Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of positively charged amino acids and electrostatic interactions in the complex of U1A protein and U1 hairpin II RNA

Previous kinetic investigations of the N-terminal RNA recognition motif (RRM) domain of spliceosomal protein U1A, interacting with its RNA target U1 hairpin II, provided experimental evidence for a ‘lure and lock’ model of binding in which electrostatic interactions first guide the RNA to the protein, and close range interactions then lock the two molecules together. To further investigate the ‘lure’ step, here we examined the electrostatic roles of two sets of positively charged amino acids in U1A that do not make hydrogen bonds to the RNA: Lys20, Lys22 and Lys23 close to the RNA-binding site, and Arg7, Lys60 and Arg70, located on ‘top’ of the RRM domain, away from the RNA. Surface plasmon resonance-based kinetic studies, supplemented with salt dependence experiments and molecular dynamics simulation, indicate that Lys20 predominantly plays a role in association, while nearby residues Lys22 and Lys23 appear to be at least as important for complex stability. In contrast, kinetic analyses of residues away from the RNA indicate that they have a minimal effect on association and stability. Thus, well-positioned positively charged residues can be important for both initial complex formation and complex maintenance, illustrating the multiple roles of electrostatic interactions in protein–RNA complexes.
Segmentation of pectoral muscle in mammograms using fractal method

This paper proposes a novel method for the detection and segmentation of pectoral muscle in mammograms using fractal-based analysis. The region of pectoral muscles containing the brightest pixels has an adverse effect, during the detection of microcalcifications in a mammogram and hence detection and segmentation of pectoral muscle is deemed as an important preprocessing step. This proposed technique segments the pectoral muscle using the principle of Fractal dimension of the image, which is image-dependent, in combination with morphological operations. This Fractal technique of pectoral muscle segmentation is proved to produce quite promising results that substantiate the merit of the proposed technique, in addition to computation simplicity.
Network anomaly detection based on Eigen equation compression

This paper addresses the issue of unsupervised network anomaly detection. In recent years, networks have played more and more critical roles. Since their outages cause serious economic losses, it is quite significant to monitor their changes over time and to detect anomalies as early as possible. In this paper, we specifically focus on the management of the whole network. In it, it is important to detect anomalies which make great impact on the whole network, and the other local anomalies should be ignored. Further, when we detect the former anomalies, it is required to localize nodes responsible for them. It is challenging to simultaneously perform the above two tasks taking into account the nonstationarity and strong correlations between nodes. We propose a network anomaly detection method which resolves the above two tasks in a unified way. The key ideas of the method are: (1)construction of quantities representing feature of a whole network and each node from the same input based on eigen equation compression, and (2)incremental anomalousness scoring based on learning the probability distribution of the quantities. We demonstrate through the experimental results using two benchmark data sets and a simulation data set that anomalies of a whole network and nodes responsible for them can be detected by the proposed method.
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution

We present a membership-query algorithm for efficiently learning DNF with respect to the uniform distribution. In fact, the algorithm properly learns the more general class of functions that are computable as a majority of polynomially-many parity functions. We also describe extensions of this algorithm for learning DNF over certain nonuniform distributions and from noisy examples as well as for learning a class of geometric concepts that generalizes DNF. The algorithm utilizes one of Freund's boosting techniques and relies on the fact that boosting does not require a completely distribution-independent weak learner. The boosted weak learner is a nonuniform extension of a Fourier-based algorithm due to Kushilevitz and Mansour (1991). <<ETX>>
Distributed source modeling of language with magnetoencephalography: Application to patients with intractable epilepsy

Purpose: To examine distributed patterns of language processing in healthy controls and patients with epilepsy using magnetoencephalography (MEG), and to evaluate the concordance between laterality of distributed MEG sources and language laterality as determined by the intracarotid amobarbital procedure (IAP).
Carrier scattering by workfunction fluctuations and interface dipoles in high-K/metal gate stacks

