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Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.

Crop rotation is a cultural practice in disease management used to break the disease cycle, resulting in a reduction of inoculum. In Ohio, crop rotations have been reduced in diversity, with many farmers shifting to corn-soy rotations from more diverse rotations featuring wheat. We investigated the impact of this shift on soil fungal communities under corn by conducting synthetic long-read amplicon sequencing. DNA was extracted from soil sampled during the corn-growing season at two locations in Ohio with replicated long-term rotation plots in a corn–soybean (CS) and corn–soybean–winter wheat (CSW) rotation in 2018, 2019, and 2020. 18s-ITS amplicons were sequenced using Illumina paired with Loopgenomics technology. PERMANOVA analysis revealed that fungal communities were significantly impacted by location, rotation, and time. Shannon and Simpson diversity indices implied fungal species diversity was significantly higher in the CS rotation. Indicator species analysis revealed 20 species indicative of the CSW rotation, including Mortierella, Hymenoschyphus, Ascobolus, and Saitozyma species, and 36 species indicative of the CS rotation, including Fusarium, Neoaschocyta, Zalerion, and Trichoderma species, among others. Indicator species amplicon sequencing variant reads for the CSW rotation were not correlated with the decline in corn yield or soil nitrogen, carbon, or active carbon. However, they were positively correlated with soil organic matter. [Figure: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

A combination product of human mesenchymal stem/stromal cells (MSCs) embedded in an extracellular matrix scaffold and preconditioned with hypoxia and the beta-adrenergic receptor antagonist, timolol, combined with sustained timolol application post implantation, has shown promising results for improving wound healing in a diabetic mouse model. In the present study, we extend those findings to the more translatable large animal porcine wound model and show that the combined treatment promotes wound reepithelialization in these excisional wounds by 40.2% and increases the CD31 immunostaining marker of angiogenesis compared with the matrix control, while maintaining an accumulated timolol plasma concentration below the clinically safe level of 0.3 ng/mL after the 15-day course of topical application. Human GAPDH was not elevated in the day 15 wounds treated with MSC-containing device relative to wounds treated with matrix alone, indicating that the xenografted human MSCs in the treatment do not persist in these immune-competent animals after 15 days. The work demonstrates the efficacy and safety of the combined treatment for improving healing in the clinically relevant porcine wound model.

Abstract Nanographene materials are promising building blocks for the growing field of low-dimensional materials for optics, electronics and biophotonics applications. In particular, bottom-up synthesized 0D graphene quantum dots show great potential as single quantum emitters. To fully exploit their exciting properties, the graphene quantum dots must be of high purity; the key parameter for efficient purification being the solubility of the starting materials. Here, we report the synthesis of a family of highly soluble and easily processable rod-shaped graphene quantum dots with fluorescence quantum yields up to 94%. This is uncommon for a red emission. The high solubility is directly related to the design of the structure, allowing for an accurate description of the photophysical properties of the graphene quantum dots both in solution and at the single molecule level. These photophysical properties were fully predicted by quantum-chemical calculations.

Ice-contact proglacial lakes affect ice dynamics and the transition of glacier termini from land- to lake-terminating has been shown to cause ice flow acceleration. In recent decades, the number and size of Greenlandic ice-marginal lakes has increased, highlighting the need to further understand these lake-terminating ice-margins as their influence on ice sheet mass balance increases. Here, time series of satellite-derived observations of ice velocity, surface elevation, and terminus position were generated at a lake-terminating outlet glacier, Isortuarsuup Sermia, and the nearby land-terminating Kangaasarsuup Sermia in south-west Greenland. At Isortuarsuup Sermia, annual surface velocity at the terminus increased by a factor of 2.5 to 214 ± 4 m yr−1 (2013–2021), with the magnitude of this acceleration declining with distance up-glacier. Meanwhile, near-terminus surface elevation changed at a rate of −2.3 ± 1.1 m yr−1 (2012–2021). Conversely, velocity change at Kangaasarsuup Sermia was minimal, while surface elevation change was approximately half at comparable elevations (−1.2 ± 0.3 m yr−1). We attribute these dynamic differences to thinning at Isortuarsuup Sermia and subsequent retreat from a stabilising sublacustrine moraine, and emphasise the potential of proglacial lakes to enhance future rates of mass loss from the Greenland Ice Sheet.

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the "black box" nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions. [ABSTRACT FROM AUTHOR]

In order to develop effective strategies to address noise annoyance, it is essential to develop an explanatory model of the psychological mechanism(s) by which noise becomes annoying. Two online studies were conducted in which a total of 193 participants completed speech perception tasks with varying degrees of background noise. Signal-to-noise ratio levels ranged from −2 to −10 dB. The crucial manipulation in both experiments concerned participants' sense of control over the noise level in the task. Dependent measures were task performance, a self-reported measure of frustration with the task, and self-reported sensitivity (trait) to noise. Results showed small but significant effects of noise sensitivity and sense of control on subjective frustration. In both experiments, more noise-sensitive individuals expressed greater frustration than did those reporting less noise sensitivity. In addition, in experiment 2 there was a significant interaction between sense of control and noise level. Listeners under the higher noise-level conditions expressed relatively similar degrees of frustration irrespective of their sense of control, while those under the lower noise-level condition showed lower frustration with greater sense of control. Results support Stallen's [(1999). Noise Health 1(3), 69–79] theoretical framework of noise annoyance, but also suggest the need for further research under more ecologically plausible conditions. [ABSTRACT FROM AUTHOR]

Given the growing emphasis on internationalizing higher education and the rapidly increasing number of students embarking on study abroad programs each year, research on student learning outcomes, especially those related to cultural learning, is assuming greater value and relevance. The author conducted a study that aims to enhance one's understanding of intercultural learning by focusing on the link between the development of intercultural sensitivity and the first of Engle and Engle's (2003) key study abroad components, program duration. The study was designed to measure and describe changes in the intercultural sensitivity of University of Maryland students who would be studying abroad in two different language-based programs of differing lengths: a seven-week summer program in Taxco, Mexico, and a 16-week semester program in Mexico City. The study relied on case study methodology and used both qualitative and quantitative measures. Data gathering techniques included face-to-face interviews with individual students, the administration of the Intercultural Development Inventory (IDI), a questionnaire, and a demographic questionnaire designed to collect information about students' previous intercultural experience. The research findings suggest that duration of the programs does indeed significantly impact the development of student intercultural sensitivity. Both quantitative and qualitative data show more development of intercultural sensitivity in the students in the Mexico City program than those in the shorter Taxco program. (Contains 3 tables and 7 notes.)