High variability in spawner–recruit data hampers learning

2001 ◽  
Vol 58 (4) ◽  
pp. 769-776 ◽  
Author(s):  
Richard A Hinrichsen
Keyword(s):  
A Priori ◽  
Time Frame ◽  
Large Error ◽  
Data Sets ◽  
Temporal Reference ◽  
Experimental Management ◽  
Number Of Treatment ◽  
A Priori Power Analysis ◽  
Short Time ◽  
And Control

Variability is a formidable opponent of experimental management aimed at detecting spawner–recruit (SR) effects in a short time frame. I fitted Ricker SR models to 214 different SR data sets and found that high residual error variability was common. For each of these data sets, in an a priori power analysis, I estimated the power of experiments that used the change in Ricker a as the treatment effect and a temporal reference alone (no subpopulation references). Power was calculated using both bootstrap resampling and the usual normal theory methods. The analysis revealed that large residual variability severely limits the power to detect large changes in recruits per spawner (R/S). At the median level of error variability, achieving the design criteria of α = 0.05 and power = 0.8 required an experiment that doubled R/S to last about 20 years (assuming an equal number of treatment and control years). Several approaches to countering large error variability are discussed along with their limitations.

scholarly journals Two case reports of corticosteroid administration-prolonged and pulsed therapy-in treatment of pruritus in cholestatic hepatitis A patients

2019 ◽  
Vol 2019 (8) ◽  
Author(s):  
Daad Daghman ◽  
Mohamad Saeed Rez ◽  
Amjad Soltany ◽  
Almotaman Alsaleh
Keyword(s):  
Hepatitis A ◽  
Medical Literature ◽  
Case Reports ◽  
Time Frame ◽  
Good Prognosis ◽  
Cholestatic Hepatitis ◽  
Corticosteroid Administration ◽  
Steroid Side Effects ◽  
Experimental Management ◽  
Short Time

Abstract Cholestasis following hepatitis A affects around 0.8% of hepatitis A patients. It is considered a distressing complication in spite of its good prognosis. Despite being subject to multiple studies, causes of cholestasis are still controversial. Many treatments (discussed later) have shown some improvements of the accompanied pruritus. In the following article, we present two cholestatic hepatitis A patients who suffered from severe pruritus. Prednisolone was administered via two different methods: prolonged and pulsed. Both showed great improvement of the pruritus in a short time frame. To the best of our knowledge, our management using pulsed corticosteroid therapy in treatment of pruritus in cholestatic hepatitis A is considered the first experimental management in medical literature. The importance of this experimental case lies in reducing the doses and the duration of steroid intake, thus reducing steroid side effects as far as possible.

scholarly journals Reaching Out to the Millions: A 5 Key Messages Rapid IEC Campaign During the COVID-19 Pandemic

2021 ◽  
pp. 0272684X2110037
Author(s):  
Cariappa P. Mudera ◽  
Rohit D. Bavdekar ◽  
Narinder Kumar ◽  
Aravind Veiraiah ◽  
Ranjith K. Nair
Keyword(s):  
Development Process ◽  
Universal Access ◽  
Training Model ◽  
Time Frame ◽  
Emergency Situations ◽  
Modified Delphi ◽  
Modified Delphi Technique ◽  
Youtube Videos ◽  
Short Time ◽  
And Control

The ongoing COVID-19 pandemic has necessitated providing populations with simple and relevant, actionable informational messages for them to be informed on individual and community level measures to combat the pandemic. Distilling guidance from various sources into simple actionable message themes in a short time frame should be the hallmark of any rapid health promotion campaign. We developed and disseminated actionable Information, Education and Communication (IEC) messages on prevention and control during the ongoing COVID-19 pandemic by selecting five key message themes of prevention. A modified Delphi technique was used for the development process which was peer reviewed after consensus was generated. An online training model using specifically developed YouTube videos and Zoom Meetings was utilized to train Master Trainers and the further hierarchy reaching out to 6989 Community Resource Persons at the peripheral echelons. Conservatively the population reached out has been estimated to be more than 6 million (across 1.2 million households), over a two week period in April 2020. The implementing agency has created 300 videos in around 22 languages based on the 5 Key Messages, which are available online for universal access. A rapid IEC campaign that can be developed, designed and rolled out in a short span is required for emergency situations such as the ongoing pandemic. NGOs play an important role in reaching out relevant messages to the community, filling in the gaps by virtue of their relative systemic agility.

Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia

2021 ◽  
pp. 216770262095934
Author(s):  
Julia M. Sheffield ◽  
Holger Mohr ◽  
Hannes Ruge ◽  
Deanna M. Barch
Keyword(s):  
A Priori ◽  
Network Connectivity ◽  
Task Learning ◽  
Human Ability ◽  
Functional Brain Networks ◽  
Task Goals ◽  
Multivariate Pattern ◽  
And Control ◽  
Goal Directed Behavior

Rapid instructed task learning (RITL) is the uniquely human ability to transform task information into goal-directed behavior without relying on trial-and-error learning. RITL is a core cognitive process supported by functional brain networks. In patients with schizophrenia, RITL ability is impaired, but the role of functional network connectivity in these RITL deficits is unknown. We investigated task-based connectivity of eight a priori network pairs in participants with schizophrenia ( n = 29) and control participants ( n = 31) during the performance of an RITL task. Multivariate pattern analysis was used to determine which network connectivity patterns predicted diagnostic group. Of all network pairs, only the connectivity between the cingulo-opercular network (CON) and salience network (SAN) during learning classified patients and control participants with significant accuracy (80%). CON-SAN connectivity during learning was significantly associated with task performance in participants with schizophrenia. These findings suggest that impaired interactions between identification of salient stimuli and maintenance of task goals contributes to RITL deficits in participants with schizophrenia.

scholarly journals A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 484
Author(s):  
Claudiu Vințe ◽  
Marcel Ausloos ◽  
Titus Felix Furtună
Keyword(s):  
Stock Market ◽  
Time Frame ◽  
Data Sets ◽  
Entropy Model ◽  
Daily Data ◽  
Volatility Models ◽  
Derivative Instruments ◽  
Securities Industry ◽  
Time Frames ◽  
Financial Securities

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Kazimierz M. Slomczynski ◽  
Irina Tomescu-Dubrow ◽  
Ilona Wysmulek
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Survey Data ◽  
A Priori ◽  
Data Sets ◽  
New Approach ◽  
Survey Quality ◽  
Survey Error ◽  
Ex Post ◽  
The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

scholarly journals Analysis of Risk Factors in Dementia Through Machine Learning

2020 ◽  
pp. 1-17
Author(s):  
Francisco Javier Balea-Fernandez ◽  
Beatriz Martinez-Vega ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
Raquel Leon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optimization Algorithms ◽  
Progressive Increase ◽  
Control Group ◽  
Data Sets ◽  
Modifiable Factors ◽  
Validation Set ◽  
The One ◽  
And Control ◽  
Potential Tool

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

scholarly journals Polyethylene Migration from Food Packaging on Cheese Detected by Raman and Infrared (ATR/FT-IR) Spectroscopy

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3872
Author(s):  
Klytaimnistra Katsara ◽  
George Kenanakis ◽  
Zacharias Viskadourakis ◽  
Vassilis M. Papadakis
Keyword(s):  
Vibrational Spectroscopy ◽  
Food Packaging ◽  
Time Frame ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Ft Ir ◽  
Comparative Results ◽  
Short Time ◽  
And Storage ◽  
Ft Ir Spectroscopy

For multiple years, food packaging migration has been a major concern in food and health sciences. Plastics, such as polyethylene, are continuously utilized in food packaging for preservation and easy handling purposes during transportation and storage. In this work, three types of cheese, Edam, Kefalotyri and Parmesan, of different hardness were studied under two complementary vibrational spectroscopy methods, ATR-FTIR and Raman spectroscopy, to determine the migration of low-density polyethylene from plastic packaging to the surface of cheese samples. The experimental duration of this study was set to 28 days due to the degradation time of the selected cheese samples, which is clearly visible after 1 month in refrigerated conditions at 4 °C. Raman and ATR-FTIR measurements were performed at a 4–3–4–3 day pattern to obtain comparative results. Initially, consistency/repeatability measurement tests were performed on Day0 for each sample of all cheese specimens to understand if there is any overlap between the characteristic Raman and ATR-FTIR peaks of the cheese with the ones from the low-density polyethylene package. We provide evidence that on Day14, peaks of low-density polyethylene appeared due to polymeric migration in all three cheese types we tested. In all cheese samples, microbial outgrowth started to develop after Day21, as observed visually and under the bright-field microscope, causing peak reverse. Food packaging migration was validated using two different approaches of vibrational spectroscopy (Raman and FT-IR), revealing that cheese needs to be consumed within a short time frame in refrigerated conditions at 4 °C.

