Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of the mammalian APC/C activator FZR1 shortens G1 and lengthens S phase but has little effect on exit from mitosis

The anaphase-promoting complex/cyclosome (APC/C) is essential for progression through mitosis. At anaphase onset, the APC/C requires the activator protein CDC20 to target securin and cyclin B1 for proteasome-dependent degradation, but then depends on the CDC20-related protein FZR1 (also known as CDH1) to remain active until the onset of the next S phase. To investigate the role of FZR1 in mammalian cells, we used RNAi in human cell lines and conditional gene targeting in mouse embryonic fibroblasts. In neither case was FZR1 required for exit from mitosis, but in cells lacking FZR1, the G1 phase was shortened and the S phase was prolonged. In several normal and transformed human cell lines, loss of FZR1 function induced DNA-damage responses and impaired proliferation independently of the p53 status. Constitutive knockdown of p53 in U2OS cells with inducible FZR1 siRNA also failed to restore their proliferative capacity. Thus, the proliferation defects are a direct consequence of the genetic damage inflicted by loss of FZR1 function and are largely independent of p53. In summary, mammalian FZR1 is not required for the completion of mitosis, but is an important regulator of G1 phase and is required for efficient DNA replication in human and mouse somatic cells.
Bank Overleverage and Macroeconomic Fragility

This paper develops a dynamic general equilibrium model that explicitly includes a banking sector with a maturity mismatch. We demonstrate that, despite the perfect competition in the banking sector, rational banks take on excessive risks systemically, resulting in overleverage and inefficiently high crisis probabilities. The model accounts for the banks' rational over-optimism regarding future capital prices which arises from pecuniary externalities on their own solvency. Using the model as an example, we introduce MSR (marginal systemic risk) as a general measure to assess the macroeconomic exposure to systemic risks.
Comparative Advantage of Maize- and Grass-Silage Based Feedstock for Biogas Production with Respect to Greenhouse Gas Mitigation

This paper analyses the comparative advantage of using silage maize or grass as feedstock for anaerobic digestion to biogas from a greenhouse gas (GHG) mitigation point of view, taking into account site-specific yield potentials, management options, and land-use change effects. GHG emissions due to the production of biogas were calculated using a life-cycle assessment approach for three different site conditions with specific yield potentials and adjusted management options. While for the use of silage maize, GHG emissions per energy unit were the same for different yield potentials, and the emissions varied substantially for different grassland systems. Without land-use change effects, silage maize-based biogas had lower GHG emissions per energy unit compared to grass-based biogas. Taking land-use change into account, results in a comparative advantage of biogas production from grass-based feedstock produced on arable land compared to silage maize-based feedstock. However, under current frame conditions, it is quite unrealistic that grass production systems would be established on arable land at larger scale.
Overexpression of the Tobacco Tsi1 Gene Encoding an EREBP/AP2–Type Transcription Factor Enhances Resistance against Pathogen Attack and Osmotic Stress in Tobacco

Using mRNA differential display analysis, we isolated a salt-induced transcript that showed a significant sequence homology with an EREBP/AP2 DNA binding motif from oilseed rape plants. With this cDNA fragment as a probe, cDNA clone Tsi1 (for Tobacco stress-induced gene1) was isolated from a tobacco cDNA library. RNA gel blot analysis indicated that transcripts homologous with Tsi1 were induced not only in NaCl-treated leaves but also in leaves treated with ethephon or salicylic acid. Transient expression analysis using a Tsi1::smGFP fusion gene in BY-2 cells indicated that the Tsi1 protein was targeted to the nucleus. Fusion protein of Tsi1 with GAL4 DNA binding domain strongly activated transcription in yeast, and the transactivating activity was localized to the 13 C-terminal amino acids of Tsi1. Electrophoretic mobility shift assays revealed that Tsi1 could bind specifically to the GCC and the DRE/CRT sequences, although the binding activity to the former was stronger than that to the latter. Furthermore, Agrobacterium-mediated transient expression and transgenic plants expressing Tsi1 demonstrated that overexpression of the Tsi1 gene induced expression of several pathogenesis-related genes under normal conditions, resulting in improved tolerance to salt and pathogens. These results suggest that Tsi1 might be involved as a positive trans-acting factor in two separate signal transduction pathways under abiotic and biotic stress.
Dynamic Linkages between the European and US Stock Markets

This paper aims to investigate the dynamic linkages between the European and US stock markets. We firstly examine the volatility spillover effects between the European and US stock markets using multivariate GARCH-BEKK model. The results suggest that volatility spillover effects widely exist in the international equity market. The UK market is the main volatility transmitter within the European stock market while the US is the main exporter worldwide. Secondly, we test the presence of time-varying correlation and tail dependence between return series using Copula-GARCH approach. The results show that correlations between the selected stock indices are not just non-linear but also significantly time-varying. In addition, this analysis also indicates asymmetric time-varying tail dependence between European and US stock market (Jan, 2004-Oct, 2009).
A Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length

