Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention.

BACKGROUND Plasma lipidomic measures may enable improved prediction of cardiovascular outcomes in secondary prevention. The aim of this study is to determine the association of plasma lipidomic measurements with cardiovascular events and assess their potential to predict such events. METHODS Plasma lipids (n = 342) were measured in a retrospective subcohort (n = 5,991) of the LIPID study. Proportional hazards regression was used to identify lipids associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) and cardiovascular death. Multivariable models adding lipid species to traditional risk factors were created using lipid ranking established from the Akaike information criterion within a 5-fold cross-validation framework. The results were tested on a diabetic case cohort from the ADVANCE study (n = 3,779). RESULTS Specific ceramide species, sphingolipids, phospholipids, and neutral lipids containing omega-6 fatty acids or odd-chain fatty acids were associated with future cardiovascular events (106 species) and cardiovascular death (139 species). The addition of 7 lipid species to a base model (11 conventional risk factors) resulted in an increase in the C-statistics from 0.629 (95% CI, 0.628-0.630) to 0.654 (95% CI, 0.653-0.656) for prediction of cardiovascular events and from 0.673 (95% CI, 0.671-0.675) to 0.727 (95% CI, 0.725-0.728) for prediction of cardiovascular death. Categorical net reclassification improvements for cardiovascular events and cardiovascular death were 0.083 (95% CI, 0.081-0.086) and 0.166 (95% CI, 0.162-0.170), respectively. Evaluation on the ADVANCE case cohort demonstrated significant improvement on the base models. CONCLUSIONS The improvement in the prediction of cardiovascular outcomes, above conventional risk factors, demonstrates the potential of plasma lipidomic profiles as biomarkers for cardiovascular risk stratification in secondary prevention. FUNDING Bristol-Myers Squibb, the National Health and Medical Research Council of Australia (grants 211086, 358395, and 1029754), and the Operational Infrastructure Support Program of the Victorian government of Australia.
Addressing the gender-knowledge gap in glucose-6-phosphate dehydrogenase deficiency: challenges and opportunities

Abstract Glucose-6-phosphate dehyrdgoenase (G6PD) deficiency is a common X-linked genetic trait, with an associated enzyme phenotype, whereby males are either G6PD deficient or normal, but females exhibit a broader range of G6PD deficiencies, ranging from severe deficiency to normal. Heterozygous females typically have intermediate G6PD activity. G6PD deficiency has implications for the safe treatment for Plasmodium vivax malaria. Individuals with this deficiency are at greater risk of serious adverse events following treatment with the only curative class of anti-malarials, 8-aminoquinolines, such as primaquine. Quantitative diagnostic tests for G6PD deficiency are complex and require sophisticated laboratories. The commonly used qualitative tests, do not discriminate intermediate G6PD activities. This has resulted in poor understanding of the epidemiology of G6PD activity in females and its corresponding treatment ramifications. New simple-to-use quantitative tests, and a momentum to eliminate malaria, create an opportunity to address this knowledge gap. While this will require additional resources for clinical studies, adequate operational research, and appropriate pharmacovigilance, the health benefits from this investment go beyond the immediate intervention for which the G6PD status is first diagnosed.
A Compact Ultra Wide Stopband Filter Based on Quarter-Mode Substrate Integrated Waveguide

In this paper, a compact quarter-mode substrate integrated waveguide bandpass filter with extremely wide stopband based on a novel integrated face-to-face complementary split ring resonators (CSRRs) is studied and developed. A combination of complementary split ring resonator and quarter-mode substrate integrated waveguide (QMSIW) is used in this development. Equivalent circuit model simulations from ADS Momentum are in good agreement with HFSS electromagnetic analysis for the QMSIW filter unit, demonstrating the feasibility of QMSIW resonators. The center frequency of the designed filter is 1.54GHz with return loss better than 19dB and insertion loss less than 0.6dB. Its first spurious appears at about 16.8GHz (about 10.9 times the central frequency) with high rejection level of 20dB, providing an ultra wide out-of-band rejection performance. The QMSIW filter was fabricated and the measured results agree very well with simulated ones.
A 5-GHz Low-Power Low-Noise Integer-N Digital Subsampling PLL With SAR ADC PD

In this paper, we present a low-power low-noise integer-N divider-less digital phase-locked loop (PLL) with high resolution. Phase detection is performed by a proposed analog-to-digital converter (ADC)-based time-to-digital converter composed of subsampling, charge pump (CP), time-domain variable-gain amplifier, and successive-approximation register (SAR) ADCs. Subsampling is well known for its high detection gain. The CP and the pulse generating circuit are also introduced to form the time-domain integral variable-gain amplifier, enhancing the resolution. An SAR ADC voltage signals into the digital domain, avoiding the use of analog filter which occupies large area. Moreover, compared to the conventional analog phase detectors, the SAR-ADC phase detector saves more area and power consumption. The novel PLL is implemented in a standard 65-nm CMOS process, occupying an area of 0.12 mm2. It presents an in-band phase noise of −108 dBc/Hz and an rms jitter of 357 fs at the operating frequency of 5 GHz. In addition, the proposed ADC-PLL achieves a competitively good figure of merit of 243 dB with a power consumption of only 3.9 mW.
Distributed Deep Reinforcement Learning using TensorFlow

Deep Reinforcement Learning is the combination of Reinforcement Learning algorithms with Deep neural network, which had recent success in learning complicated unknown environments. The trained model is a Convolutional Neural Network trained using Q-Learning Loss value. The agent takes in observation, i.e. raw pixel image and reward from the environment for each step as input. The deep Q-learning algorithm gives out the optimal action for every observation and reward pair. The hyperparameters of Deep Q-Network remain unchanged for any environment. TensorFIow, an open source machine learning and numerical computation library is used to implement the deep Q-Learning algorithm on GPU. The distributed TensorFIow architecture is used to maximize the hardware resource utilization and reduce the training time. The usage of Graphics Processing Unit (GPU) in the distributed environment accelerated the training of deep Q-network. On implementing the deep Q-learning algorithm for many environments from OpenAI Gym, the agent outperforms a decent human reference player with few days of training.
High-Level Information Fusion of Cyber-Security Expert Knowledge and Experimental Data

High-Level Information Fusion (HLIF) provides the ability to combine data from diverse sources, including documents involving analyst assessment and raw sensor reports generated by sensors, in a coherent and consistent way. Command and Control (C2) in cyber infrastructure involves gathering information from experts, merging it with field knowledge and experimental results, and selected the most appropriate cyber assets to deploy at any given time in the mission cycle. When framing cyber asset selection as a HLIF problem, one key aspect involves estimation of network-wide impacts generated by cyber assets. Cyberspace is a highly dynamic man-made domain with a high degree of uncertainty and incomplete data which must be transformed into knowledge to support precise and predictable cyber effects estimation. Current systems have to rely on human subject matter experts (SMEs) for most tasks, rendering the cyber asset planning process too time consuming and therefore operationally ineffective. This paper proposes an architecture that leverages probabilistic ontologies to expedite the cyber asset planning process, allowing for the automation of most time-consuming, error-prone, SME-based knowledge elicitation under uncertainty. We illustrate the main aspects of the proposed architecture through examples taken from the Derived and Integrated Cyber Assets (DICE) project.
Are My Arguments Trustworthy? Abstract Argumentation with Subjective Logic

An Abstract Argumentation Framework (AAF) is an abstract structure consisting of a set arguments, whose origin, nature, and possible internal organisation is not specified, and by a binary relation of attack on the set of arguments, whose meaning is not specified either. Subjective logic provides a standard set of logical operators, intended for use in domains containing uncertainty. In this paper, we define an extension of AAFs in which each argument and attacks is evaluated with an opinion, by revisiting the constellations approach developed for probabilistic AAFs. In this way, different agents can merge their opinions on how much arguments and attacks are “trustworthy”, e.g., they do not represent fallacies or enthymemes. Finally, subjective logic operators can be used to fuse the belief of different possible worlds (i.e., a constellation of sub-graphs in the original AAF) containing different arguments and attacks.
Analysis of Mesenchymal Stromal Cell Engraftment After Allogeneic HSCT in Pediatric Patients: A Large Multicenter Study

The mesenchymal stem cell (MSC) role after allogeneic hematopoietic stem cell transplantation (HSCT) is still a matter of debate; in particular, MSC engraftment in recipient bone marrow (BM) is unclear. A total of 46 patients were analyzed for MSC and hemopoietic stem cell engraftment after HSCT. The majority of patients had the BM as the stem cell source, and acute leukemia was the main indication for HSCT. Mesenchymal and hematopoietic stem cell chimerism analysis was carried out through specific polymorphic tandemly repeated regions. All patients reached complete donor engraftment; no evidence of donor-derived MSC engraftment was noted. Our data indicate that MSCs after HSCT remain of recipient origin despite the following: (i) myeloablative conditioning; (ii) the stem cell source; (iii) the interval from HSCT to BM analysis; (iv) the underlying disease before HSCT; and (v) the patients’ or the donors’ age at HSCT.
A Lumped RF Model for Nanoscale Memristive Devices and Nonvolatile Single-Pole Double-Throw Switches

