Bibliometric analysis of horticultural crop secondary metabolism.

The goal of the study was to examine the trends in recent years by analyzing 750 studies, 3619 authors, and 166 sources with the statement "Horticultural Crop Secondary Metabolism" in the article title published within the scope of SCI-Expanded and "Scopus" journals in between the years 2010 and 2023. In this case, the Web of Science Core Collection database was scanned under the heading "Horticultural Crop Secondary Metabolism", and bibliometric information was gathered. In order to advance research on horticulture crops, current problems and recommend solutions within "Horticultural Crop Secondary Metabolism" were identified in this study. The number of publications, publication kinds, reference analyses, total citations per year, most common words, most often cited local authors, most pertinent affiliations, and most pertinent sources were all examined in relation to the research. According to the findings, Horticulture Research, Frontiers in Plant Science, Plant Physiology and Biochemistry: PPB, Scientific Reports, and BMC Genomics are the journals that publish the most papers on "Horticultural Crop Secondary Metabolism". The phrases "gene expression regulation plant", "transcriptome", and "plant proteins" are used most frequently. Because of this, the increase of bibliometrics study can be very beneficial by serving as a catalyst for horticulture crop research.

Investigation of the Relationships between Coat Colour, Sex, and Morphological Characteristics in Donkeys Using Data Mining Algorithms.

This study was carried out in order to determine the morphological characteristics, body coat colour distribution, and body dimensions of donkeys raised in Turkey, as well as to determine the relationships between these factors. For this reason, the predictive performance of various machine learning algorithms (i.e., CHAID, Random Forest, ALM, MARS, and Bagging MARS) were compared, utilising the biometric data of donkeys. In particular, mean measurements were taken from a total of 371 donkeys (252 male and 119 female) with descriptive statistical values as follows: height at withers, 100.7 cm; rump height, 103.1 cm; body length, 103.8 cm; chest circumference, 112.8 cm; chest depth, 45.7 cm; chest width, 29.1 cm; front shin circumference, 13.5 cm; head length, 55 cm; and ear length, 22 cm. The body colour distribution of the donkeys considered in this study was calculated as 39.35% grey, 19.95% white, 21.83% black, and 18.87% brown. Model fit statistics, including the coefficient of determination (R2), mean square error, root-mean-square error (RMSE), mean absolute percentage error (MAPE), and standard deviation ratio (SD ratio), were calculated to measure the predictive ability of the fitted models. The MARS algorithm was found to be the best model for defining the body length of donkeys, with the highest R2 value (0.916) and the lowest RMSE, MAPE, and SD ratio values (2.173, 1.615, and 0.291, respectively). The experimental results indicate that the most suitable model is the MARS algorithm, which provides a good alternative to other data mining algorithms for predicting the body length of donkeys.

Exploiting morphobiometric and genomic variability of African indigenous camel populations-A review.

Camels (Camelus dromedarius) in Africa are adapted to arid and the semi-arid environmental conditions, and are valuable for meat, milk and fiber production. On account of the growing demand for camels in this continent, there is a need for knowledge on their phenotypic and genetic diversity. This is fundamental to sustainable herd management and utilization including the design of appropriate breeding and conservation strategies. We reviewed studies on the phenotypic and genetic characterization, breeding objectives, systems of production, productive and reproductive performances, and pathways for the sustainable rearing and use of camels in Africa. The morphological and genetic diversity, productive and reproductive abilities of African camels suggest the existence of genetic variations that can be utilized for breeds/ecotypes' genetic improvement and conservation. Possible areas of intervention include the establishment of open nucleus and community-based breeding schemes and utilization of modern reproductive technologies for the genetic improvement of milk and meat yields, sustainable management of rangelands, capacity building of the pastoralists and agro-pastoralists, institutional supports, formation of centralized conservation centres and efficient and effective marketing systems.

Modeling of COVID-19 Outbreak Indicators in China Between January and June.

OBJECTIVES: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. METHODS: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. RESULTS: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. CONCLUSION: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

Relationship among egg quality traits in Japanese quails and prediction of egg weight and color using data mining algorithms.

The present paper aims to predict egg weight from some egg quality characteristics (egg weight, yolk weight, albumen weight, shell thickness, shell weight, shape index, albumen index, yolk index, yolk scale, and Haugh unit) in Japanese quails and to classify 94 eggs collected in regards to their shell colors. In the present study, CART and MARS data mining algorithms were assessed in the prediction of egg weight of the quails with the content of detecting egg quality standards of the studied quail genotypes for breeding and marketing strategies. In the classification of the collected quail eggs on their shell colors, classification performances of CART, CHAID, exhaustive CHAID, and QUEST algorithms were measured. Among those, CART was selected as the best classification algorithm according to eggshell color. The highest significant correlations were obtained for egg weight-yolk weight (0.740) and egg weight-albumen weight (0.735), respectively, in considering egg internal and external quality traits in quails. CART algorithm more accurately classified all eggshell colors compared with other algorithms. MARS showed much better predictive performance than CART that produced 0.850 Rsq and 0.728 cross-validation Rsq for prediction of egg weight in quails. In conclusion, the obtained results revealed that data mining algorithms may be useful references in practice for quail breeders in the development of new selection strategies and characterization of the studied animal materials.

Use of MARS algorithm for predicting mature weight of different camel (Camelus dromedarius) breeds reared in Pakistan and morphological characterization via cluster analysis.

Mature weight is a significant trait that can be influenced by age, sex, breed, production system, and climate conditions in camels. In camel breeding, it is essential to describe breed standards of the studied camel breeds as part of morphological characterization and to determine morphological traits positively influencing mature weight within the scope of indirect selection criteria. This study was to find the best one among candidate models in prediction of mature weight from several morphological traits measured for eight camel breeds (Bravhi, Kachi, Kharani, Kohi, Lassi, Makrani, Pishin, and Rodbari) raised under Pakistan conditions. The morphological measurements taken from the camels in the study were birth weight (BW), weaning weight (WW), mature weight (MW), age of ridding (ARD), face length (FL), face width (FW), head length (HL), head width (HW), ear length (EL), ear width (EW), neck length (NL), neck width (NW), hump length (HL), hump width (HuW), heart girth (HG), withers height (WH), body length (BL), fore leg length (FLL), and hind leg length (HLL), respectively. In the prediction of mature body weight as a response variable, the optimal MARS predictive model with 15 terms selected by train function of the caret package produced very high predictive performance without encountering overfitting problem. Goodness of fit criteria were estimated to measure predictive quality of the MARS model using ehaGoF package available in R environment. Morphological characterization of the camel breeds was performed with hierarchical cluster analysis (HCA) on the basis of Euclidean distance-Single linkage. At the first step of hierarchical cluster analysis, the similarity level of Bravhi and Kachi camel breeds was the highest with 85.3569 (%). At the second step, Makrani joined to new cluster of Bravhi and Kachi camels found at the first step, and the similarity level of the new cluster comprising Bravhi, Kachi, and Makrani breeds was found as 84.5562 (%). MW was significantly correlated with BW (0.677), WW (0.536), HL (0.524), HuW (0.529), and ARD (0.375) at P < 0.01, and there was the highest correlation of 0.994 between HHL and FLL (P < 0.01). As a result, it could be suggested that results of MARS modeling may help camel breeders to reproduce the elite camel populations and to describe characteristics associated positively with MW within the scope of indirect selection criteria.

Comparison of tree-based regression tree methods for predicting live body weight from morphological traits in Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breeds raised in South Africa.

In animal breeding, more considerable attention is drawn to reveal the relationship between live body weight and morphological traits in identifying breed and species standards. The aim of this study was to predict live body weight from morphological characteristics in the Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breed, native to South African. In the prediction of live body weight, eleven morphological measurements, i.e., wing length, back length, beak length, shank length, shank circumference, chest circumference, wingspan, keel length, body girth, toe length, and body length, were taken. As tree-based regression tree methods, predictive performances of CART, CHAID, and exhaustive CHAID algorithms were measured for body weight prediction. Among those, CART was found to be the best decision tree algorithm that gave the highest predictive accuracy. CART visual results reflected that the heaviest body weight mean (2.000 kg) was obtained from the chickens with 10.250 cm < WL ≤ 10.500 cm. As a result, it could be suggested that the CART decision tree might help to determine breed standards of the Hy-line silver brown commercial layer and, especially, indigenous Potchefstroom Koekoek breeds for breeding programs.

Assessment of Farmers' Knowledge, Attitudes and Practices on Antibiotics and Antimicrobial Resistance.

The aim of this study was to determine knowledge, attitudes, and behaviors of farmers dealing with animal husbandry in eastern Turkey with regard to antibiotic knowledge, use, and resistance. A face to face questionnaire survey, consisting of five sections with 42 questions in total, was applied to 360 farmers located in the region. The questions in the first and fifth sections were closed-ended while those in other sections were prepared using the Likert scale. It was determined that knowledge of the farmers about antibiotic use, duration, storage, and resistance was well below desired levels. This was particularly remarkable in the participants with a low level of education, living in rural areas, and those at 48 years of age or over. In contrast, younger and highly educated participants living in urban areas were more knowledgeable about antibiotic use and they were well aware of the fact that resistance might pose a great risk for public health. Providing appropriate antibiotic use in animals through systematic training of livestock farmers is crucial in tackling the resistance problem.