scholarly journals Growth comparison of several Escherichia coli strains exposed to various concentrations of lactoferrin using linear spline regression

2012 ◽  
Vol 2 (1) ◽  
pp. 5 ◽  
Author(s):  
Camilla Sekse ◽  
Jon Bohlin ◽  
Eystein Skjerve ◽  
Gerd E Vegarud
Keyword(s):  
Escherichia Coli ◽  
Spline Regression ◽  
Linear Spline

scholarly journals Development and Validation of a New Walking Pace Function Using Crowd-Sourced GPS Data

2017 ◽  
Author(s):  
Paul D.P. Pharoah
Keyword(s):  
Spline Function ◽  
Gps Data ◽  
Spline Regression ◽  
Elevation Change ◽  
Linear Spline ◽  
Pace Function ◽  
Gps Devices ◽  
Development And Validation ◽  
Walking Time ◽  
Segment Duration

AbstractThere are several functions that hikers can use to predict walking time based on elevation change or slope of the ground. The most commonly used is the Naismith function that was first published over 100 years ago. The availability of GPS devices to record tracks now make it possible to evaluate the performance of walking time functions. Four data sources were used: 98 tracks downloaded from the Wikiloc web site; 55 tracks recorded by the author; 19 tracks recorded by the blogger Iron Hiker; and 20 tracks recorded by the blogger Hiking Guy. The ․gpx files were processed to generate segements of ~100m in length, with the associated segment duration and elevation change. The association between walking pace and elevation change was assessed in the Wikiloc data using linear spline regression. The performance of the linear spline function was then compared with the Naismith, Tobler and Laingmuir functions. The linear spline performed the best, but all four performed reasonably well. While the linear spline function could easily be programmed into the software of standard GPS devices, the Naismith function provides a simple-to-use rule-of-thumb for estimating walking time for a typical hike in the mountains.FundingNoneAcknowledgementsI thank Chris Hazzard and Keith Wilson for sharing their hiking records and for helpful comments on the manuscript.Disclosure of interestsThe authors have no interest to disclose

scholarly journals Different induction effects of antibiotic on Klebsiella pneumoniae and Escherichia coli: a retrospective study from China

2021 ◽  
Author(s):  
Jing Chen ◽  
Qian Xiang ◽  
Xiao-bo Huang ◽  
Jia-yu Wu ◽  
Min-hong Cai ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Retrospective Study ◽  
Klebsiella Pneumoniae ◽  
Antibiotic Use ◽  
Cubic Spline ◽  
Spline Regression ◽  
Carbapenem Resistant ◽  
Cox Proportional Hazard ◽  
Kaplan Meier ◽  
Rational Use Of Antibiotics

Abstract Background: One of the main factors leading to Carbapenem-resistant Enterobacteriaceae (CRE) is antibiotics usage. The main objective of this study was to assess the correlation between antibiotic use and carbapenem-resistant Klebsiella pneumoniae (CRKP) and carbapenem-resistant Escherichia coli (CREC) induction by antibiotics.Methods: A retrospective study was conducted including patients with Klebsiella pneumoniae (KP) and Escherichia coli (EC) from January 2017 to December 2020. Kaplan-Meier analysis and Cox proportional hazard model were used to estimate the hazard of CRE, while restricted cubic spline regression was used to visualize the hazard of CRE by antibiotics at different doses.Results: 2056 patients with KP and 3243 patients with EC were included. Survival analysis indicated that carbapenem, lactamase inhibitors and quinolones were associated with higher 30-day CRKP hazards than other antibiotics (χ2=33.670, P<0.001). Further restricted cubic spline regression analysis found that the hazard of CRKP induction decreased with the increase dose of lactamase inhibitors, but there was no significant change in that with the increase dose of quinolones. Moreover, there was an obvious characteristic of "parabolic curve" for the hazard of CREC induction due to lactamase inhibitors, and the hazard value gradually increased with the dose, reached the maximum at 24g, and then gradually decreased from 26g.Conclusions: Lactamase inhibitors had different drug resistance inducing effects on CRKP and CREC. Rational use of antibiotics should be implemented according to the characteristics of microorganism.

scholarly journals Pemodelan Pengaruh Imunisasi DPT Terhadap Angka Kematian Bayi di Jawa Timur Tahun 2016 Menggunakan Pendekatan Regresi Nonparametrik Spline

