scholarly journals Application of multiblock modelling to identify key drivers for antimicrobial use in pig production in four European countries

2018 ◽  
Vol 146 (8) ◽  
pp. 1003-1014 ◽  
Author(s):  
L. Collineau ◽  
S. Bougeard ◽  
A. Backhans ◽  
J. Dewulf ◽  
U. Emanuelson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Partial Least Squares Regression ◽  
Clinical Signs ◽  
Antimicrobial Use ◽  
Least Squares Regression ◽  
Pig Production ◽  
Vaccination Scheme ◽  
Key Drivers ◽  
Representative Samples ◽  
Country Specific

AbstractAntimicrobial use in pig farming is influenced by a range of risk factors, including herd characteristics, biosecurity level, farm performance, occurrence of clinical signs and vaccination scheme, as well as farmers’ attitudes and habits towards antimicrobial use. So far, the effect of these risk factors has been explored separately. Using an innovative method called multiblock partial least-squares regression, this study aimed to investigate, in a sample of 207 farrow-to-finish farms from Belgium, France, Germany and Sweden, the relative importance of the six above mentioned categories or ‘blocks’ of risk factors for antimicrobial use in pig production. Four country separate models were developed; they showed that all six blocks provided useful contribution to explaining antimicrobial use in at least one country. The occurrence of clinical signs, especially of respiratory and nervous diseases in fatteners, was one of the largest contributing blocks in all four countries, whereas the effect of the other blocks differed between countries. In terms of risk management, it suggests that a holistic and country-specific mitigation strategy is likely to be more effective. However, further research is needed to validate our findings in larger and more representative samples, as well as in other countries.

scholarly journals Risk Factors for Antimicrobial Use on Irish Pig Farms

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2828
Author(s):  
Lorcan O’Neill ◽  
Julia Adriana Calderón Díaz ◽  
Maria Rodrigues da Costa ◽  
Sinnead Oakes ◽  
Finola C. Leonard ◽  
...  
Keyword(s):  
Risk Factors ◽  
Swine Influenza ◽  
Antimicrobial Use ◽  
Respiratory Pathogens ◽  
Linear Regression Models ◽  
Pig Production ◽  
Pig Farms ◽  
Prophylactic Use ◽  
And Performance ◽  
Biosecurity Practices

The threat to public health posed by antimicrobial resistance in livestock production means that the pig sector is a particular focus for efforts to reduce antimicrobial use (AMU). This study sought to investigate the risk factors for AMU in Irish pig production. Antimicrobial use data were collected from 52 farrow-to-finish farms. The risk factors investigated were farm characteristics and performance, biosecurity practices, prevalence of pluck lesions at slaughter and serological status for four common respiratory pathogens and vaccination and prophylactic AMU practices. Linear regression models were used for quantitative AMU analysis and risk factors for specific AMU practices were investigated using logistic regression. Farms that milled their own feed had lower total AMU (p < 0.001), whereas higher finisher mortality (p = 0.043) and vaccinating for swine influenza (p < 0.001) increased AMU. Farms with higher prevalence of pericarditis (p = 0.037) and lung abscesses (p = 0.046) used more group treatments. Farms with higher prevalence of liver milk spot lesions (p = 0.018) and farms practising prophylactic AMU in piglets (p = 0.03) had higher numbers of individual treatments. Farms practising prophylactic AMU in piglets (p = 0.002) or sows (p = 0.062) had higher use of cephalosporins and fluoroquinolones. This study identified prophylactic use and respiratory disease as the main drivers for AMU in Irish pig production. These findings highlight areas of farm management where interventions may aid in reducing AMU on Irish pig farms.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

scholarly journals Low Maternal Immunoglobulin G Avidity and Single Parity as Adverse Implications of Human Cytomegalovirus Vertical Transmission in Pregnant Women with Immunoglobulin M Positivity

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 866
Author(s):  
Masatoki Kaneko ◽  
Junsuke Muraoka ◽  
Kazumi Kusumoto ◽  
Toshio Minematsu
Keyword(s):  
Risk Factors ◽  
Pregnant Women ◽  
Vertical Transmission ◽  
Human Cytomegalovirus ◽  
Multiple Logistic Regression Analysis ◽  
Clinical Signs ◽  
Immunoglobulin M ◽  
Igg Antibody ◽  
Neurological Sequelae ◽  
Previous Pregnancy

Human cytomegalovirus (CMV) is the leading cause of neurological sequelae in infants. Understanding the risk factors of primary CMV infection is crucial in establishing preventive strategies. Thus, we conducted a retrospective cohort study to identify risk factors of vertical transmission among pregnant women with immunoglobulin (Ig) M positivity. The study included 456 pregnant women with IgM positivity. Information on age, parity, occupation, clinical signs, IgM levels, and IgG avidity index (AI) was collected. The women were divided into infected and non-infected groups. The two groups showed significant differences in IgM level, IgG AI, number of women with low IgG AI, clinical signs, and number of pregnant women with single parity. In the multiple logistic regression analysis, pregnant women with single parity and low IgG AI were independent predictors. Among 40 women who tested negative for IgG antibody in their previous pregnancy, 20 showed low IgG AI in their current pregnancy. Among the 20 women, 4 had vertical transmission. These results provide better understanding of the risk factors of vertical transmission in pregnant women with IgM positivity.

Photochemical stability of warfarin potassium in powdered pharmaceutical tablets

2021 ◽  
Vol 32 (2) ◽  
pp. 115-129
Author(s):  
Emi Horiguchi-Babamoto ◽  
Makoto Otsuka
Keyword(s):  
High Performance ◽  
Partial Least Squares Regression ◽  
Pediatric Patients ◽  
Reversed Phase ◽  
Least Squares Regression ◽  
Brand Name ◽  
Pharmaceutical Tablets ◽  
Light Stability ◽  
Warfarin Potassium ◽  
The Stability

BACKGROUND: Warfarin potassium (Wf) commercial tablets originally formulated for adults are ground before administration to pediatric patients and elderly patients with dysphagia. OBJECTIVE: The present study investigated the effect of tablet grinding on the photostability of four types of commercial Wf tablets and predicted the photostability of the tablet powders by chemometric analysis. METHODS: The photodegradation of Wf content was evaluated by reversed-phase column high-performance liquid chromatography with ultraviolet (HPLC-UV). RESULTS: The bulk Wf powder was relatively photostable, whereas ground Wf tablets underwent substantial photodegradation. The photostability of the ground powders of a brand-name Wf commercial tablet and three generic Wf commercial tablets was quantitatively assessed and compared. In certain cases, the Wf in all the three ground generic tablets was less photostable than in the ground brand-name tablets. After 28 days of light irradiation, the Wf content decreased to 69.79% in the brand-name tablets, while it was 31.90% in some generic tablets. To clarify the factors influencing the relative photostability in various Wf formulations, we analyzed the intermolecular interactions between the active ingredient and the excipients by partial least-squares regression analysis based on photostability screening for each additive. CONCLUSION: The results suggested that the additives light anhydrous silicic acid and povidone adversely affect the stability of Wf tablets. In addition, the light stability of ground tablets was affected considerably by their formulation.

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