Table of Contents

Table of contents for Volume 10, Issue 3 of the Linfield Magazin

Typha latifolia paludiculture effectively improves water quality and reduces greenhouse gas emissions in rewetted peatlands

Paludiculture, the cultivation of crops on wet or rewetted agricultural peatlands, sustainably integrates productive land use with the provision of multiple ecosystem services. Paludiculture crops thrive under waterlogged conditions that stimulate nitrogen (N) and phosphorus (P) removal from soil and water and convert serious drainage-induced carbon (C) losses to C sequestration. Nutrient uptake by paludicrops can prevent mobilisation after rewetting and provide opportunities for purification of nutrient-rich water. Uncertainty remains, however, if and to what extent N loading and a subsequent increase in biomass productivity affect nutrient cycling as well as emissions of the potent greenhouse gases methane (CH4) and nitrous oxide (N2O). In this study, we use mesocosms with rewetted peat to investigate the effect of different N sources in surface water on biomass production of Typha latifolia, a typical paludiculture crop, and the emissions of CH4 and N2O. Organic (Azolla filiculoides; urea) or mineral (KNO3 ; NH4NO) N was supplied either a single time (steady state) or repeatedly (pulse) to simulate a total surface water load of 150 kg N ha(-1) . We found that N stimulated aboveground and belowground biomass production and nutrient uptake by T. latifolia. These effects were absent in Azolla treatments. Whereas after two months CH4 emissions arose to substantial amounts (> 10 mg CH4 m(-2) day(-1)) in unvegetated mesocosms loaded with organic N, they remained very low (<1 mg CH4 m(-2) day(-1)) in vegetated mesocosms, despite the labile C pool in the extensive belowground biomass and organic N loading. Overall, N2O emissions were close to zero and were only detected episodically after NO(3)(- )loading, irrespective of plant presence. Our findings support that T. latifolia as a paludicrop effectively removes various forms of N and P when harvested, and strongly mitigates CH4 emission after the rewetting of agricultural peat soils compared to unvegetated conditions

Distinct gene expression in demyelinated white and grey matter areas of patients with multiple sclerosis

Demyelination of the central nervous system is a prominent pathological hallmark of multiple sclerosis and affects both white and grey matter. However, demyelinated white and grey matter exhibit clear pathological differences, most notably the presence or absence of inflammation and activated glial cells in white and grey matter, respectively. In order to gain more insight into the differential pathology of demyelinated white and grey matter areas, we micro-dissected neighbouring white and grey matter demyelinated areas as well as normal-appearing matter from leucocortical lesions of human post-mortem material and used these samples for RNA sequencing. Our data show that even neighbouring demyelinated white and grey matter of the same leucocortical have a distinct gene expression profile and cellular composition. We propose that, based on their distinct expression profile, pathological processes in neighbouring white and grey matter are likely different which could have implications for the efficacy of treating grey matter lesions with current anti-inflammatory-based multiple sclerosis drugs

Dysfunction in Early Multiple Sclerosis: Altered Centrality Derived from Resting-State Functional Connectivity Using Magneto-Encephalography

BACKGROUND: Cognitive dysfunction in multiple sclerosis (MS) is frequent. Insight into underlying mechanisms would help to develop therapeutic strategies. OBJECTIVE: To explore the relationship of cognitive performance to patterns of nodal centrality derived from magneto-encephalography (MEG). METHODS: 34 early relapsing-remitting MS patients (median EDSS 2.0) and 28 age- and gender-matched healthy controls (HC) had a MEG, a neuropsychological assessment and structural MRI. Resting-state functional connectivity was determined by the synchronization likelihood. Eigenvector Centrality (EC) was used to quantify for each sensor its connectivity and importance within the network. A cognition-score was calculated, and normalized grey and white matter volumes were determined. EC was compared per sensor and frequency band between groups using permutation testing, and related to cognition. RESULTS: Patients had lower grey and white matter volumes than HC, male patients lower cognitive performance than female patients. In HC, EC distribution showed highest nodal centrality over bi-parietal sensors ("hubs"). In patients, nodal centrality was even higher bi-parietally (theta-band) but markedly lower left temporally (upper alpha- and beta-band). Lower cognitive performance correlated to decreased nodal centrality over left temporal (lower alpha-band) and right temporal (beta-band) sensors, and to increased nodal centrality over right parieto-temporal sensors (beta-band). Network changes were most pronounced in male patients. CONCLUSIONS: Partial functional disconnection of the temporal regions was associated with cognitive dysfunction in MS; increased centrality in parietal hubs may reflect a shift from temporal to possibly less efficient parietal processing. To better understand patterns and dynamics of these network changes, longitudinal studies are warranted, also addressing the influence of gender