In this paper, a scalable lumped model that accurately predicts the steady-state high-frequency behavior of nanoscale RF memristive devices is presented. The model is described 1) analytically by a set of closed-form equations that determine the parameters based on the device physical structure, allowing for optimized circuit design and performance through structure modifications, and 2) numerically to fit the model parameters to experimental data, allowing for evaluation of the accuracy of the model. This model is, to the best of our knowledge, the first lumped RF memristor model that includes device parasitics obtained from empirical measurements reported in the literature. Results show that the model is reasonably accurate, with 9.6% and 13% relative RMS error for the on-state magnitude and phase, respectively. Furthermore, we propose three topologies (series, shunt, and series-shunt) of nonvolatile single-pole double-throw switches using our lumped RF memristor model. The series and shunt topologies are single-voltage-controlled, while the series-shunt requires two control signals. Simulation results of these topologies exhibit low insertion loss and high isolation (below 0.25 dB and over 63 dB, respectively). The added nonvolatility and nanoscale size will result in reduced power consumption and higher density devices.
Multiple Target Tracking in Automotive FCM Radar by Multi-Bernoulli Filter with Elimination of Other Targets

To protect vulnerable road users, such as pedestrians, it is important to realize multi-target tracking in complex scenes. Due to the low signal-to-noise ratio (SNR) of pedestrian targets, the track-before-detect (TBD) approach seems to be effective. However, when an actual radar sensor is used, observation interference between targets, especially pedestrians and higher-SNR objects (such as roadside objects), may occur and lead to an incorrect tracking result. In this paper, we describe an algorithm for a multi-Bernoulli filter for TBD by eliminating targets from the original observation of an automotive fast chirp modulation (FCM) radar that is suited for complex scenes. With sequential Monte Carlo (SMC) implementation of the proposed algorithm, the approach is validated through the simulation of an urban road scene.
Accurate Modeling of the Nonlinear Characteristic of a Voltage Source Inverter for Better Performance in Near Zero Currents

In a sensorless vector control method, at low speeds and self-commissioning for AC motors, it is necessary to model dead time, rise/fall time, parasitic capacitors, and semiconductors’ voltage drop accurately. In this paper, voltage drop in each phase due to the nonlinear behavior of the inverter from the control system's viewpoint is precisely modeled by using two lookup tables. These lookup tables make the proposed model robust against the dc-link voltage and carrier frequency variations in the entire current range. In conventional methods at standstill, the measured voltage in the dc current test contains the nonlinearity of two phases. Therefore, to obtain the nonlinear voltage of each inverter phase, it is assumed that the values of inverter nonlinearity voltage at both high current and low current are equal. This simplification leads to distortion in measured voltage in the low currents. However, in this paper, the voltage drop equation of each phase is solved by numerical method that is realizable in conventional processors. The results obtained in actual voltage measurement show the superiority of the proposed method over traditional methods in the low currents. The proposed method is verified by using a voltage source inverter feeding a 2.2-kW three-phase induction motor.
Stress and Inflammation in Coronary Artery Disease: A Review Psychoneuroendocrineimmunology-Based

Recent findings have deeply changed the current view of coronary heart disease, going beyond the simplistic model of atherosclerosis as a passive process involving cholesterol build-up in the subintimal space of the arteries until their final occlusion and/or thrombosis and instead focusing on the key roles of inflammation and the immune system in plaque formation and destabilization. Chronic inflammation is a typical hallmark of cardiac disease, worsening outcomes irrespective of serum cholesterol levels. Low-grade chronic inflammation correlates with higher incidence of several non-cardiac diseases, including depression, and chronic depression is now listed among the most important cardiovascular risk factors for poor prognosis among patients with myocardial infarction. In this review, we include recent evidence describing the immune and endocrine properties of the heart and their critical roles in acute ischaemic damage and in post-infarct myocardial remodeling. The importance of the central and autonomic regulation of cardiac functions, namely, the neuro-cardiac axis, is extensively explained, highlighting the roles of acute and chronic stress, circadian rhythms, emotions and the social environment in triggering acute cardiac events and worsening heart function and metabolism in chronic cardiovascular diseases. We have also included specific sections related to stress-induced myocardial ischaemia measurements and stress cardiomyopathy. The complex network of reciprocal interconnections between the heart and the main biological systems we have presented in this paper provides a new vision of cardiovascular science based on psychoneuroendocrineimmunology.
Sentiment Analysis Based on Financial Tweets and Market Information

With the rapid development of Internet technology, people are increasingly accustomed to obtaining information and expressing their sentiments on the Internet. In the financial market, the sentiments in the financial news are supposed to have some correlation with the movement of the stock market. This paper aims to explore such correlation by conducting an empirical and data analysis. It first introduces the procedure for financial news sentiment analysis and the methods such as data mining and text processing. Then with the stock market data analysis and the calculation of the sentiment information based on Textblob, it proposes the manageable way of improving the performance of the sentiment lexicon, especially the way of text mining and choosing the feature words artificially. Finally the paper builds the model for the sentiment analysis. By doing several experiments, this paper deeply analyzes the relationship between the news sentiment values and the stock market price, and proves the effectiveness of the methods in this research. The findings demonstrate that most of the time the fluctuation of the sentiment can match with the stock market price.
3D Facial Landmark Localization Based on Two-Step Keypoint Detection

