Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasma Phospholipid Fatty Acids and Coronary Heart Disease Risk: A Matched Case-Control Study within the Women’s Health Initiative Observational Study

Background and Aims: The association of fatty acids with coronary heart disease (CHD) has been examined, mainly through dietary measurements, and has generated inconsistent results due to measurement error. Large observational studies and randomized controlled trials have shown that plasma phospholipid fatty acids (PL-FA), especially those less likely to be endogenously synthesized, are good biomarkers of dietary fatty acids. Thus, PL-FA profiles may better predict CHD risk with less measurement error. Methods: We performed a matched case-control study of 2428 postmenopausal women nested in the Women’s Health Initiative Observational Study. Plasma PL-FA were measured using gas chromatography and expressed as molar percentage (moL %). Multivariable conditional logistic regression was used to calculate odds ratios (95% CIs) for CHD associated with 1 moL % change in PL-FA. Results: Higher plasma PL long-chain saturated fatty acids (SFA) were associated with increased CHD risk, while higher n-3 polyunsaturated fatty acids (PUFA) were associated with decreased risk. No significant associations were observed for very-long-chain SFA, monounsaturated fatty acids (MUFA), PUFA n-6 or trans fatty acids (TFA). Substituting 1 moL % PUFA n-6 or TFA with an equivalent proportion of PUFA n-3 were associated with lower CHD risk. Conclusions: Higher plasma PL long-chain SFA and lower PUFA n-3 were associated with increased CHD risk. A change in diet by limiting foods that are associated with plasma PL long-chain SFA and TFA while enhancing foods high in PUFA n-3 may be beneficial in CHD among postmenopausal women.
Plunging Ranulas and Prevalence of the “Tail Sign” in 126 Consecutive Cases

Plunging (or diving) ranulas are extravasation pseudocysts arising from the sublingual gland that present as soft submandibular swelling. The “tail sign” has been widely reported as pathognomonic for their diagnosis. It is described as a smooth tapering comet‐shaped unilocular fluid mass with its “tail” in the collapsed sublingual space (SLS) and its “head” in the posterior submandibular space. This sign is based on the premise that extravasated saliva from the sublingual gland in the SLS escapes and plunges, over the posterior edge of the mylohyoid muscle into the submandibular space. Therefore, some fluid must be present in the posterior SLS in almost all patients with plunging ranulas. This study aimed to determine the frequency of fluid seen with ultrasound (US) in the posterior SLS to corroborate the tail sign.
Rorβ regulates selective axon-target innervation in the mammalian midbrain

ABSTRACT Developmental control of long-range neuronal connections in the mammalian midbrain remains unclear. We explored the mechanisms regulating target selection of the developing superior colliculus (SC). The SC is a midbrain center that directs orienting behaviors and defense responses. We discovered that a transcription factor, Rorβ, controls establishment of axonal projections from the SC to two thalamic nuclei: the dorsal lateral geniculate nucleus (dLGN) and the lateral posterior nucleus (LP). A genetic strategy used to visualize SC circuits revealed that in control animals Rorβ+ neurons abundantly innervate the dLGN but barely innervate the LP. The opposite phenotype was observed in global and conditional Rorb mutants: projections to the dLGN were strongly decreased, and projections to the LP were increased. Furthermore, overexpression of Rorb in the wild type showed increased projections to the dLGN and decreased projections to the LP. In summary, we identified Rorβ as a key developmental mediator of colliculo-thalamic innervation. Such regulation could represent a general mechanism orchestrating long-range neuronal connections in the mammalian brain. Summary: A novel genetic strategy allowing the tracing of axonal projections in mouse brain identifies Rorβ as a key factor regulating axonal targeting to distinct subcortical areas.
Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference

Model-free tracking is a widely accepted approach to track an arbitrary object in a video using a single frame annotation with no further prior knowledge about the object of interest. Extending this problem to track multiple objects is really challenging because: 1) the tracker is not aware of the objects’ type while trying to distinguish them from background (detection task) and 2) the tracker needs to distinguish one object from other potentially similar objects (data association task) to generate stable trajectories. In order to track multiple arbitrary objects, most existing model-free tracking approaches rely on tracking each target individually by updating their appearance model independently. Therefore, in this scenario they often fail to perform well due to confusion between the appearance of similar objects, their sudden appearance changes and occlusion. To tackle this problem, we propose to use both appearance and motion models, and to learn those jointly using graphical models and the deep neural networks features. We introduce an indicator variable to predict sudden appearance change and/or occlusion. When these happen, our model does not update the appearance model thus avoiding using the background and/or incorrect object to update the appearance of the object of interest mistakenly, and relies on our motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all targets simultaneously as a graphical model inference problem. We learn the joint parameters for both appearance model and motion model in an online fashion under the framework of LaRank. Experiment results show that our method achieved superior performance compared to the competitive methods.
Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.
High-Performance Nucleic Acid Sensors for Liquid Biopsy Applications.

