Fluoxetine treatment for major depression decreases the plasma levels of cytokines

Elevated levels of pro-inflammatory biomarkers have been reported in major depressive disorder (MDD). The aim of this study is to investigate the plasma levels of interleukin-18 (IL-18), macrophageinflammatory protein-1α (MIP-1α), monocyte chemoattractant protein 1 (MCP-1), stromal cell derived factor-1 (SDF-1), and regulated upon activation, normal T cell  expressed and secreted (RANTES) in patients with MDD before and after  eight week treatment of fluoxetine hydrochloride in comparison with normal controls. All subjects were assessed before and after treatment with the Hamilton Depression Rating Scale (HDRS). Our results showed that the symptoms of forty healthy controls and thirty-four patients with MDD were correlated with their plasma levels of IL-18, MIP-1α, MCP-1, SDF-1α, and RANTES. The levels of all five cytokine of patients with MDD were significantly decreased after treatment.  However, the levels remained significantly higher than those of the healthy controls (p<0.001). In the seven depressed subjects whose HDRS score fell to below seven after antidepressant therapy comparing with those subjects whose HDRS score larger than seven, the mean levels of IL-18 (p=0.01) and SDF-1α(p<0.05) were significantly lower. Conversely, higher levels of cytokines correlated with a persistently increased severity of symptoms, as measured by the HDRS scores. In conclusion, these findings suggest that MDD is associated with activation of the immune system, and the antidepressant effect of fluoxetine may be mediated in part through its anti-inflammatory effects.Key words: Fluoxetine hydrochloride, major depression, cytokine, chemokine, inflammation

Detecting cyberattacks in industrial control systems using online learning algorithms

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusion detection systems that serve as the first line of defense by monitoring and reporting potentially malicious activities. Classical machine-learning-based intrusion detection methods usually generate prediction models by learning modest-sized training samples all at once. Such approach is not always applicable to industrial control systems, as industrial control systems must process continuous control commands with limited computational resources in a nonstop way. To satisfy such requirements, we propose using online learning to learn prediction models from the controlling data stream. We introduce several state-of-the-art online learning algorithms categorically, and illustrate their efficacies on two typically used testbeds---power system and gas pipeline. Further, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance problem that is pervasive in industrial intrusion detection systems. Our experimental results indicate that the proposed algorithm can achieve an overall improvement in the detection rate of cyberattacks in industrial control systems

Aggregated Gradient Langevin Dynamics

In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic convergence of AGLD with a unified analysis for different data accessing (e.g. random access, cyclic access and random reshuffle) and snapshot updating strategies, under convex and nonconvex settings respectively. It is the first time that bounds for I/O friendly strategies such as cyclic access and random reshuffle have been established in the MCMC literature. The theoretic results also indicate that methods in AGLD possess the merits of both the low per-iteration computational complexity and the short mixture time. Empirical studies demonstrate that our framework allows to derive novel schemes to generate high-quality samples for large-scale Bayesian posterior learning tasks

Identification of the ADPR binding pocket in the NUDT9 homology domain of TRPM2

Activation of the transient receptor potential melastatin 2 (TRPM2) channel occurs during the response to oxidative stress under physiological conditions as well as in pathological processes such as ischemia and diabetes. Accumulating evidence indicates that adenosine diphosphate ribose (ADPR) is the most important endogenous ligand of TRPM2. However, although it is known that ADPR binds to the NUDT9 homology (NUDT9-H) domain in the intracellular C-terminal region, the molecular mechanism underlying ADPR binding and activation of TRPM2 remains unknown. In this study, we generate a structural model of the NUDT9-H domain and identify the binding pocket for ADPR using induced docking and molecular dynamics simulation. We find a subset of 11 residues—H1346, T1347, T1349, L1379, G1389, S1391, E1409, D1431, R1433, L1484, and H1488—that are most likely to directly interact with ADPR. Results from mutagenesis and electrophysiology approaches support the predicted binding mechanism, indicating that ADPR binds tightly to the NUDT9-H domain, and suggest that the most significant interactions are the van der Waals forces with S1391 and L1484, polar solvation interaction with E1409, and electronic interactions (including π–π interactions) with H1346, T1347, Y1349, D1431, and H1488. These findings not only clarify the roles of a range of newly identified residues involved in ADPR binding in the TRPM2 channel, but also reveal the binding pocket for ADPR in the NUDT9-H domain, which should facilitate structure-based drug design for the TRPM2 channel

Synthesis and Characterization of ZnO Nanorods and Nanodisks from Zinc Chloride Aqueous Solution

ZnO nanorods and nanodisks were synthesized by solution process using zinc chloride as starting material. The morphology of ZnO crystal changed greatly depending on the concentrations of Zn2+ion and ethylene glycohol (EG) additive in the solution. The effect of thermal treatment on the morphology was investigated. Photocatalytic activities of plate-like Zn5(OH)8Cl2 · H2O and rod-like ZnO were characterized. About 18% of 1 ppm NO could be continuously removed by ZnO particles under UV light irradiation