Nanoscopic compartmentalization of membrane protein motion at the axon initial segment

The axon initial segment (AIS) is enriched in specific adaptor, cytoskeletal, and transmembrane molecules. During AIS establishment, a membrane diffusion barrier is formed between the axonal and somatodendritic domains. Recently, an axonal periodic pattern of actin, spectrin, and ankyrin forming 190-nm-spaced, ring-like structures has been discovered. However, whether this structure is related to the diffusion barrier function is unclear. Here, we performed single-particle tracking time-course experiments on hippocampal neurons during AIS development. We analyzed the mobility of lipid-anchored molecules by high-speed single-particle tracking and correlated positions of membrane molecules with the nanoscopic organization of the AIS cytoskeleton. We observe a strong reduction in mobility early in AIS development. Membrane protein motion in the AIS plasma membrane is confined to a repetitive pattern of ∼190-nm-spaced segments along the AIS axis as early as day in vitro 4, and this pattern alternates with actin rings. Mathematical modeling shows that diffusion barriers between the segments significantly reduce lateral diffusion along the axon

The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo

Forest degradation through logging is pervasive throughout the world's tropical forests, leading to changes in the three-dimensional canopy structure that have profound consequences for wildlife, microclimate and ecosystem functioning. Quantifying these structural changes is fundamental to understanding the impact of degradation, but is challenging in dense, structurally complex forest canopies. We exploited discrete-return airborne LiDAR surveys across a gradient of logging intensity in Sabah, Malaysian Borneo, and assessed how selective logging had affected canopy structure (Plant Area Index, PAI, and its vertical distribution within the canopy). LiDAR products compared well to independent, analogue models of canopy structure produced from detailed ground-based inventories undertaken in forest plots, demonstrating the potential for airborne LiDAR to quantify the structural impacts of forest degradation at landscape scale, even in some of the world's tallest and most structurally complex tropical forests. Plant Area Index estimates across the plot network exhibited a strong linear relationship with stem basal area (R2 = 0.95). After at least 11–14 years of recovery, PAI was ~28% lower in moderately logged plots and ~52% lower in heavily logged plots than that in old-growth forest plots. These reductions in PAI were associated with near-complete lack of trees >30-m tall, which had not been fully compensated for by increasing plant area lower in the canopy. This structural change drives a marked reduction in the diversity of canopy environments, with the deep, dark understorey conditions characteristic of old-growth forests far less prevalent in logged sites. Full canopy recovery is likely to take decades. Synthesis and applications. Effective management and restoration of tropical forests requires detailed monitoring of the forest and its environment. We demonstrate that airborne LiDAR can effectively map the canopy architecture of the complex tropical forests of Borneo, capturing the three-dimensional impact of degradation on canopy structure at landscape scales, therefore facilitating efforts to restore and conserve these ecosystems

Country-level determinants of the severity of the first global wave of the COVID-19 pandemic : an ecological study

Acknowledgements We would like to thank Dr Kathryn Martin, who provided valuable advice in study design. Funding This work was supported by the Aberdeen Clinical Academic Training Scheme.Peer reviewedPublisher PD

Data on the use of dietary supplements in Danish patients with type 1 and type 2 diabetes

The data in this article describe the use of dietary supplements in Danish patients with type 1 diabetes (T1D) and type 2 diabetes (T2D). The data were collected from a web-based dietary survey on dietary habits in 774 patients with T1D (n = 426) and T2D (n = 348). The data demonstrate that 99% of the patients with diabetes use dietary supplements with no gender differences. In comparison, only 64% in the general population use dietary supplements [2].A higher proportion of people in the general population use multivitamin/mineral supplementation as compared to patients with diabetes (48% vs. 34–37%) and a higher proportion of women than men with diabetes use multivitamin/mineral supplementation (T1D: 43% women vs. 26% men and T2D: 45% women vs. 34% men). More patients with diabetes than the general population use supplements such as calcium together with vitamin D, vitamin D, vitamin B, vitamin C, vitamin E, magnesium, calcium, Q10, ginger, garlic, and other herbal supplements

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

The GimA Locus of Extraintestinal Pathogenic E. coli: Does Reductive Evolution Correlate with Habitat and Pathotype?

IbeA (invasion of brain endothelium), which is located on a genomic island termed GimA, is involved in the pathogenesis of several extraintestinal pathogenic E. coli (ExPEC) pathotypes, including newborn meningitic E. coli (NMEC) and avian pathogenic E. coli (APEC). To unravel the phylogeny of GimA and to investigate its island character, the putative insertion locus of GimA was determined via Long Range PCR and DNA-DNA hybridization in 410 E. coli isolates, including APEC, NMEC, uropathogenic (UPEC), septicemia-associated E. coli (SEPEC), and human and animal fecal isolates as well as in 72 strains of the E. coli reference (ECOR) collection. In addition to a complete GimA (∼20.3 kb) and a locus lacking GimA we found a third pattern containing a 342 bp remnant of GimA in this strain collection. The presence of GimA was almost exclusively detected in strains belonging to phylogenetic group B2. In addition, the complete GimA was significantly more frequent in APEC and NMEC strains while the GimA remnant showed a higher association with UPEC strains. A detailed analysis of the ibeA sequences revealed the phylogeny of this gene to be consistent with that obtained by Multi Locus Sequence Typing of the strains. Although common criteria for genomic islands are partially fulfilled, GimA rather seems to be an ancestral part of phylogenetic group B2, and it would therefore be more appropriate to term this genomic region GimA locus instead of genomic island. The existence of two other patterns reflects a genomic rearrangement in a reductive evolution-like manner