Since the first prototype of a transmission electron microscope was built in 1931 by Ernst Ruska and Max Knoll, Transmission Electron Microscopy (TEM) has proved to be an essential imaging tool for physicists, material scientists, and biologists. To record the TEM images for analysis, electron microscopists have used specialized electron micrograph film for a long time, until the new developments in TEM, such as electron tomography and cryo- electron microscopy, pushed for the needs of digital imaging. Recent years has seen the widespread use of the charge coupled device (CCD) in all fields of TEM. Although it does provide digital readout, its spatial resolution and sensitivity are fundamentally limited by the use of an indirect detection method based on scintillator screens to convert high energy electrons into photons. To realize the full imaging potential of the TEM, a new imaging detector for TEM, called the Direct Detection Device (DDD), was designed, built and characterized. The DDD is capable of directly detecting high energy electrons with a high signal to noise ratio, ensuring single electron sensitivity. Thanks to the small 5μm pixel size, the spatial resolution of the DDD exceeds any current imaging detectors in the TEM. Additionally, with the recent development of the fifth generations DDD prototype, the imaging performance of the DDD was further improved by using the DDD as a high speed 2-dimensional electron counter. The Modulation Transfer Function (MTF) of the DDD at half Nyquist frequency (50 lp/mm) reached 50% using the noiseless electron counter operation. The unique features of the DDD also enabled the possibility of specimen drift correction and fast wide-field image mosaic acquisition. With the advantages of direct electron detection, the DDD is expected to improve the image quality and resolution of all fields of TEM, especially for low dose imaging.

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.

Tomato fruit are especially susceptible to chilling injury (CI) when continuously exposed to temperatures below 12 °C. In this study, integrative comparative analyses of transcriptomics and metabolomics data were performed to uncover the regulatory network in CI tomato fruit. Metabolite profiling analysis found that 7 amino acids, 27 organic acids, 16 of sugars and 22 other compounds had a significantly different content while transcriptomics data showed 1735 differentially expressed genes (DEGs) were down-regulated and 1369 were up-regulated in cold-stored fruit. We found that the contents of citrate, cis-aconitate and succinate were increased, which were consistent with the expression of ATP-citrate synthase (ACS) and isocitrate dehydrogenase (IDH) genes in cold-treated tomato fruit. Cold stress promotes the expression of ACS and IDH which may increase the synthesis of citrate, cis-aconitate and succinate. Alanine and leucine had increased contents, which may result from alanine aminotransferase (ALT) and branched-chain amino acid aminotransferase (BcAT)'s high expression levels, respectively. Overall the transcriptomics and metabolomics data in our study explain the molecular mechanisms of the chilling injury and expands our understanding of the complex regulatory mechanisms of a metabolic network in response to chilling injury in tomato fruit.

Introduction

The Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using ¹¹C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation.

Methods

Using data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: ¹⁸F-florbetapir (N = 147 pairs), ¹⁸F-florbetaben (N = 22), ¹⁸F-flutemetamol (N = 10), ¹⁸F-NAV (N = 42), ¹¹C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI).

Results

In the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline.

Discussion

The universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers.

Highlights

This study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.

Introduction

The Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies.

Methods

All Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in ¹¹C-PiB-¹⁸F-Florbetapir pairs and longitudinal consistency were evaluated.

Results

The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for ¹⁸F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used.

Conclusions

FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.