We propose an approach to 3D facial landmark localization based on two-step keypoint detection. In this paper, the facial landmarking problem is devided into two subproblems that are solved independently, keypoint detection and labeling. The two steps of the keypoint detection are as follows. First, coarse position of landmarks is located by using Supervised Descent Method on depth images. Extracting the neighbourhood of landmarks' coarse position as the valid keypoint region. Second, keypoints are detected by screening the subset of facial points within the valid keypoint region combining multiple local descriptors. In the stage of labeling, combinations of landmark candidates are generated from keypoints and the one fits the facial landmark model best is labeled as the landmark combination. On the FRGC v2.0 database, we achieve mean errors from 2.85 mm to 3.81 mm for each landmark. The overall detection success rate is 96.8%, among which 97.6% for neutral expression, 97.2% for mild, 93.3% for extreme. On the Bosphorus database, we respectively achieve the success rate of 92%, 95% and 88% under three different poses. Experimental results show that the presented approach is comparable to state-of-the-art methods in terms of its accuracy and good robustness is achieved against expression and small pose variation.
Efficient Hardware Architecture for Amalgam of Blowfish and Rc6

These days, Wi-Fi is a well-liked Technology. For it there is requirement of Faster data transfer as well as high security. Presently Advanced Encryption Standard (AES) is used in Wi-Fi that is more secured comparative to other encryption algorithms. But it’s not so fast because the complex functions it has. In this paper a hardware architecture is proposed with inner-loop pipelining, loop unrolling for the amalgam of Blowfish and RC6. The used algorithm uses two random numbers “a” and “w” that helps in removing weak key attack and Known plaintext attack of Blowfish. Also the used algorithm uses one S-Box by overlapping process that eliminates the collision key attack of Blowfish. The used algorithm requires less cycles than blowfish and RC6.
Elastin molecular aging promotes MDA‐MB‐231 breast cancer cell invasiveness

Elastin is a long‐lived extracellular matrix protein responsible for the structural integrity and function of tissues. Breast cancer elastosis is a complex phenomenon resulting in both the deposition of elastotic masses and the local production of elastin fragments. In invasive human breast cancers, an increase in elastosis is correlated with severity of the disease and age of the patient. Elastin‐derived peptides (EDPs) are a hallmark of aging and are matrikines – matrix fragments having the ability to regulate cell physiology. They are known to promote processes linked to tumor progression, but their effects on breast cancer cells remain unexplored. Our data show that EDPs enhance the invasiveness of MDA‐MB‐231 breast cancer cells through the engagement of matrix metalloproteases 14 and 2. We therefore suggest that elastosis and/or an aged stroma could promote breast cancer cell invasiveness.
Optimisation of Power Electronic Switches in Cascaded H-Bridge Multilevel Inverter by using Bidirectional Switches

Multilevel inverter is a brilliant topological scheme for DC to AC conversion. Multilevel inverter blends desired voltage wave profile from numerous levels of DC voltages. Multilevel inverter has many inherent problems due to usage of higher number of semiconductor switches. This may leads excess system size and price of the inverter also increases. The increased complexity of the controlling circuits also creates problem. Complex control algorithm needs to be worked out to optimise the operation. The objective of this discussion is to work for a modified design of multilevel inverter by optimising the number of switches used in a particular multilevel inverter without affecting the performance of the system and its suitability to high power application.
Microwave Vortex Beam Generation Using Holographic Artificial Impedance Surface

In this letter, a method has been proposed to convert the surface wave into an orbital angular momentum (OAM) vortex wave at a desired angle using sinusoidally-modulated holographic artificial impedance surface. A metallic patch with a grounded plane spaced by a dielectric substrate is selected as the unit cell of impedance surface, whose effective impedance can be characterized by changing the size of the metallic patch. As an example, a holographic impedance surface, which can generate a vortex beam with +1 order OAM mode deflecting to the direction of 30○from broadside at 17 GHz, has been designed and simulated.
Multi-view Fusion with Deep Learning for 3D Shape Classification

3D model retrieval is all along a difficult and hotspot in computer vision. Recently, the view-based methods make use of the advanced convolutional neural networks which achieve excellent results. However, the structural information of the 3D model was destroyed by the projection and the relevance of multiple perspectives was not considered. In order to resolve this problem, this manuscript analyzes the process of human observation of 3D models and imitates the process of human recognition of 3D models through a combination of convolutional neural networks and recurrent neural networks. Our approach can convert the relevance between perspectives into the dependency relationship between structured recurrent neural networks and restore the structural information of the 3D model. We conducted experiments on the ModelNet40 database, it shows strong performance on par or even better than state-of-the-art results.