Miracle-dz/UniFAR-FTUs huggingface dataset:
Striking Natural Diversity in Glandular Trichome Acylsugar Composition Is Shaped by Variation at the Acyltransferase2 Locus in the Wild Tomato Solanum habrochaites1[W][OA]

Acylsugars are polyesters of short- to medium-length acyl chains on sucrose or glucose backbones that are produced in secretory glandular trichomes of many solanaceous plants, including cultivated tomato (Solanum lycopersicum). Despite their roles in biotic stress adaptation and their wide taxonomic distribution, there is relatively little information about the diversity of these compounds and the genes responsible for their biosynthesis. In this study, acylsugar diversity was assessed for 80 accessions of the wild tomato species Solanum habrochaites from throughout the Andes Mountains. Trichome metabolites were analyzed by liquid chromatography-time of flight-mass spectrometry, revealing the presence of at least 34 structurally diverse acylsucroses and two acylglucoses. Distinct phenotypic classes were discovered that varied based on the presence of glucose or sucrose, the numbers and lengths of acyl chains, and the relative total amounts of acylsugars. The presence or absence of an acetyl chain on the acylsucrose hexose ring caused clustering of the accessions into two main groups. Analysis of the Acyltransferase2 gene (the apparent ortholog of Solyc01g105580) revealed differences in enzyme activity and gene expression correlated with polymorphism in S. habrochaites accessions that varied in acylsucrose acetylation. These results are consistent with the hypothesis that glandular trichome acylsugar acetylation is under selective pressure in some populations of S. habrochaites and that the gene mutates to inactivity in the absence of selection.
VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology

Despite its wide usage in biological databases and applications, the role of the gene ontology (GO) in network analysis is usually limited to functional annotation of genes or gene sets with auxiliary information on correlations ignored. Here, we report on new capabilities of VisANT--an integrative software platform for the visualization, mining, analysis and modeling of the biological networks--which extend the application of GO in network visualization, analysis and inference. The new VisANT functions can be classified into three categories. (i) Visualization: a new tree-based browser allows visualization of GO hierarchies. GO terms can be easily dropped into the network to group genes annotated under the term, thereby integrating the hierarchical ontology with the network. This facilitates multi-scale visualization and analysis. (ii) Flexible annotation schema: in addition to conventional methods for annotating network nodes with the most specific functional descriptions available, VisANT also provides functions to annotate genes at any customized level of abstraction. (iii) Finding over-represented GO terms and expression-enriched GO modules: two new algorithms have been implemented as VisANT plugins. One detects over-represented GO annotations in any given sub-network and the other finds the GO categories that are enriched in a specified phenotype or perturbed dataset. Both algorithms take account of network topology (i.e. correlations between genes based on various sources of evidence). VisANT is freely available at http://visant.bu. edu.
An Analytical Model for IEEE 802.15.4/ ZigBee Wireless Sensor Networks with Duty Cycle Mechanism for Performance Prediction and Configuration of MAC Parameters to Achieve QoS and Energy Efficiency

The IEEE 802.15. 4 standard defines a Physical (PHY) and Medium Access Control (MAC) layer protocol for low data rate wireless network with low power requirement which makes it ideal PHY/MAC for WSNs. Currently WSNs with combination of IEEE 802.15. 4 and ZigBee are being used extensively in industrial applications such as factory automation and control, environmental monitoring etc. Such applications need Quality of Service (QoS) in terms of reliability and latency along with energy efficiency for longevity of the network. The slotted IEEE 802.15. 4 MAC protocol uses the duty cycle mechanism to save energy. However the duty cycle mechanism reduces the effective bandwidth leading to possibility of higher packet collision in the active periods and an improper choice of duty cycle may result in low packet delivery ratio and higher packet latency. Similarly there other MAC parameters such as the beacon interval which have bearing on performance of the WSN. It is therefore necessary to make accurate analysis of IEEE 802.15. 4 MAC protocol, with duty cycle mechanism enabled, so that the MAC parameters such as Duty Cycle, beacon interval etc. can be set so as to ensure satisfaction of QoS requirements of applications while achieving maximum energy efficiency. We present here an analytical model developed as a Markov Chain Process for slotted IEEE 802.15. 4 MAC protocol assuming duty cycle mechanism is enabled in the protocol. Expressions are derived based on the model to compute percentage of packet successfully delivered within a given latency and the energy consumed by the sensor nodes in the process. The model is expected to be useful in deciding on the key MAC parameter values for applications of known traffic load and QoS requirements such as Packet Delivery Ratio(PDR) within given latency while maximizing energy efficiency. The analytical model presented is validated through simulation study in NS2.
Design And Evaluation of Onset Detectors using Different Fusion Policies

