Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Vehicular Heterogeneous Networks for Green Cities

With the evolutionary development of automobile industry, modern transportation systems cause a series of critical problems, such as increased energy consumption and air pollution. To make green cities a reality, an ever expanding and evolving vehicular heterogeneous network infrastructure is required to enable fine-granularity data collection and reliable service delivery. In this paper, we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device-based vehicle-to-vehicle (D2D–V2V) transmission. We propose a two-stage energy-efficient resource allocation algorithm. In the first stage, an auction-matching-based joint relay selection, spectrum allocation, and power control algorithm is derived, which employs an English-auction approach for matching preference updating and conflict avoidance, and optimizes the energy efficiency of two-hop D2D–V2V and cellular links simultaneously in an iterative fashion. In the second stage, a nonlinear fractional programming based power control algorithm is developed to maximize the energy efficiency of the base station. Theoretical properties in terms of convergence, stability, and complexity are analyzed. Finally, the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.
Expanding the Genetic Code of Lactococcus lactis and Escherichia coli to Incorporate Non-canonical Amino Acids for Production of Modified Lantibiotics

The incorporation of non-canonical amino acids (ncAAs) into ribosomally synthesized and post-translationally modified peptides, e.g., nisin from the Gram-positive bacterium Lactococcus lactis, bears great potential to expand the chemical space of various antimicrobials. The ncAA Nε-Boc-L-lysine (BocK) was chosen for incorporation into nisin using the archaeal pyrrolysyl-tRNA synthetase–tRNAPyl pair to establish orthogonal translation in L. lactis for read-through of in-frame amber stop codons. In parallel, recombinant nisin production and orthogonal translation were combined in Escherichia coli cells. Both organisms synthesized bioactive nisin(BocK) variants. Screening of a nisin amber codon library revealed suitable sites for ncAA incorporation and two variants displayed high antimicrobial activity. Orthogonal translation in E. coli and L. lactis presents a promising tool to create new-to-nature nisin derivatives.
Biodegradable Polymers for Tissue Engineering : Review Article

Abstract: Scaffolds play a crucial role in the tissue engineering. Biodegradable polymers with great processing flex-ibility and biocompatability are predominant scaffolding materials. New developments in biodegradable polymers andtheir nanocomposites for the tissue engineering are discussed. Recent development in the scaffold designs that mimicnano and micro features of the extracellular matrix (ECM) of bones, cartilages, and vascular vessels are presentedas well.Key words: Biodegradable polymer, Tissue engineering, Scaffold, Bone, Cartilage, Vascular vessel I. 서 론 조직공학은 조직이식 및 자가 조직 보수의 한계를 극복하기 위해 1980년대 후반에 등장한 손상된 조직 및 기관을 보수 혹은 재생하는 유망한 접근법으로 지방족 에스테르 계합성 생분해성 고분자인 polyglycolide (PGA) 메쉬 상의 연골세포의 성장 및 PGA 중공 섬유 내의 간 세포 배양이 최초로 시작된 이래 10년 이내에 실질적으로 인체 내 모든 장기에 조직공학이 적용되기 시작하였다[1]. 조직공학은 세포,유전자, 단백질과 같은 바이오 인자를 스캐폴드로 알려진 다공성 분해성 임시 구조물에 이식하는 것으로 세포외기질(extracellular material, ECM) 생성을 통해 새로운 조직을 생성한다[2]. 조직공학 과정에서 핵심적인 역할을 하는스캐폴드는 조직의 원형 역할에 더하여 세포의 부착, 증식,분열, 그리고 신 조직의 생성을 지원할 수 있어야 하므로 조성, 물리적 구조, 생물학적으로 기능하는 성분들 모두가 조직공학용 스캐폴드의 중요한 속성으로 가능한 한 ECM과유사한스캐폴드를제작하는것이중요하다[3]. 이상적인스캐폴드는 i) 조절 가능한 분해속도를 가진 무해한 분해생성물을 방출하는 생체적합성 생분해성 기질로서; ii) 영양소 및폐기물의 이송을 원활히 하기 위해 서로 연결된 균일한 크기의 기공으로 이루어진 고 다공성 3-D 구조를 가지고: iii)재생을 지지할 수 있는 안정된 형체를 가지며; iv) 세포와잘 상호 반응할 수 있는 표면 화학 및 표면 형상을 가져야한다[4]. 금속, 세라믹, 고분자를 포함한 다양한 물질들이 스캐폴드용 소재로 개발되고 있지만, 가공성 및 생체적합성이 우수한 생분해성 고분자, 그 중에서도, 특히 잘 정의된 구조를가지면서 천연고분자 사용 시 나타날 수 있는 면역반응의염려가 없는 합성 생분해성 고분자가 가장 널리 사용되고있다[5]. 천연고분자는 친수성이면서 세포신호 성분을 갖고있어 세포의 부착, 증식, 분열을 증진시키며, 생체조직을 구성하는 성분과 일치 혹은 유사하여 생체적합성이 탁월한 장점이 있지만, 취약한 기계적 강도, 면역반응 및 감염의 우려,제한적인 공급, 그리고 내열성이 나빠 가공이 상대적으로 어려운 문제가 있다. 이에 반해, 합성 생분해성 고분자는 그분자구조, 분자량, 공중합 조성을 적절히 조절함으로써 분해및 기계적 특성을 용도에 맞게 다양하게 변화시킬 수 있는장점이 있지만, 그 주류를 이루는 폴리에스테르 계 생분해
Evolvable 2D computing matrix model for intrinsic evolution in commercial FPGAs with native reconfiguration support