Liquid biopsy has the potential to replace invasive, painful and costly tissue biopsies in cancer diagnosis and management. Circulating tumour nucleic acids (ctNAs) are released from tumours cells and can be detected in blood samples, providing a way to track tumors without requiring a tissue sample. The detection of mutant ctNA sequences linked to cancer, however, requires a very sensitive and speciﬁc approach to detect relatively low amounts of mutant sequences, because they are masked by the presence of high levels of wild-type sequences in patient samples. This review discusses a range of high-performance nucleic acid biosensors capable of ctNA analysis in patient samples. We compare sequencing- and amplification-based methods to next-generation sensors for ctDNA and ctRNA (including miRNA) profiling, such as electrochemical methods, surface plasmon resonance, Raman spectroscopy, and microfluidics and dielectrophoresis-based assays. We further present an overview of the analytical sensitivity and accuracy of these methods as well as biological and technical challenges they present.
Rebuilding global fisheries under uncertainty

Significance Many fisheries that have been historically overexploited are now considered to be rebuilding, with a hope that current best practices could ensure the recovery of most overfished species by mid century. Our analysis suggests this optimism may be premature, as current projections typically assume managers can have perfect measurements of current stock sizes. We demonstrate how such an assumption can undermine rebuilding efforts under current best practices and even drive unintentional stock declines. By borrowing novel decision methods from the field of robotics, we also show how stock rebuilding can be achieved in the face of measurement and environmental uncertainty, while also achieving higher economic returns than expected under current approaches. Current and future prospects for successfully rebuilding global fisheries remain debated due to uncertain stock status, variable management success, and disruptive environmental change. While scientists routinely account for some of this uncertainty in population models, the mechanisms by which this translates into decision-making and policy are problematic and can lead to unintentional overexploitation. Here, we explicitly track the role of measurement uncertainty and environmental variation in the decision-making process for setting catch quotas. Analyzing 109 well-sampled stocks from all oceans, we show that current practices may attain 55% recovery on average, while richer decision methods borrowed from robotics yield 85% recovery of global stocks by midcentury, higher economic returns, and greater robustness to environmental surprises. These results challenge the consensus that global fisheries can be rebuilt by existing approaches alone, while also underscoring that rebuilding stocks may still be achieved by improved decision-making tools that optimally manage this uncertainty.
Total Thrombus-Formation Analysis System (T-TAS): Clinical Application of Quantitative Analysis of Thrombus Formation in Cardiovascular Disease

Abstract Various antithrombotic agents are clinically used to inhibit the cascade of arterial or venous thrombosis in cardiovascular diseases. Dual antiplatelet therapy with aspirin and P2Y12 inhibitors is prescribed in patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI). Direct oral anticoagulants (DOACs) are widely used for the prevention or treatment of thromboembolism in patients with atrial fibrillation (AF) and venous thromboembolism. However, there has been no definitive tool to simultaneously monitor the antithrombotic effects of these drugs. The Total Thrombus-Formation Analysis System (T-TAS), a microchip-based flow chamber system that mimics in vivo conditions for evaluating whole blood thrombogenicity, was developed for the quantitative analysis of thrombus formation in whole blood specimens. The utility of T-TAS has been evaluated in CAD patients treated with antiplatelet therapies. The T-TAS PL chip area under the flow pressure curve (AUC) accurately assesses primary hemostasis and is sensitive to the therapeutic effects of various antiplatelet therapies. In addition, low AUC results are a significant predictor of periprocedural bleeding events in CAD patients undergoing PCI. The T-TAS AR chip AUC result is useful for assessing the efficacy of DOACs and warfarin in AF patients undergoing catheter ablation, and it is also a potential independent predictor of periprocedural bleeding events and avoidance of thrombosis in patients having undergone total knee arthroplasty. In conclusion, T-TAS is a useful index for evaluating the total antithrombotic effects of combination antithrombotic agents in patients with various cardiovascular diseases.
Exploiting the unwanted: sulphate reduction enables phosphate recovery from energy-rich sludge during anaerobic digestion

Anaerobic digestion is shifting from a single-purpose technology for renewable energy recovery from organic waste streams to a process for integrated resource recovery. The valorisation of high-rate energy- and phosphorus-rich sludge creates the opportunity for their combined recovery. This phosphate is present in a precipitated form in the sludge, and its release into the liquid phase is an important issue before recovery can be achieved. The objective of this research was to exploit the “unwanted” sulphate reduction process for the release of phosphate into the liquid phase during anaerobic digestion, thus, making it available for recovery. Two different treatments were considered, i.e., a control digester and a digester to which sulphate was added, each operated in triplicate for a period of 119 days. The control digester showed stable methane production at 628 ± 103 mL CH4 L−1 d−1, with a feedstock COD (chemical oxygen demand) conversion efficiency of 89.5 ± 14.6 %. In contrast, the digester with sulphate addition showed a 29.9 ± 15.3 % decrease in methane production, reaching an “inhibited steady state”, but phosphate release into the liquid phase increased with a factor 4.5, compared to the control digester. This inhibited steady state coincided with a clear shift from a Methanosaetaceae to a Methanosarcinaceae dominated methanogenic community. Overall, the sulphate reduction process allows phosphate release during the anaerobic digestion process, yet, at the cost of a reduced methane production rate.
Instruments measuring interprofessional collaboration in healthcare – a scoping review

ABSTRACT Worldwide there is growing understanding of the importance of interprofessional collaboration in providing well-functioning healthcare. However, little is known about how interprofessional collaboration can be measured between different health-care professionals. In this review, we aim to fill this gap, by identifying and analyzing the existing instruments measuring interprofessional collaboration in healthcare. A scoping review design was applied. A systematic literature search of two electronic databases, Medline (PubMed) and CINAHL, was conducted in 03/2018. The search yielded 1020 studies, of which 35 were selected for the review. The data were analyzed by content analysis. In total, 29 instruments measuring interprofessional collaboration were found. Interprofessional collaboration was measured predominantly between nurses and physicians with different instruments in various health-care settings. Psychometric testing was unsystematic, focusing predominantly on construct and content validity and internal consistency, thus further validation studies with comprehensive testing are suggested. The results of this review can be used to select instruments measuring interprofessional collaboration in practice or research. Future research is needed to strengthen the evidence of reliability and validity of these instruments.
Automated illustration of news stories Improving the readers experience

Forgetting what one has just read is, in some cases, linked to insufficient attention. The reader might feel either bored or distracted by something more interesting - a common trace in children and the elderly. The challenge is: how can multimedia systems assist readers in reading and remembering stories? Several studies (2, 7, 14) showed that reading memory is improved by visual stimulus. In this paper we formulate the hypothesis that an automated multimedia system can help users in reading a story by stimulating their reading memory with adequate visual illustrations. These illustrations are intended to increase the readers' attention towards the story and to help them recalling the story. The framework automatically creates a multimedia presentation of the news story by (1) rendering the news text in a sentence-by-sentence fashion, (2) providing mechanisms to select the best illustration for each sentence and (3) select the set of illustrations that guarantees the best sequence of illustrations. Users may also activate a text-to-speech functionality according to their preference or reading difficulties. Experiments used Flickr images to illustrate BBC news articles: a user survey with 23 users assessed positively the effectiveness of the system.
Electrical and Label-Free Quantification of Exosomes with Reduced Graphene Oxide Field Effect Transistor Biosensor.

Exosomes are small membrane-bound nanovesicles with a size of 50-150 nm, which contain many functional biomolecules, such as nucleic acids and proteins. Due to their high homology with parental generation, they are of great significance in clinical diagnosis. At present, the quantitative detection of low-concentration of cancer-derived exosomes present in biofluids is still a great challenge. In this study, we develop an electrical and label-free method to directly detect exosomes with high sensitivity based on reduced graphene oxide (RGO) field effect transistor (FET) biosensor. The RGO FET biosensor modified with specific antibody CD63 in the sensing area was fabricated, which was used for electrical and label-free quantification of exosomes. The method achieved a low limit of detection down to 33 particles/µL, which was lower than that of many other available methods. In addition, the FET biosensor was employed to detect exosomes in clinical serum samples, showing significant differences in detecting healthy people and prostate cancer (PC) patients. Different from other technologies, this study provides a unique technology capable of directly quantifying exosomes without labeling, indicating its potential as a tool for early diagnosis of cancer.
Unconstrained Face Recognition Quality: A Review

This document signifies the overview of the Unconstrained Face Identification Quality within an unconstrained face-recognition atmosphere. It offers the functions like imbalance, lighting, and phrase variance strategy. It may be employed for both confirmation and id. At the moment, there are lots of means of front watch face-recognition. Couples of years ago, for computer-vision, numerous face-recognition methods were prepared. But, real-globe face-detection quiet deposit a challenging work. The interest in unconstrained useful face-recognition is growing using the detonation of online media for example neighborhood systems, and video-surveillance video wherever encounter evaluation is of great significance. Within this evaluation, we show up to manage reputation within the situation of graph presumption. We are able to identify a mystical encounter utilizing a Diverse Method. This research lights-out the options recommended for unconstrained face recognition quality region and recommending the clear answer to become used by RFG (Reference Face Graph) based face-recognition. Our purpose is RFG centered unconstrained face-recognition to improve the demonstration quality. The Formula and Simulator of recommended sensing methods is likely to be completed by utilizing Mat-laboratory.
HaptiQ: A Haptic Device for Graph Exploration by People with Visual Disabilities

{ PhD Project (Ongoing): ’Managing Shared Immutable Data in a Sea of Stuff’ The focus of my PhD is to develop data and meta-data models for globally large distributed data storage systems. I have currently developed a first theoretical model and I am in the process of building a first prototype { BSc Dissertation ’HaptiQ: A Haptic Device for Graph Exploration by People with Visual Disabilities’ The HaptiQ is the first vector-based display with haptic-audio feedback for people with visual disabilities. The project is open-source and has won the the Lockheed Martin Award for Software Engineer 2014 Scotland (given to the project that best demonstrates excellent software engineering skills)
A Sensitive Carbon Monoxide Sensor Based on Photoacoustic Spectroscopy with a 2.3 μm Mid-Infrared High-Power Laser and Enhanced Gas Absorption

A photoacoustic spectroscopy (PAS)-based carbon monoxide (CO) gas sensor with a high-power laser and an enhanced gas absorption was demonstrated. The light source was a distributed feedback (DFB), continuous wave (CW) diode laser with a high output power of ~8 mW to give a strong excitation. The target gas received optical absorption enhanced two times by using a right-angle prism reflecting the laser beam. In order to reduce the noise from the background, wavelength modulation spectroscopy (WMS) and second-harmonic detection techniques were used. The modulation frequency and modulation depth were optimized theoretically and experimentally. Water vapor was added in the PAS sensor system to increase the vibrational–translational (V–T) relaxation rate of the CO molecule, which resulted in an ~8 times signal enhancement compared with the using of a dry CO/N2 gas mixture. The amplitude of the 2f signal had a 1.52-fold improvement compared to the one with only one time absorption. The experimental results showed that such a sensor had an excellent linear response to the optical power and gas concentration. At 1 s integration time, a minimum detection limit (MDL) for CO detection of 9.8 ppm was achieved. The long-term stability of the sensor system was evaluated with an Allan deviation analysis. When the integration time was 1100 s, the MDL improved to be 530 ppb. The detection performance of such a PAS-based CO sensor can be further improved when a laser with a higher output power and increasing optical absorption times is used.
Hyperspectral Images Denoising via Nonconvex Regularized Low-Rank and Sparse Matrix Decomposition

Hyperspectral images (HSIs) are often degraded by a mixture of various types of noise during the imaging process, including Gaussian noise, impulse noise, and stripes. Such complex noise could plague the subsequent HSIs processing. Generally, most HSI denoising methods formulate sparsity optimization problems with convex norm constraints, which over-penalize large entries of vectors, and may result in a biased solution. In this paper, a nonconvex regularized low-rank and sparse matrix decomposition (NonRLRS) method is proposed for HSI denoising, which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. The NonRLRS aims to decompose the degraded HSI, expressed in a matrix form, into low-rank and sparse components with a robust formulation. To enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions, a novel nonconvex regularizer named as normalized $\varepsilon $ -penalty, is presented, which can adaptively shrink each entry. In addition, an effective algorithm based on the majorization minimization (MM) is developed to solve the resulting nonconvex optimization problem. Specifically, the MM algorithm first substitutes the nonconvex objective function with the surrogate upper-bound in each iteration, and then minimizes the constructed surrogate function, which enables the nonconvex problem to be solved in the framework of reweighted technique. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method.
Why not to (over)emphasize statistical significance.

P-values should not merely be used to categorize results into significant and non-significant. This practice disregards clinical relevance, confounds non-significance with no effect and underestimates the likelihood of false-positive results. Better than to use the p-value as a dichotomizing instrument, the p-values and the confidence intervals around effect estimates can be used to put research findings in a context, thereby taking clinical relevance but also uncertainty genuinely into account.