Planning analysis for locally advanced lung cancer: dosimetric and efficiency comparisons between intensity-modulated radiotherapy (IMRT), single-arc/partial-arc volumetric modulated arc therapy (SA/PA-VMAT)

<p>Abstract</p> <p>Purpose</p> <p>To analyze the differences between the intensity-modulated radiotherapy (IMRT), single/partial-arc volumetric modulated arc therapy (SA/PA-VMAT) techniques in treatment planning for locally advanced lung cancer.</p> <p>Materials and methods</p> <p>12 patients were retrospectively studied. In each patient's case, several parameters were analyzed based on the dose-volume histograms (DVH) of the IMRT, SA/PA-VMAT plans respectively. Also, each plan was delivered to a phantom for time comparison.</p> <p>Results</p> <p>The SA-VMAT plans showed the superior target dose coverage, although the minimum/mean/maximum doses to the target were similar. For the total and contralateral lungs, the higher V_5/10, lower V_20/30and mean lung dose (MLD) were observed in the SA/PA-VMAT plans (<it>p </it>< 0.05, respectively). The PA-VMAT technique improves the dose sparing (V₂₀, V₃₀and MLD) of the controlateral lung more notably, comparing to those parameters of the IMRT and SA-VMAT plans respectively. The delivered monitor units (MUs) and treatment times were reduced significantly with VMAT plans, especially PA-VMAT plans (for MUs: mean 458.3 <it>vs</it>. 439.2 <it>vs</it>. 435.7 MUs, <it>p </it>< 0.05 and for treatment time: mean 13.7 <it>vs</it>. 10.6 <it>vs</it>. 6.4 minutes, <it>p </it>< 0.01).</p> <p>Conclusions</p> <p>The SA-VMAT technique achieves highly conformal dose distribution to the target. Comparing to the IMRT plans, the higher V_5/10, lower V_20/30and MLD were observed in the total and contralateral lungs in the VMAT plans, especially in the PA-VMAT plans. The SA/PA-VMAT plans also reduced treatment time with more efficient dose delivering. But the clinical benefit of the VMAT technique for locally advanced lung cancer needs further investigations.</p

Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images

This paper presents a semantic edge-aware multi-task neural network (SEANet) to obtain closed boundaries when delineating agricultural parcels from remote sensing images. It derives closed boundaries from remote sensing images and improves conventional semantic segmentation methods for the extraction of small and irregular agricultural parcels. SEANet integrates three correlated tasks: mask prediction, edge prediction, and distance map estimation. Related features learned from these tasks improve the generalizability of the network. We regard boundary extraction as an edge detection task and extract rich semantic edge features at multiple levels to improve the geometric accuracy of parcel delineation. Moreover, we develop a new multi-task loss that considers the uncertainty of different tasks. We conducted experiments on three high-resolution Gaofen-2 images in Shandong, Xinjiang, and Sichuan provinces, China, and on two medium-resolution Sentinel-2 images from Denmark and the Netherlands. Results showed that our method produced a better layout of agricultural parcels, with higher attribute and geometric accuracy than the existing ResUNet, ResUNet-a, R2UNet, and BsiNet methods on the Shandong and Denmark datasets. The total extraction errors of the parcels produced by our method were 0.214, 0.127, 0.176, 0.211, and 0.184 for the five datasets, respectively. Our method also obtains closed boundaries by one single segmentation, leading to superiority as compared with existing multi-task networks. We showed that it could be applied to images with different spatial resolutions for parcel delineation. Finally, our method trained on the Xinjiang dataset could be successfully transferred to the Shandong dataset with different dates and landscapes. Similarly, we obtained satisfactory results when transferring from the Denmark dataset to the Netherlands dataset. We conclude that SEANet is an accurate, robust, and transferable method for various areas and different remote sensing images. The codes of our model are available at https://github.com/long123524/SEANet_torch.</p

Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images

This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring three parallel decoders into a single encoder to improve computational efficiency. BsiNet learns three tasks: a core task for agricultural field identification and two auxiliary tasks for field boundary prediction and distance estimation, corresponding to mask, boundary, and distance tasks, respectively. A spatial group-wise enhancement module is incorporated to improve the identification of small fields. We conducted experiments on a GaoFen1 and three GaoFen2 satellite images collected in Xinjiang, Fujian, Shandong, and Sichuan provinces in China, and compared BsiNet with 13 different neural networks. Our results show that the agricultural fields extracted by BsiNet have the lowest global over-classification (GOC) of 0.062, global under-classification (GUC) of 0.042, and global total errors (GTC) of 0.062 for the Xinjiang dataset. For the Fujian dataset with irregular and complex fields, BsiNet outperformed the second-best method from the Xinjiang dataset analysis, yielding the lowest GTC of 0.291. It also produced satisfactory results on the Shandong and Sichuan datasets. Moreover, BsiNet has fewer parameters and faster computation than existing multi-task models (i.e., Psi-Net and ResUNet-a D7). We conclude that BsiNet can be used successfully in extracting agricultural fields from high-resolution satellite images and can be applied to different field settings.</p

Cellulose-starch hybrid films plasticized by aqueous ZnCl2 solution

Starch and cellulose are two typical natural polymers from plants that have similar chemical structures. The blending of these two biopolymers for materials development is an interesting topic, although how their molecular interactions could influence the conformation and properties of the resultant materials has not been studied extensively. Herein, the rheological properties of cellulose/starch/ZnCl2 solutions were studied, and the structures and properties of cellulose-starch hybrid films were characterized. The rheological study shows that compared with starch (containing mostly amylose), cellulose contributed more to the solution’s viscosity and has a stronger shear-thinning behavior. A comparison between the experimental and calculated zero-shear-rate viscosities indicates that compact complexes (interfacial interactions) formed between cellulose and starch with ≤50 wt % cellulose content, whereas a loose structure (phase separation) existed with ≥70 wt % cellulose content. For starch-rich hybrid films prepared by compression molding, less than 7 wt % of cellulose was found to improve the mechanical properties despite the reduced crystallinity of the starch; for cellulose-rich hybrid films, a higher content of starch reduced the material properties, although the chemical interactions were not apparently influenced. It is concluded that the mechanical properties of biopolymer films were mainly affected by the structural conformation, as indicated by the rheological results. View Full-Tex

Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Reduced Number of Network Stations

Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2, but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the four-month time scale studied. Both evaluations with an empirical orthogonal function (EOF) analysis and with independent dataset corroborate the validity of the results found in this study

Effect of Dietary Components on the Bioavailability of Catechins and the Application of Polyphenol Synergism

Catechins are phenolic compounds with various health benefits such as anti-allergic, antioxidant, anti-inflammatory, and anti-bacterial. However, catechins have poor thermal stability and low bioavailability. Besides, dietary intake of carbohydrates, proteins, lipids, and other substances is prone to interact with catechins in the gastrointestinal tract, affecting the absorption, distribution, metabolism, and excretion process of catechins, which affects their bioavailability and physiological activity. In addition, synergistic effects may occur when two or more natural polyphenolic compounds are applied in combination, including inhibition of food production and processing contaminants, weight loss, hypoglycemia, anti-inflammatory and antibacterial. Therefore, this paper mainly reviews the effects of dietary components on the bioavailability of catechins and elucidates the mechanisms of interaction between catechins and dietary polyphenols and the application prospects in synergistic effects

Estimating the contribution of local primary emissions to particulate pollution using high-density station observations

Local primary emission, transport, and secondary formation of aerosols constitute the major atmospheric particulate matter (PM) over a certain region. To identify and quantify major sources of ambient PM is important for pollution mitigation strategies, especially on a city scale. We developed two source apportionment methods to make the first‐order estimates of local primary contribution ratio (LCR) of PM_(2.5) (PM with diameter less than 2.5 μm) using the high‐density (about 1/km^2) network observations with high sampling frequency (about 1 hr). Measurements of PM_(2.5) mass concentration from 169 sites within a 20 km × 20 km domain are analyzed. The two methods developed here are mainly based on the spatial and temporal variations of PM_(2.5) within an urban area. The accuracy of our developed methods is subject to the assumptions on the spatial heterogeneity of primary and secondary formed aerosols as well as those from long‐range transport to a city. We apply these two methods to a typical industrial city in China in winter of 2015 with frequent severe haze events. The local primary pollution contributions calculated from the two methods agree with each other that they are often larger than 0.4. The LCR range is from 0.4 to 0.7, with an average value of 0.63. Our study indicates the decisive role of locally emitted aerosols in the urban severe haze formation during the winter time. It further suggests that reductions of local primary aerosol emissions are essential to alleviate the severe haze pollution, especially in industrial cities