Multi-contrast atherosclerosis characterization (MATCH) of carotid plaque with a single 5-min scan: technical development and clinical feasibility

BACKGROUND: Multi-contrast weighted imaging is a commonly used cardiovascular magnetic resonance (CMR) protocol for characterization of carotid plaque composition. However, this approach is limited in several aspects including low slice resolution, long scan time, image mis-registration, and complex image interpretation. In this work, a 3D CMR technique, named Multi-contrast Atherosclerosis Characterization (MATCH), was developed to mitigate the above limitations. METHODS: MATCH employs a 3D spoiled segmented fast low angle shot readout to acquire data with three different contrast weightings in an interleaved fashion. The inherently co-registered image sets, hyper T1-weighting, gray blood, and T2-weighting, are used to detect intra-plaque hemorrhage (IPH), calcification (CA), lipid-rich necrotic core (LRNC), and loose-matrix (LM). The MATCH sequence was optimized by computer simulations and testing on four healthy volunteers and then evaluated in a pilot study of six patients with carotid plaque, using the conventional multi-contrast protocol as a reference. RESULTS: On MATCH images, the major plaque components were easy to identify. Spatial co-registration between the three image sets with MATCH was particularly helpful for the reviewer to discern co-existent components in an image and appreciate their spatial relation. Based on Cohen’s kappa tests, moderate to excellent agreement in the image-based or artery-based component detection between the two protocols was obtained for LRNC, IPH, CA, and LM, respectively. Compared with the conventional multi-contrast protocol, the MATCH protocol yield significantly higher signal contrast ratio for IPH (3.1 ± 1.3 vs. 0.4 ± 0.3, p < 0.001) and CA (1.6 ± 1.5 vs. 0.7 ± 0.6, p = 0.012) with respect to the vessel wall. CONCLUSIONS: To the best of our knowledge, the proposed MATCH sequence is the first 3D CMR technique that acquires spatially co-registered multi-contrast image sets in a single scan for characterization of carotid plaque composition. Our pilot clinical study suggests that the MATCH-based protocol may outperform the conventional multi-contrast protocol in several respects. With further technical improvements and large-scale clinical validation, MATCH has the potential to become a CMR method for assessing the risk of plaque disruption in a clinical workup

Prevalence of foodborne viruses and influenza A virus from poultry processing plants to retailed chickens

IntroductionFoodborne viruses are a serious concern in public health. This study investigated the prevalence of eight foodborne viruses norovirus (NoV), adenovirus (AdV), sapovirus (SapoV), astrovirus, hepatitis A virus (HAV), hepatitis E virus (HEV), rotavirus, aichivirus, and influenza A virus (IAV).Material and methodA total of 316 chicken samples were collected from three poultry processing plants to commercial markets (local and online). RT-qPCR- and PCR-positive amplicons obtained from monitoring were confirmed by sequence analysis.ResultsFoodborne viruses and IAV were not found in poultry processing plants. Of the 100 chickens purchased from the local and online markets, 19 (19.0%) AdV and 2 (2.0%) SapoV were detected. NoV, astrovirus, HAV, HEV, rotavirus, aichivirus, and IAV were not detected in the retailed chickens. Phylogenetic analysis identified 18 human AdV-41, one porcine AdV, and two SapoV-GI.1. It was the first case of the discovery of the SapoV gene in chicken. The average contamination level of detected AdV was 2.4 log DNA copies/g, but there were cases where the highest level was 5.35 log DNA copies/g.DiscussionThis study highlights the importance of chicken's contribution to the transmission of AdV with the possibility of annual variability with emerging symptoms. The prevention of AdV contamination in the food chain from slaughterhouses to retail markets should be monitored and controlled in further study

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework

Harvesting Wind Energy Based on Triboelectric Nanogenerators

The utilization of various distributed energy is becoming a prominent research topic due to the rapid development of the Internet of Things and wireless condition monitoring systems. Among the various distributed energy sources, wind energy has the advantages of being widely distributed, renewable and pollution-free, and is a very promising mechanical energy for power supply. Traditional wind energy harvesting methods based on electromagnetic and piezoelectric effects have issues with complex structure, large size, severe mechanical structures, and high installation costs. The low frequency and irregular nature of ambient mechanical energy makes these methods generally inefficient and inevitably hinders the further exploitation of wind energy. The triboelectric nanogenerators (TENGs) based on frictional charging and electrostatic effects can also be used for wind power generation and are increasingly favored by researchers as TENGs are easier to be miniaturized and assembled, and can realize large-scale manufacturing in comparison. This paper reviews the research on TENGs for wind energy utilization in terms of structural design, material selection and potential applications. In addition, the potential difficulties and possible developments in this field are summarized and discussed

Preparation, Sintering Behavior and Consolidation Mechanism of Vanadium-Titanium Magnetite Pellets

High-quality oxidized pellets are the basis to achieve high-efficiency utilization of vanadium–titanium magnetite (VTM) ores. Bentonite was used as a binder of VTM. The main phase composition of VTM is titanomagnetite and ilmenite. When the amount of bentonite is 1%, the compressive strength and dropping strength of VTM pellets can meet the requirements. To improve metallurgical properties, the pellets need to be roasted. The best conditions for roasting are as follows: calcination temperature of 1523 K and a calcination time of 20 min. The consolidation mechanism, phase transformation, and crystal structure transformation of VTM in the process of oxidation roasting are also explained

Mechanistic and Experimental Study of the CuxO@C Nanocomposite Derived from Cu3(BTC)2 for SO2 Removal

A tunable and efficient strategy was adopted to synthesize highly porous nano-structured CuO&minus;carbonized composites (CuxO@C) using Cu3(BTC)2 as a sacrificial template. The as-synthesized CuO nanocomposites exhibited hollow octahedral structures, a large surface area (89.837 m2 g&minus;1) and a high proportion of Cu2O active sites distributed on a carbon frame. Based on DFT calculations, both the Cu atoms on the surface (CuS) and oxygen vacancy (OV) exhibited strong chemical reactivity. On the perfect CuO (111), the CuS transferred charge to O atoms on the surface and SO2 molecules. A strong adsorption energy (&minus;1.41 eV) indicated the existence of the chemisorption process. On the oxygen-deficient CuO (111), the O2 preferably adsorbed on OV and then formed SO3 by bonding with SO2, followed by the cleavage of the O&minus;O bond. Furthermore, the CuO nanocomposites exhibited an excellent ratio of S/Cu in SO2 removal experiments compared with CuO nanoparticles produced by coprecipitation

Mechanistic and Experimental Study of the Cu_xO@C Nanocomposite Derived from Cu₃(BTC)₂ for SO₂ Removal

A tunable and efficient strategy was adopted to synthesize highly porous nano-structured CuO−carbonized composites (CuxO@C) using Cu3(BTC)2 as a sacrificial template. The as-synthesized CuO nanocomposites exhibited hollow octahedral structures, a large surface area (89.837 m2 g−1) and a high proportion of Cu2O active sites distributed on a carbon frame. Based on DFT calculations, both the Cu atoms on the surface (CuS) and oxygen vacancy (OV) exhibited strong chemical reactivity. On the perfect CuO (111), the CuS transferred charge to O atoms on the surface and SO2 molecules. A strong adsorption energy (−1.41 eV) indicated the existence of the chemisorption process. On the oxygen-deficient CuO (111), the O2 preferably adsorbed on OV and then formed SO3 by bonding with SO2, followed by the cleavage of the O−O bond. Furthermore, the CuO nanocomposites exhibited an excellent ratio of S/Cu in SO2 removal experiments compared with CuO nanoparticles produced by coprecipitation