The Application of Mixed Reality to Sentinel Lymph Node Biopsy in Breast Cancer.

OBJECTIVE: To explore the value of mixed reality (MR) in sentinel lymph node biopsy (SLNB) in patients with breast cancer. METHODS: A total of 300 patients with breast cancer who underwent SLNB enrolled and were randomly divided into two groups. In group A, only dye (an injection of methylene blue) was used to detect sentinel lymph nodes, while in group B MR was used for positioning in addition to dye. (MR localization method: Before the surgery, we built a 1:1 3D reconstruction model based on the patient's CT or MRI original data, and after the patient was injected with dye, we completed MR localization by overlapping the pre-marked image with the model.) RESULTS: During surgery, the detection time in group B was significantly shorter than in group A (3.62 ± 1.20 vs.7.87 ± 1.86; p < 0.001). At 1-month post-surgery follow-up, the incidence of pain in group B was lower than that in group A (2.70 vs. 8.28%, p = 0.036). The incidence of upper limb dysfunction was lower in group B than in group A (2.03 vs. 8.97%, p = 0.009). In terms of the incidence of pain, group B was better than group A (0.68 vs. 3.45%, p = 0.094). The satisfaction of the two groups was scored, and the results showed that group B was better than group A (4.04 ± 0.91 vs.3.32 ± 0.94, p < 0.001). CONCLUSION: The application of MR to SLNB in breast cancer can significantly reduce the detection time and the occurrence of complications and improve patient satisfaction.

Characterizing the source apportionment of black carbon and ultrafine particles near urban roads in Xi'an, China.

Better knowledge of the sources of black carbon (BC) and ultrafine particles (UFPs) in urban roadway region will provide helpful information for improving road air pollution caused by vehicle emissions. For this purpose, we conducted daily observation of BC and UFPs at two trafficked sites (intersection and roadside), and a background site in Xi'an, China. The concentration data of BC and UFPs measured were combined with Aethalometer model and UFPs source apportion model, to determine and analyze the sources of BC in an urban road region. Further, the source and variation characteristics of primary and secondary UFPs at the roadside sites were clarified. The results showed that average BC concentrations at the intersection, roadside, and background were respectively 3577 ± 2771, 3078 ± 2343, and 1914 ± 1229 ng/m3. The BC source apportionment results revealed contribution rates of on-board fossil fuel combustion (BCff) at the intersection and near the road of ca. 78.7% and 73.6%, respectively. Moreover, the proportion of particles number concentrations directly emitted from vehicles and nucleated upon emission (47%) was lower than that of particles formed during the dilution and cooling of vehicle emissions and by in-situ new particle formation (53%) at the roadside site. At 49%, the proportion of primary particles number was slightly higher at the intersection. The impacts of new particle-formation events on the diurnal variation of secondary particles were explored. Generally, the majority of BC originated from traffic exhausts, while the secondary particles from non-traffic sources are dominant at the road intersections. By providing a better understanding of near-road pollution issues, this study's findings can be useful for taking effective regulatory efforts to improve air quality and reduce people's exposure to traffic-pollutants in an urban environment.

Children's exposure to BC and PM pollution, and respiratory tract deposits during commuting trips to school.

Although children have been identified as a vulnerable group highly susceptible to traffic-related air pollution, their exposure during school commutes to traffic-related pollutants and the relevant health impact is rarely studied. In this study, we measured black carbon (BC) and particulate matter (PM: PM1, PM2.5, and PM10) concentrations that children are exposed to during their multi-modal (walking, private cars, and e-bikes) commuting trips to schools in Xi'an, China. A multi-parameter inhalation rate assessment model was developed in combination with the Multi-Path Particle Dosimetry (MPPD) model to quantify the deposition dose in different parts of children's respiratory system (head, tracheobronchial (TB), pulmonary (PUL)). Results show that walking to school exposed children to the lowest PM1, PM2.5, and BC concentrations, whereas riding an e-bike led to significantly elevated exposure to PM1 and BC than the other two modes. This is due to children's closer proximity to vehicle tail pipe emissions when they bike to school on road or roadside. The PM and BC concentrations showed remarkable increases in comparison to background concentrations during children's school commutes. Urban background (UB) concentration, traffic volume (TV), time of day, and meteorological parameters could influence a child's personal exposure, and the impact of each factor vary across different transportation modes. Particle size of the pollutant affects its deposition site in the respiratory system. Deposition fractions (DFs) and deposition doses in the head region (DF > 50%) were the highest for PM and BC, for which fine particles (BC, PM1, and PM2.5) were then most easily deposited in the PUL region while coarse particles rarely reach PUL. Children inhaled higher doses of polluted air during active commuting (walking) than passive commuting (private cars, e-bikes), due to longer times of exposure coupled with more active breathing.

Exposure characteristics of ultrafine particles on urban streets and its impact on pedestrians.

In order to investigate the pedestrian exposure characteristics of ultrafine particles (UFPs) on urban streets, both mobile and fixed-point monitoring experiments were conducted. A generalized additive model and a respiratory deposition dose model were used to quantify the influencing factors and potential harm of UFPs, respectively. The results showed that UFPs' hotspots were more likely to manifest at places where vehicles tend to cluster, namely at road intersections and bus stops. The pedestrian bridge had the lowest number concentration of UFPs in comparison with the pedestrian crossing and underground passage at the same intersection. Aboveground, a "weekend effect" acting upon urban streets and evidence for periodicity at the intersections were found. The UFPs' number concentration was comprehensively explained-about 62.7% of its variation-by traffic volume, wind speed, temperature, and relative humidity. The UFPs were mainly deposited in the alveolar region of the respiratory system, but the deposition doses of males exceeded those of females under the same conditions. Based on these findings, the study also provides appropriate suggestions for better managing traffic pollution sources, traffic infrastructure, and traffic organization.

DeepSimulator1.5: a more powerful, quicker and lighter simulator for Nanopore sequencing.

MOTIVATION: Nanopore sequencing is one of the leading third-generation sequencing technologies. A number of computational tools have been developed to facilitate the processing and analysis of the Nanopore data. Previously, we have developed DeepSimulator1.0 (DS1.0), which is the first simulator for Nanopore sequencing to produce both the raw electrical signals and the reads. However, although DS1.0 can produce high-quality reads, for some sequences, the divergence between the simulated raw signals and the real signals can be large. Furthermore, the Nanopore sequencing technology has evolved greatly since DS1.0 was released. It is thus necessary to update DS1.0 to accommodate those changes. RESULTS: We propose DeepSimulator1.5 (DS1.5), all three modules of which have been updated substantially from DS1.0. As for the sequence generator, we updated the sample read length distribution to reflect the newest real reads' features. In terms of the signal generator, which is the core of DeepSimulator, we added one more pore model, the context-independent pore model, which is much faster than the previous context-dependent one. Furthermore, to make the generated signals more similar to the real ones, we added a low-pass filter to post-process the pore model signals. Regarding the basecaller, we added the support for the newest official basecaller, Guppy, which can support both GPU and CPU. In addition, multiple optimizations, related to multiprocessing control, memory and storage management, have been implemented to make DS1.5 a much more amenable and lighter simulator than DS1.0. AVAILABILITY AND IMPLEMENTATION: The main program and the data are available at https://github.com/lykaust15/DeepSimulator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Commuter PM exposure and estimated life-expectancy loss across multiple transportation modes in Xi'an, China.

