Miracle-dz/UniFAR-FTUs huggingface dataset:
Forkhead box protein C1 promotes cell proliferation and invasion in human cervical cancer

Increasing evidence has demonstrated that aberrant forkhead box protein C1 (FOXC1) expression contributes to tumorigenesis in multiple types of malignant tumor. However, the clinical significance and biological roles of FOXC1 in cervical cancer remain unknown. The expression levels of FOXC1 were examined in human cervical cancer tissues and cells using reverse transcription-quantitative polymerase chain reaction, immunohistochemistry and western blotting. Furthermore, high FOXC1 expression was significantly associated with advanced clinical stages, a high degree of malignancy and a poor outcome. FOXC1 silencing inhibited cell growth and enhanced cell apoptosis. Knockdown of FOXC1 markedly suppressed cell migration and invasion in vitro, and resulted in downregulation of phosphorylated-RAC-α serine/threonine-protein kinase, proto-oncogene c-Myc and B-cell lymphoma 2. In conclusion, these data indicated that upregulation of FOXC1 contributed to the development of cervical cancer by increasing the growth and motility of the cervical cancer cells, thereby worsening the disease progression in these patients.
A Genetic Programming Approach for Relevance Feedback in Region-Based Image Retrieval Systems

This paper presents a new relevance feedback method for content-based image retrieval using local image features. This method adopts a genetic programming approach to learn user preferences and combine the region similarity values in a query session. Experiments demonstrate that the proposed method yields more effective results than the local aggregation pattern (LAP)-based relevance feedback technique.
On Web Based Sentence Similarity for Paraphrasing Detection

Semantic similarity measures play vital roles in information retrieval, natural language processing and paraphrasing detection. With the growing plagiarisms cases in both commercial and research community, designing efficient tools and approaches for paraphrasing detection becomes crucial. This paper contrasts web-based approach related to analysis of snippets of the search engine with WordNet based measure. Several refinements of the web-based approach will be investigated and compared. Evaluations of the approaches with respect to Microsoft paraphrasing dataset will be performed and discussed.
Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.
Dynamic Perturbation of the Active Site Determines Reversible Thermal Inactivation in Glycoside Hydrolase Family 12

The temperature dependence of enzyme catalysis is highly debated. Specifically, how high temperatures induce enzyme inactivation has broad implications for both fundamental and applied science. Here, we explored the mechanism of the reversible thermal inactivation in glycoside hydrolase family 12 (GH12) using comparative molecular dynamics simulations. First, we investigated the distribution of structural flexibility over the enzyme and found that the active site was the general thermal-sensitive region in GH12 cellulases. The dynamic perturbation of the active site before enzyme denaturation was explored through principal-component analysis, which indicated that variations in the collective motion and conformational ensemble of the active site may precisely correspond to enzyme transition from its active form to the inactive form. Furthermore, the degree of dynamic perturbation of the active site was found to be negatively correlated with the melting temperatures of GH12 enzymes, further proving the importance of the dynamic stability of the active site. Additionally, analysis of the residue-interaction network revealed that the active site in thermophilic enzyme was capable of forming additional contacts with other amino acids than those observed in the mesophilic enzyme. These interactions are likely the key mechanisms underlying the differences in rigidity of the active site. These findings provide further biophysical insights into the reversible thermal inactivation of enzymes and potential applications in future protein engineering.
Coordination of olfactory receptor choice with guidance receptor expression and function in olfactory sensory neurons

Olfactory sensory neurons choose to express a single odorant receptor (OR) from a large gene repertoire and extend axons to reproducible, OR-specific locations within the olfactory bulb. This developmental process produces a topographically organized map of odorant experience in the brain. The axon guidance mechanisms that generate this pattern of connectivity, as well as those that coordinate OR choice and axonal guidance receptor expression, are incompletely understood. We applied the powerful approach of single-cell RNA-seq on newly born olfactory sensory neurons (OSNs) in young zebrafish larvae to address these issues. Expression profiles were generated for 56 individual Olfactory Marker Protein (OMP) positive sensory neurons by single-cell (SC) RNA-seq. We show that just as in mouse OSNs, mature zebrafish OSNs typically express a single predominant OR transcript. Our previous work suggests that OSN targeting is related to the OR clade from which a sensory neuron chooses to express its odorant receptor. We categorized each of the mature cells based on the clade of their predominantly expressed OR. Transcripts expressed at higher levels in each of three clade-related categories were identified using Penalized Linear Discriminant Analysis (PLDA). A genome-wide approach was used to identify membrane-associated proteins that are most likely to have guidance-related activity. We found that OSNs that choose to express an OR from a particular clade also express specific subsets of potential axon guidance genes and transcription factors. We validated our identification of candidate axon guidance genes for one clade of OSNs using bulk RNA-seq from a subset of transgene-labeled neurons that project to a single protoglomerulus. The differential expression patterns of selected candidate guidance genes were confirmed using fluorescent in situ hybridization. Most importantly, we observed axonal mistargeting in knockouts of three candidate axonal guidance genes identified in this analysis: nrp1a, nrp1b, and robo2. In each case, targeting errors were detected in the subset of axons that normally express these transcripts at high levels, and not in the axons that express them at low levels. Our findings demonstrate that specific, functional, axonal guidance related genes are expressed in subsets of OSNs that that can be categorized by their patterns of OR expression.
Consequences of endogenous and exogenous WNT signaling for development of the preimplantation bovine embryo†

Abstract The specific role of WNT signaling during preimplantation development remains unclear. Here, we evaluated consequences of activation and inhibition of β-catenin (CTNNB1)-dependent and -independent WNT signaling in the bovine preimplantation embryo. Activation of CTNNB1-mediated WNT signaling by the agonist 2-amino-4-(3,4-(methylenedioxy)benzylamino)-6-(3-methoxyphenyl)pyrimidine (AMBMP) and a glycogen synthase kinase 3 inhibitor reduced development to the blastocyst stage. Moreover, the antagonist of WNT signaling, dickkopf-related protein 1 (DKK1), alleviated the negative effect of AMBMP on development via reduction of CTNNB1. Based on labeling for phospho c-Jun N-terminal kinase, there was no evidence that DKK1 activated the planar cell polarity (PCP) pathway. Inhibition of secretion of endogenous WNTs did not affect development but increased number of cells in the inner cell mass (ICM). In contrast, DKK1 did not affect number of ICM or trophectoderm (TE) cells, suggesting that embryo-derived WNTs regulate ICM proliferation through a mechanism independent of CTNNB1. In addition, DKK1 did not affect the number of cells positive for the transcription factor yes-associated protein 1 (YAP1) involved in TE formation. In fact, DKK1 decreased YAP1. In contrast, exposure of embryos to WNT family member 7A (WNT7A) improved blastocyst development, inhibited the PCP pathway, and did not affect amounts of CTNNB1. Results indicate that embryo-derived WNTs are dispensable for blastocyst formation but participate in regulation of ICM proliferation, likely through a mechanism independent of CTNNB1. The response to AMBMP and WNT7A leads to the hypothesis that maternally derived WNTs can play a positive or negative role in regulation of preimplantation development. Summary Sentence Endogenous WNTs are dispensable for blastocyst formation, but participate in the regulation of ICM proliferation, likely through a mechanism independent of β-catenin.
Synchronization of heterogeneous dynamical networks via distributed impulsive control

This paper studies global synchronization between a heterogeneous dynamical network and a known target trajectory via distributed impulsive control. Synchronization with an error level, called quasi-synchronization, is analyzed by utilizing the time-varying Lyapunov function. Some sufficient quasi-synchronization conditions are presented and explicit expressions of error levels are derived. Furthermore, the effects of the pinning control matrix and the coupling strength are explored. Unlike the continuous pinning feedback control, it is shown that a large coupling strength will destroy synchronization in the present of impulsive controller, which is also verified by our simulations.
Erosion Characteristics of Transpiration Cooled Materials

The paper presents results of an experimental study of the erosion of transpiration air cooled blade materials. Five samples of porous wire meshes for gas turbine blade airfoil applications were subjected to erosion test with alumina. Three of the configurations of meshes were of woven structure, the other two of a wound structure. The erosion was evaluated at various angles of impact from 20 to 90 deg. The critical angle of impact was established for all five samples. The results are compared to that for a solid material sample. The paper also presents an overall plan for erosion testing of transpiration cooled materials and provides information on erosion related work.Copyright © 1980 by ASME
Direct quantification of autophagic flux by a single molecule-based probe

