Fescue toxicity causes reduced growth and reproductive issues in cattle grazing endophyte-infected tall fescue. To characterize the gut microbiota and its response to fescue toxicosis, we collected fecal samples before and after a 30-days toxic fescue seeds supplementation from eight Angus Simmental pregnant cows and heifers. We sequenced the 16 metagenomes using the whole-genome shotgun approach and generated 157 Gbp of metagenomic sequences. Through de novo assembly and annotation, we obtained a 13.1 Gbp reference contig assembly and identified 22 million microbial genes for cattle rectum microbiota. We discovered a significant reduction of microbial diversity after toxic seed treatment (P<0.01), suggesting dysbiosis of the microbiome. Six bacterial families and 31 species are significantly increased in the fecal microbiota (P-adj<0.05), including members of the top abundant rumen core taxa. This global elevation of rumen microbes in the rectum microbiota suggests a potential impairment of rumen microbiota under fescue toxicosis. Among these, Ruminococcaceae bacterium P7, an important species accounting for ~2% of rumen microbiota, was the most impacted with a 16-fold incre
Soft robots have found extensive applications in the medical field, particularly in rehabilitation exercises, assisted grasping, and artificial organs. Despite significant advancements in simulating various components of the digestive system, the rectum has been largely neglected due to societal stigma. This study seeks to address this gap by developing soft circular muscle actuators (CMAs) and rectum models to replicate the defecation process. Using soft materials, both the rectum and the actuators were fabricated to enable seamless integration and attachment. We designed, fabricated, and tested three types of CMAs and compared them to the simulated results. A pneumatic system was employed to control the actuators, and simulated stool was synthesized using sodium alginate and calcium chloride. Experimental results indicated that the third type of actuator exhibited superior performance in terms of area contraction and pressure generation. The successful simulation of the defecation process highlights the potential of these soft actuators in biomedical applications, providing a foundation for further research and development in the field of soft robotics.
Accurate dose distribution prediction is crucial in the radiotherapy planning. Although previous methods based on convolutional neural network have shown promising performance, they have the problem of over-smoothing, leading to prediction without important high-frequency details. Recently, diffusion model has achieved great success in computer vision, which excels in generating images with more high-frequency details, yet suffers from time-consuming and extensive computational resource consumption. To alleviate these problems, we propose Frequency-Decomposed Diffusion Model (FDDM) that refines the high-frequency subbands of the dose map. To be specific, we design a Coarse Dose Prediction Module (CDPM) to first predict a coarse dose map and then utilize discrete wavelet transform to decompose the coarse dose map into a low-frequency subband and three high-frequency subbands. There is a notable difference between the coarse predicted results and ground truth in high-frequency subbands. Therefore, we design a diffusion-based module called High-Frequency Refinement Module (HFRM) that performs diffusion operation in the high-frequency components of the dose map instead of the original
Among all types of cancer, gynecological malignancies belong to the 4th most frequent type of cancer among women. Besides chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an Organ-At-Risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graphs outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The com
Colon and rectum cancer share many risk factors, and are often tabulated together as ``colorectal cancer'' in published summaries. However, recent work indicating that exercise, diet, and family history may have differential impacts on the two cancers encourages analyzing them separately, so that corresponding public health interventions can be more efficiently targeted. We analyze colon and rectum cancer data from the Minnesota Cancer Surveillance System from 1998--2002 over the 16-county Twin Cities (Minneapolis--St. Paul) metro and exurban area. The data consist of two marked point patterns, meaning that any statistical model must account for randomness in the observed locations, and expected positive association between the two cancer patterns. Our model extends marked spatial point pattern analysis in the context of a log Gaussian Cox process to accommodate spatially referenced covariates (local poverty rate and location within the metro area), individual-level risk factors (patient age and cancer stage), and related interactions. We obtain smoothed maps of marginal log-relative intensity surfaces for colon and rectum cancer, and uncover significant age and stage differences b
Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using
The fundamental nature of dark matter (DM) remains unknown, with significant uncertainties in its density profile. DM environments surrounding massive binary black holes (BBHs) modify their orbital dynamics, thereby altering gravitational wave (GW) emissions. For BBH systems at the Galactic Center, dynamical friction induced by DM spikes could produce detectable deviations in GW spectra, potentially observable by future space-based detectors. To address the uncertainties in the Galactic Center's DM profile, we systematically examine two scenarios: the generalized Navarro-Frenk-White (gNFW) profile and its post-spike modification. We investigate the evolutionary effects of DM dynamical friction and accretion on the eccentricity and semi-latus rectum of secondary black holes (BHs) in elliptical orbits. By constructing orbital models with varying initial eccentricities across the mass-semi-latus rectum parameter space and utilizing 30 years of simulated pulsar timing array data from the Square Kilometer Array (SKA), we identify detectable parameter regimes of DM effects and employ these GW observational signatures to constrain different DM density profiles. Our analysis reveals that a
Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherapy (MRgART). This study trained and evaluated a deep learning DIR method for domain invariant MR-MR registration. Methods: A progressively refined registration and segmentation (ProRSeg) method was trained with 262 pairs of 3T MR simulation scans from prostate cancer patients using weighted segmentation consistency loss. ProRSeg was tested on same- (58 pairs), cross- (72 1.5T MR Linac pairs), and mixed-domain (42 MRSim-MRL pairs) datasets for contour propagation accuracy of clinical target volume (CTV), bladder, and rectum. Dose accumulation was performed for 42 patients undergoing 5-fraction MRgART. Results: ProRSeg demonstrated generalization for bladder with similar Dice Similarity Coefficients across domains (0.88, 0.87, 0.86). For rectum and CTV, performance was domain-dependent with higher accuracy on cross-domain MRL dataset (DSCs 0.89) versus same-domain data. The model's strong cross-domain performance prompted us to study the feasibility of using it for dose accumulation. Dose accumulation showed 83.3% of patients met CTV cover
Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy, yet voxel-wise DL uncertainty maps are rarely presented to clinicians. Purpose: This study assessed how DL-generated uncertainty maps impact radiation oncologists during manual correction of prostate radiotherapy DL segmentations. Methods: Two nnUNet models were trained by 10-fold cross-validation on 434 MRI-only prostate cancer cases to segment the prostate and rectum. Each model was evaluated on 35 independent cases. Voxel-wise uncertainty was calculated using the SoftMax standard deviation (n=10) and visualized as a color-coded map. Four oncologists performed segmentation in two steps: Step 1: Rated segmentation quality and confidence using Likert scales and edited DL segmentations without uncertainty maps. Step 2 ($\geq 4$ weeks later): Repeated step 1, but with uncertainty maps available. Segmentation time was recorded for both steps, and oncologists provided qualitative free-text feedback. Histogram analysis compared voxel edits across uncertainty levels. Results: DL segmentations showed high agreement with oncologist edits. Quality ratings varied: rectum segmentation ratings slightl
We consider the motion of a particle in the geometry of a Schwarzschild-like black hole embedded in a dark matter (DM) halo with Dehnen type density profile and calculate the orbital periods along with the evolution of the semi-latus rectum and eccentricity for extreme mass ratio inspirals (EMRIs). Such a system emits gravitational waves (GWs), and the particle's orbit evolves under radiation reaction. We also consider the effects of dynamical friction and accretion of DM on the orbital parameters. We find that the eccentricity and semi-latus rectum decrease faster with respect to the case in which EMRI is in empty spacetime.
Here, we explore the effect of the cloud of strings (CoS) on the gravitational waveforms of extreme mass ratio inspirals (EMRIs). The EMRI system consists of a supermassive black hole (BH) and a compact stellar mass object moving around it. We begin with studying the test particle motion around the Schwarzschild BH surrounded by a CoS by using the Lagrangian formalism. Moreover, we investigated the effect of the CoS parameter on the evolution of the semi-latus rectum and eccentricity. We then turn to the exploration of the impact of the CoS parameter on the gravitational waveforms of the EMRI system. The analysis performed shows that Laser Interferometer Space Antenna (LISA) could detect the CoS imprint in gravitational waveforms when the values of the string cloud parameter $α\gtrsim 2 \times 10^{-6}$.
We investigate the potential of extreme mass-ratio inspirals to constrain quantum Oppenheimer-Snyder black holes within the framework of loop quantum gravity. We consider a stellar-mass object orbiting a supermassive Oppenheimer-Snyder black hole in an equatorial eccentric trajectory. To explore the dynamical behavior of the system, we analyze its orbital evolution under gravitational radiation within the adiabatic approximation and the mass-quadrupole formula for different initial orbital configurations. Our results show that the quantum correction parameter $\hatα$ slows down the evolution of the orbital semi-latus rectum and eccentricity. We then employ the numerical kludge method to generate the corresponding time-domain gravitational waveforms. To assess detectability, we include Doppler modulation due to the motion of space-based detectors and compute the frequency-domain characteristic strain. By evaluating mismatches between response signals for different values of $\hatα$, we show that even small corrections $(\hatα \sim 10^{-5})$ produce distinguishable effects. Our analysis suggests that future space-based detectors such as LISA can probe quantum gravitational correction
Objective: Quantify geometric and dosimetric accuracy of a novel prostate MR-to-MR deformable image registration (DIR) approach to support MR-guided adaptive radiation therapy dose accumulation. Approach: We evaluated DIR accuracy in 25 patients treated with 30 Gy in 5 fractions on a 1.5 T MR-linac using an adaptive workflow. A reference MR was used for planning, with three images collected at each fraction: adapt MR for adaptive planning, verify MR for pretreatment position verification and beam-on for capturing anatomy during radiation delivery. We assessed three DIR approaches: intensity-based, intensity-based with controlling structures (CS) and novel intensity based with controlling structures and points of interest (CS+P). DIRs were performed between the reference and fraction images and within fractions. We propagated CTV, bladder, and rectum contours using the DIRs and compared to manual contours using Dice similarity coefficient, mean distance to agreement (DTAmean), and dose-volume metrics. Results: CS and CS+P improved geometric agreement between contours over intensity-only DIR. DTAmean for reference-to-beam-on intensity-only DIR was 0.131+/-0.009cm (CTV), 0.46+/-0.08cm
The swirling-Kerr black hole is a novel solution of vacuum general relativity and has an extra swirling parameter characterizing the rotation of spacetime background. We have studied the gravitational waves generated by extreme mass ratio inspirals (EMRIs) along eccentric orbits on equatorial plane in this novel swirling spacetime. Our findings indicate that this swirling parameter leads to a delayed phase shift in the gravitational waveforms. Furthermore, we have investigated effects of the swirling parameter on the potential issue of waveform confusion caused by the orbital eccentricity and semi-latus rectum parameters. As the swirling parameter increases, the relative variations in the eccentricity increase, while the variations in the semi-latus rectum decrease rapidly. These trends of the changes related to the orbital eccentricity and the semi-latus rectum with the swirling parameter resemble those observed with the MOG parameter in the Scalar-Tensor-Vector-Gravity (STVG) theory, but with different rates of change. Furthermore, our results also reveal that effects of the background swirling parameter on the relative variations in the eccentricity and the semi-latus rectum are
In nonparametric regression analysis, errors are possibly correlated in practice, and neglecting error correlation can undermine most bandwidth selection methods. When no prior knowledge or parametric form of the correlation structure is available in the random design setting, this issue has primarily been studied in the context of short-range dependent errors. When the data exhibits correlations that decay much more slowly, we introduce a special class of kernel functions and propose a procedure for selecting bandwidth in kernel-based nonparametric regression, using local linear regression as an example. Additionally, we provide a nonparametric estimate of the error covariance function, supported by theoretical results. Our simulations demonstrate significant improvements in estimating the nonparametric regression and error covariance functions, particularly in scenarios beyond short-range dependence. The practical application of our procedure is illustrated through the analysis of three datasets: cardiovascular disease mortality, life expectancy, and colon and rectum cancer mortality in the Southeastern United States.
