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Register a new userالكشف عن علاقات القرابة الوراثية بين أنواع الجنسين ( Triticum L. AegilopsL ) باستخدام الواسمات الجزيئية
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Tishreen University أطروحة دكتوراه
Publication date
2013
fields
Agricultural Engineering
and research's language is
العربية
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الكشف عن علاقات القرابة الوراثية بين أنواع الجنسين ( Triticum L. AegilopsL ) باستخدام الواسمات الجزيئية
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References used
معلا محمد يحيى 1993 تقييم بعض اصناف القمح المحلي من الناحية الوراثية والفيزيولوجية الموفولوجية مجلة جامعة تشرين سلسلة العلوم الزراعية المجلد15
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The research Studies genetic relationships among Triticum L. and Aegilops L.
species by direct sequencing of PCR-amplified internal transcribed spacer (ITS) of nuclear
ribosomal DNA to investigate the polymorphism in nucleotide sequences among 8
A
egilops L. and 7 Triticum L. species. ITS sequences were aligned with CLUSTAL W 2.1
multiple sequence aligment program. The phylogenetic relationships among species were
reconstructed using Unweighted Pair Group Mean Arithmetic Average (UPGMA) and
neighbor-joining (NJ) methods.
This study present first estimates of segregation distortion
as revealed by comparison of segregation ratios in DH lines and sexually
produced F٢ lines in barley using the co-dominant microsatellite markers
(STMS).
تقدير بعض المعايير الوراثية لأهم الصفات الإنتاجية ومكوناتها وأثرها في التحسين الوراثي للقمح القاسي(Triticum durum L )
MicroRNAs (miRNAs) are single-stranded, non-coding RNA
molecules, which can regulate the translation of target proteins and
thereby control biological functions. They qualify as diagnostic
markers and can help to detect diseases earlier. In case o
f a disease
the concentrations of specific miRNAs which are characteristic for
it do vary in quantity. For the detection of miRNA we are using a
surface plasmon resonance (SPR) biosensor, which allows to
measure interactions of molecules at an interface in real-time. For
this, thiolated LNA capture probes are immobilized on the gold
chip.
In this work we explore the effect of incorporating demographic metadata in a text classifier trained on top of a pre-trained transformer language model. More specifically, we add information about the gender of critics and book authors when classify
ing the polarity of book reviews, and the polarity of the reviews when classifying the genders of authors and critics. We use an existing data set of Norwegian book reviews with ratings by professional critics, which has also been augmented with gender information, and train a document-level sentiment classifier on top of a recently released Norwegian BERT-model. We show that gender-informed models obtain substantially higher accuracy, and that polarity-informed models obtain higher accuracy when classifying the genders of book authors. For this particular data set, we take this result as a confirmation of the gender bias in the underlying label distribution, but in other settings we believe a similar approach can be used for mitigating bias in the model.
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