Chronic cluster headache and the pituitary gland

Background: Cluster headache is classified as a primary headache by definition not caused by an underlying pathology. However, symptomatic cases of otherwise typical cluster headache have been reported. Case presentation: A 47-year-old male suffered from primary chronic cluster headache (CCH, ICHD-3 beta criteria fulfilled) since the age of 35 years. A magnetic resonance imaging (MRI) study of the brain in 2006 came back normal. He tried several prophylactic treatments but was never longer than 1 month without attacks. He was under chronic treatment with verapamil with only a limited effect on the attack frequency. Subcutaneous sumatriptan 6 mg injections were very effective in aborting attacks. By February 2014 the patient developed a continuous interictal pain ipsilateral to the right-sided cluster headache attacks. An indomethacin test (up to 225 mg/day orally) was negative. Because of the change in headache pattern we performed a new brain MRI, which showed a cystic structure in the pituitary gland. The differential diagnosis was between a Rathke cleft cyst and a cystic adenoma. Pituitary function tests showed an elevated serum prolactin level. A dopamine agonist (cabergoline) was started and the headache subsided completely. Potential pathophysiological mechanisms of pituitary tumor-associated headache are discussed. Conclusion: Neuroimaging should be considered in all patients with CCH, especially those with an atypical presentation or evolution. Response to acute treatment does not exclude a secondary form of cluster headache. There may be shared pathophysiological mechanisms of primary and secondary cluster headache

Abatement of ammonia emissions from livestock housing fine-tuned according to impact on protected habitats

Livestock farms are an important source of ammonia emissions, which threaten vulnerable habitats and species in nearby natural areas through a process of atmospheric nitrogen deposition. An integrated and spatially-explicit mixed integer programming model was applied to all livestock facilities in Flanders (Belgium), to evaluate the current Flemish policies aimed at limiting ammonia deposition in Natura 2000 sites. The simulations indicate that a substantial reduction in deposition is achievable with a similar cost to the currently applied policy in Flanders. Furthermore, the model allows identification of the most suitable stable type and emission abatement measures for any stable in Flanders. Such a spatially-explicit optimization approach applied to individual emission sources might assist policymakers in improving spatially-differentiated policies

Detection of corrosion on steel structures using automated image processing

The traditional method used for corrosion damage assessment is visual inspection which is time-consuming for vast areas, impossible for inaccessible areas and subjective for non-experts. A promising way to overcome the aforementioned drawbacks is to develop an artificial intelligence-based algorithm that can recognize corrosion damage in a series of photographic images. This paper reports on the implementation and use of an algorithm that quantifies and combines two visual aspects – roughness and color – in order to locate the corroded area in a given image. For the roughness analysis, the uniformity metric calculated from the gray-level co-occurrence matrix is considered. For the color analysis, the histogram of corrosion-representative colors extracted from a data-set in HSV color space is used. The algorithm has been applied to a large dataset of photographs of corroded and non-corroded components and structures. Our findings show that the developed algorithm can efficiently locate corroded areas

The impact of the COVID-19 pandemic on wellbeing and cognitive functioning of older adults

COVID-19 took a heavy toll on older adults. In Belgium, by the end of August, 93% of deaths due to COVID-19 were aged 65 or older. Similar trends were observed in other countries. As a consequence, older adults were identified as a group at risk, and strict governmental restrictions were imposed on them. This has caused concerns about their mental health. Using an online survey, this study established the impact of the COVID-19 pandemic on adults aged 65 years or older, and which factors moderate this impact. Participants reported a significant decrease in activity level, sleep quality and wellbeing during the COVID-19 pandemic. Depression was strongly related to reported declines in activity level, sleep quality, wellbeing and cognitive functioning. Our study shows that the COVID-19 pandemic had a severe impact on the mental health of older adults. This implies that this group at risk requires attention of governments and healthcare