Formation mechanism of a novel core-shell with tetradecyl dimethyl benzyl ammonium-modified montmorillonite interlayer nanofibrous membrane and its antimicrobial properties.

A novel core-shell with a tetradecyl dimethyl benzyl ammonium chloride-modified montmorillonite (TDMBA/MMT) interlayer silk fibroin (SF)/poly(lactic acid) (PLLA) nanofibrous membrane was fabricated using a simple conventional electrospinning method. Scanning electron microscopy and pore size analyses revealed that this core-shell with TDMBA/MMT interlayer maintained its nanofibrous morphology and larger pore structure more successfully than SF/PLLA nanofibrous membranes after treatment with 75% ethanol vapor. Transmission electron microscopy and energy-dispersive X-ray spectroscopy analyses testified that the SF/PLLA-TDMBA/MMT nanofibers exhibited a core-shell with an interlayer structure, with SF/PLLA in the core-shell layer and TDMBA/MMT in the interlayer. The formation of a core-shell with interlayer nanofibers was primarily attributed to the uniform dispersion of TDMBA/MMT nanosheets in a solution owing to its exfoliation using hexafluoroisopropanol and then preparing a stable spinning solution similar to an emulsion. Compared to SF/PLLA nanofibrous membranes, the core-shell structure with TDMBA/MMT interlayers of SF/PLLA nanofibrous membranes exhibited enhanced hydrophilicity, thermal stability, mechanical properties as well as improved and long-lasting antimicrobial performance against Escherichia coli and Staphylococcus aureus without cytotoxicity.

Stabilized cyclic peptides as modulators of protein-protein interactions: promising strategies and biological evaluation.

Protein-protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs "undruggable". Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, ß-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novo development of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically "intractable" PPI systems.

A Novel 1000 MPa Grade Ultrafine-Grained Dual-Phase Press Hardening Steel with Superior Oxidation Resistance and High Ductility.

1000 MPa grade low-carbon martensite press hardening steels (PHS) are widely used in energy-absorbing domains of automotive parts, such as the bottom of a B-pillar. To prevent oxide scale formation during hot forming, this PHS is often required to be protected by an additional Al-Si coating. In addition, although the low carbon martensitic microstructure grants it excellent bending toughness, the ductility tends to be limited. In this study, a novel 1000 MPa grade ultrafine-grained (UFG) martensite-ferrite (F-M) dual-phase (DP) PHS with superior oxidation resistance was designed using tailored additions of Cr, Mn, and Si, and refining the initial microstructure. Only 0.55 ± 0.18 µm thick oxide film is formed in the designed steel during austenitizing heating and stamping, which is significantly lower than the 24.6 ± 3.1 µm thick oxide film formed in conventional 1000 MPa grade low-carbon martensite PHS under the identical condition. The superior oxidation resistance of designed steel can be attributed to the rapid formation of the protective Si-rich, Cr-rich, and Mn-rich oxide layers during annealing. Moreover, due to the presence of ferrite and ultrafine microstructure, the designed steel also shows a significant improvement in ductility from 8.5% to 16.8% without sacrificing strength and bending toughness compared with conventional 1000 MPa grade low-carbon martensite PHS.

CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks.

In wireless sensor networks (WSNs), there are many challenges for outlier detection, such as fault detection, fraud detection, intrusion detection, and so on. In this paper, the participation degree of instances in the hierarchical clustering process infers the relationship between instances. However, most of the existing algorithms ignore such information. Thus, we propose a novel fault detection technique based on the participation degree, called fault detection based on participation degree (FDP). Our algorithm has the following advantages. First, it does not need data training in labeled datasets; in fact, it uses the participation degree to measure the differences between fault points and normal points without setting distance or density parameters. Second, FDP can detect global outliers without local cluster influence. Experimental results demonstrate the performance of our approach by applying it to synthetic and real-world datasets and contrasting it with four well-known techniques: isolation forest (IF), local outlier factor (LOF), one-class support vector machine (OCS), and robust covariance (RC).

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks.

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.