Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic algorithm based global optimization algorithm used to solving System Analysis of Multi-Disciplinary Optimization

Multi-Disciplinary Feasible (MDF) is a promising solving architecture for Multi-Disciplinary Optimization (MDO) problem. But traditional iteration based solving method for System Analysis (SA) of MDF has poor computational performance. The solving trouble due to SA prevents its application in complex, tight-coupled system design. The intent of this research is to improve the computational performance of SA and MDF. By analyzing the characteristics of FPI and NRI method, the reasons of computational difficulties were summarized. Then, the requirements to SA solving method were presented. To meet these requirements, original formulation of SA was changed to a Non-Linear Programming (NLP) problem. The special requirements for this NLP necessitate a new optimization algorithm. Hence, two optimization algorithms, GA and DFP, were combined in series to GA-DFP. By a test example, GA-DFP has been validated in capabilities of global search and local convergence, and meets all requirements of SA. By introducing GA-DFP into MDF, a new architecture, BO-MDF, was established. The results of typical problem show that BO-MDF has better performance than other MDO solving architectures of MDF, IDF, AAO, and CO.
Phagocytosis of Staphylococcus aureus by Macrophages Exerts Cytoprotective Effects Manifested by the Upregulation of Antiapoptotic Factors

It is becoming increasingly apparent that Staphylococcus aureus are able to survive engulfment by macrophages, and that the intracellular environment of these host cells, which is essential to innate host defenses against invading microorganisms, may in fact provide a refuge for staphylococcal survival and dissemination. Based on this, we postulated that S. aureus might induce cytoprotective mechanisms by changing gene expression profiles inside macrophages similar to obligate intracellular pathogens, such as Mycobacterium tuberculosis. To validate our hypothesis we first ascertained whether S. aureus infection could affect programmed cell death in human (hMDMs) and mouse (RAW 264.7) macrophages and, specifically, protect these cells against apoptosis. Our findings indicate that S. aureus-infected macrophages are more resistant to staurosporine-induced cell death than control cells, an effect partly mediated via the inhibition of cytochrome c release from mitochondria. Furthermore, transcriptome analysis of human monocyte-derived macrophages during S. aureus infection revealed a significant increase in the expression of antiapoptotic genes. This was confirmed by quantitative RT-PCR analysis of selected genes involved in mitochondria-dependent cell death, clearly showing overexpression of BCL2 and MCL1. Cumulatively, the results of our experiments argue that S. aureus is able to induce a cytoprotective effect in macrophages derived from different mammal species, which can prevent host cell elimination, and thus allow intracellular bacterial survival. Ultimately, it is our contention that this process may contribute to the systemic dissemination of S. aureus infection.
Use of Hepatocytes to Assess the Contribution of Hepatic Uptake to Clearance in Vivo

The wealth of information that has emerged in recent years detailing the substrate specificity of hepatic transporters necessitates an investigation into their potential role in drug elimination. Therefore, an assay in which the loss of parent compound from the incubation medium into hepatocytes (“media loss” assay) was developed to assess the impact of hepatic uptake on unbound drug intrinsic clearance in vivo (CLint ub in vivo). Studies using conventional hepatocyte incubations for a subset of 36 AstraZeneca new chemical entities (NCEs) resulted in a poor projection of CLint ub in vivo (r2 = 0.25, p = 0.002, average fold error = 57). This significant underestimation of CLint ub in vivo suggested that metabolism was not the dominant clearance mechanism for the majority of compounds examined. However, CLint ub in vivo was described well for this dataset using an initial compound “disappearance” CLint obtained from media loss assays (r2 = 0.72, p = 6.3 × 10-11, average fold error = 3). Subsequent studies, using this method for the same 36 NCEs, suggested that the active uptake into human hepatocytes was generally slower (3-fold on average) than that observed with rat hepatocytes. The accurate prediction of human CLint ub in vivo (within 4-fold) for the marketed drug transporter substrates montelukast, bosentan, atorvastatin, and pravastatin confirmed further the utility of this assay. This work has described a simple method, amenable for use within a drug discovery setting, for predicting the in vivo clearance of drugs with significant hepatic uptake.
Runx1 Transcription Factor Is Required for Myoblasts Proliferation during Muscle Regeneration

