Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature-based US to CT registration of the aortic root

A feature-based registration was developed to align biplane and tracked ultrasound images of the aortic root with a preoperative CT volume. In transcatheter aortic valve replacement, a prosthetic valve is inserted into the aortic annulus via a catheter. Poor anatomical visualization of the aortic root region can result in incorrect positioning, leading to significant morbidity and mortality. Registration of pre-operative CT to transesophageal ultrasound and fluoroscopy images is a major step towards providing augmented image guidance for this procedure. The proposed registration approach uses an iterative closest point algorithm to register a surface mesh generated from CT to 3D US points reconstructed from a single biplane US acquisition, or multiple tracked US images. The use of a single simultaneous acquisition biplane image eliminates reconstruction error introduced by cardiac gating and TEE probe tracking, creating potential for real-time intra-operative registration. A simple initialization procedure is used to minimize changes to operating room workflow. The algorithm is tested on images acquired from excised porcine hearts. Results demonstrate a clinically acceptable accuracy of 2.6mm and 5mm for tracked US to CT and biplane US to CT registration respectively.
Regular Aligned Porous Waterborne Polyurethane Composite Obtained by a Directional Freeze-Drying Process

* Corresponding author Abstract—Regular aligned porous waterborne polyurethane (WPU) composites were simply obtained by a directional freeze-drying process in the study. The morphology of the WPU composite can be tailored with different concentration of the WPU dispersion and freezing rate. Very regular aligned pores along the immersion direction can be obtained when the concentration of WPU dispersion is 20 wt. %. In addition, the periodicity of the aligned pores can be tuned by the freezing rate such as the temperature of cryogenic liquid or immersing velocity. The periodicity of the aligned pores increases when the immersed velocity decreases and the temperature of the cryogenic liquid increases. With the theory of Mullins-Sekerka, the periodicity of the aligned pores can be accurately calculated.
Adapting Color Difference for Design

CIELAB is commonly used in design as it provides a simple method for approximating color difference. However, these approximations model color perception under laboratory conditions, with correctly calibrated displays and carefully constrained viewing environments that are not reflective of complexity of viewing conditions encountered in the real world. In this paper, we present a data-driven engineering model for parametric color difference that extends CIELAB to be more broadly applicable to real-world conditions. Our model can be tuned to a desired range of viewers and conditions using a simple modeling procedure, while minimally increasing the complexity of the model. We demonstrate our approach empirically by modeling color differences for the web by leveraging crowdsourced participants.
An efficient two-dimensional blocking strategy for sparse matrix-vector multiplication on GPUs

Sparse matrix-vector multiplication (SpMV) is one of the key operations in linear algebra. Overcoming thread divergence, load imbalance and non-coalesced and indirect memory access due to sparsity and irregularity are challenges to optimizing SpMV on GPUs. In this paper we present a new blocked row-column (BRC) storage format with a novel two-dimensional blocking mechanism that effectively addresses the challenges: it reduces thread divergence by reordering and grouping rows of the input matrix with nearly equal number of non-zero elements onto the same execution units (i.e., warps). BRC improves load balance by partitioning rows into blocks with a constant number of non-zeros such that different warps perform the same amount of work. We also present an efficient auto-tuning technique to optimize BRC performance by judicious selection of block size based on sparsity characteristics of the matrix. A CUDA implementation of BRC outperforms NVIDIA CUSP and cuSPARSE libraries and other state-of-the-art SpMV formats on a range of unstructured sparse matrices from multiple application domains. The BRC format has been integrated with PETSc, enabling its use in PETSc's solvers.
A New Method for the Discovery of Essential Proteins

Background Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value. Method In this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality. Conclusions We demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.
The Pre-Application of Hydrocortisone Cream and Its Effect on Transdermal Drug Delivery by Phonophoresis

