An evaluation of the Hennessy Grading Probe for measuring fat depth in beef carcasses

1989 ◽  
Vol 29 (6) ◽  
pp. 781 ◽  
Author(s):  
DL Hopkins
Keyword(s):  
Linear Equations ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
General Validity ◽  
Measurement Analysis ◽  
Beef Carcasses ◽  
The Right ◽  
The Relationship ◽  
Methods Of Measurement

Fat depth at the P8 site on the rump was measured by the cut-and-measure (CM) technique and with the Hennessy Grading Probe (HGP) on 2501 beef carcasses at 1 abattoir over a 12-month period. CM measurements that differed by more than 1 mm between the right and left sides of the carcass were discarded. A subsequent data set of 1850 carcasses was randomly divided so that 2 models could be developed to assess the general validity of the relationship between the 2 methods of measurement. Analysis of measurements of the left side of the carcasses of these 2 subsamples showed the data were not normally distributed. Removal of outliers at the 95% confidence level and also measurements at both extremes of the data range improved the symmetry of the sets of data. From each adjusted data set, regression equations were developed to predict CM measurements from HGP measurements. Linear equations were adequate for predicting CM measurements from HGP measurements, and curvilinear analysis did not improve the predictions. Compared with the curvilinear equations, the linear equations resulted in smaller differences between the 2 data sets for the predicted CM measurements over a range of HGP measurements.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Children with 5′-end NF1 gene mutations are more likely to have glioma

2017 ◽  
Vol 3 (5) ◽  
pp. e192 ◽  
Author(s):  
Corina Anastasaki ◽  
Stephanie M. Morris ◽  
Feng Gao ◽  
David H. Gutmann
Keyword(s):  
Gene Mutation ◽  
Statistical Significance ◽  
Gene Mutations ◽  
Neurofibromatosis Type ◽  
Published Data ◽  
Data Sets ◽  
Nonsense Mutations ◽  
Data Set ◽  
Nf1 Gene ◽  
The Relationship

Objective:To ascertain the relationship between the germline NF1 gene mutation and glioma development in patients with neurofibromatosis type 1 (NF1).Methods:The relationship between the type and location of the germline NF1 mutation and the presence of a glioma was analyzed in 37 participants with NF1 from one institution (Washington University School of Medicine [WUSM]) with a clinical diagnosis of NF1. Odds ratios (ORs) were calculated using both unadjusted and weighted analyses of this data set in combination with 4 previously published data sets.Results:While no statistical significance was observed between the location and type of the NF1 mutation and glioma in the WUSM cohort, power calculations revealed that a sample size of 307 participants would be required to determine the predictive value of the position or type of the NF1 gene mutation. Combining our data set with 4 previously published data sets (n = 310), children with glioma were found to be more likely to harbor 5′-end gene mutations (OR = 2; p = 0.006). Moreover, while not clinically predictive due to insufficient sensitivity and specificity, this association with glioma was stronger for participants with 5′-end truncating (OR = 2.32; p = 0.005) or 5′-end nonsense (OR = 3.93; p = 0.005) mutations relative to those without glioma.Conclusions:Individuals with NF1 and glioma are more likely to harbor nonsense mutations in the 5′ end of the NF1 gene, suggesting that the NF1 mutation may be one predictive factor for glioma in this at-risk population.

scholarly journals Individual Tree Volume Equations for Red Pine in Michigan

1997 ◽  
Vol 14 (2) ◽  
pp. 53-58 ◽  
Author(s):  
Gary W. Fowler
Keyword(s):  
Red Pine ◽  
Data Sets ◽  
Regression Equations ◽  
Independent Data ◽  
Data Set ◽  
Individual Tree ◽  
Standing Trees ◽  
Pine Stands ◽  
Volume Estimates ◽  
Volume Equations

