Feature ranking and selection.

This figure shows how the precision values change with the deleted feature in a recursive fashion. Least important features are removed earlier.</p

Time window selection.

The three subplots represent the precision values for different time windows based on 21 start frames (x axis) and 12 window lengths (7 frames to 29 frames) for phases 1, 2, and 3 (from top to bottom) respectively, and the black bash line in each subplot indicates a precision value of 0.55.</p

iPS progenitor cells vs. MEFs and feature correlation.

(a) shows the examples of iPS progenitor cell images (blue circles) and normal MEFs images (yellow boxes) taken from phase 1, 2 and 3 of field 2 (Left, middle and right). Nucleus and cytoplasm of the enlarged progenitor cells and normal MEFs are colored in light blue and green respectively. (b) shows the Pearson coefficients between remaining types of features in three phases after the first step of feature selection. Note in this figure ellipsoid-prolate is denoted as E-prolate, intensity-StdDev as I-stdDev, intensity-min as I-Min, intensity-max as I-Max, nucleus-cytoplasm volume ratio as Ratio, ellipsoid-oblate as E-oblate.</p

Flow chart of the machine learning based approach for iPS progenitor cell identification.

In time-lapse imaging, we record the reprogramming process periodically among 54 fields after 48h of viral infection. For retrospective tracking, the figure only shows the reprogramming lineage images of the first frame of all eight phases. Only datasets from phase 1, 2 and 3 are used for model training and testing.</p

Model comparison for different missing frame number and imputation methods.

(a) shows the average precision over six time periods (TP1 to TP6) for each missing frame number and imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray). (b) shows the standard deviation, as a function of missing frame number, of imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray).</p

Model validation.

In all sub-figures, X axis indicates the start frame of the best time windows and the corresponding window length (13 frames) is indicated in the inlet. (a) 5-fold cross-validation precisions over 10 runs. (b) the standard deviation of the average precision of the neighborhood time windows in Fig 6D. (c) the standard deviation of the average precision of the distant windows in Fig 6E. (d) the average precision of seven neighborhood time windows calculated over 10 holdout validation runs. (e) the average precision over 10 independent tests for six best time windows on their corresponding distant windows.</p

Individual differences in gradients of intrinsic connectivity within the semantic network relate to distinct aspects of semantic cognition

Semantic cognition allows us to make sense of our varied experiences, including the words we hear and the objects we see. Contemporary accounts identify multiple interacting components that underpin semantic cognition, including diverse unimodal “spoke” systems that are integrated by a heteromodal “hub”, and control processes that allow us to access weakly-encoded as well as dominant aspects of knowledge to suit the circumstances. The current study examined how these dimensions of semantic cognition might be related to whole-brain-derived components (or gradients) of connectivity. A nonlinear dimensionality reduction technique was applied to resting-state functional magnetic resonance imaging from 176 participants to characterise the strength of two key connectivity gradients in each individual: the principal gradient captured the separation between unimodal and heteromodal cortex, while the second gradient corresponded to the distinction between motor and visual cortex. We then examined whether the magnitude of these gradients within the semantic network was related to specific aspects of semantic cognition by examining individual differences in semantic and non-semantic tasks. Participants whose intrinsic connectivity showed a better fit with Gradient 1 had faster identification of weak semantic associations. Furthermore, a better fit with Gradient 2 was linked to faster performance on picture semantic judgements. These findings show that individual differences in aspects of semantic cognition can be related to components of connectivity within the semantic network

Polyethylenimine Triggers Dll4 Degradation to Regulate Angiogenesis In Vitro

The Dll4-Notch signaling pathway plays a crucial role in the regulation of angiogenesis and is a promising therapeutic target for diseases associated with abnormal angiogenesis, such as cancer and ophthalmic diseases. Here, we find that polyethylenimine (PEI), a cationic polymer widely used as nucleic acid transfection reagents, can target the Notch ligand Dll4. By immunostaining and immunoblotting, we demonstrate that PEI significantly induces the clearance of cell–surface Dll4 and facilitates its degradation through the lysosomal pathway. As a result, the activation of Notch signaling in endothelial cells is effectively inhibited by PEI, as evidenced by the observed decrease in the generation of the activated form of Notch and expression of Notch target genes Hes1 and Hey1. Furthermore, through blocking Dll4-mediated Notch signaling, PEI treatment enhances angiogenesis in vitro. Together, our study reveals a novel biological effect of PEI and establishes a foundation for the development of a Dll4-targeted biomaterial for the treatment of angiogenesis-related disease

Bifunctional Compounds as Molecular Degraders for Integrin-Facilitated Targeted Protein Degradation

As effective ways to regulate protein levels, targeted protein degradation technologies have attracted great attention in recent years. Here, we established a novel integrin-facilitated lysosomal degradation (IFLD) strategy to degrade extracellular and cell membrane proteins using bifunctional compounds as molecular degraders. By conjugation of a target protein-binding ligand with an integrin-recognition ligand, the resulting molecular degrader proved to be highly efficient to induce the internalization and subsequent degradation of extracellular or cell membrane proteins in an integrin- and lysosome-dependent manner. As demonstrated in the development of BMS-L1-RGD, which is an efficient programmed death-ligand 1 (PD-L1) degrader validated both in vitro and in vivo, the IFLD strategy expands the toolbox for regulation of secreted and membrane-associated proteins and thus has great potential to be applied in chemical biology and drug discovery

Movie S6 from Targeting Polo-like Kinase 1 by a Novel Pyrrole-Imidazole Polyamide–Hoechst Conjugate Suppresses Tumor Growth <i>In Vivo</i>

Live imaging of mitotic progression in PIP3 treated hTERT-RPE1 cells</p