ABSTRACTNote onset detection is one of the most investigated tasksin Music Information Retrieval (MIR) and various detec-tion methods have been proposed in previous research. Theprimary aim of this paper is to investigate different fusionpolicies to combine existing onset detectors, thus achiev-ing better results. Existing algorithms are fused using threestrategies, ﬁrst by combining different algorithms, second,by using the linear combination of detection functions, andthird, by using a late decision fusion approach. Large scaleevaluation was carried out on two published datasets and anew percussion database composed of Chinese traditionalinstrument samples. An exhaustive search through the pa-rameter space was used enabling a systematic analysis ofthe impact of each parameter, as well as reporting the mostgenerally applicable parameter settings for the onset de-tectors and the fusion. We demonstrate improved resultsattributed to both fusion and the optimised parameter set-tings.1. INTRODUCTIONThe automatic detection of onset events is an essential partin many music signal analysis schemes and has various ap-plications in content-based music processing. Different ap-proaches have been investigated for onset detection in re-cent years [1,2]. As the main contribution of this paper, wepresent new onset detectors using different fusion policies,with improved detection rates relying on recent research inthe MIR community. We also investigate different conﬁg-urations of onset detection and fusion parameters, aimingto provide a reference for conﬁguring onset detection sys-tems.The focus of ongoing onset detection work is typicallytargeting Western musical instruments. Apart from usingtwo published datasets, a new database is incorporated intoour evaluation, collecting percussion ensembles of Jingju,also denoted as Peking Opera or Beijing Opera, a major
Efficient longest executable path search for programs with complex flows and pipeline effects

Current development tools for embedded real-time systems do not efficiently support the timing aspect. The most important timing parameter for scheduling and system analysis is the Worst-Case Execution Time (WCET) of a program.This paper presents a fast and effective WCET calculation method that takes account of low-level machine aspects like pipelining and caches, and high-level program flow like loops and infeasible paths. The method is more efficient than previous path-based approaches, and can easily handle complex programs. By separating the low-level from the high-level analysis, the method is easy to retarget.Experiments confirm that speed does not sacrifice precision, and that programs with extreme numbers of potential execution paths can be analyzed quickly.
Clinical Evaluation of a Single-Tube Multiple RT-PCR Assay for the Detection of 13 Common Virus Types/Subtypes Associated with Acute Respiratory Infection

Respiratory viruses are among the most important causes of human morbidity and mortality worldwide, especially for infants and young children. In the past years, a few commercial multiplex RT-PCR assays have been used to detect respiratory viruses in spite of the high cost. In the present study, an improved single-tube multiplex reverse transcription PCR assay for simultaneous detection of 13 respiratory viruses was evaluated and compared with a previously reported two-tube assay as the reference method using clinical nasopharyngeal aspirates samples. Of 310 prospectively tested respiratory specimens selected from children hospitalized with acute respiratory illness, 226 (72.90%, 226/310) and 214 (69.03%, 214/310) positive for one or more viruses were identified by the single-tube and the two-tube assays, respectively, with combined test results showing good concordance (Kappa value = 0.874). Individually, the single-tube assay for adenovirus (Adv), human metapneumovirus (HMPV), human rhinovirus (HRV), parainfluenza virus type 1 (PIV1), parainfluenza virus type 3 (PIV3) and parainfluenza virus type 4 (PIV4) showed the significantly superior sensitivities to those of the two-tube assay. No false positives were found. In conclusion, our results demonstrates the one-tube assay revealed significant improvements over the two-tube assay in terms of the better sensitivity, more accurate quality control, less nonspecific amplification, more cost-effective and shorter turn-around time and will be a valuable tool for routine surveillance of respiratory virus infection in China.
You Can’t Beat Frequency (Unless You Use Linguistic Knowledge) – A Qualitative Evaluation of Association Measures for Collocation and Term Extraction

In the past years, a number of lexical association measures have been studied to help extract new scientific terminology or general-language collocations. The implicit assumption of this research was that newly designed term measures involving more sophisticated statistical criteria would outperform simple counts of co-occurrence frequencies. We here explicitly test this assumption. By way of four qualitative criteria, we show that purely statistics-based measures reveal virtually no difference compared with frequency of occurrence counts, while linguistically more informed metrics do reveal such a marked difference.
A Collaborative Relevance Feedback Approach to Task-driven Recommendation

Managing knowledge is an important issue for organizations to gain sustainable advantage in today ’s competitive environments. Nevertheless, the operations of enterprises are mainly planned around task execution. Accordingly, it is important to provide knowledge support for task execution by recommending task relevant knowledge to fit the information needs of task execution. This paper contributes to the stream of knowledge management system by proposing a novel task-driven recommender system to take the merits of information retrieval and cooperative work. A task-oriented information repository based on fuzzy classification is deployed to support task-driven recommendation. Additionally, the system uses task specification to model the key contents of executing-task and to further facilitate the process of identifying relevant information needs of task execution. A collaborative relevance feedback approach is proposed to adjust task specification with the aid of cooperative workers and domain experts. The proposed system enhanced with the adaptation capability to adjust task specification makes the recommendation of relevant information more flexible and adaptable to dynamically-changing working environments.
Mesoscale ocean fronts enhance carbon export due to gravitational sinking and subduction

Significance Transport of organic carbon from the sunlit surface ocean to deeper depths drives net oceanic uptake of CO2 from the atmosphere. However, mechanisms that control this carbon export remain poorly constrained, limiting our ability to model and predict future changes in this globally important process. We show that the flux of sinking particles (typically considered the dominant form of downward transport of organic carbon) is twice as high at a frontal system, relative to surrounding waters or to nonfrontal conditions. Furthermore, downward transport by subduction leads to additional carbon export at the front that is similar in magnitude to the sinking flux. Such enhanced C export at episodic and mesoscale features needs to be incorporated into biogeochemical forecast models. Enhanced vertical carbon transport (gravitational sinking and subduction) at mesoscale ocean fronts may explain the demonstrated imbalance of new production and sinking particle export in coastal upwelling ecosystems. Based on flux assessments from 238U:234Th disequilibrium and sediment traps, we found 2 to 3 times higher rates of gravitational particle export near a deep-water front (305 mg C⋅m−2⋅d−1) compared with adjacent water or to mean (nonfrontal) regional conditions. Elevated particle flux at the front was mechanistically linked to Fe-stressed diatoms and high mesozooplankton fecal pellet production. Using a data assimilative regional ocean model fit to measured conditions, we estimate that an additional ∼225 mg C⋅m−2⋅d−1 was exported as subduction of particle-rich water at the front, highlighting a transport mechanism that is not captured by sediment traps and is poorly quantified by most models and in situ measurements. Mesoscale fronts may be responsible for over a quarter of total organic carbon sequestration in the California Current and other coastal upwelling ecosystems.
Approximately Isometric Shape Correspondence by Matching pointwise Spectral Features and Global Geodesic Structures

A practical method for finding correspondences between nonrigid isometric shapes is presented. It utilizes both pointwise surface descriptors, and metric structures defined on the shapes to perform the matching task, which is formulated as a quadratic minimization problem. Further, the paper explores the correspondence ambiguity problem arising when matching intrinsically symmetric shapes using only intrinsic surface properties. It is shown that when using isometry invariant surface descriptors based on eigendecomposition of the Laplace–Beltrami operator, it is possible to construct distinctive sets of surface descriptors for different possible correspondences. When used in a proper minimization problem, those descriptors allow us to explore number of possible correspondences between two given shapes.
Determining intonational boundaries from the acoustic signal

This article has two-fold aims: it reports ﬁrstly the improvement of a speech database in Catalan for speech synthesis (Fest-cat) with the information about prosodic boundaries using the break index labels proposed in the ToBI system; and secondly, it presents the experiments undergone to determine the acoustic markers that can differentiate among the break-indexes. Several experiments using different classiﬁcation techniques were performed in order to compare the relative merit of different attributes to characterize breaks. Results show that the prosodic phrase breaks are correlated with: presence of a pause, lengthening of the pre-break syllable and the F0 contour of the span between the stressed syllable and the following post-stressed, if there are, immediately preceding the break.
Clinical use of Ibuprofen is associated with slower FEV1 decline in children with cystic fibrosis.

RATIONALE High-dose ibuprofen in a 4-year controlled trial slowed FEV(1) decline in young subjects with cystic fibrosis, but the effectiveness of ibuprofen has not been assessed in a large group of patients treated clinically with this therapy. OBJECTIVES To assess the effect of ibuprofen therapy on FEV(1) decline in children and adolescents with cystic fibrosis, using observational data from the Cystic Fibrosis Foundation Patient Registry. METHODS The rate of decline in FEV(1) percent predicted over 2-7 years among patients age 6-17 years with FEV(1) > 60% predicted, and who were treated with ibuprofen (1,365), was compared with patients of similar age and disease severity who were not treated with this therapy (8,960). Multilevel repeated-measures mixed-regression models were used to estimate rates of decline, adjusting for characteristics and therapies that influenced FEV(1) decline. Adverse effects were compared among those treated versus not treated with ibuprofen. MEASUREMENTS AND MAIN RESULTS FEV(1) declined less rapidly among patients treated with ibuprofen (difference, 0.60% predicted per year; 95% confidence interval, 0.31 to 0.89; P < 0.0001); a 29% reduction in slope based on an average decline of 2.08% predicted per year for patients not treated. Those treated with ibuprofen were more likely to have an episode of gastrointestinal bleeding requiring hospitalization, but the occurrence was rare in both groups (annual incidence, 0.37 vs. 0.14%; relative risk, 2.72; P < 0.001). CONCLUSIONS Slower rates of FEV(1) decline are seen in children and adolescents with cystic fibrosis who are treated with ibuprofen. The apparent benefits of ibuprofen therapy outweigh the small risk of gastrointestinal bleeding.
PARAMETERIZATION OF COMPLEX CULTURAL HERITAGE SHAPES FOR ONLINE VIEWING AND INTERACTIVE PRESENTATION AND PROCESSING

We have developed algorithms and programs capable of efficiently parameterizing complex cultural heritage shapes including texture, which significantly reduces the data-set size. This is potentially significant for online viewing and interactive presentation and processing. The proposed approach is based on Non-uniform rational B-splines (NURBS) mathematical model which is also by itself suitable for analysis, especially of different artistic techniques.
The Detection of Candida Species in Patients with Halitosis

