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The aim of this study was to compare two techniques for temporary intraoperative maxillomandibular fixation (TIO-MMF) during orthognathic surgery: steel-wire ligatures versus power chains. Patients undergoing orthognathic surgery between October 2019 and March 2020 were included in a prospective cross-sectional study conducted in three participating hospitals. Data were collected using a standardized measurement form. A total of 44 patients were included, in whom TIO-MMF was applied 79 times. A statistically significant difference in intraoperative loss of stability of the segment relationship was found between steel-wire ligatures (11.4%) and power chains (0%). The mean application time of TIO-MMF differed significantly between steel-wire ligatures (99 seconds) and power chains (157 seconds) (P < 0.001). There was no statistical difference in occurrence of adverse events between the two techniques. This study found that the application of TIO-MMF with power chains is more stable compared to steel-wire ligatures. Steel-wire ligatures were significantly faster to apply, although the absolute difference (less than 1 minute) was small. Other possible advantages of the proposed technique are discussed. The results of this study suggest that power chains for the application of TIO-MMF in orthognathic surgery are a valuable alternative to steel-wire ligatures.

Rectal cancer is categorized into categories on the basis of tumor height measurements. Tumor height is used to guide initial treatment and determines the eligibility for clinical trials.This study aimed to determine the concordance between tumor heights measured by MRI and by clinical examination.This was an institutional review board-approved retrospective analysis of MRI and the clinical measurements of tumor height.This study was conducted at a single university center that was accredited by the Commission on Cancer National Accreditation Program for Rectal Cancer.Ninety-five patients who were treated between 2015 and 2019 and who had an MRI and clinical evaluation were included.The mean difference of tumor height between MRI and clinical examination was calculated. Secondary outcomes were to assess whether position in the rectum, age, BMI, or sex would affect the difference and how the measurements would change eligibility for rectal cancer trials.Tumor height measurement by MRI and clinical examination had a good correlation, with r = 0.89 and p0.001. The mean absolute difference of measurement of tumor height was 1.56 cm. Higher tumors had a larger absolute difference between measurements. Body mass index was significantly associated with the difference in measurements. The discordance in measurements led to a change in eligibility for clinical trials for 38.9% of patients. Clinical trial eligibility was not significantly associated with tumor height category, sex, or patient age.This study was conducted at a single center with retrospective methodology.Although MRI and clinical measurements showed a strong correlation, nearly 40% of our patients had a change in clinical trial eligibility depending on measurement modality. We suggest that trial investigators be consistent in establishing measurement technique as their inclusion criterion. See Video Abstract at http://links.lww.com/DCR/B756.ANTECEDENTES:El cáncer de recto se clasifica en categorías basadas en las mediciones de la altura del tumor. La altura del tumor se usa para guiar el tratamiento inicial y determina la elegibilidad para los ensayos clínicos.OBJETIVO:Determinar la concordancia entre la altura de los tumores medida por resonancia magnética (RMN) y por examen clínico.DISEÑO:Este fue un análisis retrospectivo aprobado por el IRB de la resonancia magnética y las mediciones clínicas de la altura del tumor.AJUSTE:Esto se llevó a cabo en un único centro universitario que fue acreditado por el Programa Nacional de Acreditación del Cáncer de Recto de la Comisión de Cáncer.PACIENTE:Se incluyeron 95 pacientes que fueron atendidos entre 2015 y 2019 y que tuvieron una resonancia magnética y evaluación clínica.PRINCIPALES MEDIDAS DE RESULTADOS:Se calculó la diferencia media de la altura del tumor entre la resonancia magnética y el examen clínico. Los resultados secundarios fueron evaluar si la posición en el recto, la edad, el índice de masa corporal (IMC) o el sexo afectarían la diferencia y cómo las mediciones cambiarían la elegibilidad para los ensayos de cáncer de recto.RESULTADOS:La medición de la altura del tumor por resonancia magnética y el examen clínico tuvo una buena correlación con r = 0,89 y p0,001. La diferencia absoluta media de medición de la altura del tumor fue de 1,56 cm. Los tumores más altos tenían una diferencia absoluta más grande entre las mediciones. El IMC se asoció significativamente con la diferencia en las mediciones. La discordancia en las mediciones llevó a un cambio en la elegibilidad para los ensayos clínicos para el 38,9% de los pacientes. La elegibilidad para ensayos clínicos no se asoció significativamente con la categoría de altura del tumor, el sexo o la edad del paciente.LIMITACIONES:Se realizó en un solo centro con metodología retrospectiva.CONCLUSIONES:Aunque la resonancia magnética y las mediciones clínicas mostraron una fuerte correlación, casi el 40% de nuestros pacientes tuvieron un cambio en la elegibilidad para los ensayos clínicos según la modalidad de medición. Sugerimos que los investigadores del ensayo sean coherentes al establecer la técnica de medición como criterio de inclusión. Consulte Video Resumen en http://links.lww.com/DCR/B756.

Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

Inspired by distance-constrained graph labelings that model the assignment of frequencies to transmitters, we introduce a distance-constrained labeling of connected graphs that models the assignment of frequencies to oscillators. For a graph G and a vector of nonnegative real numbers k = ( k 1 , k 2 , … , k p ) , an L ∗ ( G ; k ) -labeling is any function f from V ( G ) into R such that for each i , 1 ≤ i ≤ p , | f ( v ) − f ( w ) | ≤ k i if d ( v , w ) = i . The span of f is the absolute difference between the supremum and infimum of the image of f , and λ G , k ∗ is the maximum span over all L ∗ ( G , k ) labelings. In this paper, we explore the properties of λ G , k ∗ as a function of k , including continuity. And, for p = 2 , we establish λ G , k ∗ when G is either a tree or a cycle.

Background and purpose: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a highly accurate automatic contouring model for the heart, cardiac chambers, and great vessels for RT planning computed tomography (CT) images that can be used for dose-volume parameter estimation. Materials and methods: A neural network model was trained using a dataset of 127 expertly contoured planning CT images from RT treatment of locally advanced non-small-cell lung cancer (NSCLC) patients. Evaluation of geometric accuracy and quality of dosimetric parameter estimation was performed on 50 independent scans with contrast and without contrast enhancement. The model was further evaluated regarding the clinical acceptability of the contours in 99 scans randomly sampled from the RTOG-0617 dataset by three experienced radiation oncologists. Results: Median surface dice at 3 mm tolerance for all dedicated thoracic structures was 90% in the test set. Median absolute difference between mean dose computed with model contours and expert contours was 0.45 Gy averaged over all structures. The mean clinical acceptability rate by majority vote in the RTOG-0617 scans was 91%. Conclusion: This model can be used to contour the heart, cardiac chambers, and great vessels in large datasets of RT planning thoracic CT images accurately, quickly, and consistently. Additionally, the model can be used as a time-saving tool for contouring in clinic practice. (c) 2021 The Authors. Published by Elsevier B.V. Radiotherapy and Oncology 165 (2021) 52-59 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Hypothesis and Objective: To compare the accuracy of cephalometric landmark identification between Artificial Intelligence (AI) Deep Learning Convolutional Neural Networks YOLOv3 algorithm and Manually Traced (MT) group. Methods: AAOF Legacy Denver collection was used to obtain 110 cephalometric images for this study. Lateral cephalograms were digitized by orthodontic resident in Dolphin Imaging after intra- and inter reliability check. The same images were uploaded to AI software Ceppro DDH Inc. Cartesian system of coordinates with Sela as 0:0 was used to extract x and y coordinate for 16 cephalometric points: Nasion, A point, B point, Menton, Gonion, Upper incisor tip, Lower incisor tip, Upper incisor apex, Lower incisor apex, Anterior Nasal Spine, Posterior Nasal Spine, Pogonion, Pterigomaxillary fissure point, Basion, Articulare and Orbitale. The mean distances were assessed relative to the reference value of 2 mms. Student paired t-tests at significance level of 5 % were used to compare the mean differences in each of the X- and Y-components. SPSS (IBM-vs. 27.0) software was use for the data analysis. Results: The results showed that there is no statistical difference for 12 out of 16 points when analyzing absolute difference between MT and AI group. Success detection rate for AI within 2 mm while comparing MT and AI group was 75 % and 93% within 4 mm. Conclusions: AI could be considered a promising tool to facilitate cephalometric tracing process in routine clinical practice and in research settings. Funding: no funding. IRB and/or ACC Protocol #: 2019-1180. Notice of Determination: activity Does Not Represent Human Subjects Research.

