Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of fluence‐based dose delivery incorporating the spatial variation of dosimetric leaf gap (DLG)

The Eclipse treatment planning system uses a single dosimetric leaf gap (DLG) value to retract all multileaf collimator leaf positions during dose calculation to model the rounded leaf ends. This study evaluates the dosimetric impact of the 2D variation of DLG on clinical treatment plans based on their degree of fluence modulation. In‐house software was developed to retrospectively apply the 2D variation of DLG to 61 clinically treated VMAT plans, as well as to several test plans. The level of modulation of the VMAT cases were determined by calculating their modulation complexity score (MCS). Dose measurements were done using the MapCHECK device at a depth of 5.0 cm for plans with and without the 2D DLG correction. Measurements were compared against predicted dose planes from the TPS using absolute 3%/3 mm and 2%/2 mm gamma criteria for test plans and for VMAT cases, respectively. The gamma pass rate for the 2 mm, 4 mm, and 6 mm sweep test plans increased by 23.2%, 28.7%, and 26.0%, respectively, when the measurements were corrected with 2D variation of DLG. The clinical anal VMAT cases, which had very high MLC modulation, showed the most improvement. The majority of the improvement occurred for doses created by the 1.0 cm width leaves for both the test plans and the VMAT cases. The gamma pass rates for the highly modulated head and neck (H&N) cases, moderately modulated prostate and esophageal cases, and minimally modulated brain cases improved only slightly when corrected with 2D variation of DLG. This is because these cases did not employ the 1.0 cm width leaves for dose calculation and delivery. These data suggest that, at the very least, the TPS plans with highly modulated fluences created by the 1.0 cm fields require 2D DLG correction. Incorporating the 2D variation of DLG for the highly modulated clinical treatment plans improves their planar dose gamma pass rates, especially for fields employing the outer 1.0 cm width MLC leaves. This is because there are differences in DLG between the true DLG exhibited by the 1.0 cm width outer leaves and the constant DLG value modeled by the TPS for dose calculation. PACS numbers: 87.55. D, 87.55. Qr, 87.56. Fc, 87.56. N, 87.56. nk
Association of dispositional optimism with Life’s Simple 7’s Cardiovascular Health Index: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Sociocultural Ancillary Study (SCAS)

Objectives Mounting evidence links positive psychological functioning to restorative health processes and favourable medical outcomes. However, very little is known about the relationship between optimism, an indicator of psychological functioning and the American Heart Association (AHA)-defined concept of cardiovascular health (CVH), particularly in Hispanics/Latinos of diverse backgrounds. To address limitations of existing literature, this study investigated the association between dispositional optimism and CVH in a heterogeneous sample of Hispanics/Latinos residing in the USA. Design Cross-sectional study. Participants and setting Data were analysed from 4919 adults ages 18–75 of the Hispanic Community Health Study/Study of Latinos parent study and the Sociocultural Ancillary Study. Main outcome measures Optimism was assessed using the 6-item Life Orientation Test-Revised (range from 6 to 30). AHA classification standards were used to derive an additive CVH score with operationalisation of indicators as Ideal, Intermediate and Poor. The overall CVH score included indicators of diet, body mass index, physical activity, cholesterol, blood pressure, fasting glucose and smoking status. Multivariate linear and logistic regressions were used to examine associations of optimism with CVH (Life’s Simple 7), after adjusting for sociodemographic factors and depressive symptoms. Results Each increase in the optimism total score was associated with a greater CVH score (β=0.03 per unit increase, 95% CI 0.01 to 0.05). When modelling tertiles of optimism, participants with moderate (β=0.24 to 95% CI 0.06 to 0.42) and high (β=0.12, 95% CI 0.01 to 0.24) levels of optimism displayed greater CVH scores when compared with their least optimistic peers. Conclusion This study offers preliminary evidence for an association between optimism and CVH in a large heterogeneous group of Hispanic/Latino adults. Our study adds scientific knowledge of psychological assets that may promote CVH and suggests a novel therapeutic target for consideration. Future studies are needed to explore causality and potential mechanism underlying the relationship between positive emotion and heart health.
The Best Rated Human–Machine Interface Design for Autonomous Vehicles in the 2016 Grand Cooperative Driving Challenge

This paper provides an in-depth description of the best rated human-machine interface that was presented during the 2016 Grand Cooperative Driving Challenge. It was demonstrated by the Chalmers Truck Team as the envisioned interface to their open source software framework OpenDLV, which is used to power Chalmers’ fleet of self-driving vehicles. The design originates from the postulate that the vehicle is fully autonomous to handle even complex traffic scenarios. Thus, by including external and internal interfaces, and introducing a show, don’t tell principle, it aims at fulfilling the needs of the vehicle occupants as well as other participants in the traffic environment. The design also attempts to comply with, and slightly extend, the current traffic rules and legislation for the purpose of being realistic for full-scale implementation.
3D Mapping of Multi-floor Buildings Based on Sensor Fusion

