Optimal search: a practical interpretation of information-driven sensor management

We consider the problem of scheduling an agile sensor for performing optimal search for a target. A probability density function is created for representing our knowledge about where the target might be and it is utilized by the proposed sensor management criteria for finding optimal search strategies. The proposed criteria are: an information-driven criterion based on the Kullback-Leibler divergence and a criterion with practical meaning, i.e. performing the sensing action that will yield the maximum probability of detecting the target. It is shown that using the aforementioned criteria results in the same sensing actions when searching for a target and this result establishes a practical operational justification for using information-driven sensor management for performing search

Theoretical and paleoclimatic evidence for abrupt transitions in the Earth system

Specific components of the Earth system may abruptly change their state in response to gradual changes in forcing. This possibility has attracted great scientific interest in recent years, and has been recognized as one of the greatest threats associated with anthropogenic climate change. Examples of such components, called tipping elements, include the Atlantic Meridional Overturning Circulation, the polar ice sheets, the Amazon rainforest, as well as the tropical monsoon systems. The mathematical language to describe abrupt climatic transitions is mainly based on the theory of nonlinear dynamical systems and, in particular, on their bifurcations. Applications of this theory to nonautonomous and stochastically forced systems are a very active field of climate research. The empirical evidence that abrupt transitions have indeed occurred in the past stems exclusively from paleoclimate proxy records. In this review, we explain the basic theory needed to describe critical transitions, summarize the proxy evidence for past abrupt climate transitions in different parts of the Earth system, and examine some candidates for future abrupt transitions in response to ongoing anthropogenic forcing. Predicting such transitions remains difficult and is subject to large uncertainties. Substantial improvements in our understanding of the nonlinear mechanisms underlying abrupt transitions of Earth system components are needed. We argue that such an improved understanding requires combining insights from (a) paleoclimatic records; (b) simulations using a hierarchy of models, from conceptual to comprehensive ones; and (c) time series analysis of recent observation-based data that encode the dynamics of the present-day Earth system components that are potentially prone to tipping

A Comparative In Vitro Study of the Effects of Separate and Combined Products of Citrus e fructibus and Cydonia e fructibus on Immunological Parameters of Seasonal Allergic Rhinitis

This paper examined the effects of the combined product, Citrus e fructibus/Cydonia e fructibus (Citrus/Cydonia; Citrus and Cydonia: each 0.01 g/mL), and separate products of Citrus (0.01 g/mL) and Cydonia (0.01 g/mL) on the immunological pathways involved in seasonal allergic rhinitis (SAR). Peripheral blood mononuclear cells (PBMCs) from five healthy and five grass pollen-allergic donors were isolated and analyzed in vitro after polyclonal and allergen-specific stimulation of T cells in the presence of the three extracts. The analyses demonstrated acceptable cell survival with no signs of toxicity. Citrus mainly had a selective effect on reducing allergen-specific chronic inflammatory (TNF-α; Citrus compared to Cydonia and Citrus/Cydonia: −87.4 (P < 0.001) and −68.0 (P < 0.05), resp.) and Th2 pathway activity (IL-5; Citrus compared to Cydonia: −217.8 (P < 0.01); while, both Cydonia and Citrus/Cydonia mainly affected the induction of the allergen-specific Th1 pathway (IFN-γ; Cydonia and Citrus/Cydonia compared to Citrus: 3.8 (P < 0.01) and 3.0 (P < 0.01), resp.). Citrus and Cydonia demonstrated different working mechanisms in the treatment of SAR and the combination product did not demonstrate larger effects than the separate preparations. Further effectiveness and efficacy studies comparing the effects of the products on SAR in vivo are indicated

