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At least 1,333 records · Page 74

Characterizing how size distribution and concentration affect echogenicity of ultrasound contrast agents

Here, we investigated the effects of UCA gas bubble size distribution and concentration on the generated ultrasound echogenicity signal. Gas bubble size characterization using Coulter Counter and cryogenic-SEM revealed the hollow structure and rare presence of microbubbles >10 µm in a commercial UCA product, Lumason™. Volume-weighed size and concentration were observed to be more sensitive to changes in UCA bubble stability than number-weighted size and concentration. Size distribution measurements showed that the force (e.g., shaking/agitation energy) used to redisperse the sample did not affect the size distribution, concentration, or echogenicity of the UCA sample. The ultrasound backscattering coefficient (BSC) of size fractionated and serial diluted microbubbles showed that the echogenicity signal correlates most with UCA bubble concentration, especially volume-weighted concentration. Findings from this study may be used to support demonstrating the equivalence of a generic UCA product to the reference listed drug.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Meeting report: International soil virus conference 2024

The research field of soil viral ecology continues to advance rapidly as the roles of viruses in the functioning of soil ecosystems are increasingly recognized. To address recent developments in the field, the second International Soil Virus Conference was held in Livermore, California, USA, from June 25 to 27th, 2024, providing soil viral ecologists the opportunity to share new findings and suggest guidelines for future research, while encouraging international scientific discussion and collaboration. The meeting was held in person with sessions simultaneously streamed online. Fifty researchers attended from ten different countries and spanned a wide range of subfields and career stages. A total of 21 oral presentations were presented, followed by discussions covering key themes in soil viral research. This report summarizes the main takeaways and recommendations from the talks and discussions.

60 APPLIED LIFE SCIENCES↗

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗

Fiber-Shaped Fluidic Nanogenerator with High Power Density for Self-Powered Integrated Electronics

Flow-based hydrovoltaic nanogenerators have attracted particular attention as efficient and facile miniature energy-harvesting devices. However, they deliver relatively low output power density and limited power conversion efficiency owing to inefficient charge separation and poor charge accumulation. Here, we report the fabrication of high-performance, fiber-shaped fluidic nanogenerators (FFNGs) that use MoS 2 -encapsulated C fibers. The FFNGs deliver stable output voltages of 540 mV and an ultrahigh power density of 10.8 W m -2 . With experiments and density functional theory calculations, improvements in output performance can be attributed to smaller energy requirements for Na + cation adsorption, increased charge separation, and diffusion layer formations on the near surface. Moreover, self-powered, integrated devices achieve a maximum total power conversion-storage efficiency of ~11% and stable output voltage up to 3.1 V, which is applicable for use in consumer electronics devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Cooperative roles of water and metal-support interfaces in the selective hydrogenation of cinnamaldehyde over cobalt boride catalysts

Chemoselectivity in the hydrogenation of carbonyl over alkene bonds is valuable for producing industrial chemicals. In this study, we investigate the role of water in the selective hydrogenation of cinnamaldehyde on cobalt and cobalt boride catalysts by combining rate measurements, in situ characterization, and computational calculations. Ex situ and in situ spectroscopic analyses indicate that the boron species do not significantly modify the intrinsic electronic properties of cobalt. However, during thermal treatments, they exsolve from the bulk phase and become enriched on the surface, forming acidic species that enhance the activity and selectivity of carbonyl bond hydrogenation by three and two times, respectively. A small amount of water is able to promote hydrogenation. When titania, ceria, and zirconia are used as supports, metal-support interactions result in smaller particles that show a drop in selectivity toward the desirable unsaturated alcohol. Overall, the combination of the presence of boron species and strong metal-support interactions protects the catalysts from water attack, resulting in enhanced stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exosomes derived from thymic stromal lymphopoietin-treated dendritic cells regulate T helper 17/regulatory T cell differentiation via miR-21/Smad7 axis

Highlights: • Exosomes from TSLP-treated DCs promote Th17 differentiation. • Exosomes from TSLP-treated DCs inhibit Tregs differentiation. • MiR-21 is highly expressed in exosomes from TSLP-treated DCs. • Exosomal miR-21 regulates Th17/Treg differentiation by targeting Smad7. Thymic stromal lymphopoietin (TSLP) is associated with fungal keratitis. This work aims to investigate whether TSLP can regulate T helper (Th) 17 and regulatory T cell (Treg) differentiation. We separated dendritic cells (DCs) from peripheral blood of healthy volunteers. DCs were treated with TSLP to activate DCs, and exosomes were obtained. CD{sup +} T cells were incubated with exosomes from TSLP-treated DCs. We found that exosomes from TSLP-treated DCs notably promoted the proportions of Th17 cells and inhibited the proportions of Tregs in the CD4{sup +} T cells. Moreover, exosomes from TSLP-treated DCs enhanced the expression of retinoid-related orphan receptor γt (RORγt) and interleukin 17 (IL-17), and repressed the expression of forkhead box protein P3 (Foxp3) and interleukin 10 (IL-10) in the CD4{sup +} T cells. Furthermore, miR-21 was highly expressed in exosomes from TSLP-treated DCs. Exosomes from TSLP-treated miR-21-silenced DCs promoted Treg differentiation and suppressed Th17 differentiation. Smad7 up-regulation repressed Th17 differentiation and enhanced Treg differentiation, which was abolished by miR-21 overexpression. Smad7 overexpression rescued the effect of exosomes from TSLP-treated DCs on Th17/Treg differentiation. In conclusion, our article confirms that TSLP induces DCs to deliver miR-21 by secreting exosomes, and thus miR-21 regulates Th17/Treg differentiation by inhibiting Smad7. Thus, this work further reveals the biological role of miR-21 in fungal keratitis.

60 APPLIED LIFE SCIENCES↗

Downregulation of fascin in the first trimester placental villi is associated with early recurrent miscarriage

Highlights: • Fascin is downregulated in the placentae of pregnancies complicated by early recurrent miscarriage. • Knockdown of fascin decreased HTR-8/SVneo cells' proliferation, migration and invasion. • Fascin knockdown decreased phosphorylated ERK1/2 and phosphorylated STAT3 protein level in HTR-8/SVneo cells. • Hypoxia reoxygenation decreased fascin, N-Cadherin and Vimentin protein levels to affect HTR-8/SVneo cells' invasion. Inadequate trophoblast proliferation, shallow invasion and exaggerated rate of trophoblast apoptosis are implicated in early recurrent miscarriage (ERM). However, the mechanistic bases of this association have not been fully established. We aimed at investigating the involvement of fascin, an actin-bundling protein, in trophoblast activities and ERM. We found that fascin was downregulated in the cytotrophoblasts (CTBs) and distal cytotrophoblasts (DCTs) of ERM placentae. Knockdown of fascin altered cellular and nucleolar morphology, and inhibited the proliferation but increased apoptosis of trophoblastic HTR8/SVneo cells. Furthermore, fascin knockdown decreased the expression of transcription factors such as Snail1/2, Twist and Zeb1/2, mesenchymal molecules such as Vimentin and N-cadherin, and the protein expression of phosphorylated extracellular signal-regulated kinase 1/2 (ERK1/2) and phosphorylates signal transducer and activator of transcript 3 (STAT3). Exposure of HTR-8/SVneo cells to hypoxia reoxygenation (H/R) decreased fascin expression to affect the cells’ invasion. Our results indicate for the first time that the downregulation of fascin is involved in the pathogenesis of early recurrent miscarriage; and hence a potential therapeutic target against the disease.

60 APPLIED LIFE SCIENCES↗

The role of fascin in carcinogenesis and embryo implantation

Highlights: • Fascin promotes cancer metastasis. • Fascin interacts with transcription factors necessary for EMT, similar to that in trophoblast cells. • Fascin regulates trophoblast cells' migration and invasion. • Fascin could be implicated in trophoblast-endometrial crosstalk. The cytoskeleton, with its actin bundling proteins, plays crucial roles in a host of cellular function, such as cancer metastasis, antigen presentation and trophoblast migration and invasion, as a result of cytoskeletal remodeling. A key player in cytoskeletal remodeling is fascin. Upregulation of fascin induces the transition of epithelial phenotypes to mesenchymal phenotypes through complex interaction with transcription factors. Fascin expression also regulates mitochondrial F-actin to promote oxidative phosphorylation (OXPHOS) in some cancer cells. Trophoblast cells, on the other hand, exhibit similar physiological functions, involving the upregulation of genes crucial for its migration and invasion. Owing to the similar tumor-like characteristics among cancer and trophoblats, we review recent studies on fascin in relation to cancer and trophoblast cell biology; and based on existing evidence, link fascin to the establishment of the maternal-fetal interface.

