Miracle-dz/UniFAR-FTUs huggingface dataset:
Hedgehog Promotes Neovascularization in Pancreatic Cancers by Regulating Ang-1 and IGF-1 Expression in Bone-Marrow Derived Pro-Angiogenic Cells

Background The hedgehog (Hh) pathway has been implicated in the pathogenesis of cancer including pancreatic ductal adenocarcinoma (PDAC). Recent studies have suggested that the oncogenic function of Hh in PDAC involves signaling in the stromal cells rather than cell autonomous effects on the tumor cells. However, the origin and nature of the stromal cell type(s) that are responsive to Hh signaling remained unknown. Since Hh signaling plays a crucial role during embryonic and postnatal vasculogenesis, we speculated that Hh ligand may act on tumor vasculature specifically focusing on bone marrow (BM)-derived cells. Methodology/Principal Findings Cyclopamine was utilized to inhibit the Hh pathway in human PDAC cell lines and their xenografts. BM transplants, co-culture systems of tumor cells and BM-derived pro-angiogenic cells (BMPCs) were employed to assess the role of tumor-derived Hh in regulating the BM compartment and the contribution of BM-derived cells to angiogenesis in PDAC. Cyclopamine administration attenuated Hh signaling in the stroma rather than in the cancer cells as reflected by decreased expression of full length Gli2 protein and Gli1 mRNA specifically in the compartment. Cyclopamine inhibited the growth of PDAC xenografts in association with regression of the tumor vasculature and reduced homing of BM-derived cells to the tumor. Host-derived Ang-1 and IGF-1 mRNA levels were downregulated by cyclopamine in the tumor xenografts. In vitro co-culture and matrigel plug assays demonstrated that PDAC cell-derived Shh induced Ang-1 and IGF-1 production in BMPCs, resulting in their enhanced migration and capillary morphogenesis activity. Conclusions/Significance We identified the BMPCs as alternative stromal targets of Hh-ligand in PDAC suggesting that the tumor vasculature is an attractive therapeutic target of Hh blockade. Our data is consistent with the emerging concept that BM-derived cells make important contributions to epithelial tumorigenesis.
Activation of DNA Replication in Yeast by Recruitment of the RNA Polymerase II Transcription Complex

Abstract Activators of transcription are known to also play an important and direct role in activating DNA replication. However, the mechanism whereby they stimulate replication has remained elusive. One model suggests that, in the context of replication origins, transcriptional activators work by interacting with replication factors. We show that a defined, single interaction between a DNA-bound derivative of the activator Gal4 and Gal11P, a mutant form of the RNA polymerase II holoenzyme component Gal11, suffices for stimulating DNA replication as it does for transcription. Moreover, recruitment of TBP, which can activate transcription from a gene promoter, also stimulates DNA replication from an origin site. These results strongly argue that transcriptional activators may not necessarily need to contact DNA replication factors directly, but can stimulate replication by recruiting the RNA polymerase II transcription complex to DNA.
fMRI study of neural sensitization to hedonic stimuli in long‐term, daily cannabis users

Although there is emergent evidence illustrating neural sensitivity to cannabis cues in cannabis users, the specificity of this effect to cannabis cues as opposed to a generalized hyper‐sensitivity to hedonic stimuli has not yet been directly tested. Using fMRI, we presented 53 daily, long‐term cannabis users and 68 non‐using controls visual and tactile cues for cannabis, a natural reward, and, a sensory‐perceptual control object to evaluate brain response to hedonic stimuli in cannabis users. The results showed an interaction between group and reward type such that the users had greater response during cannabis cues relative to natural reward cues (i.e., fruit) in the orbitofrontal cortex, striatum, anterior cingulate gyrus, and ventral tegmental area compared to non‐users (cluster‐threshold z = 2.3, P < 0.05). In the users, there were positive brain‐behavior correlations between neural response to cannabis cues in fronto‐striatal‐temporal regions and subjective craving, marijuana‐related problems, withdrawal symptoms, and levels of THC metabolites (cluster‐threshold z = 2.3, P < 0.05). These findings demonstrate hyper‐responsivity, and, specificity of brain response to cannabis cues in long‐term cannabis users that are above that of response to natural reward cues. These observations are concordant with incentive sensitization models suggesting sensitization of mesocorticolimbic regions and disruption of natural reward processes following drug use. Although the cross‐sectional nature of this study does not provide information on causality, the positive correlations between neural response and indicators of cannabis use (i.e., THC levels) suggest that alterations in the reward system are, in part, related to cannabis use. Hum Brain Mapp 37:3431–3443, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
Who owns a personal home page? A discussion of sampling problems and a strategy based on a search engine

