Multiobjective Location Model Design Based on Government Subsidy in the Recycling of CDW

With the generation of a large amount of construction and demolition waste (CDW), many scholars have recently paid more attention to the recycling of CDW. In this paper, we design a classification recycling method based on the degree of CDW availability in the recycling of CDW. Considering the important role of the government in reverse logistics, a model of reverse logistics network based on the trade-off between cost and recycling rate is put forward, which is subject to government subsidy. The model includes the location of classification processing center and ensures the best route of transportation. Then, the improved particle swarm optimization algorithm is applied to solve the model to get Pareto frontier by transforming it into a multiobjective integer-programming problem. As a case study, the results of the statistical modeling used in this study indicate the feasibility of the model. Finally, according to the sensitivity analysis of government’s subsidy, we evaluate the effectiveness of this program and its applicability

Immobilizing Ni (II)-Exchanged Heteropolyacids on Silica as Catalysts for Acid-Catalyzed Esterification Reactions

Biodiesel was synthesized from oleic acid using Ni (II)-exchanged heteropolyacids immobilized on silica (Ni0.5H3SiW / SiO2 ) as a solid acid catalyst. Based on detailed analyses of FT-IR, XRD, TG and SEM, the structural, surface and thermal stability of Ni0.5H3SiW / SiO2 were investigated. Obtained results demonstrated that the Keggin structure was well in the immobilization process and possess a high thermal stability. Various esterification reaction conditions and reusability of catalyst were studied. High oleic acid conversion of 81.4 % was observed at a 1:22 mole ratio (oleic acid: methanol), 3 wt. % catalyst at 70 °C for 4 h. The Ni0.5H3SiW / SiO2 catalyst was reused for several times and presented relatively stable. More interestingly, the kinetic studies revealed the esterification process was compatible with the first order model, and a lower activation energy was obtained in this catalytic system

Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD

Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage

Background: Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI. Results: We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets. Conclusions: We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets

Design, Synthesis and Evaluation of Indene Derivatives as Retinoic Acid Receptor α Agonists

A series of novel indene-derived retinoic acid receptor α (RARα) agonists have been designed and synthesized. The use of receptor binding, cell proliferation and cell differentiation assays demonstrated that most of these compounds exhibited moderate RARα binding activity and potent antiproliferative activity. In particular, 4-((3-isopropoxy-2,3-dihydro-1H-inden-5-yl)-carbamoyl)benzoic acid (36d), which showed a moderate binding affinity, exhibited a great potential to induce the differentiation of NB4 cells (68.88% at 5 μM). Importantly, our work established indene as a promising skeleton for the development of novel RARα agonists

The Proteasome Inhibitor Bortezomib Enhances ATRA- Induced Differentiation of Neuroblastoma Cells via the JNK Mitogen-Activated Protein Kinase Pathway

Neuroblastoma (NB) is the most common extracranial solid tumor in childhood. Differentiated human NBs are associated with better outcome and lower stage; induction of differentiation is considered to be therapeutically advantageous. Alltrans retinoic acid (ATRA) has been shown to induce the differentiation of neuroblastoma (NB) cell lines. The proteasome inhibitor bortezomib inhibits cell growth and angiogenesis in NBs. Here, we investigated the synergistic effect between bortezomib and ATRA in inducing NB cell differentiation in different NB cell lines. Bortezomib combined with ATRA had a significantly enhanced antiproliferative effect. This inhibition was characterized by a synergistic increase in neuronal differentiation. At the same time, the combination therapy showed little neuronal toxicity which was assessed in primary cultures of rat cerebellar granule cells by the MTT assay, PI staining. The combination of bortezomib and ATRA triggered increased differentiation through the activation of proteins, including RARa, RARb, RARc, p-JNK and p21, compared with ATRA treatment alone. Using JNK inhibitor SP600125 to block JNK-dependent activity, the combination therapy-induced neuronal differentiation was partially attenuated. In addition, p21 shRNA had no effect on the combination therapy-induced neuronal differentiation. The in vivo antitumor activities were examined in human NB cell xenografts and GFP-labeled human NB cell xenografts. Treatment of human NB cell CHP126-bearing nude mice with ATRA plus bortezomib resulted in more significant tumor growth inhibition than mice treated with either drug alone. These findings provide the rationale fo