Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in Cervical Squamous Cell Carcinoma Incidence in 13 European Countries: Changing Risk and the Effects of Screening

Despite there being sufficient evidence for the effectiveness of screening by cytology in preventing cancer of the cervix uteri, screening policies vary widely among European countries, and incidence is increasing in younger women. This study analyzes trends in squamous cell carcinoma (SCC) of the cervix uteri in 13 European countries to evaluate effectiveness of screening against a background of changing risk. Age-period-cohort models were fitted and period and cohort effects were estimated; these were considered as primarily indicative of screening interventions and changing etiology, respectively. A unique set of estimates was derived by fixing age slopes to one of several plausible age curves under the assumption that the relation between age and cervical cancer incidence is biologically determined. There were period-specific declines in cervical SCC in several countries, with the largest decreases seen in northern Europe. A pattern emerged across Europe of escalating risk in successive generations born after 1930. In the western European countries, a decrease followed by a stabilization of risk by cohort was accompanied by period-specific declines. In southern Europe, stable period, but increasing cohort trends, were observed. Substantial changes have occurred in cervical SCC incidence in Europe and well-organized screening programs have been highly effective in reducing the incidence of cervical SCC. Screening and changing sexual mores largely explain the changing period- and cohort-specific patterns, respectively. The increasing risk in recent cohorts is of obvious concern particularly in countries where no screening programs are in place. Further investigation of the effectiveness of opportunistic screening is warranted as is the observation of differing risk patterns in young cohorts in countries with relatively similar societal structures.
A parallel implementation of Singular Value Decomposition based on Map-Reduce and PARPACK

In the e-commerce on the Web,recommender systems become a powerful technology for extracting valuable information from its customer databases. These systems also help customers find products they want to buy from a business sites. Singular Value Decomposition(SVD) is a useful technology to speedup the recommendations with very fast online performance, requiring just a few simple arithmetic operations. Unfortunately, computing the SVD of a large scale matrix is very expensive. In this paper, we propose to parallelize the SVD algorithm to run on distributed computers. Our parallel algorithm employs a parallel ARPACK algorithm to perform parallel eigenvalue decomposition. Experimental results show that the proposed method can significantly speed up the SVD computation cost while providing comparable prediction quality.
Clinical Efforts to Combine Endocrine Agents with Targeted Therapies against Epidermal Growth Factor Receptor/Human Epidermal Growth Factor Receptor 2 and Mammalian Target of Rapamycin in Breast Cancer

Enhancing the benefit of endocrine therapy by overcoming de novo or acquired resistance remains an important goal in systemic breast cancer therapy. Progress continues to be made in elucidating the molecular pathways by which estrogen receptor–positive breast cancer cells escape from endocrine therapy. The increasing recognition of the roles of epidermal growth factor receptor (EGFR) and human EGFR2 in cross-talk activation of estrogen receptor signaling has led to studies aimed at identifying whether small-molecule tyrosine kinase inhibitors targeted against these receptors give additive or synergistic effects when combined with endocrine agents. Activation of the phosphatidylinositol-3-OH kinase/Akt pathway has also been associated with resistance to either tamoxifen or estrogen deprivation, and preclinical studies have shown that the mammalian target of rapamycin antagonist temsirolimus can restore endocrine sensitivity in breast cancer cells. Randomized phase II trials of aromatase inhibitors combined with EGFR/human EGFR2 tyrosine kinase inhibitors or mammalian target of rapamycin antagonists have been completed in both the neoadjuvant and advanced breast cancer settings. Larger phase III trials with both approaches are now in progress and have been powered to detect whether either strategy can significantly prolong time to disease progression compared with endocrine therapy alone. The correlation of molecular and clinical results from these ongoing studies will be important to establish appropriate biological variables for selecting those patients who may benefit most from this combined approach.
Untraceable Email Cluster Bombs: On Agent-Based Distributed Denial of Service