In this paper, we design a highly accurate method for 3D mapping of multi-floor buildings. The basic idea is to combine the laser range sensor for metric mapping and barometric pressure sensor for detecting floor transition and kinect sensor for collection 3D environment information. Meanwhile, we adopt the Monte Carlo localization in 2D map to improve the accuracy of the localization. Finally, the barometric pressure is used to merge the 3D map of the multi-floor buildings. By using the information collected by a real robot in a typical multi-floor environment, the method is tested and compared with some other approaches, the results show that the method is efficient and has a better result in 3D mapping of multi-floor buildings.
A Skeletal Similarity Metric for Quality Evaluation of Retinal Vessel Segmentation

The most commonly used evaluation metrics for quality assessment of retinal vessel segmentation are sensitivity, specificity, and accuracy, which are based on pixel-to-pixel matching. However, due to the inter-observer problem that vessels annotated by different observers vary in both thickness and location, pixel-to-pixel matching is too restrictive to fairly evaluate the results of vessel segmentation. In this paper, the proposed skeletal similarity metric is constructed by comparing the skeleton maps generated from the reference and the source vessel segmentation maps. To address the inter-observer problem, instead of using a pixel-to-pixel matching strategy, each skeleton segment in the reference skeleton map is adaptively assigned with a searching range whose radius is determined based on its vessel thickness. Pixels in the source skeleton map located within the searching range are then selected for similarity calculation. The skeletal similarity consists of a curve similarity, which measures the structural similarity between the reference and the source skeleton maps and a thickness similarity, which measures the thickness consistency between the reference and the source vessel segmentation maps. In contrast to other metrics that provide a global score for the overall performance, we modify the definitions of true positive, false negative, true negative, and false positive based on the skeletal similarity, based on which sensitivity, specificity, accuracy, and other objective measurements can be constructed. More importantly, the skeletal similarity metric has better potential to be used as a pixelwise loss function for training deep learning models for retinal vessel segmentation. Through comparison of a set of examples, we demonstrate that the redefined metrics based on the skeletal similarity are more effective for quality evaluation, especially with greater tolerance to the inter-observer problem.
A Bridge Modular Switched-Capacitor-Based Multilevel Inverter With Optimized SPWM Control Method and Enhanced Power-Decoupling Ability

Microinverters operating into the single-phase grid from new energy source with low-voltage output face the challenges of efficiency bottleneck and twice-line-frequency variation. This paper proposed a multilevel inverter based on bridge modular switched-capacitor (BMSC) circuits with its superiority in conversion efficiency and power density. The topology is composed of dc–dc and dc–ac stages with independent control for each stage, aiming to improve system stability and simplify the control method. The BMSC dc–dc stage, which can be expanded to synthesize more levels, not only features multilevel voltage gain but also partially replaces the original bulk input capacitor and functions as an active energy buffer to enhance power-decoupling ability between dc and ac sides. In dc–ac stage, the control strategy of optimized unipolar frequency doubling sine-wave pulse width modulation is proposed to improve the quality of output waveform. Meanwhile, the multilevel voltage phase has been optimized to further reduce the power loss. Finally, a prototype has been built and tested. Associated with the simulation, the experimental results validate the practicability of these analyses.
The Implementation of Blended Learning Using Android-Based Tutorial Video in Computer Programming Course II

Computer programming course is theoretical. Sufficient practice is necessary to facilitate conceptual understanding and encouraging creativity in designing computer programs/animation. The development of tutorial video in an Android-based blended learning is needed for students’ guide. Using Android-based instructional material, students can independently learn anywhere and anytime. The tutorial video can facilitate students’ understanding about concepts, materials, and procedures of programming/animation making in detail. This study employed a Research and Development method adapting Thiagarajan’s 4D model. The developed Android-based instructional material and tutorial video were validated by experts in instructional media and experts in physics education. The expert validation results showed that the Android-based material was comprehensive and very feasible. The tutorial video was deemed feasible as it received average score of 92.9%. It was also revealed that students’ conceptual understanding, skills, and creativity in designing computer program/animation improved significantly.
The anatomy of phenotype ontologies: principles, properties and applications

&NA; The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.
Weakly Supervised Instance Segmentation Using Class Peak Response

