Powerful Method Research Articles

Mesoscopic axially swept oblique plane microscope for the imaging of freely moving organisms with near-isotropic resolution

Rapid three-dimensional imaging over extended fields of view (FOVs) is crucial to the study of organism-wide systems and biological processes in vivo. Selective-plane illumination microscopy (SPIM) is a powerful method for high spatio-temporal resolution in toto imaging of such biological specimens. However, typical SPIM implementations preclude conventional sample mounting and have anisotropic imaging performance, in particular when designed for large FOVs over 1 mm diameter. Here, we introduce axial sweeping of the illumination into a non-orthogonal dual-objective oblique plane microscope (OPM) design, thereby enabling the observation of freely moving animals over millimeter-sized FOVs, at close to isotropic, sub-cellular resolution. We apply our mesoscopic axially swept OPM (MASOPM) to image the behavioral dynamics of the sea anemone Nematostella vectensis over 1 × 0.7 × 0.4 mm at 1.7 × 2.6 × 3.7 µm resolution and 0.5 Hz volume rate.

Direct testing of natural twister ribozymes from over a thousand organisms reveals a broad tolerance for structural imperfections.

Twister ribozymes are an extensively studied class of nucleolytic RNAs. Thousands of natural twisters have been proposed using sequence homology and structural descriptors. Yet, most of these candidates have not been validated experimentally. To address this gap, we developed Cleavage High-Throughput Assay (CHiTA), a high-throughput pipeline utilizing massively parallel oligonucleotide synthesis and next-generation sequencing to test putative ribozymes en masse in a scarless fashion. As proof of principle, we applied CHiTA to a small set of known active and mutant ribozymes. We then used CHiTA to test two large sets of naturally occurring twister ribozymes: over 1600 previously reported putative twisters and ∼1000 new candidate twisters. The new candidates were identified computationally in ∼1000 organisms, representing a massive increase in the number of ribozyme-harboring organisms. Approximately 94% of the twisters we tested were active and cleaved site-specifically. Analysis of their structural features revealed that many substitutions and helical imperfections can be tolerated. We repeated our computational search with structural descriptors updated from this analysis, whereupon we identified and confirmed the first intrinsically active twister ribozyme in mammals. CHiTA broadly expands the number of active twister ribozymes found in nature and provides a powerful method for functional analyses of other RNAs.

Kinetic analysis on low‐temperature oxidation of wood pellets by isothermal microcalorimetry

AbstractLow‐temperature chemical oxidation is the major driver of self‐heating during storage of wood pellets and its kinetics is essential to describe the heat evolution. In the present work, isothermal microcalorimetry was used to characterize heat generation behavior of three types of wood pellets (pine, fir, and redwood pellets) at 30–70°C. The obtained data were employed to derive the kinetics of low‐temperature oxidation by the peak power, iso‐conversional method, and non‐steady analysis. The consistency and applicability of the kinetics derived by the three methods were evaluated. Kinetic parameters determined by the peak power method were observed to match those from the iso‐conversional method at lower conversions of the oxidation for heat generation. The kinetics derived by the iso‐conversional method indicated the oxidation reactivity generally decreasing and activation energy increasing with the conversion because of O2 consumption and reaction mechanism changing. With the impact of O2 consumption considered separately, the kinetics from the non‐steady analysis is capable of describing the evolution of heat power with the conversion and also consistent with that from the peak power method in describing intrinsic reactivity of pellet materials. The kinetics from the peak power and iso‐conversional methods lump the impact of O2 concentration with the reaction reactivity, suggesting their applications requiring additional models for connecting with O2 consumption.

Highly Sensitive One-Pot Isothermal Assay Combining Rolling Circle Amplification and CRISPR/Cas12a for Aflatoxin B1 Detection.

Occurrences of mycotoxins in cereals are widespread throughout the world. However, the lack of efficient and ultrasensitive tests has largely impeded the identification of these substances in actual samples. Herein, a novel one-pot isothermal assay that integrates rolling-circle amplification (RCA) and CRISPR/Cas12a to detect aflatoxin B1 (AFB1) is reported. Upon addition of AFB1 to the magnetic bead functionalized with a duplex of the AFB1 aptamer and its complementary DNA (cDNA), the specific recognition of AFB1 by the aptamer causes the release of cDNA to activate the RCA reaction. Subsequently, the RCA amplicon initiates both trans-cleavage and cis-cleavage activities of the endonuclease Cas12a. The synergistic coupling of RCA and CRISPR/Cas12a enables exponential amplification of cDNA, which further promotes CRISPR/Cas12a to nonspecifically cleave the single-stranded DNA reporters with enhanced detection signals. Remarkably, the CRISPR/Cas12a-assisted one-pot isothermal assay can not only achieve ultrasensitive quantitative detection through fluorescence detection, but also achieve visual detection through a lateral flow strip, which improves accessibility to mycotoxin detection in resource-limited regions. The limit of detection was 0.016 and 0.408 ng/mL, respectively. The proposed assay successfully applies in real samples with satisfactory recoveries from 90 to 114%. This study presents a powerful and versatile method for reliable and ultrasensitive detection of mycotoxins in various applications.

Lateral deflections of slender RC columns under eccentric compression loading with different end conditions

AbstractIn the literature, several methods have focused mostly on determining the lateral deflection of columns with pinned end conditions. This paper presents a powerful method that allows the calculation of lateral deflection for columns of different end conditions, with particular attention to reinforced concrete (RC) columns. By utilizing the moment–curvature relations, a numerical procedure to compute slopes and deflections of pin‐ended columns is proposed, in which the slopes and deflections are corrected via an efficient and fast technique. Accordingly, slopes and deflections of columns with different end conditions are determined via procedures that rely on a simple searching technique. The second‐order analysis is individually included in the calculations; therefore, the complexity arising from the direct impact of the axial load on the lateral deflection is averted. Therefore, the method has the potential for inclusion in many numerical models for different structural members. The equations derived via the developed method have identical responses to the expressions obtained via the exact methods. A series of numerical examples are presented to illustrate the applicability of the proposed method to RC columns with different loadings and end conditions. Finally, the proposed method is evaluated, and its accuracy is demonstrated via parametric verification. As the axial load increased, the lateral displacement increased somewhat. The greater the eccentricity is, the greater the lateral displacement. As the eccentricity increased, the failure mode of the RC column changed from compression failure to bending failure.

Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592

We examine 4 yr of Kepler 30 minutes data, and five sectors of Transiting Exoplanet Survey Satellite 2 minutes data for the dM3 star KIC-8507979/TIC-272272592. This rapidly rotating (P = 1.2 day) star has previously been identified as flare active, with a possible long-term decline in its flare output. Such slow changes in surface magnetic activity are potential indicators of solar-like activity cycles, which can yield important information about the structure of the stellar dynamo. We find that while TIC-272272592 shows evidence for both short- and long-timescale variations in its flare activity, it is unlikely physically motivated. Only a handful of stars have been subjected to such long-baseline point-in-time flare studies, and we urge caution in comparing results between telescopes due to differences in bandpass, signal-to-noise ratio, and cadence. In this work, we develop an approach to measure variations in the flare frequency distributions over time, which is quantified as a function of the observing baseline. For TIC-272272592, we find a 2.7σ detection of a sector which has a flare deficit, therefore indicating the short-term variation could be a result of sampling statistics. This quantifiable approach to describing flare-rate variation is a powerful new method for measuring the months-to-years changes in surface magnetic activity, and provides important constraints on activity cycles and dynamo models for low-mass stars.

Network community detection via neural embeddings

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.

High-performance biochar derived from the leaves of Quercus dentata thunb for triclosan removal

BackgroundTriclosan (TCS) is a common antimicrobial additive. Despite its low concentration, its frequent use and widespread application result in significant amounts of TCS entering the environment. In the environment, TCS undergoes photodegradation, producing harmful byproducts such as dioxin-like substances and chloroform, which are highly toxic. Due to TCS's lipophilic nature, it easily accumulates in animal tissues, ultimately posing a threat to human health. Therefore, there is an urgent need for a powerful and efficient method to remove TCS from water bodies. MethodsUsing discarded traditional Chinese medicine as raw material and KOH as an activator, biochar was prepared. The material was characterized, and its adsorption capacity for TCS under various conditions was evaluated. Significant FindingsCompared to the existing biochar materials' TCS adsorption capacity (13.9 mg/g-395.2 mg g-1), the KOH-modified biochar (KMBC) in this study demonstrated an exceptional adsorption capacity of 639.13 mg g-1 for TCS. KMBC can swiftly remove 98 % of TCS from water within 30 min, even with a minimal biochar concentration of 0.225 g/L, and it exhibits strong anti-interference capabilities. Most importantly, water treated with KMBC is virtually free of TCS, effectively mitigating its impact on environmental organisms.

LoMAE: Simple Streamlined Low-Level Masked Autoencoders for Robust, Generalized, and Interpretable Low-Dose CT Denoising.

