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Register a new userMethods for the Detection of Genetically Modified Plants
طرائق الكشف عن النباتات المعدلة وراثياً
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University of Aleppo رسالة ماجستير
Publication date
2013
fields
Agricultural Engineering
and research's language is
العربية
Created by
Shamra Editor
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تزرع البناتات المعدلة وراثياً في مناطق عديدة حول العامل ويزداد الاهتمام بزراعتها عاماً بعد أخر . بلغت المساحة المزروعة بالمحاصيل المعدلة وراثياً عام 2012 حوالي 170ز3 مليون هكتار موزعة على 29 دولة
Genetically modified girls are grown in many areas around the world, and the interest in planting them is increasing year after year. The area planted with genetically modified crops in 2012 amounted to about 3,170 million hectares distributed over 29 countries
References used
المنظمة العربية للتنمية الزراعية (2003) حلقة العمل حول تقييم الآثار البيئية لإدخال الأنواع النباتية والحيوانية المحورة الوراثية إلى المنطقة العربية
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This study was conducted on domestic pigeons populations in
the provinces of Hama, Idlib and Latakia using several
conventional diagnostic techniques , including pathological
examination tests and agar gel immune diffusion test and isolation
on c
hicken embryo. The number of suspected birds to be
infected by pigeon pox through clinical symptoms and macroscopic
lesions were about 37 birds. we noticed the presence of lesions
in warts and scars on the nonfeather parts of the face and on the
corner of the mouth and eyelids and other areas of the body. the
most of these accompanied by the presence of the of defteric
lesions on the mucous membrane of the oral cavity.
The results showed that all birds suffered from the presence of
infection fowlpox through histological examination of skin and
difteric lesions. the results has been confirmation by agar gel
immune diffusion test. And we successfully isolated the virus that
caused the disease by injection on the Chorioallantoic membrane
of a chicken's egg fertilized SAN.
State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks. Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can
be fine-tuned for specific downstream tasks. We propose refining a pre-trained NLP model using the objective of detecting shuffled tokens. We use a sequential approach by starting with the pre-trained RoBERTa model and training it using our approach. Applying random shuffling strategy on the word-level, we found that our approach enables the RoBERTa model achieve better performance on 4 out of 7 GLUE tasks. Our results indicate that learning to detect shuffled tokens is a promising approach to learn more coherent sentence representations.
The genus Potyvirus is one of the most important plant viral genera, as it contains about 200 virus species, some of which are of great importance. Our study showed the wide spread of viruses belonging to this genus in different regions of Syria and
during successive seasons, and the infection rate was 54.25% on the plants of the cucurbit family, and in the samples of the Solanaceae family 34.17%, while the cruciferous family was at a rate of 57.57%, which indicates the presence of these viruses and their transmission from one season to another.
Several protocols for DNA extraction from leaves of pepper Capsicum annuum
L. were evaluated to detect Begomoviruses. The extraction
methods comparison were based on DNA quantity and quality. The
DNA concentration absorbance for the evaluation of DNA concentration and contamination
were measured using spectrophotometer.
Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised
method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.
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