scholarly journals MS-EmpiRe utilizes peptide-level noise distributions for ultra sensitive detection of differentially abundant proteins

2019 ◽  
Author(s):  
Constantin Ammar ◽  
Markus Gruber ◽  
Gergely Csaba ◽  
Ralf Zimmer
Keyword(s):  
Quantitative Proteomics ◽  
Biomedical Applications ◽  
Simulated Data ◽  
General Purpose ◽  
Superior Performance ◽  
Genome Wide ◽  
Wide Range ◽  
Error Distributions ◽  
Rapid Processing ◽  
Differentially Abundant Proteins

1AbstractMass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of proteins in a wide range of biological and biomedical applications. Protein changes need to be reliably derived from a large number of measured peptide intensities and their corresponding fold changes. These fold changes vary considerably for a given protein.Numerous instrumental setups aim to reduce this variability, while current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe (github.com/zimmerlab/MS-EmpiRe), which explicitly accounts for the noise underlying peptide fold changes. We derive dataset-specific, intensity-dependent empirical error distributions, which are used for individual weighing of peptide fold changes to detect differentially abundant proteins. The method requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. In a recently published proteome-wide benchmarking dataset, MS-EmpiRe doubles the number of correctly identified changing proteins at a correctly estimated FDR cutoff in comparison to state-of-the-art tools. We confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe provides rapid processing (< 2min) and is an easy to use, general-purpose tool.

scholarly journals TiO2–Graphene Quantum Dots Nanocomposites for Photocatalysis in Energy and Biomedical Applications

Catalysts ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 319
Author(s):  
Anuja Bokare ◽  
Sowbaranigha Chinnusamy ◽  
Folarin Erogbogbo
Keyword(s):  
Quantum Dots ◽  
Biomedical Applications ◽  
Wearable Sensors ◽  
Graphene Quantum Dots ◽  
Superior Performance ◽  
Synthesis Methods ◽  
Widespread Application ◽  
Wide Range ◽  
Multifunctional Nanocomposites ◽  
Energy Conversion Devices

The focus of current research in material science has shifted from “less efficient” single-component nanomaterials to the superior-performance, next-generation, multifunctional nanocomposites. TiO2 is a widely used benchmark photocatalyst with unique physicochemical properties. However, the large bandgap and massive recombination of photogenerated charge carriers limit its overall photocatalytic efficiency. When TiO2 nanoparticles are modified with graphene quantum dots (GQDs), some significant improvements can be achieved in terms of (i) broadening the light absorption wavelengths, (ii) design of active reaction sites, and (iii) control of the electron-hole (e−-h+) recombination. Accordingly, TiO2-GQDs nanocomposites exhibit promising multifunctionalities in a wide range of fields including, but not limited to, energy, biomedical aids, electronics, and flexible wearable sensors. This review presents some important aspects of TiO2-GQDs nanocomposites as photocatalysts in energy and biomedical applications. These include: (1) structural formulations and synthesis methods of TiO2-GQDs nanocomposites; (2) discourse about the mechanism behind the overall higher photoactivities of these nanocomposites; (3) various characterization techniques which can be used to judge the photocatalytic performance of these nanocomposites, and (4) the application of these nanocomposites in biomedical and energy conversion devices. Although some objectives have been achieved, new challenges still exist and hinder the widespread application of these nanocomposites. These challenges are briefly discussed in the Future Scope section of this review.

scholarly journals Estimating barriers to gene flow from distorted isolation by distance patterns

2017 ◽  
Author(s):  
Harald Ringbauer ◽  
Alexander Kolesnikov ◽  
David Field ◽  
Nicholas H. Barton
Keyword(s):  
Gene Flow ◽  
Isolation By Distance ◽  
Covariance Structure ◽  
Simulated Data ◽  
Flower Color ◽  
Two Dimensions ◽  
Genome Wide ◽  
Linear Barrier ◽  
Wide Range ◽  
Classical Pattern

ABSTRACTIn continuous populations with local migration, nearby pairs of individuals have on average more similar genotypes than geographically well separated pairs. A barrier to gene flow distorts this classical pattern of isolation by distance. Genetic similarity is decreased for sample pairs on different sides of the barrier and increased for pairs on the same side near the barrier. Here, we introduce an inference scheme that utilizes this signal to detect and estimate the strength of a linear barrier to gene flow in two-dimensions. We use a diffusion approximation to model the effects of a barrier on the geographical spread of ancestry backwards in time. This approach allows us to calculate the chance of recent coalescence and probability of identity by descent. We introduce an inference scheme that fits these theoretical results to the geographical covariance structure of bialleleic genetic markers. It can estimate the strength of the barrier as well as several demographic parameters. We investigate the power of our inference scheme to detect barriers by applying it to a wide range of simulated data. We also showcase an example application to a Antirrhinum majus (snapdragon) flower color hybrid zone, where we do not detect any signal of a strong genome wide barrier to gene flow.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Recent trends in smartphone-based detection for biomedical applications: a review

2021 ◽  
Vol 413 (9) ◽  
pp. 2389-2406 ◽  
Author(s):  
Soumyabrata Banik ◽  
Sindhoora Kaniyala Melanthota ◽  
Arbaaz ◽  
Joel Markus Vaz ◽  
Vishak Madhwaraj Kadambalithaya ◽  
...  
Keyword(s):  
Biomedical Applications ◽  
Histopathological Examination ◽  
Dark Field ◽  
Portable Devices ◽  
Food Technology ◽  
Wide Range ◽  
Technological Interventions ◽  
Recent Trends ◽  
Increasing Demand

AbstractSmartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of teleimaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices.

scholarly journals Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Raju Bheemanahalli ◽  
Montana Knight ◽  
Cherryl Quinones ◽  
Colleen J. Doherty ◽  
S. V. Krishna Jagadish
Keyword(s):  
Grain Size ◽  
Candidate Genes ◽  
Gene Network ◽  
Genome Wide Association ◽  
Potential Candidate ◽  
Genetic Loci ◽  
Stay Green ◽  
Yield And Quality ◽  
Genome Wide ◽  
Wide Range

AbstractHigh night temperatures (HNT) are shown to significantly reduce rice (Oryza sativa L.) yield and quality. A better understanding of the genetic architecture of HNT tolerance will help rice breeders to develop varieties adapted to future warmer climates. In this study, a diverse indica rice panel displayed a wide range of phenotypic variability in yield and quality traits under control night (24 °C) and higher night (29 °C) temperatures. Genome-wide association analysis revealed 38 genetic loci associated across treatments (18 for control and 20 for HNT). Nineteen loci were detected with the relative changes in the traits between control and HNT. Positive phenotypic correlations and co-located genetic loci with previously cloned grain size genes revealed common genetic regulation between control and HNT, particularly grain size. Network-based predictive models prioritized 20 causal genes at the genetic loci based on known gene/s expression under HNT in rice. Our study provides important insights for future candidate gene validation and molecular marker development to enhance HNT tolerance in rice. Integrated physiological, genomic, and gene network-informed approaches indicate that the candidate genes for stay-green trait may be relevant to minimizing HNT-induced yield and quality losses during grain filling in rice by optimizing source-sink relationships.

scholarly journals Mapping EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS)

2019 ◽  
Vol 29 (1) ◽  
pp. 265-274
Author(s):  
Ali Kiadaliri ◽  
Monica Hernández Alava ◽  
Ewa M. Roos ◽  
Martin Englund
Keyword(s):  
Disease Severity ◽  
Knee Injury ◽  
Cruciate Ligament ◽  
Simulated Data ◽  
Probit Model ◽  
Original Data ◽  
Cost Utility ◽  
Superior Performance ◽  
Outcome Score ◽  
Mapping Models

Abstract Purpose To develop a mapping model to estimate EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS). Methods The responses to EQ-5D-3L and KOOS questionnaires (n = 40,459 observations) were obtained from the Swedish National anterior cruciate ligament (ACL) Register for patients ≥ 18 years with the knee ACL injury. We used linear regression (LR) and beta-mixture (BM) for direct mapping and the generalized ordered probit model for response mapping (RM). We compared the distribution of the original data to the distributions of the data generated using the estimated models. Results Models with individual KOOS subscales performed better than those with the average of KOOS subscale scores (KOOS5, KOOS4). LR had the poorest performance overall and across the range of disease severity particularly at the extremes of the distribution of severity. Compared with the RM, the BM performed better across the entire range of disease severity except the most severe range (KOOS5 < 25). Moving from the most to the least disease severity was associated with 0.785 gain in the observed EQ-5D-3L. The corresponding value was 0.743, 0.772 and 0.782 for LR, BM and RM, respectively. LR generated simulated EQ-5D-3L values outside the feasible range. The distribution of simulated data generated from the BM model was almost identical to the original data. Conclusions We developed mapping models to estimate EQ-5D-3L from KOOS facilitating application of KOOS in cost-utility analyses. The BM showed superior performance for estimating EQ-5D-3L from KOOS. Further validation of the estimated models in different independent samples is warranted.

scholarly journals CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Philipp Rentzsch ◽  
Max Schubach ◽  
Jay Shendure ◽  
Martin Kircher
Keyword(s):  
Prediction Models ◽  
Splice Variants ◽  
Superior Performance ◽  
Data Set ◽  
Pathogenic Variants ◽  
Genome Wide ◽  
Donor And Acceptor ◽  
Human Proteins ◽  
Variant Effect ◽  
Variant Effect Prediction

Abstract Background Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. Methods It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. Results We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. Conclusions While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Structural design and gait research of a new bionic quadruped robot

2021 ◽  
pp. 095440542199566
Author(s):  
Jiu-Peng Chen ◽  
Hong-Jun San ◽  
Xing Wu ◽  
Bin-Zhou Xiong
Keyword(s):  
Stability Margin ◽  
Static Stability ◽  
Quadruped Robot ◽  
Superior Performance ◽  
Linkage Mechanism ◽  
Wide Range ◽  
Planning Algorithm ◽  
Planning Experiment ◽  
Strength And Stiffness ◽  
Foot Trajectory

Quadruped bionic robot has a strong adaptability to the environment, compared with wheeled and tracked robots, it has superior motion performance, and has a wide range of application prospects in rescue and disaster relief, ground mine clearance, mountain transportation, so it has become a research hotspot all over the world. Leg structure is an important embodiment of the superior performance of quadruped robot, and it is also the key and difficult point of design. This article proposes a novel quadruped robot with waist structure, which can complete a variety of gait forms. Based on the theory of linkage mechanism, a novel leg structure is designed with anti-parallelogram mechanism, which improves the strength and stiffness of the robot. Using D-H description method, the kinematics analysis of this quadruped robot single leg is carried out. On this basis, in order to ensure the foot contact with the ground and achieve zero impact, polynomial programming is used to plan the foot trajectory of swing phase and support phase. Based on the static stability margin, the optimal static gait of the quadruped robot is planned. A co-simulation study has been carried out to investigate further the validity and effectiveness of the quadruped robot on gait. The simulation results clearly show the robot can walk steadily and its input and output meet the expected requirements. The solid prototype platform is built, and the trajectory planning experiment of single leg is carried out, and the foot trajectory of single leg is obtained by using laser tracker. The gait planning algorithm is applied to the whole robot, and the results show that the robot can walk according to the scheduled gait, which proves the effectiveness of the proposed algorithm.

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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