Miracle-dz/UniFAR-FTUs huggingface dataset:
Swift Is a Novel BRCT Domain Coactivator of Smad2 in Transforming Growth Factor β Signaling

ABSTRACT Transforming growth factor β (TGFβ) signaling is transduced via Smad2–Smad4–DNA-binding protein complexes which bind to responsive elements in the promoters of target genes. However, the mechanism of how the complexes activate the target genes is unclear. Here we identify Xenopus Swift, a novel nuclear BRCT (BRCA1 C-terminal) domain protein that physically interacts with Smad2 via its BRCT domains. We examine the activity of Swift in relation to gene activation in Xenopus embryos. Swift mRNA has an expression pattern similar to that of Smad2. Swift has intrinsic transactivation activity and activates target gene transcription in a TGFβ-Smad2-dependent manner. Inhibition of Swift activity results in the suppression of TGFβ-induced gene transcription and defective mesendoderm development. Blocking Swift function affects neither bone morphogenic protein nor fibroblast growth factor signaling during early development. We conclude that Swift is a novel coactivator of Smad2 and that Swift has a critical role in embryonic TGFβ-induced gene transcription. Our results suggest that Swift may be a general component of TGFβ signaling.
Head to Head Comparison of Short-Term Treatment with the NAD+ Precursor Nicotinamide Mononucleotide (NMN) and 6 Weeks of Exercise in Obese Female Mice

Obesity is well known to be a major cause of several chronic metabolic diseases, which can be partially counteracted by exercise. This is due, in part, to an upregulation of mitochondrial activity through increased nicotinamide adenine dinucleotide (NAD+). Recent studies have shown that NAD+ levels can be increased by using the NAD+ precursor, nicotinamide mononucleotide (NMN) leading to the suggestion that NMN could be a useful intervention in diet related metabolic disorders. In this study we compared the metabolic, and especially mitochondrial-associated, effects of exercise and NMN in ameliorating the consequences of high-fat diet (HFD) induced obesity in mice. Sixty female 5 week old C57BL6/J mice were allocated across five groups: Chow sedentary: CS; Chow exercise: CEX; HFD sedentary: HS; HFD NMN: HNMN; HFD exercise: HEX (12/group). After 6 weeks of diet, exercise groups underwent treadmill exercise (15 m/min for 45 min), 6 days per week for 6 weeks. NMN or vehicle (500 mg/kg body weight) was injected (i.p.) daily for the last 17 days. No significant alteration in body weight was observed in response to exercise or NMN. The HFD significantly altered adiposity, glucose tolerance, plasma insulin, NADH levels and citrate synthase activity in muscle and liver. HEX and HNMN groups both showed significantly improved glucose tolerance compared to the HS group. NAD+ levels were increased significantly both in muscle and liver by NMN whereas exercise increased NAD+ only in muscle. Both NMN and exercise ameliorated the HFD-induced reduction in liver citrate synthase activity. However, exercise, but not NMN, ameliorated citrate synthase activity in muscle. Overall these data suggest that while exercise and NMN-supplementation can induce similar reversal of the glucose intolerance induced by obesity, they are associated with tissue-specific effects and differential alterations to mitochondrial function in muscle and liver.
Development of CRIM system for the automatic speaker verification spoofing and countermeasures challenge 2015