2019 ◽  
Vol 12 (1) ◽  
pp. 26-31
Author(s):  
Harun Al Azies ◽  
Dea Trishnanti
Keyword(s):  
Regression Analysis ◽  
Nonparametric Regression ◽  
Mortality Rates ◽  
Behavior Patterns ◽  
Spline Regression ◽  
Nonparametric Regression Model ◽  
Live Births ◽  
Linear Spline ◽  
Regression Approach ◽  
Type 3

East Java is one of the provinces with a high IMR level. Based on the District / City report in East Java, in 2006 it was 0.035 live births and became 0.0032 live births in 2008. Identification of factors that influence both indicators correctly can be done by modeling, namely by nonparametric regression analysis. The nonparametric regression approach used is Spline, with its strengths the model tends to look for estimates wherever the data moves. This is because there is a knot point which is a joint fusion point which indicates a change in data behavior patterns. Based on the results of analysis and discussion using Spline analysis, it is known that the factors that influence the incidence of IMR in East Java are toddlers receiving type 3 DPT immunization. The best Spline nonparametric regression model is a linear Spline model with three point knots. The GCV value produced was 51.34. Factors of children under five obtained immunizations affecting infant mortality rates in districts / cities in East Java in 2016. This research still uses linear spline regression program with a combination of one, two, and three knots with R square of 65.92%. The need to develop programs into quadratic and cubic orders using a combination of knots. Jawa Timur merupakan salah satu provinsi dengan tingkat AKB yang tinggi. Berdasarkan laporan Kabupaten/Kota di Jawa Timur, pada tahun 2006 sebesar 0,035 kelahiran hidup dan menjadi 0,0032 kelahiran hidup pada tahun 2008. Jika suatu daerah dengan AKB yang tinggi, maka terdapat kemungkinan bahwa daerah sekitarnya akan memiliki beban AKB yang sama pula. Identifikasi faktor-faktor yang mempengaruhi kedua indikator secara tepat dapat dilakukan dengan pemodelan, yaitu dengan analisis regresi nonparametrik. Pendekatan regresi nonparametric yang digunakan adalah Spline, dengan kelebihannya model cenderung mencari estimasinya kemanapun data tersebut bergerak. Hal ini dikarenakan terdapat titik knot yang merupakan titik perpaduan bersama yang menunjukkan terjadinya perubahan pola perilaku data. Berdasarkan hasil analisis dan pembahasan dengan menggunakan analisis Spline diketahui bahwa faktor yang berpengaruh terhadap kejadian AKB di Jawa Timur adalah balita memperoleh imunisasi DPT tipe 3. Model regresi nonparametrik Spline terbaik adalah model Spline linear dengan tiga titik knot. Nilai GCV yang dihasilkan adalah 51,34. Faktor balita memperoleh imunisasi mempengaruhi angka kematian bayi di kabupaten/kota di Jawa Timur pada tahun 2016. Penelitian ini masih menggunakan program regresi spline linier dengan kombinasi satu, dua, dan tiga knot dengan R square sebesar 65,92%. Perlu adanya pengembangan program menjadi orde kuadratik dan kubik dengan menggunakan kombinasi knot.    

Adaptively refined dynamic program for linear spline regression

2014 ◽  
Vol 58 (3) ◽  
pp. 523-541 ◽  
Author(s):  
Noam Goldberg ◽  
Youngdae Kim ◽  
Sven Leyffer ◽  
Thomas D. Veselka
Keyword(s):  
Dynamic Program ◽  
Spline Regression ◽  
Linear Spline

scholarly journals Pemodelan Regresi Nonparametrik Spline Linear Persentase Penduduk Miskin di Kalimantan

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Wianita Noviani ◽  
◽  
Sifriyani Sifriyani ◽  
Ika Purnamasari ◽  
◽  
...  
Keyword(s):  
Participation Rate ◽  
Regression Method ◽  
Labor Force Participation Rate ◽  
Poor People ◽  
Spline Regression ◽  
Nonparametric Regression Model ◽  
Linear Spline ◽  
East Kalimantan ◽  
Average School ◽  
Poor Population

Poverty is a social problem faced in almost every country. Based on BPS data published in 2018, East Kalimantan Province has a population of poor people of 222.39 thousand people or around 6.06 percent. In March 2018, the number of poor people was 218.90 thousand people or about 6.03 percent, which means the number of poor people had increased by an absolute 3.49 thousand people, this caused the percentage of poor people to rise 0.03 percent. In this study identified factors that influence the percentage of poor population using a linear spline nonparametric regression model. The data used in this study are data from the Central Statistics Agency (BPS) in 5 provinces in Kalimantan. In the nonparametric linear spline regression method using the optimal knot point based on the smallest GCV value. The results obtained an R2 value of 74.48% which shows that the model formed is feasible to be used to model the data pattern and there are 5 variables that have a significant effect on the Percentage of Poor Population, namely Population Growth Rate, School Length Average, School Old School Expectancy Rate, Level Open Unemployment, Labor Force Participation Rate.

Structural organization of escherichia coli ribosomes as determined by immuno electron microscopy

1978 ◽  
Vol 36 (2) ◽  
pp. 166-167 ◽  
Author(s):  
G. Stöffler ◽  
R.W. Bald ◽  
J. Dieckhoff ◽  
H. Eckhard ◽  
R. Lührmann ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Escherichia Coli ◽  
Ribosomal Proteins ◽  
Structural Organization ◽  
Three Dimensional ◽  
Ribosomal Subunit ◽  
Spatial Arrangement ◽  
Small Subunit ◽  
Antibody Binding ◽  
Immuno Electron Microscopy

A central step towards an understanding of the structure and function of the Escherichia coli ribosome, a large multicomponent assembly, is the elucidation of the spatial arrangement of its 54 proteins and its three rRNA molecules. The structural organization of ribosomal components has been investigated by a number of experimental approaches. Specific antibodies directed against each of the 54 ribosomal proteins of Escherichia coli have been performed to examine antibody-subunit complexes by electron microscopy. The position of the bound antibody, specific for a particular protein, can be determined; it indicates the location of the corresponding protein on the ribosomal surface.The three-dimensional distribution of each of the 21 small subunit proteins on the ribosomal surface has been determined by immuno electron microscopy: the 21 proteins have been found exposed with altogether 43 antibody binding sites. Each one of 12 proteins showed antibody binding at remote positions on the subunit surface, indicating highly extended conformations of the proteins concerned within the 30S ribosomal subunit; the remaining proteins are, however, not necessarily globular in shape (Fig. 1).

Sites of Adsorption of the Bacteriophages T3 and T5 on the Wall of Escherichia Coli B.

1967 ◽  
Vol 25 ◽  
pp. 96-97
Author(s):  
Manfred E. Bayer
Keyword(s):  
Escherichia Coli ◽  
Cell Wall ◽  
Electron Microscope ◽  
Uranyl Acetate ◽  
E Coli ◽  
Receptor Sites ◽  
Rigid Cell Wall ◽  
Host Cell Surface ◽  
Bacterial Viruses ◽  
Escherichia Coli B

Bacterial viruses adsorb specifically to receptors on the host cell surface. Although the chemical composition of some of the cell wall receptors for bacteriophages of the T-series has been described and the number of receptor sites has been estimated to be 150 to 300 per E. coli cell, the localization of the sites on the bacterial wall has been unknown.When logarithmically growing cells of E. coli are transferred into a medium containing 20% sucrose, the cells plasmolize: the protoplast shrinks and becomes separated from the somewhat rigid cell wall. When these cells are fixed in 8% Formaldehyde, post-fixed in OsO4/uranyl acetate, embedded in Vestopal W, then cut in an ultramicrotome and observed with the electron microscope, the separation of protoplast and wall becomes clearly visible, (Fig. 1, 2). At a number of locations however, the protoplasmic membrane adheres to the wall even under the considerable pull of the shrinking protoplast. Thus numerous connecting bridges are maintained between protoplast and cell wall. Estimations of the total number of such wall/membrane associations yield a number of about 300 per cell.

Infection of Escherichia coli with bacteriophages

1969 ◽  
Vol 27 ◽  
pp. 370-371
Author(s):  
Manfred E. Bayer
Keyword(s):  
Escherichia Coli ◽  
Nucleic Acid ◽  
Cell Surface ◽  
Virus Type ◽  
Infection Process ◽  
Adsorption Site ◽  
The Other ◽  
Specific Receptors ◽  
Bacterial Receptors

The first step in the infection of a bacterium by a virus consists of a collision between cell and bacteriophage. The presence of virus-specific receptors on the cell surface will trigger a number of events leading eventually to release of the phage nucleic acid. The execution of the various "steps" in the infection process varies from one virus-type to the other, depending on the anatomy of the virus. Small viruses like ØX 174 and MS2 adsorb directly with their capsid to the bacterial receptors, while other phages possess attachment organelles of varying complexity. In bacteriophages T3 (Fig. 1) and T7 the small conical processes of their heads point toward the adsorption site; a welldefined baseplate is attached to the head of P22; heads without baseplates are not infective.

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