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The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread. [ABSTRACT FROM AUTHOR]

Background: Septoplasty is used to correct nasal obstruction from nasal septum deviation. However, the long‐term efficacy of septoplasty is unclear, and no literature reviews have examined long‐term outcomes of septoplasty with or without turbinate modification. This systematic review aimed to evaluate the long‐term efficacy of septoplasty with or without turbinate modification in improving nasal obstruction. Data Sources: PubMed, EMBASE, Cochrane CENTRAL. Methods: A systematic review of the literature was conducted using the aforementioned databases. Studies reporting outcomes 12+ months after functional septoplasty with or without turbinate surgery for nasal obstruction were included. Septorhinoplasties, concurrent sinus surgery, pediatric studies, and studies where septoplasty was performed for indications other than nasal obstruction were excluded. Results: After screening, 35 studies with 4,432 patients were included. Mean weighted post‐operative follow‐up time was 29.1 months (range 12–120 months). All studies reported significant improvement in subjective and objective outcomes at long‐term follow‐up compared to baseline. When comparing short‐term (<12 months) to long‐term (≥12 months) outcomes, four studies noticed that subjective outcomes worsened slightly over time, but no study found a significant change in objective outcomes over time. In addition, 23 studies reported patient satisfaction and/or improvement rates, with 75.4% (2,348/3,113) of patients expressing satisfaction/improvement at an average of 27.0 months after surgery. Conclusions: Overall, septoplasty with or without turbinate modification shows significant improvement in obstructive symptoms at long‐term follow‐up per both objective and subjective measures. Whether outcomes may worsen slightly over time remains indeterminate based on mixed results in the literature. Level of Evidence: N/A Laryngoscope, 134:2525–2537, 2024 [ABSTRACT FROM AUTHOR]

Schelling’s Naturphilosophie was, from the outset, more concerned with ontogeny than ontology, i.e. not on what nature is but on what it does: ubiquitous creation. Therefore, the processes articulated in the Philosophy of Mythology remain instances of a philosophy driven by what might be called a post-naturalist naturalism. The two aims of this paper are, firstly, to demonstrate this nature-philosophical continuity throughout Schelling’s so-called Protean philosophical projects in order, secondly, to re-prepare Schellingian themes for current debates concerning ontology. To this end, I draw on the worldmaking and abundance postulates of Goodman and Feyerabend, respectively, to demonstrate the persistence of Schellingian ontogenetic pathways for remodelling process metaphysics for contemporary philosophy. Finally, the paper argues that the logical form of the hypothesis (antecedent and consequent) is itself consequent upon the ontogeny it hypothesises.

In the early twenty-first century, philosophy stemming from the continental tradition has become overtly realist. This does not mean it abandons the sophisticated structures of reflection for a givenness on the refutation of which its earliest moments, in Kant, was premised. Nor does it entail a rediscovered faith in the adequacy of intellect to thing. Rather, we might say, new realisms have issued from a critique of transcendental dogmas. In this paper I will provisionally characterise the most salient of these transcendental dogmas before characterizing some of the experiments in removing them.

Objective: To determine how social media is used in the dissemination of new information within otolaryngology, and to emphasize the importance of standardizing Twitter hashtag use. Methods: Based on the 2019 SCImago journal rankings, the Twitter posts from the accounts of the top three journals covering each otolaryngology subspecialty were reviewed from August 1, 2020 to May 1, 2021. Twitter posts from the primary otolaryngology‐related academic societies were also reviewed during this timeframe. A list of hashtags was generated based on a combination of the most common otolaryngologic procedures and most commonly used hashtags in the social media space. This list was then crowd‐sourced with 10 fellowship‐trained otolaryngologists for each subspecialty. Results: Hashtag use among key stakeholders in the otolaryngology social media space varies considerably. For instance, #HNSCC, #HeadAndNeckSquamousCellCarcinoma, #HeadAndNeckCancer, #HeadAndNeckCancers, #OropharyngealCancer, #OropharynxCancer, #OralCancer, and #OPSCC were all commonly used hashtags to identify posts discussing oropharyngeal squamous cell carcinoma. #HeadAndNeckCancer and #HNSCC were most popular and used in a total of 85 and 65 tweets, respectively. #HeadAndNeckCancer was found alone in 32 out of 85 tweets (38%), whereas #HNSCC was found alone in 27 out of 65 tweets (42%). A standardized hashtag ontology covering all subspecialties within otolaryngology is proposed herein. Conclusions: Adoption of a standardized social media ontology within otolaryngology will improve information dissemination across all key stakeholders. Laryngoscope, 133:1595–1599, 2023 [ABSTRACT FROM AUTHOR]