MOTIVATION Transcription regulatory protein factors often bind DNA as homo-dimers or hetero-dimers. Thus they recognize structured DNA motifs that are inverted or direct repeats or spaced motif pairs. However, these motifs are often difficult to identify owing to their high divergence. The motif structure included explicitly into the motif recognition algorithm improves recognition efficiency for highly divergent motifs as well as estimation of motif geometric parameters. RESULT We present a modification of the Gibbs sampling motif extraction algorithm, SeSiMCMC (Sequence Similarities by Markov Chain Monte Carlo), which finds structured motifs of these types, as well as non-structured motifs, in a set of unaligned DNA sequences. It employs improved estimators of motif and spacer lengths. The probability that a sequence does not contain any motif is accounted for in a rigorous Bayesian manner. We have applied the algorithm to a set of upstream regions of genes from two Escherichia coli regulons involved in respiration. We have demonstrated that accounting for a symmetric motif structure allows the algorithm to identify weak motifs more accurately. In the examples studied, ArcA binding sites were demonstrated to have the structure of a direct spaced repeat, whereas NarP binding sites exhibited the palindromic structure. AVAILABILITY The WWW interface of the program, its FreeBSD (4.0) and Windows 32 console executables are available at http://bioinform.genetika. ru/SeSiMCMC
MoodSwings: A Collaborative Game for Music Mood Label Collection

There are many problems in the field of music information retrieval that are not only difficult for machines to solve, but that do not have well-defined answers. In labeling and detecting emotions within music, this lack of specificity makes it difficult to train systems that rely on quantified labels for supervised machine learning. The collection of such “ground truth” data for these subjectively perceived features necessarily requires human subjects. Traditional methods of data collection, such as the hiring of subjects, can be flawed, since labeling tasks are time-consuming, tedious, and expensive. Recently, there have been many initiatives to use customized online games to harness so-called “Human Computation” for the collection of label data, and several such games have been proposed to collect labels spanning an excerpt of music. We present a new game, MoodSwings (http://schubert.ece.drexel.edu/moodswings), which differs in that it records dynamic (per-second) labels of players’ mood ratings of music, in keeping with the unique timevarying quality of musical mood. As in prior collaborative game approaches, players are partnered to verify each others’ results, and the game is designed to maximize consensusbuilding between users. We present preliminary results from an initial set of game play data.
The impact of α/Fe enhanced stellar evolutionary tracks on the ages of elliptical galaxies

We complement our study of=Fe enhanced stellar population models of Lick absorption indices (Thomas et al. 2003) by comparing two sets of=Fe enhanced models. In both models the impact on Lick indices due to=Fe enhancement is accounted for through a modification of the stellar absorption line-strengths using the response functions of Tripicco & Bell (1995). One set of models, however, uses solar-scaled, the other =Fe enhanced stellar evolutionary tracks. Since the =Fe enhanced tracks are hotter than the solar-scaled ones (Salasnich et al. 2000), the correspondent stellar population models have slightly weaker metallic indices (i.e. Mg b,hFei etc.) and stronger Balmer line indices (H) (Maraston et al. 2003). Here we explore quantitatively the impact of this eect on the=Fe ratios, metallicities and ages that are derived for elliptical galaxies. We find that the modest decrease of the metallic indices Mg b andhFei balance each other, such that fully consistent=Fe ratios are derived for stellar systems using =Fe enhanced models with either solar-scaled or =Fe enhanced stellar tracks. The decrease of the metallic indices and the increase of H conspire in a way that also consistent metallicities are obtained. The derived ages, instead, are significantly dierent. The inclusion of=Fe enhanced stellar tracks leads to the derivation of ages as high as30 Gyr for elliptical galaxies. For the same objects, ages not older than 15 Gyr are obtained, if =Fe enhanced models using solar-scaled tracks are adopted. This may indicate that current stellar evolutionary models overestimate the bluing of stellar evolutionary tracks due to=Fe enhanced chemical mixtures at super-solar metallicities.
User Load Analysis and Pilot Sequence Design for Multi-Cell Massive MIMO Networks

We propose a novel algorithm to design user load- achieving pilot sequences that mitigate pilot contamination in multi-cell massive multiple-input multiple-output (MIMO) networks. To this end, we first derive expressions for the user load and the load region of the network considering both small- scale and large-scale propagation effects. We then develop the pilot sequence algorithm for multi- cell massive MIMO networks as per the rules of generalized Welch bound equality design. Notably, we find that our algorithm and the corresponding downlink power allocation ensure that the user load is achieved when the signal-to-interference- plus-noise ratio (SINR) requirements for the users lie within the load region. Furthermore, we demonstrate the performance advantage of our proposed design relative to the existing designs, in terms of a larger load region and a higher maximum permitted SINR. Finally, we show that our proposed design can satisfy the pre-defined SINR requirements for users with a finite number of antennas at the base station (BS), while the existing designs cannot satisfy the same requirements even with an infinite number of antennas at the BS.
Optimizing the P300-based brain–computer interface: current status, limitations and future directions

This paper summarizes the presentations and discussions at a workshop held during the Fourth International BCI Meeting charged with reviewing and evaluating the current state, limitations and future development of P300-based brain–computer interface (P300-BCI) systems. We reviewed such issues as potential users, recording methods, stimulus presentation paradigms, feature extraction and classification algorithms, and applications. A summary of the discussions and the panel's recommendations for each of these aspects are presented.
Analysis and Determination of Risk Factors Leading to Preterm Birth Using Data Mining Techniques in

333 Abstract – This paper aims to present a method of analysis a real medical dataset using Data Mining techniques. The method determines the risk factors leading to preterm birth and also analysis the quality in obstetrics. As language and development environment for statistical computation we used the R system. The purpose of this study is to highlight the influence of certain factors for premature birth by using graphics created in R. The method further, applied on a sample of Romanian births, shows that various socio-demographic, anthropometric, behavioral and medical factors act interconnected in a direct manner on the risk of premature births. To prove this statement we wanted a partitioning based on similarities between the attributes that may be risk factors. We also classified attributes that may represent risk factors according to the type of birth: term or preterm. The results will be outlined with graphics obtained by applying data exploration algorithms.
Oracle-Based Robust Optimization via Online Learning

Robust optimization is a common optimization framework under uncertainty when problem parameters are unknown, but it is known that they belong to some given uncertainty set. In the robust optimization framework, a min-max problem is solved wherein a solution is evaluated according to its performance on the worst possible realization of the parameters. In many cases, a straightforward solution to a robust optimization problem of a certain type requires solving an optimization problem of a more complicated type, which might be NP-hard in some cases. For example, solving a robust conic quadratic program, such as those arising in a robust support vector machine (SVM) with an ellipsoidal uncertainty set, leads in general to a semidefinite program. In this paper, we develop a method for approximately solving a robust optimization problem using tools from online convex optimization, where at every stage a standard (nonrobust) optimization program is solved. Our algorithms find an approximate robust solution usin.
Short-term change in kidney function and risk of end-stage renal disease.

BACKGROUND It is unclear what degree of change in the eGFR over a 1-year period indicates clinically significant progression, and whether this change adds additional information beyond that obtained by a single eGFR measure alone. METHODS We included 598 397 adults who had at least two outpatient eGFR measurements (at least 6 months apart) during 1-year accrual period in Alberta, Canada. Change in kidney function (using the first and last eGFR) was defined by change in kidney function category with confirmation based on percent (%) change in eGFR [(last eGFR - first eGFR)/first eGFR × 100]. The groups for change in kidney function were thus defined as: 'certain drop' (drop in CKD category with ≥25% decrease in the eGFR); 'uncertain drop' (drop in CKD category with <25% decrease in the eGFR); 'stable' (no change in CKD category); 'uncertain rise' (rise in CKD category with <25% rise in the eGFR) and 'certain rise' (rise in CKD category with ≥25% increase in the eGFR). Adjusted end-stage renal disease (ESRD) rates (per 1000 person-years) for each group of change in kidney function were calculated using Poisson regression. Adjusted risks of ESRD associated with change in kidney function, in reference to stable kidney function, were estimated. RESULTS Among the 598 397 participants, 74.8% (n = 447 570) had stable (no change in CKD category), 3.3% (n = 19 591) had a certain drop and 3.7% (n = 22 171) had a certain rise in kidney function. Participants who experienced a certain change in kidney function (both drop and rise) were older, more likely to be female, and had a higher prevalence of comorbidities, in comparison with those with stable kidney function. There were 1966 (0.3%) ESRD events over a median follow-up of 3.5 years. Compared with participants with stable kidney function, after adjustment for covariates, and the first eGFR measurement, those with certain drop had 5-fold increased risk of ESRD (HR: 5.11; 95% CI: 4.56-5.71), whereas those with an uncertain drop had 2-fold increased risk (HR: 2.13; 95% CI: 1.84-2.47). After adjustment for the eGFR and covariates at the last visit, neither a certain nor uncertain drop in the eGFR was associated with an increased ESRD risk. The ESRD risk associated with the last eGFR level, adjusted for the slope over time, were 2.89 (95% CI: 2.35-3.55), 10.98 (95% CI: 8.69-13.87), 35.20 (95% CI: 27.95-44.32) and 147.96 (116.92-187.23) for categories 2, 3a, 3b and 4, respectively, in reference to category 1. CONCLUSIONS A change in eGFR category accompanied by ≥25% decline (certain drop) is associated with increased ESRD risk. However, this elevated risk is captured by patient characteristics and eGFR at the last visit, suggesting that eGFR trajectories based on more than two serum creatinine measurements over a period longer than 1 year are required to determine ESRD risk and allow more reliable risk prediction.
Visual Attention Model with Cross-Layer Saliency Optimization

