Recent advancements in the breeding of sorghum crop: current status and future strategies for marker-assisted breeding

Sorghum is emerging as a model crop for functional genetics and genomics of tropical grasses with abundant uses, including food, feed, and fuel, among others. It is currently the fifth most significant primary cereal crop. Crops are subjected to various biotic and abiotic stresses, which negatively impact on agricultural production. Developing high-yielding, disease-resistant, and climate-resilient cultivars can be achieved through marker-assisted breeding. Such selection has considerably reduced the time to market new crop varieties adapted to challenging conditions. In the recent years, extensive knowledge was gained about genetic markers. We are providing an overview of current advances in sorghum breeding initiatives, with a special focus on early breeders who may not be familiar with DNA markers. Advancements in molecular plant breeding, genetics, genomics selection, and genome editing have contributed to a thorough understanding of DNA markers, provided various proofs of the genetic variety accessible in crop plants, and have substantially enhanced plant breeding technologies. Marker-assisted selection has accelerated and precised the plant breeding process, empowering plant breeders all around the world

Lead and cadmium levels in tissues of horses in Bursa, Turkey

Lead and cadmium were determined by graphite furnace atomic absorption spectrometry in liver, kidney and muscle of horses in Bursa, a highly industrialized city in Turkey. Mean levels of lead were 254 +/- 0.71 mg/kg, 3.59 +/- 1.31 mg/kg and 5.98 +/- 2.63 mg/kg (fresh weight), in liver, kidney and muscle samples, respectively, and those of cadmium were 10.62 +/- 2.90 mg/kg, 53.34 +/- 17.51 mg/kg and 0.21 +/- 0.02 mg/kg, in liver, kidney and muscle, respectively. These levels were higher than acceptable in other European countries and show that people and animals living in Bursa are subject to heavy metal pollution

Görüntü Çözünürlü?ünün Otomatik Cinsiyet ve Yaş Sınıflandırmasındakı Etkisinin Analizi

24th Signal Processing and Communication Application Conference (SIU) (2016 : Zonguldak, Turkey )In this paper, the effect of the image resolution for gender detection and age classification have been analyzed by conducting experiments with facial images that have 10 different image resolutions ranging from 2 x 1 to 329 x 264. K Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Random Forests (RF) classifiers, which have been successfully used in several applications, have been employed to extract gender and age information from the images. Experiments for age classification and gender detection have been performed separately.Bu bildiride, yüz görüntülerinde 2 × 1 ile 329 × 264 arasında degişen 10 farklı görüntü çözünürlüğü için yaş ve cinsiyet sınıflandırması deneyleri yapılarak görüntü çözünürlügünün yaş ve cinsiyet sınıflandırması başarımı üzerindeki etkisi incelenmiştir. Görüntülerden yaş ve cinsiyet bilgilerinin çıkarılabilmesi için bir çok uygulamada başarılı bir şekilde kullanılmış olan K En Yakın Komşu (k-NN), Destek Vektör Makineleri (SVM) ve Rastgele Orman (RF) sınıflandırıcıları kullanılmıştır. Deneyler yaş ve cinsiyet sınıflandırması için ayrı ayrı yapılmış, son olarak alınan sonuçlar degerlendirilmişti