Gray matter imaging in multiple sclerosis: what have we learned?

At the early onset of the 20th century, several studies already reported that the gray matter was implicated in the histopathology of multiple sclerosis (MS). However, as white matter pathology long received predominant attention in this disease, and histological staining techniques for detecting myelin in the gray matter were suboptimal, it was not until the beginning of the 21st century that the true extent and importance of gray matter pathology in MS was finally recognized. Gray matter damage was shown to be frequent and extensive, and more pronounced in the progressive disease phases. Several studies subsequently demonstrated that the histopathology of gray matter lesions differs from that of white matter lesions. Unfortunately, imaging of pathology in gray matter structures proved to be difficult, especially when using conventional magnetic resonance imaging (MRI) techniques. However, with the recent introduction of several more advanced MRI techniques, the detection of cortical and subcortical damage in MS has considerably improved. This has important consequences for studying the clinical correlates of gray matter damage. In this review, we provide an overview of what has been learned about imaging of gray matter damage in MS, and offer a brief perspective with regards to future developments in this field

Treatment and overall survival of four types of non-metastatic periampullary cancer:nationwide population-based cohort study

Background: Periampullary adenocarcinoma consists of pancreatic adenocarcinoma (PDAC), distal cholangiocarcinoma (DC), ampullary cancer (AC), and duodenal adenocarcinoma (DA). The aim of this study was to assess treatment modalities and overall survival by tumor origin. Methods: Patients diagnosed with non-metastatic periampullary cancer in 2012–2018 were identified from the Netherlands Cancer Registry. OS was studied with Kaplan–Meier analysis and multivariable Cox regression analyses, stratified by origin. Results: Among the 8758 patients included, 68% had PDAC, 13% DC, 12% AC, and 7% DA. Resection was performed in 35% of PDAC, 56% of DC, 70% of AC, and 59% of DA. Neoadjuvant and/or adjuvant therapy was administered in 22% of PDAC, 7% of DC, 7% of AC, and 12% of DA. Three-year OS was highest for AC (37%) and DA (34%), followed by DC (21%) and PDAC (11%). Adjuvant therapy was associated with improved OS among PDAC (HR = 0.62; 95% CI 0.55–0.69) and DC (HR = 0.69; 95% CI 0.48–0.98), but not AC (HR = 0.87; 95% CI 0.62–1.22) and DA (HR = 0.85; 95% CI 0.48–1.50). Conclusion: This retrospective study identified considerable differences in treatment modalities and OS between the four periampullary cancer origins in daily clinical practice. An improved OS after adjuvant chemotherapy could not be demonstrated in patients with AC and DA

Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis

Multiple sclerosis (MS), a chronic disorder of the central nervous system and common cause of neurological disability in young adults, is characterized by moderate but complex risk heritability. Here we report the results of a genome-wide association study performed in a 1000 prospective case series of well-characterized individuals with MS and group-matched controls using the Sentrix® HumanHap550 BeadChip platform from Illumina. After stringent quality control data filtering, we compared allele frequencies for 551 642 SNPs in 978 cases and 883 controls and assessed genotypic influences on susceptibility, age of onset, disease severity, as well as brain lesion load and normalized brain volume from magnetic resonance imaging exams. A multi-analytical strategy identified 242 susceptibility SNPs exceeding established thresholds of significance, including 65 within the MHC locus in chromosome 6p21.3. Independent replication confirms a role for GPC5, a heparan sulfate proteoglycan, in disease risk. Gene ontology-based analysis shows a functional dichotomy between genes involved in the susceptibility pathway and those affecting the clinical phenotyp

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems