Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.     

Insight into the Impact of Oxidative Stress on the Barrier Properties of Lipid Bilayer Models

As a new field of oxidative stress-based therapy, cold physical plasma is a promising tool for several biomedical applications due to its potential to create a broad diversity of reactive oxygen and nitrogen species (RONS). Although proposed, the impact of plasma-derived RONS on the cell membrane lipids and properties is not fully understood. For this purpose, the changes in the lipid bilayer functionality under oxidative stress generated by an argon plasma jet (kINPen) were investigated by electrochemical techniques. In addition, liquid chromatography-tandem mass spectrometry was employed to analyze the plasma-induced modifications on the model lipids. Various asymmetric bilayers mimicking the structure and properties of the erythrocyte cell membrane were transferred onto a gold electrode surface by Langmuir-Blodgett/Langmuir-Schaefer deposition techniques. A strong impact of cholesterol on membrane permeabilization by plasma-derived species was revealed. Moreover, the maintenance of the barrier properties is influenced by the chemical composition of the head group. Mainly the head group size and its hydrogen bonding capacities are relevant, and phosphatidylcholines are significantly more susceptible than phosphatidylserines and other lipid classes, underlining the high relevance of this lipid class in membrane dynamics and cell physiology.     

Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN:a "Conscious-Lab" development

Tri-flo cyclone,as a dense-medium separation device,is one of the most typical environmentally friendly industrial techniques in the coal washery plants.Surpris...     

Fluid Flow and Mixing in Non-Isothermal Water Model of Continuous Casting Tundish

Fluid flow and mixing of molten steel in a twin-slab-strand continuous casting tundish were investigated using a mixing model under non-isothermal conditions.This model led to a set of ordinary differential equations that were solved with a Runge-Kutta algorithm.Steady state water modeling was carried out under non-isothermal conditions.Experimental data obtained from the water model were used to calibrate the mixing model.Owing to the presence of a mixed convection in the non-isothermal conditions,a channelizing flow would be created in the fluid inside the tundish.A mixing model was designed that was capable of predicting RTD（residence time distribution）curves for different cases in non-isothermal conditions.The relationship between RTD parameters and the Tu（tundish Richardson number）was obtained for various cases under non-isothermal conditions.The results show that the RTD parameters were completely different under isothermal and non-isothermal conditions.The comparison of the RTD curves between the isothermal and non-isothermal conditions presents that the extent of mixing in the tundish in non-isothermal conditions is lower than the mixing extent in isothermal conditions.     

Camelid Single-Domain Antibodies: Promises and Challenges as Lifesaving Treatments

Since the discovery of camelid heavy-chain antibodies in 1993, there has been tremendous excitement for these antibody domains (VHHs/sdAbs/nanobodies) as research tools, diagnostics, and therapeutics. Commercially, several patents were granted to pioneering research groups in Belgium and the Netherlands between 1996–2001. Ablynx was established in 2001 with the aim of exploring the therapeutic applications and development of nanobody drugs. Extensive efforts over two decades at Ablynx led to the first approved nanobody drug, caplacizumab (Cablivi) by the EMA and FDA (2018–2019) for the treatment of rare blood clotting disorders in adults with acquired thrombotic thrombocytopenic purpura (TPP). The relatively long development time between camelid sdAb discovery and their entry into the market reflects the novelty of the approach, together with intellectual property restrictions and freedom-to-operate issues. The approval of the first sdAb drug, together with the expiration of key patents, may open a new horizon for the emergence of camelid sdAbs as mainstream biotherapeutics in the years to come. It remains to be seen if nanobody-based drugs will be cheaper than traditional antibodies. In this review, I provide critical perspectives on camelid sdAbs and present the promises and challenges to their widespread adoption as diagnostic and therapeutic agents.     

FQL‐RED: an adaptive scalable schema for active queue management

In a potentially congested network, random early detection (RED) active queue management (AQM) proved effective in improving throughput and average queuing delay. The main disadvantage of RED is its sensitive parameters that are impossible to estimate perfectly and adjust manually because of the dynamic nature of the network. For this reason, RED performs differently during different phases of a scenario and there is no guarantee that it will have optimal performance. Giving adaptability to RED has been the subject of broad research studies ever since RED was proposed. After a substantial study of AQM schemes and presenting a novel categorization for so‐called modern approaches utilizing artificial intelligence tools to improve AQM, this paper proposes an algorithm enhancing RED as an add‐on patch that makes minimal changes to the original RED. Being built on the basis of AQM schemes like ARED and Fuzzy‐RED, this algorithm inherits adaptability and is able to adjust RED inaccurate parameters regarding network traffic status, trying to optimize throughput and average queuing delay in a scenario. This algorithm is a Q‐learning method enhanced with a fuzzy inference system to provide RED with self‐adaptation and improved performance as a result. Given the name of FQL‐RED, this algorithm outperformed RED, ARED, and Fuzzy‐RED, as the OPNET simulations show. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.