Volume Set Research Articles

Incremental feature selection: Parallel approach with local neighborhood rough sets and composite entropy

Rough set theory is a powerful mathematical framework for managing uncertainty and is widely utilized in feature selection. However, traditional rough set-based feature selection algorithms encounter significant challenges, especially when processing large-scale incremental data and adapting to the dynamic nature of real-world scenarios, where both data volume and feature sets are continuously changing. To overcome these limitations, this study proposes an innovative algorithm that integrates local neighborhood rough sets with composite entropy to measure uncertainty in information systems more accurately. By incorporating decision distribution, composite entropy enhances the precision of uncertainty quantification, thereby improving the effectiveness of the algorithm in feature selection. To further improve performance in handling large-scale incremental data, matrix operations are employed in place of traditional set-based methods, allowing the algorithm to fully utilize modern hardware capabilities for accelerated processing. Additionally, parallel computing technology is integrated to further enhance computational speed. An incremental version of the algorithm is also introduced to better adapt to dynamic data environments, increasing its flexibility and practicality. Comprehensive experimental evaluations demonstrate that the proposed algorithm significantly surpasses existing methods in both effectiveness and efficiency.

Magnitude and kinetics of a set of neuroanatomic volume and thickness together with white matter hyperintensity is definitive of cognitive status and brain age

Even among the subjects classified as cognitively normal, there exists a subset of individuals at a given chronological age (CA) who harbor white matter hyperintensity (WMH) while another subset presents with low or undetectable WMH. Here, we conducted a comprehensive MRI segmentation of neuroanatomic structures along with WMH quantification in groups of cognitively normal (CN), cognitively impaired (CI) individuals, and individuals with an etiological diagnosis of cognitive impairment owing to Alzheimer’s Disease (CI-AD) across the early (50–64 years), intermediate (65–79 years), and late (≥80 years) age groups from the NACC cohort. Neuroanatomic volumetry quantification revealed that thinning of the parahippocampal gyrus in the early (p = 0.016) and intermediate age groups (p = 0.0001) along with an increase in CSF (p = 0.0009) delineates between CI and CI-AD subjects. Although, a significant loss of ~5–10% in volume of gray matter (p(CN vs CI) < 0.0001, p(CN vs CI-AD) < 0.0001), white matter (p(CN vs CI) = 0.002, p(CN vs CI-AD) = 0.0003) and hippocampus (p(CN vs CI) = 0.007, p(CN vs CI-AD) < 0.0001) was evident at the early age groups in the CI and CI-AD compared to CN but it was not distinct between CI and CI-AD. Using the neuroanatomic and WMH volume, and the supervised decision tree-based ML modeling, we have established that a minimum set of Three brain quantities; Total brain (GM + WM), CSF, and WMH volume, provide the Optimal quantitative features discriminative of cognitive status as CN, CI, and CI-AD. Furthermore, using the volume/thickness of 178 neuroanatomic structures, periventricular and deep WMH volume quantification for the 819 CN subjects, we have developed a quantitative index as ‘Brain Age’ (BA) depictive of neuroanatomic health at a given CA. Subjects with elevated WMH load (5–10 ml) had increased BA ( + 0.6 to +4 years) than the CA. Increased BA in the subjects with elevated WMH is suggestive of WMH-induced vascular insult leading to accelerated and early structural loss than expected for a given CA. Henceforth, this study establishes that quantification of WMH together with an optimal number of neuroanatomic features is mandatory to delve into the biological underpinning of aging and aging-associated cognitive disorders.

Rigidity of $\mathbf{\textit{U}}$-Gibbs measures near conservative Anosov diffeomorphisms on $\mathbb{T}^{3}$

We show that within a C^{1} -neighborhood {\mathcal{U}} of the set of volume preserving Anosov diffeomorphisms on the three-torus {\mathbb{T}^3} which are strongly partially hyperbolic with expanding center, any f\in{\mathcal{U}}\cap{\operatorname{Diff}^2(\mathbb{T}^3)} satisfies the dichotomy: either the strong-stable and strong-unstable bundles E^{s} , E^{u} of f are jointly integrable, or any fully supported u -Gibbs measure of f is SRB.