The introduction of a high-K/metal gate stack in metal-oxide-Semiconductor field-effect transistors can cause a significant degradation of the mobility, especially at weak inversion densities. This degradation is commonly ascribed to remote Coulomb scattering (RCS, i.e., charges trapped at the SiO2/HfO2 interface). However, very large densities of RCS charges are usually needed to reproduce the experimental data. In this work, we explore alternative scattering mechanisms by quantum calculations of the carrier mobilities. We consider, in particular, metal grain workfunction fluctuations and local dipoles at the SiO2/HfO2 interface. Similarly to RCS, both scattering mechanisms are found to reduce the carrier mobility significantly at low carrier densities. However, the mobility exhibits a different dependence on the thickness of high-κ layer, which provides a way to identify the dominant mechanism.
A 3.1–4.8-GHz delay-line-based frequency-hopping IR-UWB transmitter in 65-NM CMOS technology

A 3-5GHz frequency-hopping (FH) transmitter using an impulse radio ultra-wideband (IR-UWB) signal is designed in a standard 65-nm CMOS technology. A delay-line-based architecture utilizing only static logic gates for pulse generation is employed to achieve low power consumption. The center frequency of the transmitted IR-UWB signal with the fixed bandwidth of 500MHz covers three channels in the low band of UWB spectrum and hops successively from one to another at a rate of 200MHz. Binary phase shift keying (BPSK) modulation schemes are exploited to modulate transmitted signals with further improvement in spectrum characteristics. By employing the FH technique, the energy consumption of the transmitter is 3.6pJ/bit at a data-rate of 200Mb/s with the FCC mask well complied.
Iridovirus in the root weevil Diaprepes abbreviatus

Abstract Invertebrate iridescent virus 6 (IIV6) was evaluated for mode of transmission and ability to cause infection in the root weevil, Diaprepes abbreviatus (L.). This is the first evidence of IIV6 infection in D. abbreviatus, which caused both patent and sub-lethal covert infections in both larvae and adults. Adults and larvae were successfully infected with IIV6 by puncture, injection and per os. Transmission of IIV6 was demonstrated between infected and healthy individuals regardless of gender. Virus was detected in egg masses produced by virus-infected females suggesting IIV6 is transmitted transovarially. Virus particles were observed in the cytoplasm of weevil cells, and were shown to infect fat bodies, muscle, and nerve tissues, as visualized using transmission electron microscopy. Patent infections resulted in death of individuals within 3 to 4 days post infection. Individuals with covert infections tested positive for virus infection on day 7 by polymerase chain reaction analysis. Sequencing of PCR amplicons confirmed virus infection. Discovery of new pathogens against root weevils may provide new management tools for development of control strategies based on induced epizootics. This is the first report of a virus infecting D. abbreviatus.
Formation and evolution of galactic spheroids by mergers

Abstract Galactic spheroids can form as a result of galaxy interactions and mergers of disks. Detailed analyses of the photometric properties, the intrinsic orbital structure, the line-of-sight velocity distributions and the kinemetry of simulated merger remnants, which depend critically on the geometry and the gas content of the interacting progenitors, indicate that low and intermediate mass rotating ellipticals can form from mergers of disks. The masses and metallicities of all massive ellipticals and the kinematics of some massive non-rotating ellipticals cannot be explained by binary mergers. Thus these galaxies might have formed in a different way.
Nudging Art Lovers to Donate

Many nonprofit organizations face revenue uncertainty due to funding cuts. It is crucial for them to supplement existing revenue streams by private donations, and apply thoughtful market segmentation in their pursuit of donors. We introduce the behavioral concept of “nudge” based on the possibility of loss aversion affecting willingness-to-donate, and investigate its implications for fund-raising strategies. Potential donors are nudged to donate by the hypothetical scenario of “losing” an existing exhibition, and also by that of “gaining” an additional exhibition. We observe significant loss aversion effects as frequent gallery-goers donate more to avoid losing an exhibition. While both prospective gain and loss scenarios are effective in nudging nonfrequent gallery-goers, the prospect of enjoying “one more” event is observed to be stronger. We argue that there may be scope to increase support for nonprofit organizations, particularly in the cultural sector, by exploiting the psychological characteristics of prospective donors.
Effective Aggregation and Querying of Probabilistic RFID Data in a Location Tracking Context