Abstract New total, pulpwood, sawtimber, and residual pulpwood cubic foot individual tree volume equations were developed for red pine in Michigan using nonlinear and multiple linear regression. Equations were also developed for Doyle, International 1/4 in., and Scribner bd ft volume, and a procedure for estimating pulpwood and residual pulpwood rough cord volumes from the appropriate cubic foot equations was described. Average ratios of residual pulpwood (i.e., topwood, cubic foot or cords) to mbf were developed for 7.6 and 9.6 in. sawtimber. Data used to develop these equations were collected during May-August 1983-1985 from 3,507 felled and/or standing trees from 27 stands in Michigan. Sixteen and 11 stands were located in the Upper and Lower Peninsulas, respectively. All equations were validated on an independent data set. Rough cord volume estimates based on the new pulpwood equation were compared with contemporary tables for 2 small cruise data sets. The new equations can be used to more accurately estimate total volume and volume per acre when cruising red pine stands. North. J. Appl. For. 14(2):53-58.

Use of Enterobacteriaceae Analysis Results for Predicting Absence of Salmonella Serovars on Beef Carcasses

2009 ◽  
Vol 72 (2) ◽  
pp. 260-266 ◽  
Author(s):  
JOHN R. RUBY ◽  
STEVEN C. INGHAM
Keyword(s):  
Small Volume ◽  
Large Data ◽  
Data Sets ◽  
Data Set ◽  
A Value ◽  
Large Data Set ◽  
Intervention Treatment ◽  
Beef Carcasses ◽  
Predictive Approach ◽  
Potential Use

Previous work using a large data set (no. 1, n = 5,355) of carcass sponge samples from three large-volume beef abattoirs highlighted the potential use of binary (present or absent) Enterobacteriaceae results for predicting the absence of Salmonella on carcasses. Specifically, the absence of Enterobacteriaceae was associated with the absence of Salmonella. We tested the accuracy of this predictive approach by using another large data set (no. 2, n = 2,163 carcasses sampled before or after interventions) from the same three data set no. 1 abattoirs over a later 7-month period. Similarly, the predictive approach was tested on smaller subsets from data set no. 2 (n = 1,087, and n = 405) and on a much smaller data set (no. 3, n = 100 postintervention carcasses) collected at a small-volume abattoir over 4 months. Of Enterobacteriaceae-negative data set no. 2 carcasses, >98% were Salmonella negative. Similarly accurate predictions were obtained in the two data subsets obtained from data set no. 2 and in data set no. 3. Of final postintervention carcass samples in data set nos. 2 and 3, 9 and 70%, respectively, were Enterobacteriaceae positive; mean Enterobacteriaceae values for the two data sets were −0.375, and 0.169 log CFU/100 cm2 (detection limit = −0.204, and Enterobacteriaceae negative assigned a value of −0.505 log CFU/100 cm2). Salmonella contamination rates for final postintervention beef carcasses in data set nos. 2 and 3 were 1.1 and 7.0%, respectively. Binary Enterobacteriaceae results may be useful in evaluating beef abattoir hygiene and intervention treatment efficacy.

A Ccomparison of sedigraph and pipette methods for soil particle-size analysis

Soil Research ◽  
1993 ◽  
Vol 31 (4) ◽  
pp. 407 ◽  
Author(s):  
GD Buchan ◽  
KS Grewal ◽  
JJ Claydon ◽  
RJ Mcpherson
Keyword(s):  
Particle Size ◽  
New Zealand ◽  
Particle Size Analysis ◽  
Size Analysis ◽  
Data Sets ◽  
Regression Equations ◽  
Total Iron Content ◽  
Data Set ◽  
X Ray ◽  
The Difference

The X-ray attenuation (Sedigraph) method for particle-size analysis is known to consistently estimate a finer size distribution than the pipette method. The objectives of this study were to compare the two methods, and to explore the reasons for their divergence. The methods are compared using two data sets from measurements made independently in two New Zealand laboratories, on two different sets of New Zealand soils, covering a range of textures and parent materials. The Sedigraph method gave systematically greater mass percentages at the four measurement diameters (20, 10, 5 and 2 �m). For one data set, the difference between clay (<2 �m) percentages from the two methods is shown to be positively correlated (R2 = 0.625) with total iron content of the sample, for all but one of the soils. This supports a novel hypothesis that the typically greater concentration of Fe (a strong X-ray absorber) in smaller size fractions is the major factor causing the difference. Regression equations are presented for converting the Sedigraph data to their pipette equivalents.

