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Food safety is veryimportant in our daily life. In food processing or disinfection, microorganisms are commonly exposed to oxidative stress perturbations. However, microorganisms can adapt and respond to physicochemical interventions, leading to difficulty and complexity for food safety assurance. Therefore, understanding the response mechanisms of microbes and providing an overview of the responses under oxidative stress conditions are beneficial for ensuring food safety for the industry. The current review takes the metabolomics approach to reveal small metabolite signatures and key pathway alterations during oxidative stress at the molecular and technical levels. These alterations are involved in primary oxidative stress responses due to inactivation treatments such as using hypochlorite (HOCl), hydrogen peroxide (H2 O2 ), electrolyzed water (EW), irradiation, pulsed light (PL), electron beam (EB), and secondary oxidative stress responses due to exposures to excessive conditions such as heat, pressure, acid, and alkaline. Details on the putative origin of exogenous or endogenous reactive oxygen species (ROS) are discussed, with particular attention paid to their effects on lipid, amino acid, nucleotide, and carbohydrate metabolism. In addition, mechanisms on counteracting oxidative stresses, stabilization of cell osmolality as well as energy provision for microbes to survive are also discussed.

The increasing number of people with seafood allergy has caused a series of problems for practitioners and consumers in the seafood industry year by year. Thereby, development of efficient, convenient and low-cost allergen detection methods is urgently needed. This review introduces three important existing seafood allergen detection methods associated with DNA-based, protein-based and aptamer-based. Their principles and biological characteristics are firstly presented. The core of these three methods are DNA amplification techniques, specific binding of antigens and antibodies, and specific binding of aptamers and ligands, respectively. Among them, DNA-based detection method is an indirect analysis, which takes the gene of allergen as the detection object and is characterized by good stability and high sensitivity. Protein-based and aptamer-based, methods employ indirect analysis for allergen detection. The difference is that the latter uses an easily synthesized and highly efficient aptamer as the detection probe, showing great promising potentials. The advantages and disadvantages of the three mentioned detection methods are also discussed. In the future, as more efficient and reliable detection methods for seafood allergens come into practice, the possibility of seafood allergy patients eating seafood products by mistake will be greatly reduced, which will ensure the food safety and the health of allergy patients.

The aim of this research is to develop a probability method that can identify the flexure, shear or flexural-shear failure of RC columns. A total of 336 columns of cyclic load test results were col...

For airports with high passenger and luggage flows, a large number of staff members have to be hired to deploy the scattered passenger luggage trolleys. To release humans from the repetitive and laborious job, we develop an autonomous trolley deployment robot to detect, transport and collect the scattered idle trolleys to recycling points. This paper will firstly illustrate the entire collection pipeline of the deployment robot system and then address the key challenge: partial to full point set registration. With the perception framework, the robot can detect the idle trolleys and acquire the pose of the trolleys on the ground, and then capture the trolley from behind, along the same direction for subsequent grasping and manipulation. With RGB-D camera and a segmentation Convolutional Neural Network, the robot can generate a partial surface point cloud of the detected trolley. The resulting point cloud, data and a pre-scanned full trolley point cloud, model , are matched by an implicit pose. To tackle the low accuracy and long computation time issues, a novel searching space-constrained point set registration algorithm is proposed to register the two overlapping point sets. Based on Branch-and-Bound (BnB) mechanism, the error between data and model is iteratively optimized. The constraint of searching space speeds up the global searching of the optimal pose, by pruning the candidate spaces which is impossible to contain the optimal result. To evaluate the performance, an airport trolley segmentation dataset and a point cloud dataset for registration are constructed. Experimental results on the datasets and synthetic dataset show that our method achieves higher accuracy and success rate than the previous methods. The experiments demonstrated in video clips validate the developed system works in real-world applications.

Background Aflatoxins are one of the maximal menaces to food safety and public health and can contaminate food during the entire process from field to table, which can then be digested by the consumer, causing irreversible negative impacts such as immunotoxic, carcinogenic, and mutagenic effects. Therefore, development of novel technology to decontaminate aflatoxins is obviously important for the food industry. Cold plasma (CP) is an emerging novel nonthermal technology with eco-friendliness, high-efficiency and low cost, which has recently been widely studied for inactivating microorganisms and mycotoxins. Scope and approach This review presents a brief overview of aflatoxins and cold plasma treatment (CPT) and provides detailed information on the blocking and degradation of aflatoxins by CPT and its potential mechanisms. Furthermore, the superiority of CPT in purifying aflatoxins is highlighted by comparison with conventional methods including physical, chemical and biological treatments. Finally, the limitations and challenges of CPT are also discussed. Key findings and conclusions Recent studies show that CPT is an alternative technique to conventional methods for aflatoxins decontamination. However, the effective components and exact sites where CPT works are not completely clear, and the types of food researched are also few. Further researches should focus on breaking through these bottlenecks and provide a theoretical basis for the promotion of CPT applications.