Post-insufflation diaphragm contractions in patients receiving various modes of mechanical ventilation

BackgroundDuring mechanical ventilation, post-insufflation diaphragm contractions (PIDCs) are non-physiologic and could be injurious. PIDCs could be frequent during reverse-triggering, where diaphragm contractions follow the ventilator rhythm. Whether PIDCs happens with different modes of assisted ventilation is unknown. In mechanically ventilated patients with hypoxemic respiratory failure, we aimed to examine whether PIDCs are associated with ventilator settings, patients’ characteristics or both.MethodsOne-hour recordings of diaphragm electromyography (EAdi), airway pressure and flow were collected once per day for up to five days from intubation until full recovery of diaphragm activity or death. Each breath was classified as mandatory (without-reverse-triggering), reverse-triggering, or patient triggered. Reverse triggering was further subclassified according to EAdi timing relative to ventilator cycle or reverse triggering leading to breath-stacking. EAdi timing (onset, offset), peak and neural inspiratory time (Tineuro) were measured breath-by-breath and compared to the ventilator expiratory time. A multivariable logistic regression model was used to investigate factors independently associated with PIDCs, including EAdi timing, amplitude, Tineuro, ventilator settings and APACHE II.ResultsForty-seven patients (median[25%-75%IQR] age: 63[52–77] years, BMI: 24.9[22.9–33.7] kg/m2, 49% male, APACHE II: 21[19–28]) contributed 2 ± 1 recordings each, totaling 183,962 breaths. PIDCs occurred in 74% of reverse-triggering, 27% of pressure support breaths, 21% of assist-control breaths, 5% of Neurally Adjusted Ventilatory Assist (NAVA) breaths. PIDCs were associated with higher EAdi peak (odds ratio [OR][95%CI] 1.01[1.01;1.01], longer Tineuro (OR 37.59[34.50;40.98]), shorter ventilator inspiratory time (OR 0.27[0.24;0.30]), high peak inspiratory flow (OR 0.22[0.20;0.26]), and small tidal volumes (OR 0.31[0.25;0.37]) (all P ≤ 0.008). NAVA was associated with absence of PIDCs (OR 0.03[0.02;0.03]; P < 0.001). Reverse triggering was characterized by lower EAdi peak than breaths triggered under pressure support and associated with small tidal volume and shorter set inspiratory time than breaths triggered under assist-control (all P < 0.05). Reverse triggering leading to breath stacking was characterized by higher peak EAdi and longer Tineuro and associated with small tidal volumes compared to all other reverse-triggering phenotypes (all P < 0.05).ConclusionsIn critically ill mechanically ventilated patients, PIDCs and reverse triggering phenotypes were associated with potentially modifiable factors, including ventilator settings. Proportional modes like NAVA represent a solution abolishing PIDCs.

Regional morphological adaptations of vastus lateralis muscle in response to different progressive resistance training programs: A randomised controlled trial.

Resistance training often increases muscle size, a phenomenon known as muscle hypertrophy. These morphological adaptations were typically documented to occur in a non-uniform pattern. Investigating the specific morphological adaptations to different training programs was of interest. This study aimed to investigate two resistance training programs, a high-intensity program (HI) and a combined high-intensity with low-intensity blood flow restriction program (MIX), on morphological adaptations of vastus lateralis muscle in healthy young men. Eighteen active participants were recruited and randomly assigned to the HI (n = 10) or MIX (n = 8) groups, undergoing different 6-week resistance training programs. The training volume set was equated and progressively increased from three sets in weeks 1 and 2 to six sets, and eight sets in weeks 3-4 and 5-6, respectively. Three specific regions of vastus lateralis were assessed by magnetic resonance imaging (MRI) and ultrasound imaging (US) during pre-and post-intervention. Statistical analysis revealed statistically significant increases in muscle area at the proximal (HI: Δ12%, MIX: Δ9.2%), middle (HI: Δ8.7%, MIX: Δ9.0%), and distal (HI: Δ14%, MIX: Δ13%) regions. Additionally, both HI and MIX groups showed statistically significant increases in the sum of muscle thickness post-intervention (HI: Δ12%, MIX: Δ19%) and in the sum of fascia thickness post-intervention (HI: Δ27%, MIX: Δ54%). Despite the MIX group training with higher volume load, no statistical differences were observed between groups for any week. These findings suggested that both HI and MIX programs effectively induced increases in muscle area and sums of muscle and fascia thickness in healthy young men, allowing practitioners to choose either program based on individual preferences and constraints.

