Resilient and social health service network design to reduce the effect of COVID-19 outbreak.

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.

Design of control strategies to help prevent the spread of COVID-19 pandemic.

This paper proposes control strategies to allocate COVID-19 patients to screening facilities, health facilities, and quarantine facilities for minimizing the spread of the virus by these patients. To calculate the transmission rate, we propose a function that accounts for contact rate, duration of the contact, age structure of the population, susceptibility to infection, and the number of transmission events per contact. Moreover, the COVID-19 cases are divided into different groups according to the severity of their disease and are allocated to appropriate health facilities that provide care tailored to their needs. The multi-stage fuzzy stochastic programming approach is applied to cope with uncertainty, in which the probability associated with nodes of the scenario tree is treated as fuzzy variables. To handle the probabilistic model, we use a more flexible measure, M e measure, which allows decision-makers to adopt varying attitudes by assigning the optimistic-pessimistic parameter. This measure does not force decision-makers to hold extreme views and obtain the interval solution that provides further information in the fuzzy environment. We apply the proposed model to the case of Tehran, Iran. The results of this study indicate that assigning patients to appropriate medical centers improves the performance of the healthcare system. The result analysis highlights the impact of the demographic differences on virus transmission, and the older population has a greater influence on virus transmission than other age groups. Besides, the results indicate that behavioral changes in the population and their vaccination play a key role in curbing COVID-19 transmission.

Enabling Solution Processable COFs through Suppression of Precipitation during Solvothermal Synthesis.

Covalent organic frameworks (COFs) are crystalline, nanoporous materials of interest for various applications, but current COF synthetic routes lead to insoluble aggregates which precludes processing for practical implementation. Here, we report a COF synthesis method that produces a stable, homogeneous suspension of crystalline COF nanoparticles that enables the preparation of COF monoliths, membranes, and films using conventional solution-processing techniques. Our approach involves the use of a polar solvent, diacid catalyst, and slow reagent mixing procedure at elevated temperatures which altogether enable access to crystalline COF nanoparticle suspension that does not aggregate or precipitate when kept at elevated temperatures. On cooling, the suspension undergoes a thermoreversible gelation transition to produce crystalline and highly porous COF materials. We further show that the modified synthesis approach is compatible with various COF chemistries, including both large- and small-pore imine COFs, hydrazone-linked COFs, and COFs with rhombic and hexagonal topologies, and in each case, we demonstrate that the final product has excellent crystallinity and porosity. The final materials contain both micro- and macropores, and the total porosity can be tuned through variation of sample annealing. Dynamic light scattering measurements reveal the presence of COF nanoparticles that grow with time at room temperature, transitioning from a homogeneous suspension to a gel. Finally, we prepare imine COF membranes and measure their rejection of polyethylene glycol (PEG) polymers and oligomers, and these measurements exhibit size-dependent rejection and adsorption of PEG solutes. This work demonstrates a versatile processing strategy to create crystalline and porous COF materials using solution-processing techniques and will greatly advance the development of COFs for various applications.

Transgenic force sensors and software to measure force transmission across the mammalian nuclear envelope in vivo.

Nuclear mechanotransduction is a growing field with exciting implications for the regulation of gene expression and cellular function. Mechanical signals may be transduced to the nuclear interior biochemically or physically through connections between the cell surface and chromatin. To define mechanical stresses upon the nucleus in physiological settings, we generated transgenic mouse strains that harbour FRET-based tension sensors or control constructs in the outer and inner aspects of the nuclear envelope. We knocked-in a published esprin-2G sensor to measure tensions across the LINC complex and generated a new sensor that links the inner nuclear membrane to chromatin. To mitigate challenges inherent to fluorescence lifetime analysis in vivo, we developed software (FLIMvivo) that markedly improves the fitting of fluorescence decay curves. In the mouse embryo, the sensors responded to cytoskeletal relaxation and stretch applied by micro-aspiration. They reported organ-specific differences and a spatiotemporal tension gradient along the proximodistal axis of the limb bud, raising the possibility that mechanical mechanisms coregulate pattern formation. These mouse strains and software are potentially valuable tools for testing and refining mechanotransduction hypotheses in vivo.

An integrated socially responsible-efficient approach toward health service network design.

