Depth Function Research Articles

PD12-09 OUTCOMES OF 429 PRIMARY ROBOTIC PERITONEAL FLAP GENDER AFFIRMING VAGINOPLASTY CASES

You have accessJournal of UrologyCME1 Apr 2023PD12-09 OUTCOMES OF 429 PRIMARY ROBOTIC PERITONEAL FLAP GENDER AFFIRMING VAGINOPLASTY CASES Gaines Blasdel, Charles Dubach-Reinhold, Elijah Castle, Rachel Bluebond-Langner, and Lee Zhao Gaines BlasdelGaines Blasdel More articles by this author , Charles Dubach-ReinholdCharles Dubach-Reinhold More articles by this author , Elijah CastleElijah Castle More articles by this author , Rachel Bluebond-LangnerRachel Bluebond-Langner More articles by this author , and Lee ZhaoLee Zhao More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003259.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Gender affirming robotic peritoneal flap vaginoplasty is a promising adaptation of established vaginoplasty techniques that has been performed since 2017. We report perioperative factors and patient reported outcomes for all consecutive cases at one center. METHODS: Records of patients undergoing primary robotic peritoneal flap gender affirming vaginoplasty with the senior authors from 9/2017-8/2022 were retrospectively reviewed. Operative time, length of stay, complications, self-report of orgasm and dilation, and date of last follow up were recorded for all patients. Clavien-Dindo grading for severity of complications were organized according to occurrence prior or after 90 days postoperatively. For outcomes with incidence greater than 5%, statistical analysis for change over years was performed using nonparametric tests. RESULTS :Primary robotic peritoneal flap gender affirming vaginoplasty was performed on 429 patients with median follow up of 369 days (interquartile range 186-607). Of these, 413 patients (96%) had 90 day or longer follow up and 16 (4%) had a Clavien-Dindo 3a or 3b complication within that period (Table 1). 245 patients had a follow up of 1 year or greater, and 9 patients (4%) had Clavien-Dindo 3a or 3b complications after 90 days in this cohort. Overall elective revision rate was 17%, with 1% revision for vaginal depth. Significant decrease in operative time was observed (p<.0001), with a mean operative time of 101 mins for cases 400-429 . Rate of dilating using the largest dilator to the deepest dot increased over these first 300 cases (p<.001) and rate of elective revision decreased (p <.001). CONCLUSIONS: Primary robotic peritoneal flap gender affirming vaginoplasty has low morbidity and complication rates with durable vaginal depth and orgasmic function at long term follow up. Source of Funding: none © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e403 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gaines Blasdel More articles by this author Charles Dubach-Reinhold More articles by this author Elijah Castle More articles by this author Rachel Bluebond-Langner More articles by this author Lee Zhao More articles by this author Expand All Advertisement PDF downloadLoading ...

Robust nonparametric hypothesis tests for differences in the covariance structure of functional data

AbstractWe develop a group of robust, nonparametric hypothesis tests that detect differences between the covariance operators of several populations of functional data. These tests, called functional Kruskal–Wallis tests for covariance, or FKWC tests, are based on functional data depth ranks. FKWC tests work well even when the data are heavy‐tailed, which is shown both in simulation and theory. FKWC tests offer several other benefits: they have a simple asymptotic distribution under the null hypothesis, they are computationally cheap, and they possess transformation‐invariance properties. We show that under general alternative hypotheses, these tests are consistent under mild, nonparametric assumptions. As a result, we introduce a new functional depth function called ‐root depth that works well for the purposes of detecting differences in magnitude between covariance kernels. We present an analysis of the FKWC test based on ‐root depth under local alternatives. Through simulations, when the true covariance kernels have an infinite number of positive eigenvalues, we show that these tests have higher power than their competitors while maintaining their nominal size. We also provide a method for computing sample size and performing multiple comparisons.

The Association Between Depth of the Olfactory Sulcus, Age, Gender and Olfactory Function: An MRI-based Investigation in More Than 1000 Participants

ObjectiveThe present study aimed to investigate the relationship between olfactory sulcus (OS) depth and olfactory function considering age and gender and to provide normative data on OS depth in a population with normal olfactory function. Materials and methodsOS depth was obtained using T1 magnetic resonance imaging scans. Participants (mean age ± sd = 57 ± 16 years, ranging from 20 to 80 years) were screened for olfactory function using the Sniffin’ Sticks Screening 12 test. They were divided into an olfactory dysfunction group (n = 604) and a normosmia group (n = 493). Participants also completed questionnaires measuring depression, anxiety and quality of life. ResultsThe right OS was deeper than the left side in all age groups. On the left side, women had deeper OS compared with men, exhibiting a higher degree of symmetry in left and right OS depth in women. Variance of olfactory function was largely determined by age, OS depth explained only minor portions of this variance. Normative data for minimum OS depth was 7.55 mm on the left and 8.78 mm on the right for participants aged between 18 and 35 years (n = 144), 6.47 mm on the left and 6.99 mm on the right for those aged 36–55 years (n = 120), and 5.28 mm on the left and 6.19 mm on the right for participants older than 55 years (n = 222). ConclusionConsidering the limited resolution of the presently used T1 weighted MR scans and the nature of the olfactory screening test, OS depth explained only minor portions of the variance of olfactory function, which was largely determined by age. Age-related normative data of OS depth are presented as a reference for future work.

Statistical depth in abstract metric spaces

The concept of depth has proved very important for multivariate and functional data analysis, as it essentially acts as a surrogate for the notion of ranking of observations which is absent in more than one dimension. Motivated by the rapid development of technology, in particular the advent of ‘Big Data’, we extend here that concept to general metric spaces, propose a natural depth measure and explore its properties as a statistical depth function. Working in a general metric space allows the depth to be tailored to the data at hand and to the ultimate goal of the analysis, a very desirable property given the polymorphic nature of modern data sets. This flexibility is thoroughly illustrated by several real data analyses.

Aerosol impact on light pollution in cities and their environment

Measurements of artificial light at night represent an incredible challenge as the optical state of the atmosphere is highly unstable thus making both long-term trend analyses and inter-comparison of multiple observations difficult. Variations of atmospheric parameters, caused by either natural or anthropogenic processes, can massively influence the level of resulting night sky brightness caused by light pollution. Focusing on six parameters, either from aerosol optics or emission properties of light sources, this work literarily and numerically examines defined variations in aerosol optical depth, asymmetry parameter, single scattering albedo, ground surface reflectance, direct uplight ratio, and aerosol scale height. For each individual element the effect size and angular reliance is investigated, with results indicating that besides the aerosol scale height all play non-negligible roles in forming skyglow and environmental impact. Especially variations in aerosol optical depth and city emission function displayed severe discrepancies in consequential light pollution level. Hence, future improvement on atmospheric condition, i.e., air quality, focusing particularly on discussed elements indicates to positively influence the level of environmental impact caused by artificial light at night. We underline the need of inclusion of our outcomes to urban development and civil engineering processes in order to create or protect habitable areas for humans, wildlife and nature.

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression.

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.

Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography

Convolutional Neural Networks (CNN) consist of various hyper-parameters which need to be specified or can be altered when defining a deep learning architecture. There are numerous studies which have tested different types of networks (e.g. U-Net, DeepLabv3+) or created new architectures, benchmarked against well-known test datasets. However, there is a lack of real-world mapping applications demonstrating the effects of changing network hyper-parameters on model performance for land use and land cover (LULC) semantic segmentation. In this paper, we analysed the effects on training time and classification accuracy by altering parameters such as the number of initial convolutional filters, kernel size, network depth, kernel initialiser and activation functions, loss and loss optimiser functions, and learning rate. We achieved this using a well-known top performing architecture, the U-Net, in conjunction with LULC training data and two multispectral aerial images from North Queensland, Australia. A 2018 image was used to train and test CNN models with different parameters and a 2015 image was used for assessing the optimised parameters. We found more complex models with a larger number of filters and larger kernel size produce classifications of higher accuracy but take longer to train. Using an accuracy-time ranking formula, we found using 56 initial filters with kernel size of 5 × 5 provide the best compromise between training time and accuracy. When fully training a model using these parameters and testing on the 2015 image, we achieved a kappa score of 0.84. This compares to the original U-Net parameters which achieved a kappa score of 0.73.

The normalized depth function of squarefree powers

The depth of squarefree powers of a squarefree monomial ideal is introduced. Let I be a squarefree monomial ideal of the polynomial ring $$S=K[x_1,\ldots ,x_n]$$ . The k-th squarefree power $$I^{[k]}$$ of I is the ideal of S generated by those squarefree monomials $$u_1\cdots u_k$$ with each $$u_i\in G(I)$$ , where G(I) is the unique minimal system of monomial generators of I. Let $$d_k$$ denote the minimum degree of monomials belonging to $$G(I^{[k]})$$ . One has $${\text {depth}}(S/I^{[k]}) \ge d_k -1$$ . Setting $$g_I(k) = {\text {depth}}(S/I^{[k]}) - (d_k - 1)$$ , one calls $$g_I(k)$$ the normalized depth function of I. The computational experience strongly invites us to propose the conjecture that the normalized depth function is nonincreasing. In the present paper, especially the normalized depth function of the edge ideal of a finite simple graph is deeply studied.

Statistical Depth for Text Data: An Application to the Classification of Healthcare Data

This manuscript introduces a new concept of statistical depth function: the compositional D-depth. It is the first data depth developed exclusively for text data, in particular, for those data vectorized according to a frequency-based criterion, such as the tf-idf (term frequency–inverse document frequency) statistic, which results in most vector entries taking a value of zero. The proposed data depth consists of considering the inverse discrete Fourier transform of the vectorized text fragments and then applying a statistical depth for functional data, D. This depth is intended to address the problem of sparsity of numerical features resulting from the transformation of qualitative text data into quantitative data, which is a common procedure in most natural language processing frameworks. Indeed, this sparsity hinders the use of traditional statistical depths and machine learning techniques for classification purposes. In order to demonstrate the potential value of this new proposal, it is applied to a real-world case study which involves mapping Consolidated Framework for Implementation and Research (CFIR) constructs to qualitative healthcare data. It is shown that the DDG-classifier yields competitive results and outperforms all studied traditional machine learning techniques (logistic regression with LASSO regularization, artificial neural networks, decision trees, and support vector machines) when used in combination with the newly defined compositional D-depth.

Analytical and statistical properties of local depth functions motivated by clustering applications

Local general depth (LGD) functions are used for describing the local geometric features and mode(s) in multivariate distributions. In this paper, we undertake a rigorous systematic study of LGD and establish several analytical and statistical properties. First, we show that, when the underlying probability distribution is absolutely continuous with density f(⋅), the scaled version of LGD (referred to as τ-approximation) converges, uniformly and in Ld(Rp) to f(⋅) when τ converges to zero. Second, we establish that, as the sample size diverges to infinity the centered and scaled sample LGD converge in distribution to a centered Gaussian process uniformly in the space of bounded functions on HG, a class of functions yielding LGD. Third, using the sample version of the τ-approximation (SτA) and the gradient system analysis, we develop a new clustering algorithm. The validity of this algorithm requires several results concerning the uniform finite difference approximation of the gradient system associated with SτA. For this reason, we establish Bernstein-type inequality for deviations between the centered and scaled sample LGD, which is also of independent interest. Finally, invoking the above results, we establish consistency of the clustering algorithm. Applications of the proposed methods to mode estimation and upper level set estimation are also provided. Finite sample performance of the methodology are evaluated using numerical experiments and data analysis.

A method of estimating depth of moving sound source in shallow sea based on incoherently matched beam-wavenumber

Estimating the depth of a moving source with unknown source range is always a challenging problem in shallow water waveguides. The method of estimating the current motion source depth is sensitive to the unknown initial range and requires the horizontal synthetic aperture length formed by the motion of the source to be much longer than the modal interference period. Presented in this work is a method to estimate the depth of moving source based on the incoherently matched beam-wavenumber. In the beam-wavenumber domain, each peak amplitude only contains the modal excitation related to source depth, and each peak position corresponds to the mode propagation angle and the horizontal wavenumber. In this method, the received data are first used to perform beam-formed transformation in the vertical depth and horizontal synthetic aperture direction, and transformed into the beam-wavenumber domain. Then beam-wavenumber peak amplitudes are extracted and incoherently matched with the modal depth function to estimate the source depth. The proposed method is used to eliminate the unknown distance dependent term and improves the mode resolution by performing mode separation in the beam-wavenumber two-dimensional domain. The prominent feature of this method lies in realizing the source depth estimation at the unknown initial range and the horizontal synthetic aperture length which is smaller than the mode interference period. The simulation and SWellEx-96 experimental data processing results validate the superior performance of the proposed method.

Long-term variation characteristics of nutrients in the water and sediments of a surface flow constructed wetland with micro-polluted water sources

This study aimed to evaluate the long-term effects of a large-scale constructed wetland purifying micro-polluted water and explore accumulation characteristics and influencing factors of nutrients in sediments. For this purpose, the water quality and nutrients in sediments of the long-running large-scale constructed wetland of Yanlong Lake were monitored for eight years since the wetland began operation. The results demonstrated that the average removal rates of permanganate index (CODMn), ammonia nitrogen (NH4+-N), total nitrogen (TN) and total phosphorus (TP) were 4.39 ± 12.64%, 41.57 ± 16.36%, 32.61 ± 23.36% and 47.05 ± 20.39% in the constructed wetland from 2013 to 2020, respectively. The CODMn removal effects were the lowest. Sediments of the constructed wetland were moderately polluted and the cumulative level of nutrients was mainly affected by the organic matter content, influent water quality, water depth and plant function. Firstly, TN and TP in sediments are significantly positively correlated with total organic carbon. Besides, the nutrient concentrations in sediments increased along the water flow direction. Furthermore, the nutrient concentrations in sediments increased with water depth, and the carbon was greatly affected. In addition, the overgrowth and lodging of wetland plants could promote the nutrients accumulation in sediments. This research results provide a scientific basis for improving the purification capacity of long-running surface flow constructed wetlands.

Fungal Metabarcoding Data for Two Grapevine Varieties (Regent and Vitis vinifera ‘Cabernet-Sauvignon’) Inoculated with Powdery Mildew (Erysiphe necator) Under Drought Conditions

Fungal Metabarcoding Data for Two Grapevine Varieties (Regent and <i>Vitis vinifera</i> ‘Cabernet-Sauvignon’) Inoculated with Powdery Mildew (<i>Erysiphe necator</i>) Under Drought Conditions

Surface profile evolution model for titanium alloy machined using abrasive waterjet

The surface profile evolution model, which was initially developed for glass and polymers, can accurately predict a channel profile cross-section produced by abrasive jet (AJ) machining. In this study, the model is modified and applied for estimating the profiles of a Ti-6Al-4V alloy eroded by an abrasive waterjet (AWJ). First, the velocity and mass fraction distributions of the gas–liquid–solid phases in the AWJ at the nozzle exit were derived and compared, and several improvements were proposed, such as considering the divergence angle of the jet and particles, as well as the length of the jet core area, to precisely construct a theoretical connection of the erosion efficiency distribution before impacting the workpiece. Computational fluid dynamics (CFD) simulations were then performed to investigate the behaviour of the erosion jets during surface evolution. The results revealed that the jet diffusion provoked by the stagnation zone effect became more pronounced as the surface profile depth deepened, which led to jet directional deflection and suppressed the erosion capacities of the AWJ. Therefore, a central erosion depth function was introduced to correct this detrimental effect with the intention of obtaining an accurate channel profile. In addition, a second-order single-step fitting function was suggested to eliminate the fluctuations caused by uneven abrasive particles and the problem of reduced erosion efficiency due to channel depth variation. Finally, based on the determination of the parameters affecting the channel profile, a normalised centre erosion rate function, which only depends on the channel depth and is isolated from the material properties and the standoff distance, was recommended to simplify the calculation. The erosion function conforming to a Gaussian surface was fitted using MATLAB (R2019b, MathWorks, USA). The results demonstrated that the channel profiles predicted by the surface evolution model were consistent with the measured profiles, with an average error of 11.4%.

Determination of Horizon, Its Boundary and Depth in the Soil Profiles of North Western Himalayas

The scientific study of the soil started almost 70-80 years ago with two school of thoughts, one worked in the lab and others in field. The main aim of the researchers studying in the field was to determine the profiles of soil along with its horizons so as to extent the knowledge on physical, chemical and biological properties. The distinctness of soil with depth means that soil has unique profile. All the soils in the world has some specific depth functions. The change of soil color or soil texture in a soil profile can be considered a good indicator of the soil formation and process and has been used as a proxy for degree of development or soil age. Uniform, gradational and rapidly changing soil textures are examples of soil profile forms used for soil classification. In the current study we studied twelve profiles having four different land uses and observed several horizons having various boundaries. The upper horizons were having diffused and wavy boundaries than the lower horizons. There was seen a clear relationship between the horizons and the various land uses. The study is very important as the soils in Himalayas are not very well developed and are prone to erosion. The study will help researchers and policy makers.

Image super-resolution: A comprehensive review, recent trends, challenges and applications

Super resolution (SR) is an eminent system in the field of computer vison and image processing to improve the visual perception of the poor-quality images. The key objective of image super resolution is to address the limitations of imaging systems mainly due to hardware problems and requirements for clinical processing of medical imaging using post-processing operations. Numerous super resolution strategies have been put-forward in the computer vision community to improve and achieve high-resolution images over the years. In the past few years, there has been a significant advancement in image super-resolution algorithms. This paper aims to provide the detailed survey on recent advancements in image super-resolution in terms of traditional, deep learning and the latest transformer-based algorithms. The in-depth taxonomy of broadly classified super-resolution techniques within these categories has been broadly discussed. An extensive survey has been carried out on deep learning techniques in terms of parameters, architecture, network complexity, depth, learning rate, framework, optimization, and loss function. Furthermore, we also address some of the significant parameters such as problem definition, evaluation metrics, publicly benchmarks datasets, loss functions and applications. In addition, we have performed an experimental analysis and comparison of various benchmark algorithms on publicly available datasets both qualitively and quantitively. Lastly, we conclude our survey by emphasizing some of the prospective future directions and open issues that the community need to address in the future.

Normal mode extraction using sparse Bayesian learning in shallow water

The low-frequency signals propagating in the shallow-water waveguides are dispersive. They are composed of several normal modes according to the normal mode theory. For the vertical array data, the horizontal wavenumbers and the associated multi-frequency modal depth functions were estimated using block sparse Bayesian learning (Niu et al., JASA 2020), while a priori knowledge of sea bottom, moving source, and source locations is not needed. For the impulsive or known-form signals received by one hydrophone, the sparse Bayesian learning (SBL) approach can be also used to extract the modes (Niu et al., JASA 2021). It uses the approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, to build the dictionary matrix for SBL. Different from warping transforms based on the group slowness (or group speed) dispersion curves, mode separation using SBL is performed in frequency domain based on the phase speed (or equivalently horizontal wavenumbers) dispersion relation. The simulation results demonstrate that the proposed approach is adapted to the environment where both the reflected and refracted modes coexist, whereas the performance of the time warping transformation degrades significantly in this scenario.

Soil carbon stock assessment using depth and spatial models on afforested arable lands

The change from grasslands and natural shrubs to afforested arable land has a major impact on soil organic carbon (SOC) stocks. Grasslands are known to be SOC sinks as seen in the Chernozems of North America, Eurasia and South Africa. However, determining the SOC stocks of soils can be financially costly as each location must be sampled in depth increments. This study aimed to estimate the SOC stocks for the Mvoti catchment (30° 48′ E and 29° 18′ S) in Kwa-Zulu Natal, South Africa by developing depth functions on a limited number of soil samples and expanding these functions to known land-uses and soil types. The results showed that splines captured the short-term vertical distribution of SOC better than exponential decay functions, which has major implications on arable lands. Long-term forest plantations showed a positive correlation with SOC stocks (32.7 kg m−2), while annual crop cultivation (27.0 kg m−2) showed a negative correlation when compared to natural grasslands (28.8 kg m−2). The Cubist algorithm predicted the total SOC stock of the catchment area at between 12 248 and 17 624 Mg depending on the depth function used. Soils with yellow-brown subsoils tend to have higher SOC stocks and the lowest degree of uncertainty.

Universal approximation with neural networks on function spaces

ABSTRACT Neural networks play a central role in the construction of learning models for artificial intelligence and machine learning. This is because neural networks are highly flexible and can approximate a wide variety of maps with high accuracy. The flexibility of neural networks is theoretically guaranteed by using universal approximation theorems. When the input and output spaces have finite dimensions, the universal approximation property of neural networks has been intensively investigated under various conditions of width, depth, and activation functions. However, these finite-dimensional results cannot be directly applied to settings in which an input or output space has infinite dimensions, such as functional data analysis and neural processes. This study provides a universal approximation theorem with neural networks for uniformly continuous maps between function spaces, whose dimensions can be infinite.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors.

IntroductionThe COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world.MethodsIran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH4 (Methane), CO_1 (CO), H2O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO2 (Nitrogen oxides), O3 (ozone), SO2 (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate.ResultThe adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO2 for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH4, O3, and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO2 between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch4 is statistically significant for all countries except Azerbaijan.ConclusionsThe comparison with and without adjusted p values declares the decreases in some air pollution in these countries.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40808-022-01528-x.