Research on personal home pages is still rare. Many studies to date are exploratory, and the problem of drawing a sample that reflects the variety of existing home pages has not yet been solved. The present paper discusses sampling strategies and suggests a strategy based on the results retrieved by a search engine. This approach is used to draw a sample of 229 personal home pages that portray private identities. Findings on age and sex of the owners and elements characterizing the sites are reported.
3-D Motion Estimation Using Linear and Bilinear Constraints

This paper examines the use of the EM algorithm to perform motion segmentation on image sequences that contain independent object motion. The input data are linear constraints on 3-D translational motion and bilinear constraints on 3-D translation and rotation, derived from computed optical ow using subspace methods. The problems of outlier detection, deciding how many processes, and the initial guesses for the EM algorithm are considered. Randomly sampling from the ow eld is not a good method when a large planar surface is present in the image, due to the nature of the linear constraints. A method for choosing ow samples in this case is presented. Results from three image sequences are presented.
Different IVIG Glycoforms Affect In Vitro Inhibition of Anti-Ganglioside Antibody-Mediated Complement Deposition

Intravenous immunoglobulin (IVIG) is the first line treatment for Guillain–Barré syndrome and multifocal motor neuropathy, which are caused by anti-ganglioside antibody-mediated complement-dependent cytotoxicity. IVIG has many potential mechanisms of action, and sialylation of the IgG Fc portion reportedly has an anti-inflammatory effect in antibody-dependent cell-mediated cytotoxicity models. We investigated the effects of different IVIG glycoforms on the inhibition of antibody-mediated complement-dependent cytotoxicity. Deglycosylated, degalactosylated, galactosylated and sialylated IgG were prepared from IVIG following treatment with glycosidases and glycosyltransferases. Sera from patients with Guillain–Barré syndrome, Miller Fisher syndrome and multifocal motor neuropathy associated with anti-ganglioside antibodies were used. Inhibition of complement deposition subsequent to IgG or IgM autoantibody binding to ganglioside, GM1 or GQ1b was assessed on microtiter plates. Sialylated and galactosylated IVIGs more effectively inhibited C3 deposition than original IVIG or enzyme-treated IVIGs (agalactosylated and deglycosylated IVIGs). Therefore, sialylated and galactosylated IVIGs may be more effective than conventional IVIG in the treatment of complement-dependent autoimmune diseases.
Marine Ecology Progress Series Mar Ecol Prog Ser Discriminant Function Analysis in Marine Ecology: Some Oversights and Their Solutions

Marine ecologists commonly use discriminant function analysis (DFA) to evaluate the similarity of distinct populations and to classify individuals of unknown origin to known populations. However, investigators using DFA must account for (1) the possibility of correct classification due to chance alone, and (2) the influence of prior probabilities of group membership on classification results. A search of the recent otolith chemistry literature showed that these two concerns are some­ times ignored, so we used simulated data sets to explore the potential pitfalls of such oversights. We found that when estimating reclassification success for a training data set, small sample sizes or unbalanced sampling designs can produce remarkably high reclassification success rates by chance alone, especially when prior probabilities are estimated from sample size. When using a training data set to classify unknown individuals, maximum likelihood estimation of mixture proportions and group membership afforded up to 20 % improvement over DFA with uninformative priors when groups contributed to the sample unequally. Given these results, we recommend the use of (1) ran­ domization tests to estimate the probability that reclassification success is better than random, and (2) maximum likelihood estimation of mixture proportions in place of uninformative priors. Resale or republication not permitted without written consent of the publisher
Diagnosing genetically diverse avian malarial infections using mixed-sequence analysis and TA-cloning

