Browse Articles

Integrating climate and physical constraints into assessments of net capture from direct air capture facilities

Limiting climate change to targets enshrined in the Paris Agreement will require both deep decarbonization of the energy system and the deployment of carbon dioxide removal at potentially large scale (gigatons of annual removal). Nations are pursuing direct air capture to compensate for inertia in the expansion of low-carbon energy systems, decarbonize hard-to-abate sectors, and address legacy emissions. Global assessments of this technology have failed to integrate factors that affect net capture and removal cost, including ambient conditions like temperature and humidity, as well as emission factors of electricity and natural gas systems. We present an integrated assessment of the global deployment potential of this technology. Employing a chemical process model, climate data, grid emission factors, and fugitive methane emission factors, we predict critical performance metrics, including carbon dioxide capture rates, and water-, energy-, and emissions-intensity of capture. Our results support investors and policy makers as they site facilities and develop credible policy instruments to support expansion.

Optimization design and application of library face recognition access control system based on improved PCA

The application of face recognition technology in Library Access Control System (LACS) has an important impact on improving the security and management efficiency of the library. However, the traditional face recognition methods have some limitations in the face of complex environmental conditions such as illumination and posture change. To solve this problem, an improved method combining the Aggregating Spatial Embeddings for Face Recognition (ASEF) algorithm and Principal Component Analysis (PCA) is proposed. The PCA algorithm is optimized by introducing beta prior and full probability Bayesian model. In addition, the research also integrates K-means Clustering Algorithm (KA) to further improve the accuracy and efficiency of face recognition. The experiment showed that the improved PCA method had an average recognition rate of 92.6%, an average recognition speed of 0.40s, and higher accuracy compared to other related methods, reaching 96%. In practical applications, the system quickly and accurately completes the identification of personnel entry and exit, and improves the efficiency and security of library access management.

Mechanistic insights and atmospheric implications of the degradation reaction of 3-methoxy-1-propanol by reaction with hydroxyl radicals and identification of the end products in the presence of O2/NO

Genesis and regulation of C-terminal cyclic imides from protein damage

C-Terminal cyclic imides are posttranslational modifications that can arise from spontaneous intramolecular cleavage of asparagine or glutamine residues resulting in a form of irreversible protein damage. These protein damage events are recognized and removed by the E3 ligase substrate adapter cereblon (CRBN), indicating that these aging-related modifications may require cellular quality control mechanisms to prevent deleterious effects. However, the factors that determine protein or peptide susceptibility to C-terminal cyclic imide formation or their effect on protein stability have not been explored in detail. Here, we characterize the primary and secondary structures of peptides and proteins that promote intrinsic formation of C-terminal cyclic imides in comparison to deamidation, a related form of protein damage. Extrinsic effects from solution properties and stressors on the cellular proteome additionally promote C-terminal cyclic imide formation on proteins like glutathione synthetase that are susceptible to aggregation if the protein damage products are not removed by CRBN. This systematic investigation provides insight into the regions of the proteome that are prone to these unexpectedly frequent modifications, the effects of this form of protein damage on protein stability, and the biological role of CRBN.

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms–Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr. Model performance was also compared in a seasonally-matched pre-policy cohort. In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. The relative importance of some predictive variables differed between the pre-policy and post-policy cohorts, such as the absence of ECMO in the post-policy cohort. Survival machine learning models provide reasonable prediction of 1-year posttransplant mortality outcomes and continual updating of prediction models is warranted in the contemporary era.

Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction

Abstract Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data. The intelligent sperm motility heuristic navigates the image’s pixel space, identifying the most influential neighboring pixels for accurate imputation. Our approach includes three essential modifications: (1) integration of an identity module within the GAN architecture to mitigate the vanishing gradient problem; (2) introduction of a metaheuristic algorithm based on sperm motility to select the top 10 pixels that most effectively contribute to the generation of the missing pixel; and (3) the implementation of an adaptive interval mechanism between the discriminator’s actual value and the weighted average of the selected pixels, enhancing the generator’s efficiency and ensuring the coherence of the imputed pixels with the surrounding image context. We evaluate the proposed method on three distinct datasets (Energy Images, NREL Solar Images, and NREL Wind Turbine Dataset), demonstrating its superior performance in maintaining pixel integrity during the imputation process. Our experiments also confirm the approach’s effectiveness in addressing everyday challenges in GANs, such as mode collapse and vanishing gradients, across various GAN architectures.

Mechanism-guided engineering of a minimal biological particle for genome editing

The widespread application of genome editing to treat and cure disease requires the delivery of genome editors into the nucleus of target cells. Enveloped delivery vehicles (EDVs) are engineered virally derived particles capable of packaging and delivering CRISPR-Cas9 ribonucleoproteins (RNPs). However, the presence of lentiviral genome encapsulation and replication proteins in EDVs has obscured the underlying delivery mechanism and precluded particle optimization. Here, we show that Cas9 RNP nuclear delivery is independent of the native lentiviral capsid structure. Instead, EDV-mediated genome editing activity corresponds directly to the number of nuclear localization sequences on the Cas9 enzyme. EDV structural analysis using cryo-electron tomography and small molecule inhibitors guided the removal of ~80% of viral residues, creating a minimal EDV (miniEDV) that retains full RNP delivery capability. MiniEDVs are 25% smaller yet package equivalent amounts of Cas9 RNPs relative to the original EDVs and demonstrated increased editing in cell lines and therapeutically relevant primary human T cells. These results show that virally derived particles can be streamlined to create efficacious genome editing delivery vehicles with simpler production and manufacturing.

Integrative metabolomics and transcriptomics profiling reveals differential expression of flavonoid synthesis in Ophiopogon japonicus (L. f.) Ker-Gawl. in adaptation to drought

Drought is one of the consequences of climate change that severely affects plant growth and development. Ophiopogon japonicus (L. f.) Ker-Gawl. (Chinese name: Chuanmaidong, abbreviated as CMD) is a commonly used herbaceous plant whose growth and development are strongly affected by drought. Here, we comprehensively analyzed the transcriptomic and metabolic responses of two CMD varieties (EP and CP) to drought stress. CP utilized a small number of differentially expressed genes to regulate a greater number of differential metabolites compared to EP, suggesting that it may be more drought tolerant. In addition, integrated transcriptome and metabolome analyses revealed that transcription factors such as WRKY, TIFY, and C2H2 regulate flavonoid synthesis in CMD. These findings provide ideas for in-depth analysis of the mechanism of CMD against drought stress, and provide a theoretical basis for breeding high-quality drought-tolerant varieties.

Comparative analysis of improved m6A sequencing based on antibody optimization for low-input samples

Structural insight into sodium ion pathway in the bacterial flagellar stator from marine <i>Vibrio</i>

Many bacteria swim in liquid or swarm on surface using the flagellum rotated by a motor driven by specific ion flow. The motor consists of the rotor and stator, and the stator converts the energy of ion flow to mechanical rotation. However, the ion pathway and the mechanism of stator rotation coupled with specific ion flow are still obscure. Here, we determined the structures of the sodium-driven stator of Vibrio , namely PomAB, in the presence and absence of sodium ions and the structure with its specific inhibitor, phenamil, by cryo-electron microscopy. The structures and following functional analysis revealed the sodium ion pathway, the mechanism of ion selectivity, and the inhibition mechanism by phenamil. We propose a model of sodium ion flow coupled with the stator rotation based on the structures. This work provides insights into the molecular mechanisms of ion specificity and conversion of the electrochemical potential into mechanical functions.

