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Most damage to buildings across the contiguous United States, in terms of number and total cost, is caused by gusts in convective events associated with thunderstorms. Their assessment relies on the integrity of meteorological observations. This study examines the impact on risk due to valid gust observations culled erroneously by the real-time quality control algorithm of the US Automated Surface Observation System (ASOS) after 2013. ASOS data before 2014 are used to simulate the effect of this algorithm at 450 well-exposed stations distributed across the contiguous USA. The peak gust is culled in around 10% of these events causing significant underestimates of extreme gusts. The full ASOS record, 2000–2021, is used to estimate and map the 50-year mean recurrence interval (MRI) gust speeds, the conventional metric for structural design. It is concluded that recovery of erroneously culled observations is not possible, so the only practical option to eliminate underestimation is to ensure that the 50-year MRI gust speed at any given station is not less than the mean for nearby surrounding stations. This also affects stations where values are legitimately lower than their neighbors, which represents the price that must be paid to eliminate unacceptable risk. [ABSTRACT FROM AUTHOR]

A substantial drop in atmospheric visibility or visual range (VR) is documented in several parts of the world. We examined the long-term spatiotemporal variability in ground-based VR data for ten airports in Pakistan from 1980 to 2020. Average VR time series analysis was performed using nonparametric tests of Mann–Kendall (MK), Modified Mann–Kendall (MMK), Sen's Slope (SS), and Sequential Mann–Kendall (SMK), followed by spatiotemporal mapping. Bad VR (<5 km) conditions prevailed over more than 60% of the study period. Noticeably, VR was better during 1991–2000 due to the high average annual rainfall (905 mm) during the 1990s. Plain areas such as Faisalabad experienced the greatest number of bad VR days with hourly (92.85%), daily (95.35%), monthly (90.97%), and seasonal (90.97%) measurements, followed by Sialkot and Multan. The VR of lowlands in central, southern, and eastern Punjab and northern, central, and eastern Sindh showed decreasing trends. In contrast, the VR in coastal areas of south and southwestern Sindh, in the western highlands, and parts of Khyber Pakhtunkhwa (KPK) province, have indicated positive trends. The results of this study will support policymakers in catering to the issue of declining VR in the region by supporting and developing strategies to limit the factor associated with VR. [ABSTRACT FROM AUTHOR]

Historical wind data analysis is a key part of estimating design wind loads. Current design standards do not separately consider the wind loading effects by different wind hazard types. One reason for this lack of consideration is that the separation between thunderstorm and non-thunderstorm wind data is still an issue. A previous study about the Automated Surface Observing System (ASOS) provided a classification method of wind data as thunderstorm or non-thunderstorm based on thunderstorm 'flags' (Lombardo et al., 2009). However, this method relies mainly on manual or automated weather observations which are limited to a subset of stations worldwide. This paper first develops a revised wind hazard type recognition method based on a neural network. In this method, the historical wind data recorded is segmented in different time domains to be applied in a one-dimensional convolutional neural network (1D-CNN) for an automated thunderstorm (T) or non-thunderstorm (NT) classification. Also, based on the trained 1D-CNN, a more comprehensive wind database can be extracted. The classification result from ASOS can automatically provide different peak wind speed for different wind hazard types. • One-minute wind time history data from ASOS DSI-6405 can be extracted as the data source for a deep learning algorithm. • The K-fold validation on the 1D-CNN with the ASOS database for thunderstorm classification is reliable. • The trained ​1D-CNN can be applied to other wind datasets without the requirement for a specific signal sample format. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a fatal motor disease in adults. Its pathophysiology remains mysterious, but tremendous advances have been made with the discovery of the most frequent mutations of its more common familial form linked to the C9ORF72 gene. Although most cases are still considered sporadic, these genetic mutations have revealed the role of RNA production, processing and transport in ALS, and may be important players in all ALS forms. There are no disease-modifying treatments for adult human neurodegenerative diseases, including ALS. As in spinal muscular atrophy, RNA-targeted therapies have been proposed as potential strategies for treating this neurodegenerative disorder. Successes achieved in various animal models of ALS have proven that RNA therapies are both safe and effective. With careful consideration of the applicability of such therapies in humans, it is possible to anticipate ongoing in vivo research and clinical trial development of RNA therapies for treating ALS. [ABSTRACT FROM AUTHOR]

We propose a novel algorithm to segment a low depth-of-field (DOF) image into its focused region-of-interest (ROI) and defocused background using adaptive second-order statistics (ASOS). Most previous methods depend on the assump -tion that the images are in noise-free conditions, which leads to high false positive rates in noisy images. In this letter, we introduce a novel image segmentation algorithm for noisy low-DOF images. Specifically, we propose a novel feature transform method, called ASOS, which indicates the spatial distribution of the high-frequency components in the face of noisy low-DOF images. Experimental results demonstrate that the proposed method is effective for image segmentation in noisy images compared to several state-of-the-art methods proposed in the literature. [ABSTRACT FROM AUTHOR]