One Novel Zn(II) Nitro-containing Metal Organic Framework for Dye-Adsorption and Photo Degradation

A novel metal-organic framework [Zn0.5(L1 )(4,4'-Bpy)0.5], (HU21, HU for Hohai University, L1 = 4-hydroxy-3-nitrobenzoic acid, 4,4'-bipyridine= 4,4'-Bpy), has been isolated through hydro-thermal reaction. Single-crystal X-ray diffraction reveals the compound features a 1D fishbone-like chain. A fast adsorption rate of methylene blue with HU21 was observed in the dark, but under irritation the degradation rate of the dye was obviously increased. The degradation of methylene blue dye reached 248 mg/g under light irritation, and the photocatalytic activity reached 96.1%

Prior knowledge guided active modules identification: an integrated multi-objective approach

BACKGROUND: Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. METHODS: A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. RESULTS: Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. CONCLUSIONS: Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance

One dimensional terpyridine-based metal organic framework for stable supercapacitor

In summary, a novel structure of MOF based on 1,4–di ([2,2':6',2''terpyridin] -4'-yl)benzene and 1,4-naphthalenedicarboxylic acid has been constructed through hydrothermal reaction. The Ni-MOF displays one dimensional zigzag chain, which connect each other by hydrogen bonding to form three dimensional supramolecule with large channels. The conjugated systems of the terpyridin and benzene ligands enhance the chain rigidity, accelerate the electron transport. The massive channels provides electrolyte rapid transfer. By the structural feature aforementioned, the Ni-MOF demonstrates stable electrochemical performance as suprocapacitor

Data Deduplication Technology for Cloud Storage

With the explosive growth of information data, the data storage system has stepped into the cloud storage era. Although the core of the cloud storage system is distributed file system in solving the problem of mass data storage, a large number of duplicate data exist in all storage system. File systems are designed to control how files are stored and retrieved. Fewer studies focus on the cloud file system deduplication technologies at the application level, especially for the Hadoop distributed file system. In this paper, we design a file deduplication framework on Hadoop distributed file system for cloud application developer. Proposed RFD-HDFS and FD-HDFS two data deduplication solutions process data deduplication online, which improves storage space utilisation and reduces the redundancy. In the end of the paper, we test the disk utilisation and the file upload performance on RFD-HDFS and FD-HDFS, and compare HDFS with the disk utilisation of two system frameworks. The results show that the two-system framework not only implements data deduplication function but also effectively reduces the disk utilisation of duplicate files. So, the proposed framework can indeed reduce the storage space by eliminating redundant HDFS file

Ensembled CTR Prediction via Knowledge Distillation

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and dynamic user behaviors. The increased model complexity may slow down online inference and hinder its adoption in real-time applications. Instead, our work targets at a new model training strategy based on knowledge distillation (KD). KD is a teacher-student learning framework to transfer knowledge learned from a teacher model to a student model. The KD strategy not only allows us to simplify the student model as a vanilla DNN model but also achieves significant accuracy improvements over the state-of-the-art teacher models. The benefits thus motivate us to further explore the use of a powerful ensemble of teachers for more accurate student model training. We also propose some novel techniques to facilitate ensembled CTR prediction, including teacher gating and early stopping by distillation loss. We conduct comprehensive experiments against 12 existing models and across three industrial datasets. Both offline and online A/B testing results show the effectiveness of our KD-based training strategy.Comment: Published in CIKM'202

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients

BackgroundSurgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.MethodsPatients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots.Results118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves.ConclusionThe nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment

A control method for the single-phase three-leg unified power quality conditioner without a phase-locked loop

The single-phase three-leg unified power quality conditioner (UPQC) can achieve the functions of voltage compensation, reactive power compensation, and harmonic compensation. However, traditional control algorithms require a phase-locked loop to obtain the real-time phase angle of the grid voltage, which undoubtedly increases algorithm complexity. To simplify the phase-locked calculation, this paper proposes a control method without the phase-locked loop for the single-phase three-leg UPQC. In the proposed scheme, the instantaneous value of the grid voltage is employed to realize the grid integration control. Then, the load voltage reference is calculated in real time using a second-order generalized integrator. Moreover, a simple algorithm for reactive power and harmonic compensation is discussed, further simplifying the control algorithm. Finally, a small-scale experimental platform is built, and the effectiveness of the proposed method is verified by the experimental results

Identification of gene signatures related to hypoxia and angiogenesis in pancreatic cancer to aid immunotherapy and prognosis

BackgroundOne of the most diverse tumors is pancreatic cancer (PC), which makes predicting the prognosis challenging. PC development is directly related to hypoxia, angiogenesis, and immunotherapy. It is still unclear how the three features are related.MethodsThe Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) database were employed to obtain sequencing data for healthy pancreatic tissues and PC tissues, respectively. According to the constructed hypoxic prognostic model (HPM) and angiogenic prognostic model (APM), 4 subtypes of PC were identified. Hypoxia and angiogenesis prognostic model (HAPM) was established based on differentially expressed genes (DEGs) between high-angiogenesis/high-hypoxia (HH) and low-angiogenesis/low-hypoxia (LL) subgroups. Base on the median risk score, PC patients were separated into high-risk and low-risk groups, and clinical traits, prognosis, percentage of immune cell infiltration, PD-1 expression, and the fraction of T-cell depletion were compared between the groups. Finally, the predictive accuracy of the tumor immune dysfunction and rejection (TIDE) and tumor inflammatory signature (TIS) models, as well as HAPM, was compared.ResultWe analyzed the mRNA sequencing data from 178 PC tissues and 171 normal pancreatic tissues to obtain 9527 DEGs. We discovered 200 genes linked with hypoxia and 36 genes involved with angiogenesis through the literature. We found the core genes related with hypoxia and angiogenesis in PC by intersecting the DEGs of the HH and LL subgroups with those of PC via WGCNA. IL-17 signaling pathway, ECM-receptor interactions, cytokine receptor interactions, etc. were all enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) results of core genes. HAPM has good predictive efficiency, according to an evaluation of KM survival curves and ROC curves. The external dataset also validated the model’s ability to anticipate outcomes. Patients in the high- and low-risk groups were compared for PD1 expression and T-cell exclusion scores, which suggested that the model might be used to forecast which PC patients might benefit from immunotherapy.ConclusionsThe probable molecular processes connecting hypoxia and angiogenesis are described in this work, and a model is developed that may be utilized to forecast the prognosis for PC patients and the benefits of immunotherapy