Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveillance of avian paramyxovirus serotype-1 in migratory waterfowls in Japan between 2011 and 2013

To further understand the epidemiology of avian paramyxovirus serotype-1 (APMV-1) in migratory waterfowls in Japan, we conducted the surveillance of this virus from feces derived from the migratory waterfowls collected in 41 Japanese prefectures between October 2011 and March 2013. Six APMV-1 viruses were isolated from total 661 samples. All isolates were identified as the avirulent (lentogenic) type on the basis of intracerebral pathogenicity tests. Genetic analysis showed that these viruses possessed the deduced amino acid sequence of 112GKQGR-L117 or 112ERQER-L117 at the cleavage site of the F0 protein, which was identical to the motif in the avirulent type. Phylogenetic analysis based on the partial fusion protein gene classified these APMV-1 isolates into 2 major genetic groups. Four isolates were classified as class II genotype I, and they were genetically closely related to strains isolated in Asian countries, including Japan. In contrast, two isolates were classified as class I, and they were genetically closely related to strains mainly isolated in the U.S.A.
Differences in apparent diffusion coefficients of brain metabolites between grey and white matter in the human brain measured at 7 T

Diffusion weighted spectroscopy can provide microstructural information that is specific to compartmental geometry. So far, in human brain, apparent diffusion coefficients (ADCs) of only the metabolites N‐acetyl aspartate, creatine (tCr) and choline (tCho) have been assessed. High field MR at 7 T allows the collection and analysis of diffusion weighted spectroscopy data of additional metabolites of interest such as glutamate (Glu), N‐acetyl aspartyl glutamate, and glutamine (Gln), which are of interest due to their different compartmentalization and role in brain physiology. In this study, we performed 1H diffusion weighted spectroscopy at 7 T using a diffusion‐weighted PRESS sequence in parietal white matter (n = 6) and occipital grey matter (n = 7). Data were analyzed using the LCmodel. ADCs could reliably be obtained of N‐acetyl aspartate, tCr, tCho, Glu, Gln in grey and white matter, and N‐acetyl aspartyl glutamate in white matter. Significant differences in ADC values were observed between grey and white matter for all metabolites. ADCs in grey matter were consistently lower than in white matter. These differences can probably be attributed to different compartmentalization as well as to the differential impact of diffusion time on ADC of different molecules under conditions of restricted diffusion. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.
Neuroimaging Methods for Music Information Retrieval: Current Findings and Future Prospects

Over the past decade and a half, music information retrieval (MIR) has grown into a robust, cross-disciplinary field spanning a variety of research domains. Collaborations between MIR and neuroscience researchers, however, are still rare, and to date only a few studies using approaches from one domain have successfully reached an audience in the other. In this paper, we take an initial step toward bridging these two fields by reviewing studies from the music neuroscience literature, with an emphasis on imaging modalities and analysis techniques that might be of practical interest to the MIR community. We show that certain approaches currently used in a neuroscientific setting align with those used in MIR research, and discuss implications for potential areas of future research. We additionally consider the impact of disparate research objectives between the two fields, and how such a discrepancy may have hindered cross-discipline output thus far. It is hoped that a heightened awareness of this literature will foster interaction and collaboration between MIR and neuroscience researchers, leading to advances in both fields that would not have been achieved independently.
A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detection

Hidden Markov Models (HMMs) have been shown to provide a high level performance for detecting anomalies in intrusion detection systems. Since incomplete training data is always employed in practice, and environments being monitored are susceptible to changes, a system for anomaly detection should update its HMM parameters in response to new training data from the environment. Several techniques have been proposed in literature for on-line learning of HMM parameters. However, the theoretical convergence of these algorithms is based on an infinite stream of data for optimal performances. When learning sequences with a finite length, on-line incremental versions of these algorithms can improve discrimination by allowing for convergence over several training iterations. In this paper, the performance of these techniques is compared for learning new sequences of training data in host-based intrusion detection. The discrimination of HMMs trained with different techniques is assessed from data corresponding to sequences of system calls to the operating system kernel. In addition, the resource requirements are assessed through an analysis of time and memory complexity. Results suggest that the techniques for online incremental learning of HMM parameters can provide a higher level of discrimination than those for on-line learning, yet require significantly fewer resources than with batch training. On-line incremental learning techniques may provide a promising solution for adaptive intrusion detection systems.
Multi-thresholds Clustering Objects in a Road Network

