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A Robust Deep Learning Ensemble Framework for Waterbody Detection Using High-Resolution X-Band SAR Under Data-Constrained Conditions
No full textAccurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models—Proportional–Integral–Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)—were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision–Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms
Prolonged Growth and Extended Subadult Development in the \u3cem\u3eTyrannosaurus rex\u3c/em\u3e Species Complex Revealed by Expanded Histological Sampling and Statistical Modeling
No full textBackground
Tyrannosaurus rex, one of the most iconic non-avialan dinosaurs, remains a central focus of paleobiological research. Growth modeling suggests T. rex exceeded 8,000 kg within two decades and had a lifespan approaching 30 years. However, this understanding of T. rex growth dynamics is dependent on single-point histological sampling of multiple skeletal elements and lacks specimens encompassing the earliest growth states. Methods
We present the most comprehensive histological analysis of Tyrannosaurus ontogeny to date, based on transverse diaphyseal sections of femora and tibiae from 17 individuals ranging from small juveniles to large adults. Four alternative statistical models were tested, differing in the treatment of cortical growth marks, including annulus-like birefringent bands visible only in cross-polarized light. Due to high intraspecies morphological variability, the taxonomic status of many Tyrannosaurus specimens is debated, prompting use of the term “Tyrannosaurus rex species complex” to describe our dataset. Results
The best-supported model incorporated all visible growth marks, produced the narrowest confidence bands, and indicated lower maximum growth rates and a delayed attainment of asymptotic size (~35–40 years) compared with earlier estimates. We also find that two immature specimens within the Tyrannosaurus rex species complex are not statistically compatible with the other growth series. Our approach is the first in dinosaur skeletochronology to simultaneously estimate the position of the earliest preserved growth mark across specimens, while fitting sigmoidal curves with simultaneous confidence bands. We find the inclusion of double growth marks and those visible only with cross polarized light provide better statistical model fits and this may have implications for modeling other taxa. Additionally, we find no strong link from extant vertebrates to support the idea that the growth inflection point is biologically significant and corresponds to sexual maturity. Our results suggest that the Tyrannosaurus rex species complex grew more gradually and over a longer lifespan than indicated by prior models, with a protracted period of subadult development
Direct Effects of Capsaicin on Voltage-Dependent Calcium Channels of Mammalian Skeletal Muscle
No full textCapsaicin, a naturally occurring polyphenol, is known to affect energy expenditure and muscle fatigue and modulate contractions in skeletal muscle. The L-type Ca2+ channels are known to be an important ion channel involved in the various muscle functions and the effect of capsaicin on the skeletal L-type Ca2+ channels is currently unknown. In this study, the effects of capsaicin and capsaicin analogs on depolarization-induced Ca2+ effluxes through L-type Ca2+ channels in transverse tubule membranes from rabbit skeletal muscle and L-type Ca2+ currents recorded using the whole-cell patch clamp technique in rat myotubes were examined. Capsaicin, in the concentration range of 3–100 µM, inhibited depolarization-induced Ca2+ effluxes. The effect of capsaicin was not reversed by TRPV1 antagonist SB-366791 (10 µM). While vanilloids (30 µM) including vanillin, vanillyl alcohol, and vanillylamine were ineffective, other capsaicinoids (30 µM) including dihydrocapsaicin, nonivamide, and nordihydrocapsaicin significantly inhibited Ca2+ effluxes, suggesting that hydrocarbon chains are required for inhibition. In rat myotubes, capsaicin inhibited L-type Ca2+ currents with an IC50 value of 27.2 μM in the presence of SB-366791. Furthermore, in docking studies and molecular dynamic simulations, capsaicinoids with an aliphatic tail showed stronger binding and stable bent conformations in CaV1.1, forming hydrogen bonds with Ser1011 and Thr935 and hydrophobic/π–alkyl contacts with Phe1008, Ile1052, Met1366, and Ala1369, resembling the binding mode of amlodipine. In conclusion, the results indicate that the function of L-type Ca2+ channels in mammalian skeletal muscle was inhibited by capsaicin and capsaicin analogs in a TRPV1-independent manner
P-78. Susceptibility of Omadacycline in Bone and Joint Infections: Pathogen Susceptibility and Regimen Decisions from an Ongoing Randomized Controlled Trial
No full textBackground Treatment of Bone and joint infections (BJIs) with oral antibiotics may have benefits compared to IV therapy. Yet oral treatment options may be limited by lack of options due to antibiotic-resistant pathogens and tolerability concerns. Omadacycline has in vitro activity against common BJI pathogens, including MRSA and most Enterobacterales. However, real-world susceptibility data and implications for oral therapy are limited. Methods
We conducted a descriptive analysis of data from adult patients enrolled to date in an open-label, randomized controlled trial of comparing omadacycline-containing antibiotic regimen vs. standard of care (SOC) antibiotics for BJIs. Omadaycline susceptibility was assessed on available clinical isolates using MIC Test Strips (Liofilchem®). Susceptibility to omadacycline was interpreted using FDA breakpoints where available or extrapolated for organisms without criteria. We also described omadacycline-containing vs. SOC regimens (provider-chosen) among randomized patients to assess the proportion of oral vs. IV therapies. Results
To date, we screened 162 patients and randomized 132 (81%). Most randomized patients had diabetic foot infections (113/132 (86%)). Among screened patients, bacterial isolates were recovered from 144 (89%) participants. The most frequently identified isolates were Streptococcus spp. (37%), S. aureus (22%), 24% of which were MRSA, followed by and Enterobaterales (19%). Susceptibility data are summarized in Table 1. Among randomized patients and prior to randomization, 122/132 (92%) of omadacycline contating regimens would be oral-only therapy, compared to 100/132 (76%) in the SOC arm (p\u3c 0.001). Conclusion
In our randomized trial of BJI treatment, omadacycline demonstrated in vitro activity against most BJI pathogens, including Streptococcus, MRSA and Enterobacterales. A higher proportion of omadacycline-containing regimens were eligible for oral-only therapy compared to SOC. In light of these findings, omadacycline may warrant consideration as an oral option in select BJI cases
Systems Thinking and the Inner Development Goals
No full textIn an increasingly complex and interconnected world, it is critical to understand the tangled and interdependent nature of our current challenges, work collectively to envision just and equitable shared futures, and evolve leadership education to embrace and effectively address these challenges. In this article, we discuss the concepts of sustainability, systems thinking, and the Inner Development Goals within the context of leadership education and present ways of aligning the practices of each to lead change. Using cases from business and leadership development, this article offers suggestions for how we can focus on the individual and collective work needed to bring about lasting, sustainable change
Exploring Manifold-Based Clustering Techniques for Enhanced Inductive Thematic Analysis
No full textIn this paper, we introduce Augmented Thematic Analysis with Large Language Models (ATA-LLM), a novel framework that integrates manifold learning algorithms and clustering techniques to support inductive thematic analysis. This qualitative method, widely used in software engineering, is essential for uncovering patterns and understanding human factors and software requirements. Traditional thematic analysis involves data coding, theme identification, and the interpretation of complex narratives, making it a labor-intensive and time-consuming process. Recent advances in large language models (LLMs) offer promising opportunities; however, it remains unclear how comparable these approaches are to traditional human thematic analysis. To address this gap, we evaluated ATA-LLM using a validated qualitative dataset and compared the outcomes against human-coded analysis. Our findings indicate that within the ATA-LLM framework, DenseMAP and UMAP effectively preserve both local and global structures of high-dimensional data, resulting in more coherent and meaningful themes than other techniques. These results highlight the potential of ATA-LLM to enhance the rigor, consistency, and efficiency of inductive thematic analysis
High Spatiotemporal Resolution Monitoring of Crop Water Stress Across the Contiguous United States Using Harmonized Landsat and Sentinel-2 Data
No full textAccurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water stress was monitored by analyzing normalized difference moisture index (NDMI) time series through applying the Breaks For Additive Season and Trend Monitor (BFAST monitor) and random forest models. We used HLS data from 2016 to 2019 as the historical period, and data from 2020, a year marked by intense droughts, as the monitoring period. We used stratified random points interpreted from Standardized Precipitation Index based drought products to validate the crop water stress alerts. Our results show that HLS data enables near-real-time alerts of crop water stress with an overall accuracy of water stress of 74.0 % and kappa coefficient of 0.48. We mapped approximately 12.3 Mha of water-stressed crops across the CONUS from March to August 2020, identifying around 3.8 million crop water stress events. Among these events, nearly 41.8 % affected areas smaller than 0.5 ha. Major crop water stress events (≥ 5 ha) were the least frequent, making up 10.0 % of events, yet they dominated in terms of area, affecting 74.2 % of the total mapped extent. For temporal accuracy, the mean time lag of detected crop water stress across the CONUS using HLS data is approximately 9 days. Our detected crop water stress demonstrates the feasibility of HLS data for providing timely crop water stress monitoring at a national scale. This highlights the potential of HLS-based monitoring to inform precision irrigation and support sustainable agricultural water resource management
Structural Basis for the Subtype-Selectivity of K\u3csub\u3eCa\u3c/sub\u3e2.2 Channel Activators
No full textSmall-conductance (KCa2.2) and intermediate-conductance (KCa3.1) Ca2+-activated K+ channels are gated by a Ca2+-calmodulin dependent mechanism. NS309 potentiates the activity of both KCa2.2 and KCa3.1, while rimtuzalcap selectively activates KCa2.2. Rimtuzalcap has been used in clinical trials for the treatment of spinocerebellar ataxia and essential tremor. We report cryo-electron microscopy structures of NS309-bound KCa2.2 and KCa3.1, in addition to structures of rimtuzalcap-bound KCa2.2 and mutant KCa3.1_R355K. The different conformations of calmodulin and the cytoplasmic HC helices in the two channels underlie the subtype-selectivity of rimtuzalcap for KCa2.2. NS309 binds to pre-existing pockets in both channels, while the bulkier rimtuzalcap binds in an induced-fit pocket in KCa2.2 requiring conformational changes. In KCa2.2, calmodulin’s N-lobes are sufficiently far apart to enable conformational changes to accommodate either NS309 or rimtuzalcap. In KCa3.1, calmodulin’s N-lobes are closer to each other and constrained by KCa3.1’s HC helices, which allows binding of NS309 but not rimtuzalcap. Replacement of arginine-355 in KCa3.1’s HB helix with lysine (KCa3.1_R355K) allows the binding of rimtuzalcap and renders the mutant channel sensitive to rimtuzalcap. These structures provide a framework for structure-based drug design targeting KCa2.2 channels
“She Was My Egg Donor, Not My Mom”: Using Attribution Theory to Understand How Adult Daughters Manage Their Low-Quality Daughter-Mother Relationship
No full textThis study employs attribution theory to examine how adult daughters from low-income backgrounds perceive and manage low-quality daughter-mother relationships. Using in-depth interviews and flexible coding, we analyzed how daughters (n = 42) attribute locus, responsibility, specificity, and stability. Daughters often located control internally within their mothers, citing enduring traits as sources of conflict while noting external influences such as trauma or substance abuse. Responsibility was frequently self-assumed despite blaming mothers, positioning attribution as a means of asserting agency. Many saw their relationships as uniquely difficult and consistent, reinforcing perceptions of stability and specificity. Our study extends attribution theory to long-term family bonds, portraying adult daughters as active agents in meaning-making across the life course