Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Small extracellular vesicles of organoid-derived human retinal stem cells remodel Müller cell fate via miRNA: A novel remedy for retinal degeneration.

Remodeling retinal Müller glial fate, including gliosis inhibition and pro-reprogramming, represents a crucial avenue for treating degenerative retinal diseases. Stem cell transplantation exerts effects on modulating retinal Müller glial fate. However, the optimized stem cell products and the underlying therapeutic mechanisms need to be investigated. In the present study, we found that retinal progenitor cells from human embryonic stem cell-derived retinal organoids (hERO-RPCs) transferred extracellular vesicles (EVs) into Müller cells following subretinal transplantation into RCS rats. Small EVs from hERO-RPCs (hERO-RPC-sEVs) were collected and were found to delay photoreceptor degeneration and protect retinal function in RCS rats. hERO-RPC-sEVs were taken up by Müller cells both in vivo and in vitro, and inhibited gliosis while promoting early dedifferentiation of Müller cells. We further explored the miRNA profiles of hERO-RPC-sEVs, which suggested a functional signature associated with neuroprotection and development, as well as the regulation of stem cell and glial fate. Mechanistically, hERO-RPC-sEVs might regulate the fate of Müller cells by miRNA-mediated nuclear factor I transcription factors B (NFIB) downregulation. Collectively, our findings offer novel mechanistic insights into stem cell therapy and promote the development of EV-centered therapeutic strategies.

An improved adaptive periodical segment matrix algorithm for ECG denoising based on singular value decomposition.

BACKGROUND: Wearable devices that monitor heart health of cardiac disease patients in real time are in great demand. OBJECTIVE: We propose an algorithm of improved segment periodical matrix construction for irregular electrocardiogram (ECG) signal denoising. METHOD: While splitting the heartbeat based on each RR interval for periodical segments matrix construction, the as-filtered ECG signal is reconstructed by the maximum singular value after a singular value decomposition. RESULTS: The results demonstrate a higher noise reduction effect with lower signal distortions of our methods compared to several singular value decomposition counterpart approaches. CONCLUSION: Our method has great potential to enhance wearable devices diagnosis accuracy by denoising the complex noises such as electromyography artifacts in real-time ECG sensing.

Organoid-derived human retinal progenitor cells promote early dedifferentiation of Müller glia in Royal College of Surgeons rats.

AIM: To explore whether the subretinal transplantation of retinal progenitor cells from human embryonic stem cell-derived retinal organoid (hERO-RPCs) could promote Müller glia dedifferentiation and transdifferentiation, thus improving visual function and delaying retinal degenerative progression. METHODS: hERO-RPCs were subretinally transplanted into Royal College of Surgeons (RCS) rats. Electroretinography (ERG) recording was performed at 4 and 8wk postoperation to assess retinal function. Using immunofluorescence, the changes in outer nuclear layer (ONL) thickness and retinal Müller glia were explored at 2, 4, and 8wk postoperation. To verify the effect of hERO-RPCs on Müller glia in vitro, we cocultured hERO-RPCs with Müller glia with a Transwell system. After coculture, Ki67 staining and quantitative polymerase chain reaction (qPCR) were performed to measure the proliferation and mRNA levels of Müller glia respectively. Cell migration experiment was used to detect the effect of hERO-RPCs on Müller glial migration. Comparisons between two groups were performed by the unpaired Student's t-test, and comparisons among multiple groups were made with one-way ANOVA followed by Tukey's multiple comparison test. RESULTS: The visual function and ONL thickness of RCS rats were significantly improved by transplantation of hERO-RPCs at 4 and 8wk postoperation. In addition to inhibiting gliosis at 4 and 8wk postoperation, hERO-RPCs significantly increased the expression of dedifferentiation-associated transcriptional factor in Müller glia and promoted the migration at 2, 4 and 8wk postoperation, but not the transdifferentiation of these cells in RCS rats. In vitro, using the Transwell system, we found that hERO-RPCs promoted the proliferation and migration of primary rat Müller glia and induced their dedifferentiation at the mRNA level. CONCLUSION: These results show that hERO-RPCs might promote early dedifferentiation of Müller glia, which may provide novel insights into the mechanisms of stem cell therapy and Müller glial reprogramming, contributing to the development of novel therapies for retinal degeneration disorders.

CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection.

Multiview graph clustering has emerged as an important yet challenging technique due to the difficulty of exploiting the similarity relationships among multiple views. Typically, the similarity graph for each view learned by these methods is easily corrupted because of the unavoidable noise or diversity among views. To recover a clean graph, existing methods mainly focus on the diverse part within each graph yet overlook the diversity across multiple graphs. In this article, instead of merely considering the sparsity of diversity within a graph as previous methods do, we incline to a more suitable consideration that the diversity should be sparse across graphs. It is intuitive that the divergent parts are supposed to be inconsistent with each other, otherwise it would contradict the definition of diversity. By simultaneously and explicitly detecting the multiview consistency and cross-graph diversity, a pure graph for each view can be expected. The multiple pure graphs are further fused to the structured consensus graph with exactly r connected components where r is the number of clusters. Once the consensus graph is obtained, the cluster label to each instance can be directly allocated as each connected component precisely corresponds to an individual cluster. An alternating iterative algorithm is designed to optimize the subtasks of learning the similarity graphs adaptively, detecting the consistency as well as cross-graph diversity, fusing the multiple pure graphs, and assigning cluster label to each instance in a mutual reinforcement manner. Extensive experimental results on several benchmark multiview datasets demonstrate the effectiveness of our model, in comparison to several state-of-the-art algorithms.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, here we develop a data-driven computational model of SARS-CoV-2 transmission to investigate mechanistically the effect on COVID-19 outbreaks of school closure strategies based on syndromic surveillance and antigen screening of students. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 13.1% (95%CI: 8.6%-20.2 %), due to the low probability of timely symptomatic case identification among the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Should population-level social distancing measures unrelated to schools enable maintaining the reproduction number ( R ) at 1.3 or lower, an antigen-based screening strategy is estimated to fully prevent COVID-19 outbreaks in the general population. Depending on the contribution of schools to transmission, this strategy can either prevent COVID-19 outbreaks for R up to 1.9 or to at least greatly reduce outbreak size in very conservative scenarios about school contribution to transmission. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to roll out through 2021, especially in light of possible continued emergence of SARS-CoV-2 variants.

Constraint Co-Projections for Semi-Supervised Co-Clustering.

Co-clustering aims to simultaneously cluster the objects and features to explore intercorrelated patterns. However, it is usually difficult to obtain good co-clustering results by just analyzing the object-feature correlation data due to the sparsity of the data and the noise. Meanwhile, most co-clustering algorithms cannot take the prior information into consideration and may produce unmeaningful results. Semi-supervised co-clustering aims to incorporate the known prior knowledge into the co-clustering algorithm. In this paper, a new technique named constraint co-projections for semi-supervised co-clustering (CPSSCC) is presented. Constraint co-projections can not only make use of two popular techniques including pairwise constraints and constraint projections, but also simultaneously perform the object constraint projections and feature constraint projections. The two popular techniques are illustrated for semi-supervised co-clustering when some objects and features are believed to be in the same cluster a priori. Furthermore, we also prove that the co-clustering problem can be formulated as a typical eigen-problem and can be efficiently solved with the selected eigenvectors. To the best of our knowledge, constraint co-projections is first stated in this paper and this is the first work on using CPSSCC. Extensive experiments on benchmark data sets demonstrate the effectiveness of the proposed method. This paper also shows that CPSSCC has some favorable features compared with previous related co-clustering algorithms.