DataSheet1_Rheological and Printability Assessments on Biomaterial Inks of Nanocellulose/Photo-Crosslinkable Biopolymer in Light-Aided 3D Printing.pdf

Biomaterial inks based on cellulose nanofibers (CNFs) and photo-crosslinkable biopolymers have great potential as a high-performance ink system in light-aided, hydrogel extrusion-based 3D bioprinting. However, the colloidal stability of surface charged nanofibrils is susceptible to mono-cations in physiological buffers, which complexes the application scenarios of these systems in formulating cell-laden bioinks. In this study, biomaterial inks formulated by neutral and negatively surface charged CNFs (GrowInk-N and GrowInk-T) and photo-crosslinkable biopolymers (gelatin methacryloyl (GelMA) and methacrylated galactoglucomannan (GGMMA)) were prepared with Milli-Q water or PBS buffer. Quantitative rheological measurements were performed on the ink formulations to characterize their shear flow recovery behavior and to understand the intermolecular interactions between the CNFs of different kinds with GGMMA or GelMA. Meanwhile, printability assessments, including filament extrudability and shape fidelity of the printed scaffold under varying printing conditions, were carried out to optimize the printing process. Our study provides extensive supporting information for further developing these nanocellulose-based systems into photo-crosslinkable bioinks in the service of cell-laden 3D bioprinting.</p

Image2_Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.tif

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses.Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated.Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival.Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.</p

Table2_Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.XLSX

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses.Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated.Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival.Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.</p

Table1_Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.XLSX

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses.Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated.Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival.Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.</p

Video1_Rheological and Printability Assessments on Biomaterial Inks of Nanocellulose/Photo-Crosslinkable Biopolymer in Light-Aided 3D Printing.MP4

Biomaterial inks based on cellulose nanofibers (CNFs) and photo-crosslinkable biopolymers have great potential as a high-performance ink system in light-aided, hydrogel extrusion-based 3D bioprinting. However, the colloidal stability of surface charged nanofibrils is susceptible to mono-cations in physiological buffers, which complexes the application scenarios of these systems in formulating cell-laden bioinks. In this study, biomaterial inks formulated by neutral and negatively surface charged CNFs (GrowInk-N and GrowInk-T) and photo-crosslinkable biopolymers (gelatin methacryloyl (GelMA) and methacrylated galactoglucomannan (GGMMA)) were prepared with Milli-Q water or PBS buffer. Quantitative rheological measurements were performed on the ink formulations to characterize their shear flow recovery behavior and to understand the intermolecular interactions between the CNFs of different kinds with GGMMA or GelMA. Meanwhile, printability assessments, including filament extrudability and shape fidelity of the printed scaffold under varying printing conditions, were carried out to optimize the printing process. Our study provides extensive supporting information for further developing these nanocellulose-based systems into photo-crosslinkable bioinks in the service of cell-laden 3D bioprinting.</p

Image1_Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.tif

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses.Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated.Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival.Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.</p

Density Functional Theory-Assisted Active Learning-Driven Organic Ligand Design for CsPbBr₃ Nanocrystals

Surface modification with organic ligands is pivotal in enhancing the stability and passivation of perovskite nanocrystals. Traditionally, the design of these ligands has predominantly been dependent on the expertise and intuition of researchers. We develop a density functional theory-assisted active learning framework to screen potential surface ligands for CsPbBr3 nanocrystals in a large chemical space using dual-objective Bayesian optimization. Our approach has successfully identified a stable Pareto front after seven optimization iterations, with the resulting surrogate model demonstrating accurate predictions for the adsorption energies of newly proposed molecules. Six promising candidates in Pareto solutions without electronic traps are obtained through a mere 80 calculations from 161,900 molecule spaces. Conspicuous enrichment of featured fragments (halogen, ketone, imine, sulfide, benzene, and their combinations) is observed in the promising data set near the Pareto front, which coincides with the features of most reported excellent ligands. Our work demonstrates a highly efficient active learning framework to accelerate the ligand design for PNCs by rapidly screening the large-scale molecule space using the data-driven workflow

Supplementary document for “Invisible” pulsation and period-doubling bifurcation of harmonic mode-locking in a bidirectional fiber laser - 6680606.pdf

Supplementary explanation on relevant content in the manuscrip

Nanoporous CeO₂‑Decorated Cu-SmMn₂O₅ Composite Mullite Oxide Catalyst for Toluene Oxidation

Mullite-type AMn2O5 materials represent a group of promising candidates to substitute noble metal catalysts for the catalytic removal of volatile organic compounds (VOCs). When applied to catalytic oxidation of aromatic hydrocarbons such as toluene, AMn2O5 exhibits undesirable performance due to its poor intrinsic activity of oxygen species, leading to a light-off temperature above 250 °C. In this work, we have developed a synthesis route combining a two-step calcination and cation-exchange process to prepare a nanoporous CeO2-decorated Cu-SmMn2O5 composite mullite oxide catalyst applied in toluene oxidation. The best performing sample reached 90% toluene conversion at 190 °C, surpassing other transition metal oxide catalysts. Comprehensive characterizations reveal that Cu ion doping into the crystal lattice of the mullite phase is achieved via the cation-exchange process. In this catalyst design, surface CeO2 decoration and Cu doping promote the reduction ability and improve oxygen species activity to accelerate toluene oxidation. Overall, this work provides a strategy to prepare composite mullite oxide catalysts with high performance in the removal of VOCs, broadening the industrial application prospects of mullite-type materials