You have a satellite spacecraft or asteroid that moves under the gravitational influence of a massive central body and follows a Keplerian orbit around it ellipse parabola or hyperbola Given measurements of two positions in its orbit what is the family of possible orbital paths that connects them I use the conic section orbits semilatus rectum directly related to orbital angular momentum to parameterise these orbits The solutions have applications to orbit determination ballistic missiles interplanetary interception and targeted reentry I also show how they can be applied to solve the Lambert problem of finding the unique transfer orbit that connects two points in a specified time interval These results are accessible to advanced undergraduate students in physics or aerospace engineering. Supplementary materials are provided online
Fecal incontinence, arising from a myriad of pathogenic mechanisms, has attracted considerable global attention. Despite its significance, the replication of the defecatory system for studying fecal incontinence mechanisms remains limited largely due to social stigma and taboos. Inspired by the rectum's functionalities, we have developed a soft robotic system, encompassing a power supply, pressure sensing, data acquisition systems, a flushing mechanism, a stage, and a rectal module. The innovative soft rectal module includes actuators inspired by sphincter muscles, both soft and rigid covers, and soft rectum mold. The rectal mold, fabricated from materials that closely mimic human rectal tissue, is produced using the mold replication fabrication method. Both the soft and rigid components of the mold are realized through the application of 3D-printing technology. The sphincter muscles-inspired actuators featuring double-layer pouch structures are modeled and optimized based on multilayer perceptron methods aiming to obtain high contractions ratios (100%), high generated pressure (9.8 kPa), and small recovery time (3 s). Upon assembly, this defecation robot is capable of smoothly exp
Purpose: Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events (AEs). Methods: DLVCRO treats DLVC as soft constraints controlling the joint distribution of dose and LET. Ten prostate cancer patients were included with rectum and bladder as OARs. DLVCRO was compared with the conventional robust optimization (RO) method using the worst-case analysis method. Besides the dose-volume histogram (DVH) indices, the analogous LETVH and extra-biological-dose (xBD)-volume histogram indices were also used. The Wilcoxon signed rank test was used to measure statistical significance. Results: In nominal scenario, DLVCRO significantly improved dose, LET and xBD distributions to protect OARs (rectum: V70Gy: 3.07\% vs. 2.90\%, p = .0063, RO vs. DLVCRO; $\text{LET}_{\max}$ (keV/um): 11.53 vs. 9.44, p = .0101; $\text{xBD}
The periapsis shift (PS) of spinning test particles in the equatorial plane of arbitrary stationary and axisymmetric spacetime is studied using the post-Newtonian method. The result is expressed as a half-integer power series of $M/p$ where $M$ is the spacetime mass and $p$ is the semilatus rectum. The coefficients of the series are polynomials of the particle spin, the asymptotic expansion coefficients of the metric functions and the eccentricity of the orbit. The particle spin is shown to have a similar effect as the Lense-Thirring (LT) effect on the PS, and both of them appear from the $(M/p)^{-3/2}$ order in the PS. The coupling between the spacetime and particle spins will increase (or decrease) the PS if they are parallel (or antiparallel). For Jupiter and Saturn rotating around the Sun and exceptionally designed satellites around Mercury and Moon, the particle spin effect can be comparable to the LT one in size. The PS in other spacetime studied are not distinguishable from that in the Kerr spacetime to the $(M/p)^{-3/2}$ order.
Accurate segmentation of metastatic lymph nodes in rectal cancer is crucial for the staging and treatment of rectal cancer. However, existing segmentation approaches face challenges due to the absence of pixel-level annotated datasets tailored for lymph nodes around the rectum. Additionally, metastatic lymph nodes are characterized by their relatively small size, irregular shapes, and lower contrast compared to the background, further complicating the segmentation task. To address these challenges, we present the first large-scale perirectal metastatic lymph node CT image dataset called Meply, which encompasses pixel-level annotations of 269 patients diagnosed with rectal cancer. Furthermore, we introduce a novel lymph-node segmentation model named CoSAM. The CoSAM utilizes sequence-based detection to guide the segmentation of metastatic lymph nodes in rectal cancer, contributing to improved localization performance for the segmentation model. It comprises three key components: sequence-based detection module, segmentation module, and collaborative convergence unit. To evaluate the effectiveness of CoSAM, we systematically compare its performance with several popular segmentation m