This paper addresses the modelling and validation of an evolvable hardware architecture which can be mapped on a 2D systolic structure implemented on commercial reconfigurable FPGAs. The adaptation capabilities of the architecture are exercised to validate its evolvability. The underlying proposal is the use of a library of reconfigurable components characterised by their partial bitstreams, which are used by the Evolutionary Algorithm to find a solution to a given task. Evolution of image noise filters is selected as the proof of concept application. Results show that computation speed of the resulting evolved circuit is higher than with the Virtual Reconfigurable Circuits approach, and this can be exploited on the evolution process by using dynamic reconfiguration.
Sintered TiO2/recycled glass composites designed for the potential degradation of waterborne pollutants

Abstract Porous recycled glass beds were produced for the potential use in water remediation. The present work discusses the structural and mechanical characteristics of filters made of commercially available powdered recycled glass. The study measured the porosity and percolation performance of the sintered glass beds as a function of the sintering parameters and the particle size of the original powdered glass. Moreover, because TiO2 can serve as a photocatalyst to degrade organic pollutants, a TiO2/recycled glass composite was also studied. Optical microscopy helped measure the resulting porosity by quantitative image analysis. Finally, a performance map that specified the composite porosity, percolation, and compression characteristics was obtained as a function of sintering time, sintering temperature, glass particle size, and the percentage of TiO2 added.
Molecular Cloning and Characterization of Carbonic Anhydrase XII from Pufferfish (Takifugu rubripes)

In this study, an 1888-bp carbonic anhydrase XII (CA XII) sequence was cloned from the brain of the pufferfish, Takifugu rubripes. The cloned sequence contained a coding region of 1470-bp, which was predicted to translate into a protein of 490 amino acid residues. The predicted protein showed between 68–56% identity with the large yellow croaker (Larimichthys crocea), tilapia (Oreochromis niloticus), and Asian arowana (Scleropages formosus) CA XII proteins. It also exhibited 36% and 53% identity with human CA II and CA XII, respectively. The cloned sequence contained a 22 amino acid NH2-terminal signal sequence and three Asn-Xaa-Ser/Thr sequons, among which one was potentially glycosylated. Four cysteine residues were also identified (Cys-21, Cys-201, Cys-355, and Cys-358), two of which (Cys-21 and Cys-201) could potentially form a disulfide bond. A 22-amino acid COOH-terminal cytoplasmic tail containing a potential site for phosphorylation by protein kinase A was also found. The cloned sequence might be a transmembrane protein, as predicted from in silico and phylogenetic analyses. The active site analysis of the predicted protein showed that its active site residues were highly conserved with tilapia CA XII protein. Homology modeling of the pufferfish CA XII was done using the crystal structure of the extracellular domain of human carbonic anhydrase XII at 1.55 Å resolution as a template. Semi-quantitative reverse transcription (RT)-PCR, quantitative PCR (q-PCR), and in situ hybridization confirmed that pufferfish CA XII is highly expressed in the brain.
Application of x-ray densitometry to determine density profile in waferboard: relationship of density to thickness expansion and internal bond strength under various cycles

X-ray scanning densitometry was used to study density distribution in 2-by 2-inch waferboard samples. This method provided a detailed picture of density changes within a specimen and showed good agreement in average density value with gravimetric measurements. Results from the study of density, thickness, and IB changes in waferboard treated to up to five cycles of vacuum pressure-dry, boil-dry, 15 percent MC-dry and 15 percent MC-freeze-dry are reported. The first cycle of each treatment was found to cause the largest property change; however, the density profile shape was altered only slightly during treatment. Thickness increase was found to be strongly correlated with change in density, while IB was correlated to a lesser extent. Resin content & type have little effect on the densiometric evaluation of boards. -- AATA
Down-Regulation of SIX3 is Associated with Clinical Outcome in Lung Adenocarcinoma

Background Lung cancer is a common cancer and the leading cause of cancer-related death worldwide. SIX3 is a human homologue of the highly conserved sine oculis gene family essential during embryonic development in vertebrates, and encodes a homeo-domain containing transcription factor. Little is known about the role of SIX3 in human tumorigenesis. This study is to assess the expression/function of SIX3 and the significance of SIX3 as a prognostic biomarker in lung adenocarcinoma. Methods Quantitative real-time RT-PCR was used to analyze SIX3 mRNA expression and quantitative methylation specific PCR (MSP) was used to examine promoter methylation. MTS and colony formation assays were performed to examine cell proliferation. Wound healing assays were used to assess cell migration, and microarrays were utilized to examine genes regulated by SIX3 in lung cancer cells. Association of SIX3 expression levels with clinical outcomes of patients with lung adenocarcinoma was evaluated using the Kaplan-Meier method and a multivariate Cox proportional hazards regression model. Results SIX3 was down-regulated in lung adenocarcinoma tissues compared to their matched adjacent normal tissues, and this down-regulation was associated with methylation of the SIX3 promoter. SIX3 was also methylation-silenced in lung cancer cell lines. Restoration of SIX3 in lung cancer cells lacking endogenous SIX3 suppressed cell proliferation and migration, and downregulated a number of genes involved in proliferation and metastasis such as S100P, TGFB3, GINS3 and BAG1. Moreover, SIX3 mRNA expression was associated with significantly improved overall survival (OS) and progression-free survival (PFS) in adenocarcinoma patients and patients with bronchioloalveolar carcinoma (BAC) features. Conclusions SIX3 may play an important role as a novel suppressor in human lung cancer. SIX3 has potential as a novel prognostic biomarker for patients with lung adenocarcinomas.
Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models

Hospital readmissions have become one of the key measures of healthcare quality. Preventable readmissions have been identified as one of the primary targets for reducing costs and improving healthcare delivery. However, most data driven studies for understanding readmissions have produced black box classification and predictive models with moderate performance, which precludes them from being used effectively within the decision support systems in the hospitals. In this paper we present an application of structured sparsity-inducing norms for predicting readmission risk for patients based on their disease history and demographics. Most existing studies have focused on hospital utilization, test results, etc., to assign a readmission label to each episode of hospitalization. However, we focus on assigning a readmission risk label to a patient based on their disease history. Our emphasis is on interpreting the models to improve the understanding of the readmission problem. To achieve this, we exploit the domain induced hierarchical structure available for the disease codes which are the features for the classification algorithm. We use a tree based sparsity-inducing regularization strategy that explicitly uses the domain hierarchy. The resulting model not only outperforms standard regularization procedures but is also highly sparse and interpretable. We analyze the model and identify several significant factors that have an effect on readmission risk. Some of these factors conform to existing beliefs, e.g., impact of surgical complications and infections during hospital stay. Other factors, such as the impact of mental disorder and substance abuse on readmission, provide empirical evidence for several pre-existing but unverified hypotheses. The analysis also reveals previously undiscovered connections such as the influence of socioeconomic factors like lack of housing and malnutrition.
Ensembles of Classification Methods for Data Mining Applications

