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INTRODUCTION: Protective associations of greenspace with Parkinsons disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). METHODS: We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. RESULTS: There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. CONCLUSION: Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Abstract Current adeno-associated virus (AAV) gene therapy using nature-derived AAVs is limited by non-optimal tissue targeting. In the treatment of muscular diseases (MD), high doses are often required but can lead to severe adverse effects. Here, we rationally design an AAV capsid that specifically targets skeletal muscle to lower treatment doses. We computationally integrate binding motifs of human integrin alphaV beta6, a skeletal muscle receptor, into a liver-detargeting capsid. Designed AAVs show higher productivity and superior muscle transduction compared to their parent. One variant, LICA1, demonstrates comparable muscle transduction to other myotropic AAVs with reduced liver targeting. LICA1’s myotropic properties are observed across species, including non-human primate. Consequently, LICA1, but not AAV9, effectively delivers therapeutic transgenes and improved muscle functionality in two mouse MD models (male mice) at a low dose (5E12 vg/kg). These results underline the potential of our design method for AAV engineering and LICA1 variant for MD gene therapy.

Abstract Background Red pepper paste is a common ingredient used in food in Korea. The discrimination of the geographical origin of agricultural products is important to protect the agricultural industry and customers from the misinformation regarding the product origin. Several studies have attempted to identify the geographical origin of red pepper based on its characteristic features using diverse methods, such as inorganic elemental analysis. However, similar studies on red pepper pastes have not been conducted thus far. Results In, this study, we established methods based on inductively coupled plasma optical emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for determining inorganic elements in red pepper pastes. The limit of detection (LOD) of ICP-AES was in the range of 0.006–0.531 mg∙kg−1 and the limit of quantification (LOQ) was 0.017–1.593 mg∙kg−1. In addition, LOD and LOQ ranges for ICP-MS were 0.001–1.553, and 0.002–5.176 μg∙kg−1, respectively. The concentrations of Ca, K, Mg, Na, P, S, Cr, Mn, Co, Ni, Cu, Ga, As, Sr, Zr, Mo, Pd, Cd, Sn, Sb, Ce, Pt, Pb, and U were high in the Korean red pepper paste. All the employed discrimination models could clearly distinguish between Korean and Chinese red pepper pastes. In particular, among the four different models, CDA showed the most accurate ability to discriminate the geological origin of Korean and Chinese red pepper paste compared to that achieved using the other models with 100% accuracy. Conclusions Based on, the findings of this study, the use of ICP-AES and ICP-MS analyses for discriminating the inorganic elements in food products in combination with the aforementioned statistical analysis models could help the mitigation of issues associated with the misinformation of the geographical origin of agricultural products, aiding customer protection. Graphical Abstract

Abstract Background White standard Dendranthema grandiflorum (D. grandiflorum) is widely used for ornamental purposes. The volatile organic components (VOCs) of D. grandiflorum vary depending on the geographical origin and cultivar, which influence the market value. Here, we applied headspace extraction coupled with gas chromatography–mass spectrometry (HS–GC–MS) for the first time to determine the different origins (Korea, China, Vietnam) of D. grandiflorum in three genotypes (Baekgang, Jinba, Iwa-no-hakusen) based on volatile fingerprints and to assess the correlation among the metabolites identified in Korean and non-Korean D. grandiflorum. Results A total of 41 VOCs, mainly monoterpenoids, sesquiterpenoids, alcohols, and fatty acids, were identified. Principal component analysis showed that instead of geographical origin, genotype influences D. grandiflorum fingerprints. Cultivar discrimination was mainly affected by the metabolites associated with the alpha-terpinyl cation pathway. The orthogonal partial least squares discriminant analysis model achieved 100% and 93.3% accuracies in the calibration and validation sets, respectively. The results of volcano plots and clustering analysis, sesquiterpenoids were significantly more abundant in non-Korean than in Korean, whereas monoterpenoids were abundant in Korean samples. Conclusions 41 makers confirmed robustness maintenance over two years. These findings can be useful for reliably identifying the geographical origin of D. grandiflorum and providing a comprehensive understanding of VOCs in D. grandiflorum cultivars grown in different countries of origins. Graphical Abstract

The spectroscopic discrimination of chili pepper samples according to geographical origin was executed using analytical techniques coupled with machine learning. First, laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS), X-ray fluorescence (XRF), and near-infrared (NIR) spectroscopy were chosen for simple and rapid sample measurements. Second, to secure discrimination accuracy, eXtreme Gradient Boosting (XGBoost), a tree-based ensemble technique, was adopted as a potential classifier. Also, for explainable machine learning modeling, SHaply Additive exPlanation (SHAP) values of employed variables were calculated to assess how they contribute to the discrimination. The use of XGBoost improved discrimination accuracies in all three measurements compared to k-nearest neighbor (k-NN), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). The accuracy was 96.2 % using the LA-ICP-MS data. When the XRF and NIR data were combined, the accuracy improved to 97.5 %. The accuracy improvement was attributed to the combination of complementary atomic and molecular spectroscopic signatures of the samples.

Introduction: Protective associations of greenspace with Parkinson’s disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). Methods: We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007–2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. Results: There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. Conclusion: Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and childrens sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort. We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007-2010 (mid-childhood, mean age 7.9 years) and 2012-2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI). In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time. SVI metrics may have the potential to identify specific features of greenspace that affect sleep.

Background Dysphagia, particularly sarcopenic dysphagia, is frequent in frail older patients. Sarcopenic dysphagia is a swallowing disorder caused by sarcopenia, corresponding to a loss of muscle mass and strength. It frequently leads to inhalation and to the decrease of food intake, leading the patient to enter a vicious circle of chronic malnutrition and frailty. The awareness of the major health impacts of sarcopenic dysphagia is recent, explaining a low rate of screening in the population at risk. In this context, methods of prevention, evaluation and intervention of sarcopenic dysphagia adapted to the most at-risk population are necessary.Methods The DYSPHAGING (dysphagia & aging) pilot study is a prospective, multicentre, non-comparative study aiming to estimate the feasibility of an intervention on allied health professionals using the DYSPHAGING educational sheet designed to implement a two-step procedure ‘screen–prevent’ to mitigate swallowing disorders related to sarcopenic dysphagia. After obtaining oral consent, patients are screened using Eating Assessment Tool-10 Score. In case of a score≥2, procedures including positional manoeuvres during mealtimes, food and texture adaptation should be implemented. The primary endpoint of the study is the feasibility of this two-step procedure (screening–prevention measures) in the first 3 days after patient’s consent.The study will include 102 patients, with an expected 10% rate of non-analysable patients. Participants will be recruited from acute geriatric wards, rehabilitation centres and long-term care units, with the hypothesis to reach a feasibility rate of 50% and reject a rate lower than 35%.Ethics and dissemination The study protocol was approved according to French legislation (CPP Ile-de-France VII) on 15 February 2023. The results of the primary and secondary objectives will be published in peer-reviewed journals.Trial registration number NCT05734586.

Abstract Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.

Abstract Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world.