Efficient strategies to contain the coronavirus disease 2019 (COVID-19) pandemic are peremptory to relieve the negatively impacted public health and global economy, with the full scope yet to unfold. In the absence of highly effective drugs, vaccines, and abundant medical resources, many measures are used to manage the infection rate and avoid exhausting limited hospital resources. Wearing masks is among the non-pharmaceutical intervention (NPI) measures that could be effectively implemented at a minimum cost and without dramatically disrupting social practices. The mask-wearing guidelines vary significantly across countries. Regardless of the debates in the medical community and the global mask production shortage, more countries and regions are moving forward with recommendations or mandates to wear masks in public. Our study combines mathematical modeling and existing scientific evidence to evaluate the potential impact of the utilization of normal medical masks in public to combat the COVID-19 pandemic. We consider three key factors that contribute to the effectiveness of wearing a quality mask in reducing the transmission risk, including the mask aerosol reduction rate, mask population coverage, and mask availability. We first simulate the impact of these three factors on the virus reproduction number and infection attack rate in a general population. Using the intervened viral transmission route by wearing a mask, we further model the impact of mask-wearing on the epidemic curve with increasing mask awareness and availability. Our study indicates that wearing a face mask can be effectively combined with social distancing to flatten the epidemic curve. Wearing a mask presents a rational way to implement as an NPI to combat COVID-19. We recognize our study provides a projection based only on currently available data and estimates potential probabilities. As such, our model warrants further validation studies.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the "single disease" approach appears untenable, as does the notion of individualizing each single patient's care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.Describing aspects of biology as "heterogeneous" often has a negative connotation. It is a term that is used when we do not understand a measured or observed aspect of disease or when we need to explain data that are not consistent. However, it is evident that recognizing that there are "different kinds" of cells, genes, types of response, and severity of disease could offer a set of opportunities for therapies to work and biomarkers to be meaningful. Thus, it may be time to exploit heterogeneity rather than curse it and to use the opportunity to carve out endotypes of type 1 diabetes that have traction both in the clinic and in the laboratory.The introduction of the term "endotype" can largely be attributed to developments in the field of asthma (1) when it became apparent in the late 1990s that different pathogenic mechanisms induce a similar symptom cluster and manifest as a

Mobile memory capacity (a) Best performance achievable Mobile memory capacity (b) Performance trained on global image Mobile memory capacity (c) Performance trained on local patchesFigure 1: Inference memory and mean intersection over union (mIoU) accuracy on the DeepGlobe dataset [1]. (a): Comparison of best achievable mIoU v.s. memory for different segmentation methods. (b): mIoU/memory with different global image sizes (downsampling rate shown in scale annotations). (c): mIoU/memory with different local patch sizes (normalized patch size shown in scale annotations). GLNet (red dots) integrates both global and local information in a compact way, contributing to a well-balanced trade-off between accuracy and memory usage. See Section 4 for experiment details. Methods studied: ICNet [2], DeepLabv3+ [3], FPN [4], FCN-8s [5], UNet [6], PSPNet [7], SegNet [8], and the proposed GLNet. AbstractSegmentation of ultra-high resolution images is increasingly demanded, yet poses significant challenges for algorithm efficiency, in particular considering the (GPU) memory limits. Current approaches either downsample an ultrahigh resolution image or crop it into small patches for separate processing. In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy. We propose collaborative Global-Local Networks (GLNet) to effectively preserve both global and local information in a highly memory-efficient manner. GLNet is composed of a global branch and a local branch, taking the downsampled entire image and its cropped local patches as respective inputs. For segmentation, GLNet deeply fuses feature maps from two branches, capturing both the high-resolution fine structures from zoomed-in local patches and the contextual dependency from the downsampled input. To further resolve the potential class imbalance problem between background and foreground regions, we present a coarse-to-fine variant of GLNet, also being * The first two authors contributed equally. memory-efficient. Extensive experiments and analyses have been performed on three real-world ultra-high aerial and medical image datasets (resolution up to 30 million pixels). With only one single 1080Ti GPU and less than 2GB memory used, our GLNet yields high-quality segmentation results and achieves much more competitive accuracymemory usage trade-offs compared to state-of-the-arts.

PurposeBlockchain technology is booming in many industries. Its application in supply chain management is also gradually increasing. Supply chain management (SCM) has long been committed to reducing costs and increasing efficiency and is trying to optimise resources and reduce the sector's fragmentation. Trust has always been an important factor in managing supply chain relationships, and it also affects the efficiency of supply chain operations. To this end, this study aims to examine how trust is affected by the introduction of blockchain technology in construction supply chain management.Design/methodology/approachThis study is based on semi-structured interviews and publicly available information from experts in blockchain and construction supply chain management. Through content analysis, the data are analysed thematically to explore how various types of trust, such as system-based, cognition-based and relation-based, are affected by blockchain technology.FindingsBlockchain technology provides solutions for data tracking, contracting and transferring resources in supply chain management. These applications help enhance the various sources of trust in SCM and provide supply chain partners with protection mechanisms to avoid the risks and costs of opportunistic behaviour in collaboration, shifting trust from relational to system-based and cognition-based.Research limitations/implicationsThis study focuses only on inter-organisational rather than interpersonal trust and empirical data from experts whose knowledge and cognition could be subjective.Practical implicationsLeveraging the potential of digitalisation to manage trust requires that leaders and managers actively try to improve contractual arrangements, information sharing and being open to new innovative technologies like blockchain.Social implicationsFrom a relational view of supply chain management, the extent to which blockchain technology can develop and spread depends on the readiness of the social capital to accept decentralised governance structures.Originality/valueThis study builds upon an original data set and discusses features and applications of blockchain technology, explores the sources and dimensions of trust in supply chain management and explains the impact of blockchain technology on trust.