Following myonecrosis, muscle satellite cells proliferate, differentiate and fuse, creating new myofibers. The Runx1 transcription factor is not expressed in naïve developing muscle or in adult muscle tissue. However, it is highly expressed in muscles exposed to myopathic damage yet, the role of Runx1 in muscle regeneration is completely unknown. Our study of Runx1 function in the muscle’s response to myonecrosis reveals that this transcription factor is activated and cooperates with the MyoD and AP-1/c-Jun transcription factors to drive the transcription program of muscle regeneration. Mice lacking dystrophin and muscle Runx1 (mdx - /Runx1 f/f), exhibit impaired muscle regeneration leading to age-dependent muscle waste, gradual decrease in motor capabilities and a shortened lifespan. Runx1-deficient primary myoblasts are arrested at cell cycle G1 and consequently differentiate. Such premature differentiation disrupts the myoblasts’ normal proliferation/differentiation balance, reduces the number and size of regenerating myofibers and impairs muscle regeneration. Our combined Runx1-dependent gene expression, ChIP-seq, ATAC-seq and histone H3K4me1/H3K27ac modification analyses revealed a subset of Runx1-regulated genes that are co-occupied by MyoD and c-Jun in mdx - /Runx1 f/f muscle. The data provide unique insights into the transcriptional program driving muscle regeneration and implicate Runx1 as an important participant in the pathology of muscle wasting diseases.
Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation

In this work, we will analyze the response of a Metal Oxide Gas Sensor (MOGS) array to a flow controlled stimulus generated in a pressure controlled canister produced by a homemade olfactometer to build an E-nose. The built E-nose is capable of chocolate identification between the 26 analyzed chocolate bar samples and four features recognition (chocolate type, extra ingredient, sweetener and expiration date status). The data analysis tools used were Principal Components Analysis (PCA) and Artificial Neural Networks (ANNs). The chocolate identification E-nose average classification rate was of 81.3% with 0.99 accuracy (Acc), 0.86 precision (Prc), 0.84 sensitivity (Sen) and 0.99 specificity (Spe) for test. The chocolate feature recognition E-nose gives a classification rate of 85.36% with 0.96 Acc, 0.86 Prc, 0.85 Sen and 0.96 Spe. In addition, a preliminary sample aging analysis was made. The results prove the pressure controlled generated stimulus is reliable for this type of studies.
A performance comparison of quadtree-based access methods for thematic maps

In this paper, the efficient manipulation of thematic maps that contain multiple non-overlapping features is investigated. New methods based on Linear quadtrees are proposed and their performance is compared to that of similar structures. More specifically, window queries involving multiple features are described and tested having the number of disk accesses as a performance measure. Experimentally, it is shown that the proposed methods have a stable behavior and, in general, outperform the previous structures with respect to time and space complexity.
The Dynamics of Capital Flows and Property Returns : A Disaggregated Analysis of Metropolitan Areas and Property Types

This paper examines the shortand long-run dynamics among institutional capital flows and returns in private real estate markets. The main tool of analysis we employ is a vector autoregressive (VAR) regression model in which both institutional capital flows and returns are specified as endogenous variables in a two equation simultaneous system and in which we also control for various financial and economic variables. When aggregating across U.S. CBSAs and property types, we find evidence that both lagged NCREIF returns and lagged NCREIF flows significantly influence current returns. However, these aggregate results mask significant crosssectional variation across different metropolitan areas and property types. In particular, we provide evidence that our aggregate results are driven by a limited number of large CBSAs. We find no evidence that returns are predictive of future NPI capital flows. We also document that institutional capital are not generally predictive of relative future capital flows.
PKR Activation Favors Infectious Pancreatic Necrosis Virus Replication in Infected Cells

The double-stranded RNA-activated protein kinase R (PKR) is a Type I interferon (IFN) stimulated gene that has important biological and immunological functions. In viral infections, in general, PKR inhibits or promotes viral replication, but PKR-IPNV interaction has not been previously studied. We investigated the involvement of PKR during infectious pancreatic necrosis virus (IPNV) infection using a custom-made rabbit antiserum and the PKR inhibitor C16. Reactivity of the antiserum to PKR in CHSE-214 cells was confirmed after IFNα treatment giving an increased protein level. IPNV infection alone did not give increased PKR levels by Western blot, while pre-treatment with PKR inhibitor before IPNV infection gave decreased eukaryotic initiation factor 2-alpha (eIF2α) phosphorylation. This suggests that PKR, despite not being upregulated, is involved in eIF2α phosphorylation during IPNV infection. PKR inhibitor pre-treatment resulted in decreased virus titers, extra- and intracellularly, concomitant with reduction of cells with compromised membranes in IPNV-permissive cell lines. These findings suggest that IPNV uses PKR activation to promote virus replication in infected cells.
Spatial correlation in credit risk and its improvement in credit scoring

Credit scoring models are important tools in the credit granting process. These models measure the credit risk of a prospective client based on idiosyncratic variables and macroeconomic factors. However, small and medium sized enterprises (SMEs) are subject to the effects of the local economy. From a data set with the localization and default information of 9 million Brazilian SMEs, provided by Serasa Experian (the largest Brazilian credit bureau), we propose a measure of the local risk of default based on the application of ordinary kriging. This variable has been included in logistic credit scoring models as an explanatory variable. These models have shown better performance when compared to models without this variable. A gain around 7 percentage points of KS and Gini was observed.
On the efficiency of an autonomous cyclic motion grading system

