Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page

PP2A Mediated AMPK Inhibition Promotes HSP70 Expression in Heat Shock Response

BACKGROUND: Under stress, AMP-activated protein kinase (AMPK) plays a central role in energy balance, and the heat shock response is a protective mechanism for cell survival. The relationship between AMPK activity and heat shock protein (HSP) expression under stress is unclear. METHODOLOGY/PRINCIPAL FINDINGS: We found that heat stress induced dephosphorylation of AMPKα subunit (AMPKα) in various cell types from human and rodent. In HepG2 cells, the dephosphorylation of AMPKα under heat stress in turn caused dephosphorylation of acetyl-CoA carboxylase and upregulation of phosphoenolpyruvate carboxykinase, two downstream targets of AMPK, confirming the inhibition of AMPK activity by heat stress. Treatment of HepG2 cells with phosphatase 2A (PP2A) inhibitor okadaic acid or inhibition of PP2A expression by RNA interference efficiently reversed heat stress-induced AMPKα dephosphorylation, suggesting that heat stress inhibited AMPK through activation of PP2A. Heat stress- and other HSP inducer (CdCl(2), celastrol, MG132)-induced HSP70 expression could be inhibited by AICAR, an AMPK specific activator. Inhibition of AMPKα expression by RNA interference reversed the inhibitory effect of AICAR on HSP70 expression under heat stress. These results indicate that AMPK inhibition under stress contribute to HSP70 expression. Mechanistic studies showed that activation of AMPK by AICAR had no effect on heat stress-induced HSF1 nuclear translocation, phosphorylation and binding with heat response element in the promoter region of HSP70 gene, but significantly decreased HSP70 mRNA stability. CONCLUSIONS/SIGNIFICANCE: These results demonstrate that during heat shock response, PP2A mediated AMPK inhibition upregulates HSP70 expression at least partially through stabilizing its mRNA, which suggests a novel mechanism for HSP induction under stress

Fine-grained Private Knowledge Distillation

Knowledge distillation has emerged as a scalable and effective way for privacy-preserving machine learning. One remaining drawback is that it consumes privacy in a model-level (i.e., client-level) manner, every distillation query incurs privacy loss of one client's all records. In order to attain fine-grained privacy accountant and improve utility, this work proposes a model-free reverse $k$-NN labeling method towards record-level private knowledge distillation, where each record is employed for labeling at most $k$ queries. Theoretically, we provide bounds of labeling error rate under the centralized/local/shuffle model of differential privacy (w.r.t. the number of records per query, privacy budgets). Experimentally, we demonstrate that it achieves new state-of-the-art accuracy with one order of magnitude lower of privacy loss. Specifically, on the CIFAR-$10$ dataset, it reaches $82.1\%$ test accuracy with centralized privacy budget $1.0$; on the MNIST/SVHN dataset, it reaches $99.1\%$/$95.6\%$ accuracy respectively with budget $0.1$. It is the first time deep learning with differential privacy achieve comparable accuracy with reasonable data privacy protection (i.e., $\exp(\epsilon)\leq 1.5$). Our code is available at https://github.com/liyuntong9/rknn

Effects of dietary protein on milk yield and colostrum whey protein composition of tibetan sheep in modern intensive-fed pattern

Colostrum protein, an essential source of dietary nutrients, could improve new-born animals" immunity, and play a vital role in mammals" early development. In order to explore the milk yield and colostrum whey protein composition of Tibetan sheep, 120 Tibetan sheep were arbitrarily separated into categories, namely treatment groups (A, B, C) and control group (D). Compositional and functional differences in milk yield and colostrum whey protein composition among different dietary proteins were compared using proteomics methods. The results showed that sheep with 14% protein level diet group (group B) had the least bodyweight loss and higher milk yield during lactation compared to the other groups. Fifty differentially expressed proteins (DEPs) were recognized using iTRAQ, these DEPs were analyzed based on cluster, GO, KEGG and PPIs analysis. GO-BP involved were Protein transmembrane transport, Protein regulation metabolic process, Biological regulation, Regulation of biological process, and Response to stimulus. Meantime, DEPs participated in many KEGG pathways, including Fatty acid metabolism, Glycerophospholipid metabolism, Protein digestion and absorption, Ras signaling pathway and Cell adhesion molecules. The treatment groups showed increase in the abundance of regulation metabolic process (especially protein metabolism and fatty acid metabolism), along with decrease in stress reaction process. Lactoferrin, Alpha-S2-casein, Superoxide dismutase [Cu-Zn], Alpha-s1-casein, Alpha globin and Lactoperoxidase appeared in the center of the PPi network intersection. Interestingly, 14% protein group (group B) had exhibited the greatest variability between biological relevance in milk composition and function, these results could increase the understanding of different dietary protein on colostrum whey protein composition of Tibetan sheep, which could provide important information and potential directions for the infant milk powder and functional food industries

A Hyperthermophilic Argonaute From Ferroglobus placidus With Specificity on Guide Binding Pattern

Argonaute proteins (Agos) from thermophilic archaea are involved in several important processes, such as host defense and DNA replication. The catalytic mechanism of Ago from different microbes with great diversity and genome editing potential is attracting increasing attention. Here, we describe an Argonaute from hyperthermophilic Ferroglobus placidus (FpAgo), with a typical DNA-guided DNA endonuclease activity but adopted with only a short guide 15–20 nt length rather than a broad guide selectivity for reported Agos. FpAgo performed the precise cleavage of phosphodiester bonds between 10 and 11 nt on the target strand (counting from the guide strand) guided strictly by 5′-phosphorylated DNA at temperatures ranging from 75 to 99°C. The cleavage activity was regulated by the divalent cations Mn2+, Mg2+, Co2+, and Ni2+. In addition, FpAgo possesses guide/target mismatch tolerance in the seed region but is sensitive to mismatches in the 3′-guide region. Notably, the EMSA assay revealed that the FpAgo-guide-target ternary complex exhibited a stronger binding affinity for short 15 and 16 nt guide DNAs than longer guides. Moreover, we performed structural modeling analyses that implied the unique PAZ domain of FpAgo for 3′-guide recognition and binding to affect guide length specificity. This study broadens our understanding of thermophilic Agos and paves the way for their use in DNA manipulation

Effects of Phase-Locking Deficits on Speech Recognition in Older Adults With Presbycusis

Objective: People with presbycusis (PC) often report difficulties in speech recognition, especially under noisy listening conditions. Investigating the PC-related changes in central representations of envelope signals and temporal fine structure (TFS) signals of speech sounds is critical for understanding the mechanism underlying the PC-related deficit in speech recognition. Frequency-following responses (FFRs) to speech stimulation can be used to examine the subcortical encoding of both envelope and TFS speech signals. This study compared FFRs to speech signals between listeners with PC and those with clinically normal hearing (NH) under either quiet or noise-masking conditions.Methods: FFRs to a 170-ms speech syllable /da/ were recorded under either a quiet or noise-masking (with a signal-to-noise ratio (SNR) of 8 dB) condition in 14 older adults with PC and 13 age-matched adults with NH. The envelope (FFRENV) and TFS (FFRTFS) components of FFRs were analyzed separately by adding and subtracting the alternative polarity responses, respectively. Speech recognition in noise was evaluated in each participant.Results: In the quiet condition, compared with the NH group, the PC group exhibited smaller F0 and H3 amplitudes and decreased stimulus-response (S-R) correlation for FFRENV but not for FFRTFS. Both the H2 and H3 amplitudes and the S-R correlation of FFRENV significantly decreased in the noise condition compared with the quiet condition in the NH group but not in the PC group. Moreover, the degree of hearing loss was correlated with noise-induced changes in FFRTFS morphology. Furthermore, the speech-in-noise (SIN) threshold was negatively correlated with the noise-induced change in H2 (for FFRENV) and the S-R correlation for FFRENV in the quiet condition.Conclusion: Audibility affects the subcortical encoding of both envelope and TFS in PC patients. The impaired ability to adjust the balance between the envelope and TFS in the noise condition may be part of the mechanism underlying PC-related deficits in speech recognition in noise. FFRs can predict SIN perception performance