Commuters are reportedly exposed to severe traffic-related air pollution (TRAP) during their commuting trips. This study was designed and implemented to (1) compare particulate matter (PM) exposure across four common transportation modes; (2) examine and analyze various determining factors; and (3) estimate public health effects caused by commuting exposure to PM. All analyses and calculations were based on the experimental data collected from 13 volunteers, including heart-rate data on 336 commuting trips in four travel modes in Xi'an China. The results indicate highest PM exposure associated with cycling (average PM10, PM2.5 and PM1.0 of 114.35, 72.37 and 56.51 µg/m3, respectively), followed by riding transit buses (116.29, 67.60 and 51.12 µg/m3 for the same pollutants, respectively), then taking a taxi (97.61, 58.87 and 45.11 µg/m3), and the lowest exposure onboard subways (55.86, 46.20 and 40.20 µg/m3). A multivariable linear regression model was used to examine major influences on PM concentration variations, with results corroborating significant PM variance across commuting modes, which is also affected by background pollution concentration and relative humidity. Further, years of life expectancy (YLE) loss were estimated using an inhalation dose model together with the life table method: cycling commuters experienced the greatest YLE loss (5.51-6.43 months per capita for the studied age group). During severe pollution periods, substituting other modes (like subway) for cycling could effectively avoid acute exposure. PM2.5 levels in taxi cabins powered by CNG or methanol were comparatively lower, indicating that implementing alternative energy strategies could effectively lower traffic emissions and population exposure.

Effectiveness of additional C2 decompression of the cervical spinal canal after cervical laminoplasty: a retrospective cohort study.

PURPOSE: This study aimed to assess the effects of additional C2 decompression of the cervical spinal canal on the postoperative outcomes after cervical laminoplasty in patients with cervical stenosis caused by ossification of the posterior longitudinal ligament (OPLL). MATERIALS AND METHODS: This retrospective cohort study included patients with cervical stenosis due to OPLL and treated between April 2014 and December 2015. The patients who underwent C2-7 (additional C2 decompression) and C3-7 posterior decompression were compared using the Japanese Orthopedic Association (JOA) scores, visual analog scale (VAS) scores, axial symptom scores, and intervals between the posterior margin of the vertebral body and the K-line. RESULTS: There were 36 and 24 patients in the additional C2 decompression and control groups, respectively. The JOA scores were higher in the additional C2 decompression group than the controls at 1 and 3 years (p < 0.05). Upper extremity motor function after the operation and at 1 and 3 years and lower extremity motor function after operation were improved in the additional C2 decompression group (all p < 0.05 vs. controls). VAS scores were lower in the additional C2 decompression group than controls at 1 year (p < 0.05). Axial symptom scores in the additional C2 decompression group were decreased postoperatively but increased at 1 and 3 years (p < 0.05 vs. controls). Finally, the posterior shift of the K-line in the additional C2 decompression group was significant (from 0.98 to 1.68 cm, p < 0.05). CONCLUSIONS: Additional C2 decompression might improve the effectiveness of cervical laminoplasty in patients with cervical stenosis caused by OPLL.

A Novel Hybrid Approach for Risk Evaluation of Vehicle Failure Modes.

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes.

Emission inventory estimation of an intercity bus terminal.

Intercity bus terminals are hotspots of air pollution due to concentrated activities of diesel buses. In order to evaluate the bus terminals' impact on air quality, it is necessary to estimate the associated mobile emission inventories. Since the vehicles' operating condition at the bus terminal varies significantly, conventional calculation of the emissions based on average emission factors suffers the loss of accuracy. In this study, we examined a typical intercity bus terminal-the Southern City Bus Station of Xi'an, China-using a multi-scale emission model-(US EPA's MOVES model)-to quantity the vehicle emission inventory. A representative operating cycle for buses within the station is constructed. The emission inventory was then estimated using detailed inputs including vehicle ages, operating speeds, operating schedules, and operating mode distribution, as well as meteorological data (temperature and humidity). Five functional areas (bus yard, platforms, disembarking area, bus travel routes within the station, and bus entrance/exit routes) at the terminal were identified, and the bus operation cycle was established using the micro-trip cycle construction method. Results of our case study showed that switching to compressed natural gas (CNG) from diesel fuel could reduce PM2.5 and CO emissions by 85.64 and 6.21 %, respectively, in the microenvironment of the bus terminal. When CNG is used, tail pipe exhaust PM2.5 emission is significantly reduced, even less than brake wear PM2.5. The estimated bus operating cycles can also offer researchers and policy makers important information for emission evaluation in the planning and design of any typical intercity bus terminals of a similar scale.

