Summary Elucidation of endogenous cellular protein-protein interactions and their networks is most desirable for biological studies. Here we report our study of endogenous human coregulator protein complex networks obtained from integrative mass spectrometry-based analysis of 3,290 affinity purifications. By preserving weak protein interactions during complex isolation and utilizing high levels of reciprocity in the large dataset we identified many unreported protein associations, such as a transcriptional network formed by ZMYND8, ZNF687 and ZNF592. Furthermore, our work revealed a tiered interplay within networks that share common proteins, providing a conceptual organization of a cellular proteome composed of minimal endogenous modules (MEMOs), functional uniCOREs and regulatory complex-complex interaction networks (CCIs). This resource will effectively fill a void in linking correlative genomic studies with an understanding of transcriptional regulatory protein functions within the proteome for formulation and testing of new hypotheses.

Summary Chromatin immunoprecipitation studies have mapped protein occupancies at many genomic loci. However, a detailed picture of the complexity of coregulators (CoRs) bound to a defined enhancer along with a transcription factor is missing. To address this, we used biotin-DNA pulldown assays coupled with mass spectrometry-immunoblotting to identify at least 17 CoRs from nuclear extracts bound to 17β-estradiol (E2)-liganded estrogen receptor-α on estrogen response elements (EREs). Unexpectedly, these complexes initially are biochemically stable and contain certain atypical corepressors. Addition of ATP dynamically converts these complexes to an ‘activated’ state by phosphorylation events, primarily mediated by DNA-dependent protein kinase. Importantly, a ‘natural’ ERE-containing enhancer and nucleosomal EREs recruit similar complexes. We further discovered the mechanism whereby H3K4me3 stimulates ERα-mediated transcription as compared with unmodified nucleosomes. H3K4me3 templates promote specific CoR dynamics in the presence of ATP and AcCoA, as manifested by CBP/p300 and SRC-3 dismissal and SAGA and TFIID stabilization/recruitment.

Integrated optical phased arrays can be used for beam shaping and steering with a small footprint, lightweight, high mechanical stability, low price, and high-yield, benefiting from the mature CMOS-compatible fabrication. This paper reviews the development of integrated optical phased arrays in recent years. The principles, building blocks, and configurations of integrated optical phased arrays for beam forming and steering are presented. Various material platforms can be used to build integrated optical phased arrays, e.g., silicon photonics platforms, III/V platforms, and III–V/silicon hybrid platforms. Integrated optical phased arrays can be implemented in the visible, near-infrared, and mid-infrared spectral ranges. The main performance parameters, such as field of view, beamwidth, sidelobe suppression, modulation speed, power consumption, scalability, and so on, are discussed in detail. Some of the typical applications of integrated optical phased arrays, such as free-space communication, light detection and ranging, imaging, and biological sensing, are shown, with future perspectives provided at the end.

The depletion interactions between two large spheres in a sea of small spheres under the symmetrical or unsymmetrical geometry confinements are studied through Monte Carlo simulations in this paper. The numerical results show that both the depletion potential and depletion force are affected due to the presence of the two plates.( * )

The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications. However, the performance of traditional object detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional encoder–decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the loss of information and the interference of noises in the process of feature interweaving. Furthermore, we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that might become the edges of an object. Experimental results on an edge device show that CODCEF achieved competitive results compared with 10 state-of-the-art methods.