The Pre-Application of Hydrocortisone Cream and Its Effect on Transdermal Drug Delivery by Phonophoresis Patrick Webb Department of Exercise Sciences, BYU Master of Science Context: Transdermal delivery of hydrocortisone by phonophoresis is used for the treatment of musculoskeletal conditions. Research shows hydrocortisone and other white or opaque topical preparations transmit ultrasound energy poorly. Effective transmission of ultrasound is important in phonophoresis. Main Outcome measured: Samples of subcutaneous interstitial fluid were collected during and for 20 minutes following phonophoresis treatment. Cortisol concentrations were analyzed by an enzyme linked immune-assay (ELISA) test. Objective: Determine the subcutaneous cortisol concentration after two different phonophoresis treatments using a 2.5% hydrocortisone preparation. Design: Randomized design in which 22 healthy participants were assigned to receive a phonophoresis treatment where: 1) hydrocortisone cream was rubbed in completely prior to phonophoresis or 2) hydrocortisone powder was compounded with an ultrasound coupling gel. Test Subjects: 22 healthy individuals were recruited: 13 females with a mean age of 21 years and 9 males with a mean age of 21.8 years. Intervention: Phonophoresis consisted of pulsed ultrasound at 1 MHz, 1.0 w/cm, and a 50% duty cycle. The treatment duration was 10 minutes and was localized over the distal gastrocnemius muscle. Results: We observed no significant difference in subcutaneous cortisol concentration between the two phonophoresis treatments (p=0.05). Also no significant difference was detected between pre and posttreatment cortisol levels within each individual treatment group. Conclusions: Our data indicates that completely rubbing a topical hydrocortisone application into the skin prior to placement of ultrasound gel does not result in increased transdermal delivery of cortisol when compared with the use of a compound of ultrasound gel and hydrocortisone powder applied topically to the skin.
Empirical Treatment Effectiveness Models for Binary Outcomes

Randomized trials provide strong evidence regarding efficacy of interventions but are limited in their capacity to address potential heterogeneity in effectiveness within broad clinical populations. For example, a treatment that on average is superior may be distinctly worse in certain patients. We propose a technique for using large electronic health registries to develop and validate decision models that measure—for distinct combinations of covariate values—the difference in predicted outcomes among 2 alternative treatments. We demonstrate the methodology in a prototype analysis of in-hospital mortality under alternative revascularization treatments. First, we developed prediction models for a binary outcome of interest for each treatment. Decision criteria were then defined based on the treatment-specific model predictions. Patients were then classified as receiving concordant or discordant care (in relation to the model recommendation), and the association between discordance and outcomes was evaluated. We then present alternative decision criteria and validation methodologies, as well as sensitivity analyses that investigate 1) the imbalance between treatments on observed covariates and 2) the aggregate impact of unobserved covariates. Our methodology supplements population-average clinical trial results by modeling heterogeneity in outcomes according to specific covariate values. It thus allows for assessment of current practice, from which cogent hypotheses for improved care can be derived. Newly emerging large population registries will allow for accurate predictions of outcome risk under competing treatments, as complex functions of predictor variables. Whether or not the models might be used to inform decision making depends on the extent to which important predictors are available. Further work is needed to understand the strengths and limitations of this approach, particularly in relation to those based on randomized trials.
Cost-Sensitive Online Classification with Adaptive Regularization and Its Applications

Cost-Sensitive Online Classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies have proved that incorporating second order information could yield significant improvements on the prediction model. Hence, we propose a novel cost-sensitive online classification algorithm with adaptive regularization. We theoretically analyzed the proposed algorithm and empirically validated its effectiveness with extensive experiments. We also demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be an effective tool to tackle cost-sensitive online classification tasks in various application domains.
Lethal and Sublethal Effects of Botfly (Philornis seguyi) Parasitism on House Wren Nestlings