Detection of visually salient regions is useful for applications like image adaptation, adaptive compression, image retrieval and so on. In this paper, a new bottom-up visual attention model (VAM) is proposed based on the spirit of cross-layer optimization in the field of communication. In this model, the local saliency and global saliency are firstly extracted based on the contrast of low-level features from the local and global layers respectively, and then they are used to construct a weight model. Finally the proposed VAM is obtained by optimizing the global saliency with the weight model, which is taken as a feedback from the local layer to the global layer. Experimental results demonstrate that the proposed VAM performs competitively with four existing models on detecting out accurate salient regions and enhancing the contrast between salient and non-salient regions.
Attributed Graph Rewriting for Complex Event Processing Self-Management

The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, self-management and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism. AGeCEP represents queries in a language- and technology-agnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this research has used it to design an autonomic manager and to define a selected set of self-management policies. Finally, experiments demonstrate that AGeCEP can indeed be used to develop algorithms that can be integrated into diverse CEP systems.
A bounded neural network for open set recognition

Open set recognition is, more than an interesting research subject, a component of various machine learning applications which is sometimes neglected: it is not unusual the existence of learning systems developed on the top of closed-set assumptions, ignoring the error risk involved in a prediction. This risk is strictly related to the location in feature space where the prediction has to be made, compared to the location of the training data: the more distant the training observations are, less is known, higher is the risk. Proper handling of this risk can be necessary in various situation where classification and its variants are employed. This paper presents an approach to open set recognition based on an elaborate distance-like computation provided by a weightless neural network model. The results obtained in the proposed test scenarios are quite interesting, placing the proposed method among the current best ones.
Multi-Timescale Conformational Dynamics of the SH3 Domain of CD2-Associated Protein using NMR Spectroscopy and Accelerated Molecular Dynamics

A complete understanding of the relationship between biological activity and molecular conformation requires an understanding of the thermally accessible potential energy landscape. An extensive set of experimental NMR residual dipolar couplings (RDCs) has been used to determine the conformational behavior of CD2AP SH3C on multiple timescales, using the Gaussian Axial Fluctuation model, and comparison to restraint-free accelerated molecular dynamics simulation. These robust analyses provide a comprehensive description of conformational fluctuations on picosecond to millisecond timescales. While the β-sheets show negligible slow motions, larger amplitude slow dynamics are found in the n-SRC and RT loops that mediate physiological interactions.
Extremely Red Objects in Two Quasar Fields at z ~ 1.4

We present an investigation of the properties and environments of bright extremely red objects (EROs) found in the fields of the quasars TXS 0145+386 and 4C 15.55, both at z ~ 1.4. There is marginal evidence from Chandra Advanced CCD Imaging Spectrometer (ACIS) imaging for hot cluster gas with a luminosity of a few 1044 ergs s-1 in the field of 4C 15.55. The TXS 0145+386 field has an upper limit at a similar value, but it also clearly shows an overdensity of faint galaxies. None of the EROs are detected as X-ray sources. For two of the EROs that have spectral energy distributions and rest-frame near-UV spectra that show that they are strongly dominated by old stellar populations, we determine radial surface brightness profiles from adaptive optics images. Both of these galaxies are best fit by profiles close to exponentials, plus a compact nucleus comprising ~30% of the total light in one case and 8% in the other. Neither is well fit by an r1/4-law profile. This apparent evidence for the formation of massive ~2 × 1011 disks of old stars in the early universe indicates that at least some galaxies formed essentially monolithically, with high star formation rates sustained over a few 108 yr and without the aid of major mergers.
Life-Cycle Search, Match Quality and Japan's Labor Flow

The Japanese labor market displays U-shaped unemployment and separation rates, and declining job-finding rates as workers age. Traditional infinite horizon search models of the labor market cannot account for such patterns. We develop a life-cycle search and matching model that features random match quality and incorporates elements capturing several main characteristics of the Japanese labor market. We show that the model, calibrated for Japan, replicates the life-cycle properties of the data. Our model, following an empirically plausible productivity drop, produces changes in the steady state levels of the unemployment and finding rates similar in magnitude to those observed in Japan since the 1980s.