(Invited) Functional Fluorous Nanocarbons

Fluorination was one of the first hypothesized chemical reactions for a buckyball that could yield C60F60 with "superlubricant" properties. [1] This promise, however, has not come to fruition due to propensity of fluorofullerenes for hydrolysis. Research of fluorination of fullerenes has contributed to the basic understanding of chemical reactivity of nanocarbons, especially, in radical reactions. Overall, there are more fluorinated fullerenes than any other halogenated or hydrogenated fullerenes. Small size of F atom, its high reactivity, relative strength of C-F bond compared to C-Hal or C-H bonds, and relative thermal and environmental stability are among the major reasons of the existence of large libraries of well-characterized fluorinated fullerenes.[2] The latter include not only products of direct addition of F atoms to C(C60) atoms, i.e., C60F2n, but also numerous derivatives with fluorine-containing moieties, i.e., C60(RF)n.[3]Exploration of applications of various classes of fluorinated fullerene materials in organic optoelectronics and biomedical research that was carried out by the CSU group and collaborators in the past decade will be critically overviewed by the author.Partial support from NSF (CHE-2153922) is acknowledged.1. H.W. Kroto, J.R. Heath, S.C. O’Brien, R.F. Curl and R.E. Smalley, Nature 318 (1985) 162.2. O.V. Boltalina and S.H. Strauss (2014) Fluorofullerenes. In Dekker Encyclopedia of Nanosci. and Nanotech., CRC Press, Seven Volume Set, 1436.3. O. V. Boltalina, A. A. Popov, I. V. Kuvychko, N. B. Shustova, S. H. Strauss, Chem. Rev. (2015) 115, 1051.

Rook’s Textbook of Dermatology, 4 volume set, 10th edition

Rook’s Textbook of Dermatology, 4 volume set, 10th edition

Lattice dynamics and free energies of Fe–V alloys with thermal and chemical disorder

Molecular dynamics simulations of Fe-V binary alloys with body-centered cubic as the underlying lattice were performed using a classical potential for chemically ordered and disordered states at finite temperatures for a common set of volumes. The equation of state was fitted to the computational data to obtain temperature- and chemical-order-dependent state functions via the Moruzzi-Janak-Schwarz approximation. Additionally, vibrational entropies that account for both thermal and chemical disorder were calculated for the equiatomic compositions from phonon density-of-states curves computed using effective force constants obtained from fits to the simulations. The latter predicts that the vibrational entropy at room temperature at equiatomicity is higher for the ordered phase than for the solid solution, a peculiar behavior previously observed experimentally. The internal energy of mixing favors ordering at all compositions, with a maximum at equiatomicity that decreases as the solute concentration decreases. The configurational entropy contribution to the free energy of mixing is almost entirely responsible for the stability of the high-temperature disordered phase.

Holonomies for foliations with extreme disintegration behavior

Given a foliation, we say that it displays “extreme disintegration behavior” if either it has atomic disintegration or if it is leafwise absolutely continuous and its conditional measures are uniformly equivalent to the leaf volume, which we call UDB property. Both concepts are related to the decomposition of volume with respect to the foliation. We relate these behaviors to the measure-theoretical regularity of the holonomies, by proving that a foliation with atomic disintegration has holonomies taking full volume sets to zero volume sets, and we characterize the UBD property with the holonomies having uniformly bounded Jacobians. Both extreme phenomena appear in invariant foliations for dynamical systems.

Needle tracking in low-resolution ultrasound volumes using deep learning

PurposeClinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes.MethodsWe design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation.ResultsOur experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning.ConclusionOur study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.

Dielectric properties of tidal volume changes in rabbit lung tissue in the 100 MHz~1 GHz band

This paper investigates the variation of lung tissue dielectric properties with tidal volume under in vivo conditions to provide reliable and valid a priori information for techniques such as microwave imaging. In this study, the dielectric properties of the lung tissue of 30 rabbits were measured in vivo using the open-end coaxial probe method in the frequency band of 100 MHz to 1 GHz, and 6 different sets of tidal volumes (30, 40, 50, 60, 70, 80 mL) were set up to study the trends of the dielectric properties, and the data at 2 specific frequency points (433 and 915 MHz) were analyzed statistically. It was found that the dielectric coefficient and conductivity of lung tissue tended to decrease with increasing tidal volume in the frequency range of 100 MHz to 1 GHz, and the differences in the dielectric properties of lung tissue for the 6 groups of tidal volumes at 2 specific frequency points were statistically significant. This paper showed that the dielectric properties of lung tissue tend to vary non-linearly with increasing tidal volume. Based on this, more accurate biological tissue parameters can be provided for bioelectromagnetic imaging techniques such as microwave imaging, which could provide a scientific basis and experimental data support for the improvement of diagnostic methods and equipment for lung diseases.

