Utilising Tree-Based Ensemble Learning for Speaker Segmentation

Part 2: Learning-Ensemble LearningInternational audienceIn audio and speech processing, accurate detection of the changing points between multiple speakers in speech segments is an important stage for several applications such as speaker identification and tracking. Bayesian Information Criteria (BIC)-based approaches are the most traditionally used ones as they proved to be very effective for such task. The main criticism levelled against BIC-based approaches is the use of a penalty parameter in the BIC function. The use of this parameters consequently means that a fine tuning is required for each variation of the acoustic conditions. When tuned for a certain condition, the model becomes biased to the data used for training limiting the model’s generalisation ability.In this paper, we propose a BIC-based tuning-free approach for speaker segmentation through the use of ensemble-based learning. A forest of segmentation trees is constructed in which each tree is trained using a sampled version of the speech segment. During the tree construction process, a set of randomly selected points in the input sequence is examined as potential segmentation points. The point that yields the highest ΔBIC is chosen and the same process is repeated for the resultant left and right segments. The tree is constructed where each node corresponds to the highest ΔBIC with the associated point index. After building the forest and using all trees, the accumulated ΔBIC for each point is calculated and the positions of the local maximums are considered as speaker changing points. The proposed approach is tested on artificially created conversations from the TIMIT database. The approach proposed show very accurate results comparable to those achieved by the-state-of-the-art methods with a 9% (absolute) higher F1 compared with the standard ΔBIC with optimally tuned penalty parameter

Impact of antiepileptic-drug treatment burden on health-care-resource utilization and costs

Krithika Rajagopalan,1 Sean D Candrilli,2 Mayank Ajmera2 1Sunovion Pharmaceuticals Inc, Marlborough, MA 01752, USA; 2RTI Health Solutions, Research Triangle Park, NC 27709, USA Background: Complex titration requirements and dosing of antiepileptic drugs (AEDs) may pose a significant treatment burden for patients with epilepsy. This study evaluated health-care-resource utilization (HCRU) rates and costs by treatment burden, defined as number of daily pills and dosing frequency, among managed-care enrollees with epilepsy who initiated AED monotherapy. Methods: This retrospective longitudinal study examined administrative HC-claim data in patients aged ≥18 years with two or more pharmacy claims for an AED and two or more medical claims for epilepsy or afebrile convulsion. The number of daily AED pills was estimated at index as the total number of pills dispensed divided by the days supplied, and categorized as more than zero/one, one/two, two/three, and more than three per day. AED-dosing frequency was measured at index and categorized as one, two, three, or four times daily. Postindex 12-month all-cause and epilepsy-related HCRU and costs were estimated using multivariable Poisson regression models and generalized linear models, respectively. Results: Unadjusted total all-cause and epilepsy-related costs at 12 months postindex averaged US$26,015 per person and US$5,557 per person (2017 values), respectively. Adjusted all-cause and epilepsy-related costs were US$25,918 per person and US$5,602 per person, respectively. A pill burden of more than three a day was associated with a 6.7% increase in total annual HC costs compared with one pill/day. Patients receiving one/two, two/three, and more than three pills per day had 13.3%, 23.9%, and 38.3% higher epilepsy-related costs, respectively, than those receiving one pill per day (P<0.0001). Increase in dosing frequency was associated with greater total HCRU and higher costs, but only patients with twice-daily dosing had significantly higher epilepsy-related costs. Conclusion: Findings from this study suggest that increased treatment burden is associated with greater HCRU and higher overall and epilepsy-related costs. Reducing treatment burden via selection of AED therapy with reduced pill numbers and dosing frequency should be considered to improve health and economic outcomes. Keywords: antiepileptic drugs, health-care-resource utilization, treatment burden, epilepsy-related cost

Association Between Cytokines and Liver Histology in Children with Nonalcoholic Fatty Liver Disease.