We uncover a vulnerability that allows for an attacker to perform an email-based attack on selected victims, using only standard scripts and agents. What differentiates the attack we describe from other, already known forms of distributed denial of service (DDoS) attacks is that an attacker does not need to infiltrate the network in any manner -- as is normally required to launch a DDoS attack. Thus, we see this type of attack as a poor man's DDoS. Not only is the attack easy to mount, but it is also almost impossible to trace back to the perpetrator. Along with descriptions of our attack, we demonstrate its destructive potential with (limited and contained) experimental results. We illustrate the potential impact of our attack by describing how an attacker can disable an email account by flooding its inbox; block competition during on-line auctions; harm competitors with an on-line presence; disrupt phone service to a given victim; cheat in SMS-based games; disconnect mobile corporate leaders from their networks; and disrupt electronic elections. Finally, we propose a set of countermeasures that are light-weight, do not require modifications to the infrastructure, and can be deployed in a gradual manner.
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Significance Most biological processes rely on specific interactions between proteins, but the experimental characterization of protein−protein interactions is a labor-intensive task of frequently uncertain outcome. Computational methods based on exponentially growing genomic databases are urgently needed. It has recently been shown that coevolutionary methods are able to detect correlated mutations between residues in different proteins, which are in contact across the interaction interface, thus enabling the structure prediction of protein complexes. Here we show that the applicability of coevolutionary methods is much broader, connecting multiple scales relevant in protein−protein interaction: the residue scale of interprotein contacts, the protein scale of specific interactions between paralogous proteins, and the evolutionary scale of conserved interactions between homologous protein families. Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.
A systematic review of auditory fear conditioning protocols: Animal models and auditory conditioning stimulus

The specific circuits used to investigate fear response have been described on the basis of Pavlovian conditioning or classical fear conditioning procedures. However, prior studies did not utilize identical methods, featuring differences in animal models and exposure tones. This study aimed to identify the most frequently employed animal model and exposure tone that can be feasibly implemented to obtain consistent results in fear conditioning studies. This systematic review analyzed studies on auditory conditioning stimuli effects, i.e., fear conditioning responses, synaptic transmission, synaptic plasticity, long-term potentiation, and associative fear memory, published between 2011 and 2016. Twenty-two studies fulfilled the selection criteria of the analysis. The animal models used were mice (54.5% of studies, with C57BL/6 mice being utilized in 91.7% of the mouse studies) or rats (45.5% of studies, with Sprague–Dawley rats being utilized in 70% of the rat studies). The ranges of loudness, frequency, and duration of tone exposure in the studies were 65–100 dB, 1–12.5 kHz, and 10–30 s, respectively. The most commonly used animal in fear conditioning studies using genetic variation to elucidate certain pathogeneses or therapeutic mechanisms of drugs was the C57BL/6 mouse, whereas Sprague–Dawley rats were frequently used in studies of the physiological responses to auditory fear conditioning, fear memory, synaptic transmission, synaptic plasticity, and associative learning. Significant variation of the tone exposure was also noted.
Satisfacción laboral: una revisión de la literatura acerca de sus principales determinantes

Aun cuando el estudio de la satisfaccion laboral ha concentrado un gran interes cientifico durante buena parte del ultimo siglo, todavia resulta sorprendente cuan fragmentado se encuentra el campo y cuan limitados han sido los esfuerzos de integracion. Este articulo se propone identificar los principales factores que han sido senalados en la literatura empirica como determinantes de la satisfaccion laboral, a partir de una revision de investigaciones publicadas en revistas cientificas de alto impacto durante las ultimas dos decadas. Los resultados revelan una insuficiencia de investigaciones que hayan estudiado de manera simultanea los efectos de determinantes disposicionales y situacionales sobre la satisfaccion laboral. Finalmente, se analizan los principales vacios existentes en la literatura y se sugieren lineas potenciales de investigacion.
On the WDVV equation in quantum K-theory.

0. Introduction. Quantum cohomology theory can be described in general words as intersection theory in spaces of holomorphic curves in a given Kahler or almost Kahler manifold X. By quantum K-theory we may similarly understand a study of complex vector bundles over the spaces of holomorphic curves in X. In these notes, we will introduce a K-theoretic version of the Witten-Dijkgraaf-Verlinde-Verlinde equation which expresses the associativity constraint of the “quantum multiplication” operation on K(X). Intersection indices of cohomology theory, ∫
Deriving Combinatorial Test Design Model from UML Activity Diagram

Combinatorial test design model consists of parameters, values and the associated constraints. This model is the input for test generators. In this paper we present a rule based approach for deriving combinatorial test design model from UML activity diagrams resulting from requirement analysis. We have explored this approach on few sample cases. Our initial results suggest that this automated approach augmented with manual review and refinement step can make the test design task less effort intensive and more effective.
Spiritual Intelligence, Resiliency, and Withdrawal Time in Clients of Methadone Maintenance Treatment

Background: Reports show an increasing interest in spirituality. It has been revealed that people with spiritual tendencies, can better deal with a trauma, manage the stressful situations, and have greater improvement in their health condition. Objectives: Our aim was to examine the relationship between spiritual intelligence and resiliency, and the relation of these two variables with the withdrawal time of individuals treated with methadone. Materials and Methods: This research was conducted on patients referred to the addiction center of Baharan Psychiatric Hospital in Zahedan, Iran. Our sample included 100 referrals; they were provided with questionnaires and asked to answer them honestly. King’s spiritual intelligence questionnaire and resilience questionnaires were used. Results: There were significant positive correlations between resiliency and scores of spiritual intelligence as well as with subscales of spiritual intelligence. In addition, there were significant positive correlations between withdrawal time and scores of spiritual intelligence as well as with subscales of spiritual intelligence as well as with resiliency. Conclusions: Relationships between the spiritual intelligence and resiliency parameters with withdrawal time show that these parameters can have a role in relapse protection among addicted people.
The HMM-based Modeling for the Energy Level Prediction in Wireless Sensor Networks

The energy conservation is the intrinsic issue of the wireless sensor network (WSN). The lack of energy of some necessary sensor nodes will affect the accurate data aggregation as well as the effective lifetime of the network. In this paper, the problem of predicting which energy level the WSNs or nodes stay in is addressed. Since the primitive method that exchanges messages through nodes could not directly predict the energy level, the stochastic modeling based on hidden Markov model (HMM) is proposed to solve the problem. The energy is divided into several energy levels, i.e. different energy ranges of sensor nodes. Since the state of energy level is the random variable with the value of different energy levels at some time, which holds the characteristic of the Markov chain, it can be modeled as a left-right HMM. Since the energy level cannot be obtained directly, the energy consumption of sensor nodes at a certain time is used as observation states in HMM. Since HMM can perform training process during the running of the WSN, the method can adjust itself to make the best adaptation to the desirable one. Then the trained HMM model can be applied to the prediction of the energy level of the network or sensor nodes. The implementation and simulation of the proposed approach are given in this paper. The simulation shows that the model can effectively predict the energy level and reflect the tendency of the energy consumption, and be applied to other protocols such as energy-efficient routing protocols and so on.
Stewart platforms without computer

many ways are there to move H in such a way that P i belongs to S 1 , i = 0; : : : ; 5 ? We show in this paper that generically there are at most 40 solutions. This problem is the geometric version of a control problem for Stewart robots. A (generalized) Stewart platform is a solid with 6 points P 0 ; : : : ; P 5 on it attached through 6 legs to 6 xed points Q 0 ; : : : ; Q 5 in the space R 3. Assuming that the lengths of the legs can be varied arbitrarily (within the physical limits), the problem, rst considered by D. Stewart 8], is to control the position of the body. y In mathematical terms, we can identify the space of positions of the solid with the space SO(3) R 3 of rotations and translations of R 3. We have a map: The coordinates in the target play the role of control parameters, whereas a point in the source represents a position of the solid. The problem is to study the map. Note that both the source and target of have dimension 6, so we might expect that for a generic choice of the points P i and Q i the bers of are nite. See 7] for a more detailed discussion of Stewart platforms. The aim of this paper is to use the intersection theory in algebraic geometry to show that for a generic choice of the points P i and Q i the number of possible positions of the platform for 6 given lengths is at most 40. A rst version of this paper was inspired from results of D. Lazard 6], who considered the case of a planar platform, (i.e. the P i 's lying in a plane as well as the Q i 's) using formal calculus manipulations on a computer; he also found that the number of possible positions for given lengths of the legs is at most 40 and conjectured that this should hold in general, but could only prove that it is bounded by 320 in the general case. Our rst version in turn inspired to D. Lazard new ideas that enabled him to simplify enormously y In fact a similar device was designed already in 1947 by V.E. Gough; see 8].
A centrality-based measure of user privacy in online social networks

The risks due to a global and unaware diffusion of our personal data cannot be overlooked when more than two billion people are estimated to be registered in at least one of the most popular online social networks. As a consequence, privacy has become a primary concern among social network analysts and Web/data scientists. Some studies propose to “measure” users' profile privacy according to their privacy settings but do not consider the topological properties of the social network adequately. In this paper, we address this limitation and define a centrality-based privacy score to measure the objective user privacy risk according to the network properties. We analyze the effectiveness of our measures on a large network of real Facebook users.
Analyzing the effect of hyperparameters in a automobile classifier based on convolutional neural networks

In the recent years the convolutional neural network is used successfully in applications of image classification, due to its deep and hierarchical architecture. The hyper parameters of the convolutional neural networks are of great influence to obtain good results in binary classification without the need of a large number of layers. The activation function, the weights initialization and the sub sampling function are the three main hyper parameters. In the present work 27 models of convolutional neural network are trained and tested with automobile images taken from a surveillance camera. The illumination intensity of the test images are different from the training images, because they were taken from scenes of day, evening and night. We also demonstrate the influence of the mean of the images and the size of the filter kernel. The convolutional neural network model with the best result reached 95.6% of accuracy. The results of experiments show that neural networks predict successfully automobile images with varied illumination intensities overcome the techniques Haar Cascade and the Support Vector Machine.
The relationship between intensity and duration of physical activity and subjective well-being.

