The effects of seat width, load factor, and passenger demographics on airline passenger accommodation.

The objective of this work is to demonstrate a method for examining the competing effects of secular trends in body size, seat size and configuration, and the increased load factor of aeroplanes. The method uses statistical modelling and virtual fit testing to provide a flexible environment for exploring the impact of various parameters on passenger accommodation. A case study demonstrates the method by exploring the effect of seat width on the accommodation of US civilians (based on seated hip breadth). The case study demonstrates that recent trends of decreasing seat widths and increasing load factors lead to higher disaccommodation. Based on anthropometry and virtual fit, women are also shown to be disproportionately disaccommodated compared to men. Practitioner summary: Airlines are reducing seat width at the same time that individuals worldwide are getting larger. Flights are increasingly crowded, with load factor at a record high. This paper explores the effects of seat width on passenger accommodation under several scenarios involving load factor, demographics, and passenger seating allocation strategies.

Optimization of a pericyte therapy to improve muscle recovery after limb immobilization.

Extended bed rest or limb immobilization can significantly reduce skeletal muscle mass and function. Recovery may be incomplete, particularly in older adults. Our laboratory recently reported that vascular mural cell (pericyte) quantity is compromised after immobilization and appropriate replacement immediately before remobilization can effectively recover myofiber size in mice. Identification of a single cell surface marker for isolation of the most therapeutic pericyte would streamline efforts to optimize muscle recovery. The purpose of this study was to compare the capacity for neural/glial antigen 2 (Cspg4/NG2+) and melanoma cell adhesion molecule (Mcam/CD146+) positive pericytes to uniquely recover skeletal muscle post-disuse. A single hindlimb from adult C57BL/6J mice was immobilized in full dorsiflexion via a surgical staple inserted through the center of the foot and body of the gastrocnemius. Fourteen days after immobilization, the staple was removed and pericytes, either NG2+CD45-CD31-[Lin-], CD146+NG2-Lin-, or CD146+Lin- pericytes, were injected into the atrophied tibialis anterior (TA) muscle. TA muscles were excised 14 days after transplantation and remobilization. Pericyte transplantation did not significantly improve muscle mass or myofiber cross-sectional area (CSA) after 14 days of remobilization. However, injection of CD146+ pericytes significantly increased Type IIa quantity, capillarization, and collagen remodeling compared with NG2+ pericytes (P < 0.05). Our results suggest that selection of pericytes based on CD146 rather than NG2 results in the isolation of therapeutic mural cells with high capacity to positively remodel skeletal muscle after a period of immobilization.NEW & NOTEWORTHY In this study, pericytes were isolated from mouse skeletal muscle based on cell surface marker expression of neural/glial antigen 2 (NG2) or melanoma cell adhesion molecule (Mcam/CD146) and then compared for the capacity to recover skeletal muscle after a period of immobilization in recipient mice. We report that CD146+Lin- pericytes exhibit higher capacity than NG2+Lin- pericytes to recover Type IIa fiber quantity, capillary content, and collagen turnover after disuse.

Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

Laminin-111 Improves the Anabolic Response to Mechanical Load in Aged Skeletal Muscle.

Anabolic resistance to a mechanical stimulus may contribute to the loss of skeletal muscle mass observed with age. In this study, young and aged mice were injected with saline or human LM-111 (1 mg/kg). One week later, the myotendinous junction of the gastrocnemius muscle was removed via myotenectomy (MTE), thus placing a chronic mechanical stimulus on the remaining plantaris muscle for 2 weeks. LM-111 increased α7B integrin protein expression and clustering of the α7B integrin near DAPI+ nuclei in aged muscle in response to MTE. LM-111 reduced CD11b+ immune cells, enhanced repair, and improved the growth response to loading in aged plantaris muscle. These results suggest that LM-111 may represent a novel therapeutic approach to prevent and/or treat sarcopenia.