ilid

Cells have the ability to sense different environmental signals and position themselves accordingly in order to support their survival. Introducing analogous capabilities to the bottom-up assembled minimal synthetic cells is an important step for their autonomy. Here, a minimal synthetic cell which combines a multistimuli sensitive adhesion unit with an energy conversion module is reported, such that it can adhere to places that have the right environmental parameters for ATP production. The multistimuli sensitive adhesion unit senses light, pH, oxidative stress, and the presence of metal ions and can regulate the adhesion of synthetic cells to substrates in response to these stimuli following a chemically coded logic...

Recent years have witnessed the remarkable progress of applying deep learning models in video person re-identification (Re-ID). A key factor for video person Re-ID is to effectively construct discriminative and robust video feature representations for many complicated situations. Part-based approaches employ spatial and temporal attention to extract representative local features. While correlations between parts are ignored in the previous methods, to leverage the relations of different parts, we propose an innovative adaptive graph representation learning scheme for video person Re-ID, which enables the contextual interactions between relevant regional features...

Receptor tyrosine kinases (RTKs) play crucial roles in human health, and their misregulation is implicated in disorders ranging from neurodegenerative diseases to cancers. The highly conserved mechanism of activation of RTKs makes them especially appealing candidates for control via optogenetic dimerization methods. This work offers a strategy for using the improved Light-Induced Dimer (iLID) system with a constructed tandem-dimer of its binding partner nano (tdnano) to build light-activatable versions of RTKs...

Is recurrent network really necessary for learning a good visual representation for video based person re-identification (VPRe-id)? In this paper, we first show that the common practice of employing recurrent neural networks (RNNs) to aggregate temporal- spatial features may not be optimal. Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective learn temporal dependencies as we expected and implicitly yields an orderless representation. Based on this observation, we then present a simple yet surprisingly powerful approach for VPRe-id, where we treat VPRe-id as an efficient orderless ensemble of image based person re-identification problem...

Temporal cues embedded in videos provide important clues for person Re-Identification (ReID). To efficiently exploit temporal cues with a compact neural network, this work proposes a novel 3D convolution layer called Multi-scale 3D (M3D) convolution layer. The M3D layer is easy to implement and could be inserted into traditional 2D convolution networks to learn multi-scale temporal cues by end-to-end training. According to its inserted location, the M3D layer has two variants, i.e., local M3D layer and global M3D layer, respectively...

Ilicicolin H is a broad-spectrum antifungal agent targeting mitochondrial cytochrome bc1 reductase. Unfortunately, ilicicolin H shows reduced activities in vivo. Here, we report our effort on the identification of ilicicolin H biosynthetic gene cluster (BGC) by genomic sequencing a producing strain, Neonectria sp. DH2, and its heterologous production in Aspergillus nidulans . In addition, a shunt product with similar antifungal activities, ilicicolin J, was uncovered. This effort would provide a base for future combinatorial biosynthesis of ilicicolin H analogues...

Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually improve this task from three aspects, including a) selecting more discriminative frames, b) generating more informative temporal representations, and c) developing more effective distance metrics. To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN), which adopts the video pairs as the input and outputs their matching scores...

In this paper, we present a spatial-temporal attention-aware learning (STAL) method for video-based person re-identification. Most existing person re-identification methods aggregate image features identically to represent persons, which are extracted from the same receptive field across video frames. However, the image quality may be varying for different spatial regions and changing over time, which shall contribute to person representation and matching adaptively. Our STAL method aims to attend to the salient parts of persons in videos jointly in both spatial and temporal domains...

Video-based person re-identification is an important task with the challenges of lighting variation, low-resolution images, background clutter, occlusion, and human appearance similarity in the multi-camera visual sensor networks. In this paper, we propose a video-based person re-identification method called the end-to-end learning architecture with hybrid deep appearance-temporal feature. It can learn the appearance features of pivotal frames, the temporal features, and the independent distance metric of different features...

In this paper, we propose a novel feature learning framework for video person re-identification (re-ID). The proposed framework largely aims to exploit the adequate temporal information of video sequences and tackle the poor spatial alignment of moving pedestrians. More specifically, for exploiting the temporal information, we design a temporal residual learning (TRL) module to simultaneously extract the generic and specific features of consecutive frames. The TRL module is equipped with two bi-directional LSTM (BiLSTM), which are respectively responsible to describe a moving person in different aspects, providing complementary information for better feature representations...