AIM Previous research documented a positive effect of physical activity on subjective well-being (SWB). Yet, mainly broad activity measures (e.g. resulting from yes-no questions) were used and the effect of different participation intensities and durations has been largely neglected. The aim of this study is to examine the effect of physical activity on SWB by focusing on participation intensity and duration. METHODS Survey data from 28 European countries are used for the analysis (n = 22 971). Two regression models (Generalized Method of Moments) are estimated which analyze the effect of participation intensity and duration on SWB (measured by life satisfaction). Given the endogeneity of the participation measures, instrumental variables are used (sport opportunities, club membership, time spent sitting). The models also control for other factors that could affect SWB (e.g. age, occupation). RESULTS The results for participation intensity show that the number of days people practised at moderate intensity in the week prior to the interview have a significant and positive effect on SWB, while the number of days with vigorous-intensity activity has a significant and negative impact. Similarly, the models for duration indicate that the minutes spent on moderate-intensity activity significantly add to SWB, while the minutes spent on vigorous-intensity activity significantly reduce the level of SWB. CONCLUSIONS The findings challenge the World Health Organization's recommendation in the sense that activity at moderate and vigorous intensity is not interchangeable if the aim is to also improve SWB (and not only physical health).
NUMERICAL HYDRODYNAMIC SIMULATIONS OF MOLECULAR OUTFLOWS DRIVEN BY HAMMER JETS

Very young protostars eject collimated jets of molecular gas. Although the protostars themselves are hidden, some of their properties are revealed through the jet dynamics. We here model velocity shear, precession, pulsation and spray within dense jets injected into less-dense molecular clouds. We investigate the Hammer Jet, for which extreme velocity variations as well as strong ripping and spray actions are introduced. A three dimensional ZEUS-type hydrodynamics code, extended with molecular physics, is employed. Jet knots, previously shown to be compact in simulations of smoother jets, now appear as prominent bow shocks in H2 and as bullets in CO emission lines. High proper motions are predicted in the jet. In the lobes we uncover wide tubular low-velocity CO structures with concave bases near the nozzle. Proper motion vectors in the lobes delineate a strong accelerated flow away from the head with some superimposed turbulent-like motions. The leading bow is gradually distorted by the hammer blows and breaks up into mini-bow segments. The H2 emission line profiles are wide and twin-peaked over much of the leading bow. On comparison with the simulations, we identify observed outflows driven by various dynamical types of jet. Shear is essential to produce the jet bows, spray or precession to widen the outflows and hammer blows to generate knotty jets. We identify the proper motions of maser spots with the pattern speed of density peaks in the inner jet and shell.
Dcp2 Decaps m2,2,7GpppN-Capped RNAs, and Its Activity Is Sequence and Context Dependent

ABSTRACT Hydrolysis of the mRNA cap plays a pivotal role in initiating and completing mRNA turnover. In nematodes, mRNA metabolism and cap-interacting proteins must deal with two populations of mRNAs, spliced leader trans-spliced mRNAs with a trimethylguanosine cap and non-trans-spliced mRNAs with a monomethylguanosine cap. We describe here the characterization of nematode Dcp1 and Dcp2 proteins. Dcp1 was inactive in vitro on both free cap and capped RNA and did not significantly enhance Dcp2 activity. Nematode Dcp2 is an RNA-decapping protein that does not bind cap and is not inhibited by cap analogs but is effectively inhibited by competing RNA irrespective of RNA sequence and cap. Nematode Dcp2 activity is influenced by both 5′ end sequence and its context. The trans-spliced leader sequence on mRNAs reduces Dcp2 activity ∼10-fold, suggesting that 5′-to-3′ turnover of trans-spliced RNAs may be regulated. Nematode Dcp2 decaps both m7GpppG- and m2,2,7GpppG-capped RNAs. Surprisingly, both budding yeast and human Dcp2 are also active on m2,2,7GpppG-capped RNAs. Overall, the data suggest that Dcp2 activity can be influenced by both sequence and context and that Dcp2 may contribute to gene regulation in multiple RNA pathways, including monomethyl- and trimethylguanosine-capped RNAs.