In silico prediction of neuropeptides in Hymenoptera parasitoid wasps
نویسندگان
چکیده
منابع مشابه
In silico prediction of neuropeptides in Hymenoptera parasitoid wasps
Parasitoid wasps of the order Hymenoptera, the most diverse groups of animals, are important natural enemies of arthropod hosts in natural ecosystems and can be used in biological control. To date, only one neuropeptidome of a parasitoid wasp, Nasonia vitripennis, has been identified. This study aimed to identify more neuropeptides of parasitoid wasps, by using a well-established workflow that ...
متن کاملprediction of ignition delay period in d.i diesel engines
a semi-empirical mathematical model for predicting physical part of ignition delay period in the combustion of direct - injection diesel engines with swirl is developed . this model based on a single droplet evaporation model . the governing equations , namely , equations of droplet motion , heat and mass transfer were solved simultaneously using a rung-kutta step by step unmerical method . the...
Neuroethology of Parasitoid Wasps
Predators as diverse as snakes, scorpions, spiders, insects and snails manufacture venoms to incapacitate their prey. Most venoms contain a cocktail of neurotoxins and each neurotoxin is designed to target specific receptors in the nervous and muscular systems. Most neurotoxins act peripherally and interfere with the ability of the prey's nervous system to generate muscle contraction or relaxat...
متن کاملThe evolution of polyembryony in parasitoid wasps.
Polyembryony has evolved independently in four families of parasitoid wasps. We review three main hypotheses for the selective forces favouring this developmental mode in parasitoids: polyembryony (i) reduces the costs of egg limitation; (ii) reduces the genetic conflict among offspring; and (iii) allows offspring to adjust their numbers to the quality of the host. Using comparative data and ve...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2018
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0193561