Current State of Ventilator Setting and Their Relationship with Mortality Rate in Patients under Mechanical Ventilation: A Cross-Sectional Study

Background: Given the importance of implementing lung protective strategies to prevent lung injury caused by ventilators and death of patients, it is necessary to monitor the current condition of hospitals and examine the relationship between the parameters set on the ventilators and patient mortality. This study conducted to determine the current state of ventilator setting and their relationship with mortality rate in patients under mechanical ventilation: a cross-sectional study. Methods: This is a cross-sectional study that was conducted between June to December 2020 in one of the hospitals affiliated to Tehran University of Medical Sciences. The initial tidal volume set on the ventilator was recorded for 304 patients under mechanical ventilation and then, their heights were measured and their tidal volumes were determined based on the standard formula. Other parameters set on the ventilator as well as systolic and diastolic blood pressures of patients were also recorded and their survival rate was investigated. The data was analyzed by SPSS software, using descriptive statistics and logistic regression model. Results: Among patients, who were under mechanical ventilation, 77.6% were hospitalized in intensive care units and the rest were hospitalized in general wards. The mean adjusted tidal volume for patients was 472.91 ± 32.13 ml. The mean peak inspiratory pressure and plateau pressure were 28.00±6.98 and 13.88±4.93 CmH2O, respectively. Also, 37.2% of patients died during the hospitalization. The results of adjusted odds ratio based on multivariate logistic regression model for predictors of mortality rate showed that the variables of patients' age [OR=1.040 (1.019-1.062)], the hospital’s general ward in comparison with the ICU [OR=11.379 (5.130-25.240)] and the peak inspiratory pressure [OR=1.072 (1.007-1.141)] had a direct and significant relationship with mortality rate (in all cases P&lt;0.05). Meanwhile, the plateau pressure [OR=0.886 (0.808 -0.972)] had an inverse and significant relationship with mortality rate (P&lt;0.05). Conclusion: Despite the recommendations regarding lung protective strategies, in some cases, some parameters set in the ventilator are outside the recommended levels, which can effect on patients mortality. So monitoring and controlling the implementation of lung protective strategies and paying attention to controlling pressures set on the ventilator are among measures that should be taken in medical centers in order to prevent lung injuries and maintain patient safety.

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of features from an image or signals, which are presented in tabular form. The possibility of high-precision and high-speed processing of such large volumes of medical data requires the use of artificial intelligence tools. A linear machine learning model cannot accurately analyze such data, and traditional bagging, boosting, or stacking ensembles typically require significant computing power and time to implement. In this paper, the authors proposed a method for the analysis of large sets of medical data, based on a designed linear ensemble method with a non-iterative learning algorithm. The basic node of the new ensemble is an extended-input SGTM neural-like structure, which provides high-speed data processing at each level of the ensemble. Increasing prediction accuracy is ensured by dividing the large dataset into parts, the analysis of which is carried out in each node of the ensemble structure and taking into account the output signal from the previous level of the ensemble as an additional attribute on the next one. Such a design of a new ensemble structure provides both a significant increase in the prediction accuracy for large sets of medical data analysis and a significant reduction in the duration of the training procedure. Experimental studies on a large medical dataset, as well as a comparison with existing machine learning methods, confirmed the high efficiency of using the developed ensemble structure when solving the prediction task.

Bayesian Approach to Estimate Proglacial Lake Volume (BE‐GLAV)

AbstractWe present a new model called Bayesian Estimated Glacial Lake Volume (BE‐GLAV) to estimate the volume of proglacial lakes. Presuming the lake cross‐section as trapezoidal, BE‐GLAV uses a Bayesian calibration approach to adjust the cross‐sectional geometry to match modeled and observed lake surface widths. We validated our model using bathymetric measurements from lakes spread across High Mountain Asia (specifically, the Himalaya and Tien‐Shan), with aerial extents ranging from 0.01 to 5.5 km2. The modeled lake volumes agreed with the measured lake volume with a root‐mean‐square absolute uncertainty of ∼14%. With minimum and maximum errors of ∼0.3% and ∼61.2%, BE‐GLAV performed well compared to 10 other models in a model inter‐comparison experiment. Using the measured set of volumes, our model can constrain both the root mean square (RMS) error and the maximum percentage error in modeled lake volume, unlike other models, some of which can compute just the RMS uncertainty.

Automated multivolume placental reconstruction using three-dimensional power Doppler ultrasound and infrared camera tracking.

Placental insufficiency contributes to many obstetric pathologies however there is no bedside clinical tool to evaluate placental perfusion. We have developed a method to acquire multiple three-dimensional power Doppler ultrasound (3D PD-US) volumes of placental vasculature with infrared camera tracking providing global coordinates. These are automatically reconstructed ('stitched') into a model of the entire placenta. The purpose of the study was to evaluate the accuracy of automated reconstruction in an US phantom and apply this technique to human placentas. A custom-designed acrylic phantom was constructed with dimensions mimicking a third-trimester placenta, containing 12 quadrilateral towers of varying heights submersed in tissue-mimicking solution. Triplicated three-dimensional ultrasound volumes of this phantom were acquired at three different acquisition angles using infrared camera tracking. Data was transformed into a three-dimensional cartesian volume and automatically stitched. A single-centre, cross-sectional feasibility study was conducted on uncomplicated second-to-third trimester singleton pregnancies using standardised obstetric settings. Multiple 3D PD-US and grayscale volumes of the placenta were acquired with infrared camera tracked coordinates. Volumes were stitched to create a model of placental vasculature. 6 phantom datasets were reconstructed at each of 3 volume angles with a median of 9 volumes required. Perfect volume alignment occurred in 66.7% of 648 datapoints. Mean distance error for volume misalignment was 2.92mm. Measurements of 210 distances in each stitched volume (2160 total distances) differed an average of 1.51mm from true measurements. These compare favourably with recent literature, though for a substantially larger phantom. 17 participants were scanned with 92% reconstruction success per placental volume set and 100% participant achievability. Median reconstruction time was 10 minutes. Placental vasculature was qualitatively assessed to be present, continuous, and detailed throughout. Volume measurement of entire segmented placentas was highly repeatable (ICC 0.96). We present an automated method to model the entire structure and vasculature of second-to-third trimester placentas, with verified accuracy and clinical feasibility for grayscale and power Doppler. This study builds the foundation to develop a practical screening tool for detecting placental insufficiency, and expansion to adult organ perfusion evaluation. This article is protected by copyright. All rights reserved.

Acute Effects of Slow, Moderate and Fast Tempo Dynamic Stretching Exercises on Power in Well-Trained Male Wrestlers.

Due to the potential detrimental effects of static stretching exercises on subsequent muscle power performance, athletes and trainers have started to replace static stretching with dynamic stretching exercises in their training routines. However, there are no well-accepted guidelines regarding dynamic stretching variables, including tempo/velocity, volume (reps and sets), etc. Therefore, this study aimed to evaluate the acute effects of slow, moderate, and fast tempo dynamic stretching exercises on jump height, relative power, the reactive strength index, and leg stiffness in well-trained male wrestlers. Seventeen wrestlers (aged 20.00 ± 4.06 years, wrestling experience 6.00 ± 3.09 years, and training volume per week 10.00 ± 5.69 hours) took part in the experiment under four conditions (control session, slow tempo dynamic stretching, moderate tempo dynamic stretching, and fast tempo dynamic stretching) on separate days with a 72-h interval in between, following a randomized, crossover study design. The control session consisted of a 10-min jog on a motor-driven treadmill at 6 km/h and a 0% slope. Dynamic stretching sessions consisted of seven dynamic stretching exercises performed at 50 bpm, 100 bpm, and 120 bpm, following a 5-min warm-up on a treadmill at 6 km/h and a 0% slope. After each condition, wrestlers performed a 2 x 30-s repeated vertical jump test with 5-min rest intervals in between. The best results for jump height, relative power, the reactive strength index, and leg stiffness were registered for statistical analysis. One-way repeated ANOVA results demonstrated that there were no significant differences in pairwise comparisons of all variables obtained after the four different conditions (p > 0.05). Overall, none of the slow, moderate, and fast tempo dynamic stretching exercises led to a change in repeated jump performance of well-trained male athletes. Further studies are needed to clarify the acute effects of different tempo dynamic stretching on muscular performance in well-trained wrestlers.

Exclusion of non-Involved uterus from the target volume (EXIT-trial): An individualized treatment for locally advanced cervical cancer using modern radiotherapy and imaging techniques followed by completion surgery

Background and purposeChemoradiotherapy followed by brachytherapy is the standard of care for locally advanced cervical cancer (LACC). In this study, we postulate that omitting an iconographical unaffected uterus (+12 mm distance from the tumour) from the treatment volume is safe and that no tumour will be found in the non-targeted uterus (NTU) leading to reduction of high-dose volumes of surrounding organs at risk (OARs) Material and MethodsIn this single-arm phase 2 study, two sets of target volumes were delineated: one standard-volume (whole uterus) and an EXIT-volume (exclusion of non-tumour-bearing parts of the uterus with a minimum 12 mm margin from the tumour). All patients underwent chemoradiotherapy targeting the EXIT-volume, followed by completion hysterectomy. In 15 patients, a plan comparison between two treatment plans (PTV vs PTV_EXIT) was performed. The primary endpoint was the pathological absence of tumour involvement in the non-targeted uterus (NTU). Secondary endpoints included dosimetric impact of target volume reduction on OARs, acute and chronic toxicity, overall survival (OS), locoregional recurrence-free survival (LRFS), and progression-free survival (PFS). ResultsIn all 21 (FIGO stage I: 2; II: 14;III: 3; IV: 2) patients the NTU was pathologically negative. Ssignificant reductions in Dmean in bladder, sigmoid and rectum; V15Gy in sigmoid and rectum, V30Gy in bladder, sigmoid and rectum; V40Gy and V45Gy in bladder, bowel bag, sigmoid and rectum; V50Gy in rectum were achieved. Median follow-up was 54 months (range 7–79 months). Acute toxicity was mainly grade 2 and 5 % grade 3 urinary. The 3y- OS, PFS and LRFS were respectively 76,2%, 64,9% and 81 %. ConclusionMRI-based exclusion of the non-tumour-bearing parts of the uterus at a minimum distance of 12 mm from the tumour out of the target volume in LACC can be done without risk of residual disease in the NTU, leading to a significant reduction of the volume of surrounding OARS treated to high doses.

GAN-Based Motion Artifact Correction of 3D MR Volumes Using an Image-to-Image Translation Algorithm.

The quality of brain MRI volumes is often compromised by motion artifacts arising from intricate respiratory patterns and involuntary head movements, manifesting as blurring and ghosting that markedly degrade imaging quality. In this study, we introduce an innovative approach employing a 3D deep learning framework to restore brain MR volumes afflicted by motion artifacts. The framework integrates a densely connected 3D U-net architecture augmented by generative adversarial network (GAN)-informed training with a novel volumetric reconstruction loss function tailored to 3D GAN to enhance the quality of the volumes. Our methodology is substantiated through comprehensive experimentation involving a diverse set of motion artifact-affected MR volumes. The generated high-quality MR volumes have similar volumetric signatures comparable to motion-free MR volumes after motion correction. This underscores the significant potential of harnessing this 3D deep learning system to aid in the rectification of motion artifacts in brain MR volumes, highlighting a promising avenue for advanced clinical applications.

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans With Guidance From High-Quality Isotropic Volumes.

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality "source" volumes to thick-slice "target" scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C 2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C 2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C 2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively.

Mechanism of enhanced microfine low-rank coal flotation by jet slurry conditioning

ABSTRACT Microfine low-rank coals are subject to metamorphic changes that hinder their floatability, impeding their clean and efficient utilization. The objective of this study was to refine the jet-slurry technique to enhance the floatability of microfine low-rank coals. Furthermore, advanced methods including Focused beam reflectance measurement (FBRM), high-sensitivity microelectromechanical balancer (DCAT), Fourier transform infrared spectrometer (FTIR), and scanning electron microscope (SEM) were employed to dissect the physicochemical effects of jet slurring on microfine low-rank coal particles. The study found that as the concentration of microfine coal particles increased, surface reagent adsorption initially rose and then fell with greater feed volumes. In contrast, adhesion forces displayed an inverse trend. Optimal results were achieved with a feed volume set at 25 L/min. This phenomenon is attributable to multiple regulatory factors. Within certain limits, intensifying conditioning promotes particle agglomeration, curtails fine gangue mineral coverage, boosts sodium dodecyl sulfate (SDS) adsorption, diminishes microfine coal wettability, and better kerosene-coal particle mixing. However, at excessive jet speeds (30 L/min), substantial physical pressure variances and mechanical dispersion forces arise, undermining SDS or kerosene’s chemical adsorption to coal or modified coal, thus reducing reagent attachment. The aforementioned study demonstrates that within certain parameters, jet slurring significantly betters coal-gangue separation, thereby amplifying the flotation efficacy of microfine low-rank coals. This research offers strategic insights into microfine low-rank coal flotation processes and lays a theoretical groundwork for the selection of suitable techniques and reagents.