Annual, Local, and Individual Variation in the Inflorescence and Fruit Production of Eastern Leatherwood (Dirca palustris L. Thymelaeaceae)

2004 ◽  
Vol 131 (4) ◽  
pp. 292 ◽  
Author(s):  
Kurt Schulz ◽  
John Zasada ◽  
Elizabeth Nauertz
2018 ◽  
Vol 41 ◽  
Author(s):  
Benjamin C. Ruisch ◽  
Rajen A. Anderson ◽  
David A. Pizarro

AbstractWe argue that existing data on folk-economic beliefs (FEBs) present challenges to Boyer & Petersen's model. Specifically, the widespread individual variation in endorsement of FEBs casts doubt on the claim that humans are evolutionarily predisposed towards particular economic beliefs. Additionally, the authors' model cannot account for the systematic covariance between certain FEBs, such as those observed in distinct political ideologies.


2019 ◽  
Vol 42 ◽  
Author(s):  
Emily F. Wissel ◽  
Leigh K. Smith

Abstract The target article suggests inter-individual variability is a weakness of microbiota-gut-brain (MGB) research, but we discuss why it is actually a strength. We comment on how accounting for individual differences can help researchers systematically understand the observed variance in microbiota composition, interpret null findings, and potentially improve the efficacy of therapeutic treatments in future clinical microbiome research.


Author(s):  
M. Marko ◽  
A. Leith ◽  
D. Parsons

The use of serial sections and computer-based 3-D reconstruction techniques affords an opportunity not only to visualize the shape and distribution of the structures being studied, but also to determine their volumes and surface areas. Up until now, this has been done using serial ultrathin sections.The serial-section approach differs from the stereo logical methods of Weibel in that it is based on the Information from a set of single, complete cells (or organelles) rather than on a random 2-dimensional sampling of a population of cells. Because of this, it can more easily provide absolute values of volume and surface area, especially for highly-complex structures. It also allows study of individual variation among the cells, and study of structures which occur only infrequently.We have developed a system for 3-D reconstruction of objects from stereo-pair electron micrographs of thick specimens.


1986 ◽  
Vol 56 (03) ◽  
pp. 371-375 ◽  
Author(s):  
Peretz Weiss ◽  
Hillel Halkin ◽  
Shlomo Almog

SummaryWithin-individual variation over time in the clearance (Cl) and effect (PT%) of warfarin, was measured in 25 inpatients (group I) studied after standard single or individualized split loading doses and 1-3 times (n = 16) 8-16 weeks later during maintenance. Mean Cl (2.5 α 0.9 ml/min) was similar in both phases but significant changes occurred in 6/16 patients, exceeding those expected from within-individual variation alone (defined by its 95% tolerance limits -24% to +62%). Initial PT% (21 α 5) was unaffected by dosing schedule, total or free plasma warfarin, varying between patients by only 18-24%. Mean initial and maintenance dose-PT% ratios (8.2 mg/d: 21% and 4.1 mg/d: 40%) were similar but significant changes in sensitivity to warfarin occurred in 4/16 patients. In group I and 64 other outpatients on maintenance therapy, between-individual variability was 36-52% for Cl and 49-56% for effect. PT% correlated best (r = 0.56) with free and total plasma warfarin but poorly with dose (r = 0.29), with only 30% of PT% variance explained at best, due to high between patient variability.Warfarin dose prediction whether based on extrapolation from initial effects to the maintenance phase, or on iterative methods not allowing for between- or within-patient variation in warfarin clearance or effect which may occur independently over time, have not improved on empirical therapy. This, due to the elements of biological variability as well as the intricacy of the warfarin - prothrombin complex interaction not captured by any kinetic-dynamic model used for prediction to date.


2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra


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