scholarly journals Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P

Agriculture ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Miles Grafton ◽  
Therese Kaul ◽  
Alan Palmer ◽  
Peter Bishop ◽  
Michael White
Keyword(s):  
Soil Nutrient ◽  
Large Data ◽  
Technical Note ◽  
Soil Samples ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
Hill Country ◽  
Olsen P

This work examines two large data sets to demonstrate that hyperspectral proximal devices may be able to measure soil nutrient. One data set has 3189 soil samples from four hill country pastoral farms and the second data set has 883 soil samples taken from a stratified nested grid survey. These were regressed with spectra from a proximal hyperspectral device measured on the same samples. This aim was to obtain wavelengths, which may be proxy indicators for measurements of soil nutrients. Olsen P and pH were regressed with 2150 wave bands between 350 nm and 2500 nm to find wavebands, which were significant indicators. The 100 most significant wavebands for each proxy were used to regress both data sets. The regression equations from the smaller data set were used to predict the values of pH and Olsen P to validate the larger data set. The predictions from the equations from the smaller data set were as good as the regression analyses from the large data set when applied to it. This may mean that, in the future, hyperspectral analysis may be a proxy to soil chemical analysis; or increase the intensity of soil testing by finding markers of fertility cheaply in the field.

Effect of Ultrasonography in the Assessment of Carotid Artery Stenosis

Vascular ◽  
2005 ◽  
Vol 13 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Sergio X. Salles-Cunha ◽  
Enrico Ascher ◽  
Anil P. Hingorani ◽  
Natalia Markevich ◽  
Richard W. Schutzer ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Internal Carotid Artery ◽  
Carotid Artery Stenosis ◽  
Internal Carotid ◽  
Artery Stenosis ◽  
Regression Equations ◽  
Velocity Data ◽  
Data Set ◽  
Natural Logarithm ◽  
The Relationship

Although ultrasonography (US) advantageously portrays lumen and wall thickness, velocity criteria have been used primarily to interpret carotid artery stenosis. The relationship of US and velocity measurements was investigated. Peak-systolic and end-diastolic velocities (PSV, EDV) increase exponentially as the lumen of the internal carotid artery narrows and the percent stenosis (%S) increases. We tested the consistency of the relationship between carotid velocities and US %S in two distinct data sets. One data set was used to obtain regression equations relating velocity parameters and %S based on US. Validation of these equations was conducted using a separate, independent data set. US measurements were classified in 12 %S intervals. PSV, EDV, the ratio of the internal carotid artery to the common carotid artery PSV, and %S were entered consecutively until 10 records for each %S interval were obtained. Regression equations obtained in the first data set were used to predict %S in the second data set. Predicted %S was then compared with actual US %S. The highest correlation in the first data set ( r = .89) was between %S and the natural logarithm (ln) of PSV. This ln PSV -%S equation was then applied to a second data set of an additional 120 carotid duplex images. In the second data set, actual %S and PSV–predicted %S differed by > 10% in 38 cases (32%). When all velocity-%S regression equations were used for comparison, differences between actual and at least one velocity-predicted %S were > 10% in 19% of the arteries. Conversely, actual %S matched at least one prediction of %S based on velocity data in 81% of the cases. US %S differed significantly from single velocity-based estimates of %S in at least one-third of the cases. On the other hand, four of five US measurements were confirmed by at least one velocity parameter. Emphasis on US, in addition to velocity data, is recommended for the interpretation of duplex US carotid examinations.

scholarly journals Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

2019 ◽  
Vol 51 (4) ◽  
pp. 167-179
Author(s):  
Marcin Pietroń
Keyword(s):  
Spatial Data ◽  
Large Data ◽  
Relational Model ◽  
Data Sets ◽  
Data Set ◽  
Nosql Databases ◽  
Long Time ◽  
The Right ◽  
Spatial Operators ◽  
Gis System

Abstract Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer. Solutions based on the relational model have been the standard for a long time, but there is a new increasingly popular technological trend – solutions based on the NoSQL database which have many advantages in the context of processing of large data sets. This paper compares the performance of selected spatial relational and NoSQL databases executing queries with selected spatial operators. It has been hypothesised that a non-relational solution will prove to be more effective, which was confirmed by the results of the study. The same spatial data set was loaded into PostGIS and MongoDB databases, which ensured standardisation of data for comparison purposes. Then, SQL queries and JavaScript commands were used to perform specific spatial analyses. The parameters necessary to compare the performance were measured at the same time. The study’s results have revealed which approach is faster and utilises less computer resources. However, it is difficult to clearly identify which technology is better because of a number of other factors which have to be considered when choosing the right tool.

Environmental factors influencing maturation and release of ascospores of Venturia pirina in Victoria, Australia

2001 ◽  
Vol 52 (8) ◽  
pp. 825 ◽  
Author(s):  
O. N. Villalta ◽  
W. S. Washington ◽  
G. M. Rimmington ◽  
W. E. MacHardy
Keyword(s):  
Linear Equations ◽  
Management Program ◽  
Data Sets ◽  
Regression Equations ◽  
Degree Days ◽  
Night Time ◽  
Cumulative Percentage ◽  
Pear Scab ◽  
Linear Relationships ◽  
Venturia Pirina

The influence of moisture, light, and temperature on Venturia pirina ascospore maturation and discharge was studied during 1992–99 in 2 pear-growing regions in Victoria. In each year and site, mature ascospores were trapped over a 3-month period beginning a few days before or at the green-tip stage of pear tree development in early September and ending in late November, with the majority of ascospores ((>80%) trapped between green-tip and petal-fall. Ascospore discharge was associated with rain and dew, with 90–98% of the season’s total number of ascospores trapped during rain events and 2–10% trapped during dew events in the 12 data sets examined. Most ascospores were trapped (82.5– 99.9%) during daytime (0600–1800 hours). The 0.1–17.5% of ascospores detected during night time (1900–0500 hours) were trapped mainly within 1–3 h of dawn or dusk. There were linear relationships between the logit of cumulative percentage of ascospore maturation and temperature accumulation (above 0 degree-days), calculated both daily and for days with >= 0.2 mm of rainfall. Six linear regression equations were formulated with 10 years of field data and using the 2 methods of accumulating degree-days, to predict the cumulative percentage of matured ascospores. Predictions were compared with additional field and laboratory observations not used in the formulation of the linear equations. The importance of the temperature-based linear equations is discussed in relation to the prediction of pear scab ascospore maturity for use in a pear scab management program.

Determination of Optimal Clusters Using a Genetic Algorithm

2008 ◽  
pp. 98-117 ◽  
Author(s):  
Tushar ◽  
Tushar ◽  
Shibendu Shekhar Roy ◽  
Dilip Kumar Pratihar
Keyword(s):  
Genetic Algorithm ◽  
Threshold Value ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Fcm Algorithm ◽  
Data Points ◽  
The Relationship

Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters based on the similarity among themselves. Clusters may be either crisp or fuzzy in nature. The present chapter deals with clustering of some data sets using Fuzzy C-Means (FCM) algorithm and Entropy-based Fuzzy Clustering (EFC) algorithm. In FCM algorithm, the nature and quality of clusters depend on the pre-defined number of clusters, level of cluster fuzziness and a threshold value utilized for obtaining the number of outliers (if any). On the other hand, the quality of clusters obtained by the EFC algorithm is dependent on a constant used to establish the relationship between the distance and similarity of two data points, a threshold value of similarity and another threshold value used for determining the number of outliers. The clusters should ideally be distinct and at the same time compact in nature. Moreover, the number of outliers should be as minimum as possible. Thus, the above problem may be posed as an optimization problem, which will be solved using a Genetic Algorithm (GA). The best set of multi-dimensional clusters will be mapped into 2-D for visualization using a Self-Organizing Map (SOM).

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