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed using classifiers in both homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of real and benchmark data sets of data mining applications like intrusion detection, direct marketing and signature verification. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for real and benchmark data sets of direct marketing. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for data mining problem. The proposed ensemble methods provide significant improvement of accuracy compared to individual Classifiers and also heterogeneous models exhibit better results than homogeneous models for real and benchmark data sets of data mining applications. Index Terms — Data Mining, Ensemble, Intrusion Detection, Direct Marketing, Signature Verification, Radial Basis Function, Support Vector Machine, Accuracy.
Historical Changes in Abundance of Floating‐Leaf and Emergent Vegetation in Minnesota Lakes

Abstract To assess the consequences of shoreline development, whole-lake vegetation abundance of dense floating-leaf and emergent vegetation in 100 north-central Minnesota lakes was estimated from aerial photographs for selected years between 1939 and 2003. Lakes were randomly selected from three shoreland development classifications, which define statewide minimum shoreline development standards for Minnesota. The three classes, in order of increasing restrictions for development, are general development, recreational development, and natural environment. Image analysis techniques were used to estimate vegetation abundance (percent cover). Shoreline development varied by shoreland development class. Floating-leaf and emergent vegetative cover (percent of lake surface area) was significantly affected by development. Increases in shoreline development, indexed by dock sites per shoreline kilometer, reduced plant cover. A linear mixed-effects model estimated that in 2003, the mean floating-leaf and emergent.
Mechanotransduction, nuclear architecture and epigenetics in Emery Dreifuss Muscular Dystrophy: tous pour un, un pour tous

ABSTRACT The alteration of the several roles that Lamin A/C plays in the mammalian cell leads to a broad spectrum of pathologies that – all together – are named laminopathies. Among those, the Emery Dreifuss Muscular Dystrophy (EDMD) is of particular interest as, despite the several known mutations of Lamin A/C, the genotype–phenotype correlation still remains poorly understood; this suggests that the epigenetic background of patients might play an important role during the time course of the disease. Historically, both a mechanical role of Lamin A/C and a regulative one have been suggested as the driving force of laminopathies; however, those two hypotheses are not mutually exclusive. Recent scientific evidence shows that Lamin A/C sustains the correct gene expression at the epigenetic level thanks to the Lamina Associated Domains (LADs) reorganization and the crosstalk with the Polycomb Group of Proteins (PcG). Furthermore, the PcG-dependent histone mark H3K27me3 increases under mechanical stress, finally pointing out the link between the mechano-properties of the nuclear lamina and epigenetics. Here, we summarize the emerging mechanisms that could explain the high variability seen in Emery Dreifuss muscular dystrophy.
Clustering coefficient analysis in large wireless ad hoc network

Large wireless networks such as Internet of Things and Cyber-Physical Systems emerge as a new technology challenge in networking, particularly for scalability. Researchers recently note the potential of social network analysis to design wireless networks more efficiently. In this paper, we present a very original view to take advantage of clustering coefficient in social network analysis to develop more effective ad hoc networking without the need of global network information. We start from random geometric graph to view the communication range of a wireless node as a disc, which is equivalent to examining the outage probability of links. Under stochastic geometry analysis of interference, the impacts of density and traffic of nodes on the connectivity can be therefore analyzed. Similar to the structural holes in social networks, the ad hoc networks suffer from the void region problem in the geographical routing, which is even more harmful in ultra dense networking environments. Discarding the detailed global routing table, we surprisingly show that the local estimation of clustering coefficient of nodes can significantly improve the void problem handling to result in more effective ad hoc networking.
Nrf2 Activation Induced by Sirt1 Ameliorates Acute Lung Injury After Intestinal Ischemia/Reperfusion Through NOX4-Mediated Gene Regulation

Background/Aims: Nuclear erythroid 2-related factor-2 (Nrf2) is a major stress-response transcription factor that has been implicated in regulating ischemic angiogenesis. We investigated the effects of Nrf2 in regulating revascularization and modulating acute lung injury. Methods: The expression of Nrf2 and sirtuin1 (Sirt1) was assessed in lung tissue by western blotting and immunofluorescence staining after intestinal ischemia/reperfusion (IIR) in Nrf2–/– and wild-type (WT) mice. The involvement of Nrf2 in angiogenesis, cell viability, and migration was investigated in human pulmonary microvascular endothelial cells (PMVECs). Additionally, the influence of Nrf2 expression on NOX pathway activation was measured in PMVECs after oxygen–glucose deprivation/reoxygenation. Results: We found activation and nuclear accumulation of Nrf2 in lung tissue after IIR. Compared to IIR in WT mice, IIR in Nrf2–/– mice significantly enhanced leukocyte infiltration and collagen deposit, and inhibited endothelial cell marker CD31 expression. Nrf2 upregulation and translocation into the nucleus stimulated by Sirt1 overexpression exhibited remission of histopathologic changes and enhanced CD31 expression. Nrf2 knockdown repressed non-phagocytic cell oxidase 4 (NOX4), hypoxia-inducible factor (HIF-1α) and vascular endothelial growth factor (VEGF) expression after IIR. Nrf2 upregulation by Sirt1 enhances NOX4, HIF-1α and VEGF expression after IIR in WT mice. Furthermore, Nrf2 knockdown suppressed cell viability, capillary tube formation and cell migration in PMVECs after oxygen–glucose deprivation/reoxygenation and also inhibited NOX4, HIF-1 and VEGF expression. Moreover, NOX4 knockdown in PMVECs decreased the levels of VEGF, HIF-1α and angiogenesis. Conclusion: Nrf2 stimulation by Sirt1 plays an important role in sustaining angiogenic potential through NOX4-mediated gene regulation.
Detecting Android Malwares with High-Efficient Hybrid Analyzing Methods

In order to tackle the security issues caused by malwares of Android OS, we proposed a high-efficient hybrid-detecting scheme for Android malwares. Our scheme employed different analyzing methods (static and dynamic methods) to construct a flexible detecting scheme. In this paper, we proposed some detecting techniques such as Com+ feature based on traditional Permission and API call features to improve the performance of static detection. The collapsing issue of traditional function call graph-based malware detection was also avoided, as we adopted feature selection and clustering method to unify function call graph features of various dimensions into same dimension. In order to verify the performance of our scheme, we built an open-access malware dataset in our experiments. The experimental results showed that the suggested scheme achieved high malware-detecting accuracy, and the scheme could be used to establish Android malware-detecting cloud services, which can automatically adopt high-efficiency analyzing methods according to the properties of the Android applications.
Reasoning about the Properties of an Enterprise Information System

A condensed specification of a multi-level marketing enterprise in the Z specification language is presented and a number of proof obligations that result from operations on the state is stated. The feasibility of using certain reasoning heuristics for discharging proof obligations emerging from the specification is investigated and we show how two important proof obligations arising from the specification of a real-life enterprise may successfully be discharged using a suite of well-chosen heuristics.
The development of AZD7624 for prevention of exacerbations in COPD: a randomized controlled trial

Background p38 mitogen-activated protein kinase (MAPK) plays a central role in the regulation and activation of pro-inflammatory mediators. COPD patients have increased levels of activated p38 MAPK, which correlate with increased lung function impairment, alveolar wall inflammation, and COPD exacerbations. Objectives These studies aimed to assess the effect of p38 inhibition with AZD7624 in healthy volunteers and patients with COPD. The principal hypothesis was that decreasing lung inflammation via inhibition of p38α would reduce exacerbations and improve quality of life for COPD patients at high risk for acute exacerbations. Methods The p38 isoform most relevant to lung inflammation was assessed using an in situ proximity ligation assay in severe COPD patients and donor controls. Volunteers aged 18–55 years were randomized into the lipopolysaccharide (LPS) challenge study, which investigated the effect of a single dose of AZD7624 vs placebo on inflammatory biomarkers. The Proof of Principle study randomized patients aged 40–85 years with a diagnosis of COPD for >1 year to AZD7624 or placebo to assess the effect of p38 inhibition in decreasing the rate of exacerbations. Results The p38 isoform most relevant to lung inflammation was p38α, and AZD7624 specifically inhibited p38α and p38β isoforms in human alveolar macrophages. Thirty volunteers were randomized in the LPS challenge study. AZD7624 reduced the increase from baseline in sputum neutrophils and TNF-α by 56.6% and 85.4%, respectively (p<0.001). In the 213 patients randomized into the Proof of Principle study, there was no statistically significant difference between AZD7624 and placebo when comparing the number of days to the first moderate or severe exacerbation or early dropout. Conclusion Although p38α is upregulated in the lungs of COPD patients, AZD7624, an isoform-specific inhaled p38 MAPK inhibitor, failed to show any benefit in patients with COPD.