In sport activities and rehabilitation it is of great benefit to provide an autonomous and individual assessment system of motor activity. Providing quantitive data about the movement is not only valid for characterising the courses of training processes, it will also help the athlete to improve the technique or to enhance learning in motor rehabilitation. In this regard, significant features are required to monitor and score the movement-pattern. We analyzed different rowing parameters including grip pull out, grip force, slinding seat position and foot-rest force to derive relevant rowing features. An indoor rower was used to record several movement parameters of elite and novice athletes. Different features were extracted for each rowing parameter by assessing the difference of each feature value on elite and novice athletes. The most relevant features as well as rowing parameters were selected based on the signal-to-noise-ratio ranking. The efficiency of Naive Bayes classifier to differentiate the rowing activity between elite and novice athletes was investigated. According to the results five features are sufficient to achieve a high classification rate of 100%. Using the proposed feature set a grading system was introduced enabling to rank and score the quality related to motor activity in rowing.
Using Options on Greeks as Liquidity Protection

In this paper we suggest derivative contracts related to the Greeks of options; we show how to value them and how they can be used to manage the risk of a portfolio of derivatives. We further describe certain types of these options, namely those related to the Delta and Gamma, which can be regarded as a form of insurance against liquidity holes and transaction costs for the writer of the contract representing the underlying.
Prediction and management in energy harvested wireless sensor nodes

Solar panels are frequently used in wireless sensor nodes because they can theoretically provide quite a bit of harvested energy. However, they are not a reliable, consistent source of energy because of the Sun's cycles and the everchanging weather conditions. Thus, in this paper we present a fast, efficient and reliable solar prediction algorithm, namely, Weather-Conditioned Moving Average (WCMA) that is capable of exploiting the solar energy more efficiently than state-of-the-art energy prediction algorithms (e.g. Exponential Weighted Moving Average EWMA). In particular, WCMA is able to effectively take into account both the current and past-days weather conditions, obtaining a relative mean error of only 10%. When coupled with energy management algorithm, it can achieve gains of more than 90% in energy utilization with respect to EWMA under the real working conditions of the Shimmer node, an active sensing platform for structural health monitoring.
Unimpaired Skin Carcinogenesis in Desmoglein 3 Knockout Mice

The contribution of adherens junction inactivation, typically by downregulation or mutation of the transmembrane core component E-cadherin, to cancer progression is well recognized. In contrast, the role of the desmosomal cadherin components of the related cell-cell adhesion junction, the desmosome, in cancer development has not been well explored. Here, we use mouse models to probe the functional role of desmosomal cadherins in carcinogenesis. Because mice lacking the desmosomal cadherin Desmoglein 3 (Dsg3) have revealed a crucial role for Dsg3 in cell-cell adhesion in stratified epithelia, we investigate the consequence of Dsg3 loss in two models of skin carcinogenesis. First, using Dsg3−/− keratinocytes, we show that these cells display adhesion defects in vitro and compromised tumor growth in allograft assays, suggesting that Dsg3 enables tumor formation in certain settings. In contrast, using an autochthonous model for SCC development in response to chronic UVB treatment, we discover a surprising lack of enhanced tumorigenesis in Dsg3−/− mice relative to controls, unlike mice lacking the desmosomal component Perp. Accordingly, there is no defect in the apoptotic response to UVB or enhanced immune cell infiltration upon Dsg3 loss that could promote tumorigenesis. Thus, Dsg3 does not display a clear function as a tumor suppressor in these mouse skin cancer models. Continued unraveling of the roles of Dsg3 and other desmosomal constituents in carcinogenesis in different contexts will be important for ultimately improving cancer diagnosis, prognostication, and treatment.
cm Signals from Early Ionizing Sources