Total internal reflection is an optical imaging technique for nanoparticle tracking and observation employing the scattered light from an evanescent field near the interface or reference surface. Generally, the nanoparticle behavior is the three-dimensional Brownian motion in an aqueous medium. The motion can be traced by an optical microscopy, but it cannot be traced by an electron microscopy technique. In the three-dimensional nanoparticle moving position, the X and Y positions are parallel to the surface, which can be traced by the general microscopy techniques. However, the height position Z of a nanoparticle perpendicular to the surface could not be traced without the longitudinal scanning method. Here, a novel method is proposed to investigate the 3D position of nanoparticles by applying multi-wavelength evanescent fields microscopy, which has a high spatial resolution in the Z-direction without longitudinal scanning. This paper focuses on the verification of measurement in the Z-direction. A piezoelectric actuator was employed to control the nanoparticle displacement in height Z. Standard polystyrene 100 nm particles were randomly adhered on a spherical tip that connected with the piezoelectric actuator. The spherical tip was essentially made from an optical adhesive (n = 1.348) with a refractive index close to the water for decreasing the unnecessary signal from the tip-self during nanoparticle observation in the water. The proposed method could obtain the multi-wavelength scattering lights from the observed nanoparticles by an 8-bit color camera with higher than 50 frames per second recording to investigate the 3D nanoscale tracking. The X and Y positions of nanoparticles were determined by the centroid of the scattering light intensities. The height Z was determined from the logarithm ratios between the detected scattering light intensities of both wavelengths. The measurement repeatability of the absolute difference in height between nanoparticles could be measured less than ±16 nm by using the proposed method. The penetration height measurability range was approximated at 250 nm from the reference surface.

Accurate self-assessment of knowledge and technical skills is key to self-directed education required in surgical training. We aimed to investigate the presence and magnitude of cognitive bias in self-assessment among a cohort of surgical interns.First-year general surgery residents self-assessed performance on a battery of technical skill tasks (knot tying, suturing, vascular anastomosis, Fundamentals of Laparoscopic Skills peg transfer and intracorporeal suturing) at the beginning of residency. Each self-assessment was compared to actual performance. Bias and deviation were defined as arithmetic and absolute difference between actual and estimated scores. Spearman correlation assessed covariation between actual and estimated scores. Improvement in participant performance was analyzed after an end-of-year assessment.Participants (N = 34) completed assessments from 2017 to 2019. Actual and self-assessment scores were positively correlated (0.55, P.001). Residents generally underestimated performance (bias -4.7 + 8.1). Participants who performed above cohort average tended to assess themselves more negatively (bias -7.3 vs -2.3) and had a larger discrepancy between self and actual scores than below average performers (deviation index 9.7 + 8.2 vs 3.8 + 3.1, P.05). End-of-year total scores improved in 31 (91.2%) participants by an average of 11 points (90 possible). Least accurate residents in initial self-assessments (deviation indices75th percentile) improved less than more accurate residents (median 5 vs 16 points, P.05). All residents with a deviation index75 percentile underestimated their performance.Cognitive bias in technical surgical skills is apparent in first-year surgical residents, particularly in those who are higher performers. Inaccuracy in self-assessment may influence improvement and should be addressed in surgical training.

Purpose: Three-dimensional (3D) assessment of orthognathic surgery is often time consuming, relies on manual re-identification of anatomical landmarks or is limited to non-segmental osteotomies. The purpose of the present study was to propose and validate an automated approach for 3D assessment of the accuracy and postoperative outcome of segmental bimaxillary surgery. Methods: A semi-automatic approach was developed and validated for virtual surgical analysis (VSA) of segmental bimaxillary surgery using a pair of pre- and postoperative (2 weeks) cone-beam computerized tomography (CBCT) scans. The output of the VSA, the accuracy of the surgical outcome, was calculated as 3D translational and rotational differences between the planned and postoperative movements of the individual bone segments. To evaluate the reliability of the proposed VSA, intra-class correlation coefficients (ICC) were calculated at a 95% confidence interval on measurements of 2 observers. The VSA was deemed reliable if the ICC was excellent (> 0.80) and the absolute difference of the repeated intra- and inter-observer translational and rotational measurements were significantly lower (p < 0.05) than a hypothesized clinical relevant threshold of 1 voxel (0.45 mm) and 1 degree, respectively. Results: A total of 10 subjects (6 male; 4 women; mean age 24.4 years) with skeletal class 2 and 3, who underwent segmental bimaxillary surgery, 3-piece Le Fort I, bilateral sagittal split osteotomy and genioplasty, were recruited. The intra- and inter-observer reliability was excellent, ICC range [0.96 - 1.00]. The range of the mean absolute difference of the repeated intra- and inter-observer translational and rotational measurements were [0.07 mm (0.05) – 0.20 mm (0.19)] and [0.11˚ (0.08) - 0.63˚ (0.42)], respectively. This was significantly lower than the hypothesized clinical relevant thresholds (P

In this research, an infrared small target detection method called single-window average absolute gray difference algorithm (SW-AAGD) is proposed. This algorithm is derived from the average absolute difference algorithm which is highly capable of enhancing targets and suppressing the background clutters in infrared images. A common challenge in the average absolute gray difference algorithm is the choice of proper target and background windows due to the blurred edges of the small target. In the proposed algorithm, a single window is used for the target and background windows. To address this issue, a degree of membership is defined for each existing pixel of these windows. The membership degrees of pixels in the main window are in accordance with the properties of real targets in infrared images. These values are estimated in a way that there is no need for determining the exact target and background areas. To estimate the efficiency of the proposed algorithm, the algorithm is applied on several real images that contain real targets and the results are compared to five well-known methods in terms of the signal to clutter ratio (SCR), background suppression factor (BSF) and receiving operating characteristic (ROC) curve. The results prove the effectiveness of the membership degree assignment on the overall performance of detection algorithm.

Motivation An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins. Results The approach was extensively validated on a large test dataset (n = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman’s and Pearson’s correlation coefficients, average GDT-TS loss, sum of z-scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design. Availability and implementation http://github.com/KYZ-LSB/ProTerS-FitFun. Supplementary information Supplementary data are available at Bioinformatics online.

Recently, Computing-In-Memory (CIM) processors have been proposed to achieve high energy-efficiency by reducing data movement and solving memory bottlenecks. Furthermore, a network with high accurate image classification has been introduced through the Absolute-Difference-Accumulation (ADA) operation instead of the multiplication-and-accumulation operation, which is widely used in DNN. ADA operation provides not only opportunities for high energy-efficient DNN accelerating by reducing multiplication but also a chance to reuse computation results. However, the previous CIM processor cannot reuse previous computation results for other computations. In this brief, we propose a high accurate and high energy-efficient ADA-CIM processor that with two key features: 1) computation reuse for low-power, resulting in a 49.5% CIM operation power reduction, and 2) low-cost sign prediction core with 3-bit activation and weight quantization for high utilization. From the two key features, the proposed ADA-CIM processor is simulated in 28 nm CMOS technology and occupies 3.78 mm2. It consumes 2.77mW and achieves 43.1 TOPS/W energy-efficiency with a high-accuracy of 91.62% at CIFAR-10 (ResNet-20).

Numerous studies have identified deficits in the self-monitoring system that are associated with schizophrenia. However, the tasks used in the few previous studies generally involved complex cognitive processes and rarely compared between patients with and without passivity experiences (PE). Here, we examined the deficits in internal motor predictive representation in patients with and without PE, and in healthy controls using a novel paradigm which involved minimal cognitive processes. All participants completed a modified joint position matching (mJPM) task, in which they were required to replicate a voluntary, a passive verbally-cued, and a passive tactile-cued movement under blinded conditions. The absolute difference between the target spot and replicated spot was measured and compared. We hypothesised that if there was a failure in the internal motor predictive representation, patients with PEs would replicate less accurately in the voluntary condition, relative to passive conditions while the healthy controls would be more accurate, and, therefore, significant interactions between groups and conditions would be revealed. Both healthy controls and patients without PEs replicated more accurately in the voluntary condition compared with the passive conditions. The patients with PEs were less accurate in the voluntary condition compared with the passive tactile condition. A significant interaction was observed between patients with vs. without PEs × voluntary vs. passive tactile conditions. The findings suggested the relationship between deficits in motor self-monitoring in the prediction process and PEs, thus showing the need to highlight the link between motor performance and PEs.

Objective: To validate the PhisioPlay software for proprioceptive evaluation of the shoulder joint based on the data obtained by isokinetic dynamometry equipment. Methods: Thirty-one individuals constituted a single group for proprioceptive evaluation of both shoulder joints by means of joint position sense (JPS). The testing protocol of PhisioPlay consisted of the abduction movement of the shoulder to the target angles of 45° and 90° and the maintenance of these positions for 30 seconds. In the isokinetic dynamometer, the test was performed using its proprioceptive evaluation protocol for the same movement, target angles, and maintenance time established for the previous test. Results: For the 90° variables, statistical analysis pointed to agreement for the dominant limb in relation to the mean angulation and the absolute difference and for the non-dominant limb in relation to the absolute difference. The results of mean comparisons of the absolute differences via the Wilcoxon Test corroborate the literature regarding the behavior of the JPS considering joint amplitude, lateral dominance, and greater or lesser torque affecting the joint. Reliability and sensitivity measures also strengthen the conclusions. Conclusion: The results point to the construct validity of the PhisioPlay software for evaluating the JPS of the shoulder joint at 45° and 90° and suggest that the JPS is more influenced by the angle of the joint during the motor task than by the lateral dominance.

Key-frame selection plays an important role in facial expression recognition systems. It helps in selecting the most representative frames that capture the different poses of the face. The effect of the number of selected keyframes has been studied in this paper to find its impact on the final accuracy of the emotion recognition system. Dynamic and static information is employed to select the most effective key-frames of the facial video with a short response time. Firstly, the absolute difference between the successive frames is used to reduce the number of frames and select the candidate ones which then contribute to the clustering process. The static-based information of the reduced sets of frames is then given to the fuzzy C-Means algorithm to select the best C-frames. The selected keyframes are then fed to a graph mining-based facial emotion recognition system to select the most effective sub-graphs in the given set of keyframes. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the results show that the proposed method can effectively capture the keyframes that give the best accuracy with a mean response time equals to 2.89s.

In quantitative computed tomography (CT), manual selection of the intensity calibration phantom’s region of interest is necessary for calculating density (mg/cm3) from the radiodensity values (Hounsfield units: HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness. This cross-sectional, retrospective study included 1040 cases (520 each from two institutions) in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used. A training dataset was created by manually segmenting the phantom regions for 40 cases (20 cases for each institution). The CNN model’s segmentation accuracy was assessed with the Dice coefficient, and the average symmetric surface distance was assessed through fourfold cross-validation. Further, absolute difference of HU was compared between manually and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate the correlation coefficients between HU and phantom density. The source code and the model used for phantom segmentation can be accessed at https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation . The median Dice coefficient was 0.977, and the median average symmetric surface distance was 0.116 mm. The median absolute difference of the segmented regions between manual and automated segmentation was 0.114 HU. For the test cases, the median correlation coefficients were 0.9998 and 0.999 for the two institutions, with a minimum value of 0.9863. The proposed CNN model successfully segmented the calibration phantom regions in CT images with excellent accuracy.

In the presence of both random and interval hybrid uncertainty (RI-HU), investigating global reliability sensitivity (GRS) can identify the effect of random input on the structural safety globally. To this end, this work establishes the GRS index model and its corresponding efficient solution, and the innovation includes three aspects. Firstly, the GRS of the random input is defined by the average absolute difference between the failure probability upper bound (FP-UB) and the conditional FP-UB on the fixed random input under the RI-HU, and it can reflect the effect of the fixed random input on the structural safety. Secondly, the conditional FP-UB on fixing the random input at the realization is approximated by the conditional FP-UB on fixing the random input in a differential interval. Then the conditional probability theorem can be employed to convert estimating the GRS into the state recognition of the samples, which is the by-product of estimating the FP-UB by the numerical simulation. Finally, by the strategy of surrogating the performance function twice, the meta-importance sampling method is developed to efficiently estimate the GRS in presence of the RI-HU. The rationality of the proposed GRS index model and the efficiency of the developed estimation method are fully verified by the numerical and engineering examples.

Background Deep brain stimulation (DBS) electrode placement utilizing a frame-based technique requires registration of the stereotactic frame with computed tomography (CT) or magnetic resonance (MR) imaging. This traditionally has been accomplished with a conventional CT scanner. In recent years, intraoperative CT has become more prevalent. Objective To compare the coordinates obtained with intraoperative CT and conventional CT for registration of the stereotactic frame for DBS. Methods Patients undergoing DBS electrode placement between 2015 and 2017, who underwent both conventional and intraoperative CT for registration of the stereotactic frame, were included for analysis. The coordinates for the stereotactic target, anterior commissure, and posterior commissure for each CT method were recorded. The mean, maximum, minimum, and standard deviation of the absolute difference for each of the paired coordinates was calculated. Paired t-tests were performed to test for statistical significance of the difference. The directional difference as well as the vector error between the paired coordinates was also calculated. Results The mean absolute difference between conventional and intraoperative CT for the coordinate pairs was less than 0.279 mm or 0.211 degrees for all coordinate pairs analyzed. This was not statistically significant for any of the coordinate pairs. Moreover, the maximum absolute difference between all coordinate pairs was 1.04 mm. Conclusion Intraoperative CT imaging provides stereotactic frame registration coordinates that are similar to those obtained by a standard CT scanner. This may save time and hospital resources by obviating the need for the patient to go to the radiology department for a CT scan.

The research aimed to study dynamics of the physical fitness status in students of the “Tourism” specialized field during a trans-season stage. Totally 100 university students studying “Tourism” as a business service were involved into the physical fitness testing (48 males and 52 females). A half of the participants studied in the Classic University and another half − in the Sport University. Monthly testing was conducted seven times from September to March using a complex test Kontreks–2. The scoring system consists of eleven indicators: five of them are biomedical: age, body weight, blood pressure, heart rate, reducing pulse; a six – motor: flexibility, speed, dynamic power, power, speed and overall endurance. Linear regression was used with a purpose to study trends of the physical fitness during the trans-season stage. Two-ways ANOVA was used to determine differences between samples. One-way ANOVA with repeated measures was used to evaluate test-retest reliability (ICC=0.094). A significant superiority of students from the Sport University vs. Classic University was noticed during the seven months preparation stage (17.6%, p

Under the background of Energy Internet, the ever-growing scale of the electric power system has brought new challenges and opportunities. Numerous categories of measurement data, as the cornerstone of communication, play a crucial role in the security and stability of the system. However, the present sampling and transmission equipment inevitably suffers from data missing, which seriously degrades the stable operation and state estimation. Therefore, in this paper, we consider the load data as an example and first develop a missing detection algorithm in terms of the absolute difference sequence (ADS) and linear correlation to detect any potential missing data. Then, based on the detected results, we put forward a missing recovery model named cascaded convolutional autoencoders (CCAE), to recover those missing data. Innovatively, a special preprocessing method has been adopted to reshape the one-dimensional load data as a two-dimensional matrix, and hence, the image inpainting technologies can be conducted to address the problem. Also, CCAE is designed to reconstruct the missing data grade by grade due to its priority strategy, which enhances the robustness upon extreme missing situations. The numerical results on the load data of the Belgium grid validate the promising performance and effectiveness of the proposed solutions.

Introduction Kaplan–Meier survival analysis, the cornerstone of evaluating efficacy of oncology drugs in randomised controlled trials (RCTs), assumes censored patients are neither healthier nor sicker than those followed. We sought to examine whether censoring patterns differ between the control and experimental arms in one oncology journal that mandates the reporting of the number of patients censored. Methods In this retrospective review, proportion of censoring and study design data were gathered from RCTs published in The Lancet Oncology that reported Kaplan–Meier curves between May 2018 and August 2019. Differential censoring rates were analysed at the 1st, 3rd, 6th, and overall time points in each study. Analysis was stratified by curves reporting progression-free survival (PFS) or overall survival (OS) end-points. Results Of the 160 articles reviewed, 29 studies with 51 Kaplan–Meier curves were eligible. In both OS (N = 25) and PFS curves (N = 26), the absolute weighted difference in censoring between the control and experimental arms was initially positive, indicating more censoring in the control arm (first time point OS: 0.32%; PFS: 2.00%). The absolute difference then became negative, indicating more censoring in the experimental arm as time progressed (end-of-study OS: −7.54%; PFS: −9.09%). Conclusion Differences in censoring between control and experimental arms of cancer RCTs suggest that there could be systematic bias present at various study time points that may influence key results. Further investigation is needed, as possible reasons include study assignment disappointment, inappropriate follow-up length, lack of efficacy, or intolerable toxicity, each predominant at specific time points after randomisation.

A novel learning-based end-to-end network for stereo matching, named Multi-path Attention Stereo Matching (MPA-Net), is introduced in this study. Different from existing methods, the multi-path attention aggregation module is designed firstly, named MPA, which is a unified structure using three different parallel layers with a respective attention mechanism to extract the multi-scale informational features. Secondly, the method of cost volume construction, which differs from the traditional stereo matching methods, is extended. And then, the absolute difference between two input features is calculated. Furthermore, a u-shaped structure with 3D attention gate is selected as the encoder-decoder module. Specifically, the module is used to fuse the encoding features to their corresponding decoding features under the supervision of the authors' attention gate with skip-connection, and thus exploit more significant information for matching cost regularisation and disparity prediction. Finally, specific experiments are conducted to evaluate their network on SceneFlow, KITTI2012 and KITTI2015 data sets. The results show that their method achieves a better improvement in disparity maps prediction compared with some existing state-of-the-art methods on KITTI benchmark.

Most of the previous change detection methods are designed based on the difference of two images. However, directly using intensity or the features to generate difference image may be easily affected by the illumination and camera pose variations. In this paper, we show that accurate change detection results can be obtained by fusing the absolute difference of multiscale deep features of the reference and query images. Specifically, we build a change detection network, which computes absolute difference of the multiscale deep features of image pairs and learns adaptive features for change detection. The proposed network is based on off-the-shelf CNNs, whose convolutional layer blocks are used as feature extracting modules to extract multiscale deep features. We devise intra and cross encoding modules. The intra encoding modules are used for learning change related features from extracted features. These features are used for generating absolute difference features (ADFs). By progressively fusing the ADFs from high to low layers with cross encoding modules, we obtain full resolution of change detection result. Extensive experiments on three change detection benchmark datasets validate the superiority and effectiveness of the proposed method over the state-of-the-art change detection methods.