In this study, the plasma oxidation effect in tin-oxide (SnOx) thin film was investigated. And on this basis, we fabricated n-type thin-film transistors (TFTs) using the SnOx thin film with the plasma oxidation by experiments. By adjusting the processing time of the oxygen plasma treatment (OPT), the optimized SnOx TFT device exhibited an extremely high field-effect mobility of 87.6 cm2 V−1 s−1, a desirable on-to-off current ratio of 1.9 × 104 and a threshold voltage of − 0.9 V. Furthermore, we investigated the origin of the performance enhancements in the n-type SnOx TFTs with the optimized OPT by introducing the density of states (DOS) modeling in TCAD simulation. The numerical simulation indicated that the attributes of donor-like Gaussian defect states (oxygen vacancies) were modified in overall DOS due to the plasma oxidation effect. These present results show that the SnOx TFT treated by oxygen plasma has great promise in the future high-performance flat panel display industries.

Inertial sensors in smartphones enable the relative state measurements for pedestrian localization without global positioning system (GPS) signals or beacons. Any such navigation method should notice the reality that the phone is placed at uncontrolled variant poses with respect to the human body. In this work, we focus on pedestrian position estimation from the raw inertial measurement unit (IMU) measurements without any constraint on the device carrying manners and propose a novel deep inertial odometry solution. By presenting the expression of continuous rotating, we are able to release the reliance on the unreliable orientation provided by the sensor application program interface (API). Moreover, we propose a novel loss formulation by representing the velocity as the average velocity magnitude and the moving direction. The proposed approach was assessed via the public RoNIN dataset. The localization performance of the public dataset trained network model was then evaluated by real scenario trials in the CUHK campus. Experimental results show that our model is capable of providing robust velocity estimates and generating more accurate trajectories than state-of-the-art inertial odometry methods. Specifically, the localization evaluation in the CUHK campus contains 60-min inertial signals with a length of 3 km. The trained odometry network is with the 30th percentile accuracy of 2.26 m and the 50th percentile accuracy of 4.98 m.

Background Cold plasma (CP) as an innovative and efficient technology has been developed for food sterilization. During CP treatments, exogenous reactive oxygen species (ROS) are generated, which are usually considered as the key bactericidal agent. Therefore, understanding various resistant mechanisms developed during CP processing by bacteria and their stress responses to ROS are important for the successful application of CP treatments. Scope and approach This review summarizes the sterilizing effects of exogenous ROS induced by plasma, bacterial oxidative stress protection and repair mechanisms, and regulation of their oxidative stress responses. The damaging effects of proven free radicals to bacterial cells are introduced. Stress protection and repair mechanisms of bacteria to exogenous ROS and their antioxidant properties including changes in bacterial cell morphology, biofilm protection in quorum sensing systems, as well as multiple antioxidant regulations in bacterial cells are also discussed. Key findings and conclusions The recent studies conducted on the bacterial stress responses to ROS induced by CP treatments indicated that foodborne pathogens possess some resistance mechanisms to CP treatment in real food systems. Therefore, clarifying the development of resistance of foodborne pathogens to active substances produced by CP, balancing the relationship between minimum processing and food safety assurance, and translating laboratory results of the bacterial sterilization effectiveness of CP treatments into industrial applications should be focuses of future research.

In this work, we demonstrated a HfO2-based ferroelectric field-effect transistor (FeFET) in series with a HfAlO ferroelectric capacitor for memory application and further investigated the impact of the ferroelectric capacitor with different thicknesses and areas. It was revealed that the memory window of the FeFET has a significant correlation with the ferroelectric capacitor from the transfer curves of the transistor after the series connection. By decreasing the thickness and area of the ferroelectric capacitor, the memory window of the FeFET was improved, which could be verified through the TCAD simulation tool and capacitance matching model. With the series-connected ferroelectric capacitor, in addition to optimizing the characteristics of ferroelectric memory, we can effectively avoid the interface-caused undesirable effects in conventional ferroelectric memory during film stacking. These results provide a solution for future low-power embedded memory application.

