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2. Duplications in RB1CC1 are associated with schizophrenia; identification in large European sample sets.

Author

Degenhardt, F, Priebe, L, Meier, S, Lennertz, L, Streit, F, Witt, SH, Hofmann, A, Becker, T, Mössner, R, Maier, W, Nenadic, I, Sauer, H, Mattheisen, M, Buizer-Voskamp, J, Ophoff, RA, GROUP Consortium, Rujescu, D, Giegling, I, Ingason, A, Wagner, M, Delobel, B, Andrieux, J, Meyer-Lindenberg, A, Heinz, A, Walter, H, Moebus, S, Corvin, A, Wellcome Trust Case Control Consortium 2, International Schizophrenia Consortium, Rietschel, M, Nöthen, MM, and Cichon, S

Subjects
GROUP Consortium, Wellcome Trust Case Control Consortium 2, International Schizophrenia Consortium, Humans, Genetic Predisposition to Disease, Schizophrenia, Gene Duplication, Adult, Aged, Middle Aged, Europe, Female, Male, Protein-Tyrosine Kinases, DGCR2, exome-sequencing, intellectual disability, RB1-inducible coiled-coil 1, schizoaffective disorder, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Schizophrenia (SCZ) is a severe and debilitating neuropsychiatric disorder with an estimated heritability of ~80%. Recently, de novo mutations, identified by next-generation sequencing (NGS) technology, have been suggested to contribute to the risk of developing SCZ. Although these studies show an overall excess of de novo mutations among patients compared with controls, it is not easy to pinpoint specific genes hit by de novo mutations as actually involved in the disease process. Importantly, support for a specific gene can be provided by the identification of additional alterations in several independent patients. We took advantage of existing genome-wide single-nucleotide polymorphism data sets to screen for deletions or duplications (copy number variations, CNVs) in genes previously implicated by NGS studies. Our approach was based on the observation that CNVs constitute part of the mutational spectrum in many human disease-associated genes. In a discovery step, we investigated whether CNVs in 55 candidate genes, suggested from NGS studies, were more frequent among 1637 patients compared with 1627 controls. Duplications in RB1CC1 were overrepresented among patients. This finding was followed-up in large, independent European sample sets. In the combined analysis, totaling 8461 patients and 112 871 controls, duplications in RB1CC1 were found to be associated with SCZ (P=1.29 × 10(-5); odds ratio=8.58). Our study provides evidence for rare duplications in RB1CC1 as a risk factor for SCZ.

Published
2013

6. Copy number variations of chromosome 16p13.1 region associated with schizophrenia

Author

Ingason, A, Rujescu, D, Cichon, S, Sigurdsson, E, Sigmundsson, T, Pietiläinen, O P H, Buizer-Voskamp, J E, Strengman, E, Francks, C, Muglia, P, Gylfason, A, Gustafsson, O, Olason, P I, Steinberg, S, Hansen, T, Jakobsen, K D, Rasmussen, H B, Giegling, I, Möller, H-J, Hartmann, A, Crombie, C, Fraser, G, Walker, N, Lonnqvist, J, Suvisaari, J, Tuulio-Henriksson, A, Bramon, E, Kiemeney, L A, Franke, B, Murray, R, Vassos, E, Toulopoulou, T, Mühleisen, T W, Tosato, S, Ruggeri, M, Djurovic, S, Andreassen, O A, Zhang, Z, Werge, T, Ophoff, R A, Rietschel, M, Nöthen, M M, Petursson, H, Stefansson, H, Peltonen, L, Collier, D, Stefansson, K, and Clair, D M St

Published
2011
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7. Correction: Automatic detection of break-over phase onset in horses using hoof-mounted inertial measurement unit sensors

Author

Tijssen, M., Hernlund, E., Rhodin, M., Bosch, S., Voskamp, J. P., Nielen, M., Serra Braganςa, F. M., FAH Evidence based Veterinary Medicine, dFAH AVR, Afd Algemeen Paard, Hafd Onderwijsadvies en training, and Pervasive Systems

Subjects
Hoof and Claw, Inertia, Physiology, Velocity, Biosensing Techniques, Walking, 0403 veterinary science, Gait (human), Medical Bioscience, Medicine and Health Sciences, Musculoskeletal Diseases, Musculoskeletal System, Mammals, Multidisciplinary, Feet, Physics, Applied Mathematics, Simulation and Modeling, Classical Mechanics, Eukaryota, 04 agricultural and veterinary sciences, Biomechanical Phenomena, Signal Filtering, Warmblood, Physical Sciences, Vertebrates, Medicine, Legs, Engineering and Technology, Anatomy, Wireless Technology, Algorithms, Research Article, Materials science, 040301 veterinary sciences, Hoof, Science, Acoustics, Musculoskeletal Physiological Phenomena, Acceleration, Equines, Phase (waves), Angular velocity, Research and Analysis Methods, Motion, Inertial measurement unit, Animals, Force platform, Horses, Biological Locomotion, 0402 animal and dairy science, Organisms, Correction, Biology and Life Sciences, Toes, 040201 dairy & animal science, Body Limbs, Amniotes, Signal Processing, Horse Diseases, Mathematics
Abstract

A prolonged break-over phase might be an indication of a variety of musculoskeletal disorders and can be measured with optical motion capture (OMC) systems, inertial measurement units (IMUs) and force plates. The aim of this study was to present two algorithms for automatic detection of the break-over phase onset from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. The performance of these algorithms was evaluated by internal validation with an OMC system and a force plate separately. Seven Warmblood horses were equipped with two wireless IMUs which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted over a force plate for internal validation while simultaneously the 3D position of three reflective markers, attached to lateral heel, lateral toe and lateral coronet of each hoof, were measured by six infrared cameras of an OMC system. The performance of the algorithms was evaluated by linear mixed model analysis. The acceleration algorithm was the most accurate with an accuracy between -9 and 23 ms and a precision around 24 ms (against OMC system), and an accuracy between -37 and 20 ms and a precision around 29 ms (against force plate), depending on gait and hoof. This algorithm seems promising for quantification of the break-over phase onset although the applicability for clinical purposes, such as lameness detection and evaluation of trimming and shoeing techniques, should be investigated more in-depth.

Published
2020

9. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Author

Braganca, F. M. Serra, Broomé, Michael, Rhodin, M., Bjornsdottir, S., Gunnarsson, V, Voskamp, J. P., Persson-Sjödin, E., Back, W., Lindgren, G., Novoa-Bravo, M., Roepstorff, C., van der Zwaag, B. J., Van Weeren, P. R., Hernlund, E., Braganca, F. M. Serra, Broomé, Michael, Rhodin, M., Bjornsdottir, S., Gunnarsson, V, Voskamp, J. P., Persson-Sjödin, E., Back, W., Lindgren, G., Novoa-Bravo, M., Roepstorff, C., van der Zwaag, B. J., Van Weeren, P. R., and Hernlund, E.

Abstract

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms., QC 20201127

Published
2020
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10. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Author

Equine Musculoskeletal Biology, dES RMSC, Hafd Onderwijsadvies en training, dES AVR, Dep Clinical Sciences, Afd Algemeen Paard, Serra Bragança, F M, Broomé, S, Rhodin, Marie, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, C, van der Zwaag, B J, Van Weeren, P R, Hernlund, E, Equine Musculoskeletal Biology, dES RMSC, Hafd Onderwijsadvies en training, dES AVR, Dep Clinical Sciences, Afd Algemeen Paard, Serra Bragança, F M, Broomé, S, Rhodin, Marie, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, C, van der Zwaag, B J, Van Weeren, P R, and Hernlund, E

Published
2020

11. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Author

Serra Bragança, F M, Broomé, S, Rhodin, M, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, Christoffer, van der Zwaag, B J, van Weeren, P R; https://orcid.org/0000-0002-6654-1817, Hernlund, E, Serra Bragança, F M, Broomé, S, Rhodin, M, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, Christoffer, van der Zwaag, B J, van Weeren, P R; https://orcid.org/0000-0002-6654-1817, and Hernlund, E

Abstract

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.