An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO 3 -based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba 0.5 Ca 0.5 )TiO 3 -Ba(Ti 0.7 Zr 0.3 )O 3 , with piezoelectric properties that show better temperature reliability than other BaTiO 3 -based piezoelectrics in our initial training data.piezoelectric materials | materials informatics | Bayesian learning | morphotropic phase boundary | Pb-free materials A ccelerating the process of materials design and discovery is an emerging theme in materials science (1). The emphasis has so far largely been on screening databases or using datadriven approaches that infer predictions directly from the data, be it from high-throughput calculations or experimental measurements (2-6). However, a distinguishing aspect of materials science is that in addition to data there exists a substantial body of knowledge in the form of phenomenological models and physical theories that could be used to constrain the inference models. Hence, a key challenge in materials informatics is to incorporate knowledge to make predictions that are more robust than would be possible by using data alone. Although such knowledge is used in choosing features or descriptors for materials informatics (7-9), it has seldom been used to encode prior information in the form of probability distributions and uncertainties for predicting novel materials with desired properties. Bayesian inference, which permits integration of prior knowledge or beliefs with the observed data, has shown considerable promise in cancer genomics (10) using metabolic pathway information, and in systems biology (11), but has been little explored in materials science. Our objective is to combine empirical data and materials knowledge within a Bayesian approach coupled to the results of Landau-Devonshire theory (12, 13) to design better BaTiO3-based lead-free piezoelectrics.Piezoelectric materials, such as the solid solutions of BaTiO3, are best suited for exploring Bayesian inference methods because historically they are well modeled by Landau-Devonshire theory (12-14) and equations exist for describing some of the key characteristics that determine the functional response, such as the morphotropic phase boundary (MPB) (15, 16). These equations serve as "constraints" that encode prior knowledge within our Bayesian formalism. Furthermore, BaTiO3-based solid solutions represent an important class of potential substitutes for Pb-based materials, which suffer from environmental concerns. Akin to the Pb-based piezoelectrics, MPBs can be established in BaTiO3-based solid solutions that enable polarization and structural inst...

The accelerated exploration of the materials space in order to identify configurations with optimal properties is an ongoing challenge. Current paradigms are typically centered around the idea of performing this exploration through high-throughput experimentation/computation. Such approaches, however, do not account for-the always present-constraints in resources available. Recently, this problem has been addressed by framing materials discovery as an optimal experiment design. This work augments earlier efforts by putting forward a framework that efficiently explores the materials design space not only accounting for resource constraints but also incorporating the notion of model uncertainty. The resulting approach combines Bayesian Model Averaging within Bayesian Optimization in order to realize a system capable of autonomously and adaptively learning not only the most promising regions in the materials space but also the models that most efficiently guide such exploration. The framework is demonstrated by efficiently exploring the MAX ternary carbide/nitride space through Density Functional Theory (DFT) calculations.

Real-world problems often involve complex systems that cannot be perfectly modeled or identified, and many engineering applications aim to design operators that can perform reliably in the presence of such uncertainty. In this paper, we propose a novel Bayesian framework for objective-based uncertainty quantification (UQ), which quantifies the uncertainty in a given system based on the expected increase of the operational cost that it induces. This measure of uncertainty, called MOCU (mean objective cost of uncertainty), provides a practical way of quantifying the effect of various types of system uncertainties on the operation of interest.Furthermore, the proposed UQ framework provides a general mathematical basis for designing robust operators, and it can be applied to diverse applications, including robust filtering, classification, and control. We demonstrate the utility and effectiveness of the proposed framework by applying it to the problem of robust structural intervention of gene regulatory networks, an important application in translational genomics. Index TermsMean objective cost of uncertainty (MOCU), objective-based uncertainty quantification (UQ), robust operator design, robust network intervention.Byung-Jun Yoon is with the

Summary Cryptococcus neoformans (Cn) is a deadly fungal pathogen whose intracellular lifestyle is important for virulence. Host mechanisms controlling fungal phagocytosis and replication remain obscure. Here, we perform a global phosphoproteomic analysis of the host response to Cryptococcus infection. Our analysis reveals numerous and diverse host proteins that are differentially phosphorylated following fungal ingestion by macrophages, thereby indicating global reprogramming of host kinase signaling. Notably, phagocytosis of the pathogen activates the host autophagy initiation complex (AIC) and the upstream regulatory components LKB1 and AMPKα1, which regulate autophagy induction through their kinase activities. AMPKα1 deletion in monocytes results in resistance to fungal colonization of mice. Finally, the recruitment of AIC components to nascent Cryptococcus-containing vacuoles (CnCVs) regulates the intracellular trafficking and replication of the pathogen. These findings demonstrate that host AIC regulatory networks confer susceptibility to infection and establish a proteomic resource for elucidating host mechanisms that regulate fungal intracellular parasitism.