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In computer vision and natural language processing, masked autoencoders (MAE) have been recognized as a powerful self-pretraining method for transformers, due to their exceptional capability to extract representative features. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective streamlined low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and generability across a variety of noise levels. Experimental findings show that the proposed LoMAE enhances the denoising capabilities of the transformer and substantially reduce their dependency on high-quality, ground-truth data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. In summary, the proposed LoMAE provides promising solutions to the major issues in LDCT including interpretability, ground truth data dependency, and model robustness/generalizability.

Predictive Modelling of Mineral Prospectivity Using Satellite Remote Sensing and Machine Learning Algorithms

In today’s world of falling returns on fixed exploration budgets, complex targets, and ever-increasing volumes of multi-parameter datasets, the effective management and integration of existing data are essential to any mineral exploration operation. Machine learning (ML) algorithms like Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM) are powerful data-driven methods that are not implemented very often with remote sensing-derived hydrothermal alternation information and limited field datasets for mapping mineral prospectivity. The application of machine learning algorithms with satellite remote sensing data and limited field data, they have not been compared and evaluated together thoroughly in this field. A data science approach was applied to create nine predictor maps, incorporating limited field data and satellite remote sensing information. A confusion matrix, statistical measures, and a Receiver Operating Characteristic (ROC) curve were used to evaluate the prediction models efficacy on both the training and test datasets. The results suggested that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. These results imply that the RF model is the most suitable for Cu potential mapping in the Pakistan’s North Waziristan region. Consequently, all the models including the RF model were used to generate a prospectivity map, which contained low to very-high potential zones, to support further exploration in the region. The newly discovered deposit inside the predicted prospective areas demonstrates the robustness and efficacy of the prospectivity modelling approach as proposed in this research for generating exploration targets.

Comprehensive phenotypic assessment of nonsense mutations in mitochondrial ND5 in mice.

Mitochondrial dysfunction induced by mitochondrial DNA (mtDNA) mutations has been implicated in various human diseases. A comprehensive analysis of mitochondrial genetic disorders requires suitable animal models for human disease studies. While gene knockout via premature stop codons is a powerful method for investigating the unique functions of target genes, achieving knockout of mtDNA has been rare. Here, we report the genotypes and phenotypes of heteroplasmic MT-ND5 gene-knockout mice. These mutant mice presented damaged mitochondrial cristae in the cerebral cortex, hippocampal atrophy, and asymmetry, leading to learning and memory abnormalities. Moreover, mutant mice are susceptible to obesity and thermogenetic disorders. We propose that these mtDNA gene-knockdown mice could serve as valuable animal models for studying the MT-ND5 gene and developing therapies for human mitochondrial disorders in the future.

Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet’s randomized t-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.

Clinical information extraction for lower-resource languages and domains with few-shot learning using pretrained language models and prompting

Abstract A vast amount of clinical data are still stored in unstructured text. Automatic extraction of medical information from these data poses several challenges: high costs of clinical expertise, restricted computational resources, strict privacy regulations, and limited interpretability of model predictions. Recent domain adaptation and prompting methods using lightweight masked language models showed promising results with minimal training data and allow for application of well-established interpretability methods. We are first to present a systematic evaluation of advanced domain-adaptation and prompting methods in a lower-resource medical domain task, performing multi-class section classification on German doctor’s letters. We evaluate a variety of models, model sizes (further-pre)training and task settings, and conduct extensive class-wise evaluations supported by Shapley values to validate the quality of small-scale training data and to ensure interpretability of model predictions. We show that in few-shot learning scenarios, a lightweight, domain-adapted pretrained language model, prompted with just 20 shots per section class, outperforms a traditional classification model, by increasing accuracy from $48.6\%$ to $79.1\%$ . By using Shapley values for model selection and training data optimization, we could further increase accuracy up to $84.3\%$ . Our analyses reveal that pretraining of masked language models on general-language data is important to support successful domain-transfer to medical language, so that further-pretraining of general-language models on domain-specific documents can outperform models pretrained on domain-specific data only. Our evaluations show that applying prompting based on general-language pretrained masked language models combined with further-pretraining on medical-domain data achieves significant improvements in accuracy beyond traditional models with minimal training data. Further performance improvements and interpretability of results can be achieved, using interpretability methods such as Shapley values. Our findings highlight the feasibility of deploying powerful machine learning methods in clinical settings and can serve as a process-oriented guideline for lower-resource languages and domains such as clinical information extraction projects.

Van der Waals interactions between nonpolar alkyl chains and polar oxide surfaces prevent catalyst deactivation in aldehyde gas sensing

Catalysis-based electrical sensing of volatile organic compounds on metal oxide surfaces is a powerful method for molecular discrimination. However, catalyst deactivation caused by the poisoning of catalytic sites by analytes and/or catalyzed products remains a challenge. This study highlights the underestimated role of van der Waals interactions between hydrophobic aliphatic alkyl chains and hydrophilic ZnO surfaces in mitigating catalyst deactivation during aliphatic aldehyde sensing. By immobilizing octadecylphosphonic acid (ODPA) on ZnO nanowire sensors, recovery times for nonanal detection are significantly reduced without compromising sensitivity. Temperature-programmed measurements demonstrate a reduction in desorption temperature of carboxylates on ODPA-modified ZnO to below 150 °C, whereas carboxylates on bare ZnO remain above 300 °C, indicating a significant decrease in catalyst deactivation. Density functional theory calculations reveal that accumulated van der Waals interactions between alkyl chains and ZnO surfaces significantly contributed to adsorption molecular kinetics. IR spectroscopy using deuterated self-assembled monolayers (SAMs) reveals conformational changes of alkyl chains within the SAMs caused by aldehyde adsorption, supporting the suggested adsorption kinetics. A model is proposed based on the dynamic surface-covering by alkyl chains destabilizes catalytically oxidized carboxylic acids.

Antioxidant, cytotoxic, anti-migratory, and pro-apoptotic effects of Bolanthus turcicus extracts on head and neck cancer cells.

Investigation of various plant extracts using in-vitro/in-vivo assays has emerged as a promising avenue for identifying potential pharmacophores that can be developed into therapeutic drugs. This study aims to assess the bioactive compounds and antioxidant capacity of the Bolanthus turcicus (B. turcicus) and to investigate the effects on head and neck cancer (HNC) cell lines. Methanol (MeOH), ethyl acetate (EA) and aqueous (Aq) extracts were prepared from B. turcicus, and the amount of total phenolic content (TPC) and total flavonoid content (TFC) in the extracts were analyzed by the Folin-Ciocalteu and Aluminum chloride method, respectively. In addition, the total antioxidant capacity and iron reducing potential of B. turcicus extracts were determined by the Phosphomolybdenum and Ferric ion reducing antioxidant power (FRAP) method. The effect of B. turcicus on HEp-2, SCC-90, SCC-9, FaDu HNC cell viability, motility, and cell-nuclear morphology was evaluated by MTT, scratch-wound healing assay, and Pllalloidin-DAPI staining, respectively. The effect of B. turcicus on the expression of CASP-3, BAX, and BCL-2 genes at the mRNA, protein, and intracellular level was evaluated by quantitative PCR (qPCR), western blot, and immunofluorescence staining. Moreover, Annexin V-FITC/PI, was used in flow cytometry to investigate the effect of B. turcicus on apoptosis. The MeOH extract exhibited the highest phenolic content, flavonoid content and antioxidant activity (p < 0.05 for all). HNC cells treated with extracts indicated delayed wound healing and decreased motility (p < 0.05 for all). Analysis of annexin V-PI staining indicated that the B. turcicus extracts induced apoptosis but not viability and necrosis in the HNC cell (p < 0.05 for all). Moreover, qPCR data regarding the apoptotic mechanism showed that the extracts could induce apoptosis by upregulation of pro-apoptotic CASP-3 and BAX genes and downregulation of anti-apoptotic BCL-2 gene (p < 0.05 for all). The expression of protein and intracellular levels of CASP-3 and BAX were increased, while the BCL-2 was decreased in cells treated with the extracts (p < 0.05 for all). In addition, diffuse pycnosis and DNA condensation in HNC cell nuclei, confirming apoptotic cell death (p < 0.05 for all). This study data indicated that B. turcicus extracts have antioxidant, cytotoxic, anti-migratory and pro-apoptotic activity. In conclusion, it has been shown that B. turcicus can be used as a potential therapeutic agent against HNC.

Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps.

Cryo-electron microscopy (cryo-EM) is one of the most powerful experimental methods for macromolecular structure determination. However, accurate DNA/RNA structure modeling from cryo-EM maps is still challenging especially for protein-DNA/RNA or multi-chain DNA/RNA complexes. Here we propose a deep learning-based method for accurate de novo structure determination of DNA/RNA from cryo-EM maps at <5 Å resolutions, which is referred to as EM2NA. EM2NA is extensively evaluated on a diverse test set of 50 experimental maps at 2.0-5.0 Å resolutions, and compared with state-of-the-art methods including CryoREAD, ModelAngelo, and phenix.map_to_model. On average, EM2NA achieves a residue coverage of 83.15%, C4' RMSD of 1.06 Å, and sequence recall of 46.86%, which outperforms the existing methods. Moreover, EM2NA is applied to build the DNA/RNA structures with 10 to 5347 nt from an EMDB-wide data set of 263 unmodeled raw maps, demonstrating its ability in the blind model building of DNA/RNA from cryo-EM maps. EM2NA is fast and can normally build a DNA/RNA structure of <500 nt within 10 minutes.