Measuring tropical rainforest resilience under non-Gaussian disturbances

The Amazon rainforest is considered one of the Earth's tipping elements and may lose stability under ongoing climate change. Recently a decrease in tropical rainforest resilience has been identified globally from remotely sensed vegetation data. However, the underlying theory assumes a Gaussian distribution of forest disturbances, which is different from most observed forest stressors such as fires, deforestation, or windthrow. Those stressors often occur in power-law-like distributions and can be approximated by $\alpha$-stable L\'evy noise. Here, we show that classical critical slowing down indicators to measure changes in forest resilience are robust under such power-law disturbances. To assess the robustness of critical slowing down indicators, we simulate pulse-like perturbations in an adapted and conceptual model of a tropical rainforest. We find few missed early warnings and few false alarms are achievable simultaneously if the following steps are carried out carefully: First, the model must be known to resolve the timescales of the perturbation. Second, perturbations need to be filtered according to their absolute temporal autocorrelation. Third, critical slowing down has to be assessed using the non-parametric Kendall-$\tau$ slope. These prerequisites allow for an increase in the sensitivity of early warning signals. Hence, our findings imply improved reliability of the interpretation of empirically estimated rainforest resilience through critical slowing down indicators

Detection of Dural Metastases Before the Onset of Clinical Symptoms by 16 alpha-[F-18]Fluoro-17 beta-Estradiol PET in a Patient With Estrogen Receptor-Positive Breast Cancer

We offer an illustrative case about estrogen receptor (ER) imaging (also known as 16 alpha-[F-18]fluoro-17 beta-estradiol ([F-18]-FES) PET) and the detection of metastatic lesions in the dural region. We present a case of a woman with ER-positive metastatic breast cancer and high [F-18]-FES uptake in the dural region on PET imaging, without associated clinical symptoms. These lesions were missed on [F-18]-FDG PET because of physiological [F-18]-FDG uptake in the brain. This case highlighted some difficulties in the interpretation of imaging of brain metastases and demonstrated the added value of [F-18]-FES PET imaging. [F-18]-FES PET could be used to prove the presence of ER-positive metastases in the brain

Application of PET Tracers in Molecular Imaging for Breast Cancer

Purpose of Review: Molecular imaging with positron emission tomography (PET) is a powerful tool to visualize breast cancer characteristics. Nonetheless, implementation of PET imaging into cancer care is challenging, and essential steps have been outlined in the international “imaging biomarker roadmap.” In this review, we identify hurdles and provide recommendations for implementation of PET biomarkers in breast cancer care, focusing on the PET tracers 2-[18F]-fluoro-2-deoxyglucose ([18F]-FDG), sodium [18F]-fluoride ([18F]-NaF), 16α-[18F]-fluoroestradiol ([18F]-FES), and [89Zr]-trastuzumab. Recent Findings: Technical validity of [18F]-FDG, [18F]-NaF, and [18F]-FES is established and supported by international guidelines. However, support for clinical validity and utility is still pending for these PET tracers in breast cancer, due to variable endpoints and procedures in clinical studies. Summary: Assessment of clinical validity and utility is essential towards implementation; however, these steps are still lacking for PET biomarkers in breast cancer. This could be solved by adding PET biomarkers to randomized trials, development of imaging data warehouses, and harmonization of endpoints and procedures

Image Quality and Interpretation of [18F]-FES-PET:Is There any Effect of Food Intake?

BACKGROUND: High physiological 16α-[18F]-fluoro-17β-estradiol ([18F]-FES) uptake in the abdomen is a limitation of this positron emission tomography (PET) tracer. Therefore, we investigated the effect of food intake prior to PET acquisition on abdominal background activity in [18F]-FES-PET scans. METHODS: Breast cancer patients referred for [18F]-FES-PET were included. Three groups were designed: (1) patients who consumed a chocolate bar (fatty meal) between tracer injection and imaging (n = 20), (2) patients who fasted before imaging (n = 20), and (3) patients without diet restrictions (control group, n = 20). We compared the physiological [18F]-FES uptake, expressed as mean standardized uptake value (SUVmean), in the abdomen between groups. RESULTS: A significant difference in [18F]-FES uptake in the gall bladder and stomach lumen was observed between groups, with the lowest values for the chocolate group and highest for the fasting group (p = 0.015 and p = 0.011, respectively). Post hoc analysis showed significant differences in the SUVmean of these organs between the chocolate and fasting groups, but not between the chocolate and control groups. CONCLUSION: This exploratory study showed that, compared to fasting, eating chocolate decreases physiological gall bladder and stomach [18F]-FES uptake; further reduction through a normal diet was not seen. A prospective study is warranted to confirm this finding

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

Objective: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. Background: RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. Methods: Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. Results: The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. Conclusion: This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.</p