scholarly journals Human body-fluid proteome: quantitative profiling and computational prediction

2020 ◽  
Author(s):  
Lan Huang ◽  
Dan Shao ◽  
Yan Wang ◽  
Xueteng Cui ◽  
Yufei Li ◽  
...  
Keyword(s):  
High Throughput ◽  
Human Body ◽  
Body Fluid ◽  
Biomarker Discovery ◽  
Interaction Network ◽  
Computational Prediction ◽  
Computational Effort ◽  
The Body ◽  
Support Vector ◽  
Learning Approaches

Abstract Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

scholarly journals Computational prediction of implantation outcome after embryo transfer

2019 ◽  
Vol 26 (3) ◽  
pp. 1810-1826 ◽  
Author(s):  
Behnaz Raef ◽  
Masoud Maleki ◽  
Reza Ferdousi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Model ◽  
Embryo Transfer ◽  
Area Under The Curve ◽  
Computational Prediction ◽  
Support Vector ◽  
Human Menopausal Gonadotropin ◽  
Optimum Number ◽  
Learning Approaches

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

scholarly journals A simple serum depletion method for proteomics analysis

BioTechniques ◽  
2020 ◽  
Vol 69 (2) ◽  
pp. 148-151
Author(s):  
Alexandre Zougman ◽  
John P Wilson ◽  
Rosamonde E Banks
Keyword(s):  
Sample Preparation ◽  
Serum Protein ◽  
Body Fluid ◽  
Biomarker Discovery ◽  
The Body ◽  
Protein Digestion ◽  
Proteomics Analysis ◽  
Serum Proteome ◽  
Serum Fractions

Serum is the body fluid most often used in biomarker discovery. Albumin, the most abundant serum protein, contributes approximately 50% of the serum protein content, with an additional dozen abundant proteins dominating the rest of the serum proteome. To profile this challenging protein mixture by proteomics, the abundant proteins must be depleted to allow for detection of the low-abundant proteins, the primary biomarker targets. Current serum depletion approaches for proteomics are costly and relatively complex to couple with protein digestion. We demonstrate a simple, affordable serum depletion methodology that, within a few minutes of processing, results in two captured serum fractions – albumin-depleted and albumin-rich – which are digested in situ. We believe our method is a useful addition to the biomarker sample preparation toolbox.

scholarly journals Characteristics of Intracellular and Extracellular Fluid Ratio for the Varying Body Impedances in Fixed Total Body Fluid

2017 ◽  
Author(s):  
JungHun Choi
Keyword(s):  
Human Body ◽  
High Frequency ◽  
Body Fluid ◽  
Extracellular Fluid ◽  
Bioelectrical Impedance ◽  
The Body ◽  
Horizontal Axis ◽  
Fluid Status ◽  
Total Body ◽  
Frequency Current

A bioelectrical impedance analysis is a proven method to measure body composition in clinical situations. It uses the relation between the body fluid and the impedances in a variety of frequencies. A body model can be simplified as a parallel combination of a capacitor and two resistors which represent a cell membrane, Intracellular Fluid (ICF), and Extracellular Fluid (ECF). Low frequency current passes through ECF and high frequency current also passes through ICF in a body. A Cole-Cole plot is a graphical interpretation of the path of impedances and each axis represents resistance and reactance with variable frequencies. A high value of resistance in a horizontal axis is a resistance value of ECF and a low value of resistance at a high frequency presents ICF. Interpolation technique is needed to find out the exact cross-point between impedance values and the horizontal axis. The two estimated impedance values are used to derive Total Body Water (TBW), ICF, ECF, Fat Free Mass (FFM), and Fat Mass (FM) from various published equations [1]. Minimizing the possible error of fluid volume assessment and accurate prediction of fluid status in a human body is essential for appropriate therapy. Different techniques of fluid status assessment in a human body can be applicable, such as physical examination, orthostatic vital signs, blood volume measurement, acoustic cardiograph, chest radiography, and thoracic ultrasonography [2]. In this study, a bioelectrical impedance spectroscopy device and simple body models were used to collect data such as TBW, ICF, ECF, FM, and FFM. The ratio between ICF and ECF was investigated for the same values of TBW, FM, and FFM by varying impedance values.

scholarly journals Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset

2022 ◽  
Vol 12 ◽  
Author(s):  
Radek Zenkl ◽  
Radu Timofte ◽  
Norbert Kirchgessner ◽  
Lukas Roth ◽  
Andreas Hund ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Developmental Stages ◽  
Agricultural Practices ◽  
Support Vector ◽  
Learning Approaches ◽  
Support Vector Classifier ◽  
Wide Range ◽  
Field Phenotyping

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

scholarly journals Human Body 3D Posture Estimation Using Significant Points and Two Cameras

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chia-Feng Juang ◽  
Teng-Chang Chen ◽  
Wei-Chin Du
Keyword(s):  
Human Body ◽  
Color Space ◽  
Three Dimensional ◽  
The Body ◽  
Support Vector ◽  
Segmentation Method ◽  
Current Frame ◽  
Depth Sensors ◽  
Posture Estimation ◽  
Estimation System

This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.

scholarly journals The Application of SILAC Mouse in Human Body Fluid Proteomics Analysis Reveals Protein Patterns Associated with IgA Nephropathy

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Shilin Zhao ◽  
Rongxia Li ◽  
Xiaofan Cai ◽  
Wanjia Chen ◽  
Qingrun Li ◽  
...  
Keyword(s):  
Human Serum ◽  
Human Body ◽  
Body Fluid ◽  
Biomarker Discovery ◽  
Internal Standard ◽  
Complement C3 ◽  
Mouse Serum ◽  
Protein Patterns ◽  
Human Body Fluid ◽  
Serum And Urine

Body fluid proteome is the most informative proteome from a medical viewpoint. But the lack of accurate quantitation method for complicated body fluid limited its application in disease research and biomarker discovery. To address this problem, we introduced a novel strategy, in which SILAC-labeled mouse serum was used as internal standard for human serum and urine proteome analysis. The SILAC-labeled mouse serum was mixed with human serum and urine, and multidimensional separation coupled with tandem mass spectrometry (IEF-LC-MS/MS) analysis was performed. The shared peptides between two species were quantified by their SILAC pairs, and the human-only peptides were quantified by mouse peptides with coelution. The comparison for the results from two replicate experiments indicated the high repeatability of our strategy. Then the urine from Immunoglobulin A nephropathy patients treated and untreated was compared by this quantitation strategy. Fifty-three peptides were found to be significantly changed between two groups, including both known diagnostic markers for IgAN and novel candidates, such as Complement C3, Albumin, VDBP, ApoA,1 and IGFBP7. In conclusion, we have developed a practical and accurate quantitation strategy for comparison of complicated human body fluid proteome. The results from such strategy could provide potential disease-related biomarkers for evaluation of treatment.

Identification of Cancer Biomarkers in Human Body Fluids by Using Enhanced Physicochemical-incorporated Evolutionary Conservation Scheme

2020 ◽  
Vol 20 (21) ◽  
pp. 1888-1897
Author(s):  
Jian Zhang ◽  
Yu Zhang ◽  
Yanlin Li ◽  
Song Guo ◽  
Guifu Yang
Keyword(s):  
Human Body ◽  
Body Fluid ◽  
Cancer Biomarkers ◽  
Evolutionary Conservation ◽  
Body Fluids ◽  
The Body ◽  
Selection Strategy ◽  
Feature Subset ◽  
Scoring Matrices ◽  
Related Proteins

Objective: Cancer is one of the most serious diseases affecting human health. Among all current cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for labor-intensive and time-consuming biochemical experiments. Methods: In this work, we propose a novel method for high-throughput identification of cancer biomarkers in human body fluids. We incorporate physicochemical properties into the weighted observed percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature subset. Results: The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed model. We also test our proposed method on independent testing datasets and apply it to the identification of potential cancer biomarkers in human body fluids. Conclusion: The testing results promise a good generalization capability of our approach.

scholarly journals A Two-Stage Fall Recognition Algorithm Based on Human Posture Features

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6966
Author(s):  
Kun Han ◽  
Qiongqian Yang ◽  
Zefan Huang
Keyword(s):  
Human Body ◽  
Stable State ◽  
Recognition Algorithm ◽  
The Body ◽  
Support Vector ◽  
Unstable State ◽  
Detection Accuracy ◽  
Two Stage ◽  
Human Posture ◽  
Key Features

Falls are seriously threatening the health of elderly. In order to reduce the potential danger caused by falls, this paper proposes a two-stage fall recognition algorithm based on human posture features. For preprocessing, we construct the new key features: deflection angles and spine ratio to describe the changes of human posture based on the human skeleton extracted by OpenPose. In the first stage, based on the variables: tendency symbol and steady symbol integrated by the scattered key features, we divide the human body state into three states: stable state, fluctuating state, and disordered state. By analyzing whether the body is in a stable state, the ADL (activities of daily living) actions with high stability can be preliminarily excluded. In the second stage: to further identify the confusing ADL actions and the fall actions, we innovatively design a time-continuous recognition algorithm. When human body is constantly in an unstable state, the human posture features: compare value γ, energy value ε, state score τ are proposed to form a feature vector, and support vector machine (SVM), K nearest neighbors (KNN), decision tree (DT), random forest (RF) are utilized for classification. Experiment results demonstrate that SVM with linear kernel function can distinguish falling actions best and our approach achieved a detection accuracy of 97.34%, precision of 98.50%, and the recall, F1 score are 97.33%, 97.91% respectively. Compared with previous state-of-art algorithms, our algorithm can achieve the highest recognition accuracy. It proves that our fall detection method is effective.

scholarly journals Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia

2021 ◽  
Vol 15 ◽  
Author(s):  
Qing Zhang ◽  
Xihui Zhou ◽  
Yajun Li ◽  
Xiaodong Yang ◽  
Qammer H. Abbasi
Keyword(s):  
Cerebellar Ataxia ◽  
Back Propagation ◽  
Principal Component ◽  
Economic Cost ◽  
The Body ◽  
Test Time ◽  
Support Vector ◽  
Learning Approaches ◽  
Sensory Ataxia ◽  
External Characteristics

Ataxia is a kind of external characteristics when the human body has poor coordination and balance disorder, it often indicates diseases in certain parts of the body. Many internal factors may causing ataxia; currently, observed external characteristics, combined with Doctor’s personal clinical experience play main roles in diagnosing ataxia. In this situation, different kinds of diseases may be confused, leading to the delay in treatment and recovery. Modern high precision medical instruments would provide better accuracy but the economic cost is a non-negligible factor. In this paper, novel non-contact sensing technique is used to detect and distinguish sensory ataxia and cerebellar ataxia. Firstly, Romberg’s test and gait analysis data are collected by the microwave sensing platform; then, after some preprocessing, some machine learning approaches have been applied to train the models. For Romberg’s test, time domain features are considered, the accuracy of all the three algorithms are higher than 96%; for gait detection, Principal Component Analysis (PCA) is used for dimensionality reduction, and the accuracies of Back Propagation (BP) neural Network, Support Vector Machine (SVM), and Random Forest (RF) are 97.8, 98.9, and 91.1%, respectively.

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