Weakly supervised instance segmentation with image-level labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by exploiting class peak responses to enable a classification network for instance mask extraction. With image labels supervision only, CNN classifiers in a fully convolutional manner can produce class response maps, which specify classification confidence at each image location. We observed that local maximums, i.e., peaks, in a class response map typically correspond to strong visual cues residing inside each instance. Motivated by this, we first design a process to stimulate peaks to emerge from a class response map. The emerged peaks are then back-propagated and effectively mapped to highly informative regions of each object instance, such as instance boundaries. We refer to the above maps generated from class peak responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed instance-level representation, which allows instance masks to be extracted even with some off-the-shelf methods. To the best of our knowledge, we for the first time report results for the challenging image-level supervised instance segmentation task. Extensive experiments show that our method also boosts weakly supervised pointwise localization as well as semantic segmentation performance, and reports state-of-the-art results on popular benchmarks, including PASCAL VOC 2012 and MS COCO.
High density of CD68+ tumor-associated macrophages predicts a poor prognosis in gastric cancer mediated by IL-6 expression

The aim of the present study was to explore the potential role of cluster of differentiation CD68+ tumor-associated macrophages (TAMs) induced by interleukin (IL)-6 in the progression of gastric cancer (GC) and patient prognosis. The expression levels of IL-6 and CD68 were detected by immunohistochemical staining in 60 samples of tumor and non-tumor gastric tissues. CD14+ monocytes were isolated from peripheral blood mononuclear cells and stimulated with macrophage colony stimulation factor (M-CSF) and IL-6, and the expression levels of IL-10, IL-12, vascular endothelial growth factor (VEGF)-C and transforming growth factor (TGF)-β were measured by reverse transcription polymerase chain reaction and ELISA. The GC MGC-803 cell line was co-cultured with monocytes stimulated by M-CSF and IL-6 and the invasion ability of the MGC-803 was evaluated by Transwell analysis. The levels of STAT3, P-STAT3 and interferon-regulatory factor 4 (IRF4) in the monocytes stimulated by M-CSF and IL-6 were detected by western blotting. The results demonstrated that the frequencies of IL-6+ macrophages (Mφs) and CD68+ Mφs were significantly higher in tumor regions compared with the corresponding non-tumor regions of GC tissues. Kaplan-Meier analysis revealed that the densities of tumor-infiltrating CD68+ or IL-6+ Mφs were inversely associated with the overall survival rates of the patients. In vitro, the expression levels of IL-10, VEGF-C and TGF-β significantly increased in CD14+ monocytes subsequent to M-CSF and IL-6 stimulation. The invasion abilities of MGC-803 were increased by the monocytes stimulated with M-CSF and IL-6. The levels of STAT3, P-STAT3 and IRF4 proteins increased in the monocytes stimulated by M-CSF and IL-6. In conclusion, the results from the present study suggest that a high density of CD68+ TAMs predicts a poor prognosis in GC. IL-6 may polarize the Mφs and promote tumor invasion through the IL-6/STAT3/IRF4 signaling pathway.
Genetic Characterization of the Gypsy Moth from China (Lepidoptera, Lymantriidae) Using Inter Simple Sequence Repeats Markers

This study provides the first genetic characterization of the gypsy moth from China ( Lymantria dispar ), one of the most recognized pests of forests and ornamental trees in the world. We assessed genetic diversity and structure in eight geographic populations of gypsy moths from China using five polymorphic Inter simple sequence repeat markers, which produced reproducible banding patterns. We observed 102 polymorphic loci across the 176 individuals sampled. Overall genetic diversity (Nei’s, H) was 0.2357, while the mean genetic diversity within geographic populations was 0.1845 ± 0.0150. The observed genetic distance among the eight populations ranged from 0.0432 to 0.1034. Clustering analysis (using an unweighted pair-group method with arithmetic mean and multidimensional scaling), revealed strong concordance between the strength of genetic relationships among populations and their geographic proximity. Analysis of molecular variance demonstrated that 25.43% of the total variability (F ST = 0.2543, P < 0.001) was attributable to variation among geographic populations. The results of our analyses investigating the degree of polymorphism, genetic diversity (Nei’s and Shannon) and genetic structure, suggest that individuals from Hebei may be better able to adapt to different environments and to disperse to new habitats. This study provides crucial genetic information needed to assess the distribution and population dynamics of this important pest species of global concern.
Contact Invariant Model Learning for Legged Robot Locomotion

In this letter, we present a new formulation for learning the dynamics of legged robots performing locomotion tasks. Using sensor data we learn error terms at the level of rigid body dynamics and actuation dynamics. The learning framework deals with the hybrid nature of legged systems given by different contact configurations: We use the projection of the rigid body dynamics into a subspace consistent with the contact constraints. The equations of motion in such subspace do not depend on the contact forces, allowing to formulate a learning problem where force sensor data are not required. Additionally, we propose to use the columns of end-effector Jacobians as basis vectors, obtaining a model that generalizes across contact configurations. Both locally weighted projection regression and sparse Gaussian process regression are used as supervised learning techniques. As application of the learned model, an inverse dynamics control method is extended. Hardware experiments with a quadruped robot show reduced RMS tracking error and a significant reduction in RMS feedback effort during base-only, walking, and trotting motions.
Analysis of Bus Station Services with Importance-Performance Analysis: Empirical Results of Gerbangkertosusila Region

Gresik, Bangkalan, Mojokerto, Surabaya, Sidoarjo, and Lamongan (Gerbangkertosusila) areas serve as National Activity Centre in East Java Province especially in export-import activities regionally, nationally, and internationally. Because of its strategic position, in order to develop industries, the supporting factors such as the availability of infrastructure particularly in transportation should be well prepared. There are many problems emerging in transportation including congestion, a number of private vehicles, public transport facilities, and transportation development policies. The transition of land transportation modes, especially from private vehicles to mass transportation (buses, trains, etc.) becomes an alternative problem-solving. The study aimed to analyze passenger satisfaction who experienced the services in the provincial bus stations in the area of Gerbangkertosusila. Performance level and customer expectations of customers were examined and analyzed using Importance-Performance Analysis (IPA). There were 139 respondents interviewed. The result showed the customer satisfaction index for the best bus station was Gresik, followed by Sidoarjo, Lamongan, Surabaya, Mojokerto, Bangkalan, and Mojokerto (district). Passengers (customers) have different expectations especially for the services of each station in every region, so local governments need to create new policies and breakthroughs in order to increase consumer interest in mass transportation facilities.
Algebraic Matching Techniques for Fast Decoding of Polar Codes with Reed-Solomon Kernel

We propose to reduce the decoding complexity of polar codes with non-Arikan kernels by employing a (near) ML decoding algorithm for the codes generated by kernel rows. A generalization of the order statistics algorithm is presented for soft decoding of Reed-Solomon codes. Algebraic properties of the Reed-Solomon code are exploited to increase the reprocessing order. The obtained algorithm is used as a building block to obtain a decoder for polar codes with Reed-Solomon kernel.
Feature based analysis friendly video coding scheme for HEVC

This paper proposes an algorithm to maintain feature information of compressed video in high efficiency video coding (HEVC), where feature information is defined by SIFT (scale invariant feature transform) detector. Adaptive largest coding unit (LCU) selection is defined to determine LCU into two different groups, important LCU group and non-important LCU group. Moreover, two different bit allocations are generated in rate control to each group based on coding mode selection, Intra or Inter mode, to achieve target bit rate and also guarantee the feature information can be maintained. Experimental results show proposed method can achieve better SIFT similarity than original HEVC encoder from 1% to 10% at the same target bit.
Surface imaging‐based analysis of intrafraction motion for breast radiotherapy patients

Breast treatments are becoming increasingly complex as the use of modulated and partial breast therapies becomes more prevalent. These methods are predicated on accurate and precise positioning for treatment. However, the ability to quantify intrafraction motion has been limited by the excessive dose that would result from continuous X‐ray imaging throughout treatment. Recently, surface imaging has offered the opportunity to obtain 3D measurements of patient position throughout breast treatments without radiation exposure. Thirty free‐breathing breast patients were monitored with surface imaging for 831 monitoring sessions. Mean translations and rotations were calculated over each minute, each session, and over all sessions combined. The percentage of each session that the root mean squares (RMS) of the linear translations were outside of defined tolerances was determined for each patient. Correlations between mean translations per minute and time, and between standard deviation per minute and time, were evaluated using Pearson's r value. The mean RMS translation averaged over all patients was 2.39mm±1.88mm. The patients spent an average of 34%, 17%, 9%, and 5% of the monitoring time outside of 2 mm, 3 mm, 4 mm, and 5 mm RMS tolerances, respectively. The RMS values averaged over all patients were 2.71mm±1.83mm, 2.76±2.27, and 2.98mm±2.30mm over the 5th, 10th, and 15th minutes of monitoring, respectively. The RMS values (r=0.73,p=0) and standard deviations (r=0.88,p=0) over all patients showed strong significant correlations with time. We see that the majority of patients' treatment time is spent within 5 mm of the isocenter and that patient position drifts with increasing treatment time. Treatment length should be considered in the planning process. An 8 mm margin on a target volume would account for 2 SDs of motion for a treatment up to 15 minutes in length. PACS numbers: 87.53. Jw, 87.53. Kn, 87.56. Da, 87.63. L‐