A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks

PI3-K/Akt pathway contributes to IL-6-dependent growth of 7TD1 cells

BACKGROUND: Recently, growing evidence suggests the involvement of PI 3-K/Akt in IL-6-dependent survival and proliferative responses in several types of cells. However, whether PI 3-K/Akt plays the same role in IL-6-dependent growth of 7TD1 mouse-mouse B cell hybridoma is not known. METHODS: We investigated the activation status of Akt in 7TD1 cells induced by IL-6. With PI 3-K specific inhibitor wortmannin, we also investigated the biological roles of Akt activation in 7TD1 cells. RESULTS: IL-6 stimulated phosphorylation of Akt in a dose- and time-dependent manner in 7TD1 cells. Wortmannin significantly reduced IL-6-induced phosphorylation of Akt and IL-6-dependent growth of 7TD1 cells. Furthermore, wortmannin blocked IL-6-induced up-regulation of XIAP, but not Bcl-2 in 7TD1 cells. CONCLUSION: The data suggest that IL-6-induced PI 3-K/Akt activation is essential for the optimal growth of 7TD1 cells through up-regulation of anti-apoptosis proteins such as XIAP

The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis

A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion. The algorithm adopts the federated architecture, and attracts increasing research interest; however, it's performance analysis is still incomplete, e.g., the error performance under noisy condition has not yet been investigated. In this paper, we focus on providing a generalized analysis by the use of the linear system theory, such that the error performance of the APC algorithm for solving linear systems in presence of additive noise can be clarified. We specifically provide a closed-form expression of the error of solution attained by the APC algorithm. Numerical results demonstrate the error performance of the APC algorithm, validating the presented analysis.Comment: 6 pages, 3 figure

Selective unresponsiveness to the inhibition of p38 MAPK activation by cAMP helps L929 fibroblastoma cells escape TNF-α-induced cell death

<p>Abstract</p> <p>Background</p> <p>The cyclic AMP (cAMP) signaling pathway has been reported to either promote or suppress cell death, in a cell context-dependent manner. Our previous study has shown that the induction of dynein light chain (DLC) by cAMP response element-binding protein (CREB) is required for cAMP-mediated inhibition of mitogen-activated protein kinase (MAPK) p38 activation in fibroblasts, which leads to suppression of NF-κB activity and promotion of tumor necrosis factor-α (TNF-α)-induced cell death. However, it remains unknown whether this regulation is also applicable to fibroblastoma cells.</p> <p>Methods</p> <p>Intracellular cAMP was determined in L929 fibroblastoma cells after treatment of the cells with various cAMP elevation agents. Effects of cAMP in the presence or absence of the RNA synthesis inhibitor actinomycin D or small interfering RNAs (siRNAs) against CREB on TNF-α-induced cell death in L929 cells were measured by propidium iodide (PI) staining and subsequent flow cytomety. The activation of p38 and c-Jun N-terminal protein kinase (JNK), another member of MAPK superfamily, was analyzed by immunoblotting. JNK selective inhibitor D-JNKi1 and p38 selective inhibitor SB203580 were included to examine the roles of JNK and p38 in this process. The expression of DLC or other mediators of cAMP was analyzed by immunoblotting. After ectopic expression of DLC with a transfection marker GFP, effects of cAMP on TNF-α-induced cell death in GFP+ cells were measured by PI staining and subsequent flow cytomety.</p> <p>Results</p> <p>Elevation of cAMP suppressed TNF-α-induced necrotic cell death in L929 fibroblastoma cells via CREB-mediated transcription. The pro-survival role of cAMP was associated with selective unresponsiveness of L929 cells to the inhibition of p38 activation by cAMP, even though cAMP significantly inhibited the activation of JNK under the same conditions. Further exploration revealed that the induction of DLC, the major mediator of p38 inhibition by cAMP, was impaired in L929 cells. Enforced inhibition of p38 activation by using p38 specific inhibitor or ectopic expression of DLC reversed the protection of L929 cells by cAMP from TNF-α-induced cell death.</p> <p>Conclusion</p> <p>These data suggest that the lack of a pro-apoptotic pathway in tumor cells leads to a net survival effect of cAMP.</p

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding window algorithm combined with entropy is applied to divide the input and output datasets across different operational conditions, establishing clear data boundaries. Following this, the prediction errors derived from the neural network under different operational states are harnessed to define a set of event-triggered relearning criteria. Once these conditions are triggered, the relevant dataset is used to recalibrate the model to the specific operational condition and predict the data under this operating condition. When the predicted data fall within the training input range of a pre-trained model, we switch to that model for immediate prediction. Compared with the conventional BP neural network model and random vector functional-link network, the proposed model can produce a better estimation accuracy and reduce computation costs. Finally, the effectiveness of our proposed method is validated through numerical simulation tests using nonlinear Hammerstein models with Gaussian noise, reflecting complex stochastic industrial processes

Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.Comment: WSDM 23, Best Paper Candidat

Pathogenic alpha-synuclein aggregates preferentially bind to mitochondria and affect cellular respiration

Abstract Misfolded alpha-synuclein (αSyn) is a major constituent of Lewy bodies and Lewy neurites, which are pathological hallmarks of Parkinson’s disease (PD). The contribution of αSyn to PD is well established, but the detailed mechanism remains obscure. Using a model in which αSyn aggregation in primary neurons was seeded by exogenously added, preformed αSyn amyloid fibrils (PFF), we found that a majority of pathogenic αSyn (indicated by serine 129 phosphorylated αSyn, ps-αSyn) was membrane-bound and associated with mitochondria. In contrast, only a minuscule amount of physiological αSyn was mitochondrial bound. In vitro, αSyn PFF displayed a stronger binding to purified mitochondria than did αSyn monomer, revealing a preferential mitochondria binding by aggregated αSyn. This selective mitochondrial ps-αSyn accumulation was confirmed in other neuronal and animal αSyn aggregation models that do not require exogenously added PFF and, more importantly, in postmortem brain tissues of patients suffering from PD and other neurodegenerative diseases with αSyn aggregation (α-synucleinopathies). We also showed that the mitochondrial ps-αSyn accumulation was accompanied by defects in cellular respiration in primary neurons, suggesting a link to mitochondrial dysfunction. Together, our results show that, contrary to physiological αSyn, pathogenic αSyn aggregates preferentially bind to mitochondria, indicating mitochondrial dysfunction as the common downstream mechanism for α-synucleinopathies. Our findings suggest a plausible model explaining the formation and the peculiar morphology of Lewy body and reveal that disrupting the interaction between ps-αSyn and the mitochondria is a therapeutic target for α-synucleinopathies.https://deepblue.lib.umich.edu/bitstream/2027.42/148288/1/40478_2019_Article_696.pd

Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking

Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage ranking often uses efficient models to generate candidates out of a set of retrieved ads. The candidates are then fed into a more computationally intensive but accurate final stage ranking system to produce the final ads recommendation. As the early and final stage ranking use different features and model architectures because of system constraints, a serious ranking consistency issue arises where the early stage has a low ads recall, i.e., top ads in the final stage are ranked low in the early stage. In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i.e. ads clicks and ads quality events) and their task relations. With our multi-task learning framework, we can not only achieve serving cost saving from the model consolidation, but also improve the ads recall and ranking consistency. In the online A/B testing, our framework achieves significantly higher click-through rate (CTR), conversion rate (CVR), total value and better ads-quality (e.g. reduced ads cross-out rate) in a large scale industrial ads ranking system.Comment: Accepted by AdKDD 2