Impact of urban tree arrangement on pedestrian exposure to the size-fractional particulate matter in a city boulevard.

Trees act as natural filters that mitigate roadside air pollution. However, the filtration impact of different tree arrangements on traffic pollutants with different particle diameters has rarely been analysed in real street canyon environments. To quantify how roadside tree arrangements impact pedestrian exposure to particle number concentrations (PNCs) of different diameters (0.25-32 µm), in situ field measurements were carried out in a boulevard-type street canyon in the city of Xi'an, China. This study analysed the experimental data of PNCs collected along segments of a pedestrian lane under four typical tree arrangements: open space without trees, a sparse-spaced tree arrangement, a medium-spaced tree arrangement, and a dense-spaced tree arrangement in a street canyon. Our results reveal that the effect of tree arrangement on PNCs depended on the particle diameter. In general, trees can significantly reduce coarse PNC (particles with diameters > 2.5 µm) but not the fine PNC. Quantitative analysis showed that a medium-spaced tree arrangement, in which tree crowns are adjacent to each other but do not overlap, is the most capable of reducing PNC, followed by a sparse-spaced tree arrangement, while a the dense-spaced tree arrangement has the least impact. The attenuation effect of trees on the PNCs increased with increasing particle diameter. Moreover, the presence of trees altered the local microclimate, which also affected how exposure to PNCs changed. Our empirical findings further highlight the complexity of how trees affect particulate pollutants in street canyons and provide timely insights for enhancing tree-planning management in cities from the perspective of air quality improvement.

A systematic review of transportation carbon emissions based on CiteSpace.

Transportation sector has become a major contributor to the escalation of carbon emissions and subsequent climate change. In this study, a bibliometric analysis was conducted using CiteSpace on published papers (1991-2022). Then a theoretical framework was proposed through traditional content analysis from three aspects: measurement, mechanism analysis, and low-carbon pathways analysis. The clustering results show that the research topics have involved mainly factor analysis, evaluation, system analysis, control measurement and pollutants. A further summary of the content of the relevant literature shows that there are five main accounting methods for measuring transportation carbon emissions (TCEs), which can be applied to different scenarios. Studies involving the spatio-temporal distribution of TCEs is limited and mainly focus on macroperspectives. The mechanism of TCEs involves three main aspects: system assessment, efficiency measurement, and driver analysis, which serve to identify the internal patterns of TCEs. Finally, the outlook regarding TCEs is presented.

Impact assessment of vehicle electrification pathways on emissions of CO2 and air pollution in Xi'an, China.

To assess the environmental impact of promoting the use of electric vehicles in road traffic on emissions of CO2 and air pollution in Xi'an, China, both the proportion of electric vehicles and the power generation mix should be considered. Here, vehicle ownership in 2021 served as the baseline scenario, and the vehicle development trend through 2035 was projected. Using emission factor models for fuel vehicles and the electricity generation required for running electric vehicles, this study estimated the related pollutants' emission inventories at 81 corresponding scenarios, in which differing vehicle electrification paths were coupled with power generation mix. Further, the degree to which different vehicle electrification paths impacted the CO2 and air pollutant emissions was also evaluated. The results show that, to achieve the goal of peak carbon emission in the road transport sector in Xi'an by 2030, the penetration rate of electric vehicles must reach at least 40 % in 2035, and the thermal power generation rate should satisfy the necessary coupling conditions. Although reducing the thermal power generation rate could mitigate the environmental problems, we find that electric vehicle development in Xi'an during 2021-2035 would still exacerbate SO2 emissions despite reducing the thermal power generation rate to 10 %. Finally, to avoid exacerbating the adverse effects on public health from vehicle-related pollutants, the penetration rate of electric vehicles should be at least 40 % in 2035, at which time for the 40 %, 50 %, 60 %, and 70 % scenarios, the corresponding thermal power generation rate should not exceed 10 %, 30 %, 50 %, and 60 %. This study systematically analyzed plausible development paths of electric vehicles from the perspectives of peak carbon emissions, air pollution control, and human health, whose findings can serve as a timely and valuable reference for reducing pollution and carbon in the field of road transport.

MAE-TransRNet: An improved transformer-ConvNet architecture with masked autoencoder for cardiac MRI registration.

The heart is a relatively complex non-rigid motion organ in the human body. Quantitative motion analysis of the heart takes on a critical significance to help doctors with accurate diagnosis and treatment. Moreover, cardiovascular magnetic resonance imaging (CMRI) can be used to perform a more detailed quantitative analysis evaluation for cardiac diagnosis. Deformable image registration (DIR) has become a vital task in biomedical image analysis since tissue structures have variability in medical images. Recently, the model based on masked autoencoder (MAE) has recently been shown to be effective in computer vision tasks. Vision Transformer has the context aggregation ability to restore the semantic information in the original image regions by using a low proportion of visible image patches to predict the masked image patches. A novel Transformer-ConvNet architecture is proposed in this study based on MAE for medical image registration. The core of the Transformer is designed as a masked autoencoder (MAE) and a lightweight decoder structure, and feature extraction before the downstream registration task is transformed into the self-supervised learning task. This study also rethinks the calculation method of the multi-head self-attention mechanism in the Transformer encoder. We improve the query-key-value-based dot product attention by introducing both depthwise separable convolution (DWSC) and squeeze and excitation (SE) modules into the self-attention module to reduce the amount of parameter computation to highlight image details and maintain high spatial resolution image features. In addition, concurrent spatial and channel squeeze and excitation (scSE) module is embedded into the CNN structure, which also proves to be effective for extracting robust feature representations. The proposed method, called MAE-TransRNet, has better generalization. The proposed model is evaluated on the cardiac short-axis public dataset (with images and labels) at the 2017 Automated Cardiac Diagnosis Challenge (ACDC). The relevant qualitative and quantitative results (e.g., dice performance and Hausdorff distance) suggest that the proposed model can achieve superior results over those achieved by the state-of-the-art methods, thus proving that MAE and improved self-attention are more effective and promising for medical image registration tasks. Codes and models are available at https://github.com/XinXiao101/MAE-TransRNet.

Study on modified poplar wood powder/polylactic acid high toughness green 3D printing composites.

In order to alleviate environmental pollution and the shortage of petroleum resources, improve the utilization of renewable materials, the research of biodegradable green composite materials has become a research hotspot. In this paper, Poplar Wood powder(PWP) and Polylactic acid(PLA) were selected, adding poly lactic acid graft maleic anhydride (MPLA) and Silane coupling agent KH-550 (KH550) as a compatibilizer and coupling agent to improve interface compatibility, at the same time, poly Butylenedioate-co-terephthalate (PBAT) and poly Butylene Succinate (PBS) were added to improve the toughness of the composites. The experimental results show that, the impact strength of 20 %-KMPP/PBAT/PBS composite modified by MPLA and KH550 was 20.70 kJ/m-2. Secondly, the hydrophobic angle of the composite material is as high as 112°. It is found that the high content of PWP with small particle size (200 mesh) can make it more evenly dispersed in the composite material, and the cross section of the composite material was smooth. The modified composite was 4.24$/kg, which reduced the cost by 28.07 %. The research results have opened up a new way to develop 3D printed biomass composites with low cost, high compatibility, high toughness and good environmental adaptability, and broadened the application scope and value of the composites.

Six-month follow-up after recovery of COVID-19 Delta variant survivors via CT-based deep learning.

