3,253 research outputs found
Spontaneous spatial fractals: universal contexts and applications
We report on our latest research in the field of spontaneous spatial fractal patterns. New analyses, results and potential applications are reported for nonlinear ring cavities and kaleidoscope laser systems
FKBPL is associated with metabolic parameters and is a novel determinant of cardiovascular disease.
Type 2 diabetes (T2D) is associated with increased risk of cardiovascular disease (CVD). As disturbed angiogenesis and endothelial dysfunction are strongly implicated in T2D and CVD, we aimed to investigate the association between a novel anti-angiogenic protein, FK506-binding protein like (FKBPL), and these diseases. Plasma FKBPL was quantified by ELISA cross-sectionally in 353 adults, consisting of 234 T2D and 119 non-diabetic subjects with/without CVD, matched for age, BMI and gender. FKBPL levels were higher in T2D (adjusted mean: 2.03 ng/ml ± 0.90 SD) vs. non-diabetic subjects (adjusted mean: 1.79 ng/ml ± 0.89 SD, p = 0.02), but only after adjustment for CVD status. In T2D, FKBPL was negatively correlated with fasting blood glucose, HbA1c and diastolic blood pressure (DBP), and positively correlated with age, known diabetes duration, waist/hip ratio, urinary albumin/creatinine ratio (ACR) and fasting C-peptide. FKBPL plasma concentrations were increased in the presence of CVD, but only in the non-diabetic group (CVD: 2.02 ng/ml ± 0.75 SD vs. no CVD: 1.68 ng/ml ± 0.79 SD, p = 0.02). In non-diabetic subjects, FKBPL was positively correlated with an established biomarker for CVD, B-type Natriuretic Peptide (BNP), and echocardiographic parameters of diastolic dysfunction. FKBPL was a determinant of CVD in the non-diabetic group in addition to age, gender, total-cholesterol and systolic blood pressure (SBP). FKBPL may be a useful anti-angiogenic biomarker in CVD in the absence of diabetes and could represent a novel CVD mechanism
Measurement of 222Rn dissolved in water at the Sudbury Neutrino Observatory
The technique used at the Sudbury Neutrino Observatory (SNO) to measure the
concentration of 222Rn in water is described. Water from the SNO detector is
passed through a vacuum degasser (in the light water system) or a membrane
contact degasser (in the heavy water system) where dissolved gases, including
radon, are liberated. The degasser is connected to a vacuum system which
collects the radon on a cold trap and removes most other gases, such as water
vapor and nitrogen. After roughly 0.5 tonnes of H2O or 6 tonnes of D2O have
been sampled, the accumulated radon is transferred to a Lucas cell. The cell is
mounted on a photomultiplier tube which detects the alpha particles from the
decay of 222Rn and its daughters. The overall degassing and concentration
efficiency is about 38% and the single-alpha counting efficiency is
approximately 75%. The sensitivity of the radon assay system for D2O is
equivalent to ~3 E(-15) g U/g water. The radon concentration in both the H2O
and D2O is sufficiently low that the rate of background events from U-chain
elements is a small fraction of the interaction rate of solar neutrinos by the
neutral current reaction.Comment: 14 pages, 6 figures; v2 has very minor change
A neurogenetic model for the study of schizophrenia spectrum disorders: The International 22q11.2 Deletion Syndrome Brain Behavior Consortium
Rare copy number variants contribute significantly to the risk for schizophrenia, with the
22q11.2 locus consistently implicated. Individuals with the 22q11.2 deletion syndrome
(22q11DS) have an estimated 25-fold increased risk for schizophrenia spectrum disorders,
compared to individuals in the general population. The International 22q11DS Brain Behavior
Consortium is examining this highly informative neurogenetic syndrome phenotypically and
genomically. Here we detail the procedures of the effort to characterize the neuropsychiatric and
neurobehavioral phenotypes associated with 22q11DS, focusing on schizophrenia and
subthreshold expression of psychosis. The genomic approach includes a combination of whole
genome sequencing and genome-wide microarray technologies, allowing the investigation of all
possible DNA variation and gene pathways influencing the schizophrenia-relevant phenotypic
expression. A phenotypically rich data set provides a psychiatrically well-characterized sample
of unprecedented size (n=1,616) that informs the neurobehavioral developmental course of
22q11DS. This combined set of phenotypic and genomic data will enable hypothesis testing to
elucidate the mechanisms underlying the pathogenesis of schizophrenia spectrum disorders
3-D Ultrastructure of O. tauri: Electron Cryotomography of an Entire Eukaryotic Cell
The hallmark of eukaryotic cells is their segregation of key biological functions into discrete, membrane-bound organelles. Creating accurate models of their ultrastructural complexity has been difficult in part because of the limited resolution of light microscopy and the artifact-prone nature of conventional electron microscopy. Here we explored the potential of the emerging technology electron cryotomography to produce three-dimensional images of an entire eukaryotic cell in a near-native state. Ostreococcus tauri was chosen as the specimen because as a unicellular picoplankton with just one copy of each organelle, it is the smallest known eukaryote and was therefore likely to yield the highest resolution images. Whole cells were imaged at various stages of the cell cycle, yielding 3-D reconstructions of complete chloroplasts, mitochondria, endoplasmic reticula, Golgi bodies, peroxisomes, microtubules, and putative ribosome distributions in-situ. Surprisingly, the nucleus was seen to open long before mitosis, and while one microtubule (or two in some predivisional cells) was consistently present, no mitotic spindle was ever observed, prompting speculation that a single microtubule might be sufficient to segregate multiple chromosomes
DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vision is the richest and most cost-effective technology for Driver
Monitoring Systems (DMS), especially after the recent success of Deep Learning
(DL) methods. The lack of sufficiently large and comprehensive datasets is
currently a bottleneck for the progress of DMS development, crucial for the
transition of automated driving from SAE Level-2 to SAE Level-3. In this paper,
we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which
includes real and simulated driving scenarios: distraction, gaze allocation,
drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth
and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A
comparison with existing similar datasets is included, which shows the DMD is
more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated
by extracting a subset of it, the dBehaviourMD dataset, containing 13
distraction activities, prepared to be used in DL training processes.
Furthermore, we propose a robust and real-time driver behaviour recognition
system targeting a real-world application that can run on cost-efficient
CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated
with different types of fusion strategies, which all reach enhanced accuracy
still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and
Robotic
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