The automatic speaker verification spoofing and countermeasures challenge 2015 provides a common framework for the evaluation of spoofing countermeasures or anti-spoofing techniques in the presence of various seen and unseen spoofing attacks. This contribution proposes a system consisting of amplitude, phase, linear prediction residual, and combined amplitude - phase-based countermeasures for the detection of spoofing attacks. In this task we use following features: Mel-frequency cepstral coefficients (MFCC), product spectrum-based cepstral coefficients, modified group delay cepstral coefficients, weighted linear prediction group delay cepstral coefficients, linear prediction residual cepstral coefficients, cosine normalized phase-based cepstral features (CNPCC), and a combination of MFCC-CNPCC. The product spectrum-based features are influenced by both the amplitude and phase spectra. The Gaussian Mixture Model (GMM) classifier is used for the discrimination of the human and spoofed speech signals. Our primary submitted system is a linear fusion of the sub-systems based on the features mentioned above with fusion weights trained on the development dataset. Experimental results on the challenge evaluation data provided an average EER (equal error rate) of 0.041%, 5.347%, and 2.69% on the known, unknown and all (known + unknown) spoofing attacks, respectively. Among all the systems product spectrum-based cepstral coefficients- and conventional MFCC (without any feature normalization)based systems performed the best in terms of EER measure. On the known, unknown and all conditions the EER obtained by the MFCC and product spectrum-based features are 0.78% & 0.65%, 5.39% & 5.37% and 3.09% & 3.01%, respectively.
Implementation of an Urban Canopy Parameterization in a Mesoscale Meteorological Model

Abstract An urban canopy parameterization (UCP) is implemented into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to improve meteorological fields in the urban boundary layer for finescale (∼1-km horizontal grid spacing) simulations. The UCP uses the drag-force approach for dynamics and a simple treatment of the urban thermodynamics to account for the effects of the urban environment. The UCP is evaluated using a real-data application for Philadelphia, Pennsylvania. The simulations show that the UCP produces profiles of wind speed, friction velocity, turbulent kinetic energy, and potential temperature that are more consistent with the observations taken in urban areas and data from idealized wind tunnel studies of urban areas than do simulations that use the roughness approach. In addition, comparisons with meteorological measurements show that the UCP simulations are superior to those that use the roughness approach. This improvement of .
Who Benefits from Jurisdictional Competition?

When local governments compete, do their constituents win? Much empirical and theoretical work, inspired by the canonical Tiebout (1956) model, suggests that they do, with jurisdictional competition creating a quasi-market for public goods. In this paper, I consider a case in which special interests, rather than local taxpayers, emerge as the primary winners from jurisdictional competition. My theory predicts that competition among local governments will drive up the wages of highly specialized public-sector employees, a hypothesis for which I find support using a unique survey of employee compensation. I conclude by drawing more general conclusions from the theory, identifying the conditions under which constituents end up as the beneficiaries of jurisdictional competition, and the conditions under which they become its losers.
Joint transmit/receive one-tap minimum mean square error frequency-domain equalisation for broadband multicode direct-sequence code division multiple access

Multicode direct-sequence code division multiple access (DS-CDMA) can flexibly support multimedia services with various data rates simply by changing the code multiplexing order. The use of simple one-tap frequency-domain equalisation (FDE) at a receiver is known to improve the bit error rate (BER) performance of multicode DS-CDMA in a severe frequency-selective fading environment. However, the BER performance improvement is limited due to the presence of the residual inter-chip interference (ICI). The authors propose a joint transmit/receive minimum mean square error (MMSE) FDE, which carries out one-tap transmit FDE and one-tap receive FDE jointly based on the MMSE criterion. The authors theoretically derive a suboptimal set of transmit and receive FDE weights and investigate the BER performance improvement by computer simulation in a freqeuncy-selective Rayleigh fading channel. The proposed scheme improves the received signal-to-interference plus noise ratio after despreading, and consequently, the BER performance can be significantly improved compared to the conventioal receive MMSE-FDE by (a) making the variations in the equivalent channel gain shallower to reduce the residual ICI for large code multiplexing order U and (b) allocating the transmit power to the frequencies having good condition to improve the received signal-to-noise ratio for a small U. This is conformed by the computer simualtion.
Spheroplasts of Lactobacillus casei and the cellular distribution of bactoprenol.

Spheroplasts of L. casei were prepared by the simultaneous action of trypsin and lysozyme in the presence of EDTA and 20% sucrose. Electron microscopy of thin sections of cells during the formation of spheroplasts showed that the spheroplasts emerged from the cell wall through a weak point at one end of the cell. Electron microscopy of negatively stained preparations showed that the structure of spheroplast membranes obtained by disruption in hypotonic medium varied according to the nature of the hypotonic medium used. Disruption in water gave small, delicate membrane fragments; disruption in 0.1 M tris-Cl gave large pieces with tubulo-vesicular attachments. The effects of Mg 2+ and EDTA were also investigated. Bactoprenol, the C 55 isoprenoid alcohol concerned in cell wall biosynthesis, was located by growing L. casei in [ 14 C]mevalonic acid. Over 90% of the cellular bactoprenol was found in the spheroplast membrane when this was obtained with 0.1 M tris-Cl, but only 42% when the spheroplasts were burst in water. It is suggested that the missing bactoprenol may be solubilized with the membranes of mesosomal orgin, and that bactoprenol is therefore a component of both plasma and mesosomal membranes.
Divergent evolutionary processes associated with colonization of offshore islands

Oceanic islands have been a test ground for evolutionary theory, but here, we focus on the possibilities for evolutionary study created by offshore islands. These can be colonized through various means and by a wide range of species, including those with low dispersal capabilities. We use morphology, modern and ancient sequences of cytochrome b (cytb) and microsatellite genotypes to examine colonization history and evolutionary change associated with occupation of the Orkney archipelago by the common vole (Microtus arvalis), a species found in continental Europe but not in Britain. Among possible colonization scenarios, our results are most consistent with human introduction at least 5100 bp (confirmed by radiocarbon dating). We used approximate Bayesian computation of population history to infer the coast of Belgium as the possible source and estimated the evolutionary timescale using a Bayesian coalescent approach. We showed substantial morphological divergence of the island populations, including a size increase presumably driven by selection and reduced microsatellite variation likely reflecting founder events and genetic drift. More surprisingly, our results suggest that a recent and widespread cytb replacement event in the continental source area purged cytb variation there, whereas the ancestral diversity is largely retained in the colonized islands as a genetic ‘ark’. The replacement event in the continental M. arvalis was probably triggered by anthropogenic causes (land‐use change). Our studies illustrate that small offshore islands can act as field laboratories for studying various evolutionary processes over relatively short timescales, informing about the mainland source area as well as the island.
Clustering in Wireless Sensor Networks based on near optimal bi-partitions

Wireless Sensor Networks (WSNs) have recently become an area of attractive research interest. A WSN consists of low-cost, low power, and energy-constrained sensors responsible for monitoring and reporting a physical phenomenon to the sink node where the end-user can access the data. Reducing energy consumption and enlarging lifetime of the whole WSNs are the important challenges in these fields of applications. To deal with these challenges, clustering algorithms can be used. In this paper we present a new approach called the Spectral Classification based on Near Optimal Clustering in Wireless Sensor Networks (SCNOpC-WSNs). This protocol uses spectral graph theory in order to subdivide the network such that each cluster includes the highest inter-correlated sensors. Simulation results demonstrate that SCNOpC-WSNs distribute energy consumption more effectively among the sensors. Thus, the proposed approach enlarges the network lifetime by as much as 44.8% compared to LEACH.
Alternative Method for Increnentally Constructing the FP-Tree

The FP-tree is an effective data structure that facilitates the mining of frequent patterns from transactional databases. But, transactional databases are dynamic in general, and hence modifications on the database must be reflecting onto the FP-tree. Constructing the FP-tree from scratch and incrementally updating the FP-tree are two possible choices. However, from scratch construction turns unfeasible as the database size increases. So, this paper addresses incremental update by extending the FP-tree concepts and manipulation process. Our new approach is capable of handling all kinds of changes; include additions, deletions and modifications. The target is achieved by constructing and incrementally dealing with the complete FP-tree, i.e., with one minimum support. Constructing the complete FP-tree has the other advantage that it provides the freedom of mining for lower minimum support values without the need to reconstruct the tree. However, directly reflecting the changes onto the FP-tree may invalidate the basic FP-tree structure. Thus, we apply a sequence of shuffling and merging operations to validate and maintain the modified tree. The experiments conducted on synthetic and real datasets clearly highlight advantages of the proposed incremental approach over constructing the FP-tree from scratch
The Determining Factors of Customer Loyalty for Luxury Hotels in US

This research aims to examine which determinants (i .e. perceived quality, trust, satisfaction, perceived value, and membership programs) have significant effect(s) on customer loyalty for luxury hotel. In order to get the answer, this study surveyed 308 online users to investigate the determining factors that affect customer loyalty fo r luxury hotel in US. The multiple regression statistical method, ANOVA, frequency tests and factor analysis were used for d ata analysis. The study results show that among the determining facto rs and their relationship against attitudinal and b ehavioral loyalty, “satisfaction” was consistently valued the most, fo llowed by “trust. ” The attitudinal loyalty measurem ent regarded “membership program” as its third most valued while the behavioral loyalty positioned “perceived quali ty” as its third. Results for the fourth most valued factor, indicate d “perceived quality” from the attitudinal loyalty measurement, while “perceived value” was behavioral loyalty’s fourth m ost valued. The least valued factors for attitudina l and behavioral loyalty were “perceived value” and “membership prog ram,” respectively. On a greater scale, in measurin g overall loyalty, the determining factors were ranked as fol lows, in descending order from most to least import ant: “satisfaction,” “trust,” perceived quality,” “membe rship program,” and “perceived value. ” This study can serve as a model to be adopted for yto come in the determining factors of loyalty in luxury hotels in US. The constructs, specifically “perceiv ed value,” “perceived quality,” “satisfaction,” and “trust” have been of use for years, through various industries. These particular determinants will continue to be of use across industries as efforts are made to measure customer loyalty.
Molecular Evolution and Functional Divergence of Sulfate Transporter Gene Family in Plants

The plant sulfate transporter proteins play important roles in transport and absorption of sulfate. It has been demonstrated that the members of this family in plants may be classified into 5 main groups. In the present study, we found the statistical evidence of shifted evolutionary rate between groups that contributed to functional divergence of this family in plants. Furthermore, evolutionary analysis illustrated that positive selection played important role during the long period of evolution for this family, and Sulfate_transp and STAS domains had co-evolved in the evolution of proteins containing both domains.
Malware Profiler Based on Innovative Behavior-Awareness Technique

In order to steal valuable data, hackers are uninterrupted research and development new techniques to intrude computer systems. Opposite to hackers, security researchers are uninterrupted analysis and tracking new malicious techniques for protecting sensitive data. There are a lot of existing analyzers can be used to help security researchers to analyze and track new malicious techniques. However, these existing analyzers cannot provide sufficient information to security researchers to perform precise assessment and deep analysis. In this paper, we introduce a behavior-based malicious software profiler, named Holography platform, to assist security researchers to obtain sufficient information. Holography platform analyzes virtualization hardware data, including CPU instructions, CPU registers, memory data and disk data, to obtain high level behavior semantic of all running processes. High level behavior semantic can provide sufficient information to security researchers to perform precise assessment and deep analysis new malicious techniques, such as malicious advertisement attack(malvertising attack).
Microphone Identification Using Higher-Order Statistics

This paper presents a statistical framework for microphone identification using only digital audio recordings. To accomplish this task, the microphone-induced artifacts are modeled using a nonlinear function and then a statistical tool based on higher-order statistics is used to capture these artifacts. More specifically, polyspectral analysis is used to capture microphone-induced artifacts. Distanceand correlation-based similarity measures are used for automatic microphone identification. More specifically, distance between scale-invariant Hu moments of the bicoherence magnitude spectrum and cross-correlation between bicoherence phase spectra are used. The effectiveness of the proposed framework has been tested with 24 audio recordings captured using eight microphones during three recording sessions. Performance of the proposed scheme is evaluated using ambient noise recordings captured using eight microphones of four different types.
Predictability of foreign exchange rates : On the chaos ’ routes

