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.

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.

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.

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.

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.

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.

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.

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.