Robust Allocation of Reserve Policies for a Multiple-Cell Based Power System

This paper applies a robust optimization technique for coordinating reserve allocations in multiple-cell based power systems. The linear decision rules (LDR)-based policies were implemented to achieve the reserve robustness, and consist of a nominal power schedule with a series of linear modifications. The LDR method can effectively adapt the participation factors of reserve providers to respond to system imbalance signals. The policies considered the covariance of historic system imbalance signals to reduce the overall reserve cost. When applying this method to the cell-based power system for a certain horizon, the influence of different time resolutions on policy-making is also investigated, which presents guidance for its practical application. The main results illustrate that: (a) the LDR-based method shows better performance, by producing smaller reserve costs compared to the costs given by a reference method; and (b) the cost index decreases with increased time intervals, however, longer intervals might result in insufficient reserves, due to low time resolution. On the other hand, shorter time intervals require heavy computational time. Thus, it is important to choose a proper time interval in real time operation to make a trade off

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training data. To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i.e., minimizing the dependency between inputs (i.e., features) and outputs (i.e., labels) during training the classifier. However, such a minimization process conflicts with minimizing the supervised loss that aims to maximize the dependency between inputs and outputs, causing an explicit trade-off between model robustness against MI attacks and model utility on classification tasks. In this paper, we aim to minimize the dependency between the latent representations and the inputs while maximizing the dependency between latent representations and the outputs, named a bilateral dependency optimization (BiDO) strategy. In particular, we use the dependency constraints as a universally applicable regularizer in addition to commonly used losses for deep neural networks (e.g., cross-entropy), which can be instantiated with appropriate dependency criteria according to different tasks. To verify the efficacy of our strategy, we propose two implementations of BiDO, by using two different dependency measures: BiDO with constrained covariance (BiDO-COCO) and BiDO with Hilbert-Schmidt Independence Criterion (BiDO-HSIC). Experiments show that BiDO achieves the state-of-the-art defense performance for a variety of datasets, classifiers, and MI attacks while suffering a minor classification-accuracy drop compared to the well-trained classifier with no defense, which lights up a novel road to defend against MI attacks.Comment: Accepted to KDD 2022 (Research Track

Niacin downregulates chemokine (c-c motif) ligand 2 (CCL2) expression and inhibits fat synthesis in rat liver cells

Purpose: To elucidate the role of chemokine (c-c motif) ligand 2 (CCL2) in fat metabolism in hepatocytes. Methods: Following partial hepatectomy, regenerated rat liver cells were isolated and cultured for 24 h were transfected with recombinant plasmid pEGFP-N1-CCL2 using liposomes. Niacin was added to the culture medium to inhibit fat synthesis. CCL2 expression was measured using western blot, while the expression of acly-coa synthetase long chain family 4 (ACSL4) and apolipoprotein E (ApoE) were assessed using real-time PCR. Results: At 12 h after transfection, GFP-positive rates in the pEGFP-N1 and pEGFP-N1-CCL2 transfection groups were 42.4 ± 5.6 % and 45.1 ± 3.5 %, respectively. Expression levels of CCL2 increased over time in pEGFP-N1 transfection group, pEGFP-N1-ccl2 transfection group, and niacin and pEGFP-N1-ccl2 transfection co-treatment group; however, CCL2 expression levels in the niacin and pEGFP-N1-ccl2 transfection co-treatment groups were similar to that of pEGFP-N1 transfection group, which were significantly lower than those of the pEGFP-N1-ccl2 transfection group. Expressionlevel trends of fat-related genes ACSL4 and ApoE were similar to that of CCL2. Conclusion: Niacin downregulates the expression of CCL2, thereby inhibiting lipid synthesis in liver cells. Keywords: Chemokine 2, Niacin, Hepatectomy, Lipid synthesis, Transfectio

Tet3 regulates synaptic transmission and homeostatic plasticity via DNA oxidation and repair.

Contrary to the long-held belief that DNA methylation of terminally differentiated cells is permanent and essentially immutable, post-mitotic neurons exhibit extensive DNA demethylation. The cellular function of active DNA demethylation in neurons, however, remains largely unknown. Tet family proteins oxidize 5-methylcytosine to initiate active DNA demethylation through the base-excision repair (BER) pathway. We found that synaptic activity bi-directionally regulates neuronal Tet3 expression. Functionally, knockdown of Tet or inhibition of BER in hippocampal neurons elevated excitatory glutamatergic synaptic transmission, whereas overexpressing Tet3 or Tet1 catalytic domain decreased it. Furthermore, dysregulation of Tet3 signaling prevented homeostatic synaptic plasticity. Mechanistically, Tet3 dictated neuronal surface GluR1 levels. RNA-seq analyses further revealed a pivotal role of Tet3 in regulating gene expression in response to global synaptic activity changes. Thus, Tet3 serves as a synaptic activity sensor to epigenetically regulate fundamental properties and meta-plasticity of neurons via active DNA demethylation

Willingness of people living with HIV to receive a second COVID-19 booster dose: a multicenter cross-sectional study in China

