The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Characterization of the role of Drosophila JAK/STAT signalling in cellular proliferation

Der Janus Kinase / signal transducer and activator of transcription (JAK/STAT) Signal- transduktionsweg wird für viele Entwicklungsvorgänge benötigt und spielt eine zentrale Rolle bei der Hämatopoese und bei der Immunantwort. Obwohl der JAK/STAT-Signalweg in den vergangenen Jahren Gegenstand intensiver Forschung war, erschwert die Redundanz des Signalwegs bei Wirbeltieren genetische Untersuchungen zur Identifizierung derjenigen Mechanismen, die den JAK/STAT-Signalweg regulieren. Der JAK/STAT-Signaltransduktionsweg ist evolutionär konserviert und ebenfalls bei der Taufliege Drosophila melanogaster vorhanden. Im Gegensatz zu Wirbeltieren ist der Signaltransduktionsweg von Drosophila weniger redundant und beinhaltet folgende Hauptkomponenten: den Liganden Unpaired (Upd), den Transmembranrezeptor Domeless (Dome), die einzige JAK-Tyrosinkinase Hopscotch (hop), sowie den Transkriptionsfaktor STAT92E. In der vorliegenden Arbeit wird die Rolle des JAK/STAT-Signalwegs bei der zellulären Proliferation mithilfe der Modellsysteme der Flügel- und der Augen-Imaginalscheiben von Drosophila charakterisiert. "Loss-of-function"- und "Gain-of-function"-Experimente zur Verminderung beziehungs-weise Erhöhung der Signalaktivität zeigten, dass der JAK/STAT-Signalweg eine Rolle bei der zellulären Proliferation der Flügel-Imaginalscheiben spielte, ohne die Zellgröße oder Apoptose zu verändern. Bei der Flügelentwicklung während des zweiten und des frühen dritten Larvalstadiums war die Aktivität des JAK/STAT-Signalwegs sowohl notwendig für die zelluläre Proliferation als auch hinreichend, um Überproliferation anzutreiben. Allerdings änderte sich während der späten dritten Larvalstadien die JAK/STAT-Signalaktivität, sodass endogene STAT92E-Mengen einen anti-proliferativen Effekt im gleichen Gewebe aufwiesen. Weiterhin reichte die ektopische Aktivierung des JAK/STAT-Signalwegs zu diesem späten Entwicklungszeitpunkt aus, um die Mitose zu inhibieren und die Zellen in der Phase G2 des Zellzyklus zu arretieren. Diese Ergebnisse legen den Schluss nahe, dass der JAK/STAT-Signalweg sowohl pro-proliferativ in frühen Flügelscheiben als auch anti-proliferativ zu späten Stadien der Flügelscheiben-Entwicklung wirken kann. Dieser späte anti-proliferative Effekt wurde durch einen nicht-kanonischen Mechanismus der STAT92E-Aktivierung vermittelt, da späte hop defiziente Zellverbände im Vergleich zu Wildtyp-Zellen keine Veränderungen im Ausmaß der zellulären Proliferation aufwiesen. Ferner konnte gezeigt werden, dass eine während der Larvalstadien exprimierte dominant-negative und im N-Terminus deletierte Form von STAT92E (?NSTAT92E) nicht für den anti-proliferativen Effekt verantwortlich ist. Diese Tatsache ist ein weiteres Indiz dafür, dass das vollständige STAT92E den späten anti-proliferativen Effekt verursacht. Um Modulatoren für die von JAK/STAT vermittelte zelluläre Proliferation zu identifieren, wurde ein P-Element-basierter genetischer Interaktions-Screen in einem sensibilisierten genetischen Hintergrund durchgeführt. Insgesamt wurden dazu 2267 unabhängige P-Element-Insertionen auf ihre Wechselwirkung mit der JAK/STAT-Signalaktivität untersucht und 24 interagierende Loci identifiziert. Diese Kandidaten können in folgende Gruppen eingeordnet werden: Zellzyklusproteine, Transkriptionsfaktoren, DNA und RNA bindende Proteine, ein Mikro-RNA-Gen, Komponenten anderer Signaltransduktionswege und Zelladhäsionsproteine. In den meisten Fällen wurden mehrere Allele der interagierenden Kandidatengene getestet. 18 Kandidatengene mit übereinstimmend interagierenden Allelen wurden dann zur weiteren Analyse ausgewählt. Von diesen 18 Kandidaten-Loci wurden 7 mögliche JAK/STAT-Signalwegskomponenten und 6 neue Zielgene des Signalwegs gefunden. Zusammenfassend wurde das Verständnis um STAT92E verbessert. Dieses Protein hat die gleiche Funktion wie das STAT3-Protein der Wirbeltiere und treibt die zelluläre Proliferation voran. Analog zu STAT1 hat STAT92E aber auch einen anti-proliferativen Effekt. Ferner wurden 24 mögliche Modulatoren der JAK/STAT-Signalaktivität identifiziert. Die Charakterisierung dieser Wechselwirkungen eröffnet vielversprechende Wege zu dem Verständnis, wie JAK/STAT die zelluläre Proliferation reguliert und könnte bei der Entwicklung von neuartigen therapeutischen Targets zur Behandlung von Krebskrankheiten und Entwicklungsstörungen beitragen.