ABSTRACT Macroautophagy/autophagy remains a rapidly advancing research topic, and there continues to be a need for constantly evolving methodology to investigate this pathway at each individual step. Accordingly, new assays to measure autophagic flux in a robust and reliable manner are essential to understand the mechanism and physiological roles of autophagy. Kaizuka et al. recently reported a new fluorescence probe, GFP-LC3-RFP-LC3ΔG to directly demonstrate autophagic flux without being combined with lysosomal inhibitors (see the Kaizuka et al. punctum in this issue of the journal). When expressed in cells, the probe is cleaved into a degradable/quenchable part, GFP-LC3, and stable/cytosolic part, RFP-LC3ΔG. The latter serves as an internal control and a decrease of the GFP:RFP ratio indicates the occurrence of autophagy. Since the key index of this probe is the degradation of GFP-LC3, it can be used to measure the cumulative effect of basal autophagy. The assay is applicable to high-throughput drug discovery as well as in vivo analysis.
A micromechanics-based creep damage model for brittle rocks

For brittle rocks, the essential non-linear mechanism of material degradation and structure failure can be related to cracking-induced inelastic deformation and damage evolution. This paper is devoted to modelling non-linear time-dependent mechanical behaviour of brittle rocks under compression. A micromechanics-based damage-friction coupling model is developed based on the linear homogenisation method and within the irreversible thermodynamics framework. Firstly, the free energy in the matrix-cracks representative volume is determined, from which are derived the thermodynamic forces associated with the inelastic strains and the damage. Under isotropic assumption, the evolutions of the two internal variables are calculated, respectively, using an associated Coulomb-type friction criterion and a strain energy release rate-based damage criterion. By accounting for subcritical cracking mechanism, the basic model is then extended to describe time-dependent creep deformation and applied to simulate triaxial compression tests and triaxial creep tests on granite. Comparisons of numerical predictions with experimental data are performed to show the predictive capacity and the advantage of the proposed model.
Surface Features of Jute Fiber Using Scanning Electron Microscopy

The surface features of jute fiber were studied after various degrees of delignification and bleaching treatments. Surface features progressively change with gradual removal of lignin, leading to the "cottonization" of jute fiber at 93% delignification. Bleaching with peroxide or bleaching powder causes better cleaning and separation of ultimates. Degradation or damage of fibers or cells occurs when the chlorine used is nearly neutral or acidic.
Village Building Identification Based on Ensemble Convolutional Neural Networks

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86.
Searching Time Series via Semantic Annotations

Time series are used in several domains of knowledge. During their analysis, experts often create or analyze associations between time series and annotations. In order to study a problem, for example, patient behavior or crop patterns, experts need to search and correlate several time series. However, finding appropriate series related with a problem is a difficult task. Search is usually performed using a few parameters, such as series geographic location. Annotations may be used to help the search using string match. Given this scenario, this paper discusses a work in progress to design and partially develop a software framework to search time series via semantic annotations. It will support experts in the correlation of time series, foster collaboration among experts, and allow the use of Linked Data concepts to aggregate knowledge to content. This paper extends the short paper accepted for BRESCI Brazilian Workshop e-Science 2014. The extensions include a time series semantic annotation model, implementation details, and a longer theoretical related work section.
Involvement of Smad3 pathway in atrial fibrosis induced by elevated hydrostatic pressure

Hypertension is a main risk factor for atrial fibrillation, but the direct effects of hydrostatic pressure on the atrial fibrosis are still unknown. The present study investigated whether hydrostatic pressure is responsible for atrial fibrosis, and addressed a potential role of the Smad pathway in this pathology. Biochemical assays were used to study regulation and expression of fibrotic factors in spontaneously hypertensive rats (SHRs) and Wistar rats, and in cardiac fibroblasts (CFs) cultured under standard (0 mmHg) and elevated (20, 40 mmHg) hydrostatic pressure. Levels of atrial fibrosis and protein expression of fibrotic factors Col‐1A1/‐3A1, TGF‐β1, and MMP‐2 in SHRs' left atrial tissues were higher than those in Wistar rats. Exposure to elevated pressure was associated with the proliferation of CFs. The protein expression of Col‐1A1/‐3A1, TGF‐β1, and MMP‐2 in CFs was also up‐regulated in a pressure‐dependent manner. The proliferation of CFs and increased expressions of fibrotic markers induced by elevated hydrostatic pressure could be reversed by the Smad3 inhibitor naringenin. The activation of Smad3 pathway was also stimulated by elevated hydrostatic pressure. These results demonstrate that CF secretory function and proliferation can be up‐regulated by exposure to elevated pressure, and that Smad3 may modulate CF activation induced by high hydrostatic pressure.
High-Performance Systems for Crowdsourced Data Analysis

Author(s): Haas, Daniel | Advisor(s): Franklin, Michael J | Abstract: In spite of the dramatic recent progress in automated techniques for computer vision and natural language understanding, human effort, often in the form of crowd workers recruited on marketplaces such as Amazon’s Mechanical Turk, remains a necessary part of data analysis workflows for machine learning and data cleaning. However, embedding manual steps in automated workflows comes with a performance cost, since humans seldom process data at the speed of computers. In order to rapidly iterate between hypotheses and evidence, data analysts need tools that can provide human processing at close to machine latencies.In this dissertation, I describe the design, theory, and implementation of performant crowd-powered systems. After discussing the performance implications of involving humans in data analysis workflows, I present an example of a data cleaning system that requires low-latency crowd input. Then, I describe CLAMShell, a system that accurately labels large-scale datasets in one to two minutes, and its evaluation on over a thousand workers processing nearly a quarter million tasks. Next, I consider the design of multi-tenant crowd systems running many heterogeneous applications at once. I describe Cioppino, a system designed to improve throughput and reduce cost in this setting, while taking into account worker preferences. Finally, I explore the theory of identifying fast individuals in an unknown population of workers, which can be modeled as an instance of the infinite-armed bandit problem. The analysis results in novel near-optimal algorithms with applications to broader statistical theory. Together, these components provide for the implementation of human computation systems that are cost-efficient, scalable, and fast enough to integrate into existing data analysis workflows without compromising performance.
Information Theoretic Optimal Learning of Gaussian Graphical Models

What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered? Information-theoretic arguments provide a lower bound on the minimum number of samples necessary to perfectly identify the support of any multivariate normal distribution as a function of model parameters. For a model defined on a sparse graph with $p$ nodes, a maximum degree $d$ and minimum normalized edge strength $\kappa$, this necessary number of samples scales at least as $d \log p/\kappa^2$. The sample complexity requirements of existing methods for perfect graph reconstruction exhibit dependency on additional parameters that do not enter in the lower bound. The question of whether the lower bound is tight and achievable by a polynomial time algorithm remains open. In this paper, we constructively answer this question and propose an algorithm, termed DICE, whose sample complexity matches the information-theoretic lower bound up to a universal constant factor. We also propose a related algorithm SLICE that has a slightly higher sample complexity, but can be implemented as a mixed integer quadratic program which makes it attractive in practice. Importantly, SLICE retains a critical advantage of DICE in that its sample complexity only depends on quantities present in the information theoretic lower bound. We anticipate that this result will stimulate future search of computationally efficient sample-optimal algorithms.
Beyond Strain

Introduction: Life expectancy is increasing in Latin America resulting in the need for more family caregivers for older adults with dementia. The purpose of the current study was to examine the relationships between personal strengths (optimism, sense of coherence [SOC], and resilience) and the mental health of dementia caregivers from Latin America. Method: Primary family dementia caregivers (n = 127) were identified via convenience sampling at the Instituto de Neurociencias de San Lucas, Argentina, and CETYS University, in Baja California, Mexico and completed measures of these constructs. Findings: Personal strengths explained between 32% and 50% of the variance in caregiver mental health. In a series of hierarchical multiple regressions, more manageability (β = −.38, p = .001), general resilience (β = −.24, p = .012), and social competence (β = −.21, p = .034) were uniquely associated with lower depression. Greater comprehensibility (β = −.28, p = .008) was uniquely associated with decreased burden, and manageability was marginally related (β = −.21, p< .10). Greater optimism (β = .37, p< .001) and manageability (β = .27, p = .004) were uniquely associated with increased life satisfaction. Discussion: The personal strengths of caregivers in Latin America may be particularly important for their mental health because of the culturally imbedded sense of duty toward older family members. Implications: Incorporating strengths-based approaches into research on caregiver interventions in regions where caregiving is a highly culturally valued role such as Latin America may have the potential to improve the mental health of dementia caregivers.