To examine the effects of Candida on halitosis, the carrier state of Candida was examined in patients who made a visit with a chief complaint of halitosis. Methods. Subjects were 123 patients (42 males and 81 females) who visited our clinic, with a chief complaint of halitosis. Their average age was 45.8 years. To examine halitosis, an organoleptic test was conducted, and volatile sulfur compounds (VSCs) were measured by gas chromatography. Tongue-coating samples collected at the initial visit were cultured in CHROMagar Candida medium. The results of a Candida culture test, an organoleptic test, and VSC measurements were examined. Results. The male-to-female ratio of the patients was about 1 : 2. Patients with severe halitosis accounted for less than 20%. In the Candida culture test, the positive rate was about 25.2%, and C. albicans was the most frequently detected. Two kinds of Candida species were detected in 75% (6/8) of the strongly Candida-positive group. The VSC measurements were correlated with the Candida culture test results. Methyl mercaptan concentration was higher in the strongly C. albicans-positive group or the subjects having two kinds of Candida species. Conclusion. We suggest that imbalance of oral microbial community exists in the strongly Candida-positive group.
When positive studies of novel therapies are subsequently nullified: cumulative meta-analyses in preeclampsia.

PURPOSE The purpose of this study was to examine changes over time in the pooled effect size of randomized, double-blinded, placebo-controlled trials (RCTs) published on the protective effects of antioxidants and low dose aspirin against preeclampsia, and to identify determinants that may affect such changes. METHODS Two recently published meta-analyses of RCTs examining the effects of antioxidant treatment or low dose aspirin on the rates of preeclampsia and its adverse effects were used. Chronological, cumulative meta-analyses were conducted to investigate the possibility of a time-dependent effect. The journal's impact factor, citation numbers of each paper, and their sample size, were correlated with the risk ratio (RR) of the study. RESULTS The median sample size of positive antioxidant trials (i.e., showing protective effect) was tenfold smaller (median 267) than that of the negative trials (median 2120) (P = 0.017). A similar trend was seen for low dose aspirin studies. There was a significant correlation between study size and RR for the effects of antioxidants and low dose aspirin on intrauterine growth restriction (IUGR). There was no correlation between RR and citation number, or between RR and the journal's impact factor for the two therapeutic modalities. For both modalities, the journal's impact factor correlated significantly with the number of citations per year. Cumulative meta-analyses revealed that during the first few years and studies, there was a seeming significant protective effect of antioxidant or aspirin against preeclampsia. For both treatment, the initial protective effects gradually disappeared and nullified by larger, later studies. CONCLUSIONS Initial studies, often published in high impact factor journals, are cited significantly more times but do not exhibit a higher likelihood of predicting a correct long term answer. Studies with smaller sample sizes are more likely to be biased against the null hypothesis. As such, cumulative meta-analysis is an effective tool in predicting potential bias against the null hypothesis and the need for additional studies.
Multiperiod Asset Allocation with Dynamic Volatilities

In this paper we extend and generalize the framework of Testing and Valuing Dynamic Correlations for Asset Allocation by focussing on a multistep version of the variance minimization criterion. We consider both a closed and an open loop strategy. It is shown both theoretically and empirically that this approach amplifies the benefits of using dynamic correlations (this has not yet been shown). This draft: February 25, 2004. Preliminary and Incomplete.
Comparison of machine learning methods for estimating energy consumption in buildings

The increasing number of decentralized renewable energy sources together with the grow in overall electricity consumption introduce many new challenges related to dimensioning of grid assets and supply-demand balancing. Approximately 40% of the total energy consumption is used to cover the needs of commercial and office buildings. To improve the design of the energy infrastructure and the efficient deployment of resources, new paradigms have to be thought up. Such new paradigms need automated methods to dynamically predict the energy consumption in buildings. At the same time these methods should be easily expandable to higher levels of aggregation such as neighborhoods and the power grid. Predicting energy consumption for a building is complex due to many influencing factors, such as weather conditions, performance and settings of heating and cooling systems, and the number of people present. In this paper, we investigate a newly developed stochastic model for time series prediction of energy consumption, namely the Conditional Restricted Boltzmann Machine (CRBM), and evaluate its performance in the context of building automation systems. The assessment is made on a real dataset consisting of 7 weeks of hourly resolution electricity consumption collected from a Dutch office building. The results showed that for the energy prediction problem solved here, CRBM outperforms Artificial Neural Networks (ANNs), and Hidden Markov Models (HMMs).