BackgroundReliable non-invasive markers to characterize inflammation, hepatocellular ballooning, and fibrosis in nonalcoholic fatty liver disease (NAFLD) are lacking. We investigated the relationship between plasma cytokine levels and features of NAFLD histology to gain insight into cellular pathways driving NASH and to identify potential non-invasive discriminators of NAFLD severity and pattern.MethodsCytokines were measured from plasma obtained at enrollment in pediatric participants in NASH Clinical Research Network studies with liver biopsy-proven NAFLD. Cytokines were chosen a priori as possible discriminators of NASH and its components. Minimization of Akaike Information Criterion (AIC) was used to determine cytokines retained in multivariable models.ResultsOf 235 subjects, 31% had "Definite NASH" on liver histology, 43% had "Borderline NASH", and 25% had NAFLD but not NASH. Total plasminogen activator inhibitor 1 (PAI1) and activated PAI1 levels were higher in pediatric participants with Definite NASH and with lobular inflammation. Interleukin-8 (IL-8) was higher in those with stage 3-4 fibrosis and lobular inflammation. sIL-2rα was higher in children with stage 3-4 fibrosis and portal inflammation. In multivariable analysis, PAI1 variables were discriminators of Borderline/Definite NASH, definite NASH, lobular inflammation and ballooning. IL-8 increased with steatosis and fibrosis severity; sIL-2rα increased with fibrosis severity and portal inflammation. IL-7 decreased with portal inflammation and fibrosis severity.ConclusionsPlasma cytokines associated with histology varied considerably among NASH features, suggesting promising avenues for investigation. Future, more targeted analysis is needed to identify the role of these markers in NAFLD and to evaluate their potential as non-invasive discriminators of disease severity

334phealth related quality of life in women with hr her2 advanced or metastatic breast cancer treated in real world settings in italy and germany

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Integrated root phenotypes for improved rice performance under low nitrogen availability

Greater nitrogen efficiency would substantially reduce the economic, energy and environmental costs of rice production. We hypothesized that synergistic balancing of the costs and benefits for soil exploration among root architectural phenes is beneficial under suboptimal nitrogen availability. An enhanced implementation of the functional-structural model OpenSimRoot for rice integrated with the ORYZA_v3 crop model was used to evaluate the utility of combinations of root architectural phenes, namely nodal root angle, the proportion of smaller diameter nodal roots, nodal root number; and L-type and S-type lateral branching densities, for plant growth under low nitrogen. Multiple integrated root phenotypes were identified with greater shoot biomass under low nitrogen than the reference cultivar IR64. The superiority of these phenotypes was due to synergism among root phenes rather than the expected additive effects of phene states. Representative optimal phenotypes were predicted to have up to 80% greater grain yield with low N supply in the rainfed dry direct-seeded agroecosystem over future weather conditions, compared to IR64. These phenotypes merit consideration as root ideotypes for breeding rice cultivars with improved yield under rainfed dry direct-seeded conditions with limited nitrogen availability. The importance of phene synergism for the performance of integrated phenotypes has implications for crop breeding.Peer reviewe

What factors determine the severity of hepatitis A‐related acute liver failure?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87020/1/j.1365-2893.2010.01410.x.pd

BioModels: ten-year anniversary

BioModels (http://www.ebi.ac.uk/biomodels/) is a repository of mathematical models of biological processes. A large set of models is curated to verify both correspondence to the biological process that the model seeks to represent, and reproducibility of the simulation results as described in the corresponding peer-reviewed publication. Many models submitted to the database are annotated, cross-referencing its components to external resources such as database records, and terms from controlled vocabularies and ontologies. BioModels comprises two main branches: one is composed of models derived from literature, while the second is generated through automated processes. BioModels currently hosts over 1200 models derived directly from the literature, as well as in excess of 140 000 models automatically generated from pathway resources. This represents an approximate 60-fold growth for literature-based model numbers alone, since BioModels’ first release a decade ago. This article describes updates to the resource over this period, which include changes to the user interface, the annotation profiles of models in the curation pipeline, major infrastructure changes, ability to perform online simulations and the availability of model content in Linked Data form. We also outline planned improvements to cope with a diverse array of new challenges

