DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy

Treatment planning is a critical component of the radiotherapy workflow, typically carried out by a medical physicist using a time-consuming trial-and-error manner. Previous studies have proposed knowledge-based or deep learning-based methods for predicting dose distribution maps to assist medical physicists in improving the efficiency of treatment planning. However, these dose prediction methods usuallylack the effective utilization of distance information between surrounding tissues andtargets or organs-at-risk (OARs). Moreover, they are poor in maintaining the distribution characteristics of ray paths in the predicted dose distribution maps, resulting in a loss of valuable information obtained by medical physicists. In this paper, we propose a distance-aware diffusion model (DoseDiff) for precise prediction of dose distribution. We define dose prediction as a sequence of denoising steps, wherein the predicted dose distribution map is generated with the conditions of the CT image and signed distance maps (SDMs). The SDMs are obtained by a distance transformation from the masks of targets or OARs, which provide the distance information from each pixel in the image to the outline of the targets or OARs. Besides, we propose a multiencoder and multi-scale fusion network (MMFNet) that incorporates a multi-scale fusion and a transformer-based fusion module to enhance information fusion between the CT image and SDMs at the feature level. Our model was evaluated on two datasets collected from patients with breast cancer and nasopharyngeal cancer, respectively. The results demonstrate that our DoseDiff outperforms the state-of-the-art dose prediction methods in terms of both quantitative and visual quality

Research of the size effect on shear strength of metal-plate connector joints in China

According to the reliability theory, the size effect has a great impact on the design value on shear strength of metal-plate connector. But little research has been done. So, based on GB/T50329-2002 of China, firstly, determining the size of metal-plate at different conditions, size effect tests were then conducted on metal-plate connectors composed of a type of Chinese metal-plate and 2# SPF dimension lumber from North America. A total of 125 metal-plate connectors are tested at five angles (90°, 60°T, 120°C, 150°C, 30°T), with Five kinds of widths (50mm，85mm，125mm，150mm，180mm) for each angle. Based on the testing data, fitting curve of size effect is presented, and width-effect parameters are estimated with SPSS(Statistic Package for Social Science). Results indicate that the width effect is significant; shear strength increases with the increase of width, and stays stable after a certain width

Single Image Super Resolution via Neighbor Reconstruction

Super Resolution (SR) is a complex, ill-posed problem where the aim is to construct the mapping between the low and high resolution manifolds of image patches. Anchored neighborhood regression for SR (namely A+  [27]) has shown promising results. In this paper we present a new regression-based SR algorithm that overcomes the limitations of A+ and benefits from an innovative and simple Neighbor Reconstruction Method (NRM). This is achieved by vector operations on an anchored point and its corresponding neighborhood. NRM reconstructs new patches which are closer to the anchor point in the manifold space. Our method is robust to NRM sparsely-sampled points: increasing PSNR by 0.5 dB compared to the next best method. We comprehensively validate our technique on standardised datasets and compare favourably with the state-of-the-art methods: we obtain PSNR improvement of up to 0.21 dB compared to previously-reported work

Multi-omics approaches reveal the molecular mechanisms underlying the interaction between Clonorchis sinensis and mouse liver

IntroductionClonorchiasis remains a serious global public health problem, causing various hepatobiliary diseases. However, there is still a lack of overall understanding regarding the molecular events triggered by Clonorchis sinensis (C. sinensis) in the liver.MethodsBALB/c mouse models infected with C. sinensis for 5, 10, 15, and 20 weeks were constructed. Liver pathology staining and observation were conducted to evaluate histopathology. The levels of biochemical enzymes, blood routine indices, and cytokines in the blood were determined. Furthermore, alterations in the transcriptome, proteome, and metabolome of mouse livers infected for 5 weeks were analyzed using multi-omics techniques.ResultsThe results of this study indicated that adult C. sinensis can cause hepatosplenomegaly and liver damage, with the most severe symptoms observed at 5 weeks post-infection. However, as the infection persisted, the Th2 immune response increased and symptoms were relieved. Multi-omics analysis of liver infected for 5 weeks identified 191, 402 and 232 differentially expressed genes (DEGs), proteins (DEPs) and metabolites (DEMs), respectively. Both DEGs and DEPs were significantly enriched in liver fibrosis-related pathways such as ECM-receptor interaction and cell adhesion molecules. Key molecules associated with liver fibrosis and inflammation (Cd34, Epcam, S100a6, Fhl2, Itgax, and Retnlg) were up-regulated at both the gene and protein levels. The top three metabolic pathways, namely purine metabolism, arachidonic acid metabolism, and ABC transporters, were associated with liver cirrhosis, fibrosis, and cholestasis, respectively. Furthermore, metabolites that can promote liver inflammation and fibrosis, such as LysoPC(P-16:0/0:0), 20-COOH-leukotriene E4, and 14,15-DiHETrE, were significantly up-regulated.ConclusionOur study revealed that the most severe symptoms in mice infected with C. sinensis occurred at 5 weeks post-infection. Moreover, multi-omics analysis uncovered predominant molecular events related to fibrosis changes in the liver. This study not only enhances our understanding of clonorchiasis progression but also provides valuable insights into the molecular-level interaction mechanism between C. sinensis and its host liver

Lamellipodin-Deficient Mice: A Model of Rectal Carcinoma

During a survey of clinical rectal prolapse (RP) cases in the mouse population at MIT animal research facilities, a high incidence of RP in the lamellipodin knock-out strain, C57BL/6-Raph1[superscript tm1Fbg] (Lpd[superscript -/-]) was documented. Upon further investigation, the Lpd[superscript -/-] colony was found to be infected with multiple endemic enterohepatic Helicobacter species (EHS). Lpd[superscript -/-] mice, a transgenic mouse strain produced at MIT, have not previously shown a distinct immune phenotype and are not highly susceptible to other opportunistic infections. Predominantly male Lpd[superscript -/-] mice with RP exhibited lesions consistent with invasive rectal carcinoma concomitant to clinically evident RP. Multiple inflammatory cytokines, CD11b+Gr1+ myeloid-derived suppressor cell (MDSC) populations, and epithelial cells positive for a DNA damage biomarker, H2AX, were elevated in affected tissue, supporting their role in the neoplastic process. An evaluation of Lpd[superscript -/-] mice with RP compared to EHS-infected, but clinically normal (CN) Lpd[superscript -/-] animals indicated that all of these mice exhibit some degree of lower bowel inflammation; however, mice with prolapses had significantly higher degree of focal lesions at the colo-rectal junction. When Helicobacter spp. infections were eliminated in Lpd[superscript -/-] mice by embryo transfer rederivation, the disease phenotype was abrogated, implicating EHS as a contributing factor in the development of rectal carcinoma. Here we describe lesions in Lpd[superscript -/-] male mice consistent with a focal inflammation-induced neoplastic transformation and propose this strain as a mouse model of rectal carcinoma.United States. National Institutes of Health (T32-OD010978)United States. National Institutes of Health (R01-OD011141)United States. National Institutes of Health (P30-ES002109)Massachusetts Institute of Technology. Ludwig Center for Molecular Oncology (U54- CA114462)National Cancer Institute (U.S.) (P30-CA14051

Incremental feedforward collective pitch control method for wind turbines

In recent years, wind turbines are becoming larger, which will exacerbate the complexity of loads Complex load change affect the output power quality and wind turbine service life so that must be studied. Pitch control is usually used to reduce wind turbine load. In this paper, based on the Light Detection and Ranging (LiDAR) technology and incremental feedforward control theory, an incremental feedforward collective pitch controller is proposed. The controller can be directly superimposed on the traditional collective pitch controller so that the incremental pitch angle can fully compensate wind influence. The effectiveness of the controller is verified by multi-software platform joint simulation and hardware-in-the-loop experiment. The results show that the controller can effectively reduce the wind turbine power and load fluctuation when the variation trend of wind speed in the rotor plane estimate by LiDAR data is the same as the actual wind speed

Mist1 Expressing Gastric Stem Cells Maintain the Normal and Neoplastic Gastric Epithelium and Are Supported by a Perivascular Stem Cell Niche

The regulation and stem cell origin of normal and neoplastic gastric glands are uncertain. Here, we show that Mist1 expression marks quiescent stem cells in the gastric corpus isthmus. Mist1+ stem cells serve as a cell-of-origin for intestinal-type cancer with the combination of Kras and Apc mutation and for diffuse-type cancer with the loss of E-cadherin. Diffuse-type cancer development is dependent on inflammation mediated by Cxcl12+ endothelial cells and Cxcr4+ gastric innate lymphoid cells (ILCs). These cells form the perivascular gastric stem cell niche, and Wnt5a produced from ILCs activates RhoA to inhibit anoikis in the E-cadherin-depleted cells. Targeting Cxcr4, ILCs, or Wnt5a inhibits diffuse-type gastric carcinogenesis, providing targets within the neoplastic gastric stem cell niche

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning

Improving Photoelectrochemical Activity of ZnO/TiO2 Core–Shell Nanostructure through Ag Nanoparticle Integration

In solar energy harvesting using solar cells and photocatalysts, the photoexcitation of electrons and holes in semiconductors is the first major step in the solar energy conversion. The lifetime of carriers, a key factor determining the energy conversion and photocatalysis efficiency, is shortened mainly by the recombination of photoexcited carriers. We prepared and tested a series of ZnO/TiO2-based heterostructures in search of designs which can extend the carrier lifetime. Time-resolved photoluminescence tests revealed that, in ZnO/TiO2 core–shell structure the carrier lifetime is extended by over 20 times comparing with the pure ZnO nanorods. The performance improved further when Ag nanoparticles were integrated at the ZnO/TiO2 interface to construct a Z-scheme structure. We utilized these samples as photoanodes in a photoelectrochemical (PEC) cell and analyzed their solar water splitting performances. Our data showed that these modifications significantly enhanced the PEC performance. Especially, under visible light, the Z-scheme structure generated a photocurrent density 100 times higher than from the original ZnO samples. These results reveal the potential of ZnO-Ag-TiO2 nanorod arrays as a long-carrier-lifetime structure for future solar energy harvesting applications