QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation

Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). We introduce QuickDrop, an efficient and original FU method that utilizes dataset distillation (DD) to accelerate unlearning and drastically reduces computational overhead compared to existing approaches. In QuickDrop, each client uses DD to generate a compact dataset representative of the original training dataset, called a distilled dataset, and uses this compact dataset during unlearning. To unlearn specific knowledge from the global model, QuickDrop has clients execute Stochastic Gradient Ascent with samples from the distilled datasets, thus significantly reducing computational overhead compared to conventional FU methods. We further increase the efficiency of QuickDrop by ingeniously integrating DD into the FL training process. By reusing the gradient updates produced during FL training for DD, the overhead of creating distilled datasets becomes close to negligible. Evaluations on three standard datasets show that, with comparable accuracy guarantees, QuickDrop reduces the duration of unlearning by 463.8x compared to model retraining from scratch and 65.1x compared to existing FU approaches. We also demonstrate the scalability of QuickDrop with 100 clients and show its effectiveness while handling multiple unlearning operations

Improvement of Culture Conditions and Plant Growth Regulators for In Vitro Callus Induction and Plant Regeneration in <i>Paeonia lactiflora</i> Pall.

Owing to its high ornamental, medicinal and horticultural values, herbaceous peony (Paeonia lactiflora Pall.) has been widely used as a landscaping and economical plant around the world. However, the lack of an efficient and stable regeneration system in P. lactiflora restricts its rapid propagation and large-scale production. By testing the key factors affecting callus formation, proliferation, adventitious bud induction and rooting, here, we developed an in vitro system for callus induction and regeneration in P. lactiflora. Our results show that callus formation was affected by explant types, culture environment, basal medium and plant growth regulators. Using cotyledons as explants, we established good conditions for P. lactiflora callus induction and callus proliferation. We effectively obtained adventitious buds differentiated from callus in Murashige and Skoog (MS) medium containing kinetin (KT) and thidiazuron (TDZ). Adventitious bud growth can be further promoted by adding gibberellin 3 (GA3), 1-naphthaleneacetic acid (NAA) and 6-benzyleaminopurine (6-BA) into the MS medium. A high percentage of rooting can be achieved by adding indolebutyric acid (IBA) and activated carbon (AC) to ½ MS medium. Overall, our system promotes callus induction and adventitious bud regeneration for P. lactiflora through improved culture conditions and plant growth regulators in the culture media, and lays a foundation for subsequent genetic engineering research

In situ electrochemical synthesis of graphene-poly(arginine) composite for p-nitrophenol monitoring

Para-Nitrophenol (p-nitrophenol) is a common industrial pollutant occurring widely in water bodies, yet actual monitoring methods are limited. Herein we proposed a fully electrochemically in situ synthesized graphenepolyarginine composite functionalized screen printed electrode, as a novel p-nitrophenol sensing platform. The electrode was characterized by morphologic, spectrometric and electrochemical techniques. p-nitrophenol in both pure aqueous solution and real water samples was tested. Results show a detection limit as low as the nanomolar level, and display a linear response and high selectivity in the range of 0.5-1250 mu M. Molecular simulation reveals a detailed synergy between graphene and poly-arginine. The preferable orientation of nitrophenol molecules on the graphene interface in the presence of poly-arginine induces H- and ionic binding. This sensor is an ideal prototype for p-nitrophenol quantification in real waters

Histone methyltransferase SET8 is regulated by miR-192/215 and induces oncogene-induced senescence via p53-dependent DNA damage in human gastric carcinoma cells

Gastric cancer (GC) is the most common cancer throughout the world. Despite advances of the treatments, detailed oncogenic mechanisms are largely unknown. In our previous study, we investigated microRNA (miR) expression profiles in human GC using miR microarrays. We found miR-192/215 were upregulated in GC tissues. Then gene microarray was implemented to discover the targets of miR-192/215. We compared the expression profile of BGC823 cells transfected with miR-192/215 inhibitors, and HFE145 cells transfected with miR-192/-215 mimics, respectively. SET8 was identified as a proposed target based on the expression change of more than twofold. SET8 belongs to the SET domain-containing methyltransferase family and specifically catalyzes monomethylation of H4K20me. It is involved in diverse functions in tumorigenesis and metastasis. Therefore, we focused on the contributions of miR-192/215/SET8 axis to the development of GC. In this study, we observe that functionally, SET8 regulated by miR-192/215 is involved in GC-related biological activities. SET8 is also found to trigger oncogene-induced senescence (OIS) in GC in vivo and in vitro, which is dependent on the DDR (DNA damage response) and p53. Our findings reveal that SET8 functions as a negative regulator of metastasis via the OIS-signaling pathway. Taken together, we investigated the functional significance, molecular mechanisms, and clinical impact of miR-192/215/SET8/p53 in GC