogen fixation, plant growth promotion and resistance to heavy metals, which provided effective rhizobia strains for bioremediation of VTM tailings or other contaminated soils. This study demonstrated that there are at least three genera of culturable rhizobia in symbiosis with P. pinnata in VTM tailings.DiscussionAbundant culturable rhizobia with the capacity of nitrogen fixation, plant growth promotion and resistance to heavy metals survived in VTM tailings, indicating more valuable functional microbes could be isolated from extreme soil environments such as VTM tailings

Dynamics of sewage charge policies, environmental protection industry and polluting enterprises - a case study in China

The game model of sewage charges, based on incremental mode, was developed. Four modes of high/low sewage charge standards were utilized to analyze the relationship between control strategy on sewage discharge and the development of environmental protection industry. Results showed that the selection of control strategy was heavily dependent on the level of sewage charges. An empirical study was carried out to investigate the environmental protection industry in 31 regions in China in 2010. It was revealed that the sewage charge standards, formulated by the local governments and the development of environmental protection industry, varied significantly in different regions. The optimal level of environmental quality and social welfare, as a whole, was not achieved in China. It is proposed that different regions should adopt different sewage charges standards based on their economic development level and the current environmental quality. Similarly, the scientific and technological innovation of environmental protection industry should be further strengthened.Qingsong Wang, Xueliang Yuan, Jian Zuo, Ruimin Mu, Lixin Zhou, and Mingxia Su