Abstract. We studied the effect of botfly (Philornis seguyi) parasitism on survival and growth of House Wren (Troglodytes aedon) nestlings. We investigated whether nestling survival was related to (1) the intensity of botfly infestation, (2) age of the nestling at the time it was parasitized, and (3) the order in which a chick hatched within a brood. The prevalence of botfly parasitism was 25%; the mean intensity and age at parasitism were 12.8 larvae per nestling and 3.9 days, respectively. Nestling survival was 42% lower in infested than in noninfested broods. Nestling survival was negatively associated with the mean intensity of parasitism of the brood and positively associated with the age of the nestling at the time it was parasitized. Within infested broods, nestling survival was higher in chicks hatched first than in chicks hatched last. Infested nestlings that survived until fledging grew at lower rates and remained in the nest longer than did nestlings in noninfested broods. Our results reveal that P. seguyi has lethal and sublethal effects on House Wren nestlings and that the intensity of parasitism, age of nestlings at the time they are infested, and sequence of hatching are important factors that influence the survival of parasitized nestlings.
2D Human Pose Estimation based on Object Detection using RGB-D information

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.
Experimental Study on the Impact of Rotation Speed Rate and Filling Rate on the Particle Size of the Grinding Product of Ball Mill

Combination experiments were carried out by selecting three different value of rotation speed rate and four different value of filling rate using a Φ600 mm × 400 mm experimental prototype ball mill.The size distribution results were obtained at different rotation speed rate and different filling rate.The yield of different size fractions was calculated.The influence of rotation speed rate and filling rate on particle size was analyzed according to the mechanism of rotation.
Three-dimensionally gradient harmonic structure design: an integrated approach for high performance structural materials

ABSTRACT This paper presents an overview on the peculiar microstructural design, called ‘harmonic structure’ (HS), for improved mechanical performance of structural materials. A well designed powder metallurgy processing approach has been developed to create a unique three-dimensionally gradient HS with controlled bimodal grain size distribution in metals and alloys. The bulk materials with HS exhibited considerably higher strength and improved toughness as compared to coarse-grained structures. The unique HS design promotes uniformity of deformation by avoiding strain localization during plastic deformation. A possible mechanism of deformation behavior of HS has also been proposed based on the available experimental results. GRAPHICAL ABSTRACT
The Lymphocyte-Monocyte Ratio Predicts Patient Survival and Aggressiveness of Endometrial Cancer

Objective: We assessed the prognostic implications of preoperative lymphocyte-monocyte ratio (LMR) in patients with endometrial cancer (EC). Methods: We retrospectively examined the LMR as a prognostic variable in a cohort of 255 patients with EC who underwent surgical resection. Patients were categorized into two groups according to the LMR (LMR-low and LMR-high) using cutoff points determined by receiving operator characteristic (ROC) curve analysis. The primary objective was to correlate the LMR to clinicopathological factors; the secondary objective was to determine the survival significance of the LMR in patients with EC. Results: Using data from the entire cohort, the most discriminative LMR cutoff value selected on the ROC curve was 3.28 for both disease-free survival (DFS) and overall survival (OS). The LMR-low and LMR-high groups included 33 (12.9%) and 222 patients (87.1%), respectively. The 5-year DFS rates in the LMR-low and LMR-high groups were 64.5 and 93.9% (P < 0.0001), respectively, and the 5-year OS rates in the two groups were 76.7 and 96.5% (P < 0.0001), respectively. On multivariate analysis, we identified histologic grade, International Federation of Gynecology and Obstetrics (FIGO) stage, and LMR levels as the strongest prognostic factors affecting DFS (P = 0.0037, P < 0.0001, and P < 0.0001, respectively), and FIGO stage and the LMR as the strongest prognostic factors predicting OS (P < 0.0001 and P < 0.0001, respectively). Conclusion: The LMR is an independent prognostic factor for both DFS and OS after surgical resection, and it provides additional prognostic value beyond standard clinicopathological parameters.
Normal Map Acquisition of Nearly Flat Objects Using a Flatbed Scanner

We propose a new efficient technique for acquiring normal maps of nearly flat objects using a flatbed scanner. The technique requires no hardware modification or dedicated calibration. Our setup consists of a conventional flatbed scanner and a clamp on which a planar checkerboard is glued. We take four scans of the object manually rotated by roughly 90 degrees. The scans are then registered by sub pixel checkerboard corners. Detailed normal map of the scanned surface is obtained by solving a linear least squares problem.
Use of Bauru Yardstick in Patients With Complete Bilateral Cleft Lip and Palate at an Indian Center

Objective: To evaluate dental arch relationship in treated bilateral cleft lip and palate (BCLP) cases at an Indian cleft center using the Bauru yardstick. Patients and methods: Digital photographs of the dental cast of 50 consecutively treated patients (28 males and 22 females) with nonsyndromic BCLP at an Indian cleft center were rated by 2 examiners as per the Bauru yardstick for the 12-year-old age group. The average age group was 12 ± 0.62 years. All cases were treated with same surgical protocol. Results: The inter-examiner agreement between the 3 examiners was found to be very high, with weighted kappa values ranging from 0.894 to 0.951. The intraexaminer agreement between the 2 examinations for all the examiners was also found to be very high, with weighted kappa values ranging from 0.894 to 0.931. Seventy-eight percent of patients were rated with a Bauru yardstick score of 1+2. In addition, 10% of patients were rated with a score of 3, 8% as 4, and 4% as 5. The overall Bauru yardstick score for the center was 2.36. Conclusion: The protocol followed for the repair of BCLP cases by the center was found to be a good regimen in regard to the Bauru yardstick score.
A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling

In this paper, a novel time series heteroskedastic model is proposed for sea clutter modeling application. In the light of characteristics of the practical clutter at low grazing angle, the original generalized autoregressive conditional heteroskedasticity process, which has been widely used in various fields of economics, is extended from three aspects. First, the autoregressive moving-average terms are introduced for modeling the temporal correlation of both clutter returns and innovations. Second, the exponential of the conditional variance is generalized from one to arbitrary positive value, to capture the nonlinearity existing in the practical clutter. Third, the traditional Gaussian innovation is replaced by the Johnson $S_{u}$ random variable, which is a monotonic transformation of the Gaussian random variable and is capable of modeling the skewness and kurtosis. By systematically analyzing a large number of practical sea clutter data sets, we show that the proposed time series model fits the data better than some commonly used statistic-based distributions, such as the Weibull and compound Gaussian distributions.
Dressed up: Baiting Attackers through Endpoint Service Projection

Honeypots have been widely employed to track attackers' activities and divert potential threats against real assets. A critical challenge of honeypot research is how to better integrate deceptive honeypots as part of an overall production network. Conventional honeypots are typically deployed as separate assets near those they are protecting---they are not in the direct line of fire. Such a setup does not effectively protect real assets since attackers do not require a full network scan to identify certain production hosts. In this paper, we present a novel framework to transparently project vulnerable honey services atop real production systems without interfering the production system. The key idea is to use SDN technology to divide a production network into segments of production and decoy servers. Traffic intended for production workloads is redirected to decoys based on port or service information. The decoy servers run "vulnerable" services that are heavily monitored. From the attackers' perspective, these vulnerable services run on production systems, but traffic is instead relayed to a honeypot with the same configuration (e.g., IP address, MAC address, running services) of the protected production system. In this way, our approach capitalizes on capturing attacks before they reach protected assets. We demonstrate its feasibility with a prototype implementation and practical deployment model. Evaluation shows that our approach incurs negligible overhead and resists potential side channel fingerprinting attacks.
Tracing Ca and F during Remelting of Titanium-Aluminides in ESR and VAR

Pressure electroslag remelting (PESR) of titanium and its alloys under a Ca-CaF 2-CaO slag has been under investigation as a suitable means for the refining and deoxidation of scraps and intermediates from alternative titanium winning processes. Little is known on the solution of Ca and F in the metal phase during ESR and less on the impact of these elements on properties of the remelted material. Modelling of calcium and fluorine activities in CaF 2-based slags suggest that a solution of Ca and F in the metal can be expected in the ppm range. This paper presents a review of the analytical techniques presently applied to precisely track traces of Ca and F in titanium and titanium aluminides.