Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional analysis of MKP-1 and MKP-2 in breast cancer tamoxifen sensitivity

Increased activation of ERK signaling has been reported in breast cancer models of acquired tamoxifen resistance. Here, we examined the expression of Mitogen-Activated Protein Kinase Phosphatases (MKPs) 1 and 2 following tamoxifen treatment and the effects of MKP-1/MKP-2 overexpression on tamoxifen sensitivity. Treatment of MCF7 breast cancer cells with tamoxifen increased MKP-2, but not MKP-1, protein levels. Overexpression of MKP-1 or MKP-2 inhibited estrogen-induced MCF7 cell proliferation compared to vector controls. MCF7-MKP-2 cells displayed significantly increased sensitivity to tamoxifen as compared to vector control or MCF7-MKP-1 cells. MKP-1 or MKP-2 overexpression eliminated ERK1/2 phosphorylation, suggesting that decreases in estrogen-induced proliferation of MKP-1 and MKP-2 overexpressing cells are due to ERK1/2 dephosphorylation. JNK1/2 activation was not detectable in any of these cells. These data suggest that tamoxifen-induced death of these cells is not dependent upon JNK signaling, but rather that ERK is the major MAPK driving their proliferation. MCF7-TAMR cells express higher levels of MKP-2 mRNA and protein than MCF7 cells. MKP-2 and phospho-ERK1/2 proteins are constitutively expressed in MCF7-TAMR cells, and activated JNK1/2 is not detectable. These data suggest that MKP-2 rather than MKP-1 is tamoxifen-regulated and that the elevated expression of MKP-2 in MCF7-TAMR cells potentially functions to restore tamoxifen sensitivity.
Influence of molecular configuration on the passage of macromolecules across the glomerular capillary wall

The influence of molecular configuration on the filtration of macromolecules across glomerular capillary walls was examined by comparing fractional clearances of two uncharged polysaccharides of distinctly different molecular configuration in the Munich-Wistar rat. The macromolecules employed were dextran, a slightly branched polymer of glucopyranose, and ficoll, a highly cross-linked copolymer of sucrose and epichlorohydrin. Differences in effective shape between these two polymers were determined from measurements of several physical properties of aqueous solutions containing either dextran or ficoll. It was found that dextran is best represented as a prolate ellipsoid with axial ratios of 4, 9, and 16 for molecules with Stokes- Einstein radii of 22, 32, and 40 A, respectively. On the other hand, ficoll is more closely approximated as spherical since the axial ratio was found to be between 1 and 2 for all molecular sizes. Fractional clearances of dextran and ficoll ranging in effective radius from 18 to 44 A were determined in each of seven Munich-Wistar rats. Fractional clearances of dextran were found to be greater than those of ficoll, the difference being significant for molecular radii ranging from 24 to 44 A. In addition, as shown previously for dextran, ficoll was found to be neither secreted nor reabsorbed by the renal tubules. These results, therefore, suggest that in addition to molecular size and charge, molecular configuration is also a determinant of the filtration of macromolecules across the glomerular capillary wall.
Induction of NF-κB Activity during Haloperidol-Induced Oxidative Toxicity in Clonal Hippocampal Cells: Suppression of NF-κB and Neuroprotection by Antioxidants

Haloperidol (HP), a dopamine receptor antagonist, is cytotoxic to mouse clonal hippocampal HT22 cells in a concentration-dependent manner and causes cell death by oxidative stress. The addition of HP to HT22 cells led to an increase in intracellular peroxides and a time-dependent drop in the intracellular glutathione levels. HP-induced oxidative cell death was prevented by the pineal hormone melatonin, its precursor N-acetyl serotonin, and most effectively by vitamin E (α-tocopherol). These antioxidants inhibited the intracellular peroxide accumulation and stabilized the glutathione content of HT22 cells after the challenge with HP. At the molecular level, HP specifically induced the DNA binding activity and the transcriptional activity of the redox-sensitive transcription factor NF-κB. This enhanced NF-κB activity could be blocked by the neuroprotective antioxidants. The specific suppression of NF-κB by its inhibitor IκBα partially protected the cells against HP, indicating that the activation of NF-κB may be involved in HP-induced oxidative cell death in vitro.
An Aggressive Graph-Based Selective Sampling Algorithm for Classification