Threshold selection is an important topic and also a critical preprocessing step, which directly affects the accuracy of the clustering in a road network. This paper analyzes the necessity of multiple thresholds selection in a road network, extracts the similar nature of the objects, proposes firstly the scheme of multiple thresholds based on support vector regression (SVR) and improves on the existing algorithm. Performance analysis and experimental result show that the multiple thresholds scheme achieves high efficiency and accuracy for clustering objects based in a road network.
3-D AUDIO WITH DYNAMIC TRACKING FOR MULTIMEDIA ENVIRONTMENTS

This papers deals with a 3-D audio system that has been developed for desktop multimedia environments. The system has the ability to place virtual sources at arbitrary azimuths and elevations around the listener’s head based on HRTF binaural synthesis. A listener seated in front of a computer and two loudspeakers placed at each side of the monitor have been considered. Transaural reproduction using loudspeakers has been used for rendering the sound field to listener ears. Furthermore the system can cope with slight movements of the listener head. Head position is monitored by means of a simple computer vision algorithm. Four head position coordinates (x,y,z,φ) in order to allow free movements of the listener are continuously estimated. Cross-talk cancellation filters and virtual sources locations are updated depending on these head coordinates.
The Impact of a Clinical Decision Support System in Diabetes Primary Care Patients in a Developing Country.

BACKGROUND Telehealth strategies have the potential to improve diabetes care, but there is a lack of evidence about the impact of these strategies in developing countries. Our objective was to analyze the feasibility, usability, and clinical impact of a decision support system (DSS) in Brazilian primary care diabetes patients. MATERIALS AND METHODS This was a quasi-experimental study that included type 2 diabetes primary care patients >40 years of age. Patients were assessed before (during 6 months) and after the implementation of the DSS application (4 months). The DSS application, used by health professionals, included clinical evaluations and blood glucose measurements and generated specific recommendations based on the data entered. RESULTS In total, 145 patients were included (mean age, 62.0 ± 9.9 years), 62.1% were female, and 70.0% had been diagnosed with diabetes more than 5 years ago. Overall, there was no decrease in median hemoglobin A1c (HbA1c), from 7.7% (range, 6.5-9.8%) to 7.4% (range, 6.5-9.2%) (P for slope = 0.347). Subgroup analysis showed that patients with an HbA1c level of ≥9% at baseline had a significant reduction in median HbA1c level, from 10.5% (range, 9.9-11.3%) to 10.0% (range, 8.9-10.9%) (P for difference of slope between subgroups = 0.004). The reduction occurred in the first phase of the study, before the DSS use. Healthcare practitioners considered the DSS easy to use (99%) and believed that it provided useful information for patient care (100%). CONCLUSIONS In this study the improvement of glycemic control before the application in more decompensated patients (HbA1c ≥9%) probably reflects the systematization of diabetes care. The DSS use did not improve the HbA1c level, possibly because of the short follow-up and/or infrequent use by the healthcare practitioners.
An efficient multi-view rasterization architecture

TV have been designed and built. However, these displays have received relatively little attention in the context of real-time computer graphics. We present a novel rasterization architecture that rasterizes each triangle to multiple views simultaneously. When determining which tile in which view to rasterize next, we use an efficiency measure that estimates which tile is expected to get the most hits in the texture cache. Once that tile has been rasterized, the efficiency measure is updated, and a new tile and view are selected. Our traversal algorithm provides significant reductions in the amount of texture fetches, and bandwidth gains on the order of a magnitude have been observed. We also present an approximate rasterization algorithm that avoids pixel shader evaluations for a substantial amount (up to 95%) of fragments and still maintains high image quality.
Measuring the effect of countries ’ ESG performance on their sovereign borrowing costs ∗

We examine whether the extra-financial performance of countries on environmental, social and governance (ESG) factors matters for sovereign bonds markets. Using a panel regression model over a data set with 23 OECD countries, we show that ESG ratings significantly decrease government bond spreads. Moreover, we find that the impact of ESG ratings on the cost of sovereign borrowing is more pronounced in bonds of shorter maturities.
Novel Introner-Like Elements in fungi Are Involved in Parallel Gains of Spliceosomal Introns

