1
|
Corral-Lopez A, Kotrschal A, Szorkovszky A, Garate-Olaizola M, Herbert-Read J, van der Bijl W, Romenskyy M, Zeng HL, Buechel SD, Fontrodona-Eslava A, Pelckmans K, Mank JE, Kolm N. Evolution of schooling drives changes in neuroanatomy and motion characteristics across predation contexts in guppies. Nat Commun 2023; 14:6027. [PMID: 37758730 PMCID: PMC10533906 DOI: 10.1038/s41467-023-41635-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
One of the most spectacular displays of social behavior is the synchronized movements that many animal groups perform to travel, forage and escape from predators. However, elucidating the neural mechanisms underlying the evolution of collective behaviors, as well as their fitness effects, remains challenging. Here, we study collective motion patterns with and without predation threat and predator inspection behavior in guppies experimentally selected for divergence in polarization, an important ecological driver of coordinated movement in fish. We find that groups from artificially selected lines remain more polarized than control groups in the presence of a threat. Neuroanatomical measurements of polarization-selected individuals indicate changes in brain regions previously suggested to be important regulators of perception, fear and attention, and motor response. Additional visual acuity and temporal resolution tests performed in polarization-selected and control individuals indicate that observed differences in predator inspection and schooling behavior should not be attributable to changes in visual perception, but rather are more likely the result of the more efficient relay of sensory input in the brain of polarization-selected fish. Our findings highlight that brain morphology may play a fundamental role in the evolution of coordinated movement and anti-predator behavior.
Collapse
Affiliation(s)
- Alberto Corral-Lopez
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada.
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden.
- Division of Biosciences, University College London, London, UK.
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden.
| | - Alexander Kotrschal
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Behavioural Ecology, Wageningen University & Research, Wageningen, Netherlands
| | - Alexander Szorkovszky
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
| | - Maddi Garate-Olaizola
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
| | - James Herbert-Read
- Department of Zoology, University of Cambridge, Cambridge, UK
- Aquatic Ecology, Lund University, Lund, Sweden
| | - Wouter van der Bijl
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada
| | - Maksym Romenskyy
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Department of Life Sciences, Imperial College London, London, UK
| | - Hong-Li Zeng
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Severine Denise Buechel
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Behavioural Ecology, Wageningen University & Research, Wageningen, Netherlands
| | - Ada Fontrodona-Eslava
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK
| | | | - Judith E Mank
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada
| | - Niclas Kolm
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
| |
Collapse
|
2
|
Kotrschal A, Szorkovszky A, Herbert-Read J, Bloch NI, Romenskyy M, Buechel SD, Eslava AF, Alòs LS, Zeng H, Le Foll A, Braux G, Pelckmans K, Mank JE, Sumpter D, Kolm N. Rapid evolution of coordinated and collective movement in response to artificial selection. Sci Adv 2020; 6:6/49/eaba3148. [PMID: 33268362 PMCID: PMC7710366 DOI: 10.1126/sciadv.aba3148] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 10/21/2020] [Indexed: 05/28/2023]
Abstract
Collective motion occurs when individuals use social interaction rules to respond to the movements and positions of their neighbors. How readily these social decisions are shaped by selection remains unknown. Through artificial selection on fish (guppies, Poecilia reticulata) for increased group polarization, we demonstrate rapid evolution in how individuals use social interaction rules. Within only three generations, groups of polarization-selected females showed a 15% increase in polarization, coupled with increased cohesiveness, compared to fish from control lines. Although lines did not differ in their physical swimming ability or exploratory behavior, polarization-selected fish adopted faster speeds, particularly in social contexts, and showed stronger alignment and attraction responses to multiple neighbors. Our results reveal the social interaction rules that change when collective behavior evolves.
Collapse
Affiliation(s)
- Alexander Kotrschal
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden.
