Smart observation of management impacts on peatlands function (SmartBog)

Peatlands are an important ecosystem due to their role in carbon sequestration as well as other ecosystem services including; climate regulation and water regulation. Peatlands cover a small fraction (~3 %) of the terrestrial surface. Nevertheless, they account for approximately one-third of global Soil Organic Carbon stock. In Ireland, peatlands cover ~21% of the land area and account for between 50-75% of the total SOC stock. However, much of this area has been degraded through anthropogenic activities such as drainage and peat extraction. Therefore, there is a need to develop a system to identify management-related impacts on peatland function. The system will directly support rehabilitation and conservation activities, aiding identification of candidate sites for rewetting and restoration. Both high-resolution satellite data (Copernicus Sentinel-2) and very high-resolution aerial photography will be used. Peatlands will be delineated using the Derived Irish Peat map (DIPM2) in both datasets. Semi-automatic object-based image analysis and machine learning-based techniques will be used to extract the extent of drains on Irish peatlands. Furthermore, a multi-scale approach will be implemented to generate Normalized Difference Vegetation Index maps. NDVI will be generated from both in-situ and remote sensors (Sentinel-2/Aerial imagery). Overall, the outputs generated from these datasets (LULC, drainage and NDVI maps) will be integrated into a GIS framework. The main aim of this study is to assess the impact of anthropogenic management of peatlands, using GIS, Earth Observation and Machine Learning (ML)

Group analysis in functional neuroimaging: selecting subjects using similarity measures.

International audienceStandard group analyses of fMRI data rely on spatial and temporal averaging of individuals. This averaging operation is only sensible when the mean is a good representation of the group. This is not the case if subjects are not homogeneous, and it is therefore a major concern in fMRI studies to assess this group homogeneity. We present a method that provides relevant distances or similarity measures between temporal series of brain functional images belonging to different subjects. The method allows a multivariate comparison between data sets of several subjects in the time or in the space domain. These analyses assess the global intersubject variability before averaging subjects and drawing conclusions across subjects, at the population level. We adapt the RV coefficient to measure meaningful spatial or temporal similarities and use multidimensional scaling to give a visual representation of each subject's position with respect to other subjects in the group. We also provide a measure for detecting subjects that may be outliers. Results show that the method is a powerful tool to detect subjects with specific temporal or spatial patterns, and that, despite the apparent loss of information, restricting the analysis to a homogeneous subgroup of subjects does not reduce the statistical sensitivity of standard group fMRI analyses

Amyloidosis: Systems-Based Therapies

In this chapter, the authors will discuss the epidemiology and clinical presentations of amyloidosis. The main body of this chapter will concentrate on treatment options, both FDA-approved and experimental, specific to the various forms of amyloidosis. Since this set of diseases can affect multiple organ systems, we tackle the therapeutic avenues and the current challenges in each system under clinical investigation, including neurological, psychiatric, gastrointestinal, cardiovascular, endocrine, renal and hematologic, in addition to options for palliative treatment for severe symptom management and improved quality of life. Several recent groundbreaking discoveries have opened up the potential for successful treatment of peripheral and central neurological amyloidoses making this an exciting and evolving field

Integrin-linked kinase is required for laminin-2–induced oligodendrocyte cell spreading and CNS myelination

Early steps in myelination in the central nervous system (CNS) include a specialized and extreme form of cell spreading in which oligodendrocytes extend large lamellae that spiral around axons to form myelin. Recent studies have demonstrated that laminin-2 (LN-2; α2β1γ1) stimulates oligodendrocytes to extend elaborate membrane sheets in vitro (cell spreading), mediated by integrin α6β1. Although a congenital LN-2 deficiency in humans is associated with CNS white matter changes, LN-2–deficient (dy/dy) mice have shown abnormalities primarily within the peripheral nervous system. Here, we demonstrate a critical role for LN-2 in CNS myelination by showing that dy/dy mice have quantitative and morphologic defects in CNS myelin. We have defined the molecular pathway through which LN-2 signals oligodendrocyte cell spreading by demonstrating requirements for phosphoinositide 3-kinase activity and integrin-linked kinase (ILK). Interaction of oligodendrocytes with LN-2 stimulates ILK activity. A dominant negative ILK inhibits LN-2–induced myelinlike membrane formation. A critical component of the myelination signaling cascade includes LN-2 and integrin signals through ILK

Risk algorithm using serial biomarker measurements doubles the number of screen-detected cancers compared with a single-threshold rule in the United Kingdom collaborative trial of ovarian cancer screening

PURPOSE: Cancer screening strategies have commonly adopted single-biomarker thresholds to identify abnormality. We investigated the impact of serial biomarker change interpreted through a risk algorithm on cancer detection rates. PATIENTS AND METHODS: In the United Kingdom Collaborative Trial of Ovarian Cancer Screening, 46,237 women, age 50 years or older underwent incidence screening by using the multimodal strategy (MMS) in which annual serum cancer antigen 125 (CA-125) was interpreted with the risk of ovarian cancer algorithm (ROCA). Women were triaged by the ROCA: normal risk, returned to annual screening; intermediate risk, repeat CA-125; and elevated risk, repeat CA-125 and transvaginal ultrasound. Women with persistently increased risk were clinically evaluated. All participants were followed through national cancer and/or death registries. Performance characteristics of a single-threshold rule and the ROCA were compared by using receiver operating characteristic curves. RESULTS: After 296,911 women-years of annual incidence screening, 640 women underwent surgery. Of those, 133 had primary invasive epithelial ovarian or tubal cancers (iEOCs). In all, 22 interval iEOCs occurred within 1 year of screening, of which one was detected by ROCA but was managed conservatively after clinical assessment. The sensitivity and specificity of MMS for detection of iEOCs were 85.8% (95% CI, 79.3% to 90.9%) and 99.8% (95% CI, 99.8% to 99.8%), respectively, with 4.8 surgeries per iEOC. ROCA alone detected 87.1% (135 of 155) of the iEOCs. Using fixed CA-125 cutoffs at the last annual screen of more than 35, more than 30, and more than 22 U/mL would have identified 41.3% (64 of 155), 48.4% (75 of 155), and 66.5% (103 of 155), respectively. The area under the curve for ROCA (0.915) was significantly (P = .0027) higher than that for a single-threshold rule (0.869). CONCLUSION: Screening by using ROCA doubled the number of screen-detected iEOCs compared with a fixed cutoff. In the context of cancer screening, reliance on predefined single-threshold rules may result in biomarkers of value being discarded

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

A Hybrid Magneto-Optic Capacitive Memory with Picosecond Writing Time

The long-term future of information storage requires the use of sustainable nanomaterials in architectures operating at high frequencies. Interfaces can play a key role in this pursuit via emergent functionalities that break out from conventional operation methods. Here, spin-filtering effects and photocurrents are combined at metal-molecular-oxide junctions in a hybrid magneto-capacitive memory. Light exposure of metal-fullerene-metal oxide devices results in spin-polarized charge trapping and the formation of a magnetic interface. Because the magnetism is generated by a photocurrent, the writing time is determined by exciton formation and splitting, electron hopping, and spin-dependent trapping. Transient absorption spectroscopy measurements show changes in the electronic states as a function of the magnetic history of the device within picoseconds of the optical pumping. The stored information is read using time-resolved scanning magneto optic Kerr effect measurements during microwave irradiation. The emergence of a magnetic interface in the picosecond timescale opens new paths of research to design hybrid magneto-optic structures operating at high frequencies for sensing, computing, and information storage

Percent atheroma volume: Optimal variable to report whole-heart atherosclerotic plaque burden with coronary CTA, the PARADIGM study.

BACKGROUND AND AIMS:Different methodologies to report whole-heart atherosclerotic plaque on coronary computed tomography angiography (CCTA) have been utilized. We examined which of the three commonly used plaque burden definitions was least affected by differences in body surface area (BSA) and sex. METHODS:The PARADIGM study includes symptomatic patients with suspected coronary atherosclerosis who underwent serial CCTA >2 years apart. Coronary lumen, vessel, and plaque were quantified from the coronary tree on a 0.5 mm cross-sectional basis by a core-lab, and summed to per-patient. Three quantitative methods of plaque burden were employed: (1) total plaque volume (PV) in mm3, (2) percent atheroma volume (PAV) in % [which equaled: PV/vessel volume * 100%], and (3) normalized total atheroma volume (TAVnorm) in mm3 [which equaled: PV/vessel length * mean population vessel length]. Only data from the baseline CCTA were used. PV, PAV, and TAVnorm were compared between patients in the top quartile of BSA vs the remaining, and between sexes. Associations between vessel volume, BSA, and the three plaque burden methodologies were assessed. RESULTS:The study population comprised 1479 patients (age 60.7 ± 9.3 years, 58.4% male) who underwent CCTA. A total of 17,649 coronary artery segments were evaluated with a median of 12 (IQR 11-13) segments per-patient (from a 16-segment coronary tree). Patients with a large BSA (top quartile), compared with the remaining patients, had a larger PV and TAVnorm, but similar PAV. The relation between larger BSA and larger absolute plaque volume (PV and TAVnorm) was mediated by the coronary vessel volume. Independent from the atherosclerotic cardiovascular disease risk (ASCVD) score, vessel volume correlated with PV (P < 0.001), and TAVnorm (P = 0.003), but not with PAV (P = 0.201). The three plaque burden methods were equally affected by sex. CONCLUSIONS:PAV was less affected by patient's body surface area then PV and TAVnorm and may be the preferred method to report coronary atherosclerotic burden