Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

An Efficient Lossless Telemetry Data Compression and Fault Analysis System Using 2SMLZ and CMOW-DLNN

Wireless Personal Communications - In this paper, the effect of perfect electric conductor (PEC) as a ground plane on antenna array is investigated. Vertical electric dipole which is of...     

Closed loop identification and control loop reconfiguration: an industrial case study

The results of a joint university–industry collaborative project for control loop reconfiguration using closed loop experimental data from a fuel gas pressure control system are described in this paper. The fuel gas pressure was being regulated using a butane stream. For economic reasons, it was necessary to switch control to the ethane stream. Previous attempts at effecting this changeover had proved unsuccessful. In this study, a powerful system identification technique namely Canonical Variate Analysis (CVA) was employed to obtain the empirical plant models. A PI controller was then designed using the direct synthesis method. Acceptable closed loop behavior was obtained with little online tuning.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

Reduction of speckle noise from optical coherence tomography images using multi-frame weighted nuclear norm minimization method

In this paper, we propose a speckle noise reduction method for spectral-domain optical coherence tomography (SD-OCT) images called multi-frame weighted nuclear norm minimization (MWNNM). This method is a direct extension of weighted nuclear norm minimization (WNNM) in the multi-frame framework since an adequately denoised image could not be achieved with single-frame denoising methods. The MWNNM method exploits multiple B-scans collected from a small area of a SD-OCT volumetric image, and then denoises and averages them together to obtain a high signal-to-noise ratio B-scan. The results show that the image quality metrics obtained by denoising and averaging only five nearby B-scans with MWNNM method is considerably better than those of the average image obtained by registering and averaging 40 azimuthally repeated B-scans.     

A comparative study of three advanced controllers for the regulation of hypnosis

The anesthetic state is a dynamic combination of hypnosis, analgesia and neuromuscular blockade that is maintained by infusing a cocktail of drugs. This work focuses on controlling the hypnosis during a surgical procedure by automatic regulation of isoflurane and employs Bispectral Index (BIS) as the primary controlled variable. A seventh-order nonlinear pharmacokinetic–pharmacodynamic representation has been used for the hypnosis dynamics of patients. This study uses a model predictive control structure for the regulation of BIS. Performance of this controller has been tested for a range of patients and compared with two previously employed control strategies (cascade internal model controller and cascade controller with modeling error compensation). Performance of the three controllers has also been studied for a step change in BIS, measured disturbances and noise in the measured variables. Numerical simulations show that the model predictive controller performed better than the other two controllers.     

Securing User-Controlled Routing Infrastructures

Designing infrastructures that give untrusted third parties (such as end-hosts) control over routing is a promising research direction for achieving flexible and efficient communication. However, serious concerns remain over the deployment of such infrastructures, particularly the new security vulnerabilities they introduce. The flexible control plane of these infrastructures can be exploited to launch many types of powerful attacks with little effort. In this paper, we make several contributions towards studying security issues in forwarding infrastructures (FIs). We present a general model for an FI, analyze potential security vulnerabilities, and present techniques to address these vulnerabilities. The main technique that we introduce in this paper is the use of simple lightweight cryptographic constraints on forwarding entries. We show that it is possible to prevent a large class of attacks on end-hosts and bound the flooding attacks that can be launched on the infrastructure nodes to a small constant value. Our mechanisms are general and apply to a variety of earlier proposals such as , DataRouter, and Network Pointers.     

Predicting the dilution of plasma transferred arc hardfacing of stellite on carbon steel using response surface methodology

Control of dilution is important in hardfacing, where low dilution is typically desirable. At present, most fabrication industries use shielded metal are welding, gas metal arc welding, gas tungsten arc welding and submerged are welding processes for hardfacing purposes. In these processes, the percentage of the dilution level is higher, ranging between 10% and 30%. In Plasma Transferred Arc (PTA) hardfacing, a solidified metallurgical bond between the deposit and the substrate is obtained with minimum dilution (less than 10%). This paper highlights the application of response surface methodology to predict and optimize the percentage of the dilution of a cobalt-based hardfaced surface produced by the PTA process. Experiments were conducted based on a fully replicable five-factor, five-level central composite rotatable design and a mathematical model was developed using response surface methodology. Furthermore, the response surface methodology was used to optimize the process parameters that yield the lowest percentage of dilution.     

Immune and Anticancer Responses Elicited by Fully Synthetic Aberrantly Glycosylated MUC1 Tripartite Vaccines Modified by a TLR2 or TLR9 Agonist

The mucin MUC1 is overexpressed and aberrantly glycosylated by many epithelial cancer cells manifested by truncated O‐linked saccharides. Although tumor‐associated MUC1 has generated considerable attention because of its potential for the development of a therapeutic cancer vaccine, it has been difficult to design constructs that consistently induce cytotoxic T‐lymphocytes (CTLs) and ADCC‐mediating antibodies specific for the tumor form of MUC1. We have designed, chemically synthesized, and immunologically examined vaccine candidates each composed of a glycopeptide derived from MUC1, a promiscuous T_helper peptide, and a TLR2 (Pam₃CysSK₄) or TLR9 (CpG‐ODN 1826) agonist. It was found that the Pam₃CysSK₄‐containing compound elicits more potent antigenic and cellular immune responses, resulting in a therapeutic effect in a mouse model of mammary cancer. It is thus shown, for the first time, that the nature of an inbuilt adjuvant of a tripartite vaccine can significantly impact the quality of immune responses elicited against a tumor‐associated glycopeptide. The unique adjuvant properties of Pam₃CysSK₄, which can reduce the suppressive function of regulatory T cells and enhance the cytotoxicity of tumor‐specific CTLs, are likely responsible for the superior properties of the vaccine candidate 1 .