Traditional online learning algorithms are designed for vector data only, which assume that the labels of all the training examples are provided. In this paper, we study graph classification where only limited nodes are chosen for labelling by selective sampling. Particularly, we first adapt a spectral-based graph regularization technique to derive a novel online learning linear algorithm which can handle graph data, although it still queries the labels of all nodes and thus is not preferred, as labelling is typically time-consuming. To address this issue, we then propose a new confidence-based query method for selective sampling. The theoretical result shows that our online learning algorithm with a fraction of queried labels can achieve a mistake bound comparable with the one learning on all labels of the nodes. In addition, the algorithm based on our proposed query strategy can achieve a mistake bound better than the one based on other query methods. However, our algorithm is conservative to update the model whenever error happens, which obviously wastes training labels that are valuable for the model. To take advantage of these labels, we further propose a novel aggressive algorithm, which can update the model aggressively even if no error occurs. The theoretical analysis shows that our aggressive approach can achieve a mistake bound better than its conservative and fully-supervised counterpart, with substantially fewer queried times. We empirically evaluate our algorithm on several real-world graph datasets and the experimental results demonstrate that our method is highly effective.
Didactic Cache Memory Simulator Project Using a Structural Modeling

This document presents the teaching and learning aid tool DCMSim (Didactic Cache Memory Simulator). It is a didactic simulator that uses a structural modeling of cache memories and to be used in undergraduate classes in memory hierarchy topics. The DCMSim was designed to motivate and grow the student participation and reflection through an alternative learning environment. The simulator goal is to represent the cache memory structurally and not only functionally.
Cheetah

Large-scale data analysis has become increasingly important for many enterprises. Recently, a new distributed computing paradigm, called MapReduce, and its open source implementation Hadoop, has been widely adopted due to its impressive scalability and flexibility to handle structured as well as unstructured data. In this paper, we describe our data warehouse system, called Cheetah, built on top of MapReduce. Cheetah is designed specifically for our online advertising application to allow various simplifications and custom optimizations. First, we take a fresh look at the data warehouse schema design. In particular, we define a virtual view on top of the common star or snowflake data warehouse schema. This virtual view abstraction not only allows us to design a SQL-like but much more succinct query language, but also makes it easier to support many advanced query processing features. Next, we describe a stack of optimization techniques ranging from data compression and access method to multi-query optimization and exploiting materialized views. In fact, each node with commodity hardware in our cluster is able to process raw data at 1GBytes/s. Lastly, we show how to seamlessly integrate Cheetah into any ad-hoc MapReduce jobs. This allows MapReduce developers to fully leverage the power of both MapReduce and data warehouse technologies.
Inventories as Factors of Production and Economic Fluctuations

This paper presents a theory of inventory investment by stage-of-processing, and uses it to examine the role of inventory investment in economic fluctuations. The model, in which inventories are treated as factors of production, is estimated for four durable goods industries. Three conclusions emerge from the estimation. First, the estimates generally satisfy the theoretical restrictions. Second, the elasticity of inventory demand with respect to output is high, indicating an important accelerator effect. Finally, the estimates suggest that shifts in the demand for inventories are an important source of economic fluctuations. Copyright 1989 by American Economic Association.
Chaos Theory: Implications for Supply Chain Management

Since the late 1950's it has been recognized that the systems used internally within supply chains can lead to oscillations in demand and inventory as orders pass through the system. The uncertainty generated can result in late deliveries, order cancellations and an increased reliance on inventory to buffer these effects. Despite the best efforts of organizations to stabilize the dynamics generated, industry still experiences a high degree of uncertainty. The failure to significantly reduce uncertainty through traditional approaches may in part be explained by chaos theory. This paper defines deterministic chaos and demonstrates that supply chains can display some of the key characteristics of chaotic systems, namely: chaos exhibits sensitivity to initial conditions; it has Islands of Stability; generates patterns; invalidates the reductionist view; and, undermines computer accuracy. The implications for the management and design of supply chains are discussed.
Annotation and Extraction of Multiword Expressions in Turkish Treebanks

Multiword expressions (MWEs) present particular and distinctive semantic properties, hence their automatic extraction receives special attention from the natural language processing (NLP) and corpus linguistics community, and is still an active research area. Unfortunately, the creation of necessary resources for this task is quite rigorous and many languages suffer from the lack of these; as in the case for Turkish. This study presents our MWE annotations on recently introduced Turkish Treebanks, which focuses on annotating various types of linguistic units and expressions, including named entities, numerical expressions, idiomatic phrases, verb phrases with auxiliaries and duplications. The paper aims to provide a benchmark and pave the way towards further MWE extraction research for Turkish. To this end, the paper also introduces our experimental results with seven baseline approaches, a dependency parser and a previously introduced rule-based extractor on these annotated corpora. Our highest performances achieved over these resources are about 60% F-scores.
Technology Integration around the Geographic Information: A State of the Art

One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented.
Social inhibition modulates the effect of negative emotions on cardiac prognosis following percutaneous coronary intervention in the drug-eluting stent era.

AIMS Negative emotions have an adverse effect on cardiac prognosis. We investigated whether social inhibition (inhibited self-expression in social interaction) modulates the effect of negative emotions on clinical outcome following percutaneous coronary intervention (PCI). METHODS AND RESULTS Eight hundred and seventy-five consecutive patients from the RESEARCH registry (Erasmus Medical Centre, Rotterdam) completed depression, anxiety, negativity (negative emotions in general), and social inhibition scales 6 months following PCI. The endpoint was major adverse cardiac event (MACE-death, myocardial infarction, coronary artery bypass graft (CABG), or PCI) at 9 months following assessment. There were 100 MACE; patients who were high in both negativity and inhibition were at increased risk of MACE (38/254=15%) when compared with high negativity/low inhibition patients (13/136=10%; P=0.018). Depression (P=0.23) or anxiety (P=0.63) did not explain away this moderating effect of inhibition. High negativity/high inhibition (HR=1.92, 95%CI 1.22-3.01, P=0.005) and previous CABG (HR=1.90, 95%CI 1.04-3.47, P=0.038) were independent predictors of MACE. Patients with high negativity but low inhibition were not at increased risk (P=0.76). High negativity/high inhibition also independently predicted death/MI (n=20) as a more specific endpoint (HR=5.85, P=0.001). CONCLUSION The interaction effect of social inhibition and negative emotions, rather than negative emotions per se, predicted poor clinical outcome following PCI. Social inhibition should not be overlooked as a modulating factor.
Sitting Posture Recognition and Location Estimation for Human-Aware Environment

Realization of human-computer symbiosis is an important idea in the context of ubiquitous computing. Symbiotic Computing is a concept that bridges the gap between situations in Real Space RS and data in Digital Space DS. The main purpose is to develop an intelligent software application as well as establish the next generation information platform to develop the symbiotic system. In this paper, the authors argue that it is necessary to build 'Mutual Cognition' between human and system. Mutual cognition consists of two functions: 'RS Cognition' and 'DS Cognition'. This paper examines RS Cognition, which consists of many software functions for perceiving various situations like events or humans' activities in RS. The authors develop two perceptual functions, sitting posture recognition and human's location estimation for a person, as RS perception tasks. In the resulting experiments, developed functions are quite competent to recognize a human's activities.
International Political Earthquakes

"International Political Earthquakes" is the master work of the preeminent scholar Michael Brecher. Brecher, who came of age during WWII, has witnessed more than eight decades of conflict and spent his career studying the dynamics of relations among nations throughout the world.When terrorism, ethnic conflict, military build-up, or other local tensions spark an international crisis, Brecher argues, the structure of global politics determines its potential to develop into open conflict. The conflict, in turn, may generate world-wide political upheaval.Comparing international crises to earthquakes, Brecher proposes a scale analogous to the Richter scale to measure the severity and scope of a crisis' impact on the landscape of international politics.Brecher's conclusions about the causes of international conflict and its consequence for global stability make a convincing case for gradual, non-violent approaches to crisis resolution.In this book a preeminent scholar examines the global causes and consequences of international conflict.
Multilevel Bayesian Models of Categorical Data Annotation

Abstract This paper demonstrates the utility of multilevel Bayesian models of data annotation for classifiers (also known as coding or rating). The observable data is the set of categorizations of items by annotators (also known as raters or coders) from which data may be missing at random or may be replicated (that is, it handles fixed panel and varying panel designs). Estimated model parameters include the prevalance of category 1 outcomes, the “true” category of each item (the latent class), the accuracy in terms of sensitivity and specificity of each annotator (latent annotator traits), the difficulty of each item (latent item traits). The multilevel parameters represent the average behavior and variances among the annotators and the items. We perform inference with Gibbs sampling, which approximates the full posterior distribution of parameters as a set of samples. Samples from the posterior category distribution may be used for probabilistic supervision and evaluation of classifiers, as well as in gold-standard adjudication and active learning. We evaluate our approach with simulated data and two real data sets, including data for which a prior “gold standard” exists.
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or protein-protein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GraMi, a novel framework for frequent subgraph mining in a single large graph. GraMi undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GraMi that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGraMi, a version supporting structural and semantic constraints, and AGraMi, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches.
Causality Relationship among Foreign Direct Investments, Gross Domestic Product and Exports for Pakistan

The study of economic indicator is very vital for the assessment of overall performance of the economy in term of countrywide earnings. We are identifying the impact of some factors like FDI, GDP & export on economic growth and also recognize the relationship among GDP, FDI and exports in explaining the financial performance of the major nation like Pakistan and also identify the linkage on each other. With the help of time-series data from 1970 to 2012, this study investigates the causality relationship between FDI, GDP and exports in case of Pakistan economy. For this undertaking, statistical techniques like Unit root test of (ADF), Phillip Perron, Johansen’s co integration analysis and Granger causality have been used. By apply the unit root test of Phillip all variables become stationery at first difference which leads to co integration test. Co integration test shows that there is existence of a long run relationship ship among the variables. Granger causality consequences suggest that there is bi directional causality relationship exist among FDI and Exports and uni-directional relationship exist between GDP-Exports and GDP-FDI running from GDP to Exports and FDI. No causal relationship found between Exports to GDP and FDI to GDP. Keywords: Foreign Direct Investment (FDI), Gross Domestic Product (GDP), Exports , Pakistan.
A companding technique for PAPR reduction in DWT OFDM systems

Orthogonal frequency division multiplexing (OFDM) technique is considered as a advance technology in realizing high-speed wireless digital communications. However, high peak-to-average power ratio (PAPR) of transmitted signals is a major drawback for multi-carrier modulation systems such as orthogonal frequency division multiplexing and discrete wavelet division multiplexing. In this paper, proposed scheme will be introduced, which combines wavelet transform and companding technique to reduce PAPR. The μ-law companding scheme, which enlarges only small signals so that the average power increases. By using proposed scheme, the PAPR of OFDM signal can be reduced by 6.8 dB over the original system. Wavelet based Multi-Carrier Modulation (WMCM) offers an alternative to the well-established OFDM as an efficient multicarrier modulation technique. The schemes are compared with simulation results.
Tractability Guarantees for DL-Lite Query Answering

It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the core dialect of DL-Lite. The proof exploits known properties of query matches in this logic and involves a query-dependent modification of the data. To obtain a more practical approach, we next propose a concrete polynomial-time algorithm for answering acyclic CQs based upon a rewriting into datalog. We also show how the algorithm can be extended to a larger class of (nearly acyclic) CQs. A preliminary evaluation of our proof-of-concept implementation suggests the interest of our approach for handling large acyclic CQs.
Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis

Social networks are dynamically changing over time e.g., some accounts are being created and some are being deleted or become private. This ephemerality at both an account level and content level results from a combination of privacy concerns, spam, and de-ceptive behaviors. In this study we analyze a large dataset of 180,340 accounts active during the Russian-Ukrainian crisis to discover a series of predictive features for the removal or shutdown of a suspicious account. We ﬁnd that unlike previously reported proﬁle and network features, lexical features form the basis for highly accurate prediction of the deletion of an account.
Use of a generalized thouless energy in describing transport properties of Josephson junctions

The concept of a Thouless energy, introduced originally in the context of Anderson localization (as the inverse diffusion time through a finite-size disordered conductor), has turned out to be an essential energy scale for understanding proximity-induced superconductivity in a normal metal connected to a superconductor; in particular, it has had significant success in the quasiclassical description of phenomena in superconductor-normal-metal-superconductor Josephson junctions. We generalize the concept of a Thouless energy to include transport through strongly correlated insulators, and show how it provides a unified description of transport and indicates how the quasiclassical picture breaks down as we turn on electron-electron correlations in the normal metal layer of a Josephson junction to tune it through the Mott metal-insulator transition.
Genetic nonlinear controller of a skid steering mobile robot with slip conditions

In this paper, the kinematics of a skid steering mobile robot is controlled using a nonlinear controller augmented with a genetic algorithm which is used to tune the controller parameters. The controller is simple in terms of design and implementation. Moreover, it maintains good performance and has stronger robustness. Furthermore, the proposed controller is used to control an extended kinematics model taking into account slip effects to test the validity and enhancement of the controller. Simulation results are included.