Spliceosomal introns are key components of the eukaryotic gene structure. Although they contributed to the emergence of eukaryotes, their origin remains elusive. In fungi, they might originate from the multiplication of invasive introns named Introner-Like Elements (ILEs). However, so far ILEs have been observed in six fungal species only, including Fulvia fulva and Dothistroma septosporum (Dothideomycetes), arguing against ILE insertion as a general mechanism for intron gain. Here, we identified novel ILEs in eight additional fungal species that are phylogenetically related to F. fulva and D. septosporum using PCR amplification with primers derived from previously identified ILEs. The ILE content appeared unique to each species, suggesting independent multiplication events. Interestingly, we identified four genes each containing two gained ILEs. By analysing intron positions in orthologues of these four genes in Ascomycota, we found that three ILEs had inserted within a 15 bp window that contains regular spliceosomal introns in other fungal species. These three positions are not the result of intron sliding because ILEs are newly gained introns. Furthermore, the alternative hypothesis of an inferred ancestral gain followed by independent losses contradicts the observed degeneration of ILEs. These observations clearly indicate three parallel intron gains in four genes that were randomly identified. Our findings suggest that parallel intron gain is a phenomenon that has been highly underestimated in ILE-containing fungi, and likely in the whole fungal kingdom.
Relaxation and Transients in a Time-Dependent logistic Map

We study the one-dimensional logistic map with control parameter perturbed by a small periodic function. In the pure constant case, scaling arguments are used to obtain the exponents related to the relaxation of the trajectories at the exchange of stability, period-doubling and tangent bifurcations. In particular, we evaluate the exponent z which describes the divergence of the relaxation time τ near a bifurcation by the relation τ ~ | R - Rc |-z. Here, R is the control parameter and Rc is its value at the bifurcation. In the time-dependent case new attractors may appear leading to a different bifurcation diagram. Beside these new attractors, complex attractors also arise and are responsible for transients in many trajectories. We obtain, numerically, the exponents that characterize these transients and the relaxation of the trajectories.
Evolutionary Multiobjective Ensemble Learning Based on Bayesian Feature Selection

This paper proposes to incorporate evolutionary multiobjective algorithm and Bayesian Automatic Relevance Determination (ARD) to automatically design and train ensemble. The algorithm determines almost all the parameters of ensemble automatically. Our algorithm adopts different feature subsets, selected by Bayesian ARD, to maintain accuracy and promote diversity among individual NNs in an ensemble. The multiobjective evaluation of the fitness of the networks encourages the networks with lower error rate and fewer features. The proposed algorithm is applied to several real-world classification problems and in all cases the performance of the method is better than the performance of other ensemble construction algorithms.
An adaptive communication system for user's resource environment

Although we can construct intercommunication environments by high quality video multicast streams such as DV (digital video) streams on super high speed networks, it is difficult for the reasonable network service users to join the high quality communication session. If we can use transcoding functions that include translator and mixer functions defined by RTP, it is able to transcode a media stream format into another stream format. Media transcoding functions are usefulness for intercommunication on heterogeneous computer networks. However, it is difficult for distributed communication systems to prepare the suitable transcoding nodes in advance because the required intercommunication environments are not always static. Accordingly, we should consider dynamic transcoding functions with relocatable decision methods. In this paper we propose a novel middleware system that realizes dynamic transcoding functions by extendable media stream.
Breaking the bandwidth wall in chip multiprocessors

In throughput-aware CMPs like GPUs and DSPs, software-managed streaming memory systems are an effective way to tolerate high latencies. E.g., the Cell/B.E. incorporates local memories, and data transfers to/from those memories are overlapped with computation using DMAs. In such designs, the latency of the memory system has little impact on performance; instead, memory bandwidth becomes critical. With the increase in the number of cores, conventional DRAMs no longer suffice to satisfy the bandwidth demand. Hence, recent throughput-aware CMPs adopted caches to filter off-chip traffic. However, such caches are optimized for latency, not bandwidth. This work presents a re-design of the memory system in throughput-aware CMPs. Instead of a traditional latency-aware cache, we propose to spread the address space using fine-grained interleaving all over a shared non-coherent last-level cache (LLC). In this way, on-chip storage is optimally used, with no need to keep coherency. On the memory side, we also propose the use of interleaving across DRAMs but with a much finer granularity than usual page-size approaches. Our proposal is highly optimized for bandwidth, not latency, by avoiding data replication in the LLC and by using fine-grained address space interleaving in both the LLC and the memory. For a CMP with 128 cores and 64-MB LLC, performance is improved by 21% due to the LLC optimizations and an extra 42% due to the off-chip memory optimizations, for a total 1.7 times performance improvement.
Theory and Methods of Local Tissue-pO2 Monitoring in Experimental Angiology