Innovameter: Agent-based modeling of innovation determinants in American and European countries

This article discusses the dynamics of innovation in America and Europe, focusing on variables such as access to technology, education, and life expectancy. To do this, the article proposes an agent-based model called the Innovameter. The dependent variable is the Global Innovation Index. The paper focuses on data analysis through correlation analysis and multiple hierarchical regressions to determine the contribution of specific variables related to the pillars of the Global Innovation Index and indicators of the Human Development Index. After analyzing the data, an agent-based model was built to parameterize these main variables by defining two levels of abstraction: at the global level, there is the country, where birth rates, life expectancy, ICT use, and research and development are defined. At the local level, we define the individuals who have an age, years of schooling, and income. A series of experiments were conducted by selecting data from 30 countries. From the results of the experiments, a nonparametric correlation analysis was performed, and correlation indices were obtained indicating a relationship between the predicted outcomes and the outcomes in the global index. The proposed model aims to provide suggestions on how the different variables can become the norm in most of the countries studied.

Hybrid spin coating and evaporation techniques for fabricating double-layer stacked dual-color perovskite LEDs

Palmitoylation-dependent association with Annexin II directs hepatitis E virus ORF3 sorting into vesicles and quasi-enveloped virions

Historically considered to be nonenveloped, hepatitis E virus (HEV), an important zoonotic pathogen, has recently been discovered to egress from infected cells as quasi-enveloped virions. These quasi-enveloped virions circulating in the blood are resistant to neutralizing antibodies, thereby facilitating the stealthy spread of infection. Despite abundant evidence of the essential role of the HEV-encoded ORF3 protein in quasi-enveloped virus formation, the underlying mechanism remains unclear. Here, we demonstrate that the HEV ORF3 protein possesses an inherent capacity for self-secretion and that palmitoylation at two cysteine residues within the ORF3 N-terminal region is essential for its secretion and quasi-enveloped virus formation. We further found that only palmitoylated ORF3 proteins hijacked Annexin II for transport to the cytoskeleton and are then directed into multivesicular bodies through the nSMase-endosomal sorting complexes required for transport-III pathway for secretion. Finally, we show that infection of gerbils with HEV mutants harboring mutations at palmitoylation sites within ORF3 showed no fecal viral shedding but competent replication in the liver. Our study fills a gap in the understanding of the assembly and release of quasi-enveloped virions mediated by ORF3 and offers the potential for designing therapeutic strategies to control HEV infection.

Experimental research on the structural optimization of grouting holes in circular and rectangular pipe sections for trenchless pipe jacking

Profile of Stefan W. Hell

Ln-MOF in production of durable antimicrobial and UV-Protective fluorescent cotton fabric for potential application in military textiles

AbstractIndustrialization of military textiles faces many challenges and some requirements such as durability, protection and suitability for hostile environment must be provided. Herein, fluorescent protective cotton with ultraviolet radiation (UVR)-protection and antimicrobial property was currently prepared via the immobilization of lanthanide-metal organic framework (Ln-MOF). Cotton fabrics were primarily activated via cationization process with 3-Chloro-2-hydroxypropyltrimethyl ammonium chloride to obtain the cationized cotton (Q-cotton). Subsequently, Ln-MOFs based on Europium (Eu) and Terbium (Tb) were separately immobilized within cotton and Q-cotton fabrics. The obtained Ln-MOF@fabrics showed good fluorescent character, while three and four emission bands were estimated for Eu-MOF@fabric and Tb-MOF@fabric, respectively, related to the electron transition from 5D0 to 7F0-4 in Eu3+ and from 5D4 to 7F3-6 in Tb3+. After Ln-MOF incorporation, UVR-protection factor (UPF) was significantly enlarged from 1.9 (insufficient UPF) to 22.1–25.6 (good UPF) without cationization and to 32.4–37.8 (very good UPF) for Q-cotton. Against three different pathogens (Escherichia coli, staphylococcus Aureus and Candida albicans), Ln-MOF@fabrics exhibited good microbial reduction of 68–79% and 81–91% in case of cotton and Q-cotton, respectively. The cationization improved the functionality and durability of fabrics, while the acquired functions were still existed even after 10 repetitive washings.

A single-center, single-arm, prospective, open-label, and comparative trial to evaluate the safety and tolerability profile of a 90-day oral L-arginine hydrochloride intervention for patients with amyotrophic lateral sclerosis

Modular DNA origami–based electrochemical detection of DNA and proteins

The diversity and heterogeneity of biomarkers has made the development of general methods for single-step quantification of analytes difficult. For individual biomarkers, electrochemical methods that detect a conformational change in an affinity binder upon analyte binding have shown promise. However, because the conformational change must operate within a nanometer-scale working distance, an entirely new sensor, with a unique conformational change, must be developed for each analyte. Here, we demonstrate a modular electrochemical biosensor, built from DNA origami, which is easily adapted to diverse molecules by merely replacing its analyte binding domains. Instead of relying on a unique nanometer-scale movement of a single redox reporter, all sensor variants rely on the same 100-nm scale conformational change, which brings dozens of reporters close enough to a gold electrode surface that a signal can be measured via square-wave voltammetry, a standard electrochemical technique. To validate our sensor’s mechanism, we used single-stranded DNA as an analyte, and optimized the number of redox reporters and various linker lengths. Adaptation of the sensor to streptavidin and Platelet-Derived Growth Factor-BB (PDGF-BB) analytes was achieved by simply adding biotin or anti-PDGF aptamers to appropriate DNA linkers. Geometrically optimized streptavidin sensors exhibited signal gain and limit of detection markedly better than comparable reagentless electrochemical sensors. After use, the same sensors could be regenerated under mild conditions: Performance was largely maintained over four cycles of DNA strand displacement and rehybridization. By leveraging the modularity of DNA nanostructures, our work provides a straightforward route to the single-step quantification of arbitrary nucleic acids and proteins.

Wavefront-aberration-tolerant diffractive deep neural networks using volume holographic optical elements

AbstractAs the demand for computational performance in artificial intelligence (AI) continues to increase, diffractive deep neural networks (D2NNs), which can perform AI computing at the speed of light by repeated optical modulation with diffractive optical elements (DOEs), are attracting attention. DOEs are varied in terms of fabrication methods and materials, and among them, volume holographic optical elements (vHOEs) have unique features such as high selectivity and multiplex recordability for wavelength and angle. However, when those are used for D2NNs, they suffer from unknown wavefront aberrations compounded by multiple fabrication errors. Here, we propose a training method to adapt the model to be unknown wavefront aberrations and demonstrate a D2NN using vHOEs. As a result, the proposed method improved the classification accuracy by approximately 58 percentage points in the optical experiment, with the model trained to classify handwritten digits. The achievement of this study can be extended to the D2NN that enables the independent modulation of multiple wavelengths owing to their wavelength selectivity and wavelength division multiplex recordability. Therefore, it might be promising for various applications that require multiple wavelengths in parallel optical computing, bioimaging, and optical communication.

Geometrically modulated contact forces enable hula hoop levitation

Mechanical systems with moving points of contact—including rolling, sliding, and impacts—are common in engineering applications and everyday experiences. The challenges in analyzing such systems are compounded when an object dynamically explores the complex surface shape of a moving structure, as arises in familiar but poorly understood contexts such as hula hooping. We study this activity as a unique form of mechanical levitation against gravity and identify the conditions required for the stable suspension of an object rolling around a gyrating body. We combine robotic experiments involving hoops twirling on surfaces of various geometries and a model that links the motions and shape to the contact forces generated. The in-plane motions of the hoop involve synchronization to the body gyration that is shown to require damping and sufficiently high launching speed. Further, vertical equilibrium is achieved only for bodies with “hips” or a critical slope of the surface, while stability requires an hourglass shape with a “waist” and whose curvature exceeds a critical value. Analysis of the model reveals dimensionless factors that successfully organize and unify observations across a wide range of geometries and kinematics. By revealing and explaining the mechanics of hula hoop levitation, these results motivate strategies for motion control via geometry-dependent contact forces and for accurately predicting the resulting equilibria and their stability.