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has significantly affected the global population, with People Living with HIV (PLWH) being particularly vulnerable due to their compromised immune systems. Although vaccination is a crucial preventative measure against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, little is understood about the willingness of PLWH to receive a second COVID-19 booster dose and the factors that may influence this decision. This study investigates the willingness of PLWH in China to receive a second COVID-19 booster dose and its influencing factors, comparing these with a group of healthy individuals.MethodsA multicenter cross-sectional study was conducted across five Chinese cities, namely, Beijing, Tianjin, Zhengzhou, Hohhot, and Harbin. Participants were recruited through five community-based organizations. Data were collected via participant self-administered questionnaires included demographic information, willingness to receive a second COVID-19 booster dose, and knowledge about HIV and COVID-19 vaccination. Factors influencing vaccination willingness were identified using multivariable logistic regression analyzes.ResultsA total of 156 PLWH and 151 healthy individuals were included in the study. After adjusting for potential confounders, it was found that PLWH demonstrated a lower willingness to receive a second COVID-19 booster dose compared to healthy individuals (77.6% vs. 88.7%, p = 0.009). Lower willingness was associated with HIV positive status (Adjusted Odds Ratio [AOR]: 0.39, 95%CI: 0.20, 0.75), perceived barriers (AOR: 0.05, 95%CI: 0.01, 0.26), and perceived severity (AOR: 0.32, 95%CI: 0.12, 0.90).ConclusionPLWH in China demonstrated a lower willingness to receive a second COVID-19 booster dose compared to healthy individuals. The findings suggest that perceptions and understanding of the COVID-19 vaccination and its necessity for protection against SARS-CoV-2 could influence this willingness. Efforts should be made to strengthen and disseminate knowledge about HIV and COVID-19 vaccinations among this population. In addition, developing interventions and policies that target specific subgroups and address misconceptions about vaccination could be instrumental in improving vaccination rates among PLWH

Lumen contour segmentation in ivoct based on n-type cnn

Automatic segmentation of lumen contour plays an important role in medical imaging and diagnosis, which is the first step towards the evaluation of morphology of vessels under analysis and the identification of possible atherosclerotic lesions. Meanwhile, quantitative information can only be obtained with segmentation, contributing to the appearance of novel methods which can be successfully applied to intravascular optical coherence tomography (IVOCT) images. This paper proposed a new end-to-end neural network (N-Net) for the automatic lumen segmentation, using multi-scale features based deep neural network, for IVOCT images. The architecture of the N-Net contains a multi-scale input layer, a N-type convolution network layer and a cross-entropy loss function. The multi-scale input layer in the proposed N-Net is designed to avoid the loss of information caused by pooling in traditional U-Net and also enriches the detailed information in each layer. The N-type convolutional network is proposed as the framework in the whole deep architecture. Finally, the loss function guarantees the degree of fidelity between the output of proposed method and the manually labeled output. In order to enlarge the training set, data augmentation is also introduced. We evaluated our method against loss, accuracy, recall, dice similarity coefficient, jaccard similarity coefficient and specificity. The experimental results presented in this paper demonstrate the superior performance of the proposed N-Net architecture, comparing to some existing networks, for enhancing the precision of automatic lumen segmentation and increasing the detailed information of edges of the vascular lumen

Distributed Equivalent Substitution Training for Large-Scale Recommender Systems

We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features. DES introduces fully synchronous training to large-scale recommendation system for the first time by reducing communication, thus making the training of commercial recommender systems converge faster and reach better CTR. DES requires much less communication by substituting the weights-rich operators with the computationally equivalent sub-operators and aggregating partial results instead of transmitting the huge sparse weights directly through the network. Due to the use of synchronous training on large-scale Deep Learning Recommendation Models (DLRMs), DES achieves higher AUC(Area Under ROC). We successfully apply DES training on multiple popular DLRMs of industrial scenarios. Experiments show that our implementation outperforms the state-of-the-art PS-based training framework, achieving up to 68.7% communication savings and higher throughput compared to other PS-based recommender systems.Comment: Accepted by SIGIR '2020. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 202

Metformin represses bladder cancer progression by inhibiting stem cell repopulation via COX2/PGE2/STAT3 axis

Cancer stem cells (CSCs) are a sub-population of tumor cells playing essential roles in initiation, differentiation, recurrence, metastasis and development of drug resistance of various cancers, including bladder cancer. Although multiple lines of evidence suggest that metformin is capable of repressing CSC repopulation in different cancers, the effect of metformin on bladder cancer CSCs remains largely unknown. Using the N-methyl-N-nitrosourea (MNU)-induced rat orthotropic bladder cancer model, we demonstrated that metformin is capable of repressing bladder cancer progression from both mild to moderate/severe dysplasia lesions and from carcinoma in situ (CIS) to invasive lesions. Metformin also can arrest bladder cancer cells in G1/S phases, which subsequently leads to apoptosis. And also metformin represses bladder cancer CSC repopulation evidenced by reducing cytokeratin 14 (CK14+) and octamer-binding transcription factor 3/4 (OCT3/4+) cells in both animal and cellular models. More importantly, we found that metformin exerts these anticancer effects by inhibiting COX2, subsequently PGE2 as well as the activation of STAT3. In conclusion, we are the first to systemically demonstrate in both animal and cell models that metformin inhibits bladder cancer progression by inhibiting stem cell repopulation through the COX2/PGE2/STAT3 axis