Identification of Problem-based learning components and their effects on graduates' competencies

This study investigated the relationship between higher education and the requirement of the world of work with an emphasis on the effect of problem-based learning (PBL) on graduates' competencies. The implementation of full PBL method is costly (Albanese & Mitchell, 1993; Berkson, 1993; Finucane, Shannon, & McGrath, 2009). However, the implementation of PBL in a less than curriculum-wide mode is more achievable in a broader context (Albanese, 2000). This means higher education institutions implement only a few PBL components in the curriculum. Or a teacher implements a few PBL components at the courses level. For this kind of implementation there is a need to identify PBL components and their effects on particular educational outputs (Hmelo-Silver, 2004; Newman, 2003). So far, however there has been little research about this topic. The main aims of this study were: (1) to identify each of PBL components which were manifested in the development of a valid and reliable PBL implementation questionnaire and (2) to determine the effect of each identified PBL component to specific graduates' competencies. The analysis was based on quantitative data collected in the survey of medicine graduates of Gadjah Mada University, Indonesia. A total of 225 graduates responded to the survey. The result of confirmatory factor analysis (CFA) showed that all individual constructs of PBL and graduates' competencies had acceptable GOFs (Goodness-of-fit). Additionally, the values of the factor loadings (standardize loading estimates), the AVEs (average variance extracted), CRs (construct reliability), and ASVs (average shared squared variance) showed the proof of convergent and discriminant validity. All values indicated valid and reliable measurements. The investigation of the effects of PBL showed that each PBL component had specific effects on graduates' competencies. Interpersonal competencies were affected by Student-centred learning (β = .137; p < .05) and Small group components (β = .078; p < .05). Problem as stimulus affected Leadership (β = .182; p < .01). Real-world problems affected Personal and organisational competencies (β = .140; p < .01) and Interpersonal competencies (β = .114; p < .05). Teacher as facilitator affected Leadership (β = 142; p < .05). Self-directed learning affected Field-related competencies (β = .080; p < .05). These results can help higher education institution and educator to have informed choice about the implementation of PBL components. With this information higher education institutions and educators could fulfil their educational goals and in the same time meet their limited resources. This study seeks to improve prior studies' research method in four major ways: (1) by indentifying PBL components based on theory and empirical data; (2) by using latent variables in the structural equation modelling instead of using a variable as a proxy of a construct; (3) by using CFA to validate the latent structure of the measurement, thus providing better evidence of validity; and (4) by using graduate survey data which is suitable for analysing PBL effects in the frame work of the relationship between higher education and the world of work.

A Survey of the Relationship between Climatic Heat Stress Indices and Fundamental Milk Components Considering Uncertainty

The main purpose of this study is to assess the relationship between four bioclimatic indices for cattle (environmental stress, heat load, modified heat load, and respiratory rate predictor indices) and three main milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when the cows use the natural pasture. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty information in the confidence intervals. The main results identify an interesting relationship between the milk compounds and climate indices under all climate conditions. During spring, there are reasonably high correlations between the fat and protein concentrations vs. the climate indices, whereas there are insignificant dependencies between the milk yield and climate indices. During summer, the correlation between the fat and protein concentrations with the climate indices decreased in comparison with the spring results, whereas the correlation for the milk yield increased. This methodology is suggested for studies investigating the impacts of climate variability/change on food and agriculture using short term data considering uncertainty.