The platinum-multiwire-surface electrode after Kessler and Lübbers gives the opportunity of direct measuring of the local tissue-pO2-pressure; this 8-wire electrode with a Pt-wire-diameter of 15 μm operates on the polarographic principle. Each Pt-wire has a hemispheric collecting area of about 30 μm. Pressure ischemia by compression of capillaries is prevented by proportioning the weight (2.1 g) of the electrode. Data output is done by 1) continuous pO2 measuring; 2) pO2 histograms. During the continuous pO2 measurement the electrode stays on the tissue measuring point thus enabling the direct record of quickly proceeding reactions. The pO 2-histogram exhibits the statistical distribution of the local partial oxygen pressures in a studied organ, a quasi sample inquiry is performed in the tissue, based on minimum 100 single values that are obtained by repeated manual repositioning of the electrode. By this, values are obtained on the level and distribution of local pO2 in the tissue so that condition and function of the microcirculation can be objectively assessed. Immediate alterations in the local tissue pO2 suggest regulatory adjustment after hemodynamic changes. Technical problems connected with pO2 measurement in clinical use (sterilization, data acquisition and presentation) are described. The presented technique gives the chance to exactly assess the effects of vasoactive drugs on the microcirculation and to quantify the results of operations, for example after bypass-surgery.
Highly Elastic and Tough Interpenetrating Polymer Network-Structured Hybrid Hydrogels for Cyclic Mechanical Loading-Enhanced Tissue Engineering

Although hydrogels are extensively investigated as biomaterials due to their ability to mimic cellular microenvironments, they are often limited by their poor physical properties in response to mechanical loads, including weak gel strength, brittleness, and permanent deformation. Recently, interpenetrating polymer network (IPN) hydrogels have gained substantial attention for their use in investigating changes in encapsulated cell behaviors under mechanical stimulation. However, despite recent success in developing highly elastic IPN-structured hydrogels, it remains a great technical challenge to endow them with biocompatibility and biodegradability due to use of toxic chemicals, nonbiodegradable prepolymers, and harsh reaction conditions. In this study, we report on the synthesis and formation of highly elastic and tough IPN-structured hydrogels based on alginate and gelatin, which are biocompatible and biodegradable. Mechanical stimulation enhanced the proliferation and osteogenic differentiation of enca.
Comparing Features for Classification of MEG Responses to Motor Imagery

Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.
The value of residual liver volume as a predictor of hepatic dysfunction and infection after major liver resection

Background and aims: Major liver resection incurs a risk of postoperative liver dysfunction and infection and there is a lack of objective evidence relating residual liver volume to these complications. Patients and methods: Liver volumetry was performed on computer models derived from computed tomography (CT) angioportograms of 104 patients with normal synthetic liver function scheduled for liver resection. Relative residual liver volume (%RLV) was calculated as the relation of residual to total functional liver volume and related to postoperative hepatic dysfunction and infection. Receiver operator characteristic curve analysis was undertaken to determine the critical %RLV predicting severe hepatic dysfunction and infection. Univariate analysis and multivariate logistic regression analysis were performed to delineate perioperative predictors of severe hepatic dysfunction and infection. Results: The incidence of severe hepatic dysfunction and infection following liver resection increased significantly with smaller %RLV. A critical %RLV of 26.6% was identified as associated with severe hepatic dysfunction (p<0.0001). Additionally, body mass index (BMI), operating time, and intraoperative blood loss were significant prognostic indicators for severe hepatic dysfunction. It was not possible to predict the individual risk of postoperative infection precisely by %RLV. However, in patients undergoing major liver resection, infection was significantly more common in those who developed postoperative severe hepatic dysfunction compared with those who did not (p = 0.030). Conclusions: The likelihood of severe hepatic dysfunction following liver resection can be predicted by a small %RLV and a high BMI whereas postoperative infection is more related to liver dysfunction than precise residual liver volume. Understanding the relationship between liver volume and synthetic and immune function is the key to improving the safety of major liver resection.