Interclutch variability in egg characteristics in two species of rail: Is maternal identity encoded in eggshell patterns?

Maternal signatures are present in the eggs of some birds, but quantifying interclutch variability within populations remains challenging. Maternal assignment of eggs with distinctive appearances could be used to non-invasively identify renesting females, including hens returning among years, as well as to identify cases of conspecific brood parasitism. We explored whether King Rail (Rallus elegans) eggs with shared maternity could be matched based on eggshell pattern. We used NaturePatternMatch (NPM) software to match egg images taken in the field in conjunction with spatial and temporal data on nests. Since we had only a small number of marked breeders, we analyzed similar clutch images from a study of Eurasian Common Moorhens (Gallinula chloropus chloropus) with color-banded breeders for which parentage at many nests had been verified genetically to validate the method. We ran 66 King Rail clutches (n = 338 eggs) and 58 Common Moorhen clutches (n = 364 eggs) through NPM. We performed non-metric multidimensional scaling and permutational analysis of variance using the best egg match output from NPM. We also explored whether eggs could be grouped by clutch using a combination of egg dimensions and pattern data derived from NPM using linear discriminant analyses. We then scrutinized specific matches returned by NPM for King Rail eggs to determine whether multiple matches between the same clutches might reveal maternity among nests and inform our understanding of female laying behavior. To do this, we ran separate NPM analyses for clutches photographed over several years from two spatially distant parts of the site. With these narrower datasets, we were able to identify four instances where hens likely returned to breed among years, four likely cases of conspecific brood parasitism, and a within-season re-nesting attempt. Thus, the matching output was helpful in identifying congruent egg patterns among clutches when used in conjunction with spatial and temporal data, revealing previously unrecognized site fidelity, within-season movements, and reproductive interference by breeding females. Egg pattern data in combination with nest mapping can be used to inform our understanding of female reproductive effort, success, and longevity in King Rails. These methods may also be applied to other secretive birds and species of conservation concern.

A Panel Asymmetric Causality Between Health and Climate Change

As climate change threatens human life and health by causing severe storms, floods, temperature fluctuations and droughts, it is predicted that in the coming decades, most of the global population will be impacted and the lives of millions will be at risk. In this context, the article investigates the existence of a symmetric and asymmetric causality between climate change and health between 1990 and 2015 for European countries, including EU, EFTA member and EU candidate states. In the first stage of the analysis, health scores are estimated by cluster and discriminant analyses; in the second stage, the relationships among these scores and climate variables are examined. The country-specific findings are obtained for the health effects of climate change variables according to factors such as geographical structure and seasonal characteristics. According to the results, while the health effects of changes in temperature and greenhouse emissions differ from country to country, the reduction in precipitation for nearly half of the countries is found to have a negative effect on health.

Business management and methods of predictive financial analysis companies

The focus of this presented work is the application of one-dimensional discriminant analysis in specific conditions of economic practice. The research sample of the enterprises has shown, that even these methods can better warn against nearing bankruptcy by predicting whether business will or will not be sustainable. Generally, these discriminant analyses use the financial ratios methods. The future situation of an enterprise can be predicted, among other things, by means of one-dimensional and multidimensional discriminant analysis methods, which are dealt with by several authors. Given the different approaches of authors, one-dimensional discriminant analysis methods that are "older" can be assumed to have a different reliability than multidimensional discriminant analysis methods. The assumptions of our research were verified in a group consisting of prosperous and non-prosperous business entities. The results of the original research show that one-dimensional discriminatory methods had a higher reliability than the multidimensional ones on the sample of enterprises surveyed. At the same time, it has not been established that a 100% reliable method will be found, but it is good to know the assumptions on which these existing methods work and use a combination of multiple methods.

Why Do Agricultural Cooperative Mergers Not Cross the Finishing Line?

Mergers have played a relevant role in the business development of many agri-food cooperatives and have led to the consolidation of large cooperative groups which are leaders in their respective business sectors. However, many of the merger processes undertaken fail: some are aborted at the negotiation stage, and others are not approved by members. These failures entail financial and social costs due to frustrated expectations and the time invested in the negotiation process. The objective of this paper is to establish the economic, socio-cultural, organisational and process management factors that underlie this outcome. A survey was conducted among the directors and administrators of a sample of Spanish agri-food cooperatives that had participated in merger processes which were aborted at the negotiation stage or were not approved by their members. Factor and discriminant analyses established the aspects which had the greatest impact on the failure of the merger processes. Far from being economic factors, these analyses reveal that defensive localisms, a lack of commitment to the merger on the part of members and directors, and communication failures were more significant.

