No abstract

Synergistic actions for mixtures of abamectin with other insecticides in some insect pests were evaluated, and the possible synergistic mechanism was studied by the comparison in toxicity and cuticular penetration of abamectin between with and without other insecticides or synergists in Helicoverpa armigera larvae. The results of bioassay showed that horticultural mineral oil (HMO), hexaflumuron, chlorpyrifos, and some other insecticides were synergistic to abamectin with 152.0J420.0 of co-toxicity coefficient (CTC) in some agricultural insect pests. In topical application tests, HMO or piperonyl butoxide (PBO) increased the toxicity of abamectin in larvae of H. armigera, but the mortality was not affected by s,s,s-tributylphorotrithioate (DEF) and triphenylphosphate (TPP). The synergistic action of HMO was obviously higher than PBO, and when treated simultaneously with abamectin, HMO gave a more significant synergism than if treated 2 hours ahead. The highest synergistic effect (SE) was found in the mixture of abamectin+HMO (1:206) . The mortality did not increase or the toxicity drop, when a synergist or HMO was added into the mixture of abamectin+HMO or abamectin+synergist , respectively. Results from the isotope tracing experiments showed that HMO significantly enhanced the penetration of 3 H-abamectin through the cuticle of H. armigera larvae, which resulted in the synergism of the mixture. The cuticular penetration of 3 H-abamectin was not accumulatively affected by chlorpyrifos, nor by hexaflumuron, though there was an inhibition within 30 seconds or 1 hour after treated by these two chemicals respectively. Results suggested that the synergism of abamectin mixed with hexaflumuron or chlorpyrifos might be related to inhibition of metabolic enzymes or target sites in the larvae.Insect Science (2005) 12, 109J119 10 56 12of chemicals used: abamectin at 0.03696 µg/larva, HMO at 30.41 µg/larva, DEF at 2.99 µg/larva, TPP at 2.97 µg/larva, PBO at 2.99 µg/larva, ‡ Chemicals applied at sequence: with 2 hours of interval between 2 applications; § HMO+PBO : PBO was applied with HMO simultaneously; ¶DAT: days after treatment. 4 90 0 HMO+Abamectin ‡ J J 50 74 10 82 J8 DEF HMO+Abamectin J 50 80 16 92 2 Untreated J J 50 0 J 0 J † dosages of chemicals used: abamectin at 0.1478 µg/larva, HMO at 30.41 µg/larva, DEF at 2.99 µg/larva; ‡ HMO+abamectin: HMO was applied with abamectin simultaneously. 50 0

Effects of copper (Cu) accumulation by the flesh fly Boettcherisca peregrina (R.-D.) (Diptera: Sarcophagidae) on the ectoparasitic wasp Nasonia vitripennis (Walker) (Hymenoptera: Pteromalidae) were investigated experimentally by exposing host larvae to contaminated diets with final Cu concentrations of 400 +g/g and 800 +g/g diet fresh weight (DFW), respectively. Results showed that Cu can be transferred along food chains to secondary consumers (parasitoids) in small amounts, resulting in negative effects on parasitoid growth and development (body weight and developmental duration) as well as fecundity (number of offspring per female). Copper exposure also inhibited vitellogenesis of parasitoids from Cu-contaminated host pupae. It is suggested that the decreased fecundity and inhibition of vitellogenesis of N. vitripennis resulted from poor host nutritional state rather than from direct effects of Cu stress.

No abstract

Click-through rate(CTR) prediction plays an important role in online advertising and recommender systems. In practice, the training of CTR models depends on click data which is intrinsically biased towards higher positions since higher position has higher CTR by nature. Existing methods such as actual position training with fixed position inference and inverse propensity weighted training with no position inference alleviate the bias problem to some extend. However, the different treatment of position information between training and inference will inevitably lead to inconsistency and sub-optimal online performance. Meanwhile, the basic assumption of these methods, i.e., the click probability is the product of examination probability and relevance probability, is oversimplified and insufficient to model the rich interaction between position and other information. In this paper, we propose a Deep Position-wise Interaction Network (DPIN) to efficiently combine all candidate items and positions for estimating CTR at each position, achieving consistency between offline and online as well as modeling the deep non-linear interaction among position, user, context and item under the limit of serving performance. Following our new treatment to the position bias in CTR prediction, we propose a new evaluation metrics named PAUC (position-wise AUC) that is suitable for measuring the ranking quality at a given position. Through extensive experiments on a real world dataset, we show empirically that our method is both effective and efficient in solving position bias problem. We have also deployed our method in production and observed statistically significant improvement over a highly optimized baseline in a rigorous A/B test.