Effects of Cations and PH on Antimicrobial Activity of Thanatin and s-Thanatin against _Escherichia coli_ ATCC25922 and _B. subtilis_ ATCC 21332

Thanatin and s-thanatin were insect antimicrobial peptides which have shown potent antimicrobial activities on a variety of microbes. In order to investigate the effect of cations and pH on the activity of these peptides against Gram-negative bacteria and Gram-positive bacteria, the antimicrobial activities of both peptides were studied in increasing concentrations of monovalent cations (K^+^ and Na^+^), divalent cations (Ca^2+^ and Mg^2+^) and H^+^. The NCCLS broth microdilution method showed that both peptides were sensitive to the presence of cations. The divalent cations showed more antagonized effect on the activity against Gram-negative bacteria than the monovalent cations, since the two peptides lost the ability to inhibit bacterial growth at a very low concentration. In addition, the activities of both peptides tested were not significantly affected by pH. Comparing to studies of other antibacterial peptide activities, our data support a hypothesis that positive ions affect the sensitivity to cation peptides

Photo-RAFT Polymerization Regulated by NIR Light for Well-Defined Polymer Synthesis.

Photoinduced reversible deactivation radical polymerization (Photo-RDRP) systems enable the preparation of functional and complex materials under light irradiation at room temperature. This technique confers spatiotemporal control towards the synthesis of well-defined polymer, which has been applied in biomedicine and manufacturing. Moreover, owing to the retention of functional groups in polymer material prepared by RDRP, photo-RDRP can be utilized for the post-modification under light irradiation. Although photo-RDRP regulated by UV and visible light have been well-established, there have been few systems utilizing NIR wavelengths for RDRP due to their lower energy. In contrast to UV and visible light, the unique properties of NIR light are promising for the development of new and exciting applications. For example, in NIR light-induced RDRP (NIR-RDRP) systems, well-defined polymers can be synthesized through opaque barriers owing to enhanced penetration of longer wavelengths. Moreover, the lower light scattering in colloidal media afforded by longer wavelengths benefits heterogenous photopolymerization systems. Notably, the low absorption of biomolecules by NIR light is less likely to cause issues in sensitive systems, such as proteins or cells are present. These advantages of NIR light open new applications of RDRP for the fabrication of advanced polymeric materials in various fields. Herein, taking advantage of the unique properties of NIR wavelengths, this thesis aims to develop a series of novel NIR-light-induced reversible addition-fragmentation chain-transfer (NIR-RAFT) polymerization systems, enabling facile synthesis of well-defined polymers through non-transparent (opaque) barriers. The first study in the thesis demonstrates an efficient photo-RAFT polymerization catalyzed by metal naphthalocyanines under NIR irradiation in the organic solvent. In the following work, we expand NIR-RAFT systems from organic solutions to aqueous media. Besides a homogenous system, a heterogenous photopolymerization system is developed for the preparation of polymeric nanoparticles. Owing to the enhanced penetration and low scattering of NIR light, the photoinduced synthesis of nanoparticles and hydrogels through non-transparent barriers has been successfully demonstrated. Finally, due to the presence of RAFT end groups, photoinduced hydrogel healing was demonstrated under NIR irradiation in the final work, creating opportunities for more exciting applications

Using Socialization and Personalization Strategies to Mitigate Intrusiveness of Social Network Advertising

As the rapid expansion of social network advertising (SNA), advertising intrusiveness becomes a constant challenge to marketers, platforms and users. Normally, socialization (i.e., anthropomorphism cues, reference group cues and social endorsement cues) and personalization advertising strategies are employed to minimize SNA intrusiveness. However, limited theoretical insights have been provided by prior research. Hence, this study aims to shed light on the influence of socialization and personalization from a information processing perspective. A 4 × 2 experiment was designed and conducted on the self-developed system. By doing these, this study significantly advances the literature on socialization and personalization in the context of SNA, and provides theoretical and managerial insight