Convalescent Plasma Reduces Mortality and Decreases Hospitalization Stay in Patients with Moderate COVID-19 Pneumonia

Humans infected with SARS-CoV-2 may develop COVID-19, which manifests across a wide spectrum of clinical severity ranging from mild upper respiratory tract illnesses to diffuse viral pneumonia, causing acute respiratory failure. Many therapies have been tested for their efficacy in treating COVID-19. Controversy surrounds convalescent plasma transfusions as an effective treatment for COVID-19. This study discusses the efficacy of this treatment on COVID-19 patients. Electronic medical record data were collected from patients diagnosed with COVID-19, from November 2020 to August 2021, in the Galilee Medical Center’s COVID-19 departments. Epidemiological, clinical, laboratory and imaging variables were analyzed. Multivariate stepwise regression and discriminant analyses were used to identify and validate the correlation between convalescent treatment and either death or time to negative PCR and hospitalization length. The study population included 270 patients, 100 of them treated with convalescent plasma. The results show that convalescent plasma therapy significantly prevented mortality in moderate patients, reduced hospitalization length and time to negative PCR. Additionally, high BMI, elderly age, high CRP and 4C-scores correlated with the severity and mortality of COVID-19 patients. Convalescent plasma also significantly reduced inflammatory markers, especially in moderate COVID-19 patients. In non-critical hospitalized patients, convalescent plasma therapy reduces morbidity and mortality in moderate COVID-19 patients and hospitalization length. Identifying patients who could benefit from this treatment could reduce the risk of death and shorten their hospitalization stay.

Clinical Predictors of Mortality and Critical Illness in Patients with COVID-19 Pneumonia

Background/Aim: Early identification of patients with COVID-19 who will develop severe or critical disease symptoms is important for delivering proper and early treatment. We analyzed demographic, clinical, immunological, hematological, biochemical and radiographic findings that may be of utility to clinicians in predicting COVID-19 severity and mortality. Methods: Electronic medical record data from patients diagnosed with COVID-19 from November 2020 to June 2021 in the COVID-19 Department in the Galilee Medical Center, Nahariya, Israel, were collected. Epidemiologic, clinical, laboratory and imaging variables were analyzed. Multivariate stepwise regression analyses and discriminant analyses were used to identify and validate powerful predictors. The main outcome measure was invasive ventilation, or death. Results: The study population included 390 patients, with a mean age of 61 ± 18, and 51% were male. The non-survivors were mostly male, elderly and overweight and significantly suffered from hypertension, diabetes mellitus type 2, lung disease, hemodialysis and past use of aspirin. Four predictive factors were found that associated with increased disease severity and/or mortality: age, NLR, BUN, and use of high flow oxygen therapy (HFNC). The AUC or diagnostic accuracy was 87%, with a sensitivity of 97%, specificity of 60%, PPV of 87% and NPP of 91%. The cytokine levels of CXCL-10, GCSF, IL-2 and IL-6 were significantly reduced upon the discharge of severely ill COVID-19 patients. Conclusions: The predictive factors associated with increased mortality include age, NLR, BUN, and use of HFNC upon admission. Identifying those with higher risks of mortality could help in early interventions to reduce the risk of death.

Wealthier is healthier? Healthier is wealthier? A comprehensive econometric analysis of the European countries

Healthier people contribute more to the development of the economy. Besides, in a better economy, people have a better quality and healthier life. At this stage, one has to ask which one precedes the other: health or wealth? To find the answer, this study aims to investigate the causality relationship between health and inclusive wealth in the European countries for the period of 1990–2015. The causality between health and inclusive wealth scores, which are estimated by cluster and discriminant analyses, is investigated by the panel causality test. The research results indicate bidirectional causality between health and inclusive wealth. A one-way causality is detected in 11 cases as being from inclusive wealth to health and in 8 the other way. Furthermore, a two-way causality is found in 2 countries. Among the results, it is noteworthy that 91% of the countries with causality from inclusive wealth to health are among the healthy countries.

Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seeds

In this study, eggplant seeds of fifteen different varieties were selected for discriminant analyses with a multispectral imaging technique. Seventy-eight features acquired with the multispectral images were extracted from individual eggplant seeds, which were then classified using SVM and a one-dimensional convolutional neural network (1D-CNN), and the overall accuracy was 90.12% and 94.80%, respectively. A two-dimensional convolutional neural network (2D-CNN) was also adopted for discrimination of seed varieties, and an accuracy of 90.67% was achieved. This study not only demonstrated that multispectral imaging combining machine learning techniques could be used as a high-throughput and nondestructive tool to discriminate seed varieties but also revealed that the shape of the seed shell may not be exactly the same as the female parents due to the genetic and environmental factors.

Business Process Management Culture in Public Administration and Its Determinants

Public administration institutions increasingly use business process management (BPM) to innovate internal operations, increase process performance and improve their services. Research on private sector companies has shown that organizational culture may impact an organization's BPM and this culture is often referred to as BPM culture. However, similar research on public administration is yet missing. Thus, this article assesses BPM culture in Germany’s municipal administration. 733 online survey responses were gathered and analyzed using MANOVA and follow-up discriminant analyses to identify possible determinants of public administration’s BPM culture. The results indicate that the employees’ professional experience and their responsibility influence the assessment of BPM culture, as does the size of a municipality. Based on these findings, the article proposes testable relationships and an agenda for further research on BPM culture in public administration.