Cell motility is an important but complex process; as cells move, new adhesions form at the front and adhesions disassemble at the back. To replicate this dynamic and spatiotemporally controlled asymmetry of adhesions and achieve motility in a minimal synthetic cell, we controlled the adhesion of a model giant unilamellar vesicle (GUV) to the substrate with light. For this purpose, we immobilized the proteins iLID and Micro, which interact under blue light and dissociate from each other in the dark, on a substrate and a GUV, respectively...

Partial face recognition (PFR) in an unconstrained environment is a very important task, especially in situations where partial face images are likely to be captured due to occlusions, out-of-view, and large viewing angle, e.g., video surveillance and mobile devices. However, little attention has been paid to PFR so far and thus, the problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a novel partial face recognition approach, called Dynamic Feature Matching (DFM), which combines Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC) to address partial face recognition problem regardless of various face sizes...

Light-induced dimerizing systems, e.g. iLID, are an increasingly utilized optogenetics tool to perturb cellular signaling. The major benefit of this technique is that it allows external spatiotemporal control over protein localization with sub-cellular specificity. However, when it comes to local recruitment of signaling components to the plasmamembrane, this precision in localization is easily lost due to rapid diffusion of the membrane anchor. In this study, we explore different approaches of countering the diffusion of peripheral membrane anchors, to the point where we detect immobilized fractions with iFRAP on a timescale of several minutes...

Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in complex real-world scenarios. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper. First, we propose a Cross-view Dictionary Learning (CDL) model, which is a general solution to the multi-view learning problem...

Toward the bottom-up assembly of synthetic cells from molecular building blocks, it is an ongoing challenge to assemble micrometer sized compartments that host different processes into precise multicompartmental assemblies, also called prototissues. The difficulty lies in controlling interactions between different compartments dynamically both in space and time, as these interactions determine how they organize with respect to each other and how they work together. In this study, we have been able to control the self-assembly and social self-sorting of four different types of colloids, which we use as a model for synthetic cells, into two separate families with visible light...

The growth of cancer metastases in the liver depends on a permissive interaction with the hepatic microenvironment and neutrophils can contribute to this interaction, either positively or negatively, depending on their phenotype. Here we investigated the role of IGF-I in the control of the tumor microenvironment in the liver, using mice with a conditional, liver-specific, IGF-I deficiency (iLID) induced by a single tamoxifen injection. In mice that had a sustained (3 weeks) IGF-I deficiency prior to the intrasplenic/portal inoculation of colon carcinoma MC-38 cells, we observed an increase in neutrophil accumulation in the liver relative to controls...

Methods to acutely manipulate protein interactions at the subcellular level are powerful tools in cell biology. Several blue-light-dependent optical dimerization tools have been developed. In these systems one protein component of the dimer (the bait) is directed to a specific subcellular location, while the other component (the prey) is fused to the protein of interest. Upon illumination, binding of the prey to the bait results in its subcellular redistribution. Here, we compared and quantified the extent of light-dependent dimer occurrence in small, subcellular volumes controlled by three such tools: Cry2/CIB1, iLID, and Magnets...

The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment...

The blue light-dependent interaction between the proteins iLID and Nano allows recruiting and patterning proteins on GUV membranes, which thereby capture key features of patterns observed in nature. This photoswitchable protein interaction provides non-invasive, reversible and dynamic control over protein patterns of different sizes with high specificity and spatiotemporal resolution.

We developed a novel optogenetic tool, SxIP-improved light-inducible dimer (iLID), to facilitate the reversible recruitment of factors to microtubule (MT) plus ends in an end-binding protein-dependent manner using blue light. We show that SxIP-iLID can track MT plus ends and recruit tgRFP-SspB upon blue light activation. We used this system to investigate the effects of cross-linking MT plus ends and F-actin in Drosophila melanogaster S2 cells to gain insight into spectraplakin function and mechanism. We show that SxIP-iLID can be used to temporally recruit an F-actin binding domain to MT plus ends and cross-link the MT and F-actin networks...