Published
2020

16. Validation of distal limb mounted inertial-measurement-unit sensors for stride detection in Warmblood horses at walk and trot

Author

Serra Braganca, Filipe, Bosch, S, Voskamp, J P, Marin-Perianu, M, Van der Zwaag, B J, Vernooij, J C M, van Weeren, P R, Back, W, dES RMSC, dES AVR, Regenerative Medicine, Stem Cells & Cancer, Applied Veterinary Research, and LS Equine Muscoskeletal Biology

Subjects
kinematics, inertial measurement unit, gait analysis, stride events, horse
Abstract

BACKGROUND: Inertial-measurement-unit (IMU)-sensor-based techniques are becoming more popular in horses as a tool for objective locomotor assessment. OBJECTIVES: To describe, evaluate and validate a method of stride detection and quantification at walk and trot using distal limb mounted IMU-sensors. STUDY DESIGN: Prospective validation study comparing IMU-sensors and motion capture with force plate data. METHODS: Seven Warmblood horses equipped with metacarpal/metatarsal IMU-sensors and reflective markers for motion capture were hand walked and trotted over a force plate. Using four custom-built algorithms hoof-on/off timing over the force plate were calculated for each trial from the IMU data. Accuracy of the computed parameters was calculated as the mean difference in milliseconds between the IMU or motion capture generated data and the data from the force plate, precision as the s.d. of these differences and percentage of error with accuracy of the calculated parameter as a percentage of the force plate stance duration. RESULTS: Accuracy, precision and percentage of error of the best performing IMU algorithm for stance duration at walk were 28.5 ms, 31.6 ms and 3.7% for the forelimbs and -5.5 ms, 20.1 ms and -0.8% for the hindlimbs respectively. At trot the best performing algorithm achieved accuracy, precision and percentage of error of -27.6 ms/8.8 ms/-8.4% for the forelimbs and 6.3 ms/33.5 ms/9.1% for the hind limbs. MAIN LIMITATIONS: The described algorithms have not been assessed on different surfaces. CONCLUSIONS: IMU technology can be used to determine temporal kinematic stride variables at walk and trot justifying its use in gait and performance analysis. However, precision of the method may not be sufficient to detect all possible lameness-related changes. These data seem promising enough to warrant further research to evaluate whether this approach will be useful for appraising the majority of clinically relevant gait changes encountered in practice. This article is protected by copyright. All rights reserved.

Published
2017

17. Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot

Author

Serra Braganca, Filipe, Bosch, S, Voskamp, J P, Marin-Perianu, M, Van der Zwaag, B J, Vernooij, J C M, van Weeren, P R, Back, W, dES RMSC, dES AVR, Regenerative Medicine, Stem Cells & Cancer, Applied Veterinary Research, LS Equine Muscoskeletal Biology, and Pervasive Systems

Subjects
0301 basic medicine, Hoof and Claw, Kinematics, Computer science, ACCURACY, Biosensing Techniques, Walking, Horse, 0403 veterinary science, Gait (human), Forelimb, Computer vision, Prospective Studies, Gait, FORELIMB LAMENESS, 04 agricultural and veterinary sciences, General Medicine, TIMINGS, horse, Biomechanical Phenomena, Warmblood, Inertial measurement unit, kinematics, gait analysis, Gait analysis, medicine.medical_specialty, 040301 veterinary sciences, STRIDE, Motion capture, PARAMETERS, Article, 03 medical and health sciences, medicine, Animals, Veterinary Sciences, Horses, SPEED, business.industry, inertial measurement unit, stride events, RECOGNITION, Stride events, Experimental and Basic Research Studies, Distal limb, PRECISION, GRAPHICAL REPRESENTATIONS, 030104 developmental biology, Physical therapy, HINDLIMB LAMENESS, Artificial intelligence, business
Abstract

BACKGROUND: Inertial-measurement-unit (IMU)-sensor-based techniques are becoming more popular in horses as a tool for objective locomotor assessment. OBJECTIVES: To describe, evaluate and validate a method of stride detection and quantification at walk and trot using distal limb mounted IMU-sensors. STUDY DESIGN: Prospective validation study comparing IMU-sensors and motion capture with force plate data. METHODS: Seven Warmblood horses equipped with metacarpal/metatarsal IMU-sensors and reflective markers for motion capture were hand walked and trotted over a force plate. Using four custom-built algorithms hoof-on/off timing over the force plate were calculated for each trial from the IMU data. Accuracy of the computed parameters was calculated as the mean difference in milliseconds between the IMU or motion capture generated data and the data from the force plate, precision as the s.d. of these differences and percentage of error with accuracy of the calculated parameter as a percentage of the force plate stance duration. RESULTS: Accuracy, precision and percentage of error of the best performing IMU algorithm for stance duration at walk were 28.5 ms, 31.6 ms and 3.7% for the forelimbs and -5.5 ms, 20.1 ms and -0.8% for the hindlimbs respectively. At trot the best performing algorithm achieved accuracy, precision and percentage of error of -27.6 ms/8.8 ms/-8.4% for the forelimbs and 6.3 ms/33.5 ms/9.1% for the hind limbs. MAIN LIMITATIONS: The described algorithms have not been assessed on different surfaces. CONCLUSIONS: IMU technology can be used to determine temporal kinematic stride variables at walk and trot justifying its use in gait and performance analysis. However, precision of the method may not be sufficient to detect all possible lameness-related changes. These data seem promising enough to warrant further research to evaluate whether this approach will be useful for appraising the majority of clinically relevant gait changes encountered in practice. This article is protected by copyright. All rights reserved.

