Biofilms are structured microbial communities attached to surfaces, which play a significant role in the persistence of biofoulings in both medical and industrial settings. Bacteria in biofilms are mostly embedded in a complex matrix comprised of extracellular polymeric substances that provide mechanical stability and protection against environmental adversities. Once the biofilm is matured, it becomes extremely difficult to kill bacteria or mechanically remove biofilms from solid surfaces. Therefore, interrupting the bacterial surface sensing mechanism and subsequent initial binding process of bacteria to surfaces is essential to effectively prevent biofilm-associated problems. Noting that the process of bacterial adhesion is influenced by many factors, including material surface properties, this review summarizes recent works dedicated to understanding the influences of surface charge, surface wettability, roughness, topography, stiffness, and combination of properties on bacterial adhesion. This review also highlights other factors that are often neglected in bacterial adhesion studies such as bacterial motility and the effect of hydrodynamic flow. Lastly, the present review features recent innovations in nanotechnology-based antifouling systems to engineer new concepts of antibiofilm surfaces.

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Deep neural networks (DNNs) have been widely applied in various applications involving image, text, audio, and graph data. However, recent studies have shown that DNNs are vulnerable to adversarial attack. Though there are several works studying adversarial attack and defense on domains such as images and text processing, it is difficult to directly transfer the learned knowledge to graph data due to its representation challenge. Given the importance of graph analysis, increasing number of works start to analyze the robustness of machine learning models on graph. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial attack strategies on graph data and provide an unified problem formulation which can cover all current adversarial learning studies on graph. We also compare different attacks on graph data and discuss their corresponding contributions and limitations. Finally, we discuss several future research directions in this area.

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Probabilistic methods have been widely used to account for uncertainty of various sources in predicting fatigue life for components or materials. The Bayesian approach can potentially give more complete estimates by combining test data with technological knowledge available from theoretical analyses and/or previous experimental results, and provides for uncertainty quantification and the ability to update predictions based on new data, which can save time and money. The aim of the present article is to develop a probabilistic methodology for low cycle fatigue life prediction using an energy-based damage parameter with Bayes’ theorem and to demonstrate the use of an efficient probabilistic method, moreover, to quantify model uncertainty resulting from creation of different deterministic model parameters. For most high-temperature structures, more than one model was created to represent the complicated behaviors of materials at high temperature. The uncertainty involved in selecting the best model from among all the possible models should not be ignored. Accordingly, a black-box approach is used to quantify the model uncertainty for three damage parameters (the generalized damage parameter, Smith–Watson–Topper and plastic strain energy density) using measured differences between experimental data and model predictions under a Bayesian inference framework. The verification cases were based on experimental data in the literature for the Ni-base superalloy GH4133 tested at various temperatures. Based on the experimentally determined distributions of material properties and model parameters, the predicted distributions of fatigue life agree with the experimental results. The results show that the uncertainty bounds using the generalized damage parameter for life prediction are tighter than that of Smith–Watson–Topper and plastic strain energy density methods based on the same available knowledge.

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Chronic neuroinflammation is thought to play an etiological role in Alzheimer’s disease (AD), which is characterized pathologically by amyloid and tau formation, as well as neuritic dystrophy and synaptic degeneration. The causal relationship between these pathological events is a topic of ongoing research and discussion. Recent data from transgenic AD models point to a tight spatiotemporal link between neuritic and amyloid pathology, with the obligatory enzyme for β-amyloid (Aβ) production, namely β-secretase-1 (BACE1), is overexpressed in axon terminals undergoing dystrophic change. However, the axonal pathology inherent with BACE1 elevation seen in transgenic AD mice may be secondary to increased soluble Aβ in these genetically modified animals. Here we explored the occurrence of the AD-like axonal and dendritic pathology in adult rat brain affected by LPS-induced chronic neuroinflammation. Unilateral intracerebral LPS injection induced prominent inflammatory response in glial cells in the ipsilateral cortex and hippocampal formation. BACE1 protein levels were elevated the ipsilateral hippocampal lysates in the LPS treated animals relative to controls. BACE1 immunoreactive dystrophic axons appeared in the LPS-treated ipsilateral cortex and hippocampal formation, colocalizing with increased β-amyloid precursor protein and Aβ antibody (4G8) immunolabeling. Quantitative Golgi studies revealed reduction of dendritic branching points and spine density on cortical layer III and hippocampal CA3 pyramidal neurons in the LPS-treated ipsilateral cerebrum. These findings suggest that Alzheimer-like amyloidogenic axonal pathology and dendritic degeneration occur in wildtype mammalian brain in partnership with neuroinflammation following LPS injection.