Realizing High-Detectivity Near-Infrared Photodetectors in Tin–Lead Perovskites by Double-Sided Surface-Preferred Distribution of Multifunctional Tin Thiocyanate Additive

Tin–lead perovskite photodetectors are attractive alternatives to silicon counterparts for near-infrared photodetection, due to their outstanding optoelectronic properties, solution processability, and flexible compatibility. However, uncontrollable crystallization and easy oxidation problems of tin-containing perovskites severely hinder advances in their perfomance. Herein, we develop a high-detectivity near-infrared photodetector using a tailored tin-lead perovskite structure. Notably, we employ tin thiocyanate to form a double-sided surface-preferred distribution in tin-lead perovskites, in which the majority is located at bottom and top surfaces, and the tiny minority positioned inside the films. The tailored perovskite structure with the unique additive distribution significantly improves the film morphology and antioxidation ability. Finally, self-powered tin-lead perovskite photodetectors achieve a peak responsivity of 0.57 A W–1, a detectivity of 8.48 × 1012 Jones at 910 nm, and a large linear dynamic range of 213 dB, accompanied by an outstanding lifetime of 2300 h. This work opens up a new avenue to developing high-performance near-infrared photodetectors

Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU

We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study. Five machine-learning (ML) algorithms were applied for training on mortality predictive models with the data from a surgical intensive care unit (ICU) database of the Fujian Provincial Hospital in China. The accuracy, precision, recall, f1 score and the area under the receiver operator characteristic curve (AUC) were used to evaluate the performance of different models, and the calibration of the model was evaluated by brier score. We demonstrated that eXtreme Gradient Boosting (XGBoost) was more suitable for the task, demonstrating a AUC of 0.84. We analyzed the feature importance with the Local Interpretable Model-agnostic Explanations (LIME) analysis and further identified the high-risk factors of mortality in ICU through this study. This study established the mortality predictive model of patients who had undergone craniotomy in ICU. Identification of the factors that had great influence on mortality has the potential to provide auxiliary decision support for clinical medical staff on their practices.</p

Synergistic co-regulation and competition by a SOX9-GLI-FOXA phasic transcriptional network coordinate chondrocyte differentiation transitions

<div><p>The growth plate mediates bone growth where SOX9 and GLI factors control chondrocyte proliferation, differentiation and entry into hypertrophy. FOXA factors regulate hypertrophic chondrocyte maturation. How these factors integrate into a Gene Regulatory Network (GRN) controlling these differentiation transitions is incompletely understood. We adopted a genome-wide whole tissue approach to establish a <u><b>G</b></u>rowth <u><b>P</b></u>late <u><b>D</b></u>ifferential <u><b>G</b></u>ene <u><b>E</b></u>xpression <u><b>L</b></u>ibrary (GP-DGEL) for fractionated proliferating, pre-hypertrophic, early and late hypertrophic chondrocytes, as an overarching resource for discovery of pathways and disease candidates. <i>De novo</i> motif discovery revealed the enrichment of SOX9 and GLI binding sites in the genes preferentially expressed in proliferating and prehypertrophic chondrocytes, suggesting the potential cooperation between SOX9 and GLI proteins. We integrated the analyses of the transcriptome, SOX9, GLI1 and GLI3 ChIP-seq datasets, with functional validation by transactivation assays and mouse mutants. We identified new SOX9 targets and showed SOX9-GLI directly and cooperatively regulate many genes such as <i>Trps1</i>, <i>Sox9</i>, <i>Sox5</i>, <i>Sox6</i>, <i>Col2a1</i>, <i>Ptch1</i>, <i>Gli1</i> and <i>Gli2</i>. Further, FOXA2 competes with SOX9 for the transactivation of target genes. The data support a model of SOX9-GLI-FOXA phasic GRN in chondrocyte development. Together, SOX9-GLI auto-regulate and cooperate to activate and repress genes in proliferating chondrocytes. Upon hypertrophy, FOXA competes with SOX9, and control toward terminal differentiation passes to FOXA, RUNX, AP1 and MEF2 factors.</p></div

Model of the growth plate GRN directing chondrocyte differentiation.

(A) Stage-specific gene transcriptional programs regulating chondrocyte proliferation (PZ), pre-hypertrophy (PHZ), early hypertrophy (UHZ) and terminal differentiation (LHZ). Each node represents a transcription factor gene or its protein product. All the genes in the network are SOX9 targets identified from the ChIP-sequencing experiment. Each solid line represents an identified GLI-target gene relationship, pointing from GLI1/GLI3 to the target. The TF genes that were targeted both by SOX9 and GLI1/GLI3 are in the inner circle of the network, while those targeted only by SOX9 were placed in the outer circle. The dotted blue line indicates the presence of the predicted evolutionarily conserved SOX9/FOXA2 binding consensus in the target genes. The changes of color codes in the gene nodes from dark to light red and white indicate the dynamics of gene expression levels during the chondrocyte state transition (red representing high expression level; white, low expression). (B) Schematic model of a SOX9-GLI-FOXA phasic GRN coordinating chondrocyte differentiation and transitions in the growth plate.</p

Co-localization of SOX9 and FOXA2 in the growth plate.

<p>(A-C) <i>In vivo</i> expression patterns of SOX9 (A, red), FOXA2 (B, green) and co-localization (C) were shown on the cryosectioned growth plate (P10). Boxed regions of PCs (a-a"), PHCs (b-b") and HCs (c-c") were shown in higher magnification to demonstrate the differential expression and co-localization of SOX9 and FOXA2. (Bar = 100μm).</p

DNA motif enrichment analyses.

<p>(A) DNA motifs were identified in the promoter regions of DEGs in the PZ, PHZ, UHZ and LHZ. The output motifs from the DME program were prioritized according to the FG/BG ratio of frequency of occurrence. Of the 50 most highly ranked motifs with at least 2-fold ratio difference, those not expressed or constantly expressed were eliminated from the list. The identified motifs were matched in the TRANSFAC database. Top ranked and matched TFs that were differentially expressed across the growth plate were selected for further analysis. (B) SOX9 motif identified from DME program was utilized for prediction of monomer binding sites within 10kb distance of TSS of DEGs in each zone. The 25bp DNA sequences flanking the monomer SOX9 binding sites were analyzed by using the MEME program. SOX9 dimer motifs were significantly enriched for PZ and PHZ. The width of the spacer sequences in the dimer motifs ranges from 4- to 11-bp for PZ and 10-bp for PHZ genes, counted from the last base pair position of the 5’-AACAA-3’ SOX9 binding core consensus. (C) The numbers of SOX9 monomer motifs (Regular TRANSFAC consensus, COL2C1, COL2C2 and COL2C3), the SOX9 dimer motifs and the GLI binding motifs that are located within 250-bp from the SOX9 binding peaks identified in DEGs.</p

Global gene expression profiling in different chondrocyte populations.

<p>(A) 10-day-old mouse growth plates were cryosectioned and mRNA was extracted from the pooled samples of chondrocytes in the PZ, PHZ, UHZ and LHZ. Microarray data were then generated for the expression profiling of 21464 genes in each population of chondrocytes. (B) A total of 1891 genes showed differential expression patterns over the 4 zones with coefficient of standard deviation (CSD) of mRNA levels greater than 0.15. Four major distinct patterns of gene expression over the growth plates were identified in Heatmap by using <i>K</i>-Means Clustering. (C) Enriched biological processes representing the main functions of the co-expressed genes in each cluster by GO term analysis.</p

Competition between SOX9 and FOXA2 during chondrocyte differentiation.

<p>(A-D) Competition between SOX9 and FOXA2 on <i>Col2a1</i> intron 1 and <i>Col10a1</i> enhancer by luciferase assay in ATDC5. Enh: enhancer; Pro: promoter. The amount of SOX9 was kept constant and FOXA2 was increased and vice versa. (E) EMSA was performed using probes for the <i>Col2a1</i> intron I (Gel I and II) and <i>Col10a1</i> enhancer (Gel III and IV), and SOX9 and FOXA proteins at indicated concentrations. The sequences from <i>Col2a1</i> intron I and <i>Col10a1</i> enhancer used in the EMSAs are shown in the upper panel, and the SOX9/FOXA binding motifs are indicated by capital letters. **: p-value < 0.01; *: p-value < 0.05; NS: Non-significant.</p

Predicted regulation by SOX9 and GLI1 cooperation.

<p>(A-C) SOX9, GLI1 (Generated from E11.5 developing limb) ChIP-seq signals and conservation score in the loci of <i>Cyr61</i>, <i>Trps1</i> and <i>Ptch1</i>. BS: binding site; BR: binding region. (D-I) SOX9, GLI1 ChIP-seq signals and conservation score in the loci of Hedgehog target genes (<i>Gli1</i> and <i>Gli2</i>) and Sox9 target genes (<i>Sox9</i>, <i>Col2a1</i>, <i>Sox5</i> and <i>Sox6</i>).</p

Expression patterns of putative SOX9 target genes and SOX9 binding regions in their genomic loci.

<p>(A-I) Genes with SOX9 binding motifs located within 200bp from SOX9 ChIP-seq peaks (Generated from newborn rib chondrocytes) were selected for validation. <i>In vivo</i> expression patterns of potential SOX9 targets were validated by in situ hybridization: <i>Zbtb20</i> (A), <i>Wwp2</i> (B), <i>Ppa1</i> (D), <i>Bnip3</i> (E), <i>Slc8a3</i> (F), <i>Wnk4</i> (G) and <i>Col10a1</i> (H); or immunostaining: FOXP2 (C) and SOX9 (I), revealing similar expression trends with the microarray data as shown on the left side. The PZ, PHZ and HZ were separated by the white-dot lines. (Bar = 100μm). Predicted SOX9 binding sites (BS: binding site) and SOX9 ChIP-seq signals (BR: binding region) (Green) are shown under each gene.</p