scholarly journals Breeding Strategies to Improve Miscanthus as a Sustainable Source of Biomass for Bioenergy and Biorenewable Products

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 673 ◽  
Author(s):  
John Clifton-Brown ◽  
Kai-Uwe Schwarz ◽  
Danny Awty-Carroll ◽  
Antonella Iurato ◽  
Heike Meyer ◽  
...  
Keyword(s):  
Significant Contribution ◽  
Perennial Grass ◽  
Time Frame ◽  
Eastern Asia ◽  
Germplasm Collections ◽  
Naturally Occurring ◽  
Wide Range ◽  
Yield Quality ◽  
Short Time ◽  
Commercial Expansion

Miscanthus, a C4 perennial grass native to Eastern Asia, is being bred to provide biomass for bioenergy and biorenewable products. Commercial expansion with the clonal hybrid M. × giganteus is limited by low multiplication rates, high establishment costs and drought sensitivity. These limitations can be overcome by breeding more resilient Miscanthus hybrids propagated by seed. Naturally occurring fast growing indigenous Miscanthus species are found in diverse environments across Eastern Asia. The natural diversity provides for plant breeders, the genetic resources to improve yield, quality, and resilience for a wide range of climates and adverse abiotic stresses. The challenge for Miscanthus breeding is to harness the diversity through selections of outstanding wild types, parents, and progenies over a short time frame to deploy hybrids that make a significant contribution to a world less dependent on fossil resources. Here are described the strategies taken by the Miscanthus breeding programme at Aberystwyth, UK and its partners. The programme built up one of the largest Miscanthus germplasm collections outside Asia. We describe the initial strategies to exploit the available genetic diversity to develop varieties. We illustrate the success of combining diverse Miscanthus germplasm and the selection criteria applied across different environments to identify promising hybrids and to develop these into commercial varieties. We discuss the potential for molecular selections to streamline the breeding process.

Origins and Destinations – Social Security Claimant Dynamics

1998 ◽  
Vol 27 (3) ◽  
pp. 351-369 ◽  
Author(s):  
MICHAEL NOBLE ◽  
SIN YI CHEUNG ◽  
GEORGE SMITH
Keyword(s):  
The United States ◽  
Data Sets ◽  
Income Support ◽  
Data Set ◽  
Family Structures ◽  
Highly Educated ◽  
Lone Parents ◽  
Lone Mothers ◽  
Time Period ◽  
Short Time

This article briefly reviews American and British literature on welfare dynamics and examines the concepts of welfare dependency and ‘dependency culture’ with particular reference to lone parents. Using UK benefit data sets, the welfare dynamics of lone mothers are examined to explore the extent to which they inform the debates. Evidence from Housing Benefits data show that even over a relatively short time period, there is significant turnover in the benefits-dependent lone parent population with movement in and out of income support as well as movement into other family structures. Younger lone parents and owner-occupiers tend to leave the data set while older lone parents and council tenants are most likely to stay. Some owner-occupier lone parents may be relatively well off and on income support for a relatively short time between separation and a financial settlement being reached. They may also represent a more highly educated and highly skilled group with easier access to the labour market than renters. Any policy moves paralleling those in the United States to time limit benefit will disproportionately affect older lone parents.

scholarly journals Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Working Condition ◽  
Sequence Data ◽  
A Priori ◽  
Real Data ◽  
Data Sets ◽  
Main Task ◽  
Event Sequence ◽  
Telemetry Data ◽  
Condition Monitoring Systems ◽  
Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

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