Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity.

Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results.

Einstein-Stokes relation for small bubbles at the nanoscale.

As the physicochemical properties of ultrafine bubble systems are governed by their size, it is crucial to determine the size and distribution of such bubble systems. At present, the size or size distribution of nanometer-sized bubbles in suspension is often measured by either dynamic light scattering or the nanoparticle tracking analysis. Both techniques determine the bubble size via the Einstein-Stokes equation based on the theory of the Brownian motion. However, it is not yet clear to which extent the Einstein-Stokes equation is applicable for such ultrafine bubbles. In this work, using atomic molecular dynamics simulation, we evaluate the applicability of the Einstein-Stokes equation for gas nanobubbles with a diameter less than 10 nm, and for a comparative analysis, both vacuum nanobubbles and copper nanoparticles are also considered. The simulation results demonstrate that the diffusion coefficient for rigid nanoparticles in water is found to be highly consistent with the Einstein-Stokes equation, with slight deviation only found for nanoparticle with a radius less than 1 nm. For nanobubbles, including both methane and vacuum nanobubbles, however, large deviation from the Einstein-Stokes equation is found for the bubble radius larger than 3 nm. The deviation is attributed to the deformability of large nanobubbles that leads to a cushioning effect for collision-induced bubble diffusion.

Steering the Reconstruction of Oxide-Derived Cu by Secondary Metal for Electrosynthesis of n-Propanol from CO.

As fuel and an important chemical feedstock, n-propanol is highly desired in electrochemical CO2/CO reduction on Cu catalysts. However, the precise regulation of the Cu localized structure is still challenging and poorly understood, thus hindering the selective n-propanol electrosynthesis. Herein, by decorating Au nanoparticles (NPs) on CuO nanosheets (NSs), we present a counterintuitive transformation of CuO into undercoordinated Cu sites locally around Au NPs during CO reduction. In situ spectroscopic techniques reveal the Au-steered formation of abundant undercoordinated Cu sites during the removal of oxygen on CuO. First-principles accuracy molecular dynamic simulation demonstrates that the localized Cu atoms around Au tend to rearrange into disordered layer rather than a Cu (111) close-packed plane observed on bare CuO NSs. These Au-steered undercoordinated Cu sites facilitate CO binding, enabling selective electroreduction of CO into n-propanol with a high Faradaic efficiency of 48% in a flow cell. This work provides new insight into the regulation of the oxide-derived catalysts reconstruction with a secondary metal component.

Orbital angular momentum mode fiber force sensing technology based on intensity interrogation.

Micromanipulation and biological, materials science, and medical applications often require controlling or measuring the forces exerted on small objects. Based on the high linearity and sensitivity of OAM beams in the sensing field, this article proposes for the first time to apply OAM beams to force sensing. In this paper, a fiber optic force sensing technology based on the intensity distribution change of orbital angular momentum (OAM) mode is proposed and realized. This technique detects the magnitude of the external force applied to the fiber by exciting the OAM mode with a topological charge 3, thereby tracking changes in light intensity caused by mode coupling. Applying this technique to force measurement, we have experimentally verified that when the sensor is subjected to a force in the range of 0mN to 10mN, the change in speckle light intensity at the sensor output has a good linear relationship with the force. Meanwhile, theoretical analysis and experimental results indicate that compared with previous force sensing methods, this sensing technology has a simple structure, is easy to implement, has good stability, and has practical application potential.

ER stress mediates Angiotensin II-augmented innate immunity memory and facilitates distinct susceptibilities of thoracic from abdominal aorta to aneurysm development.

To determine the roles of endoplasmic reticulum (ER) stress and trained immunity, we performed transcriptome analyses on the thoracic aorta (TA) and abdominal aorta (AA) from the angiotensin II (Ang II)-HFD-ApoE-KO aneurysm model and made significant findings: 1) Ang II bypassed HFD-induced metabolic reprogramming and induced stronger inflammation in AA than in TA; 2) Ang II and HFD upregulated 890 genes in AA versus TA and induced cytokine signaling; 3) Ang II AA and TA upregulated 73 and 68 cytokines, scRNA-Seq identified markers of macrophages and immune cells, cell death regulators, respectively; transdifferentiation markers of neuron, glial, and squamous epithelial cells were upregulated by Ang II-AA and TA; and pyroptosis signaling with IL-1ß and caspase-4 were more upregulated in Ang II-AA than in TA; 4) Six upregulated transcriptomes in patients with AAA, Ang II AA, Ang II TA, additional aneurysm models, PPE-AAA and BAPN-Ang II-AAA, were partially overlapped with 10 lists of new ER stress gene sets including 3 interaction protein lists of ER stress regulators ATF6, PERK, and IRE1, HPA ER localization genes, KEGG signal genes, XBP1 transcription targets, ATF4 (PERK) targets, ATF6 targets, thapsigargin ER stress genes, tunicamycin-ER stress genes, respectively; 5) Ang II-AA and TA upregulated ROS regulators, MitoCarta genes, trained immunity genes, and glycolysis genes; and 6) Gene KO transcriptomes indicated that ATF6 and PERK played more significant roles than IRE1 in promoting AAA and trained immunity whereas antioxidant NRF2 inhibited them. Our unprecedented ER-focused transcriptomic analyses have provided novel insights on the roles of ER as an immune organelle in sensing various DAMPs and initiating ER stress that triggers Ang II-accelerated trained immunity and differs susceptibilities of thoracic and abdominal aortas to diseases.

Suppression of cytokine release syndrome during CAR-T-cell therapy via a subcutaneously injected interleukin-6-adsorbing hydrogel.

The infusion of chimaeric antigen receptor (CAR) T cells can trigger the release of life-threatening supraphysiological levels of pro-inflammatory cytokines. However, uncertainty regarding the timing and severity of such cytokine release syndrome (CRS) demands careful monitoring of the conditions required for the administration of neutralizing antibodies. Here we show that a temperature-sensitive hydrogel conjugated with antibodies for the pro-inflammatory cytokine interleukin-6 (IL-6) and subcutaneously injected before the infusion of CAR-T cells substantially reduces the levels of IL-6 during CRS while maintaining the therapy's antitumour efficacy. In immunodeficient mice and in mice with transplanted human haematopoietic stem cells, the subcutaneous IL-6-adsorbing hydrogel largely suppressed CAR-T-cell-induced CRS, substantially improving the animals' survival and alleviating their levels of fever, hypotension and weight loss relative to the administration of free IL-6 antibodies. The implanted hydrogel, which can be easily removed with a syringe following a cooling-induced gel-sol transition, may allow for a shift in the management of CRS, from monitoring to prevention.

Enhancing Fairness in Disease Prediction by Optimizing Multiple Domain Adversarial Networks.

Predictive models in biomedicine need to ensure equitable and reliable outcomes for the populations they are applied to. Unfortunately, biases in medical predictions can lead to unfair treatment and widening disparities, underscoring the need for effective techniques to address these issues. To enhance fairness, we introduce a framework based on a Multiple Domain Adversarial Neural Network (MDANN), which incorporates multiple adversarial components. In an MDANN, an adversarial module is applied to learn a fair pattern by negative gradients back-propagating across multiple sensitive features (i.e., characteristics of individuals that should not be used to discriminate unfairly between individuals when making predictions or decisions.) We leverage loss functions based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address the class imbalance, promoting equitable classification performance for minority groups (e.g., a subset of the population that is underrepresented or disadvantaged.) Moreover, we utilize pre-trained convolutional autoencoders (CAEs) to extract deep representations of data, aiming to enhance prediction accuracy and fairness. Combining these mechanisms, we alleviate biases and disparities to provide reliable and equitable disease prediction. We empirically demonstrate that the MDANN approach leads to better accuracy and fairness in predicting disease progression using brain imaging data for Alzheimer's Disease and Autism populations than state-of-the-art techniques.

Assembly of 43 human Y chromosomes reveals extensive complexity and variation.

The prevalence of highly repetitive sequences within the human Y chromosome has prevented its complete assembly to date1 and led to its systematic omission from genomic analyses. Here we present de novo assemblies of 43 Y chromosomes spanning 182,900 years of human evolution and report considerable diversity in size and structure. Half of the male-specific euchromatic region is subject to large inversions with a greater than twofold higher recurrence rate compared with all other chromosomes2. Ampliconic sequences associated with these inversions show differing mutation rates that are sequence context dependent, and some ampliconic genes exhibit evidence for concerted evolution with the acquisition and purging of lineage-specific pseudogenes. The largest heterochromatic region in the human genome, Yq12, is composed of alternating repeat arrays that show extensive variation in the number, size and distribution, but retain a 1:1 copy-number ratio. Finally, our data suggest that the boundary between the recombining pseudoautosomal region 1 and the non-recombining portions of the X and Y chromosomes lies 500 kb away from the currently established1 boundary. The availability of fully sequence-resolved Y chromosomes from multiple individuals provides a unique opportunity for identifying new associations of traits with specific Y-chromosomal variants and garnering insights into the evolution and function of complex regions of the human genome.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review.

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

A supramolecular metalloenzyme possessing robust oxidase-mimetic catalytic function.

Enzymes fold into unique three-dimensional structures to distribute their reactive amino acid residues, but environmental changes can disrupt their essential folding and lead to irreversible activity loss. The de novo synthesis of enzyme-like active sites is challenging due to the difficulty of replicating the spatial arrangement of functional groups. Here, we present a supramolecular mimetic enzyme formed by self-assembling nucleotides with fluorenylmethyloxycarbonyl (Fmoc)-modified amino acids and copper. This catalyst exhibits catalytic functions akin those of copper cluster-dependent oxidases, and catalytic performance surpasses to date-reported artificial complexes. Our experimental and theoretical results reveal the crucial role of periodic arrangement of amino acid components, enabled by fluorenyl stacking, in forming oxidase-mimetic copper clusters. Nucleotides provide coordination atoms that enhance copper activity by facilitating the formation of a copper-peroxide intermediate. The catalyst shows thermophilic behavior, remaining active up to 95 °C in an aqueous environment. These findings may aid the design of advanced biomimetic catalysts and offer insights into primordial redox enzymes.

IGFBP7 Fuels the Glycolytic Metabolism in B-Cell Precursor Acute Lymphoblastic Leukemia by Sustaining Activation of the IGF1R-Akt-GLUT1 Axis.

Increased glycolytic metabolism plays an important role in B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL). We previously showed that IGFBP7 exerts mitogenic and prosuvival effects in ALL by promoting IGF1 receptor (IGF1R) permanence on the cell surface, thus prolonging Akt activation upon IGFs/insulin stimulation. Here, we show that sustained activation of the IGF1R-PI3K-Akt axis concurs with GLUT1 upregulation, which enhances energy metabolism and increases glycolytic metabolism in BCP-ALL. IGFBP7 neutralization with a monoclonal antibody or the pharmacological inhibition of the PI3K-Akt pathway was shown to abrogate this effect, restoring the physiological levels of GLUT1 on the cell surface. The metabolic effect described here may offer an additional mechanistic explanation for the strong negative impact seen in ALL cells in vitro and in vivo after the knockdown or antibody neutralization of IGFBP7, while reinforcing the notion that it is a valid target for future therapeutic interventions.

FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms.

(1) Background: Phenotype prediction is a pivotal task in genetics in order to identify how genetic factors contribute to phenotypic differences. This field has seen extensive research, with numerous methods proposed for predicting phenotypes. Nevertheless, the intricate relationship between genotypes and complex phenotypes, including common diseases, has resulted in an ongoing challenge to accurately decipher the genetic contribution. (2) Results: In this study, we propose a novel feature selection framework for phenotype prediction utilizing a genetic algorithm (FSF-GA) that effectively reduces the feature space to identify genotypes contributing to phenotype prediction. We provide a comprehensive vignette of our method and conduct extensive experiments using a widely used yeast dataset. (3) Conclusions: Our experimental results show that our proposed FSF-GA method delivers comparable phenotype prediction performance as compared to baseline methods, while providing features selected for predicting phenotypes. These selected feature sets can be used to interpret the underlying genetic architecture that contributes to phenotypic variation.

An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method.

The complex phase diagram and bonding nature of the TiAl system make it difficult to accurately describe its various properties and phases by traditional atomistic force fields. Here, we develop a machine learning interatomic potential with a deep neural network method for the TiAlNb ternary alloy based on a dataset built by first-principles calculations. The training set includes bulk elementary metals and intermetallic structures with slab and amorphous configurations. This potential is validated by comparing bulk properties-including lattice constant and elastic constants, surface energies, vacancy formation energies, and stacking fault energies-with their respective density functional theory values. Moreover, our potential could accurately predict the average formation energy and stacking fault energy of γ-TiAl doped with Nb. The tensile properties of γ-TiAl are simulated by our potential and verified by experiments. These results support the applicability of our potential under more practical conditions.

Surface hydrophilicity-mediated migration of nano/microparticles under temperature gradient in a confined space.

HYPOTHESIS: Particle transport by a temperature gradient is prospective in many biomedical applications. However, the prevalence of boundary confinement in practical use introduces synergistic effects of thermophoresis and thermo-osmosis, causing controversial phenomena and great difficulty in understanding the mechanisms. EXPERIMENTS: We developed a microfluidic chip with a uniform temperature gradient and switchable substrate hydrophilicity to measure the migrations of various particles (d = 200 nm - 2 µm), through which the effects of particle thermophoresis and thermo-osmotic flow from the substrate surface were decoupled. The contribution of substrate hydrophilicity on thermo-osmosis was examined. Thermophoresis was measured to clarify its dependence on particle size and hydrophilicity. FINDINGS: This paper reports the first experimental evidence of a large enthalpy-dependent thermo-osmotic mobility χ âˆ¼ ΔH on a hydrophobic polymer surface, which is 1-2 orders of magnitude larger than that on hydrophilic surfaces. The normalized Soret coefficient for polystyrene particles, ST/d = 18.0 K-1µm-1, is confirmed to be constant, which helps clarify the controversy of the size dependence. Besides, the Soret coefficient of hydrophobic proteins is approximately-four times larger than that of hydrophilic extracellular vesicles. These findings suggest that the intrinsic slip on the hydrophobic surface could enhance both surface thermo-osmosis and particle thermophoresis.

Innate immunity of vascular smooth muscle cells contributes to two-wave inflammation in atherosclerosis, twin-peak inflammation in aortic aneurysms and trans-differentiation potential into 25 cell types.

Introduction: Vascular smooth muscle cells (VSMCs) are the predominant cell type in the medial layer of the aorta, which plays a critical role in aortic diseases. Innate immunity is the main driving force for cardiovascular diseases. Methods: To determine the roles of innate immunity in VSMC and aortic pathologies, we performed transcriptome analyses on aortas from ApoE-/- angiotensin II (Ang II)-induced aortic aneurysm (AAA) time course, and ApoE-/- atherosclerosis time course, as well as VSMCs stimulated with danger-associated molecular patterns (DAMPs). Results: We made significant findings: 1) 95% and 45% of the upregulated innate immune pathways (UIIPs, based on data of 1226 innate immune genes) in ApoE-/- Ang II-induced AAA at 7 days were different from that of 14 and 28 days, respectively; and AAA showed twin peaks of UIIPs with a major peak at 7 days and a minor peak at 28 days; 2) all the UIIPs in ApoE-/- atherosclerosis at 6 weeks were different from that of 32 and 78 weeks (two waves); 3) analyses of additional 12 lists of innate immune-related genes with 1325 cytokine and chemokine genes, 2022 plasma membrane protein genes, 373 clusters of differentiation (CD) marker genes, 280 nuclear membrane protein genes, 1425 nucleoli protein genes, 6750 nucleoplasm protein genes, 1496 transcription factors (TFs) including 15 pioneer TFs, 164 histone modification enzymes, 102 oxidative cell death genes, 68 necrotic cell death genes, and 47 efferocytosis genes confirmed two-wave inflammation in atherosclerosis and twin-peak inflammation in AAA; 4) DAMPs-stimulated VSMCs were innate immune cells as judged by the upregulation of innate immune genes and genes from 12 additional lists; 5) DAMPs-stimulated VSMCs increased trans-differentiation potential by upregulating not only some of 82 markers of 7 VSMC-plastic cell types, including fibroblast, osteogenic, myofibroblast, macrophage, adipocyte, foam cell, and mesenchymal cell, but also 18 new cell types (out of 79 human cell types with 8065 cell markers); 6) analysis of gene deficient transcriptomes indicated that the antioxidant transcription factor NRF2 suppresses, however, the other five inflammatory transcription factors and master regulators, including AHR, NF-KB, NOX (ROS enzyme), PERK, and SET7 promote the upregulation of twelve lists of innate immune genes in atherosclerosis, AAA, and DAMP-stimulated VSMCs; and 7) both SET7 and trained tolerance-promoting metabolite itaconate contributed to twin-peak upregulation of cytokines in AAA. Discussion: Our findings have provided novel insights on the roles of innate immune responses and nuclear stresses in the development of AAA, atherosclerosis, and VSMC immunology and provided novel therapeutic targets for treating those significant cardiovascular and cerebrovascular diseases.

An autoencoder-based deep learning method for genotype imputation.

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

A Membrane Curvature Modulated Lipopeptide to Broadly Combat Multidrug-Resistant Bacterial Pneumonia with Low Resistance Risk.

The extensive spread of multidrug resistance to Gram-negative bacteria has become a huge threat to human health, where peptide-based antibacterial agents have emerged as a powerful star weapon. Here we report a lipopeptide (LP-20) constructed nanomicelle with a different antibacterial mechanism of membrane curvature modulation, which induced dynamic membrane fission resulting in acceleration and enhancement of antibacterial activity to clinically isolated ESKAPE strains, including multidrug-resistant (MDR) pathogens. The minimum inhibitory concentration was reduced to 2-10 µM, and the minimum duration for killing was shortened to less than an hour by LP-20. This is an improvement over antimicrobial peptides and traditional antibiotics, such as ciprofloxacin and tetracycline, significantly enhancing antibacterial activity for MDR, and we observed no acquisition of resistance for one month. This accelerated germicidal mechanism was attributed to multitargeting with lipopolysaccharides, phosphoethanolamine, phosphatidylglycerol, and cardiolipin, and the synergetic interactions induced a high curvature of the bacterial membrane, which facilitated simultaneously efficient damage to both inner and outer membrane. The LP-20 effectively prolonged the lifetime of myositis mice with Escherichia coli MDR and pneumonia mice with Klebsiella pneumoniae through a hepatic metabolism with ignorable toxicity. This study provides critical information for the fabrication of lipopeptide-based nano-antibiotics for the efficient control of intractable MDR caused by Gram-negative pathogens.

Graphdiyne oxide nanosheets display selective anti-leukemia efficacy against DNMT3A-mutant AML cells.

DNA methyltransferase 3 A (DNMT3A) is the most frequently mutated gene in acute myeloid leukemia (AML). Although chemotherapy agents have improved outcomes for DNMT3A-mutant AML patients, there is still no targeted therapy highlighting the need for further study of how DNMT3A mutations affect AML phenotype. Here, we demonstrate that cell adhesion-related genes are predominantly enriched in DNMT3A-mutant AML cells and identify that graphdiyne oxide (GDYO) display an anti-leukemia effect specifically against these mutated cells. Mechanistically, GDYO directly interacts with integrin ß2 (ITGB2) and c-type mannose receptor (MRC2), which facilitate the attachment and cellular uptake of GDYO. Furthermore, GDYO binds to actin and prevents actin polymerization, thus disrupting the actin cytoskeleton and eventually leading to cell apoptosis. Finally, we validate the in vivo safety and therapeutic potential of GDYO against DNMT3A-mutant AML cells. Collectively, these findings demonstrate that GDYO is an efficient and specific drug candidate against DNMT3A-mutant AML.

High-Strength Plus Reversible Supramolecular Adhesives Achieved by Regulating Intermolecular PtII ⋠⋠⋠PtII Interactions.

Surface adhesion has a great contradiction in high strength and good reversibility given their mutually exclusive requirements of fixed crosslinked networks and dynamic chain motion. Herein, we demonstrate a supramolecular organoplatinum(II) adhesive system regulated by intermolecular PtII ⋅⋅⋅PtII interactions that can simultaneously achieve high-strength and excellent reversible adhesion to various substrates. Upon alternating temperature, the assembly of suitably substituted organoplatinum(II) molecules can switch between well-ordered and disordered states via tuning PtII ⋅⋅⋅PtII interactions, resulting in stable reversible adhesion even after 100 cycles with a robust strength of ≈1.25 MPa and a large on-off ratio of ≈25. Along with the switch of PtII ⋅⋅⋅PtII contacts, the surface adhesion of organoplatinum(II) adhesives can be monitored by their changes in electrical signals. This study will open up new inspirations for developing high-performance reversible adhesives.