Federated Learning for Tabular Data:Exploring Potential Risk to Privacy

Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning method-ology, since it avoids exchanging data between participants, but instead exchanges model parameters. FL has traditionally been applied to image, voice and similar data, but recently it has started to draw attention from domains including financial services where the data is predominantly tabular. However, the work on tabular data has not yet considered potential attacks, in particular attacks using Generative Adversarial Networks (GANs), which have been successfully applied to FL for non-tabular data. This paper is the first to explore leakage of private data in Federated Learning systems that process tabular data. We design a Generative Adversarial Networks (GANs)-based attack model which can be deployed on a malicious client to reconstruct data and its properties from other participants. As a side-effect of considering tabular data, we are able to statistically assess the efficacy of the attack (without relying on human observation such as done for FL for images). We implement our attack model in a recently developed generic FL software framework for tabular data processing. The experimental results demonstrate the effectiveness of the proposed attack model, thus suggesting that further research is required to counter GAN-based privacy attacks.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Distributed System

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns

Dynamic patterns are characterized by complex spatial and motion patterns. Understanding dynamic patterns requires a disentangled representational model that separates the factorial components. A commonly used model for dynamic patterns is the state space model, where the state evolves over time according to a transition model and the state generates the observed image frames according to an emission model. To model the motions explicitly, it is natural for the model to be based on the motions or the displacement fields of the pixels. Thus in the emission model, we let the hidden state generate the displacement field, which warps the trackable component in the previous image frame to generate the next frame while adding a simultaneously emitted residual image to account for the change that cannot be explained by the deformation. The warping of the previous image is about the trackable part of the change of image frame, while the residual image is about the intrackable part of the image. We use a maximum likelihood algorithm to learn the model that iterates between inferring latent noise vectors that drive the transition model and updating the parameters given the inferred latent vectors. Meanwhile we adopt a regularization term to penalize the norms of the residual images to encourage the model to explain the change of image frames by trackable motion. Unlike existing methods on dynamic patterns, we learn our model in unsupervised setting without ground truth displacement fields. In addition, our model defines a notion of intrackability by the separation of warped component and residual component in each image frame. We show that our method can synthesize realistic dynamic pattern, and disentangling appearance, trackable and intrackable motions. The learned models are useful for motion transfer, and it is natural to adopt it to define and measure intrackability of a dynamic pattern

Etiologic subtype predicts outcome in mild stroke: prospective data from a hospital stroke registry

BACKGROUND: Few studies on whether etiologic subtype can predict outcome in mild stroke are available. The study aim to explore the effect of different etiologic subtype on prognosis of these patients. METHODS: We prospectively registered consecutive cases of acute ischemic stroke from September. 01, 2009 to August. 31, 2011. Patients with National Institute of Health Stroke Scale (NIHSS) ≦3 and within 30 days of symptom onset were included. All cause death or disability (defined as modified Rankin Scale >2) were followed up at 3 months. The multivariate logistical regression model was used to analyse relationship between etiologic subtype and clinical outcomes. RESULTS: We included 680 cases, which accounted for 41.1% (680/1655) of the total registered cases. Mean age were 62.54 ± 13.51 years, and males were 65.4%. The median time of symptoms onset to admission was 72 hours. 3.8% (26/680) of cases admitted within 3 hours and 4.7% (32/680) admitted within 4.5 hours. However, no patient received intravenous thrombolysis. Of included patients, 21.5% large-artery atherosclerosis, 40.6% small-vessel disease, 7.5% cardioembolisms, 2.2% other causes and 28.2% undetermined causes. The rate of case fatality and death/disability was 2.2% and 10.1% respectively at 3 months. After adjustment of potential confounders, such as age, sex, NIHSS on admission and vascular risk factors et al., cardioembolism (RR = 3.395;95%CI 1.257 ~ 9.170) was the predictor of death or disability at 3 months and small vessel occlusion (RR = 0.412;95%CI 0.202 ~ 0.842) was the protective factor of death or disability at 3 months. CONCLUSION: Different etiologic subtype can predict the outcome in patients with mild stroke and it can help to stratify these patients for individual decision-making