- Behavioural Ecology, Wageningen University, Wageningen, Netherlands
| | - Alexander Szorkovszky
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | - James Herbert-Read
- Department of Zoology, University of Cambridge, Cambridge, UK
- Aquatic Ecology, Lund University, Lund, Sweden
| | - Natasha I Bloch
- Department of Biomedical Engineering, University of Los Andes, Bogotá, Colombia
| | - Maksym Romenskyy
- Department of Life Sciences, Imperial College London, London, UK
| | | | - Ada Fontrodona Eslava
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Centre for Biological Diversity, University of St. Andrews, St. Andrews, UK
| | | | - Hongli Zeng
- School of Science, Nanjing University of Posts and Telecommmunications, Nanjing, China
| | - Audrey Le Foll
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
| | - Ganaël Braux
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
| | | | - Judith E Mank
- University College London, London, UK
- Department of Zoology, University of British Columbia, Vancouver, Canada
| | - David Sumpter
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | - Niclas Kolm
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
| |
Collapse
|
3
|
Szorkovszky A, Kotrschal A, Herbert-Read JE, Buechel SD, Romenskyy M, Rosén E, van der Bijl W, Pelckmans K, Kolm N, Sumpter DJ. Assortative interactions revealed by sorting of animal groups. Anim Behav 2018. [DOI: 10.1016/j.anbehav.2018.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
4
|
Kotrschal A, Szorkovszky A, Romenskyy M, Perna A, Buechel SD, Zeng HL, Pelckmans K, Sumpter D, Kolm N. Brain size does not impact shoaling dynamics in unfamiliar groups of guppies (Poecilia reticulata). Behav Processes 2017; 147:13-20. [PMID: 29248747 DOI: 10.1016/j.beproc.2017.12.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 12/13/2017] [Accepted: 12/13/2017] [Indexed: 11/26/2022]
Abstract
Collective movement is achieved when individuals adopt local rules to interact with their neighbours. How the brain processes information about neighbours' positions and movements may affect how individuals interact in groups. As brain size can determine such information processing it should impact collective animal movement. Here we investigate whether brain size affects the structure and organisation of newly forming fish shoals by quantifying the collective movement of guppies (Poecilia reticulata) from large- and small-brained selection lines, with known differences in learning and memory. We used automated tracking software to determine shoaling behaviour of single-sex groups of eight or two fish and found no evidence that brain size affected the speed, group size, or spatial and directional organisation of fish shoals. Our results suggest that brain size does not play an important role in how fish interact with each other in these types of moving groups of unfamiliar individuals. Based on these results, we propose that shoal dynamics are likely to be governed by relatively basic cognitive processes that do not differ in these brain size selected lines of guppies.
Collapse
Affiliation(s)
| | | | - Maksym Romenskyy
- Department of Mathematics, Uppsala University, 75106, Uppsala, Sweden
| | - Andrea Perna
- Department of Life Sciences, University of Roehampton, London, United Kingdom
| | - Severine D Buechel
- Department of Zoology, Stockholm University, SE-10691, Stockholm, Sweden
| | - Hong-Li Zeng
- Department of Mathematics, Uppsala University, 75106, Uppsala, Sweden
| | | | - David Sumpter
- Department of Mathematics, Uppsala University, 75106, Uppsala, Sweden
| | - Niclas Kolm
- Department of Zoology, Stockholm University, SE-10691, Stockholm, Sweden
| |
Collapse
|
5
|
Kotrschal A, Zeng HL, van der Bijl W, Öhman-Mägi C, Kotrschal K, Pelckmans K, Kolm N. Evolution of brain region volumes during artificial selection for relative brain size. Evolution 2017; 71:2942-2951. [PMID: 28986929 DOI: 10.1111/evo.13373] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 09/14/2017] [Accepted: 09/24/2017] [Indexed: 12/28/2022]
Abstract
The vertebrate brain shows an extremely conserved layout across taxa. Still, the relative sizes of separate brain regions vary markedly between species. One interesting pattern is that larger brains seem associated with increased relative sizes only of certain brain regions, for instance telencephalon and cerebellum. Till now, the evolutionary association between separate brain regions and overall brain size is based on comparative evidence and remains experimentally untested. Here, we test the evolutionary response of brain regions to directional selection on brain size in guppies (Poecilia reticulata) selected for large and small relative brain size. In these animals, artificial selection led to a fast response in relative brain size, while body size remained unchanged. We use microcomputer tomography to investigate how the volumes of 11 main brain regions respond to selection for larger versus smaller brains. We found no differences in relative brain region volumes between large- and small-brained animals and only minor sex-specific variation. Also, selection did not change allometric scaling between brain and brain region sizes. Our results suggest that brain regions respond similarly to strong directional selection on relative brain size, which indicates that brain anatomy variation in contemporary species most likely stem from direct selection on key regions.
Collapse
Affiliation(s)
| | - Hong-Li Zeng
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | | | | | - Kurt Kotrschal
- Department of Behavioural Biology, University of Vienna, Vienna, Austria.,Konrad Lorenz Forschungsstelle, University of Vienna, Vienna, Austria.,Wolf Science Center, University of Veterinary Medicine Vienna, Ernstbrunn, Austria
| | | | - Niclas Kolm
- Department of Zoology, Stockholm University, Stockholm, Sweden
| |
Collapse
|
6
|
Szorkovszky A, Kotrschal A, Herbert Read JE, Sumpter DJT, Kolm N, Pelckmans K. An efficient method for sorting and quantifying individual social traits based on group‐level behaviour. Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12813] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Alex Szorkovszky
- Mathematics DepartmentUppsala University Uppsala Sweden
- IT DepartmentUppsala University Uppsala Sweden
| | | | | | | | - Niclas Kolm
- Zoology DepartmentStockholm University Stockholm Sweden
| | | |
Collapse
|
7
|
Spiegelberg J, Rusz J, Pelckmans K. Tensor decompositions for the analysis of atomic resolution electron energy loss spectra. Ultramicroscopy 2017; 175:36-45. [DOI: 10.1016/j.ultramic.2016.12.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 12/01/2016] [Accepted: 12/04/2016] [Indexed: 11/25/2022]
|
8
|
Spiegelberg J, Rusz J, Thersleff T, Pelckmans K. Analysis of electron energy loss spectroscopy data using geometric extraction methods. Ultramicroscopy 2017; 174:14-26. [DOI: 10.1016/j.ultramic.2016.12.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 12/08/2016] [Accepted: 12/13/2016] [Indexed: 10/20/2022]
|
9
|
Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK. Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif Intell Med 2011; 53:107-18. [PMID: 21821401 DOI: 10.1016/j.artmed.2011.06.006] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2009] [Revised: 05/11/2011] [Accepted: 06/18/2011] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. METHODS The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. RESULTS We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. CONCLUSIONS This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included.
Collapse
Affiliation(s)
- Vanya Van Belle
- Department of Electrical Engineering (ESAT), Division SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg, Belgium.
| | | | | | | |
Collapse
|
10
|
Karsmakers P, Pelckmans K, De Brabanter K, Van hamme H, Suykens JAK. Sparse conjugate directions pursuit with application to fixed-size kernel models. Mach Learn 2011. [DOI: 10.1007/s10994-011-5253-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
11
|
Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK. Improved performance on high-dimensional survival data by application of Survival-SVM. ACTA ACUST UNITED AC 2010; 27:87-94. [PMID: 21062763 DOI: 10.1093/bioinformatics/btq617] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION New application areas of survival analysis as for example based on micro-array expression data call for novel tools able to handle high-dimensional data. While classical (semi-) parametric techniques as based on likelihood or partial likelihood functions are omnipresent in clinical studies, they are often inadequate for modelling in case when there are less observations than features in the data. Support vector machines (svms) and extensions are in general found particularly useful for such cases, both conceptually (non-parametric approach), computationally (boiling down to a convex program which can be solved efficiently), theoretically (for its intrinsic relation with learning theory) as well as empirically. This article discusses such an extension of svms which is tuned towards survival data. A particularly useful feature is that this method can incorporate such additional structure as additive models, positivity constraints of the parameters or regression constraints. RESULTS Besides discussion of the proposed methods, an empirical case study is conducted on both clinical as well as micro-array gene expression data in the context of cancer studies. Results are expressed based on the logrank statistic, concordance index and the hazard ratio. The reported performances indicate that the present method yields better models for high-dimensional data, while it gives results which are comparable to what classical techniques based on a proportional hazard model give for clinical data.
Collapse
Affiliation(s)
- V Van Belle
- Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium.
| | | | | | | |
Collapse
|
12
|
Abstract
This work studies a new survival modeling technique based on least-squares support vector machines. We propose the use of a least-squares support vector machine combining ranking and regression. The advantage of this kernel-based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non-linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high-dimensional Norway/Stanford Breast Cancer Data set.
Collapse
Affiliation(s)
- V Van Belle
- Katholieke Universiteit Leuven, ESAT-SCD, Kasteelpark Arenberg 10 bus 2446, B-3001 Leuven, Belgium.
| | | | | | | |
Collapse
|
13
|
|
14
|
Herpe TV, Pelckmans K, Brabanter JD, Janssens F, Moor BD, den Berghe GV. Statistical approach of assessing the reliability of glucose sensors: the GLYCENSIT procedure. J Diabetes Sci Technol 2008; 2:939-47. [PMID: 19885283 PMCID: PMC2769813 DOI: 10.1177/193229680800200604] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND In healthcare, patients with diabetes are instructed on how to apply intensified insulin therapy in an optimal manner. Tight blood glucose control is also performed on patients treated in the intensive care unit (ICU). Different blood glucose meters and glucose monitoring systems (GMSs) are used to achieve this goal, and some may lack reliability. METHODS The GLYCENSIT procedure is a statistical assessment tool we are proposing for evaluating the significant difference of paired glucose measurements. The performance of the GlucoDay system in the ICU is analyzed with GLYCENSIT. RESULTS THE GLYCENSIT ANALYSIS COMPRISES THREE PHASES: testing possible persistent measurement behavior as a function of the glycemic range, testing the number of measurement errors with respect to a standard criterion for binary assessment of glucose sensors, and computing the tolerance intervals that indicate possible test sensor deviations for new observations. The probability of the tolerance intervals directly reflects the number of samples and additionally improves current assessment techniques. The method can be tuned according to the clinician's preferences regarding significance level, tolerance level, and glycemic range cutoff values. The measurement behavior of the GlucoDay sensor is found to be persistent but inaccurate and returns wide tolerance intervals, suggesting that the GlucoDay sensor may not be sufficiently reliable for glycemia control in the ICU. CONCLUSIONS The GLYCENSIT procedure aims to serve as statistical guide for clinicians in the assessment of glucose sensor devices.
Collapse
Affiliation(s)
- Tom Van Herpe
- Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Heverlee (Leuven), Belgium.
| | | | | | | | | | | |
Collapse
|
15
|
|
16
|
Abstract
This letter investigates a tight convex relaxation to the problem of tuning the regularization constant with respect to a validation based criterion. A number of algorithms is covered including ridge regression, regularization networks, smoothing splines, and least squares support vector machines (LS-SVMs) for regression. This convex approach allows the application of reliable and efficient tools, thereby improving computational cost and automatization of the learning method. It is shown that all solutions of the relaxation allow an interpretation in terms of a solution to a weighted LS-SVM.
Collapse
|
17
|
|
18
|
Pelckmans K, De Brabanter J, Suykens JA, De Moor B. The differogram: Non-parametric noise variance estimation and its use for model selection. Neurocomputing 2005. [DOI: 10.1016/j.neucom.2005.02.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
19
|
Abstract
This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved. It is shown that this approach generalizes the approach of mean imputation in the linear case and the resulting kernel machine reduces to the standard Support Vector Machine (SVM) when no input values are missing. Furthermore, the method is extended to the multivariate case of fitting additive models using componentwise kernel machines, and an efficient implementation is based on the Least Squares Support Vector Machine (LS-SVM) classifier formulation.
Collapse
Affiliation(s)
- K Pelckmans
- Katholieke Universiteit Leuven, ESAT-SCD/SISTA, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
| | | | | | | |
Collapse
|
20
|
|
21
|
|
22
|
Espinoza M, Pelckmans K, Hoegaerts L, Suykens JA, Moor BD. A comparative study of ls-svm’s applied to the silver box identification problem. ACTA ACUST UNITED AC 2004. [DOI: 10.1016/s1474-6670(17)31251-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
23
|
|
24
|
Brabanter JD, Pelckmans K, Suykens JA, Moor BD, Vandewalle J. Robust complexity criteria for nonlinear regression in NARX models. ACTA ACUST UNITED AC 2003. [DOI: 10.1016/s1474-6670(17)34742-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|