A family of thermostable fungal cellulases created by structure-guided recombination

SCHEMA structure-guided recombination of 3 fungal class II cellobiohydrolases (CBH II cellulases) has yielded a collection of highly thermostable CBH II chimeras. Twenty-three of 48 genes sampled from the 6,561 possible chimeric sequences were secreted by the Saccharomyces cerevisiae heterologous host in catalytically active form. Five of these chimeras have half-lives of thermal inactivation at 63°C that are greater than the most stable parent, CBH II enzyme from the thermophilic fungus Humicola insolens, which suggests that this chimera collection contains hundreds of highly stable cellulases. Twenty-five new sequences were designed based on mathematical modeling of the thermostabilities for the first set of chimeras. Ten of these sequences were expressed in active form; all 10 retained more activity than H. insolens CBH II after incubation at 63°C. The total of 15 validated thermostable CBH II enzymes have high sequence diversity, differing from their closest natural homologs at up to 63 amino acid positions. Selected purified thermostable chimeras hydrolyzed phosphoric acid swollen cellulose at temperatures 7 to 15°C higher than the parent enzymes. These chimeras also hydrolyzed as much or more cellulose than the parent CBH II enzymes in long-time cellulose hydrolysis assays and had pH/activity profiles as broad, or broader than, the parent enzymes. Generating this group of diverse, thermostable fungal CBH II chimeras is the first step in building an inventory of stable cellulases from which optimized enzyme mixtures for biomass conversion can be formulated

Holistic Deep Learning

There is much interest in deep learning to solve challenges in applying neural network models in real-world environments. In particular, three areas have received considerable attention: adversarial robustness, parameter sparsity, and output stability. Despite numerous attempts to solve these problems independently, little work simultaneously addresses the challenges. In this paper, we address the problem of constructing holistic deep learning models by proposing a novel formulation that solves these issues in combination. Real-world experiments on both tabular and MNIST datasets show that our formulation can simultaneously improve the accuracy, robustness, stability, and sparsity over traditional deep learning models among many others.Comment: In preparation for Machine Learnin

SCHEMA Recombination of a Fungal Cellulase Uncovers a Single Mutation That Contributes Markedly to Stability

A quantitative linear model accurately (R^2 = 0.88) describes the thermostabilities of 54 characterized members of a family of fungal cellobiohydrolase class II (CBH II) cellulase chimeras made by SCHEMA recombination of three fungal enzymes, demonstrating that the contributions of SCHEMA sequence blocks to stability are predominantly additive. Thirty-one of 31 predicted thermostable CBH II chimeras have thermal inactivation temperatures higher than the most thermostable parent CBH II, from Humicola insolens, and the model predicts that hundreds more CBH II chimeras share this superior thermostability. Eight of eight thermostable chimeras assayed hydrolyze the solid cellulosic substrate Avicel at temperatures at least 5 °C above the most stable parent, and seven of these showed superior activity in 16-h Avicel hydrolysis assays. The sequence-stability model identified a single block of sequence that adds 8.5 °C to chimera thermostability. Mutating individual residues in this block identified the C313S substitution as responsible for the entire thermostabilizing effect. Introducing this mutation into the two recombination parent CBH IIs not featuring it (Hypocrea jecorina and H. insolens) decreased inactivation, increased maximum Avicel hydrolysis temperature, and improved long time hydrolysis performance. This mutation also stabilized and improved Avicel hydrolysis by Phanerochaete chrysosporium CBH II, which is only 55–56% identical to recombination parent CBH IIs. Furthermore, the C313S mutation increased total H. jecorina CBH II activity secreted by the Saccharomyces cerevisiae expression host more than 10-fold. Our results show that SCHEMA structure-guided recombination enables quantitative prediction of cellulase chimera thermostability and efficient identification of stabilizing mutations

MEGA: Multimodal Alignment Aggregation and Distillation For Cinematic Video Segmentation

Previous research has studied the task of segmenting cinematic videos into scenes and into narrative acts. However, these studies have overlooked the essential task of multimodal alignment and fusion for effectively and efficiently processing long-form videos (>60min). In this paper, we introduce Multimodal alignmEnt aGgregation and distillAtion (MEGA) for cinematic long-video segmentation. MEGA tackles the challenge by leveraging multiple media modalities. The method coarsely aligns inputs of variable lengths and different modalities with alignment positional encoding. To maintain temporal synchronization while reducing computation, we further introduce an enhanced bottleneck fusion layer which uses temporal alignment. Additionally, MEGA employs a novel contrastive loss to synchronize and transfer labels across modalities, enabling act segmentation from labeled synopsis sentences on video shots. Our experimental results show that MEGA outperforms state-of-the-art methods on MovieNet dataset for scene segmentation (with an Average Precision improvement of +1.19%) and on TRIPOD dataset for act segmentation (with a Total Agreement improvement of +5.51%)Comment: ICCV 2023 accepte

Motion-Guided Masking for Spatiotemporal Representation Learning

Several recent works have directly extended the image masked autoencoder (MAE) with random masking into video domain, achieving promising results. However, unlike images, both spatial and temporal information are important for video understanding. This suggests that the random masking strategy that is inherited from the image MAE is less effective for video MAE. This motivates the design of a novel masking algorithm that can more efficiently make use of video saliency. Specifically, we propose a motion-guided masking algorithm (MGM) which leverages motion vectors to guide the position of each mask over time. Crucially, these motion-based correspondences can be directly obtained from information stored in the compressed format of the video, which makes our method efficient and scalable. On two challenging large-scale video benchmarks (Kinetics-400 and Something-Something V2), we equip video MAE with our MGM and achieve up to +$1.3\%$ improvement compared to previous state-of-the-art methods. Additionally, our MGM achieves equivalent performance to previous video MAE using up to $66\%$ fewer training epochs. Lastly, we show that MGM generalizes better to downstream transfer learning and domain adaptation tasks on the UCF101, HMDB51, and Diving48 datasets, achieving up to +$4.9\%$ improvement compared to baseline methods.Comment: Accepted to ICCV 202

Transpiration of Eucalyptus woodlands across a natural gradient of depth-to-groundwater

© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]. Water resources and their management present social, economic and environmental challenges, with demand for human consumptive, industrial and environmental uses increasing globally. However, environmental water requirements, that is, the allocation of water to the maintenance of ecosystem health, are often neglected or poorly quantified. Further, transpiration by trees is commonly a major determinant of the hydrological balance of woodlands but recognition of the role of groundwater in hydrological balances of woodlands remains inadequate, particularly in mesic climates. In this study, we measured rates of tree water-use and sapwood 13C isotopic ratio in a mesic, temperate Eucalypt woodland along a naturally occurring gradient of depth-to-groundwater (DGW), to examine daily, seasonal and annual patterns of transpiration. We found that: (i) the maximum rate of stand transpiration was observed at the second shallowest site (4.3 m) rather than the shallowest (2.4 m); (ii) as DGW increased from 4.3 to 37.5 m, stand transpiration declined; (iii) the smallest rate of stand transpiration was observed at the deepest (37.5 m) site; (iv) intrinsic water-use efficiency was smallest at the two intermediate DGW sites as reflected in the Δ13C of the most recently formed sapwood and largest at the deepest and shallowest DGW sites, reflecting the imposition of flooding at the shallowest site and the inaccessibility of groundwater at the deepest site; and (v) there was no evidence of convergence in rates of water-use for co-occurring species at any site. We conclude that even in mesic environments groundwater can be utilized by trees. We further conclude that these forests are facultatively groundwater-dependent when groundwater depth is <9 m and suggest that during drier-than-average years the contribution of groundwater to stand transpiration is likely to increase significantly at the three shallowest DGW sites

Deterministic Chaos in a Model of Discrete Manufacturing

Abstract: A natural extension of the bucket brigade model of manufacturing is capable of chaotic behavior in which the product intercompletion times are, in effect, random, even though the model is completely deterministic. This is, we believe, the first proven instance of chaos in discrete manufacturing. Chaotic behavior represents a new challenge to the traditional tools of engineering management to reduce variability in production lines. Fortunately, if configured correctly, a bucket brigade assembly line can avoi

A comprehensive review on sub-zero temperature cold thermal energy storage materials, technologies, and applications:state of the art and recent developments

The energy industry needs to take action against climate change by improving efficiency and increasing the share of renewable sources in the energy mix. On top of that, refrigeration, air-conditioning, and heat pump equipment account for 25-30% of the global electricity consumption and will increase dramatically in the next decades. However, some waste cold energy sources have not been fully used. These challenges triggered an interest in developing the concept of cold thermal energy storage, which can be used to recover the waste cold energy, enhance the performance of refrigeration systems, and improve renewable energy integration. This paper comprehensively reviews the research activities about cold thermal energy storage technologies at sub-zero temperatures (from around 270 ◦C to below 0 ◦C). A wide range of existing and potential storage materials are tabulated with their properties. Numerical and experimental work conducted for different storage types is systematically summarized. Current and potential applications of cold thermal energy storage are analyzed with their suitable materials and compatible storage types. Selection criteria of materials and storage types are also presented. This review aims to provide a quick reference for researchers and industry experts in designing cold thermal energy systems. Moreover, by identifying the research gaps where further efforts are needed, the review also outlines the progress and potential development directions of cold thermal energy storage technologies.The authors would like to acknowledge the funding support from SJ-NTU Corporate Lab. This work was partically funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE), and the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia program