Optimal Design of Experiments on Riemannian Manifolds

The theory of optimal design of experiments has been traditionally developed on an Euclidean space. In this article, new theoretical results and an algorithm for finding the optimal design of an experiment located on a Riemannian manifold are provided. It is shown that analogously to the results in Euclidean spaces, D-optimal and G-optimal designs are equivalent on manifolds, and we provide a lower bound for the maximum prediction variance of the response evaluated over the manifold. In addition, a converging algorithm that finds the optimal experimental design on manifold data is proposed. Numerical experiments demonstrate the importance of considering the manifold structure in a designed experiment when present, and the superiority of the proposed algorithm. Supplementary materials for this article are available online.</p

DataSheet_1_Identification of ribosomal protein family as immune-cell-related biomarkers of NAFLD by bioinformatics and experimental analyses.docx

BackgroundImmune cells play an integral role in the development and progression of non-alcoholic fatty liver disease (NAFLD). This study was to identify immune-cell-related biomarkers for the diagnosis and treatment of NAFLD.Methods and findingsFirst, we introduced human liver transcriptome data from the GEO database (GSE48452 and GSE126848) and performed a weighted gene co-expression network analysis (WGCNA) to screen out the modules related to immune cell infiltration and to identify immune-cell-related differentially expressed genes (ICR-DEGs) associated with NAFLD progression. Further, the protein-protein interaction (PPI) network of ICR-DEGs was established to obtain hub genes and subsequently, the expression trend analysis was conducted to identify immune-cell-related biomarkers of NAFLD. Finally, the mRNA expression of biomarkers was validated in a NAFLD mouse model induced by high-fat diet (HFD) feeding. In total, we identified 66 ICR-DEGs and 13 hub genes associated with NAFLD. Among them, 9 hub genes (CD247, CD74, FCGR2B, IL2RB, INPP5D, MRPL16, RPL35, RPS3A, RPS8) were correlated with the infiltrating immune cells by the Pearson correlation analysis. Subsequently, 4 immune-cell-related biomarkers (RPL35, RPS3A, RPS8, and MRPL16) with the same expression trends in GSE48452 and GSE126848 datasets were identified. These biomarkers were enriched in immune-related pathways and had a good ability to distinguish between NASH and healthy samples. Moreover, we constructed a competing endogenous RNA (ceRNA) network of biomarkers and predicted twenty potential therapeutic drugs targeting RPS3A such as taxifolin and sitagliptin. Finally, experimental validation indicated that the hepatic mRNA expression of Rpl35, Rps3A, and Rps8 was significantly decreased in NAFLD mice.ConclusionsThis study identified four ribosomal protein genes (RPL35, RPS3A, RPS8, and MRPL16) as immune-cell-related biomarkers of NAFLD, which may actively participate in the immune processes during NAFLD progression and could serve as potential targets for the diagnosis and treatment of NAFLD.</p

Configuration, Anion-Specific Effects, Diffusion, and Impact on Counterions for Adsorption of Salt Anions at the Interfaces of Clay Minerals

Interfacial interactions of clay minerals with salt solutions are ubiquitous and play a crucial role in a wide range of fields, where salt cations are the focus while anions are generally regarded as spectators. Here, molecular dynamics simulations show that the various anions are strongly adsorbed on the surfaces of clay minerals, and the resulting anion-specific effects are pronounced. Although constructing only H-bonds, anions form stable inner- and outer-sphere complexes with clay minerals, and F^– and OH^– can result in even more stable complexes than metal ions. The underlying anion-specific effects abide by the sequence OH^– > F^– > Cl^– > I^– and show apparent enhancements with increase of salt concentrations. OH^– is particular at relatively high concentrations, forming clusters and capturing metal ions at octahedral AlO₆ surfaces and approaching tetrahedral SiO₄ surfaces with help of metal ions in addition to the monodispersive inner- and outer-sphere species at octahedral AlO₆ surfaces that are similar for all anions. Diffusion coefficients of anions are the same order of magnitude as those of metal ions and are affected by counterions, concentrations, and distances to the surfaces of clay minerals. Diffusion coefficients of both inner- and outer-sphere anions decrease as I^– > Cl^– > F^– > OH^–. Adsorption of anions is affected by counterions (metal ions) and vice versa. Impact of anions on the adsorption of counterions also shows ion-specific effects that follow the sequence OH^– > F^– > Cl^– > I^–, and OH^– can even alter the adsorption structure and distribution of counterions

3D Differentiation of Neural Stem Cells in Macroporous Photopolymerizable Hydrogel Scaffolds

<div><p>Neural stem/progenitor cells (NSPCs) are the stem cell of the adult central nervous system (CNS). These cells are able to differentiate into the major cell types found in the CNS (neurons, oligodendrocytes, astrocytes), thus NSPCs are the mechanism by which the adult CNS could potentially regenerate after injury or disorder. Microenviromental factors are critical for guiding NSPC differentiation and are thus important for neural tissue engineering. In this study, D-mannitol crystals were mixed with photocrosslinkable methacrylamide chitosan (MAC) as a porogen to enhance pore size during hydrogel formation. D-mannitol was admixed to MAC at 5, 10 and 20 wt% D-mannitol per total initial hydrogel weight. D-mannitol crystals were observed to dissolve and leave the scaffold within 1 hr. Quantification of resulting average pore sizes showed that D-mannitol addition resulted in larger average pore size (5 wt%, 4060±160 µm², 10 wt%, 6330±1160 µm², 20 wt%, 7600±1550 µm²) compared with controls (0 wt%, 3150±220 µm²). Oxygen diffusion studies demonstrated that larger average pore area resulted in enhanced oxygen diffusion through scaffolds. Finally, the differentiation responses of NSPCs to phenotypic differentiation conditions were studied for neurons, astrocytes and oligodendrocytes in hydrogels of varied porosity over 14 d. Quantification of total cell numbers at day 7 and 14, showed that cell numbers decreased with increased porosity and over the length of the culture. At day 14 immunohistochemistry quantification for primary cell types demonstrated significant differentiation to the desired cells types, and that total percentages of each cell type was greatest when scaffolds were more porous. These results suggest that larger pore sizes in MAC hydrogels effectively promote NSPC 3D differentiation.</p> </div

Fig.13.mp4

The movement of a water drip dropping through the FOV

Total cell number at day 7 and 14 for porous scaffolds cultured in control (+EGF+FGF, -GF) and differentiation (INF-γ, PDGF-AA, BMP-2) media.

<p>NSPCs were initially seeded at 200 × 10³ cells/scaffold. *** denotes significance by two-factor ANOVA (p<0.0001). Mean ± SD with n = 3. All scaffolds were cultured for 1 d in expansion media (+EGF+FGF) then switched to conditions labeled in the caption.</p

Figure 1

<p>(A) Methodology for creating 3D porous MAC scaffolds and procedure for NSPC culture and differentiation in 3D environments. (B) Images of a 20% D-mannitol scaffold captured immediately after crosslinking and after PBS dissolution for 1 hr at 37°C.</p

Fig14.mp4

The movement of a water surface and splashed drops after a water drip dropping on the surface

Fluorescence staining results for NSPC differentiation.

<p>Quantification of IHC at day 14 shows that more porous scaffolds (up to 20 wt% D-mannitol initially) in (A) neuron specific media (IFN-γ) favor neurons. (B) Oligodendrocyte specific media (PDGF-AA) favor oligodendrocytes and (C) in astrocyte specific media (BMP-2) favor astrocytes. (D) Control media with no growth factors (-GF) as well as with proliferation growth factors (+EGF+FGF) maintain nestin expression (note: error bars are included but too small to see). Letters denote significance by single factor ANOVA (p<0.001). Mean ± SD with n = 3.</p