Criteria usually used by economists to evaluate the accuracy of forecasts generated by a model are based on mean and quadratic errors. The ability to give directional forecast, raise or drop, is not very used whereas it is of big practical interest. If a model was able to give good directional forecasts with a rate greater than 50% over long periods, trading strategies with excess return could exist. Using this criterion, the predictability of daily major foreign exchange rates is examined. The forecasting model we used is non-parametric and based on nearest neighbors. First of all, a description of the data is done. We focus on possible chaotic properties and independence of the data. First results illustrate the need to select few neighbors and an embedding dimension adapted to the data, the relevance of the directional forecast criterion compared to MAE and RMSE. Ten years of daily forecasts, from 1989 to 1998, were computed. If it is not possible to find a model with significant results all over the period, some sub periods show results far from the behaviour of a random walk. Deutsche Mark and British Pound sign forecasting rate for the year 1991 is greater than 57%.
Active Degradation Explains the Distribution of Nuclear Proteins during Cellular Senescence

The amount of cellular proteins is a crucial parameter that is known to vary between cells as a function of the replicative passages, and can be important during physiological aging. The process of protein degradation is known to be performed by a series of enzymatic reactions, ranging from an initial step of protein ubiquitination to their final fragmentation by the proteasome. In this paper we propose a stochastic dynamical model of nuclear proteins concentration resulting from a balance between a constant production of proteins and their degradation by a cooperative enzymatic reaction. The predictions of this model are compared with experimental data obtained by fluorescence measurements of the amount of nuclear proteins in murine tail fibroblast (MTF) undergoing cellular senescence. Our model provides a three-parameter stationary distribution that is in good agreement with the experimental data even during the transition to the senescent state, where the nuclear protein concentration changes abruptly. The estimation of three parameters (cooperativity, saturation threshold, and maximal velocity of the reaction), and their evolution during replicative passages shows that only the maximal velocity varies significantly. Based on our modeling we speculate the reduction of functionality of the protein degradation mechanism as a possible competitive inhibition of the proteasome.
Discovery of Globular Clusters in the Proto-Spiral NGC2915: Implications for Hierarchical Galaxy Evolution

We have discovered three globular clusters beyond the Holmberg radius in Hubble Space Telescope Advanced Camera for Surveys images of the gas-rich dark matter dominated blue compact dwarf galaxy NGC2915. The clusters, all of which start to resolve into stars, have M_{V606} = -8.9 to -9.8 mag, significantly brighter than the peak of the luminosity function of Milky Way globular clusters. Their colors suggest a metallicity [Fe/H] ~ -1.9 dex, typical of metal-poor Galactic globular clusters. The specific frequency of clusters is at a minimum normal, compared to spiral galaxies. However, since only a small portion of the system has been surveyed it is more likely that the luminosity and mass normalized cluster content is higher, like that seen in elliptical galaxies and galaxy clusters. This suggests that NGC2915 resembles a key phase in the early hierarchical assembly of galaxies - the epoch when much of the old stellar population has formed, but little of the stellar disk. Depending on the subsequent interaction history, such systems could go on to build-up larger elliptical galaxies, evolve into normal spirals, or in rare circumstances remain suspended in their development to become systems like NGC2915.
Forecasting time-dependent conditional densities. A neural network approach.

In financial econometrics the modeling of asset return series is closely related to the estimation of the corresponding conditional densities. One reason why one is interested in the whole conditional density and not only in the conditional mean, is that the conditional variance can be interpreted as a measure of time-dependent volatility of the return series. In fact, the modeling and the prediction of volatility is one of the central topics in asset pricing. In this paper we propose to estimate conditional densities semi-nonparametrically in a neural network framework. Our recurrent mixture density networks realize the basic ideas of prominent GARCH approaches but they are capable of modeling any continuous conditional density also allowing for time-dependent higher-order moments. Our empirical analysis on daily DAX data shows that out-of-sample volatility predictions of the neural network model are superior to predictions of GARCH models in that they have a higher correlation with implied volatilities. (author's abstract)