Birds harbouring several malarial parasites are common in the wild, and resolving such multiple infections is important for our understanding of host–parasite relationships. We propose a simple and reasonably accurate method for detecting and resolving multiple infections, based on the analysis of parasite cytochrome b DNA sequences: genetically mixed infections are first identified by double nucleotide peaks on sequence electropherograms, and later retrieved by TA-cloning. We applied this method to wild birds, and to experimentally created mixes with varying proportion of two parasites (Plasmodium spp. and Haemoproteus spp.). In general, the method was very efficient in detecting and resolving multiple infections, but some problems were encountered. Several multiple infections were erroneously scored as simple, either because one of the parasite lineages was a better target for the primers used, or because it was much more abundant in the mix. On the other hand, single nucleotide substitutions and template switching during PCR produced artificial sequences in some clones. We discuss the utility of the method, and propose a framework for its use when screening for genetically diverse avian malarial parasites.
Holographic Flavor Transport in Arbitrary Constant Background Fields

We use gauge-gravity duality to compute a new transport coefficient associated with a number Nf of massive N=2 supersymmetric hypermultiplet fields propagating through an N=4 SU(Nc) super-Yang-Mills theory plasma in the limits of large Nc and large 't Hooft coupling, with Nf << Nc. We introduce a baryon number density as well as arbitrary constant electric and magnetic fields, generalizing previous calculations by including a magnetic field with a component parallel to the electric field. We can thus compute all components of the conductivity tensor associated with transport of baryon number charge, including a component never before calculated in gauge-gravity duality. We also compute the contribution that the flavor degrees of freedom make to the stress-energy tensor, which exhibits divergences associated with the rates of energy and momentum loss of the flavor degrees of freedom. We discuss two currents that are free from these divergences, one of which becomes anomalous when the magnetic field has a component parallel to the electric field and hence may be related to recent study of charge transport in the presence of anomalies.
Health, Quality of Life and GDP: An ASEAN Experience

Governments all over the world would like to improve the well being of its citizen. One aspects of well being can be seen through the quality of life (QoL) a person enjoys. Measuring and determining what is QoL is not an easy task. In this paper, using per-capita income as the yardstick for QOL, and the role of government through the way it spend its public money would be able to tell how ASEAN governments’ expenditure influences the QoL. Particularly government expenditure on health signifies the commitment of a government in improving the QoL. QoL can also be seen through the availability of health care in country. When health care improves, life span and earning abilities will raise too. It is the government’s responsibility in providing the best health care to the people. Government’s commitment can be gauged through the amount it spends on these items. At the same time government spending would bring spillover effects in terms of raising per-capita income, which reflects the QoL. Using data on government expenditure of ASEAN countries for the last 15 years, it would be possible to determine on what extent expenditures on health do influence the per-capita income. Initial findings reveal that the impact of both these government expenditures on per-capita income is not the same for all the ASEAN countries.
Psychosocial risk factors and health outcomes in the offspring of parents with bipolar disorder in adolescence and young adulthood: A 10-year longitudinal study

It is well known that the offspring of parents with bipolar disorder (OBD) are at heightened risk of developing mental disorders, particularly the affective disorders. However, we have much less information about deleterious non-psychiatric outcomes, such a health-risk behaviors, and the family-environmental risk factors associated with negative outcomes in the OBD. It has been proposed that the OBD, through genetic mechanisms and early family interactions, develop a heightened sensitivity to stress, maladaptive coping, and dysregulated behavior, which ultimately increases the risk for affective disorders. The current dissertation was designed to test parts of this model by conducting a comprehensive assessment of social risk factors, personality, and mental health in the OBD, and to examine putative antecedents of negative outcomes in the OBD using 10-year longitudinal data. The first study within this dissertation assessed different psychosocial and health-related outcomes in the OBD, including mental health, personality, coping style, smoking, suicidality, high-risk sexual behaviors, criminality, and mental health. These factors were compared across affected and unaffected offspring in order to differentiate potential prodromal markers from correlates of mood episodes. It was found that unaffected OBD engaged in less task-oriented and more avoidant coping strategies than controls. Furthermore, after controlling for current affective disorders, the OBD were more likely than controls to engage in sexual risk behaviors (SRBs). In the second study, parents’ personality, specifically high neuroticism, was assessed to determine whether it predicted SRBs among the offspring 10 years later, and whether the relationship between parents’ personality and offspring SRBs was mediated by behavioral problems in middle childhood. High neuroticism, low agreeableness, and low extraversion in the parents predicted SRBs in their offspring in late adolescence-early adulthood. The offspring’s externalizing problems in middle childhood partly mediated the association between parents’ personality and offspring SRBs. The findings highlight a risk profile in the OBD characterized by poor stress coping and engagement in risky sexual behaviors. Furthermore, SRBs were related to a developmental trajectory that included markers of family stress and early externalizing problems. Together, these studies highlight the importance of behavior problems as markers of vulnerability in the OBD. Importantly, this pattern of results emphasizes the need for targeted, early interventions aimed at increasing familial stability and stress management before the development of behavioral problems.
GROUND-BASED OPTICAL AND SPITZER INFRARED IMAGING OBSERVATIONS OF COMET 21P/GIACOBINI-ZINNER

We present ground-based optical and Spitzer Space Telescope infrared (IR) imaging observations of the ecliptic (Jupiter-family) comet 21P/Giacobini-Zinner, the parent body of the Draconid meteor stream, during its 2005 apparition. The onset of nucleus activity occurred at a pre-perihelion heliocentric distance, rh ≃ 3.80 AU, while post-perihelion 21P was dusty (peak Afρ = 131 cm−1) and active out to heliocentric distances ≳3.3 AU following a logarithmic slope with rh of −2.04. Coma colors, V − R = 0.524 ± 0.003, R − I = 0.487 ± 0.004, are redder than solar, yet comparable to colors derived for other Jupiter-family comets. A nucleus radius of 1.82 ± 0.05 km is derived from photometry at quiescence. Spitzer images post-perihelion exhibit an extensive coma with a prominent dust tail, where excess emission (over the dust continuum) in the 4.5 μm Infrared Array Camera (IRAC) image arises from volatile gaseous CO and/or CO2. No dust trail was detected (3σ surface brightness upper limit of 0.3 MJy sr−1 pixel−1) along the projected velocity vector of comet 21P in the MIPS 24 μm image suggesting that the number density of trail particles is ≲7 × 10−11 m−3. The bolometric albedo of 21P derived from the contemporaneous optical and Spitzer observations is A(θ = 22°) = 0.11, slightly lower than values derived for other comets at the same phase angle.
Dynamic Replica Selection Using Improved Kernel Density Estimation

Replication service in Distributed Systems can reduce access latency and bandwidth consumption. When different nodes hold replicas accessed, there will be a significant benefit by selecting the best replica. Most of the existed replication strategies deal with the prediction of the response time. However, these strategies do not take fully into account the network dynamic and access locality. To solve this problem, a dynamic replica selection strategy using improved Kernel Density Estimation (KDE) is presented. Firstly, it distinguishes old replicas from new ones. Then, it predicts the network load and available bandwidth to choose the best replica. The improved KDE can select accurately the best accessed replica with only a little history data, which is very useful in a dynamic network. Simulation results demonstrate the efficiency and effectiveness of improved KDE in comparison with other approaches.
Efficient Implementation of Native Software Simulation for MPSoC

Efficient and precise simulation models at a high abstraction level are required in order to perform early design validations and architecture explorations of multi- processor system-on-chip (MPSoC) platforms. Although native software simulation approaches provide interesting capabilities, they quickly become unsuitable when complex hardware architecture have to be considered. In this paper, we present a SystemC-based MPSoC platform implementation that allows native software simulation while keeping details of the underlying hardware model. The key contribution of this work is a realistic memory mapping modelling that makes possible the simulation of operating systems and software applications on complex hardware models with multiple processors and DMA devices. This method also allows the reuse of different software components for the target processor(s). Experimental results show the efficiency of the proposed method to validate software on complex hardware architectures.
Adaptive User Distance Modeling in Social Media

One important challenge in social network analysis is how to model users’ distance as a single measure. We propose to model this distance by simultaneously exploring users’ profile attributes and local network structures. Due to the sparsity of data, where each user may interact with just a few people and only a few users provide their profile information, it is typically difficult to learn effective distance measures for any individual network. One important observation is that, people nowadays engage in multiple social networks, such as Facebook, Twitter, etc., where auxiliary knowledge from related networks can help alleviate the data sparsity problem. Nonetheless, due to the network differences, borrowing knowledge directly does not work well. Instead, we propose an adaptive metric learning framework. The basic idea is to exploit knowledge from related networks collectively through embedding and employ boosting-based techniques to eliminate irrelevant attributes. We evaluate the adaptive user distance measure on link prediction problem an important social modeling task. Empirical studies demonstrate that the proposed approach significantly improves the link-prediction precision over state-of-theart metric learning and link prediction approaches on two large-scale social networking datasets significantly.
The Tnt1 Retrotransposon Escapes Silencing in Tobacco, Its Natural Host

Retrotransposons' high capacity for mutagenesis is a threat that genomes need to control tightly. Transcriptional gene silencing is a general and highly effective control of retrotransposon expression. Yet, some retrotransposons manage to transpose and proliferate in plant genomes, suggesting that, as shown for plant viruses, retrotransposons can escape silencing. However no evidence of retrotransposon silencing escape has been reported. Here we analyze the silencing control of the tobacco Tnt1 retrotransposon and report that even though constructs driven by the Tnt1 promoter become silenced when stably integrated in tobacco, the endogenous Tnt1 elements remain active. Silencing of Tnt1-containing transgenes correlates with high DNA methylation and the inability to incorporate H2A.Z into their promoters, whereas the endogenous Tnt1 elements remain partially methylated at asymmetrical positions and incorporate H2A.Z upon induction. Our results show that the promoter of Tnt1 is a target of silencing in tobacco, but also that endogenous Tnt1 elements can escape this control and be expressed in their natural host.
Subjective and objective quality assessment of single-channel speech separation algorithms

Previous studies on performance evaluation of single-channel speech separation (SCSS) algorithms mostly focused on automatic speech recognition (ASR) accuracy as their performance measure. Assessing the separated signals by different metrics other than this has the benefit that the results are expected to carry on to other applications beyond ASR. In this paper, in addition to conventional speech quality metrics (PESQ and SNRloss), we also evaluate the separation systems output using different source separation metrics: blind source separation evaluation (BSS EVAL) and perceptual evaluation methods for audio source separation (PEASS) measures. In our experiments, we apply these measures on the separated signals obtained by two well-known systems in the SCSS challenge to assess the objective and subjective quality of their output signals. Comparing subjective and objective measurements shows that PESQ and PEASS quality metrics predict well the subjective quality of separated signals obtained by the separation systems. From the results it is observed that the short-time objective intelligibility (STOI) measure predict the speech intelligibility results.