Chlorine substitution-dependent toxicities of polychlorinated biphenyls to the earthworm Eisenia fetida in soil

Polychlorinated biphenyls(PCBs)with different chlorine substitution patterns often coexist in e-waste-processing sites.However,the single and combined toxicity of PCBs to soil organisms and the influence of chlorine substitution patterns remain largely unknown.Herein,we evaluated the distinct in vivo toxicity of PCB28(a trichlorinated PCB),PCB52(a tetrachlorinated PCB),PCB101(a pentachlorinated PCB),and their mixture to earthworm Eisenia fetida in soil,and looked into the underlining mechanisms in an in vitro test using coelomocytes.After a 28-days exposure,all PCBs(up to 10 mg/kg)were not fatal to earthworms,but could induce intestinal histopathological changes and microbial community alterations in the drilosphere system,along with a significant weight loss.Notably,pentachlorinated PCBs with a low bioaccumulation ability showed greater inhibitory effects on the growth of earthworm than lowly chlorinated PCBs,suggesting that bioaccumulation was not the main determinant of chlorine substitution-dependent toxicity.Furthermore,in vitro assays showed that the highly chlorinated PCBs induced a high-percentage apoptosis of eleocytes in the coelomocytes and significantly activated antioxidant enzymes,indicating that the distinct cellular vulnerability to lowly/highly chlorinated PCBs was the main contributor to the PCBs toxicity.These findings emphasize the specific advantage of using earthworms in the control of lowly chlorinated PCBs in soil due to their high tolerance and accumulation ability.

MPHM:Model poisoning attacks on federal learning using historical information momentum

Federated learning(FL)development has grown increasingly strong with the increased emphasis on data for individuals and industry.Federated learning allows individual participants to jointly train a global model without sharing local data,which significantly enhances data privacy.However,federated learning is vulnerable to poisoning attacks by malicious participants.Since federated learning does not have access to the participants’training process,i.e.,attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants.Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible.In contrast,we propose a novel model poisoning attack based on the momentum of history information(MPHM),that is,the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training,which will make the new malicious updates more effective and stealthy.Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information.Experiments show that in the classical defense case,our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks.