Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Argument based Relation Properties in Opinions

We consider the problem of identifying properties which can relate propositions in opinionated texts that are part of an argument structure. Given a statement or a set of statements, the term stance refers to the overall viewpoint present, whether it is in favour or against the topic in discussion — a form of persuasion as in computational argumentation. Stance classification has become a prominent topic among NLP researchers and while stance alone cannot help in identifying the argumentative context behind a text, we show that opinions containing a stance can be classified in terms of the way the opinions are expressed: either as explicit or implicit opinions. We use three supervised methods — surface-based, embeddings-based and hybrid — and analyse the performance of these methods. We also propose three types of domain-based ontology relation, based on the implicit/explicit opinion classification, which can be used to relate appropriate propositions within an argument structure.
Validation of water vapour profiles (version 13) retrieved by the IMK/IAA scientific retrieval processor based on full resolution spectra measured by MIPAS on board Envisat

Abstract. Vertical profiles of stratospheric water vapour measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) with the full resolution mode between September 2002 and March 2004 and retrieved with the IMK/IAA scientific retrieval processor were compared to a number of independent measurements in order to estimate the bias and to validate the existing precision estimates of the MIPAS data. The estimated precision for MIPAS is 5 to 10% in the stratosphere, depending on altitude, latitude, and season. The independent instruments were: the Halogen Occultation Experiment (HALOE), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), the Improved Limb Atmospheric Spectrometer-II (ILAS-II), the Polar Ozone and Aerosol Measurement (POAM III) instrument, the Middle Atmospheric Water Vapour Radiometer (MIAWARA), the Michelson Interferometer for Passive Atmospheric Sounding, balloon-borne version (MIPAS-B), the Airborne Microwave Stratospheric Observing System (AMSOS), the Fluorescent Stratospheric Hygrometer for Balloon (FLASH-B), the NOAA frostpoint hygrometer, and the Fast In Situ Hygrometer (FISH). For the in-situ measurements and the ground based, air- and balloon borne remote sensing instruments, the measurements are restricted to central and northern Europe. The comparisons to satellite-borne instruments are predominantly at mid- to high latitudes on both hemispheres. In the stratosphere there is no clear indication of a bias in MIPAS data, because the independent measurements in some cases are drier and in some cases are moister than the MIPAS measurements. Compared to the infrared measurements of MIPAS, measurements in the ultraviolet and visible have a tendency to be high, whereas microwave measurements have a tendency to be low. The results of χ2-based precision validation are somewhat controversial among the comparison estimates. However, for comparison instruments whose error budget also includes errors due to uncertainties in spectrally interfering species and where good coincidences were found, the χ2 values found are in the expected range or even below. This suggests that there is no evidence of systematically underestimated MIPAS random errors.
A genome-wide microRNA screen identifies regulators of tetraploid cell proliferation

Tetraploid cells, which are most commonly generated by errors in cell division, are genomically unstable and have been shown to promote tumorigenesis. Recent genomic studies have estimated that ∼40% of all solid tumors have undergone a genome-doubling event during their evolution, suggesting a significant role for tetraploidy in driving the development of human cancers. To safeguard against the deleterious effects of tetraploidy, nontransformed cells that fail mitosis and become tetraploid activate both the Hippo and p53 tumor suppressor pathways to restrain further proliferation. Tetraploid cells must therefore overcome these antiproliferative barriers to ultimately drive tumor development. However, the genetic routes through which spontaneously arising tetraploid cells adapt to regain proliferative capacity remain poorly characterized. Here, we conducted a comprehensive gain-of-function genome-wide screen to identify microRNAs (miRNAs) that are sufficient to promote the proliferation of tetraploid cells. Our screen identified 23 miRNAs whose overexpression significantly promotes tetraploid proliferation. The vast majority of these miRNAs facilitate tetraploid growth by enhancing mitogenic signaling pathways (e.g., miR-191-3p); however, we also identified several miRNAs that impair the p53/p21 pathway (e.g., miR-523-3p), and a single miRNA (miR-24-3p) that potently inactivates the Hippo pathway via down-regulation of the tumor suppressor gene NF2. Collectively, our data reveal several avenues through which tetraploid cells may regain the proliferative capacity necessary to drive tumorigenesis.
An ant colony algorithm-based approach for cost-effective data hosting with high availability in multi-cloud environments

In this age of information, data plays an increasingly important role. Nowadays, with the development and popularization of cloud computing, more and more enterprises and personal users choose to put their applications and data into the cloud. However, most of them tend to use single cloud because of convenience and cheap storage price in the short term. This may lead to some challenges and risks, such as vendor lock-in, data low-availability, and data privacy risks. Recently, multi-cloud has attracted considerable interests. In this paper, an ant colony algorithm-based approach is proposed for data hosting in multi-cloud environments, which is subjected to optimization objectives including cost and availability. Extensive experiments are performed to evaluate the proposed method, which are driven by real-world cloud services information.
What makes Federer look so elegant ?

Everyday we come across thousands of sportsmen in action. Each of them have their own style of play and make varied impressions on the viewers. Despite this expected variability in the impression they make, there are certain inherent qualities of their play like their poise, the economy of their movement, flow of their movement etc. which make some of them more watchable to most of us. For example in tennis, Federer is widely regarded as the one of the most elegant players in the history. In cricket, the upright stance, the perfect balance, the precision in shot-making are some of the qualities which makes Sachin Tendulkar look more ’elegant’or ’better’ than others. In this project, we wish to the measure the ‘watchability’ of a player by quantifying the quality of the various movements of a player. In particular, we concentrate mainly on the movements of a batsman in cricket and provide principled ways to measure the ‘watchability’ of a batsman in terms of the three typical movements of a batsman, viz stance, back-lift and follow-through. ‘Watchability’ scores can be very useful for a qualitative video summarization of sports videos, analyzing the change in style of a single players play over the course of a long career, or even to determine the amount of influence of one players style on another.
Towards a DDC-based Topic Network Model of Wikipedia

This paper presents a network-theoretical approach to modeling the semantics of large text networks. By example of the German Wikipedia we demonstrate how to estimate the structuring of topics focused by large corpora of natural language texts. Algorithms of this sort are needed to implement distributional semantics of textual manifestations in large online social networks. Our algorithm is based on a comparative study of short text classification starting from two state-of-the-art approaches: Latent Dirichlet Allocation (LDA) and Neural Network Language Models (NNLM). We evaluate these models by example of (i) OAI metadata, (ii) a TREC dataset and (iii) the Google Snippets dataset to demonstrate their performance. We additionally show that a combination of both classifiers is better than any of its constitutive models. Finally, we exemplify our text classifier by plotting the topic structuring of all articles of the German Wikipedia.
The Mediating Effect of Social Media Marketing Adoption between Competitive Intelligence and SME Performance

SMEs play an important role and have been major contributors to the socio-economic development of Malaysia. However, due to increasing number of competition, many organisations, either in the public or private sector, are instigating their own competitive intelligence (CI) services to guide their decision makers. However, most companies still do not establish a formal department of CI even though it is apparent that CI is becoming increasingly vital to a company? s survival in today? s dynamic economies. In addition, in studies of internet adoption by Malaysian SMEs in a developing country, it is found that although access to social media has improved, engagement and use of the Internet is still at low levels in small companies. Thus, this study was conducted to identify the mediating effect of social media marketing adoption between competitive intelligence and SME performance. Theoretical framework of this study was derived from Technology Acceptance Model (TAM) that figure out whether the user were able to adopt the new technologies. The study was carried out in East Coast Region of Malaysia using 339 respondents and analysed using Smart PLS 3.0. Overall, the study provides supportive evidence that social media marketing adoption mediates the relationship between competitive intelligence and SME performance. Competitive intelligence also found to be significantly affect the SME performance.
Content-Based Viewer Estimation Using Image Features for Recommendation of Video Clips

Content-based recommendation is a promising approach to overcome the cold-start problem, which is a fundamental problem facing recommendations on video sharing Web sites and smart television. In this work, we propose and investigate an approach in which viewer profiles are estimated from the image features of video clips. Image features are extracted from representative images such as the key frames and thumbnails of video clips and then used as explanatory variables to construct classifiers for predicting the attributes of audiences having a high probability of watching those clips. We evaluated the proposed method using video clips and corresponding viewer demographics data from YouTube. A thorough investigation of various image features and their combinations demonstrated the effectiveness of the proposed method for this task. Our method is completely contentbased and we expect it to enable recommendations and target advertising even for users and videos with little historical data and meta-data.
Brain prints for biometrics: An introduction and review of current trends

INTRODUCTION The field of biometrics has received considerable attention from the academic community, technology companies, and by the public. This is mainly due to the multiple security issues caused by alarming number of frauds in banking and internet transactions, threats to national security due to terrorism etc. Biometrics involve the use of behavioral or physiological characteristics where traditionally finger prints have been used as the standard de facto method in biometrics. Alternative biometrics such as those based on signature, face features, palmprint, hand geometry, iris and voice have also been proposed (Wayman et al, 2004). There is also a more recent trend to this where measurements from the heart, brain and shape of inner ear etc that have been suggested either to replace the existing biometrics or to augment them to obtain improved performance (Revett 2012). Because of this trend, it is desirable to present an introduction and review of current trends to serve as a guide to people outside the field. The goal of this article is to present an introduction to biometric technology using electrical patterns from the brain, known as brain prints and discuss the current trends and challenge problems that are likely to shape the field in the near future.
Theory of compressional and shear waves in fluidlike marine sediments

An unconsolidated, saturated marine sediment consists of a more or less loose assemblage of mineral grains in contact, with seawater in the interstices. It is postulated that the two-phase medium possesses no skeletal frame, implying that the elastic rigidity modulus of the material is zero. A theory of wave propagation in such a sediment is developed, in which the medium is treated as a fluid that supports a specific form of intergranular dissipation. Two important equations emerge from the analysis, one for compressional wave propagation and the second describing transverse disturbances. For the type of dissipation considered, which exhibits hysteresis or memory, the shear equation admits a wavelike solution, and is thus a genuine wave equation, even though the sediment shows no elastic rigidity. In effect, the medium possesses a “dissipative” rigidity, which is capable of supporting shear. This behavior is distinct from that of a viscous fluid, for which the shear equation is diffusionlike in character.
Smart steel bridge construction enabled by BIM and Internet of Things in industry 4.0: A framework

To deal with the uncontrollability and inefficiency problem of construction progress, quality, and cost in traditional steel bridge construction projects, this paper discusses smart steel bridge construction enabled by Building Information Modeling (BIM) and Internet of Things (IoT). The framework of smart steel bridge construction is firstly proposed. The research directions and roadmap are further pointed out. The core idea of smart steel bridge construction is to apply data-driven methods and model-based analytics to realize real-time collaborative management and closed-loop control of steel bridge lifecycle activities. It is expected that this paper will position the BIM- and IoT-enabled steel bridge construction mode in the context of industry 4.0.
Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions.

AIM AND OBJECTIVE Adverse drug reactions (ADRs) present a major burden for patients and the healthcare industry. Various computational methods have been developed to predict ADRs for drug molecules. However, many of these methods require experimental or surveillance data and cannot be used when only structural information is available. MATERIALS AND METHODS We collected 1,231 small molecule drugs and 600 human proteins and utilized molecular docking to generate binding features among them. We developed machine learning models that use these docking features to make predictions for 1,533 ADRs. RESULTS These models obtain an overall area under the receiver operating characteristic curve (AUROC) of 0.843 and an overall area under the precision-recall curve (AUPR) of 0.395, outperforming seven structural fingerprint-based prediction models. Using the method, we predicted skin striae for fluticasone propionate, dermatitis acneiform for mometasone, and decreased libido for irinotecan, as demonstrations. Furthermore, we analyzed the top binding proteins associated with some of the ADRs, which can help to understand and/or generate hypotheses for underlying mechanisms of ADRs. CONCLUSION Machine learning combined with molecular docking can help to predict ADRs for drug molecules and provide possible explanations for the ADR mechanisms.
Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments

A two-groups mixed-effects model for the comparison of (normalized) microarray data from two treatment groups is considered. Most competing parametric methods that have appeared in the literature are obtained as special cases or by minor modification of the proposed model. Approximate maximum likelihood fitting is accomplished via a fast and scalable algorithm, which we call LEMMA (Laplace approximated EM Microarray Analysis). The posterior odds of treatment $\times$ gene interactions, derived from the model, involve shrinkage estimates of both the interactions and of the gene specific error variances. Genes are classified as being associated with treatment based on the posterior odds and the local false discovery rate (f.d.r.) with a fixed cutoff. Our model-based approach also allows one to declare the non-null status of a gene by controlling the false discovery rate (FDR). It is shown in a detailed simulation study that the approach outperforms well-known competitors. We also apply the proposed methodology to two previously analyzed microarray examples. Extensions of the proposed method to paired treatments and multiple treatments are also discussed.
Enhanced Amendment Delivery to Low Permeability Zones for Chlorinated Solvent Source Area Bioremediation

Abstract : The shear-thinning technology is designed to enhance delivery of remedial amendments to lowpermeability (low-k) zones for which treatment is typically limited when standard amendment delivery processes are utilized. Improved distribution of injected fluids is achieved by exploiting the rheological properties of shear-thinning fluids (STFs) during injection and transport within a formation, such as cross-flow from high-permeability (high-k) to low-k zones. The overall goal of this project was to demonstrate and validate the use of shear-thinning delivery fluid for enhanced delivery of bioremediation amendments at a chlorinated solvent-impacted site and to develop guidance for its use at other sites. The term shear-thinning is applied to fluids to describe their dynamic viscosity-reducing behavior when shear rates are increased. Shear-thinning fluids are non-Newtonian, meaning that their viscosities exhibit a temporary drop when the applied shear rate is increased. A viscositymodifying shear force can be applied using methods as simple as mixing or shaking of the solution, or in the context of subsurface delivery by injecting the fluid through a well screen and into porous media. For the enhanced amendments delivery process, a non-toxic biodegradable polymer, such as xanthan gum, is added to the injection solution to form a non- Newtonian fluid with shear-thinning properties. The shear-thinning behavior causes a more significant viscosity reduction to the fluid flowing through the lower-k zones relative to the viscosity reduction of the fluid flowing in higher permeable zones, i.e., the fluid mobility in the higher-k zone is controlled. Therefore, the preferential flow through the more permeable zones is significantly reduced while the flow into the lower-k zone is increased.
DGC-specific RHOA mutations maintained cancer cell survival and promoted cell migration via ROCK inactivation

RHOA missense mutations exist specifically in diffuse type gastric cancers (DGC) and are considered one of the DGC driver genes, but it is not fully understood how RHOA mutations contribute to DGC development. Here we examined how RHOA mutations affect cancer cell survival and cell motility. We revealed that cell survival was maintained by specific mutation sites, namely G17, Y42, and L57. Because these functional mutations suppressed MLC2 phosphorylation and actin stress fiber formation, we realized they act in a dominant-negative fashion against the ROCK pathway. Through the same inactivating mechanism that maintained cell survival, RHOA mutations also increased cell migration activity. Cell survival and migration studies on CLDN18-ARHGAP (CLG) fusions, which are known to be mutually exclusive to RHOA mutations, showed that CLG fusions complemented cell survival under RHOA knockdown condition and also induced cell migration. Site-directed mutagenesis analysis revealed the importance of the GAP domain and indicated that CLG fusions maintained RHOA in the inactive form. Taken together, these findings show that the inactivation of ROCK would be a key step in DGC development, so ROCK activation might provide novel therapeutic opportunities.
Dual Role of Dietary Curcumin Through Attenuating AFB1-Induced Oxidative Stress and Liver Injury via Modulating Liver Phase-I and Phase-II Enzymes Involved in AFB1 Bioactivation and Detoxification

It is well understood that liver cytochrome p450 enzymes are responsible for AFB1 bioactivation, while phase-II enzymes regulated by the transcription factor nuclear factor-erythroid-2-related factor 2 (Nrf2) are involved in detoxification of AFB1. In this study, we explored the potential of curcumin to prevent AFB1-induced liver injury by modulating liver phase-I and phase-II enzymes along with Nrf2 involved in AFB1 bioactivation and detoxification. Arbor Acres broiler were divided into four groups including control group (G1; fed only basal feed), curcumin alone-treated group (G2; 450 mg/kg feed), AFB1-fed group (G3; 5 mg/kg feed), and curcumin plus AFB1 group (G4; 5 mg AFB1+450 mg curcumin/kg feed). After 28 days, liver and blood samples were collected for different analyses. Histological and phenotypic results revealed that AFB1-induced liver injury was partially ameliorated by curcumin supplementation. Compared to AFB1 alone-treated group, serum biochemical parameters and liver antioxidant status showed that curcumin supplementation significantly prevented AFB1-induced liver injury. RT-PCR and western blot results revealed that curcumin inhibited CYP enzymes-mediated bioactivation of AFB1 at mRNA and protein level. Transcription factor Nrf2, its downstream genes such as GSTA3, and GSTM2 mRNA, and protein expression level significantly upregulated via dietary curcumin. In addition, GSTs enzyme activity was enhanced with dietary curcumin which plays a crucial role in AFB1-detoxification. Conclusively, the study provided a scientific basis for the use of curcumin in broiler’s diet and contributed to explore the multi-target preventive actions of curcumin against AFB1-induced liver injury through the modulation of phase-I and phase-II enzymes, and its potent anti-oxidative effects.
Endothelial Dysfunction in Steatotic Human Donor Livers: A Pilot Study of the Underlying Mechanism During Subnormothermic Machine Perfusion

Background Steatosis is a major risk factor for primary nonfunction in liver transplantations. Steatotic livers recover poorly from ischemia reperfusion injury, in part due to alterations in the microcirculation, although the exact mechanism is unclear. In this study, we tested if there were any alterations in the shear stress sensing Kruppel-like factor 2 (KLF2) and its likely downstream consequences in the ex vivo perfused human liver endothelium, which would imply perturbations in microcirculatory flow in macrosteatotic livers disrupts laminar flow to evaluate if this is a potential therapeutic target for steatotic livers. Methods Using a subnormothermic machine perfusion system, 5 macrosteatotic and 4 nonsteatotic human livers were perfused for 3 hours. Flow, resistance, and biochemical profile were monitored. Gene expression levels of nitric oxide synthase 3 (eNOS), KLF2, and thrombomodulin were determined. Nitric oxide (NO) was measured in the perfusion fluid and activation of eNOS was measured with Western blotting. Results Flow dynamics, injury markers, and bile production were similar in both groups. Kruppel-like factor 2 expression was significantly higher in nonsteatotic livers. Western blotting analyses showed significantly higher levels of activated eNOS in nonsteatotic livers, consistent with an increase in NO production over time. Macrosteatotic livers showed decreased KLF2 upregulation, eNOS activity, and NO production during machine perfusion. Conclusions These results indicate a perturbed KLF2 sensing in steatotic livers, which aligns with perturbed microcirculatory state. This may indicate endothelial dysfunction and contribute to poor posttransplantation outcomes in fatty livers, and further studies to confirm by evaluation of flow and testing treatments are warranted.