Miracle-dz/UniFAR-FTUs huggingface dataset:
Absolute Proteome Quantification in the Gas-Fermenting Acetogen Clostridium autoethanogenum

Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C1 waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. ABSTRACT Microbes that can recycle one-carbon (C1) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO2 and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model acetogen Clostridium autoethanogenum grown autotrophically on three gas mixtures (CO, CO+H2, or CO+CO2+H2). We detect the prioritization of proteome allocation for C1 fixation and the significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are likely relevant in vivo for CO oxidation, H2 metabolism, and ethanol production. The integration of proteomic and metabolic flux data demonstrated that enzymes catalyze high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied by changing enzyme catalytic rates rather than concentrations. IMPORTANCE Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C1 waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. Here, we perform reliable absolute proteome quantification for the first time in an acetogen. This is important as our work advances both rational metabolic engineering of acetogen cell factories and accurate in silico reconstruction of their phenotypes. Furthermore, this absolute proteomics data set serves as a reference toward a better systems-level understanding of the ancient metabolism of acetogens.
Analysis of the role of N-linked glycosylation in cell-surface expression, function and binding properties of SARS-CoV-2 receptor ACE2

Human angiotensin I-converting enzyme 2 (hACE2) is a type-I transmembrane glycoprotein that serves as the major cell entry receptor for SARS-CoV and SARS-CoV-2. The viral spike (S) protein is required for attachment to ACE2 and subsequent virus-host cell membrane fusion. Previous work has demonstrated the presence of N-linked glycans in ACE2. N-glycosylation is implicated in many biological activities, including protein folding, protein activity, and cell surface expression of biomolecules. However, the contribution of N-glycosylation to ACE2 function is poorly understood. Here, we examined the role of N-glycosylation in the activity and localization of two species with different susceptibility to SARS-CoV-2 infection, porcine ACE2 (pACE2) and hACE2. The elimination of N-glycosylation by tunicamycin (TM) treatment or mutagenesis, showed that N-glycosylation is critical for the proper cell surface expression of ACE2 but not for its carboxiprotease activity. Furthermore, nonglycosylable ACE2 localized predominantly in the endoplasmic reticulum (ER) and not at the cell surface. Our data also revealed that binding of SARS-CoV and SARS-CoV-2 S protein to porcine or human ACE2 was not affected by deglycosylation of ACE2 or S proteins, suggesting that N-glycosylation plays no role in the interaction between SARS coronaviruses and the ACE2 receptor. Impairment of hACE2 N-glycosylation decreased cell to cell fusion mediated by SARS-CoV S protein but not SARS-CoV-2 S protein. Finally, we found that hACE2 N-glycosylation is required for an efficient viral entry of SARS-CoV/SARS-CoV-2 S pseudotyped viruses, which could be the result of low cell surface expression of the deglycosylated ACE2 receptor. Importance Elucidating the role of glycosylation in the virus-receptor interaction is important for the development of approaches that disrupt infection. In this study, we show that deglycosylation of both ACE2 and S had a minimal effect on the Spike-ACE2 interaction. In addition, we found that removal of N-glycans of ACE2 impaired its ability to support an efficient transduction of SARS-CoV and SARS-CoV-2 S pseudotyped viruses. Our data suggest that the role of deglycosylation of ACE2 on reducing infection is likely due to a reduced expression of the viral receptor on the cell surface. These findings offer insight into the glycan structure and function of ACE2, and potentially suggest that future antiviral therapies against coronaviruses and other coronavirus-related illnesses involving inhibition of ACE2 recruitment to the cell membrane could be developed.
Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.
Mitogen-activated protein kinases contribute to temperature induced cardiac remodelling in rainbow trout (Oncorhynchus mykiss) heart

Rainbow trout (Oncorhynchus mykiss) live in temperate environments and experience seasonal changes in temperature that range between 4°C and 20°C. Laboratory studies demonstrate that cold and warm acclimation of male trout can have oppositional effects on cardiac hypertrophy and the collagen content of the heart. The cellular mechanisms behind temperature induced cardiac remodelling are unclear, as is why this response differs between male and female fish. Recent work utilizing cultured trout cardiac fibroblasts suggests that collagen deposition is regulated, at least in part, by mitogen-activated protein kinase (MAPK) cell signalling pathways. We therefore hypothesized that temperature-dependent cardiac remodelling is regulated by these same cell signalling pathways. To test this, male and female trout were acclimated to 18°C (warm) in the summer and to 4°C (cold) in the winter and the activation of MAPK pathways in the hearts were characterized and compared to that of control fish maintained at 12°C. Animals, maintained under a natural photoperiod matched to time of year, were sampled throughout each acclimation. p38 MAPK phosphorylation increased in the hearts of female fish during the cold acclimation protocol and the phosphorylation of extracellular signal-regulated kinase (ERK) increased in the hearts of male fish with warm acclimation. These results indicate that thermal acclimation has transient and sex-specific effects on the phosphorylation of MAPKs.
Termites have developed wider thermal limits to cope with environmental conditions in savannas

The most diverse and abundant family of termites, the Termitidae, evolved in warm, wet African tropical forests. Since then, they have colonised grassy biomes such as savannas. These environments have more extreme temperatures than tropical forests, and greater temporal fluctuations (both annually and diurnally) that are challenging for soft-bodied ectotherms. We propose that that a likely mechanism that facilitated the expansion from forest to savanna was the widening of physiological limits of savanna termite species in order to cope with more extreme environmental conditions. We sampled termites directly from mound structures across an environmental gradient in Ghana, and recorded the thermal tolerance of individual termites, both critical thermal maximum (CTmax) and critical thermal minimum (CTmin). We estimated colony thermal tolerance by taking an average of each tested individual, and modelled these data against several environmental factors (canopy cover above the mound, rainfall and temperature). We found that savanna termite species had significantly higher CTmax values, and significantly lower CTmin values, than forest species. In addition, areas with high canopy cover were significantly associated with low CTmax values, and areas with higher average daily rainfall were significantly associated with higher CTmin values. Our results suggest that the widening of thermal tolerances has occurred in savanna termite species, probably in response to the more extreme temperatures found in those environments.
Super-Resolution Imaging by Dual Iterative Structured Illumination Microscopy

Three-dimensional (3D) multicolor super-resolution imaging in the 50-100 nm range in fixed and living cells remains challenging. We extend the resolution of structured illumination microscopy (SIM) by an improved nonlinear iterative reconstruction algorithm that enables 3D multicolor imaging with improved spatiotemporal resolution at low illumination intensities. We demonstrate the performance of dual iterative SIM (diSIM) imaging cellular structures in fixed cells including synaptonemal complexes, clathrin coated pits and the actin cytoskeleton with lateral resolutions of 60-100 nm with standard fluorophores. Furthermore, we visualize dendritic spines in 70 µm thick brain slices with an axial resolution < 200 nm. Finally, we image dynamics of the endoplasmatic reticulum and microtubules in living cells with up to 255 frames/s.
Polymerization of C9 enhances bacterial cell envelope damage and killing by membrane attack complex pores

Complement proteins can form Membrane Attack Complex (MAC) pores that directly kill Gram-negative bacteria. MAC pores assemble by stepwise binding of C5b, C6, C7, C8 and finally C9, which can polymerize into a transmembrane ring of up to 18 C9 monomers. It is still unclear if the assembly of a polymeric-C9 ring is necessary to sufficiently damage the bacterial cell envelope to kill bacteria, because a robust way to specifically prevent polymerization of C9 has been lacking. In this paper, polymerization of C9 was prevented without affecting the binding of C9 to C5b-8 by locking the first transmembrane helix domain of C9. We show that polymerization of C9 strongly enhanced bacterial cell envelope damage and killing by MAC pores for several Escherichia coli and Klebsiella strains. Moreover, we show that polymerization of C9 is impaired on complement-resistant E. coli strains that survive killing by MAC pores. Altogether, these insights are important to understand how MAC pores kill bacteria and how bacterial pathogens can resist MAC-dependent killing.
Programming large target genomic deletion and concurrent insertion via a prime editing-based method: PEDAR

Genomic insertions, duplications, and insertion/deletions (indels) account for ~14% of human pathogenic mutations. Current gene editing methods cannot accurately or efficiently correct these abnormal genomic rearrangements, especially larger alterations (>100 bp). Thus, developing a method to accurately delete insertions/duplications and repair the deletion junction could improve the scope of gene therapies. Here, we engineer a novel gene editor, PE-Cas9, by conjugating Cas9 nuclease to reverse transcriptase. Combined with two prime editing guide RNAs (pegRNAs) targeting complementary DNA strands, PE-Cas9 can direct the replacement of a genomic fragment, ranging from to ~1-kb to >10-kb, with a desired sequence at the target site without requiring an exogenous DNA template. In a reporter cell line, this PE-Cas9-based deletion and repair (PEDAR) method restored mCherry expression through in-frame deletion of a disrupted GFP sequence. We further show that PEDAR efficiency could be enhanced by using pegRNAs with high cleavage activity or increasing transfection efficiency. In tyrosinemia mice, PEDAR removed a 1.38-kb pathogenic insertion within the Fah gene and precisely repaired the deletion junction to restore FAH expression in liver. This study highlights PEDAR as a tool for correcting pathogenic mutations.
Enacting shared responsibility in biosecurity governance: insights from adaptive governance

Amidst an increasingly complex global environment of trade and travel, with heightened concerns for the unintended or deliberate spread of species and diseases, biosecurity is a key policy goal in many parts of the world. In Australia, there is concern that invasive species (plants, animals, and diseases) enter, spread, and establish, threatening local industries such as agriculture, as well as human health and biodiversity. Shared responsibility for biosecurity is a recent policy direction that has gained great traction but requires improved conceptual and practical clarity in how local citizens are co-opted into or experience biosecurity programs. In this paper, we interrogate the framing and enactment of shared responsibility for biosecurity, propose a repositioning informed by attributes of adaptive governance that involves a clearer structuring of partnerships, and illustrate this through a case example of network-based, passive surveillance. This repositioning is organized around four pillars, where biosecurity is a part of dynamic cosmopolitan territories; enacted through diverse networks; integrating with existing types of knowledge, concerns, and practices; and forming networks of partnership. We consider implications for adaptive governance more generally, centering on structure, power, and decision making.
Probabilistic Load Flow by Monte Carlo Simulation to Determine the Amount of Use of the Transmission System

Access to the transmission network by generators and distributors is essential in the unbundled configuration of the electricity market. Regulatory agents seek to improve the relationship between network users and their operators in order to optimize investments, increase reliability and lower tariffs. In this context, the need arises to foresee the use of transmission networks as accurately as possible to correctly signal to planners and to reduce users' tariff costs. This paper aims to present a methodology for obtaining the amounts of power at the connection points between users and the transmission network, in addition to a search system for the least probable charge. A probabilistic load flow method by numerical method was used, with the search for the lowest charge value. The inputs were modeled as random variables with known probability distribution functions. High concentrations of wind generators were considered and modeled as Weibull distributions. A modified IEEE 14 bus system was used as a case study. The results showed the amounts by risk and by lower value, being the user's option to choose each of the results. The presented method proved to be an interesting proposal for situations that require a forecast of the amounts of use of the transmission system in the medium and long term, with the presence of wind generators.
Computational fluid dynamics modeling and multi-objective optimization of flat tubes partially filled with a porous layer using ANFIS, GMDH, and NSGA II approaches

In this work, the fluid flow in flat tubes armed with a porous layer is modeled and multiobjectively optimized utilizing computational fluid dynamics methods, Adaptive-network-based fuzzy inference system, grouped technique of data handling type artificial neural network, and non-dominated sorting genetic algorithm II. The variables design includes the tubes’ two geometrical parameters, porous layer thickness ratio, tube flattening, porosity, entrance flow rate, and wall heat flux. The purposes are to minimize the pressure drop and to maximize the convection heat transfer coefficient. Initially, utilizing computational fluid dynamics methods the problem is solved numerically in different flat tubes to calculate two objective parameters in tubes. Using numerical results of the preceding step, and are modeled through adaptive-network-based fuzzy inference system and grouped technique for handling the data. Then, Pareto-based multi-objective optimizing will be performed employing grouped technique of data handling model and non-dominated sorting genetic algorithm II. The results revealed that a better predicting is obtained by the adaptive-network-based fuzzy inference system model compared to the other approaches and the significant design information is included in the attained Pareto solution on flow parameters in flat tubes partly with porous insert. Based on the findings, the best configuring for the highest heat transfer and the least pressure loss is Hp= 0.75 and H=4mm. Review History: Received: Revised: Accepted: Available Online:
How to reconstruct aerosol-induced diffuse radiation scenario for simulating GPP in land surface models? An evaluation of reconstruction methods with ORCHIDEE_DFv1.0_DFforc

The impact of diffuse radiation on photosynthesis has been widely documented in field measurements. This impact may have evolved over time during the last century due to changes in cloudiness, increased anthropogenic aerosol loads over polluted regions, and to sporadic volcanic eruptions curtaining the stratosphere with sulfate aerosols. The effects of those changes in diffuse light on large-scale photosynthesis (GPP) are difficult to quantify, and land surface models have been designed to simulate them. Investigating how anthropogenic aerosols have impacted GPP through diffuse light in those models requires carefully designed factorial simulations and a reconstruction of background diffuse light levels during the preindustrial period. Currently, it remains poorly understood how diffuse radiation reconstruction methods can affect GPP estimation and what fraction of GPP changes can be attributed to aerosols. In this study, we investigate different methods to reconstruct spatiotemporal distribution of the fraction of diffuse radiation (Fdf) under preindustrial aerosol emission conditions using a land surface model with a two-stream canopy light transmission scheme that resolves diffuse light effects on photosynthesis in a multi-layered canopy, ORCHIDEE_DF. We show that using a climatologically averaged monthly Fdf, as has been done by earlier studies, can bias the global GPP by up to 13 PgC yr−1 because this reconstruction method dampens the variability of Fdf and produces Fdf that is inconsistent with shortwave incoming surface radiation. In order to correctly simulate preindustrial GPP modulated by diffuse light, we thus recommend that the Fdf forcing field should be calculated consistently with synoptic, monthly, and inter-annual aerosol and cloud variability for preindustrial years. In the absence of aerosol and cloud data, alternative reconstructions need to retain the full variability in Fdf. Our results highlight the importance of keeping consistent Fdf and radiation for land surface models in future experimental designs that seek to investigate the impacts of diffuse radiation on GPP and other carbon fluxes.
Polka Lines: Learning Illumination Patterns and Reconstruction for Active Stereo Supplemental Document

In this Supplemental Document, we present additional details and analysis. Specifically, we provide • Additional prototype details of diffractive optical elements and stereo cameras. • Details on the experimental real-time capture system. • Optimization details of designing a DOE phase map for a specific illumination pattern. • Examples of the stereo NIR dataset. • Calibration details including geometric parameters of the stereo cameras and the illumination module and radiometric calibration of the DOE-laser module. • Details on self-supervised finetuning and the proposed network architecture. • Analysis on the DOE initialization in the proposed end-to-end training. • Insights from Polka Lines illuminations. • Additional results on environment-specific end-to-end optimization. • Comparison to existing illumination patterns and conventional DOE designs.
Texture Browser: Feature‐based Texture Exploration

Texture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre‐trained convolutional neural network to extract high‐level semantic image features, which are then mapped to a 2‐dimensional location using an adaptation of t‐SNE, a dimensionality‐reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t‐SNE, scalable clustering, and multi‐resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.
Deep Learning: Basic Concepts

This note introduces the problems of classification and regression in machine learning, and then discusses issues related to the data that are necessary to train these predictors. Basic architectures of neural networks are described next, followed by a discussion of general principles underlying their training. Back-propagation (a specific training algorithm) and more task-specific neural network architectures will be discussed in subsequent notes.
Securing the Internet of Things from the Bottom Up Using Physical Unclonable Functions

Cyberattacks that target hardware are becoming increasingly prevalent. These include probing attacks that aim at physically extracting sensitive information including cryptographic keys from non-volatile memory. Internet of Things devices that communicate with the Cloud are susceptible to such attacks. Therefore, the integrity of data and ability to authenticate devices are threatened. Physical Unclonable Functions (PUFs) offer a countermeasure to such attacks. A market analysis of products containing PUFs was carried out. An extract of the market analysis and the inferences that were drawn from it is provided. The analysis showed that although many different types of PUFs have been integrated into a variety of devices, most of them are still used in very rudimentary ways. Keywords–Cloud; Physical Unclonable Function; Critical Infrastructure; Internet of Things; Hardware Security.