RFID applications usually rely on RFID deployments to manage high-level events such as tracking the location that products visit for supply-chain management, localizing intruders for alerting services, and so on. However, transforming low-level streams into high-level events poses a number of challenges. In this paper, we deal with the well known issues of data redundancy and data-information mismatch: we propose an on-line summarization mechanism that is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningfulness of the information. We also show that common information needs, i.e. detecting complex events meaningful to applications, can be effectively answered by executing temporal probabilistic SQL queries directly on the summarized data. All the techniques presented in this paper are implemented in a complete framework and successfully evaluated in real-world location tracking scenarios. Key–Words: RFID data streams, Data summarization, Probabilistic Data Management, Object tracking, Probabilistic Database
Somatosensory Focused Balance Training without cues can improve balance and gait in early Parkinson’s disease – a randomised pilot study

Abstract Purpose: To study the effect of Somatosensory Focused Balance Training without Cues, on gait and balance in people with early Parkinson’s disease. Materials and methods: This was a randomised pilot study. Twenty-eight community-dwelling people with early Parkinson’s disease were randomised to immediate or delayed start of a 8w-group training in a community fitness location. Outcomes were measured at preintervention and postintervention. In addition, the early start group performed a 6-month follow up. Clinical outcome measures included: Berg Balance Scale (BBS), BDL Balance Scale, Timed Up and Go, 10 m walk test and the motor part of Unified Parkinson’s disease Rating Scale. Nonparametric statistics was used for analysis. Results: Twenty-two participants (10 men, 12 female) were analysed (median age 69, Hoehn & Yahr 2.0). The participants improved significantly in performance of BBS (p = .007), BDL Balance Scale (p = .005), 10 m walk (p = .012) and mUPDRS (p = .027). At follow up, the improvement had declined. The small sample size restricts generalisability of the results. Conclusions: The intervention showed positive effects on balance, gait and mUPDRS, suggesting that this specific approach should be further explored as a rehabilitation method to delay balance decline in people in the early stages of Parkinson’s disease.
Generalised mixtures of experts, independent expert training, and learning classifier systems

We present a generalisation to the Mixtures of Experts model that introduces prior localisation of the experts as part of the model structure, and as such relates them strongly to the evolutionary computation ML method known as Learning Classifier Systems. While the introduced generalisa-tion allows specification of more complex localisation patterns, identifying good models becomes more difficult. We approach this tradeoff by introducing a new training schema that makes fitting a single model computationally less demanding and shifts the importance to searching the space of possible model structures, guided by approximate variational Bayesian inference to fit the model and find the model evidence. We demonstrate model search for simple non-linear curve fitting tasks by sampling from the model posterior, as a proof-of-concept alternative to the genetic algorithm used in Learning Classifier Systems for that purpose.
Emerging perception: Tracking the process of visual object recognition

My studies using gaze behavior and V1 activity showed that, as part of the object recognition process, object features are prioritized over other parts of a scene. Such prioritization can be deduced from changes in eye movement behavior, as well as from the selective enhancement and suppression of V1 activity during the transition from initial viewing to the recognition of an object. Moreover, I found that changes in perception are accompanied by activity in cortical attention circuits, and that early and later sensory areas modulate their exchange of information during object recognition. From this, I conclude that visual object recognition does not occur at once and in a single cortical area, but is a constructive and distributed cortical process.
Whanaungatanga: Sybil-proof routing with social networks

Decentralized systems, such as distributed hash tables, are subject to the Sybil attack, in which an adversary creates many false identities to increase its influence. This paper proposes a routing protocol for a distributed hash table that is strongly resistant to the Sybil attack. This is the first solution to this problem with sublinear run time and space usage. The protocol uses the social connections between users to build routing tables that enable Sybil-resistant distributed hash table lookups. With a social network ofnwell-connected honest nodes, the protocol can tolerate up to O(n/ logn) “attack edges” (social links from honest users to phoney identities). This means that an adversary has to fool a large fraction of the honest users before any lookups will fail. The protocol builds routing tables that containO( √ n log n) entries per node. Lookups take O(1) time. Simulation results, using social network graphs from LiveJournal, Flickr, and YouTube confirm the analytical results.
Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from 15N-NOESY

