Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

Genomic and antigenic properties of Newcastle Disease virus genotypes 2.XX1 and 2.VII from Egypt do not point to antigenic drift as a driving force of spread

Newcastle disease (ND), caused by avian orthoavulavirus type-1 (NDV), is endemic in poultry in the Middle East causing continuing outbreaks in poultry populations despite efforts to vaccinate. In the past, genotype 2.XXI (former 2.VI) was present in poultry in Egypt but has been replaced by genotype 2.VII. We investigated whether virus evolution contributed to superseding, and focused on the antigenic sites within the Heamagglutinin-Neuramindase (HN) spike protein. Full length sequences of a NDV genotype 2.VII isolate currently circulating in Egypt was compared to a genotype 2.XXI isolate that was present as co-infection with vaccine type viruses (2.II) in an historical isolate of the year 2011. Amino acid differences in the HN glycoprotein for both 2.XXI and 2.VII viruses amounted to 11,7% and 11,9 % compared to LaSota vaccine type. However, mutations within the globular head (aa 126-570), bearing relevant antigenic sites, were underrepresented (aa divergence of 8,8% and 8,1 % compared to 22,4% and 25,6% within the fragment encompassing cytoplasmic tail, transmembrane part and stalk regions (aa 1-125) for genotypes 2.XXI and 2.VII, respectively. Nevertheless, reaction patterns of HN-specific monoclonal antibodies revealed differences between vaccine type viruses and genotype 2.XXI and 2.VII viruses for specific epitopes. Accordingly, compared to Egyptian vaccine type isolates and the LaSota vaccine reference strain, single aa substitutions in 6 of 10 described neutralizing epitopes were found within the attachment protein. However, the same alterations in neutralization sensitive epitopes were present in old genotype 2.XXI as well as in newly emerged genotype 2.VII isolates. In addition, isolates were indistinguishable by polyclonal chicken sera raised against different genotypes including vaccine viruses. These findings suggest, that factors other than antigenic differences within the HN-protein account for facilitating spread of genotype 2.VII while displacing genotype 2.XXI viruses in Egypt.

Two distinct immunopathological profiles in autopsy lungs of COVID-19

Coronavirus Disease 19 (COVID-19) is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has grown to a worldwide pandemic with substantial mortality. Immune mediated damage has been proposed as a pathogenic factor, but immune responses in lungs of COVID-19 patients remain poorly characterized. Here we show transcriptomic, histologic and cellular profiles of post mortem COVID-19 (n = 34 tissues from 16 patients) and normal lung tissues (n = 9 tissues from 6 patients). Two distinct immunopathological reaction patterns of lethal COVID-19 are identified. One pattern shows high local expression of interferon stimulated genes (ISGhigh) and cytokines, high viral loads and limited pulmonary damage, the other pattern shows severely damaged lungs, low ISGs (ISGlow), low viral loads and abundant infiltrating activated CD8+ T cells and macrophages. ISGhigh patients die significantly earlier after hospitalization than ISGlow patients. Our study may point to distinct stages of progression of COVID-19 lung disease and highlights the need for peripheral blood biomarkers that inform about patient lung status and guide treatment.

Nuclear Reaction Network Unveils Novel Reaction Patterns and Nuclei Stability

Despite the advances in discovering new nuclei, modeling microscopic nuclear structure, nuclear reactors, and stellar nucleosynthesis, we lack a systemic tool, in the form of a network framework, to understand the structure and dynamics of 70 thousands reactions discovered until now. We assemble here a nuclear reaction network in which a node represents a nuclide, and a link represents a direct reaction between nuclides. Interestingly, the degree distribution of nuclear network exhibits a bimodal distribution that significantly deviates from the power-law distribution of scale-free networks and Poisson distribution of random networks. The distribution is universal for reactions with a rate below the threshold, λ-Tγ, where T is the temperature and γ≈1.05. We discovered three rules that govern the structure pattern of nuclear reaction network: (i) reaction-type is determined by linking choices, (ii) spatial distances between the reacting nuclides are short, and (iii) each node in- and out- degrees are close to each other. By incorporating these three rules, our model unveils the underlying nuclear reaction patterns hidden in a large and dense nuclear reaction network. It enables us to predict missing links that represent possible new nuclear reactions not yet discovered.

Two distinct immunopathological profiles in lungs of lethal COVID-19

Immune responses in lungs of Coronavirus Disease 2019 (COVID-19) are poorly characterized. We conducted transcriptomic, histologic and cellular profiling of post mortem COVID-19 and normal lung tissues. Two distinct immunopathological reaction patterns were identified. One pattern showed high expression of interferon stimulated genes (ISGs) and cytokines, high viral loads and limited pulmonary damage, the other pattern showed severely damaged lungs, low ISGs, low viral loads and abundant immune infiltrates. Distinct patterns of pulmonary COVID-19 immune responses correlated to hospitalization time and may guide treatment and vaccination approaches.