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Lightweight detection model for safe wear at worksites using GPD-YOLOv8 algorithm
Unlocking the catalytic precision of ligand-controlled enzymatic halogenation
A single-component flavin-dependent halogenase, AetF, has emerged as an attractive biocatalyst for catalyzing halogenation. However, its flavin chemistry remains unexplored and cannot be predicted due to its uniqueness in sequence and structure compared to other flavin-dependent monooxygenases. Here, we investigated the flavin reactions of AetF using transient kinetics. Our data revealed that NADP + binding is required for formation of C4a-hydroperoxy flavin adenine dinucleotide (FAD) (FAD C4aOOH ), a key flavin-oxygen adduct required for generating a halogenating species. In the presence of NaBr without L-tryptophan, the flavin oxygen adduct intermediates [possibly FAD C4aOOH and C4a-hydroxy FAD (FAD C4aOH )] are highly stabilized (>4,000 s) before returning to the oxidized FAD state. In the presence of L-tryptophan, the rate of FAD C4aOH dehydration to form oxidized FAD increased by ~825-fold. These data suggest that the presence of all substrates is required for speeding up AetF’s catalytic cycle. Our findings underscore the adeptness of AetF in managing its reactivity through ligand control. Structural and tunnel analyses revealed that the binding of NADP + and L-tryptophan induces changes in protein tunnels which may potentially link to the ligand-controlled mechanisms. Leveraging these catalytic insights, we employed light-induced flavin reduction and NADP + stimulation to enable AetF halogenation of various compounds. Our findings demonstrate the mechanisms of precise control over flavin chemistry by AetF. These mechanistic insights may be useful for the biocatalytic development of single-component flavin-dependent halogenases.
Restoration effect of chemically modified microRNA-143-3p on acute myocardial infarction in animal models
Income inequality and the erosion of democracy in the twenty-first century
Among the most pressing problems societies face today are economic inequality and the erosion of democratic norms and institutions. In fact the two problems—inequality and democratic erosion—are linked. In a large cross-national statistical study of risk factors for democratic erosion, we establish that economic inequality is one of the strongest predictors of where and when democracy erodes. Even wealthy and longstanding democracies are vulnerable if they are highly unequal (though national wealth might provide some resiliency). The association between inequality and risk of democratic backsliding is robust, and holds under different measures and structures of both income inequality and wealth inequality. The association is unlikely to be a case of reverse causation. For concerned citizens seeking to understand why so many democracies are eroding and how to stop this process, our study indicates that policies for ameliorating inequality are a promising path forward.
Optoelectronic device library containing multiple Verilog-A models
A hybrid meta on-top functional for multiconfiguration pair-density functional theory
Multiconfiguration pair-density functional theory (MC-PDFT) was proposed a decade ago, but it is still in the early stage of density functional development. MC-PDFT uses functionals that are called on-top functionals; they depend on the density and the on-top pair density. Most MC-PDFT calculations to date have been unoptimized translations of generalized gradient approximations (GGAs) of Kohn–Sham density functional theory (KS-DFT). A hybrid MC-PDFT has also been developed, in which one includes a fraction of the complete active space self-consistent-field wave function energy in the total energy. Meta-GGA functionals, which use kinetic-energy densities in addition to GGA ingredients, have shown higher accuracy than GGAs in KS-DFT, yet the translation of meta-GGAs has not been previously proposed for MC-PDFT. In this paper, we propose a way to include kinetic energy density in a hybrid on-top functional for MC-PDFT, and we optimize the parameters of the resulting functional by training with a database developed as part of the present work that contains a wide variety of systems with diverse characters. The resulting hybrid meta functional is called the MC23 functional. We find that MC23 has improved performance as compared to KS-DFT functionals for both strongly and weakly correlated systems. We recommend MC23 for future MC-PDFT calculations.
Importance of EQA/PT for the detection of genetic variants in comprehensive cancer genome testing
Synapse-specific catecholaminergic modulation of neuronal glutamate release
Norepinephrine in vertebrates and its invertebrate analog, octopamine, regulate the activity of neural circuits. We find that, when hungry, Drosophila larvae switch activity in type II octopaminergic motor neurons (MNs) to high-frequency bursts, which coincide with locomotion-driving bursts in type I glutamatergic MNs that converge on the same muscles. Optical quantal analysis across hundreds of synapses simultaneously reveals that octopamine potentiates glutamate release by tonic type Ib MNs, but not phasic type Is MNs, and occurs via the G q -coupled octopamine receptor (OAMB). OAMB is more abundant in type Ib terminals and acts through diacylglycerol and its target Unc13A, a key component of the glutamate release machinery. Potentiation varies significantly—by up to 1,000%—across synapses of a single Ib axon, with synaptic Unc13A levels determining both release probability and potentiation. We propose that a dual molecular mechanism—an upstream neuromodulator receptor and a downstream transmitter release controller—fine-tunes catecholaminergic modulation so that strong tonic synapses exhibit large potentiation, while weaker tonic and all phasic synapses maintain consistency, yielding a sophisticated regulation of locomotor behavior.
Comprehensive analysis of inhibin-β A as a potential biomarker for gastrointestinal tract cancers through bioinformatics approaches
Tetrameric PilZ protein stabilizes stator ring in complex flagellar motor and is required for motility in <i>Campylobacter jejuni</i>
Rotation of the bacterial flagellum, the first identified biological rotary machine, is driven by its stator units. Knowledge gained about the function of stator units has increasingly led to studies of rotary complexes in different cellular pathways. Here, we report that a tetrameric PilZ family protein, FlgX, is a structural component underneath the stator units in the flagellar motor of Campylobacter jejuni . FlgX forms a stable tetramer that does not bind cyclic di-GMP (c-di-GMP), unlike other canonical PilZ domain–containing proteins. Cryoelectron tomography and subtomogram averaging of flagellar motors in situ provide evidence that FlgX interacts with each stator unit and plays a critical role in stator ring assembly and stability. Furthermore, FlgX is conserved and was most likely present in the common ancestor of the phylum Campylobacterota . Overall, FlgX represents a divergence in function for PilZ superfamily proteins as well as a player in the key stator–rotor interaction of complex flagellar motors.
Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning
AbstractBackground and objectives: For the planning of surgical procedures involving the bony reconstruction of the mandible, the autologous iliac crest graft, along with the fibula graft, has become established as a preferred donor region. While computer-assisted planning methods are increasingly gaining importance, the necessary preparation of geometric data based on CT imaging remains largely a manual process. The aim of this work was to develop and test a method for the automated segmentation of the iliac crest for subsequent reconstruction planning. Methods: A total of 1,398 datasets with manual segmentations were obtained as ground truth, with a subset of 400 datasets used for training and validation of the Neural Networks and another subset of 177 datasets used solely for testing. A deep Convolutional Neural Network implemented in a 3D U-Net architecture using Tensorflow was employed to provide a pipeline for automatic segmentation. Transfer learning was applied for model training optimization. Evaluation metrics included the Dice Similarity Coefficient, Symmetrical Average Surface Distance, and a modified 95% Hausdorff Distance focusing on regions relevant for transplantation. Results: The automated segmentation achieved high accuracy, with qualitative and quantitative assessments demonstrating predictions closely aligned with ground truths. Quantitative evaluation of the correspondence yielded values for geometric agreement in the transplant-relevant area of 92% +/- 7% (Dice coefficient) and average surface deviations of 0.605 +/- 0.41 mm. In all cases, the bones were identified as contiguous objects in the correct spatial orientation. The geometries of the iliac crests were consistently and completely recognized on both sides without any gaps. Conclusions: The method was successfully used to extract the individual geometries of the iliac crest from CT data. Thus, it has the potential to serve as an essential starting point in a digitized planning process and to provide data for subsequent surgical planning. The complete automation of this step allows for efficient and reliable preparation of anatomical data for reconstructive surgeries.
Deep conservation complemented by novelty and innovation in the insect eye ground plan
A spectacular diversity of forms and features allow species to thrive in different environments, yet some structures remain relatively unchanged. Insect compound eyes are easily recognizable despite dramatic differences in visual abilities across species. It is unknown whether distant insect species use similar or different mechanisms to pattern their eyes or what types of genetic changes produce diversity of form and function. We find that flies, mosquitos, butterflies, moths, beetles, wasps, honeybees, and crickets use homologous developmental programs to pattern their retinas. Transcription factor expression can be used to establish homology of different photoreceptor (PR) types across the insects: Prospero (Pros) for R7, Spalt (Sal) for R7+R8, and Defective proventriculus (Dve) for R1-6. Using gene knockout (CRISPR/Cas9) in houseflies, butterflies, and crickets and gene knockdown (RNAi) in beetles, we found that like Drosophila , EGFR and Sevenless (Sev) signaling pathways are required to recruit motion and color vision PRs, though Drosophila have a decreased reliance on Sev signaling relative to other insects. Despite morphological and physiological variation across species, retina development passes through a highly conserved phylotypic stage when the unit eyes (ommatidia) are first patterned. This patterning process likely represents an “insect eye ground plan” that is established by an ancient developmental program. We identify three types of developmental patterning modifications (ground plan modification, nonstochastic patterns, and specialized regions) that allow for the diversification of insect eyes. We suggest that developmental divergence after the ground plan is established is responsible for the exceptional diversity observed across insect visual systems.
Early postoperative bevacizumab for preventing neovascular glaucoma in phacovitrectomy for proliferative diabetic retinopathy
Learning the language of antibody hypervariability
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose “foundational” PLMs have limited performance in modeling antibodies due to the latter’s hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP’s transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.
Muon spectroscopy of a 12-phosphatetraphene with extremely efficient radical trapping properties
A minimal vertex model explains how the amnioserosa avoids fluidization during <i>Drosophila</i> dorsal closure
Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster . During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, both shape index and aspect ratio of amnioserosa cells increase markedly. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrinkage of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts nonmonotonic junction tension that we confirm with laser ablation experiments.
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
Unveiling hidden reaction kinetics of carbon dioxide in supercritical aqueous solutions
Dissolution of CO 2 in water followed by the subsequent hydrolysis reactions is of great importance to the global carbon cycle, and carbon capture and storage. Despite numerous previous studies, the reactions are still not fully understood at the atomistic scale. Here, we combined ab initio molecular dynamics (AIMD) simulations with Markov state models to elucidate the reaction mechanisms and kinetics of CO 2 in supercritical water both in the bulk and nanoconfined states. The integration of unsupervised learning with first-principles data allows us to identify complex reaction coordinates and pathways automatically instead of a priori human speculation. Interestingly, our unbiased modeling found an unknown pathway of dissolving CO 2 (aq) under graphene nanoconfinement, involving the pyrocarbonate anion [C 2 O 5 2 − (aq)] as an intermediate state. The pyrocarbonate anion was previously hypothesized to have a fleeting existence in water; however, our study reveals that it is a crucial reaction intermediate and stable carbon species in the nanoconfined solutions. We even observed the formation of pyrocarbonic acid [H 2 C 2 O 5 (aq)], which was unknown in water, in our AIMD simulations. The unexpected appearance of pyrocarbonates is related to the superionic behavior of the confined solutions. We also found that carbonation reactions involve collective proton transfer along transient water wires, which exhibits concerted behavior in the bulk solution but proceeds stepwise under nanoconfinement. The first-principles Markov state models show substantial promise for elucidating complex reaction kinetics in aqueous solutions. Our study highlights the importance of large oxocarbons in aqueous carbon reactions, with great implications for the deep carbon cycle and the sequestration of CO 2 .