Despite the fact that medical responses are crucial for saving precious lives during any humanitarian crisis (e.g., the COVID-19 pandemic), healthcare infrastructure in many communities are partially covered or are not covered yet. In order to strengthen the health system response to such crisis, especially in low- to middle-income communities, this paper extends a novel multi-objective model for designing a health service network under uncertainty which simultaneously considers efficiency, social responsibility, and network cost. For efficiency, a modified data envelopment analysis model is introduced and inserted into the proposed model to decrease the inefficiency of healthcare facilities belonging to the different tiers of the health system. For social responsibility, two measures of job creation and balanced development are incorporated into the extended model. This is not only considered to cope with the increased numbers of patients and disaster victims to healthcare facilities but also to deal with the challenge of the economy and the livelihoods of people during the crisis. Moreover, a novel mixed possibilistic-flexible robust programming (MPFRP) approach is developed to protect the considered network against uncertainty. To show the applicability of the extended model, a real-world case study is presented. The results reveal that contrary to fuzzy programming models, the MPFRP performs well in terms of social responsibility (72%), cost (8%), and efficiency (28%) and is able to make a trade-off between these three measures. In this study, the resilience level of the designed network is not addressed while disregarding any short-term stoppage owing to internal or external sources of disruption in designing may bring about a considerable loss.

A novel capacity sharing mechanism to collaborative activities in the blood collection process during the COVID-19 outbreak.

Because of government intervention, such as quarantine and cancellation of public events at the peak of the COVID-19 outbreak and donors' health scare of exposure to the virus in medical centers, the number of blood donors has considerably decreased. In some countries, the rate of blood donation has reached lower than 30%. Accordingly, in this study, to fill the lack of blood product during COVID-19, especially at the outbreak's peak, we propose a novel mechanism by providing a two-stage optimization tool for coordinating activities to mitigate the shortage in this urgent situation. In the first stage, a blood collection plan considering disruption risk in supply to minimize the unmet demand will be solved. Afterward, in the second stage, the collected units will be shared between regions by applying the capacity sharing concept to avoid the blood shortage in health centers. Moreover, to tackle the uncertainty and disruption risk, a novel stochastic model combining the mixed uncertainty approach is tailored. A rolling horizon planning method is implemented under an iterative procedure to provide and share the limited blood resources to solve the proposed model. A real-world case study of Iran is investigated to examine the applicability and performance of the proposed model; it should be noted that the designed mechanism is not confined just to this case. Obtained computational results indicate the applicability of the model, the superior performance of the capacity sharing concept, and the effectiveness of the designed mechanism for mitigating the shortage and wastage during the COVID-19 outbreak.

Designing a testing kit supply network for suspected COVID-19 cases under mixed uncertainty approach.

Motivated by the COVID-19 (C-19) pandemic and the challenges it poses to global health and the medical communities, this research aims to investigate the factors affecting of reduction health inequalities related to the C-19 to tackle the increasing number of outbreaks and their social consequences in such a pandemic. Hence, we design a COVID-19 testing kit supply network (C-19TKSN) to allocate various C-19 test kits to the suspected C-19 cases depending on the time between the emergence of their first symptoms and the time they are tested. In particular, this model aims to minimize the total network cost and decrease false results C-19 test by considering the fundamental characteristics of a diagnostic C-19 test (i.e., specificity and sensitivity). In the sensitivity characteristic, a gamma formula is presented to estimate the error rate of false-negative results. The nature of the C-19TKSN problem is dynamic over time due to difficult predictions and changes in the number of C-19 patients. For this reason, we consider the potential demands relating to different regions of the suspected C-19 cases for various C-19 test kits and the rate of prevalence of C-19 as stochastic parameters. Accordingly, a multi-stage stochastic programming (MSSP) method with a combined scenario tree is proposed to deal with the stochastic data in a dynamic environment. Then, a fuzzy approach is employed based on M e measure to cope with the epistemic uncertainty of input data. Eventually, the practicality and capability of the proposed model are shown in a real-life case in Iran. The results demonstrate that the performance of the MSSP model is significantly better in comparison with the two-stage stochastic programming (TSSP) model regarding the false results and the total cost of the network.

Memory Seeds Enable High Structural Phase Purity in 2D Perovskite Films for High-Efficiency Devices.

2D perovskites are a class of halide perovskites offering a pathway for realizing efficient and durable optoelectronic devices. However, the broad chemical phase space and lack of understanding of film formation have led to quasi-2D perovskite films with polydispersity in perovskite layer thicknesses, which have hindered device performance and stability. Here, a simple and scalable approach is reported, termed as the "phase-selective method", to fabricate 2D perovskite thin films with homogenous layer thickness (phase purity). The phase-selective method involves the dissolution of single-crystalline powders with a homogeneous perovskite layer thickness in desired solvents to fabricate thin films. In situ characterizations reveal the presence of sub-micrometer-sized seeds in solution that preserve the memory of the dissolved single crystals and dictate the nucleation and growth of grains with an identical thickness of the perovskite layers in thin films. Photovoltaic devices with a p-i-n architecture are fabricated with such films, which yield an efficiency of 17.1% enabled by an open-circuit voltage of 1.20 V, while preserving 97.5% of their peak performance after 800 h under illumination without any external thermal management.

A reactive phase against disruptions for designing a proactive platelet supply network.

Previous studies in blood supply chain network design often follow a commonly used approach in protecting the chain against disruptions, considering the effects of disruptions on the designing phase. However, in many real-world situations, disruptions cannot be adequately measured in advance. Moreover, using disruptions in the designing phase through the common two-stage stochastic programming models impose high costs on the network, since they cannot be updated based on unpredicted disruptions. This paper proposes an updatable two-phase approach which deals with disruptions in the operational phase, not in the strategic design phase. In the first step, called the proactive phase, a nominal platelet supply chain network is designed under operational uncertainty, using the whole-blood collection method. In the event of disruptions, the second step, called the reactive phase, is applied, and the tailored network is updated based on the realized data, using apheresis as the collection mechanism. The operational risks are captured using a fuzzy programming approach in the model. Based on the real data from Fars province of Iran, we compare the performance of the two-phase approach with the commonly used approaches in the literature, resulting in more flexible decisions, and consequently, less conservatism degree rather than the existing approaches.

Remote Doping of Scalable Nanowire Branches.

Selective-area epitaxy provides a path toward high crystal quality, scalable, complex nanowire networks. These high-quality networks could be used in topological quantum computing as well as in ultrafast photodetection schemes. Control of the carrier density and mean free path in these devices is key for all of these applications. Factors that affect the mean free path include scattering by surfaces, donors, defects, and impurities. Here, we demonstrate how to reduce donor scattering in InGaAs nanowire networks by adopting a remote-doping strategy. Low-temperature magnetotransport measurements indicate weak anti-localization-a signature of strong spin-orbit interaction-across a nanowire Y-junction. This work serves as a blueprint for achieving remotely doped, ultraclean, and scalable nanowire networks for quantum technologies.

Spatial mapping of tissue properties in vivo reveals a 3D stiffness gradient in the mouse limb bud.

Numerous hypotheses invoke tissue stiffness as a key parameter that regulates morphogenesis and disease progression. However, current methods are insufficient to test hypotheses that concern physical properties deep in living tissues. Here we introduce, validate, and apply a magnetic device that generates a uniform magnetic field gradient within a space that is sufficient to accommodate an organ-stage mouse embryo under live conditions. The method allows rapid, nontoxic measurement of the three-dimensional (3D) spatial distribution of viscoelastic properties within mesenchyme and epithelia. Using the device, we identify an anteriorly biased mesodermal stiffness gradient along which cells move to shape the early limb bud. The stiffness gradient corresponds to a Wnt5a-dependent domain of fibronectin expression, raising the possibility that durotaxis underlies cell movements. Three-dimensional stiffness mapping enables the generation of hypotheses and potentially the rigorous testing of mechanisms of development and disease.

Oscillatory cortical forces promote three dimensional cell intercalations that shape the murine mandibular arch.

Multiple vertebrate embryonic structures such as organ primordia are composed of confluent cells. Although mechanisms that shape tissue sheets are increasingly understood, those which shape a volume of cells remain obscure. Here we show that 3D mesenchymal cell intercalations are essential to shape the mandibular arch of the mouse embryo. Using a genetically encoded vinculin tension sensor that we knock-in to the mouse genome, we show that cortical force oscillations promote these intercalations. Genetic loss- and gain-of-function approaches show that Wnt5a functions as a spatial cue to coordinate cell polarity and cytoskeletal oscillation. These processes diminish tissue rigidity and help cells to overcome the energy barrier to intercalation. YAP/TAZ and PIEZO1 serve as downstream effectors of Wnt5a-mediated actomyosin polarity and cytosolic calcium transients that orient and drive mesenchymal cell intercalations. These findings advance our understanding of how developmental pathways regulate biophysical properties and forces to shape a solid organ primordium.