Shuttle HAT for mild alkene transfer hydrofunctionalization.

Hydrogen atom transfer (HAT) from a metal-hydride is a reliable and powerful method for functionalizing unsaturated C-C bonds in organic synthesis. Cobalt hydrides (Co-H) have garnered significant attention in this field, where the weak Co-H bonds are most commonly generated in a catalytic fashion through a mixture of stoichiometric amounts of peroxide oxidant and silane reductant. Here we show that the reverse process of HAT to an alkene, i.e. hydrogen atom abstraction of a C-H adjacent to a radical, can be leveraged to generate catalytically active Co-H species in an application of shuttle catalysis coined shuttle HAT. This method obviates the need for stoichiometric reductant/oxidant mixtures thereby greatly simplifying the generation of Co-H. To demonstrate the generality of this shuttle HAT platform, five different reaction manifolds are shown, and the reaction can easily be scaled up to 100 mmol.

A Computational Model Reveals How Varying Muscle Activation in the Lateral Pharyngeal Wall and Soft Palate Differentiates Velopharyngeal Closure Patterns.

Finite element (FE) models have emerged as a powerful method to study biomechanical complexities of velopharyngeal (VP) function. However, existing models have overlooked the active contributions of the lateral pharyngeal wall (LPW) in VP closure. This study aimed to develop and validate a more comprehensive FE model of VP closure to include the superior pharyngeal constrictor (SPC) muscle within the LPW as an active component of VP closure. The geometry of the velum and the lateral and posterior pharyngeal walls with biomechanical activation governed by the levator veli palatini (LVP) and SPC muscles were incorporated into an FE model of VP closure. Differing muscle activations were employed to identify the impact of anatomic contributions from the SPC muscle, LVP muscle, and/or velum for achieving VP closure. The model was validated against normative magnetic resonance imaging data at rest and during speech production. A highly accurate and validated biomechanical model of VP function was developed. Differing combinations and activation of muscles within the LPW and velum provided insight into the relationship between muscle activation and closure patterns, with objective quantification of anatomic change necessary to achieve VP closure. This model is the first to include the anatomic properties and active contributions of the LPW and SPC muscle for achieving VP closure. Now validated, this method can be utilized to build robust, comprehensive models to understand VP dysfunction. This represents an important advancement in patient-specific modeling of VP function and provides a foundation to support development of computational tools to meet clinical demand.

Copper‐Catalyzed Enantioselective Dehydro‐Diels–Alder Reaction: Atom‐Economical Synthesis of Axially Chiral Carbazoles

AbstractThe dehydro‐Diels–Alder (DDA) reaction is a powerful method for the construction of aromatic compounds. However, the enantioselective DDA reaction has been rarely developed, probably due to the competitive thermal reaction. Herein, we report a copper‐catalyzed enantioselective DDA reaction through vinyl cation pathway. The reaction leads to the atom‐economical synthesis of axially chiral phenyl and indolyl carbazoles in generally excellent yields with good to excellent atroposelectivities. This methodology represents the first example of non‐noble metal‐catalyzed enantioselective DDA reaction. Notably, new chiral ligand and organocatalyst derived from the constructed axially chiral carbazole are demonstrated to be useful in asymmetric catalysis.

Gain-based Green Ant Colony Optimization for 3D Path Planning on Remote Sensing Images

Metaheuristic algorithms are powerful methods for handling complexities in 3D environments because of their adaptability property. This paper proposes a gain-based, green-ant colony optimization (GGACO) method for 3D path planning on remote sensing images. Shortest paths do not always imply minimum energy consumption. Moreover, computational complexity tends to increase in the case of higher-dimensional data. A novel method is proposed to alleviate this issue, one that provides an efficient path with minimum energy consumption by adding a gain quantity during its search. The results are validated using performance measures, viz., path length, time, and energy cost. Real-time images, along with their corresponding ground truth and “digital surface models (DSM)”, have been sourced from the “International Society for Photogrammetry and Remote Sensing (ISPRS)”. Comparisons have been made against state-of-the-art algorithms and analyzed. Finally, the convergence and stability of the proposed method have been verified; it has been found that the proposed method outperforms the existing method by 6%, 11% and 5% regarding length, computation time, and energy, respectively.