In this paper, we investigated the performance of an n-type tin-oxide (SnOx) thin film transistor (TFT) by experiments and simulation. The fabricated SnOx TFT device by oxygen plasma treatment on the channel exhibited n-type conduction with an on/off current ratio of 4.4×104, a high field-effect mobility of 18.5 cm2/V.s and a threshold swing of 405 mV/decade, which could be attributed to the excess reacted oxygen incorporated to the channel to form the oxygen-rich n-type SnOx. Furthermore, a TCAD simulation based on the n-type SnOx TFT device was performed by fitting the experimental data to investigate the effect of the channel traps on the device performance, indicating that performance enhancements were further achieved by suppressing the density of channel traps. In addition, the n-type SnOx TFT device exhibited high stability upon illumination with visible light. The results show that the n-type SnOx TFT device by channel plasma processing has considerable potential for next-generation high-performance display application.

Fabrication, physical modeling and dynamic response of p-type Al-doped SnOx active channel thin film transistors (TFTs) are presented for the potential application of ultra-high definition (UHD) displays. After deposition of Al-doped SnOx active layer using reactive co-sputtering, the channel was treated with plasma fluorination which improve the device performance of high ION/IOFF ratio of > 106, low subthreshold swing of ~100 mV/dec and high field-effect mobility (μFE) of 4.8 cm2V-1s-1. To understand the origin of such high performance, physical modeling and numerical simulations were performed using density of state (DOS) model of defects/traps of oxide semiconductor. This model describes the modifications of donor-like tail states and acceptor-like Gaussian defect states due to Al doping on SnOx and fluorine treatment. To evaluate the device performance for UHD large scale displays, the dynamic responses of p-type TFT pixel circuit for various requirements are simulated with physical models. These results suggest that the Al-doped SnOx TFTs are potential candidates for future high-definition displays and many applications in transparent electronics.

Transparent electronics has attracted much attention and been widely studied for next-generation high-performance flat-panel display application in the past few years, because of its excellent electrical properties. In display application, thin film transistors (TFTs) play an important role as the basic units by controlling the pixels. Among them, oxide-based TFTs have become promising candidates and gradually replaced the conventional amorphous and polycrystalline silicon TFTs, due to high mobility, good transparency, excellent uniformity and low processing temperature. Even though n-type oxide TFTs have shown high device performance and been used in commercial display application, p-type oxide TFTs with the equal performance have been rarely reported. Hence, in this paper, recent progress and challenges in p-type oxide-based TFTs are reviewed. After a short introduction, the TFT device structure and operation are presented. Then, recent developments in p-type oxide TFTs are discussed in detail, with the emphasis on the potential p-type oxide candidates as copper oxide, tin oxide and nickel oxide. Moreover, miscellaneous applications of p-type oxide TFTs are also presented. Despite this, the performance of p-type oxide TFTs still lags behind, as compared with that of n-type counterparts. Thus, the current issues and challenges of p-type oxide TFTs are briefly discussed.

Optimal device integrity was achieved in Ni/SiGeOx/TiOy/TaN resistive memory by using a forming-free switch with a low switching power of 790 μW, stable endurance of 104 cycles, optimal retention time of 105 s, resistance window of at least 1150×, and tight current distributions at 85 °C. These characteristics are attributed to the low current switching obtained using SiGeOx with a high oxygen vacancy density and highly defective TiOy grain boundaries.

Leucine deprivation improves insulin sensitivity; however, whether and how this effect can be extended is unknown. We hypothesized that intermittent leucine deprivation (ILD) might produce a long-term effect on improved insulin sensitivity via the formation of metabolic memory. Consistently, seven ILD cycles treatment (1-day leucine-deficient diet, 3-day control diet) in mice produced a long-lasting (after resuming a control diet for 49 days) effect on improved whole-body and hepatic insulin sensitivity in mice, indicating the potential formation of metabolic memory. Furthermore, the effects of ILD depended on hepatic general control nondepressible 2 (GCN2) expression as verified by gain-and loss-of-function experiments. Moreover, ILD increased Gcn2 expression by reducing its DNA methylation at two CpG promoter sites controlled by demethylase growth arrest and DNA damage inducible b. Finally, ILD also improved insulin sensitivity in insulin-resistant mice. Thus, ILD induces long-lasting improvements in insulin sensitivity by increasing hepatic Gcn2 expression via a reduction in its DNA methylation. These results provide novel insights into understanding the link between leucine deprivation and insulin sensitivity, as well as potential nutritional intervention strategies for treating insulin resistance and related diseases. We also provide evidence for liver-specific metabolic memory after ILD and novel epigenetic mechanisms for Gcn2 regulation.

Graphical abstract, A novel technique for treating grass carp by combining plasma functionalized liquids and ultrasound to inactivate bacteria was developed. The effects of the plasma functionalized liquids (PFL) including plasma functionalized water (PFW) and buffer (PFB) and their respective combination with ultrasound treatment (USPFW and USPFB) on the oxidative and physical qualities of grass carp were also investigated. Individual applications of PFW and PFB significantly reduced the populations of Escherichia coli and Shewanella putrefaciens in the range of 0.31–1.18 log CFU/g, compared with the control with a reduction of 0.18 log CFU/g, while combined treatments of USPFW and USPFB presented additional reductions of 0.05–0.65 log CFU/g, with potential synergy demonstrated for PFW and ultrasound. The treatment resulted in improved biomedical index and nutritional value of fatty acids and lipids, protein structural unfolding, increased lipid oxidation and protein degradation with values within the acceptable limits, and the combined treatment was more effective for retarding the hardness reduction in grass carp, while the colour change was also significantly affected, resulting in increased whiteness. The results indicated that the combined treatments may be a promising approach to improving the quality of seafood products.

Multimodal imaging is increasingly used to address neuropathology associated with alcohol use disorder (AUD). Few studies have investigated relationships between metabolite concentrations and white matter (WM) integrity; currently, there are no such data in AUD. In this preliminary study, we used complementary neuroimaging techniques, magnetic resonance spectroscopy (MRS), and diffusion weighted imaging (DWI), to study AUD neurophysiology. We tested for relationships between metabolites in the dorsal anterior cingulate cortex (dACC) and adjacent WM microstructure in young adult AUD and control (CON) subjects. Sixteen AUD and fourteen CON underwent whole-brain DWI and MRS of the dACC. Outcomes were dACC metabolites, and diffusion tensor metrics of dACC-adjacent WM. Multiple linear regression terms included WM region, group, and region × group for prediction of dACC metabolites. dACC myo-inositol was positively correlated with axial diffusivity in the left anterior corona radiata (p

A miniaturized Antipodal Vivaldi Antenna (AVA) with flat gain is proposed in this paper for UWB applications. This antenna is implemented by loading a pair of open stubs, comb-shaped slot array, and semi-elliptical patches on a traditional AVA. The miniaturization property is obtained as a result of the introduction of comb-shaped slits which increases the surface current path. The function of the pair of open stubs is to improve the gain in the lower frequency band, while the effect of the pair of semi-elliptical patches is to maintain the gain in the higher frequency band. In order to verify this design concept, a prototype of the proposed AVA is fabricated and measured. The measured results show that VSWR < 2 in the frequency band of 3 – 11GHz and the average gain in is 8.9 dBi in the end-fire direction. Besides the gain fluctuation is ± 1.1dB within this impedance bandwidth, achieving a relatively flat gain.

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

Coastal bridges are continually attacked by chloride ions during their lifetime, making the structures vulnerable. Piers, which are the main components of bridge structures to resist later...

In this paper, the effect of channel annealing and oxygen flow rate in P-type tin-monoxide (SnO) thin film transistor (TFT) was investigated to reach the process compatibility with n-type oxide-based TFT. The optimized P-type SnO TFT with a small threshold voltage of -0.1 V, a high field-effect mobility of 4 cm² V-1 s-1 and an on/off current ratio of >10² was proposed. From the experimental results, the 150 °C channel annealing revealed a metallic behavior but a semiconductor-like property at a higher 200 °C that was favorable for transistor rectification. Besides, the higher oxygen flow rate was also helpful for improving device mobility and driving current, but shows a slight increase in off-state leakage, which is unavoidable due to the increase of grain in SnO channel.

In this work, we successfully demonstrated the 9.6-nm-thick HfAlOx ferroelectric memory using light aluminium doping of 6.5% to form ferroelectric orthorhombic phase. Compared to ferroelectric HfZrOx film with zirconium diffusion issue, the HfAlOx film showed excellent thermal stability under high temperature annealing. Besides, the ferroelectric HfAlOx also exhibited robust polarization characteristics under endurance cycling test. A >106 stressed cycles with 4 MV/cm can be measured and a long endurance was sustained over 5 × 107 cycles under 3.6 MV/cm. Therefore, the HfAlOx film showed the great promise for integrating in high performance ferroelectric memory with thermal budget concerns.

Organometal halide perovskites offer tremendous potential in developing optoelectronic and photovoltaic devices because of their spectacular band gap properties. Pressure has been demonstrated to b...

This paper presents a methodology to perform seismic fragility analysis of deteriorating reinforced concrete (RC) bridge columns induced by corrosion based on time-variant capacity estimates. The typically simplified uniform reduction model of cross-section of a corroded reinforcing steel bar is assumed, and the strain penetration and slippage of reinforcements anchored to the foundation are considered in the performance assessment of corroded bridge columns. To be conveniently used in engineering practice, the drift ratio is taken as the capacity index to quantify various damage states, and the plastic hinge model is adopted and verified to calculate the tip displacement of corroded columns due to its simple form and wide use in elastic–plastic analysis. Seismic fragility curves are then developed with reference to both the pristine (or original) construction state and current deteriorated state to illustrate the differences of potential damage probabilities calculated by time-invariant and time-variant capacity indexes. Further, significance test is performed. The results highlight that some of the empirical plastic hinge length models can also be used to predict the ultimate displacements of corroded columns by introducing the corroded constitutive models of materials; the seismic performance of a deteriorating RC bridge column will be significantly underestimated by using pristine capacity index as the measurement criterion. As a matter of fact, in engineering practice, one of the main concerned issues should be the current structural failure probability when evaluating the performance of a deteriorated RC structure. Hence, it is suggested that the time-variant capacity index should be adopted to estimate the seismic performance of a deteriorating RC structure during its life cycle.

For the first time, we successfully demonstrated the dopant-free HfO2 negative capacitance (NC) transistor featuring a low gate-overdrive voltage and a nearly hysteresis-free sub-40 mV/dec swing. These excellent performances can be ascribed to the reduction of the monoclinic phase suppressed with the thickness scaling and gate stress engineering favorably rearranging oxygen vacancies to boost orthorhombic phase transition. The highly scalable and dopant-free NC transistor shows the potential for the application of advanced CMOS technology.

Objective This study aimed to assess and compare the clinical efficacy and safety of acupuncture and related therapies (ARTs) add-on to conventional treatment (CT) for heart failure (HF) through pairwise and network meta-analyses. Methods Six electronic databases, including PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI) and Wanfang Database were searched from inceptions to December 2017. Randomized controlled trials (RCTs) regarding ARTs combining with CT for HF were eligible. The primary outcomes were changes in heart function classification (HFC) according to New York Heart Association class and left ventricular ejection fraction (LVEF). Risk of bias assessment was conducted by two independent authors. Pairwise and network meta-analyses were performed using STATA 13.0 and WinBUGS 1.4.3 software. Results A total of 26 RCTs were enrolled for analyses, with 5 kinds of ARTs and 2116 patients in all. Pairwise meta-analyses showed that acupoint application (OR: 3.28, 95%CI[2.26, 4.76]), acupuncture (OR: 2.78, 95%CI[1.21, 6.41]), acupoint injection (OR: 3.33, 95%CI[1.85, 6.00]) and moxibustion (OR: 2.51, 95%CI[1.02, 6.21]) could significantly improve HFC when they were used as add-on to CT. Acupoint application (MD: 3.57, 95%CI[1.45, 5.70]), acupuncture (MD: 7.75, 95%CI[2.33, 13.17]), acupoint injection (MD: 4.81, 95%CI[2.99, 6.63]) and moxibustion (MD: 6.99, 95%CI[3.62, 10.36]) were significantly beneficial in improving LVEF. Network meta-analyses showed that acupoint injection (SUCRA = 70.0%) and acupuncture (SUCRA = 90.4%) respectively had the greatest probability in improving HFC and LVEF. Conclusion Most of the included ARTs add-on to CT was effective in improving HFC and LVEF. Acupoint injection and acupuncture may respectively have better effect than others for HFC and LVEF. However, due to the small sample size and poor quality of the included studies, hence well-designed RCTs are needed to confirm our findings. This study was registered in PROSPERO, CRD42018087700.

Image sensors have widespread use in many robotics applications and, in particular, in target tracking. While existing methods assume continuous image feedback, the novelty of this brief stems from the development of dwell time conditions to guarantee convergence of state estimates to an ultimate bound for a class of image-based observers in the presence of intermittent measurements. A Lyapunov analysis for the switched system is performed to develop convergence conditions based on the minimum amount of time the object must be visible and on the maximum amount of time the object may remain outside the camera view. Experimental results are included to verify the theoretical results and elucidate real-world performance.

We demonstrated for the first time a triple-level operation of a resistive-gate Flash (RG-Flash) on a FinFET platform. Comprehensive reliabilities have been examined. The results show the forming-free property, low programming (PGM) current (

Pressure calibration for most diamond-anvil cell (DAC) experiments is mainly based on the ruby scale, which is key to implementing this powerful tool for high-pressure study. However, the ruby scale can often hardly be used for programmably controlled DAC devices, especially the piezoelectric-driving cells, where a continuous pressure calibration is required. In this work, we present an effective pressure gauge for DACs made of Manganin metal based on the four-probe resistivity measurements. Pressure dependence of its resistivity is well established and shows excellent linear relations in the 0-30 GPa pressure range with a slope of 23.4 (9) GPa for the first-cycle compression, in contrast to that of multiple-cycle compression and decompression having a nearly identical slope of 33.7 (4) GPa likely due to the strain effect. In addition, the such-established Manganin scale can be used for continuously monitoring the cell pressure of piezoelectric-driving DACs, and the reliability of this method is also verified by the fixed-point method with a Bi pressure standard. Realization of continuous pressure calibration for programmably controlled DACs would offer many opportunities for the study of dynamics, kinetics, and critical behaviors of pressure-induced phase transitions.

The state-of-the-art methods of Magnetic Resonance Imaging (MRI) denoising technologies have improved significantly in the past decade, particularly those based in deep learning. However, the major issues in deep learning based denoising algorithms is both that the model architectures are not built for the complex noise distributions inherent in MRI, and that the data given to these algorithms is typically synthetic, and thus, they fail to generalize to spatially variant noise distributions. The noise varies greatly dependent upon such factors as pulse sequence of the MRI sequence, reconstruction method, coil configuration, physiological activities, etc. To overcome these issues, we have created HydraNet, a multi-branch deep neural network architecture that learns to denoise MR images at a multitude of noise levels, and which has critically been trained using only real image pairs of high and low signal-to-noise ratio (SNR) images. We prove the superiority of HydraNet at denoising complex noise distributions in comparison to the leading deep learning method in our experimentation, in addition to non-local collaborative filtering-based methods, quantitatively in both Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and qualitatively upon inspection of denoised MRI samples.

Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect's visual neuron, LGMD is considered to be an ideal basis for building UAV's collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts' synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial-temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.

In this paper, a gallium-nitride (GaN) ultrahigh-speed permanent magnet synchronous motor (PMSM) sensorless drive is proposed. The ultra-high-speed PMSM has a maximum electrical frequency of 3 kHz. Thus the utilization of high-switching frequency devices becomes essential. The developed 3-phase GaN inverter achieves 40 kHz switching frequency, thereby increasing the control performances of the machine. Besides, due to the absence of speed sensors in ultra-high-speed applications, a secondorder sliding-mode observer (SMO) is implemented for position and speed estimation. The SMO shows a fast dynamic response and strong robustness in the results of the recent simulations. A harmonic injection modulation algorithm is implemented to reduce the computation load of the digital signal processor (DSP). Synthesized by the GaN inverter, the SMO, and the harmonic injection modulation algorithm, the ultra-high-speed PMSM drive achieves highefficiency and high-dynamic performances.

While neuroimaging and blood biomarker have been two of the most active areas of research in the neurotrauma community, these fields rarely intersect to delineate subconcussive brain injury. The aim of the study was to examine the association between diffusion MRI techniques [diffusion tensor imaging (DTI) and neurite orientation/dispersion density imaging (NODDI)] and brain-injury blood biomarker levels [tau, neurofilament-light (NfL), glial-fibrillary-acidic-protein (GFAP)] in high-school football players at their baseline, aiming to detect cumulative neuronal damage from prior seasons. Twenty-five football players were enrolled in the study. MRI measures and blood samples were obtained during preseason data collection. The whole-brain, tract-based spatial statistics was conducted for six diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial/radial diffusivity (AD, RD), neurite density index (NDI), and orientation dispersion index (ODI). Five players were ineligible for MRIs, and three serum samples were excluded due to hemolysis, resulting in 17 completed set of diffusion metrics and blood biomarker levels for association analysis. Our permutation-based regression model revealed that serum tau levels were significantly associated with MD and NDI in various axonal tracts; specifically, elevated serum tau levels correlated to elevated MD (p = 0.0044) and reduced NDI (p = 0.016) in the corpus callosum and surrounding white matter tracts (e.g., longitudinal fasciculus). Additionally, there was a negative association between NfL and ODI in the focal area of the longitudinal fasciculus. Our data suggest that high school football players may develop axonal microstructural abnormality in the corpus callosum and surrounding white matter tracts, such as longitudinal fasciculus. A future study is warranted to determine the longitudinal multimodal relationship in response to repetitive exposure to sports-related head impacts.

Objective. Intrahepatic cholestasis of pregnancy (ICP) is a common pregnancy-related liver disease and is associated with an increased risk of adverse neonatal outcomes. Ursodeoxycholic acid (UDCA) is the most effective treatment. This study was aimed at investigating the adverse outcomes of ICP and evaluating the effects of treatment with UDCA in patients with ICP. Methods. We included 114 women with ICP and 3725 women without ICP (no-ICP group) who delivered in our hospital between September 2017 and August 2019. The prevalence of ICP in this study was 3.15%. We matched each woman with ICP to five controls. Of all the 114 women with ICP, 73 (64.04%) received UDCA while 41 (35.96%) did not. Logistic multivariate regression analysis was used to compare the adverse outcomes between those with ICP and matched controls as well as between those who received UDCA (UDCA group) and those who did not (non-UDCA group). Results. Compared with controls, women with ICP were more likely to have preeclampsia (adjusted odds ratio, aOR = 16.74 , 95% CI 5.29–52.98), cesarean section ( aOR = 1.76 , 95% CI 1.10–2.81), and preterm birth ( aOR = 24.35 , 95% CI 2.74–216.67). Administration of UDCA reduced the rate of preterm birth (1.37% vs. 14.63%, aOR = 0.10 , 95% CI 0.01–0.90). Conclusion. ICP increased the risk of preeclampsia, cesarean section, and preterm birth. UDCA could reduce the rate of preterm birth.

In this paper, we propose a novel algorithm that estimates the pose of the robot end effector using depth vision. The input to our system is the segmented robot hand point cloud from a depth sensor. Then a neural network takes a point cloud as input and outputs the position and orientation of the robot end effector in the camera frame. The estimated pose can serve as the input of the controller of the robot to reach a specific pose in the camera frame. The training process of the neural network takes the simulated rendered point cloud generated from different poses of the robot hand mesh. At test time, one estimation of a single robot hand pose is reduced to 10ms on gpu and 14ms on cpu, which makes it suitable for close loop robot control system that requires to estimate hand pose in an online fashion. We design a robot hand pose estimation experiment to validate the effectiveness of our algorithm working in the real situation. The platform we used includes a Kinova Jaco 2 robot arm and a Kinect v2 depth sensor. We describe all the processes that use vision to improve the accuracy of pose estimation of the robot end-effector. We demonstrate the possibility of using point cloud to directly estimate the robot’s end-effector pose and incorporate the estimated pose into the controller design of the robot arm.

In recent years, applications of cold plasma treatments have shown high efficiency in sterilization of fresh fruit and vegetables. With encouraging results, development of this technique focuses on influences of cold plasma on the quality attributes of these fresh produce. More studies found that the quality attributes are maintained in an acceptable range or can even be improved under certain plasma treatment conditions. This review provides an overview of the multiple effects of cold plasma on the fresh produce, in terms of physical, chemical and physiological aspects. Besides, the possible mechanisms of how plasma influences these quality attributes is elaborated, which is useful for further modification and improvement of the plasma technology, so that quality enhancement and shelf life extension can be achieved. Future trends in the development of cold plasma technology are also presented. Cold plasma treatment is a potential technology for treating fresh fruit and vegetables to enhance their quality and extend their shelf life. However, knowledge of the effects of cold plasma on the quality changes of fresh fruit and vegetables is still limited. Therefore, there is a need for future studies to understand the mechanisms of plasma effects on controlling the interactions between plasma and food products in order to realize the early adoption of the technology to the food industry.

We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io., Measurement(s) brain measurement Technology Type(s) magnetic resonance imaging (MRI) Factor Type(s) athlete/non-athlete group • concussion history Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13394012

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

In this work, we propose a FinFET resistance gate switching nonvolatile memory (RG-FinFET) which comprises a simple RRAM structure on top of a HKMG FinFET gate. The readout is taken from the FinFET V T or I D and its operation is based on the resistance switching instead of the conventional charge storage. The SET/RESET operation of the memory is made by the edge tunneling between top gate and source. The RG-FinFET shows ultra-low switching current, FORMing-free and ultra-fast SET/RESET speed. Comparing to conventional drain-type 1T1R, proposed gate-type 1T features low power consumption, smaller size in layout and larger window. It also exhibits excellent reliabilities, e.g., a very large window with highly stable retention, no sneak path, immunity to disturbances etc. Moreover, RG-FinFET is fully compatible with the logic CMOS technology and well-suited for NOR type memories, showing great potential for the future embedded applications.

Learning-based grasp pose detection algorithms have boosted the performance of robot grasping, but they usually need manually fine-tuning steps to find the balance between detection accuracy and efficient. In this paper, we discard these intermediate procedures, like sampling grasps and generating grasp proposals, and propose an end-to-end grasp pose detection model. Our model uses the RGB image as the input and predicts the single grasp pose in each small grid of the image. Furthermore, the best grasps are found by non-maximum suppression (NMS) strategy. The clustering and ranking procedures are left for NMS while the network only generates dense grasp predictions, which keeps the network simple and efficient. To achieve dense predictions, the predicted grasps of our detection model are represented by the 6 channels images with each pixel location representing a rated grasp. To the best of our knowledge, our model is the first neural network that attaches a grasp pose in pixel level. The model achieves 96.5% accuracy which costs 14ms for prediction of a 480×360 resolution RGB image in Cornell Grasp Dataset, and 90.4% robot grasping success rate for unknown objects with a parallel plate gripper in the real environment.

We investigate a systematic improvement for 3D range-gated imaging in scattering environments. Drawbacks including absorption, ambient light, and scattering effect are studied. The former two are compensated through parameter estimation and preprocessing. With regard to the scattering effect, we propose a new 3D reconfiguration algorithm using a Bayesian approach that incorporates spatial constraints through a general Gaussian Markov random field. The model takes both scene depth and albedo into account, which provides a more informative and accurate restoration result. Hyper-parameters for the statistical mechanism are evaluated adaptively in the procedure and an iterated conditional mode optimization algorithm is employed to find an optimum solution. The performance of our method was assessed via conducting various experiments and the results also indicate that the proposed method is helpful for restoring the 2D image of a scene with improved visibility.

Meat and seafood products present a viable medium for microbial propagation, which contributes to foodborne illnesses and quality losses. The development of novel and effective techniques for microbial decontamination is therefore vital to the food industry. Water presents a unique advantage for large-scale applications, which can be functionalized to inactivate microbial growth, ensuring the safety and quality of meat and seafood products. By taking into account the increased popularity of functionalized water utilization through electrolysis, ozonation and cold plasma technology, relevant literature regarding their applications in meat and seafood safety and quality are reviewed. In addition, the principles of generating functionalized water are presented, and the safety issues associated with their uses are also discussed.Functionalization of water is a promising approach for the microbiological safety and quality of meat and seafood products and possesses synergistic effects when combined with other decontamination approaches. However, functionalized water is often misused since the active antimicrobial component is applied at a much higher concentration, despite the availability of applicable regulations. Functionalized water also shows reduced antimicrobial efficiency and may produce disinfection by-products (DBPs) in the presence of organic matter, especially at a higher concentration of active microbial component. Utilization should be encouraged within regulated guidelines, especially as hurdle technology, while plasma functionalized water which emerges with great potentials should be exploited for future applications. It is hoped that this review should encourage the industry to adopt the functionalized water as an effective alternative technique for the food industry.

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration. The broad and local-scale airborne information collected by these infrastructures can answer many ecological questions. However, extracting and interpreting the biological information from such massive weather radar data remains an intractable problem. Recently, many big-data problems have been solved using the deep learning technology. In this study, the biological information in the weather radar data is identified using the advanced deep learning method. The proposed method consists of two main parts, i.e., a rendering and casting procedure and an image segmentation procedure based on a convolutional neural network. The biological data are automatically extracted by rendering and mapping, image segmentation, and result masking. By analyzing the typical radar data from single and multiple stations, we partly reveal the intensity and speed of the migration pattern. We present the first feasibility study of the extraction of local and large-scale biological phenomena from the Chinese weather radar network data.

The objective of the study was to examine the association between diffusion MRI techniques [diffusion tensor imaging (DTI) and neurite orientation/dispersion density imaging (NODDI)] and brain-injury blood biomarker levels [Tau, neurofilament-light (NfL), glial-fibrillary-acidic-protein (GFAP)] in high-school football and cross-country runners at their baseline, aiming to detect cumulative neuronal damage from prior seasons. Twenty-five football players and 8 cross-country runners underwent MRI and blood biomarker measures during preseason data collection. The whole-brain, tract-based spatial statistics was conducted for six diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial/radial diffusivity (AD, RD), neurite density index (NDI), and orientation dispersion index (ODI). Diffusion metrics and blood biomarker levels were compared between groups and associated within each group. The football group showed lower AD and MD than the cross-country group in various axonal tracts of the right hemisphere. Elevated ODI was observed in the football group in the right hemisphere of the corticospinal tract. Blood biomarker levels were consistent between groups except for elevated Tau levels in the cross-country group. Tau level was positively associated with MD and negatively associated with NDI in the corpus callosum of football players, but not in cross-country runners. Our data suggest that football players may develop axonal microstructural abnormality. Levels of MD and NDI in the corpus callosum were associated with serum Tau levels, highlighting the vulnerability of the corpus callosum against cumulative head impacts. Despite observing multimodal associations in some brain areas, neuroimaging and blood biomarkers may not strongly correlate to reflect the severity of brain damage.

Segmentation of brain tissue in diffusion MRI image space has some unique advantages. A novel segmentation method using the direction-averaged diffusion weighted imaging (DWI) signal is proposed. Two images can be obtained from the fitting of the direction-averaged DWI signal as a function of b-value: one with superior contrast between the gray matter and white matter; one with prominent CSF contrast. A pseudo T1 weighted image can be constructed and standard segmentation tools can be applied. The method was tested on the HCP dataset using SPM12, and showed good agreement with segmentation using the T1 weighted image with the same resolution. The Dice score was all greater than 0.88 for GM or WM with full DWI data and very stable against subsampling of the DWI data in number of diffusion directions, number of shells, and spatial resolution.