Purpose: Using computer-aided diagnosis (CAD) methods to analyze the discharge and 6-month follow-up data of COVID-19 Delta variant survivors, evaluate and summarize the recovery and prognosis, and improve people's awareness of this disease. Methods: This study collected clinical data, SGRQ questionnaire results, and lung CT scans (at both discharge and 6-month follow-up) from 41 COVID-19 Delta variant survivors. Two senior radiologists evaluated the CT scans before in-depth analysis. Deep lung parenchyma enhancing (DLPE) method was used to accurately segment conventional lesions and sub-visual lesions in CT images, and then quantitatively analyze lung injury and recovery. Patient recovery was also measured using the SGRQ questionnaire. The follow-up examination results from this study were combined with those of the original COVID-19 for further comparison. Results: The participants include 13 males (31.7%) and 28 females (68.3%), with an average age of 42.2 ± 17.7 years and an average BMI of 25.2 ± 4.4 kg/m2. Compared discharged CT and follow-up CT, 48.8% of survivors had pulmonary fibrosis, mainly including irregular lines (34.1%), punctuate calcification (12.2%) and nodules (12.2%). Compared with discharged CT, the ground-glass opacity basically dissipates at follow-up. The mean SGRQ score was 0.041 (0-0.104). The sequelae of survivors mainly included impaired sleep quality (17.1%), memory decline (26.8%), and anxiety (21.9%). After DLPE process, the lesion volume ratio decreased from 0.0018 (0.0003, 0.0353) at discharge to 0.0004 (0, 0.0032) at follow-up, p < 0.05, and the absorption ratio of lesion was 0.7147 (-1.0303, 0.9945). Conclusion: The ground-glass opacity of survivors had dissipated when they were discharged from hospital, and a little fibrosis was seen in CT after 6-month, mainly manifested as irregular lines, punctuate calcification and nodules. After DLPE and quantitative calculations, we found that the degree of fibrosis in the lungs of most survivors was mild, which basically did not affect lung function. However, there are a small number of patients with unabsorbed or increased fibrosis. Survivors mainly had non-pulmonary sequelae such as impaired sleep quality and memory decline. Pulmonary prognosis of Delta variant patients was better than original COVID-19, with fewer and milder sequelae.

Short-term effects of air pollution and weather changes on the occurrence of acute aortic dissection in a cold region.

Background: Air pollution and severe weather conditions can adversely affect cardiovascular disease emergencies. Nevertheless, it remains unclear whether air pollutants and low ambient temperature can trigger the occurrence of acute aortic dissection (AAD) in cold regions. Methods: We applied a retrospective analysis to assess the short-term effects of air pollution and ambient temperature on the occurrence of AAD in Harbin, China. A total of 564 AAD patients were enrolled from a major hospital in Harbin between January 1, 2017, and February 5, 2021. Weather condition data and air pollutant concentrations, including fine particulate matter smaller than 10 µm (PM10) and 2.5 µm in diameter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), were collected every day. Conditional logistic regressions and correlation analysis were applied to analyze the relationship of environmental and atmospheric parameters with AAD occurrence at lags of 0 to 7 days. Specifically, we appraised the air quality index, CO, NO2, SO2, O3, PM10, PM2.5, temperature, dew point temperature, atmospheric pressure, and cloud amount. Results: A total of 1,496 days at risk were assessed, of which 564 patients developed AAD. Specifically, AAD did not occur on 1,043 (69.72%) days, while 1 or more cases occurred on 453 (30.28%) days. Several pollution and weather predictors for AAD were confirmed by multilevel modeling. The air quality index (p = 0.0012), cloud amount (p = 0.0001), and concentrations of PM2.5 (p = 0.0004), PM10 (p = 0.0013), NO2 (p = 0.0007) and O3 (p = 0.0001) predicted AAD as early as 7 days before the incident (lag of 7 days) in the study period. However, only concentrations of the air pollutants NO2 (p = 0.0468) and O3 (p = 0.011) predicted the occurrence of AAD after the COVID-19 outbreak. Similar predictive effects were observed for temperature, dew point temperature, and atmospheric pressure (all p < 0.05) on all days. Conclusion: The risk of AAD is closely related to air pollution and weather characteristics in Harbin. While causation was not determined, the impact of air pollutants on the risk of AAD was reduced after the COVID-19 outbreak.

Using mixed reality technique combines multimodal imaging signatures to adjuvant glioma photodynamic therapy.

Background: Photodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT's key operating zones. Methods: We used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival. Results: We constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment. Conclusion: Using the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography.

Cardiac diseases have high mortality rates and are a significant threat to human health. Echocardiography is a commonly used imaging technique to diagnose cardiac diseases because of its portability, non-invasiveness and low cost. Precise segmentation of basic cardiac structures is crucial for cardiologists to efficiently diagnose cardiac diseases, but this task is challenging due to several reasons, such as: (1) low image contrast, (2) incomplete structures of cardiac, and (3) unclear border between the ventricle and the atrium in some echocardiographic images. In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively and made use of unlabeled data to achieve a great performance, which could help doctors improve the accuracy of CVD diagnosis and screening. We evaluated this method on a public dataset (CAMUS), achieving mean Dice Similarity Coefficient (DSC) of 0.898, 0.911, 0.916 with 1/4, 1/2 and full labeled data on two-chamber (2CH) echocardiography images, and of 0.903, 0.921, 0.928 with 1/4, 1/2 and full labeled data on four-chamber (4CH) echocardiography images. Compared with other existing methods, the proposed method had fewer parameters and better performance. The code and models are available at https://github.com/gpgzy/CL-Cardiac-segmentation.

Exploring New Characteristics: Using Deep Learning and 3D Reconstruction to Compare the Original COVID-19 and Its Delta Variant Based on Chest CT.

Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient's lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI). Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0-3 days), progressive and peak stage (4-16 days), and absorption stage (17-42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17-30] vs. 39 [31-44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

Chemical characterization of PM2.5 emitted from China IV and China V light-duty vehicles in China.

This study reported the emission factors (EFs) and detailed chemical compositions of PM2.5 collected from China IV and China V light-duty vehicles (LDVs) through dynamometer test. The China IV LDVs containing 4 gasoline vehicles (GVs) and 4 natural gas vehicles (NGVs) had port fuel injection (PFI) engines, while the China V LDVs included 2 GVs with PFI engines and 2 GVs with gasoline direct injection (GDI) engines. The average EFs of PM2.5 were 1.90 ± 0.70 mg km-1, 1.44 ± 0.29 mg km-1, and 0.56 ± 0.05 mg km-1 for China IV GVs, China IV NGVs, and China V GVs, respectively. PM2.5 profiles of LDVs were characterized by abundant carbon species (60.59-68.58%) with low amounts of water soluble ions (WSIs, 6.96-16.37%) and elements (5.20-7.53%). In general, the EFs of PM2.5 constituents including organic carbon (OC), elemental carbon (EC), WSIs, and elements were reduced obviously by strengthening emission standards from China IV to China V. While the contributions of most WSIs and elements to PM2.5 increased as vehicle technology improved. Furthermore, the EFs of PM2.5 components from China IV LDVs also decreased when shifting fuels from gasoline to natural gas. While the fractions of OC, WSIs and most elements in PM2.5 increased due to the highest reduction rate of EC mass. For China V LDVs, GDI vehicles emitted less OC but more EC compared to PFI vehicles, and the EFs of most WSIs and elements also increased. Overall, GDI vehicles exhibited lower fractions OC and WSIs but higher contents of EC and elements in PM2.5. Besides, PM2.5 and its chemical species were heavily dependent on vehicle's driving patterns. The average EFs of PM2.5 components under aggressive driving pattern increased significantly compared to those under moderate driving pattern.