60 APPLIED LIFE SCIENCES↗

A transfer learning approach for acoustic emission zonal localization on steel plate-like structure using numerical simulation and unsupervised domain adaptation

The detection and localization of damage in metallic structures using acoustic emission (AE) monitoring and artificial intelligence technology such as deep learning has been widely studied. However, a current challenge of this approach is the difficulty of obtaining sufficient labeled historical AE signals for the training process of deep learning models. This problem can be approached through the implementation of transfer learning. The innovation of this paper lies in the development of a transfer learning approach for AE source localization on a stainless-steel structure when no historical labeled AE signals are available for training. A finite element model is developed to generate numerical AE signals for the training. Unsupervised domain adaptation (UDA) technology is utilized to reduce the distribution difference between the numerical and the realistic AE signals and to derive the localization results of the unlabeled realistic AE signals. Finally, the results suggest that the proposed approach is capable of localizing AE signals with high accuracy in the absence of labeled training data.

42 ENGINEERING↗

Economic Incentives for Agrivoltaics Systems with Commodity Crops in the Midwestern United States

Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.

14 SOLAR ENERGY↗

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗

Molecular Specificity and Proton Transfer Mechanisms in Aerosol Prenucleation Clusters Relevant to New Particle Formation

Atmospheric aerosol particles influence Earth’s radiative energy balance and cloud properties either directly through interacting with the solar radiation or indirectly through acting as cloud condensation nuclei (CCN), thus impacting Earth’s climate change, air quality, and human health. Regarding both the important scientific and overarching practical implications of aerosols, the past two decades have seen an extensive research effort, with emphasis on the chemical compositions and underlying mechanisms of aerosol formation. New particle formation (NPF) has been recognized as an important pathway, contributing up to 50% of atmospheric aerosols. Nowadays, the general consensus in NPF is that it proceeds via two distinct stages—the nucleation from gaseous precursors to form critical nuclei of sub-1–2 nanometer size, and the subsequent growth into large particles. However, a fundamental understanding of both the NPF process and the molecular-level characterization of the critical size aerosol clusters is still largely missing, hampering the development of reliable and predictive aerosol nucleation and climate models.

Hou, Gao-Lei↗

Programming Amphiphilic Peptoid Oligomers for Hierarchical Assembly and Inorganic Crystallization

Natural organisms make a wide variety of exquisitely complex, nano-, micro-, and macroscale structured materials in an energy-efficient and highly reproducible manner. During these processes, the information-carrying biomolecules (e.g., proteins, peptides, and carbohydrates) enable (1) hierarchical organization to assemble scaffold materials and execute high-level functions and (2) exquisite control over inorganic materials synthesis, generating biominerals whose properties are optimized for their functions. Inspired by nature, significant efforts have been devoted to developing functional materials that can rival those natural molecules by mimicking in vivo functions using engineered proteins, peptides, DNAs, sequence-defined synthetic molecules (e.g., peptoids), and other biomimetic polymers. Among them, peptoids, a new type of synthetic mimetics of peptides and proteins, have received particular attention because they combine the merits of both synthetic polymers (e.g., high chemical stability and efficient synthesis) and biomolecules (e.g., sequence programmability and biocompatibility). The lack of both chirality and hydrogen bonds in their backbone results in a highly designable peptoid-based system with reduced structural complexity and side chain-chemistry-dominated properties. Here in this Account, we present our recent efforts in this field by programming amphiphilic peptoid sequences for (1) the controlled self-assembly into different hierarchically structured nanomaterials with favorable properties and (2) manipulating inorganic (nano)crystal nucleation, growth, and assembly into superstructures. First, we designed a series of amphiphilic peptoids with controlled side chain chemistries that self-assembled into 1D highly stiff and dynamic nanotubes, 2D membrane-mimetic nanosheets, hexagonally patterned nanoribbons, and 3D nanoflowers. These crystalline nanostructures exhibited sequence-dependent properties and showed promise for different applications. The corresponding peptoid self-assembly pathways and mechanisms were also investigated by leveraging in situ atomic force microscopy studies and molecular dynamics simulations, which showed precise sequence dependency. Second, inspired by peptide- and protein-controlled formation of hierarchical inorganic nanostructures in nature, we developed peptoid-based biomimetic approaches for controlled synthesis of inorganic materials (e.g., noble metals and calcite), in which we took advantage of the substantial side chain chemistry of peptoids and investigated the relationship between the peptoid sequences and the morphology and growth kinetics of inorganic materials. For example, to overcome the challenges (e.g., complexity of protein- and peptide-folding, poor thermal and chemical stabilities) facing the area of protein- and peptide-controlled synthesis of inorganic materials, we recently reported the design of sequence-defined peptoids for controlled synthesis of highly branched plasmonic gold particles. Moreover, we developed a rule of thumb for designing peptoids that predictively enabled the morphological evolution from spherical to coral-shaped gold nanoparticles (NPs). With this Account, we hope to stimulate the research interest of chemists and materials scientists and promote the predictive synthesis of functional and robust materials through the design of sequence-defined synthetic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