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Abstract Since the first report on single-layer MoS2 based transistor, rapid progress has been achieved in two-dimensional (2D) material-based atomically thin electronics, providing an alternative approach to solve the bottleneck in silicon device miniaturization. In this scenario, reliable contact between the metal electrodes and the subnanometer-thick 2D materials becomes crucial in determining the device performance. Here, utilizing the quasi-van der Waals (vdW) epitaxy of metals on fluorophlogopite mica, we demonstrate an all-stacking method for the fabrication of 2D devices with high-quality vdW contacts by mechanically transferring pre-deposited metal electrodes. This technique is applicable for complex device integration with sizes up to the wafer scale and is also capable of tuning the electric characteristics of the interfacial junctions by transferring selective metals. Our results provide an efficient, scalable, and low-cost technique for 2D electronics, allowing high-density device integration as well as a handy tool for fundamental research in vdW materials.

Background and ObjectiveCoronary artery disease remains a leading cause of mortality among individuals with cardiovascular conditions. The therapeutic use of bioresorbable vascular scaffolds (BVSs) through stent implantation is common, yet the effectiveness of current BVS segmentation techniques from Intravascular Optical Coherence Tomography (IVOCT) images is inadequate.MethodsThis paper introduces an enhanced segmentation approach using a novel Wavelet-based U-shape network to address these challenges. We developed a Wavelet-based U-shape network that incorporates an Attention Gate (AG) and an Atrous Multi-scale Field Module (AMFM), designed to enhance the segmentation accuracy by improving the differentiation between the stent struts and the surrounding tissue. A unique wavelet fusion module mitigates the semantic gaps between different feature map branches, facilitating more effective feature integration.ResultsExtensive experiments demonstrate that our model surpasses existing techniques in key metrics such as Dice coefficient, accuracy, sensitivity, and Intersection over Union (IoU), achieving scores of 85.10%, 99.77%, 86.93%, and 73.81%, respectively. The integration of AG, AMFM, and the fusion module played a crucial role in achieving these outcomes, indicating a significant enhancement in capturing detailed contextual information.ConclusionThe introduction of the Wavelet-based U-shape network marks a substantial improvement in the segmentation of BVSs in IVOCT images, suggesting potential benefits for clinical practices in coronary artery disease treatment. This approach may also be applicable to other intricate medical imaging segmentation tasks, indicating a broad scope for future research.

Abstract Synthetic dimensions, wherein dynamics occurs in a set of internal states, have found great success in recent years in exploring topological effects in cold atoms and photonics. However, the phenomena thus far explored have largely been restricted to the non-interacting or weakly interacting regimes. Here, we extend the synthetic dimensions playbook to strongly interacting systems of Rydberg atoms prepared in optical tweezer arrays. We use precise control over driving microwave fields to introduce a tunable U(1) flux in a four-site lattice of coupled Rydberg levels. We find highly coherent dynamics, in good agreement with theory. Single atoms show oscillatory dynamics controllable by the gauge field. Small arrays of interacting atoms exhibit behavior suggestive of the emergence of ergodic and arrested dynamics in the regimes of intermediate and strong interactions, respectively. These demonstrations pave the way for future explorations of strongly interacting dynamics and many-body phases in Rydberg synthetic lattices.

Background Several studies have suggested an association between vitamin deficiency and the development of tuberculosis; however, the precise impact remains unclear. This study aimed to elucidate the relationship between distinct vitamin statuses and the occurrence of tuberculosis.Materials and methods Retrieval was conducted using several databases without language restrictions to capture the eligible studies on tuberculosis and vitamin status. Pooled odds ratios (ORs), relative risks (RRs), and hazard ratios (HRs) were used with 95% confidence intervals (CIs) to clarify the relationship between the different vitamin statuses (A, B, D, and E) and the occurrence of tuberculosis. Subgroup analysis, sensitivity analysis, meta-regression analysis, and Galbraith plot were performed to determine sources of heterogeneity. Potential publication biases were detected using Begg’s test, Egger’s test, and the trim-and-fill test.Results We identified 10,266 original records from our database searches, and 69 eligible studies were considered in this study. The random-effect model showed that people with tuberculosis may exhibit vitamin A deficiency (OR = 10.66, 95%CI: 2.61–43.63, p = .001), while limited cohort studies showed that vitamin A supplementation may reduce tuberculosis occurrence. Additionally, vitamin D deficiency was identified as a risk factor for tuberculosis development (RR = 1.69, 95%CI: 1.06–2.67, p = .026), and people with tuberculosis generally had lower vitamin D levels (OR = 2.19, 95%CI: 1.76–2.73, p

In general, carriers are designed to deliver pharmaceutical (featuring small drug‐molecules) and biopharmaceutical (featuring large‐molecule drugs, e.g., antibodies, proteins, etc.) active ingredients and platform that is capable of transporting nanoparticles is rare. Herein, the fabrication of a supramolecular carrier with a nano‐grid structure for the entrapment of liposomes and exosomes is reported. The nano‐grid particles (NGP) are made of cyclodextrins as elementary units which are widely used to load drugs through host–guest inclusion complexes, whilst its secondary structure further formed the NGP tertiary architectures, enabling the entrapment of nanoparticles. The NGP features stimuli responsiveness, tunable surface charge, and good biocompatibility. The entrapped liposomes in NGP present enhanced distribution in lungs and extended duration of action in mice. And elongated release profiles for over 14 days is obtained for exosomes—making one‐of‐its‐kind unique platform to load and deliver nanoparticles.