Relationship of ELF and PIIINP With Liver Histology and Response to Vitamin E or Pioglitazone in the PIVENS Trial

Enhanced liver fibrosis score (ELF) and one of its components, amino-terminal propeptide of type III procollagen (PIIINP) are promising noninvasive biomarkers of liver histology in patients with nonalcoholic steatohepatitis (NASH). We evaluated the association of ELF and PIIINP with fibrosis stages at baseline and end of treatment (EOT) with vitamin E or pioglitazone in the PIVENS trial (Pioglitazone vs. Vitamin E vs. Placebo for the Treatment of Nondiabetic Patients With NASH) and characterized ELF and PIIINP changes and their associations with changes in the histological endpoints. ELF and PIIINP were measured at baseline and weeks 16, 48, and 96 on sera from 243 PIVENS participants. Baseline and EOT ELF were significantly associated with fibrosis stage (P < 0.001). The area under the curve for ELF's detection of clinically significant and advanced fibrosis in baseline biopsies was 0.74 and 0.79, respectively (P < 0.001). There was a significant drop in ELF score at weeks 48 and 96 in patients who achieved the NAFLD activity score (NAS)-based primary end point (P = 0.007) but not in those who experienced NASH resolution (P = 0.24) or fibrosis improvement (P = 0.50). Change in PIIINP was significantly associated with NASH resolution and improvement in NAS-based histological endpoint and fibrosis (P < 0.05 for all). Over the study period, both ELF and PIIINP significantly decreased with vitamin E (P < 0.05), but only PIIINP decreased with pioglitazone (P < 0.001). Conclusion: ELF is significantly associated with clinically significant and advanced fibrosis in patients with NASH, but its longitudinal changes were not associated with improvement in fibrosis or NASH resolution. PIIINP, one of its components, appears promising for identifying longitudinal histologic changes in patients with NASH and is worthy of further investigation

Multiseriate cortical sclerenchyma enhance root penetration in compacted soils

Mechanical impedance limits soil exploration and resource capture by plant roots. We examine the role of root anatomy in regulating plant adaptation to mechanical impedance and identify a root anatomical phene in maize (Zea mays) and wheat (Triticum aestivum) associated with penetration of hard soil: multiseriate cortical sclerenchyma (MCS). We characterize this trait and evaluate the utility of MCS for root penetration in compacted soils. Roots with MCS had a greater cell wall to lumen ratio and a distinct UV emission spectrum in outer cortical cells. Genome-wide association mapping revealed that MCS is heritable and genetically controlled. We identified a candidate gene associated with MCS. Across all root classes and nodal positions, maize genotypes with MCS had 13% greater root lignin concentration compared to genotypes without MCS. Genotypes without MCS formed MCS upon exogenous ethylene exposure. Genotypes with MCS had greater lignin concentration and bending strength at the root tip. In controlled environments, MCS in maize and wheat was associated improved root tensile strength and increased penetration ability in compacted soils. Maize genotypes with MCS had root systems with 22% greater depth and 39% greater shoot biomass in compacted soils in the field compared to lines without MCS. Of the lines we assessed, MCS was present in 30-50% of modern maize, wheat, and barley cultivars but was absent in teosinte and wild and landrace accessions of wheat and barley. MCS merits investigation as a trait for improving plant performance in maize, wheat, and other grasses under edaphic stress

Modelling the effects of glucagon during glucose tolerance testing.

From Europe PMC via Jisc Publications RouterHistory: ppub 2019-12-01, epub 2019-12-12Publication status: PublishedBACKGROUND:Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. RESULTS:Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). CONCLUSIONS:The models are used to investigate how different degrees of pax'tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted