Lessons from the Classroom – assessing the work of postgraduate students to support better hygrothermal risk assessment

The widespread adoption of transient simulation modelling tools by building design professionals to support hygrothermal risk assessment of building design specifications is a crucial component in a multi-pronged drive to reduce moisture risk in buildings. Structured upskilling is essential. Much can be learnt about the ways practitioners use such tools by reviewing the work of professional postgraduate student groups. Such review could inform the creation of a user protocol. Peer-review under the responsibility of the organizing committee of the ICMB21

Effect of aging on esophageal motility in patients with and without GERD

Background/Aims: The impact of aging on esophageal motility is not completely understood. This study aims at assessing 1) whether degeneration of esophageal body motility occurs with age and 2) whether this development is influenced by gastroesophageal reflux disease (GERD)

Effect of aging on esophageal motility in patients with and without GERD

Background/Aims: The impact of aging on esophageal motility is not completely understood. This study aims at assessing 1) whether degeneration of esophageal body motility occurs with age and 2) whether this development is influenced by gastroesophageal reflux disease (GERD)

Bringing Anatomical Information into Neuronal Network Models

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication

A phytolith supported biosphere-hydrosphere predictive model for Southern Ethiopia:Insights into paleoenvironmental changes and human landscape preferences since the last glacial maximum

During the past 25 ka, southern Ethiopia has undergone tremendous climatic changes, from dry and relatively cold during the Last Glacial Maximum (LGM, 25–18 ka) to the African Humid Period (AHP, 15–5 ka), and back to present-day dry conditions. As a contribution to better understand the effects of climate change on vegetation and lakes, we here present a new Predictive Vegetation Model that is linked with a Lake Balance Model and available vegetation-proxy records from southern Ethiopia including a new phytolith record from the Chew Bahir basin. We constructed a detailed paleo-landcover map of southern Ethiopia during the LGM, AHP (with and without influence of the Congo Air Boundary) and the modern-day potential natural landcover. Compared to today, we observe a 15–20% reduction in moisture availability during the LGM with widespread open landscapes and only few remaining forest refugia. We identify 25–40% increased moisture availability during the AHP with prevailing forests in the mid-altitudes and indications that modern anthropogenic landcover change has affected the water balance. In comparison with existing archaeological records, we find that human occupations tend to correspond with open landscapes during the late Pleistocene and Holocene in southern Ethiopia

Prevalence and prognostic value of neurological affections in hospitalized patients with moderate to severe COVID-19 based on objective assessments.

Neurological manifestations of coronavirus disease 2019 (COVID-19) have been frequently described. In this prospective study of hospitalized COVID-19 patients without a history of neurological conditions, we aimed to analyze their prevalence and prognostic value based on established, standardized and objective methods. Patients were investigated using a multimodal electrophysiological approach, accompanied by neuropsychological and neurological examinations. Prevalence rates of central (CNS) and peripheral (PNS) nervous system affections were calculated and the relationship between neurological affections and mortality was analyzed using Firth logistic regression models. 184 patients without a history of neurological diseases could be enrolled. High rates of PNS affections were observed (66% of 138 patients receiving electrophysiological PNS examination). CNS affections were less common but still highly prevalent (33% of 139 examined patients). 63% of patients who underwent neuropsychological testing (n = 155) presented cognitive impairment. Logistic regression models revealed pathology in somatosensory evoked potentials as an independent risk factor of mortality (Odds Ratio: 6.10 [1.01-65.13], p = 0.049). We conclude that hospitalized patients with moderate to severe COVID-19 display high rates of PNS and CNS affection, which can be objectively assessed by electrophysiological examination. Electrophysiological assessment may have a prognostic value and could thus be helpful to identify patients at risk for deterioration

Detection of a novel human coronavirus by real-time reverse-transcription polymerase chain reaction

We present two real-time reverse-transcription polymerase chain reaction assays for a novel human coronavirus (CoV), targeting regions upstream of the E gene (upE) or within open reading frame (ORF)1b, respectively. Sensitivity for upE is 3.4 copies per reaction (95% confidence interval (CI): 2.5-6.9 copies) or 291 copies/mL of sample. No cross-reactivity was observed with coronaviruses OC43, NL63, 229E, SARS-CoV, nor with 92 clinical specimens containing common human respiratory viruses. We recommend using upE for screening and ORF1b for confirmation

Baufeuchte vom Keller bis zum Dach

Anhand von Beispielen wird gezeigt, dass während der Bauphase eingebrachte Feuchte an vielen Stellen im Gebäude auftreten und Einfluss nehmen kann. Eine Betonwand im Keller kann während der Austrocknung die Dämmwirkung der Perimeterdämmung beeinflussen, wenn keine vollflächige Dichtung zwischen Wand und Keller besteht. Bei Niedrigenergiehäusern kann die Baufeuchte ungewollt und unberücksichtigt in den ersten Jahren zu erhöhtem Heizenergiebedarf führen. Bei Fehlstellen in der Verlegung der Dämmplatten eines Wärmedämmverbundsystems oder auch bei zu früher Aufbringung des Außenputzes kann die austrocknende Baufeuchte zu Pilzbildung an der Außenoberfläche führen. Schließt eine Wand aus Beton oder Mauerwerk in eine Dachkonstruktion ein, kann hier zusätzliche Feuchte in die Dachkonstruktion gelangen und zu Schäden am Holz oder Schimmelbildung führen. Die Beispiele belegen, dass der Einfluss der Baufeuchte während der Planung und Erstellung eines Gebäudes beachtet werden muss, um Folgeschäden zu vermeiden

BigBrain Analysis: Cellular-Level Precision at 1µm Resolution

IntroductionThe BigBrain model [1] is a cornerstone for extracting quantitative measures of brain architecture at 20μm isotropic resolution. While this model has proven instrumental in extracting 3D histological features, there's a growing need for even higher spatial resolution to obtain measures at the level of individual cells. Building on previous work from 2022 [2], this project utilizes 2D 1μm sections to provide a more detailed characterization of cellular distributions in the human brain, and to further enhance the BigBrain model with accurate estimates of layer-wise cell densities across the entire cortex.MethodsExpanding on our previous work [2], we investigated 78 additional areas of the Julich-Brain cytoarchitectonic atlas [3]. These patches were sampled by registering each section to BigBrain space, and then sampling cortical locations corresponding to a probability >60% of being the specific area. In each patch, cortical layer boundaries were annotated by experts and validated using a four-eye procedure. Automatic cell body detection state of the art Deep Learning model [4] was applied to all patches, enabling the extraction of laminar cell numbers and cell body sizes for all areas under investigation (Fig.1).ResultsThe expanded dataset now encompasses 900 cortical patches, with a size of about 52GB, selected from well-defined cytoarchitectonic areas of the Julich-Brain Atlas. Each patch in the dataset includes the 1μm raw image, manual annotations of isocortical layers, and contours and spatial properties of the extracted cell body segmentations (Fig.2). This enhancement in resolution from the native 20μm BigBrain resolution to 1μm has unveiled significant differences in cell packing density across various laminae of the brain. A trend of decreasing cell densities from posterior to anteriorly located areas was observed across all lamina of the human cortex. This trend was especially pronounced in granular layers II and IV. Moreover, the new patches can be utilized to refine previously generated cortical laminae [5], which were based on a limited number of areas.ConclusionsThe shift from 20 to a 1µm resolution image data has enabled quantitative analysis of individual cell bodies. This approach gives precise cell counts from specific brain areas and integrates them with overall brain data, revealing both known and new brain architectural insights. The resulting dataset, rooted in the BigBrain framework, provides a well structured and accurate spatial representation. This dataset can potentially replace the century-old cell counts from von Economo and Koskinas [6]. Its strengths lie in its reproducibility, precise 3D anchoring in the BigBrain, and the availability of original images for each patch, allowing detailed verification down to individual cells.[1] Amunts K, et al. (2013). BigBrain: An ultrahigh-resolution 3D human brain model. Science[2] EBRAINS https://search.kg.ebrains.eu/instances/f06a2fd1-a9ca-42a3-b754-adaa025adb10[3] Amunts K, et al. (2020). Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science[4] Upschulte E, et al. (2022). Contour Proposal Networks for Biomedical Instance Segmentation. Medical Image Analysis.[5] Wagstyl K, et al. (2020). BigBrain 3D atlas of cortical layers. PLOS Biology[6] von Economo C, Koskinas GN. (1925). Die Cytoarchitektonik der Hirnrinde des Erwachsenen Menschen. Springe