We investigate the 21 cm signals from the UV ionizing sources in the reionization epoch. The formation and evolution of 21 cm emission and absorption regions depend essentially on the kinetics of photons in the physical and frequency spaces. To solve the radiative transfer equation, we use the WENO algorithm, which is effective to capture the sharp ionization profile and the cut-off at the front of light (r = ct) and to handle the small fraction of neutral hydrogen and helium in the ionized sphere. We show that a spherical shell of 21 cm emission and absorption will develop around a point source once the speed of the ionization front (I-front) is significantly lower than the speed of light. The 21 cm shell extends from the I-front to the front of light; its inner part is the emission region and its outer part is the absorption region. The 21 cm emission region depends strongly on the intensity, frequency-spectrum and life-time of the UV ionizing source. At redshift 1+z = 20, for a UV ionizing source with an intensity Ė ≃ 10ergs and a power law spectrum ν with α = 2, the emission region has a comoving size of 1 3 Mpc at time ≃ 2 Myr. Nevertheless, the emission regions are very small, and would be erased by thermal broadening if the intensity is less than Ė ≃ 10ergs, the frequency spectrum is thermal at temperature T ≃ 10 K, or the frequency spectrum is a power law with α ≥ 3. On the other hand, the 21 cm absorption regions are developed in all these cases. For a source of short life-time, no 21 cm emission region can be formed if the source dies out before the I-front speed is significantly lower than the speed of light. Yet, a 21 cm absorption region can form and develop even after the emission of the source ceases. Subject headings: cosmology: theory intergalactic medium radiation transfer methods: numerical Department of Physics, University of Arizona, Tucson, AZ 85721 Division of Applied Mathematics, Brown University, Providence, RI 02912 Purple Mountain Observatory, Nanjing, 210008, P.R. China. National Astronomical Observatories, Chinese Academy of Science, Chao-Yang District, Beijing 100012, P.R. China
Fast LIDAR-based Road Detection Using Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean height and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast convolutional neural network (CNN). The CNN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieves state-of-the-art results on the KITTI road benchmark. It is currently the top-performing algorithm among the published methods in the overall category urban road and outperforms the second best LIDAR-only approach by 7.4 percentage points. Its fast inference makes it particularly suitable for real-time applications.
Simultaneous spectrophotometric determination of copper(II), lead(II) and cadrnium(II)

The full spectrum technique in the fourier domain was studied for simultaneous determination of Cu(II), Pb(II) and Cd(II) with 4-(2-pyridylazo) resorcinol (PAR). A program called SPGRFSQ. was designed to perform the calculations. Seven error functions were calculated for deducing the number of factors. Data reduction was performed using principal component analysis (PCA) algorithm. Experimental results showed the method to be successful even where there was severe overlap of spectra.
Analytical Methodologies for the Determination of Endocrine Disrupting Compounds in Biological and Environmental Samples

Endocrine-disruptor compounds (EDCs) can mimic natural hormones and produce adverse effects in the endocrine functions by interacting with estrogen receptors. EDCs include both natural and synthetic chemicals, such as hormones, personal care products, surfactants, and flame retardants, among others. EDCs are characterised by their ubiquitous presence at trace-level concentrations and their wide diversity. Since the discovery of the adverse effects of these pollutants on wildlife and human health, analytical methods have been developed for their qualitative and quantitative determination. In particular, mass-based analytical methods show excellent sensitivity and precision for their quantification. This paper reviews recently published analytical methodologies for the sample preparation and for the determination of these compounds in different environmental and biological matrices by liquid chromatography coupled with mass spectrometry. The various sample preparation techniques are compared and discussed. In addition, recent developments and advances in this field are presented.
Determination of cell fate selection during phage lambda infection

Bacteriophage lambda infection of Escherichia coli can result in distinct cell fate outcomes. For example, some cells lyse whereas others survive as lysogens. A quantitative biophysical model of lambda infection supports the hypothesis that spontaneous differences in the timing of individual molecular events during lambda infection leads to variation in the selection of cell fates. Building from this analysis, the lambda lysis–lysogeny decision now serves as a paradigm for how intrinsic molecular noise can influence cellular behavior, drive developmental processes, and produce population heterogeneity. Here, we report experimental evidence that warrants reconsidering this framework. By using cell fractioning, plating, and single-cell fluorescent microscopy, we find that physical differences among cells present before infection bias lambda developmental outcomes. Specifically, variation in cell volume at the time of infection can be used to help predict cell fate: a ≈2-fold increase in cell volume results in a 4- to 5-fold decrease in the probability of lysogeny. Other cell fate decisions now thought to be stochastic might also be determined by pre-existing variation.
Clustering Based on Fuzzy Tolerance Quotient Spaces ?

The quotient space theory based on fuzzy tolerance relation is put forward to solve the problem of clustering in this paper. The similarity matrix does not always satisfy ultrametric inequality, theoretically and practically. We give the method to construct the hierarchical quotient space chain if the similarity matrix is only reflexive and symmetric. We consider not only the subset of data (cluster) but also the structure of the different granular clusters. The main contributions in this paper include four parts. (1) We introduce sufficient and necessary condition that normalized distance d(x, y) is an equicrural distance; (2) The relation between equicrural distance and ultrametric inequality is discussed; (3) We propose a method to get the proximate partition of a covering from a tolerance relation; (4) An example for using fuzzy tolerance quotient spaces to clustering is given.