The classic SIR model is often used to evaluate the effectiveness of controlling infectious diseases. Moreover, when adopting strategies such as isolation and vaccination based on changes in the size of susceptible populations and other states, it is necessary to develop a non-smooth SIR infectious disease model. To do this, we first add a non-linear term to the classical SIR model to describe the impact of limited medical resources or treatment capacity on infectious disease transmission, and then involve the state-dependent impulsive feedback control, which is determined by the convex combinations of the size of the susceptible population and its growth rates, into the model. Further, the analytical methods have been developed to address the existence of non-trivial periodic solutions, the existence and stability of a disease-free periodic solution (DFPS) and its bifurcation. Based on the properties of the established Poincaré map, we conclude that DFPS exists, which is stable under certain conditions. In particular, we show that the non-trivial order-1 periodic solutions may exist and a non-trivial order-$ k $ ($ k\geq 1 $) periodic solution in some special cases may not exist. Moreover, the transcritical bifurcations around the DFPS with respect to the parameters $ p $ and $ AT $ have been investigated by employing the bifurcation theorems of discrete maps.

Abstract By 31 May 2022, original/Alpha, Delta and Omicron strains induced 101 outbreaks of COVID-19 in mainland China. Most outbreaks were cleared by combining non-pharmaceutical interventions (NPIs) with vaccines, but continuous virus variations challenged the dynamic zero-case policy (DZCP), posing questions of what are the prerequisites and threshold levels for success? And what are the independent effects of vaccination in each outbreak? Using a modified classic infectious disease dynamic model and an iterative relationship for new infections per day, the effectiveness of vaccines and NPIs was deduced, from which the independent effectiveness of vaccines was derived. There was a negative correlation between vaccination coverage rates and virus transmission. For the Delta strain, a 61.8% increase in the vaccination rate (VR) reduced the control reproduction number (CRN) by about 27%. For the Omicron strain, a 20.43% increase in VR, including booster shots, reduced the CRN by 42.16%. The implementation speed of NPIs against the original/Alpha strain was faster than the virus’s transmission speed, and vaccines significantly accelerated the DZCP against the Delta strain. The CRN ( $${R}_{c1}$$ R c 1 ) during the exponential growth phase and the peak time and intensity of NPIs were key factors affecting a comprehensive theoretical threshold condition for DZCP success, illustrated by contour diagrams for the CRN under different conditions. The DZCP maintained the $${R}_{c1}$$ R c 1 of 101 outbreaks below the safe threshold level, but the strength of NPIs was close to saturation especially for Omicron, and there was little room for improvement. Only by curbing the rise in the early stage and shortening the exponential growth period could clearing be achieved quickly. Strengthening China's vaccine immune barrier can improve China's ability to prevent and control epidemics and provide greater scope for the selection and adjustment of NPIs. Otherwise, there will be rapid rises in infection rates and an extremely high peak and huge pressure on the healthcare system, and a potential increase in excess mortality.

Abstract Background The continuous emergence of novel SARS-CoV-2 variants with markedly increased transmissibility presents major challenges to the zero-COVID policy in China. It is critical to adjust aspects of the policy about non-pharmaceutical interventions (NPIs) by searching for and implementing more effective ways. We use a mathematical model to mimic the epidemic pattern of the Omicron variant in Shanghai to quantitatively show the control challenges and investigate the feasibility of different control patterns in avoiding other epidemic waves. Methods We initially construct a dynamic model with a core step-by-step release strategy to reveal its role in controlling the spread of COVID-19, including the city-based pattern and the district-based pattern. We used the least squares method and real reported case data to fit the model for Shanghai and its 16 districts, respectively. Optimal control theory was utilized to explore the quantitative and optimal solutions of the time-varying control strength (i.e., contact rate) to suppress the highly transmissible SARS-CoV-2 variants. Results The necessary period for reaching the zero-COVID goal can be nearly 4 months, and the final epidemic size was 629,625 (95%CI: [608,049, 651,201]). By adopting the city-based pattern, 7 out of 16 strategies released the NPIs more or earlier than the baseline and ensured a zero-resurgence risk at the average cost of 10 to 129 more cases in June. By adopting the district-based pattern, a regional linked release can allow resumption of social activity to ~ 100% in the boundary-region group about 14 days earlier and allow people to flow between different districts without causing infection resurgence. Optimal solutions of the contact rate were obtained with various testing intensities, and higher diagnosis rate correlated with higher optimal contact rate while the number of daily reported cases remained almost unchanged. Conclusions Shanghai could have been bolder and more flexible in unleashing social activity than they did. The boundary-region group should be relaxed earlier and more attention should be paid to the centre-region group. With a more intensive testing strategy, people could return to normal life as much as possible but still ensure the epidemic was maintained at a relatively low level.

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.

Abstract Humic acids (HAs) widely exist in water environment, and has an important impact on the adsorption of pollutants. Herein, HAs (both dissolved and coated) was employed to assess the effect on the removal of the organic contaminant tetracycline (TC) by K2CO3 modified magnetic biochar (KMBC). Results showed that low concentration of dissolved HAs promoted TC removal, likely due to a bridging effect, while higher concentration of dissolved HAs inhibited TC adsorption because of the competition of adsorption sites on KMBC. By characterization analysis, coated HAs changed the surface and pore characteristics of KMBC, which suppressed the TC removal. In a sequential adsorption experiment involving dissolved HAs and TC, the addition of HAs at the end of the experiment led to the formation of HAs-TC ligands with free TC, which improved the adsorption capacity of TC. TC adsorption by KMBC in the presence of dissolved HAs and coated HAs showed a downward trend with increasing pH from 5.0 to 10.0. The TC adsorption process was favorable and endothermic, and could be better simulated by pseudo-second-order kinetics and Freundlich isotherm model. Hydrogen bonds and π–π interactions were hypothesized to be the underlying influencing mechanisms.

The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration.

The ultrashort-term wind power prediction (USTWPP) technology assists the grid to arrange spare capacity, which is important to optimize power investment reasonably. To improve the accuracy of USTWPP and optimize power investment requirements, a USTWPP method with dynamic switching of multiple models is proposed. For high wind speed fluctuation samples, the wind speed-power curve (WSPC) is fitted in a large sample of historical data, and the corrected wind speed is the input of WSPC. The spatiotemporal attentive network model (STAN) is built for the prediction of low wind speed fluctuation samples. According to the real-time fluctuation characteristics of the correction wind speed, a switching mechanism between multiple models is established to reconstruct the prediction results along the time axis direction, and the predicted power is set to zero for the samples whose correction wind speed is lower than the cut-in wind speed. We conducted simulation experiments with data provided by a wind farm with an installed capacity of 130.5 MW in China. The normalized root mean square error (NRMSE) for the 4 h ahead predicted power reaches 0.0907, which verified the validity and applicability of the proposed model.

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.

Staphylococcus aureus (S. aureus) is a major foodborne pathogen. Rapid and specific detection is crucial for controlling staphylococcal food poisoning. This study reported a Staphylococcus phage named LSA2302 showing great potential for applications in the rapid detection of S. aureus. Its biological characteristics were identified, including growth properties and stability under different pH and temperature conditions. The genomic analysis revealed that the phage has no genes associated with pathogenicity or drug resistance. Then, the phage-functionalized magnetic beads (pMB), serving as a biological recognition element, were integrated with ATP bioluminescence assays to establish a biosensing method for S. aureus detection. The pMB enrichment brought high specificity and a tenfold increase in analytical sensitivity during detection. The whole detection process could be completed within 30 min, with a broad linear range of 1 × 104 to 1 × 108 CFU/mL and a limit of detection (LOD) of 2.43 × 103 CFU/mL. After a 2 h pre-cultivation, this method is capable of detecting bacteria as low as 1 CFU/mL. The recoveries of S. aureus in spiked skim milk and chicken samples were 81.07% to 99.17% and 86.98% to 104.62%, respectively. Our results indicated that phage-based biosensing can contribute to the detection of target pathogens in foods.

This study examines the spreading behavior of window spill fire under the action of lateral wind and its influence on the temperature distribution of polyethylene sandwich panels on the outer surface of buildings. Twelve working conditions were set up based on different opening sizes and wind speeds. Under the action of lateral wind, with a constant opening size, this study has found greater wind speed, lower average flame height, and longer horizontal flame propagation length. This study is based on the feature-length. The facade flame height model without wind was developed, and the average flame height model under extreme conditions was established based on the speculation of a lateral wind Froude number. In addition, under constant opening size, the peak surface temperature of the PE sandwich panel is the highest when the wind speed is 1 m/s. These conclusions are of great significance for fire studies.

Pure high impact polystyrene (HIPS) is widely used, but it is flammable. During combustion, it produces black smoke, drips, and poses other hazards to human safety, making it necessary to study flame-retardant HIPS. In this study, nitrogen-phosphorus-silicon flame retardants were prepared by using melamine cyanurate (MC), aluminum hypophosphite (ALHP), and nano-silica (nano-SiO2) to improve the flame retardancy of HIPS. The flame retardancy and mechanism of HIPS composites were studied. The results showed that when the ratio of MC to ALHP was 1:4 and nano-SiO2 was 1.5 wt%, the limiting oxygen index (LOI) value of HIPS composite was 26.9%, and PHRR and THR were reduced to 254 kW/m2 and 85 MJ/m2, respectively. The smoke production decreased by 32%, and the carbon residue increased by 70%. ALHP and nano-SiO2 mainly play a flame retardant role in the condensed phase. MC mainly plays a flame retardant role in the gas phase, as well as the condensed phase.

Psathyrostachys huashanica Keng (2n = 2x = 14, NsNs) is an excellent gene resource for wheat breeding, which is characterized by early maturity, low plant height, and disease resistance. The wheat-P. huashanica derivatives were created by the elite genes of P. huashanica and permeate into common wheat through hybridization. Among them, a long-glume material 20JH1155 was identified, with larger grains and longer spike than its parents. In the present study, the methods of cytological observation, GISH, and sequential FISH analysis showed that 20JH1155 contained 21 pairs of wheat chromosomes and a pair of P. huashanica. There were some differences in 5A and 7B chromosomes between 20JH1155 and parental wheat 7182. Molecular marker, FISH, and sequence cloning indicated 20JH1155 alien chromosomes were 3Ns of P. huashanica. In addition, differentially expressed genes during immature spikelet development of 20JH1155 and 7182 and predicted transcription factors were obtained by transcriptome sequencing. Moreover, a total of 7 makers derived from Ph#3Ns were developed from transcriptome data. Taken together, the wheat-P. huashanica derived line 20JH1155 provides a new horizon on distant hybridization of wheat and accelerates the utilization of genes of P. huashanica.

The remote sensing scene images classification has been of great value to civil and military fields. Deep learning models, especially the convolutional neural network (CNN), have achieved great success in this task, however, they may suffer from two challenges: first, the sizes of the category objects are usually different, but the conventional CNN extracts the features with fixed convolution extractor, which could cause the failure in learning the multiscale features; second, some image regions may not be useful during the feature learning process, therefore, how to guide the network to select and focus on the most relevant regions is crucially vital for remote sensing scene image classification. To address these two challenges, we propose a multiscale attention network (MSA-Network), which integrates a multiscale (MS) module and a channel and position attention (CPA) module to boost the performance of the remote sensing scene classification. The proposed MS module learns multiscale features by adopting various sizes of sliding windows from different depths’ layers and receptive fields. The CPA module is composed of two parts: the channel attention (CA) module and the position attention (PA) one. The CA module learns the global attention features from channel-level, and the PA module extracts the local attention features from pixel-level. Thus, fusing both of those two attention features, the network is apt to focus on the more critical and salient regions automatically. Extensive experiments on UC Merced, AID, NWPU-RESISC45 datasets demonstrate that the proposed MSA-Network outperforms several state-of-the-art methods.

At present, owing to the convenience of stylus pen-based input devices and touch-sensitive screens, users often use tablet computers to draw, take notes, and annotate documents. The aim of the present research was to investigate, for tablet computer users, the writing speed, tracing accuracy, comfort perception, and satisfaction in terms of five angles formed by the use of an external peripheral. In two kinds of tasks (writing and tracing lines), a control experimental design with various tilt angles (5°, 15°, 25°, 35° and 45° from horizontal) was adopted. The efficiency of writing tasks and the accuracy of line tracing were calculated following each incline. Using the five-point Likert Scale, the tablet computer users’ perceived comfort and ease of use were also evaluated. The results reveal that when tablet personal computers (Tablet PCs) were mounted at a high tilt angle (35°), the writing speed was high (P = 0.028). Meanwhile, when the tablet computer was mounted at a low tilt angle (5°), user comfort and satisfaction were high (P < 0.001). The findings of the present article suggest that users who need to write or draw for short periods increase the angle (approximate 35°) of their Tablet PC to improve their writing speed. Conversely, users who need long periods of use could position the Tablet PC at a lower tilt angle (approximate 5°) to reduce the risk of any musculoskeletal disorder.

Food is essential for human life and has been the concern of many healthcare conventions. Nowadays new dietary assessment and nutrition analysis tools enable more opportunities to help people understand their daily eating habits, exploring nutrition patterns and maintain a healthy diet. In this paper, we develop a deep model based food recognition and dietary assessment system to study and analyze food items from daily meal images (e.g., captured by smartphone). Specifically, we propose a three-step algorithm to recognize multi-item (food) images by detecting candidate regions and using deep convolutional neural network (CNN) for object classification. The system first generates multiple region of proposals on input images by applying the Region Proposal Network (RPN) derived from Faster R-CNN model. It then indentifies each region of proposals by mapping them into feature maps, and classifies them into different food categories, as well as locating them in the original images. Finally, the system will analyze the nutritional ingredients based on the recognition results and generate a dietary assessment report by calculating the amount of calories, fat, carbohydrate and protein. In the evaluation, we conduct extensive experiments using two popular food image datasets - UEC-FOOD100 and UEC-FOOD256. We also generate a new type of dataset about food items based on FOOD101 with bounding. The model is evaluated through different evaluation metrics. The experimental results show that our system is able to recognize the food items accurately and generate the dietary assessment report efficiently, which will benefit the users with a clear insight of healthy dietary and guide their daily recipe to improve body health and wellness.

Human pose estimation localizes body keypoints to accurately recognizing the postures of individuals given an image. This step is a crucial prerequisite to multiple tasks of computer vision which include human action recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. Therefore, we present this survey article to fill the knowledge gap and shed light on the researches of 2D human pose estimation. A brief introduction is followed by classifying it as a single or multi-person pose estimation based on the number of people needed to be tracked. Then gradually the approaches used in human pose estimation are described before listing some applications and also flaws facing in pose estimation. Following that, a center of attention is given on briefly discussing researches with a significant effect on human pose estimation and examine the novelty, motivation, architecture, the procedures (working principles) of each model together with its practical application and drawbacks, datasets implemented, as well as the evaluation metrics used to evaluate the model. This review is presented as a baseline for newcomers and guides researchers to discover new models by observing the procedure and architecture flaws of existing researches.

When injectors are used in petroleum industries or sea water desalination system, it is a special case that the induced gas is accompanied by water, the ejector is in two-phase operation. In the present study, the ejector performance at two phase flow is investigated experimentally and numerically. The results indicate that: the entrainment ratio would decrease when the water is injected into the induced flow, as well as the pressure ratio. The maximum decrement of entrainment ratio is around 40.5% at sub-critical mode when the motive pressure is 1.0 MPa, the induced pressure is 0.2 MPa, liquid-air ratio is 22%. For the pressure ratio, the maximum decrement is 62.5% when the motive pressure is 1.0 MPa, the induced pressure is 0.5 MPa, liquid-air ratio is 2.87%. However, the lower the induced pressure is, the more obvious the effect of mass flow rate on entrainment rate is, while the effect on pressure ratio is just opposite. Moreover, the performance becomes worse in sub-critical mode than that in critical mode. The effect of NXP on ejector performance also has been analyzed. There exists an optimum NXP whether the two phase flow ejector is in critical mode or sub-critical mode. The optimum NXP for entrainment ratio enlarges from 3.6 mm to 4.4 mm with the increase of water mass flowrate, and is bigger than that of single phase ejector (the optimum NXP is 2.8 mm). This study may provide a beneficial reference for the operation of supersonic ejectors on two phase flow.

Abstract Machine learning (ML) techniques have become ubiquitous and indispensable for solving intricate problems in most disciplines. To determine the extent of funding for clinical research projects applying ML techniques by the National Institutes of Health (NIH) in 2017, we searched the NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER) system using relevant keywords. We identified 535 projects, which together received a total of $264 million, accounting for 2% of the NIH extramural budget for clinical research.

In this article, a coding metasurface is specifically designed to switch transmission and reflection functionalities between two different frequency bands for linearly polarized waves within wide incidence angles. A metasurface consists of four metallic patterns, where the middle two structures are inserted to ensure effective performance of transmission and reflection, while the top and bottom patterns are designed based on simultaneously controlling the reflection phase for both polarization states. It has been experimentally demonstrated that the proposed metasurface can convert a transmission band into a complete reflection band (meanwhile, the reflection band is translated into a complete transmission band) by changing the incident polarization state. Highly efficient transmission and reflection characteristics have been achieved from 21.1 to 24.5 GHz as well as from 33.3 to 38.3 GHz, whereas more than 10 dB radar cross-section (RCS) reduction has also been obtained for both TE and TM modes in their respective reflection bands. The performance of the proposed metasurface is well sustained up to 40° oblique incidence. This work will help to open a new aspect in metasurfaces to manipulate the electromagnetic waves at preferable frequency bands to achieve desirable functionalities.

BackgroundHarnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. ObjectiveThis study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. MethodsWe used natural language processing (NLP) algorithms to identify symptom- and medical condition–related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. ResultsWithin a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition–related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P

BackgroundCOVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. ObjectiveThe aim of this study is to measure the public’s behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic. MethodsNatural language processing (NLP) algorithms were used to identify COVID-19–related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States. ResultsThe final sample size included 9,065,733 posts, 70% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69%), compared with COVID-19 in general (n=3,390,139, 37%) and COVID-19 public health measures (n=1,836,200, 20%). ConclusionsThere was a decline in COVID-19–related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue.

Abstract Background Plant non-specific lipid transfer proteins (nsLTPs) are small, basic proteins that are abundant in higher plants. They have been reported to play an important role in various plant physiological processes, such as lipid transfer, signal transduction, and pathogen defense. To date, a comprehensive analysis of the potato nsLTP gene family is still lacking after the completion of potato (Solanum tuberosum L.) genome sequencing. A genome-wide characterization, classification and expression analysis of the StnsLTP gene family was performed in this study. Results In this study, a total of 83 nsLTP genes were identified and categorized into eight types based on Boutrot’s method. Multiple characteristics of these genes, including phylogeny, gene structures, conserved motifs, protein domains, chromosome locations, and cis-elements in the promoter sequences, were analyzed. The chromosome distribution and the collinearity analyses suggested that the expansion of the StnsLTP gene family was greatly enhanced by the tandem duplications. Ka/Ks analysis showed that 47 pairs of duplicated genes tended to undergo purifying selection during evolution. Moreover, the expression of StnsLTP genes in various tissues was analyzed by using RNA-seq data and verified by quantitative real-time PCR, revealing that the StnsLTP genes were mainly expressed in younger tissues. These results indicated that StnsLTPs may played significant and functionally varied roles in the development of different tissues. Conclusion In this study, we comprehensively analyzed nsLTPs in potato, providing valuable information to better understand the functions of StnsLTPs in different tissues and pathways, especially in response to abiotic stress.

Early fault detection and diagnosis can increase the stability, reliability and safety of manufacturing equipment. It can be used for protection against unforeseen emergencies in manufacturing system. Recently, fault diagnosis (FD) methods based on deep learning (DL) have become a research hotspot for their excellent performance. However, the training process of deep learning (DL) models is time-consuming because of their high computation complexity. Moreover, most of DL-based FD methods have an assumption the distribution of training datasets in the source domain is the same as that of test datasets in the target domain. However, it is impossible in typical real-world manufacturing applications. In order to cope with these two problems, this paper proposes a FD method based on convolutional neural network (CNN) and transfer learning (TL). Firstly, a CNN model based on LeNet-5 is designed to extract fault features from images which is converted from raw signal data by continuous wavelet transform (CWT), then the performance of the CNN model are improved by fine-tuning which is an effective way of TL. The proposed method is conducted on two well-known datasets and the experimental results show that the proposed method can significantly improve the accuracy and efficiency performance a lot compared with the standard CNN model.

Artificial stent implantation is one of the most effective ways to treat vascular diseases. However, commonly used metal stents have many negative effects, such as being difficult to remove and recover, whereas bio-absorbable stents have become the best way to treat vascular diseases because of their absorbability and harmlessness. It is very important in vascular medical imaging, such as optical coherence tomography (OCT), to be able to effectively track the position of stents in blood vessels. This task is undoubtedly labor-intensive, and it is inefficient to rely on experts to identify various scaffolds from medical images. In this paper, a novel automatic detection method for bioresorbable vascular scaffolds (BVSs) via a U-shaped convolutional neural network is developed. The method is composed of three steps: data preparation, network training, and network testing. First, in the data preparation step, we complete the task of labeling related samples based on expert experience, and then, these labeled OCT images are divided into the original and masked OCT images (corresponding to X and Y in supervised learning, respectively). Next, we train our data on a U-shaped convolutional neural network, which consists of five downsampling modules and four upsampling modules. We can obtain a related training model, which can be used to predict the related samples. In the testing stage, we can easily utilize the trained model to predict the input OCT data so that we can obtain the relevant information about a BVS in an OCT image. Obviously, this method can assist doctors in diagnosing the disease and in making important decisions. Finally, some experiments are performed to validate our proposed method, and the IoU criterion is used to measure the superiority of our proposed method. The results show that our proposed method is completely feasible and superior.

Volume rendering is an emerging technique widely used in the medical field to visualize human organs using tomography image slices. In volume rendering, sliced medical images are transformed into attributes, such as color and opacity through transfer function. Thus, the design of the transfer function directly affects the result of medical images visualization. A well-designed transfer function can improve both the image quality and visualization speed. In one of our previous paper, we designed a multi-dimensional transfer function based on region growth to determine the transparency of a voxel, where both gray threshold and gray change threshold are used to calculate the transparency. In this paper, a new approach of the transfer function is proposed based on clustering analysis of gray-gradient mode histogram, where volume data is represented in a two-dimensional histogram. Clustering analysis is carried out based on the spatial information of volume data in the histogram, and the transfer function is automatically generated by means of clustering analysis of the spatial information. The dataset of human thoracic is used in our experiment to evaluate the performance of volume rendering using the proposed transfer function. By comparing with the original transfer function implemented in two popularly used volume rendering systems, visualization toolkit (VTK) and RadiAnt DICOM Viewer, the effectiveness and performance of the proposed transfer function are demonstrated in terms of the rendering efficiency and image quality, where more accurate and clearer features are presented rather than a blur red area. Furthermore, the complex operations on the two-dimensional histogram are avoided in our proposed approach and more detailed information can be seen from our final visualized image.

Automatic segmentation of lumen contour plays an important role in medical imaging and diagnosis, which is the first step towards the evaluation of morphology of vessels under analysis and the identification of possible atherosclerotic lesions. Meanwhile, quantitative information can only be obtained with segmentation, contributing to the appearance of novel methods which can be successfully applied to intravascular optical coherence tomography (IVOCT) images. This paper proposed a new end-to-end neural network (N-Net) for the automatic lumen segmentation, using multi-scale features based deep neural network, for IVOCT images. The architecture of the N-Net contains a multi-scale input layer, a N-type convolution network layer and a cross-entropy loss function. The multi-scale input layer in the proposed N-Net is designed to avoid the loss of information caused by pooling in traditional U-Net and also enriches the detailed information in each layer. The N-type convolutional network is proposed as the framework in the whole deep architecture. Finally, the loss function guarantees the degree of fidelity between the output of proposed method and the manually labeled output. In order to enlarge the training set, data augmentation is also introduced. We evaluated our method against loss, accuracy, recall, dice similarity coefficient, jaccard similarity coefficient and specificity. The experimental results presented in this paper demonstrate the superior performance of the proposed N-Net architecture, comparing to some existing networks, for enhancing the precision of automatic lumen segmentation and increasing the detailed information of edges of the vascular lumen.

Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment.

Fusarium head blight (Fhb), powdery mildew, and stripe rust are major wheat diseases globally. Aegilops geniculata Roth (UgUgMgMg, 2n = 4x = 28), a wild relative of common wheat, is valuable germplasm of disease resistance for wheat improvement and breeding. Here, we report the development and characterization of two substitution accessions with high resistance to powdery mildew, stripe rust and Fhb (W623 and W637) derived from hybrid progenies between Ae. geniculata and hexaploid wheat Chinese Spring (CS). Fluorescence in situ hybridization (FISH), Genomic in situ hybridizations (GISH), and sequential FISH-GISH studies indicated that the two substitution lines possess 40 wheat chromosomes and 2 Ae. geniculata chromosomes. Furthermore, compared that the wheat addition line parent W166, the 2 alien chromosomes from W623 and W637 belong to the 7Mg chromosomes of Ae. geniculata via sequential FISH-GISH and molecular marker analysis. Nullisomic-tetrasomic analysis for homoeologous group-7 of wheat and FISH revealed that the common wheat chromosomes 7A and 7B were replaced in W623 and W637, respectively. Consequently, lines W623, in which wheat chromosomes 7A were replaced by a pair of Ae. geniculata 7Mg chromosomes, and W637, which chromosomes 7B were substituted by chromosomes 7Mg, with resistance to Fhb, powdery mildew, and stripe rust. This study has determined that the chromosome 7Mg from Ae. geniculata exists genes resistant to Fhb and powdery mildew.

Traditional Chinese state sacrificial ritual represented a symbolic system of integrating religious belief, divine authority, and political legitimacy. The Northern Stronghold (Beizhen 北鎮, i.e., Mount Yiwulü 醫巫閭山) was equal in status to the other four strongholds, which, moreover, served as a strategic military fortress and represented the earth virtue in the early state sacrifice system. In the late imperial era of China, and during the Yuan (1279–1368) and Qing (1644–1911) dynasties in particular, the Northern Stronghold swiftly achieved prominence and eventually became an instrument used by minority ethnic groups, namely the Mongolians and Manchus, when elaborating upon the legitimacy of their political regimes. During the Yuan dynasty, the mountain spirits of the five strongholds (Wuzhen 五鎮) were formally invested as kings and, as a result, were accorded equivalent sacrifices in comparison to those given to the five sacred peaks (Wuyue 五嶽). Given that the Northern Stronghold was located near the northeast of Beijing, the Yuan government considered it the foundation of the state. Thereafter, the Northern Stronghold was regarded as the most important of the five stronghold mountains. In the Ming dynasty (1368–1644), the Northern Stronghold Temple (Beizhenmiao 北鎮廟) was reconstructed as both a military fortress and religious site, while its representation as a significant site for a foreign conquest dynasty diminished and its significance as a bastion of anti-insurgent suppression emerged. By the Qing dynasty, the Northern Stronghold was regarded as an integral component of the geographic origin of the Manchu people and thereby assumed once again a position of substantial political significance. Several Qing emperors visited the Northern Stronghold and left poems and prose written in graceful Chinese to present their high respect and their mastery of Chinese culture. The history of the Northern Stronghold demonstrates how the ethnic minority regimes successfully utilized the traditional Chinese state sacrificial ritual to serve their political purpose.

Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.

Robust cooling and nondestructive imaging are prerequisites for many emerging applications of neutral atoms trapped in optical tweezers, such as their use in quantum information science and analog quantum simulation. The tasks of cooling and imaging can be challenged, however, by the presence of large trap-induced shifts of their respective optical transitions. Here, we explore a system of ^{39}K atoms trapped in a near-detuned (780nm) optical tweezer, which leads to relatively minor differential (ground versus excited state) Stark shifts. We demonstrate that simple and robust loading, cooling, and imaging can be achieved through combined addressing of the D_{1} and D_{2} transitions. While imaging on the D_{2} transition, we can simultaneously apply Λ-enhanced gray molasses (GM) on the D_{1} transition, preserving low backgrounds for single-atom imaging through spectral filtering. Using D_{1} cooling during and after trap loading, we demonstrate enhanced loading efficiencies as well as cooling to low temperatures. These results suggest a simple and robust path for loading and cooling large arrays of potassium atoms in optical tweezers through the use of resource-efficient near-detuned optical tweezers and GM cooling.

Magnetic Resonance Imaging (MRI) is an indispensable tool in the diagnosis of brain diseases due to painlessness and safety. Nevertheless, Rician noise is inevitably injected during the image acquisition process, which leads to poor observation and interferes with the treatment. Owing to the complexity of Rician noise, using the elimination method of Gaussian to remove it does not perform well. Therefore, the feature fusion and attention network (FFA-DMRI) is proposed to separate noise from observed MRI. Inspired by the attention-guided CNN network (ADNet) and Convolutional block attention module (CBAM), a spatial attention mechanism has been specially designed to obtain the area of interest in MRI. Furthermore, the feature fusion block concatenates local with global information, which makes full use of the multilevel structure and boosts the expressive ability of network. The comprehensive experiments on Alzheimer’s disease neuroimaging initiative dataset (ADNI) have demonstrated high effectiveness of FFA-DMRI with maintaining the crucial brain details. Moreover, in terms of visual inspections, the denoising results are also consistent with human perception.

The subjective cognitive decline (SCD) may last for decades prior to the onset of dementia and has been proposed as a risk population for development to amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD). Disruptions of functional connectivity and causal connectivity (CC) in the salience network (SN) are generally perceived as prominent hallmarks of the preclinical AD. Nevertheless, the alterations in anterior SN (aSN), and posterior SN (pSN) remain unclear. Here, we hypothesized that both the functional connectivity (FC) and CC of the SN subnetworks, comprising aSN and pSN, were distinct disruptive in the SCD and aMCI. We utilized resting-state functional magnetic resonance imaging to investigate the altered FC and CC of the SN subnetworks in 28 healthy controls, 23 SCD subjects, and 29 aMCI subjects. In terms of altered patterns of FC in SN subnetworks, aSN connected to the whole brain was significantly increased in the left orbital superior frontal gyrus, left insula lobule, right caudate lobule, and left rolandic operculum gyrus (ROG), whereas decreased FC was found in the left cerebellum superior lobule and left middle temporal gyrus when compared with the HC group. Notably, no prominent statistical differences were obtained in pSN. For altered patterns of CC in SN subnetworks, compared to the HC group, the aberrant connections in aMCI group were separately involved in the right cerebellum inferior lobule (CIL), right supplementary motor area (SMA), and left ROG, whereas the SCD group exhibited more regions of aberrant connection, comprising the right superior parietal lobule, right CIL, left inferior parietal lobule, left post-central gyrus (PG), and right angular gyrus. Especially, SCD group showed increased CC in the right CIL and left PG, whereas the aMCI group showed decreased CC in the left pre-cuneus, corpus callosum, and right SMA when compared to the SCD group. Collectively, our results suggest that analyzing the altered FC and CC observed in SN subnetworks, served as impressible neuroimaging biomarkers, may supply novel insights for designing preclinical interventions in the preclinical stages of AD.

Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow‐up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health System between January 2013 and December 2017. We described the prevalence and follow‐up pattern of patients with at least 1 systolic blood pressure (SBP) ≥160 mm Hg or diastolic blood pressure (DBP) ≥100 mm Hg and patients with at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Of 373 861 patients included, 56 909 (15.2%) had at least 1 SBP ≥160 mm Hg or DBP ≥100 mm Hg, and 10 476 (2.8%) had at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Among patients with SBP ≥160 mm Hg or DBP ≥100 mm Hg, only 28.3% had a follow visit within 1 month (time window of follow‐up recommended by the guideline) and 19.9% subsequently achieved control targets (SBP

Background: Although increased age is associated with higher systolic blood pressure (SBP) in general, there may be variation across individuals in how SBP changes over time. The goal of this paper is to identify heterogeneity in SBP trajectories among young adults with similar initial values and identify personal characteristics associated with different trajectory patterns. This may have important implications for prevention and prognosis. Methods: A cohort of 12,468 individuals aged 18–35 years in the Qingdao Port Cardiovascular Health Study in China was followed yearly during 2000–2011. Individuals were categorized into three strata according to their baseline SBP: ≤110 mmHg, 111–130 mmHg, and >130 mmHg. Within each stratum, group-based trajectory analyses were conducted to identify distinct SBP trajectory patterns, and their association with sociodemographic and baseline health characteristics was assessed by ordinal logistic regression. Results: Five distinct groups of individuals exhibiting divergent patterns of increasing, stable or decreasing SBP trends were identified within each stratum. This is a first report to identify a subgroup with decreasing trend in SBP. Individuals with more advanced age, having less than high school education, family history of cardiovascular diseases, greater body mass index, greater waist circumference, and hyperlipidemia at baseline were more likely to experience trajectories of higher SBP within each stratum. Conclusions: The diverging trajectories among young adults with similar initial SBP highlight the need for prevention and feasibility of effective blood pressure control, while the identified risk factors may inform targeted interventions.

Alzheimer’s disease (AD), which most commonly occurs in the elder, is a chronic neurodegenerative disease with no agreed drugs or treatment protocols at present. Amnestic mild cognitive impairment (aMCI), earlier than AD onset and later than subjective cognitive decline (SCD) onset, has a serious probability of converting into AD. The SCD, which can last for decades, subjectively complains of decline impairment in memory. Distinct altered patterns of default mode network (DMN) subnetworks connected to the whole brain are perceived as prominent hallmarks of the early stages of AD. Nevertheless, the aberrant phase position connectivity (PPC) connected to the whole brain in DMN subnetworks remains unknown. Here, we hypothesized that there exist distinct variations of PPC in DMN subnetworks connected to the whole brain for patients with SCD and aMCI, which might be acted as discriminatory neuroimaging biomarkers. We recruited 27 healthy controls (HC), 20 SCD and 28 aMCI subjects, respectively, to explore aberrant patterns of PPC in DMN subnetworks connected to the whole brain. In anterior DMN (aDMN), SCD group exhibited aberrant PPC in the regions of right superior cerebellum lobule (SCL), right superior frontal gyrus of medial part (SFGMP), and left fusiform gyrus (FG) in comparison of HC group, by contrast, no prominent difference was found in aMCI group. It is important to note that aMCI group showed increased PPC in the right SFGMP in comparison with SCD group. For posterior DMN (pDMN), SCD group showed decreased PPC in the left superior parietal lobule (SPL) and right superior frontal gyrus (SFG) compared to HC group. It is noteworthy that aMCI group showed decreased PPC in the left middle frontal gyrus of orbital part (MFGOP) and right SFG compared to HC group, yet increased PPC was found in the left superior temporal gyrus of temporal pole (STGTP). Additionally, aMCI group exhibited decreased PPC in the left MFGOP compared to SCD group. Collectively, our results have shown that the aberrant regions of PPC observed in DMN are related to cognitive function, and it might also be served as impressible neuroimaging biomarkers for timely intervention before AD occurs.

Background Characterizing and assessing the prevalence, awareness, and treatment patterns of patients with isolated diastolic hypertension (IDH) can generate new knowledge and highlight opportunities to improve their care. Methods and Results We used data from the China PEACE (Patient‐centered Evaluative Assessment of Cardiac Events) Million Persons Project, which screened 2 351 035 participants aged 35 to 75 years between 2014 and 2018. IDH was defined as systolic and diastolic blood pressure of 28 kg/m2, consumed alcohol, had diabetes mellitus, and prior cardiovascular events were more likely to have IDH (all P28 kg/m2, higher income, diabetes mellitus, prior cardiovascular events, and Central or Eastern region (all P

With the arrival of the Web 2.0 era and the rapid development of virtual reality (VR) technology in recent years, WebVR technology has emerged as the combination of Web 2.0 and VR. Moreover, the concept of “WebVR + medical science”is also proposed to advance medical applications. However, due to the limited storage space and low computing capability of Web browsers, it is difficult to achieve real-time rendering of large-scale medical vascular models on the Web, let alone large-scale vascular animation simulations. The framework proposed in this paper can achieve virtual display of the medical blood vasculum, including lightweight processing of the vasculum and virtual realization of blood flow. This innovative framework presents a simulation algorithm for the virtual blood path based on the Catmull-Rom spline. The mechanisms of progressive compression and online recovery of the lightweight vascular structure are further proposed. The experimental results show that our framework has a shorter browser-side response time than existing methods and achieves efficient real-time simulation.

The ischaemic heart disease has become one of the leading causes of mortality worldwide. Dynamic single-photon emission computed tomography (D-SPECT) is an advanced routine diagnostic tool commonly used to validate the myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a 3-D myocardial model and assisting clinicians to perform assessments of myocardial function. Thus, image segmentation is a key technology in preclinical cardiac studies. Intensity inhomogeneity is one of the common challenges in image segmentation and is caused by image artifacts and instrument inaccuracy. In this paper, a novel region-based active contour model that can segment the myocardial D-SPECT image accurately is presented. First, a local region-based fitting image is defined based on the information related to the intensity. Second, a likelihood fitting image energy function is built in a local region around each point in a given vector-valued image. Next, the level set method is used to present a global energy function with respect to the neighborhood center. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy model and local image fitting energy model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation.

The integrative analysis of multimodal medical images plays an important role in the diagnosis of coronary artery disease by providing additional comprehensive information that cannot be found in an individual source image. Intravascular ultrasound (IVUS) and optical coherence tomography (IV-OCT) are two imaging modalities that have been widely used in the medical practice for the assessment of arterial health and the detection of vascular lumen lesions. IV-OCT has a high resolution and poor penetration, while IVUS has a low resolution and high detection depth. This paper proposes a new approach for the fusion of intravascular ultrasound and optical coherence tomography pullbacks to significantly improve the use of those two types of medical images. It also presents a new two-phase multimodal fusion framework using a coarse-to-fine registration and a wavelet fusion method. In the coarse-registration process, we define a set of new feature points to match the IVUS image and IV-OCT image. Then, the improved quality image is obtained based on the integration of the mutual information of two types of images. Finally, the matched registered images are fused with an approach based on the new proposed wavelet algorithm. The experimental results demonstrate the performance of the proposed new approach for significantly enhancing both the precision and computational stability. The proposed approach is shown to be promising for providing additional information to enhance the diagnosis and enable a deeper understanding of atherosclerosis.

Panax notoginseng is the most important valued and endemic medicinal herb to China. It belongs to the Araliaceae family, which has a longer medical history in China. The complete chloroplast genome of Panax notoginseng is 156,387 bp in size and displays a typical quadripartite structure of the large single-copy region (LSC, 86,112 bp), small single-copy region (SSC, 18,005 bp) that separate by a pair of inverted repeat regions (IRs, each for 26,135 bp). The base nucleotide composition of the cpDNA is 30.8% of A, 31.1% of T, 19.9% of C, and 18.2% of G, with a total G + C content of 38.1%. The whole chloroplast genome of P. notoginseng contains 134 genes, including 89 protein-coding genes (PCGs), 37 transfer RNA (tRNAs) genes, and eight ribosomal RNA (rRNAs) genes species. Phylogenetic relationship analysis based on 37 medicinal herbs species confirmed the position of P. notoginseng closely related to Panax japonicus and Panax vietnamensis.

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

Despite the new ideas were inspired in medical treatment by the rapid advancement of three-dimensional (3D) printing technology, there is still rare research work reported on 3D printing of coronary arteries being documented in the literature. In this work, the application value of 3D printing technology in the treatment of cardiovascular diseases has been explored via comparison study between the 3D printed vascular solid model and the computer aided design (CAD) model. In this paper, a new framework is proposed to achieve a 3D printing vascular model with high simulation. The patient-specific 3D reconstruction of the coronary arteries is performed by the detailed morphological information abstracted from the contour of the vessel lumen. In the process of reconstruction which has 5 steps, the morphological details of the contour view of the vessel lumen are merged along with the curvature and length information provided by the coronary angiography. After comparing with the diameter of the narrow section and the diameter of the normal section in CAD models and 3D printing model, it can be concluded that there is a high correlation between the diameter of vascular stenosis measured in 3D printing models and computer aided design models. The 3D printing model has high-modeling ability and high precision, which can represent the original coronary artery appearance accurately. It can be adapted for prevascularization planning to support doctors in determining the surgical procedures.

BACKGROUND:The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. METHODS AND FINDINGS:We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. CONCLUSIONS:Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies.