A genetic switch for stable, long-term fermentative production of anabolic products in yeast

Amyris is the integrated renewable products company that is enabling the world’s leading brands to achieve sustainable growth. Amyris applies its innovative bioscience solutions to convert plant sugars into hydrocarbon molecules and produce specialty ingredients and consumer products. Production and marketing of the molecule farnesene (Biofene®) has already been commercialized with production scale in some markets. Farnesene has many applications as a renewable feedstock for polymers, nutraceuticals and cosmetics. To reduce the production cost of farnesene, at Amyris we engineer strains using a state-of-the art industrial synthetic biology platform to have high titer, yield, and productivity, and we perform fermentations in 200 m3 vessels over the course of many days or weeks. The challenge is that high-producer cells grow more slowly than spontaneous mutant low- or non-producer cells, especially in the nutrient-unlimited conditions of the seed train expansion, and yet must comprise the vast majority of the population. We have successfully addressed this challenge by developing an industrially-scalable genetic switch to successfully maintain high performance throughout lengthy fermentations. This genetic switch uses maltose (a cheap, non-toxic and metabolizable molecule) to control transcription such that when maltose is added in the seed train, product formation is shut off. This increased the growth of high-producer cells, resulting in higher inoculum purity and improved performance in bioreactors

Moving Beyond CHO: Alternative host systems may be the future of biotherapeutics

CHO cells are the primary expression system for recombinant proteins with significant investment over the last three decades resulting in robust cell lines and processes. The flexible nature of CHO has lent itself to multiple process formats, such as fed batch, perfusion and continuous cultures, and advances in omics technology has enabled customization of media formulations and targeted engineering of CHO cells. This knowledge has led to large gains in protein productivity that can be captured with culture duration and/or scale. Despite this, constant pressure exists to reduce cost of manufacturing and improve per batch productivity to meet the needs of increased patient populations and increase accessibility of these therapeutics. Biogen has partnered with MIT to take a holistic view of the potential future of biomanufacturing to identify technologies that can make step changes in productivity and cost reduction. This effort has identified the host system as the most important factor to enabling this vision. Specifically, a non-mammalian host could be the key to realizing the most significant gains in productivity and reduction in cost of manufacturing. Through this initiative, we sought to take a more comprehensive approach to investigate alternative hosts for recombinant antibody production. Eight non-mammalian hosts were selected based on several properties, including proven secretion of recombinant protein products, ability to glycosylate proteins, established genome or molecular biology toolkit, amongst others. The final panel of organisms included yeast, filamentous fungi, a diatom, and a trypanosome. In collaboration with Amyris, we evaluated these eight non-mammalian host cell lines to compare their suitability as a potential primary host for the biotechnology industry. Only non-engineered, wild-type strains were used as a starting point for this evaluation, which assessed the ability of each host to express the same IgG1 model antibody. The outcome of this comparative analysis demonstrated that several of the alternative hosts could express full length antibody with acceptable glycoforms. Additionally, the ease of culture, ability to engineer the genome, and flexibility of carbon source were assessed. As an output of this work, the most productive strains will be made available for use without restrictions to allow others in the community to freely work with these hosts. Based on this initial assessment, a strategy to further investigate the potential of the most promising hosts will be shared

RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation

Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments. Prior work has proposed building scene graphs of the environment but typically assumes that the environment is static, omitting regions that require active interactions. This severely limits their ability to handle more complex tasks in household and office environments: before setting up a table, robots must explore drawers and cabinets to locate all utensils and condiments. In this work, we introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment. The ACSG accounts for both low-level information, such as geometry and semantics, and high-level information, such as the action-conditioned relationships between different entities in the scene. To this end, we present the Robotic Exploration (RoboEXP) system, which incorporates the Large Multimodal Model (LMM) and an explicit memory design to enhance our system's capabilities. The robot reasons about what and how to explore an object, accumulating new information through the interaction process and incrementally constructing the ACSG. We apply our system across various real-world settings in a zero-shot manner, demonstrating its effectiveness in exploring and modeling environments it has never seen before. Leveraging the constructed ACSG, we illustrate the effectiveness and efficiency of our RoboEXP system in facilitating a wide range of real-world manipulation tasks involving rigid, articulated objects, nested objects like Matryoshka dolls, and deformable objects like cloth.Comment: Project Page: https://jianghanxiao.github.io/roboexp-web

Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation

We contribute the Habitat Synthetic Scene Dataset, a dataset of 211 high-quality 3D scenes, and use it to test navigation agent generalization to realistic 3D environments. Our dataset represents real interiors and contains a diverse set of 18,656 models of real-world objects. We investigate the impact of synthetic 3D scene dataset scale and realism on the task of training embodied agents to find and navigate to objects (ObjectGoal navigation). By comparing to synthetic 3D scene datasets from prior work, we find that scale helps in generalization, but the benefits quickly saturate, making visual fidelity and correlation to real-world scenes more important. Our experiments show that agents trained on our smaller-scale dataset can match or outperform agents trained on much larger datasets. Surprisingly, we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10,000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments

A New Framework Combining Local-Region Division and Feature Selection for Micro-Expressions Recognition

Micro-expressions are deliberate or unconscious movements of people's psychological activities, reflecting the transient facial true expressions. Previous works focus on the whole face for micro-expressions recognition. These methods can extract a number of feature vectors which are relevant or irrelevant to the micro-expressions recognition. Besides, the high-dimension feature vectors can result in longer computational time and increased computational complexity. In order to address these problems, we propose a new framework which combines the local-region division and the feature selection. Based on the proposed framework, the original images can retain more efficient regions and filter out the invalid components of feature vectors. Specifically, with the joint efforts of the facial deformation identification model and facial action coding system, the global region is divided into seven local regions with their corresponding actions units. The ReliefF algorithm is used to select effective components of feature vectors and reduce the dimension. To evaluate the proposed framework, we conduct experiments on both the Chinese Academy of Sciences Micro-expression II Database and Spontaneous Micro-expression Database with Leave-One-Subject-Out Cross Validation method. The results show that the performance in local combined regions outperforms its counterpart in the global region, and the recognition accuracy is further improved with the combination of feature selection

Analysis of the expression and distribution of protein O-linked mannose β1,2-N-acetylglucosaminyltransferase 1 in the normal adult mouse brain

IntroductionProtein O-linked mannose β1,2-N-acetylglucosaminyltransferase 1 (POMGNT1) is crucial for the elongation of O-mannosyl glycans. Mutations in POMGNT1 cause muscle-eye-brain (MEB) disease, one of the main features of which is anatomical aberrations in the brain. A growing number of studies have shown that defects in POMGNT1 affect neuronal migration and distribution, disrupt basement membranes, and misalign Cajal-Retzius cells. Several studies have examined the distribution and expression of POMGNT1 in the fetal or neonatal brain for neurodevelopmental studies in the mouse or human brain. However, little is known about the neuroanatomical distribution and expression of POMGNT1 in the normal adult mouse brain.MethodsWe analyzed the expression of POMGNT1 mRNA and protein in the brains of various neuroanatomical regions and spinal cords by western blotting and RT-qPCR. We also detected the distribution profile of POMGnT1 in normal adult mouse brains by immunohistochemistry and double-immunofluorescence.ResultsIn the present study, we found that POMGNT1-positive cells were widely distributed in various regions of the brain, with high levels of expression in the cerebral cortex and hippocampus. In terms of cell type, POMGNT1 was predominantly expressed in neurons and was mainly enriched in glutamatergic neurons; to a lesser extent, it was expressed in glial cells. At the subcellular level, POMGNT1 was mainly co-localized with the Golgi apparatus, but expression in the endoplasmic reticulum and mitochondria could not be excluded.DiscussionThe present study suggests that POMGNT1, although widely expressed in various brain regions, may has some regional and cellular specificity, and the outcomes of this study provide a new laboratory basis for revealing the possible involvement of POMGNT1 in normal physiological functions of the brain from a morphological perspective

Antibody production in micro-organisms

Global demand for monoclonal antibody-based therapeutics (Mab’s) far exceeds current production capacity, and is expected to continue to grow based on current development pipelines. Despite their proven efficacy in a large number of indications, equitable use of these drugs is limited by the high cost of CHO-cell based production and purification. Micro-organisms such as yeasts and filamentous fungi present an attractive alternative for antibody production, but will require extensive genetic modification to achieve both high titers and mammalian-like glycosylation patterns in a secreted product that is easily purified. Towards this end, we developed state-of-the-art genetic engineering tools for eight micro-organisms to enable the highly efficient, targeted multiplexed integrations necessary for antibody production in these hosts. We demonstrated successful antibody production in several of these micro-organisms, paving the way to low-cost microbial fermentation to replace CHO fermentation