Chemical shift mapping is an important technique in NMR-based drug screening for identifying the atoms of a target protein that potentially bind to a drug molecule upon the molecule's introduction in increasing concentrations. The goal is to obtain a mapping of peaks with known residue assignment from the reference spectrum of the unbound protein to peaks with unknown assignment in the target spectrum of the bound protein. Although a series of perturbed spectra help to trace a path from reference peaks to target peaks, a one-to-one mapping generally is not possible, especially for large proteins, due to errors, such as noise peaks, missing peaks, missing but then reappearing, overlapped, and new peaks not associated with any peaks in the reference. Due to these difficulties, the mapping is typically done manually or semi-automatically. However, automated methods are necessary for high-throughput drug screening. We present PeakWalker, a novel peak walking algorithm for fast-exchange systems that models the errors explicitly and performs many-to-one mapping. On the proteins: hBclxl, UbcH5B, and histone H1, it achieves an average accuracy of over 95% with less than 1.5 residues predicted per target peak. Given these mappings as input, we present PeakAssigner, a novel combined structure-based backbone resonance and NOE assignment algorithm that uses just 15N-NOESY, while avoiding TOCSY experiments and 13C-labeling, to resolve the ambiguities for a one-to-one mapping. On the three proteins, it achieves an average accuracy of 94% or better.
Wavelets, curvelets, and multiresolution analysis techniques in fast Z-pinch research

Z pinches produce an X ray rich plasma environment where backlighting imaging of imploding targets can be quite challenging to analyze. What is required is a detailed understanding of the implosion dynamics by studying snapshot images of its in flight deformations away from a spherical shell. We have used wavelets, curvelets and multiresolution analysis techniques to address some of these difficulties and to establish the Shell Thickness Averaged Radius (STAR) of maximum density, r*(N,θ) where N is the percentage of the shell thickness over which we average. The non-uniformities of r*(N,θ) are quantified by a Legendre polynomial decomposition in angle, θ, and the identification of its largest coefficients. Undecimated wavelet decompositions outperform decimated ones in denoising and both are surpassed by the curvelet transform. In each case, hard thresholding based on noise modeling is used.
The empiricist’s challenge: Asking meaningful questions in political science in the age of big data

ABSTRACT The continuously growing use of digital services has provided social scientists with an expanding reservoir of data, potentially holding valuable insights into human behavior and social systems. This has often been associated with the terms “big data” and “computational social science. ” Using such data, social scientists have argued, will enable us to better understand social, political, and economic life. Yet this new data type comes not only with promises but with challenges as well. These include developing standards for data collection, preparation, analysis, and reporting; establishing more systematic links between established theories within the existing body of research in the social sciences; and moving away from proofs-of-concepts toward the systematic development and testing of hypotheses. In this article, we map these promises and challenges in detail and introduce five highly innovative contributions collected in this special issue. These articles illustrate impressively the potential of digital trace data in the social science all the while remaining conscious of its pitfalls.
Video communication best for female friends

Video-mediated communication is widely available for decades, but not universally accepted and appreciated. Little is known about system factors, user factors and environment factors that govern its acceptance and efficacy. Recent research points to gender and relationship as important factors in the effect of VMC on outcome of negotiation tasks (Swaab & Swaab, 2009), respectively the outcome of impression formation of strangers (Fullwood, 2007). Research in this article presents evidence how factors gender, task and relationship influence the paperand-pencil measurements of social presence according to Short, Williams and Christie (1976).
Behavioral Modeling Intrusion Detection System (BMIDS) Using Internet of Things (IoT) Behavior-Based Anomaly Detection via Immunity-Inspired Algorithms

In this research, a real-world simulation service uses Internet of Things (IoT) capable objects to detect behavioral-based anomalies within a simulated smart home/vicinity. In the simulation, the smart home and the surrounding world are monitored with a behavioral modeling intrusion detection system (BMIDS). The BMIDS uses immunity-inspired algorithms to distinguish whether behavioral patterns extracted match a desired behavior or indicate deviation from the desired behavior. The simulation is used to monitor and scale all relevant activity in order to verify construction of accurate behavioral models. The BMIDS demonstrates how the capture of IoT sensor data activity concatenated as a set of event sequences along with the simulated world state provide the components to establish a numerical representation for behavioral identity. Creating behavioral models via pervasive system monitoring prior to and alongside cyber-physical system experimentation with the BMIDS simulation is cost efficient, easily verifiable and promotes autonomous monitoring for an autonomic monitoring mechanism. Also, performance measurements can be scaled more accurately during qualitative and quantitative analysis, thus producing the desired behavior model as depicted by the specified scripted or non-scripted behavior.