Published
2016

18. Validation of distal limb mounted inertial-measurement-unit sensors for stride detection in Warmblood horses at walk and trot

Author

dES RMSC, dES AVR, Regenerative Medicine, Stem Cells & Cancer, Applied Veterinary Research, LS Equine Muscoskeletal Biology, Serra Braganca, Filipe, Bosch, S, Voskamp, J P, Marin-Perianu, M, Van der Zwaag, B J, Vernooij, J C M, van Weeren, P R, Back, W, dES RMSC, dES AVR, Regenerative Medicine, Stem Cells & Cancer, Applied Veterinary Research, LS Equine Muscoskeletal Biology, Serra Braganca, Filipe, Bosch, S, Voskamp, J P, Marin-Perianu, M, Van der Zwaag, B J, Vernooij, J C M, van Weeren, P R, and Back, W

Published
2017

20. Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot.

Author

Bragança, F. M., Bosch, S., Voskamp, J. P., Marin‐Perianu, M., Van der Zwaag, B. J., Vernooij, J. C. M., Weeren, P. R., and Back, W.

Abstract

Background Inertial measurement unit ( IMU) sensor-based techniques are becoming more popular in horses as a tool for objective locomotor assessment. Objectives To describe, evaluate and validate a method of stride detection and quantification at walk and trot using distal limb mounted IMU sensors. Study design Prospective validation study comparing IMU sensors and motion capture with force plate data. Methods A total of seven Warmblood horses equipped with metacarpal/metatarsal IMU sensors and reflective markers for motion capture were hand walked and trotted over a force plate. Using four custom built algorithms hoof-on/hoof-off timing over the force plate were calculated for each trial from the IMU data. Accuracy of the computed parameters was calculated as the mean difference in milliseconds between the IMU or motion capture generated data and the data from the force plate, precision as the s.d. of these differences and percentage of error with accuracy of the calculated parameter as a percentage of the force plate stance duration. Results Accuracy, precision and percentage of error of the best performing IMU algorithm for stance duration at walk were 28.5, 31.6 ms and 3.7% for the forelimbs and −5.5, 20.1 ms and −0.8% for the hindlimbs, respectively. At trot the best performing algorithm achieved accuracy, precision and percentage of error of −27.6/8.8 ms/−8.4% for the forelimbs and 6.3/33.5 ms/9.1% for the hindlimbs. Main limitations The described algorithms have not been assessed on different surfaces. Conclusions Inertial measurement unit technology can be used to determine temporal kinematic stride variables at walk and trot justifying its use in gait and performance analysis. However, precision of the method may not be sufficient to detect all possible lameness-related changes. These data seem promising enough to warrant further research to evaluate whether this approach will be useful for appraising the majority of clinically relevant gait changes encountered in practice. [ABSTRACT FROM AUTHOR]

Published
2017
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21. Digital Simulation of the Steady State Behaviour of Moving Bed Processes;

Author

Voskamp, J. H. and Brasz, J.

Abstract

A mathematical description of a complicated pellet indurating machine is presented that is simple and useful for digital computation and optimisation purposes. The arguments for some of the approximations used are given together with the resulting differential equations. A short discussion is devoted to some of the computational aspects.The verification of the model is described briefly, and typical computational results are presented from which some process conclusions are drawn.Although the process is specialised, the approach followed in this paper may be useful to other types of packed bed processes including sintering and drying of foods.

Published
1975
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