Per-Pixel Versus Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per- pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.

Effects of A Short-term Cardio Tai Chi Program on Cardiorespiratory Fitness and Hemodynamic Parameters in Sedentary Adults: A Pilot Study

This study evaluates the effects of a short-term Cardio Tai Chi program on the cardiorespiratory fitness and hemodynamic parameters in sedentary adults. Thirty-one sedentary participants (age: 58 ± 9 years, body mass: 63 ± 12 kg) were subjected to an exercise program during 10 sessions over a 10-day period within 2 weeks. The Cardio Tai Chi program consisted in a series of three to five intervals lasting 90 s each at ∼70% maximal heart rate separated by 2-min of low-intensity recovery. Primary outcome measures were cardiorespiratory fitness (peak oxygen uptake, V˙O2peak) assessed by the Rockport walking test and resting hemodynamic parameters (systolic, diastolic, mean, and pulse pressures). We observed a significant difference of means on post-pre V˙O2peak [4.5 ml/kg/min, 95% confidence interval (CI): 3.1 to 5.8, p = 0.004], systolic blood pressure (-5.5 mmHg, 95% CI:-7.3 to -3.8, p = 0.010) and pulse pressure (-3.7 mmHg, 95% CI: -5.2 to -2.3, p = 0.028). No significant differences were observed for diastolic pressure (−1.8 mmHg, 95% CI: -2.6 to -1.0, p = 0.226), mean blood pressure (2.5 mmHg, 95% CI: 1.4 to 3.6, p = 0.302), or resting heart rate (-0.9 beat/min, 95% CI: -2.0 to 0.1, p = 0.631). Our findings suggest that engaging in a short-term Cardio Tai Chi program can improve cardiorespiratory fitness and hemodynamic parameters in sedentary adults

Dasatinib vs. imatinib in patients with chronic myeloid leukemia in chronic phase (CML-CP) who have not achieved an optimal response to 3 months of imatinib therapy: the DASCERN randomized study

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Landscape-scale drivers of glacial ecosystem change in the montane forests of the eastern Andean flank, Ecuador

Understanding the impact of landscape-scale disturbance events during the last glacial period is vital in accu- rately reconstructing the ecosystem dynamics of montane environments. Here, a sedimentary succession from the tropical montane cloud forest of the eastern Andean flank of Ecuador provides evidence of the role of non- climate drivers of vegetation change (volcanic events, fire regime and herbivory) during the late-Pleistocene. Multiproxy analysis (pollen, non-pollen palynomorphs, charcoal, geochemistry and carbon content) of the se- diments, radiocarbon dated to ca. 45–42 ka, provide a snap shot of the depositional environment, vegetation community and non-climate drivers of ecosystem dynamics. The geomorphology of the Vinillos study area, along with the organic‐carbon content, and aquatic remains suggest deposition took place near a valley floor in a swamp or shallow water environment. The pollen assemblage initially composed primarily of herbaceous types (Poaceae-Asteraceae-Solanaceae) is replaced by assemblages characterised by Andean forest taxa, (first Melastomataceae-Weinmannia-Ilex, and later, Alnus-Hedyosmum-Myrica). The pollen assemblages have no modern analogues in the tropical montane cloud forest of Ecuador. High micro-charcoal and rare macro-charcoal abundances co-occur with volcanic tephra deposits suggesting transportation from extra-local regions and that volcanic eruptions were an important source of ignition in the wider glacial landscape. The presence of the coprophilous fungi Sporormiella reveals the occurrence of herbivores in the glacial montane forest landscape. Pollen analysis indicates a stable regional vegetation community, with changes in vegetation population co- varying with large volcanic tephra deposits suggesting that the structure of glacial vegetation at Vinillos was driven by volcanic activity

Prostaglandin metabolite induces inhibition of TRPA1 and channel-dependent nociception

BACKGROUND: The Transient Receptor Potential (TRP) ion channel TRPA1 is a key player in pain pathways. Irritant chemicals activate ion channel TRPA1 via covalent modification of N-terminal cysteines. We and others have shown that 15-Deoxy-Δ12, 14-prostaglandin J(2) (15d-PGJ(2)) similarly activates TRPA1 and causes channel-dependent nociception. Paradoxically, 15d-PGJ(2) can also be anti-nociceptive in several pain models. Here we hypothesized that activation and subsequent desensitization of TRPA1 in dorsal root ganglion (DRG) neurons underlies the anti-nociceptive property of 15d-PGJ(2). To investigate this, we utilized a battery of behavioral assays and intracellular Ca(2+) imaging in DRG neurons to test if pre-treatment with 15d-PGJ(2) inhibited TRPA1 to subsequent stimulation. RESULTS: Intraplantar pre-injection of 15d-PGJ(2), in contrast to mustard oil (AITC), attenuated acute nocifensive responses to subsequent injections of 15d-PGJ(2) and AITC, but not capsaicin (CAP). Intraplantar 15d-PGJ(2)—administered after the induction of inflammation—reduced mechanical hypersensitivity in the Complete Freund’s Adjuvant (CFA) model for up to 2 h post-injection. The 15d-PGJ(2)-mediated reduction in mechanical hypersensitivity is dependent on TRPA1, as this effect was absent in TRPA1 knockout mice. Ca(2+) imaging studies of DRG neurons demonstrated that 15d-PGJ(2) pre-exposure reduced the magnitude and number of neuronal responses to AITC, but not CAP. AITC responses were not reduced when neurons were pre-exposed to 15d-PGJ(2) combined with HC-030031 (TRPA1 antagonist), demonstrating that inhibitory effects of 15d-PGJ(2) depend on TRPA1 activation. Single daily doses of 15d-PGJ(2), administered during the course of 4 days in the CFA model, effectively reversed mechanical hypersensitivity without apparent tolerance or toxicity. CONCLUSIONS: Taken together, our data support the hypothesis that 15d-PGJ(2) induces activation followed by persistent inhibition of TRPA1 channels in DRG sensory neurons in vitro and in vivo. Moreover, we demonstrate novel evidence that 15d-PGJ(2) is analgesic in mouse models of pain via a TRPA1-dependent mechanism. Collectively, our studies support that TRPA1 agonists may be useful as pain therapeutics

ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight adapter architecture using images paired with corresponding free-text radiology reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13 findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898 across five findings (atelectasis, cardiomegaly, consolidation, pleural effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images) training data), and semantic search (0.76 normalized discounted cumulative gain (NDCG) across nineteen queries, including perfect retrieval on twelve of them). Compared to existing data-efficient methods including supervised contrastive learning (SupCon), ELIXR required two orders of magnitude less data to reach similar performance. ELIXR also showed promise on CXR vision-language tasks, demonstrating overall accuracies of 58.7% and 62.5% on visual question answering and report quality assurance tasks, respectively. These results suggest that ELIXR is a robust and versatile approach to CXR AI