thesis morphometric study

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Home > Books > New Insights into Morphometry Studies

Introductory Chapter - Morphometric Studies: Beyond Pure Anatomical Form Analysis

Submitted: 09 May 2017 Published: 12 July 2017

DOI: 10.5772/intechopen.69682

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New Insights into Morphometry Studies

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Pere m. parés‐casanova *.

• Department of Animal Science, University of Lleida, Catalonia, Spain

*Address all correspondence to: [email protected]

Morphometrics (or morphometry) 1 refers to the study of shape variation of organs and organisms and its covariation with other variables [ 1 ]: “ Defined as the fusion of geometry and biology, morphometrics deals with the study of form in two‐ or three‐dimensional space ” [ 2 ]. Shape encompasses, together with size , the form in Needham’s equation (1950) [ 3 ], two aspects with differing properties.

Scientific production in the morphometric field has increased dramatically over the last few decades. I do not doubt that largely this has resulted from easily available and (usually) fairly comprehensive computer programs, cheaper and more powerful personal computers, and more specialized and less expensive equipment for raw data acquisition: “ Fortunately, the morphometric community is replete with theorists who also generate software, and thus numerous packages are available ” [ 4 ].

Therefore, in addition to the “classical” tools for obtaining data (such as images), there is currently a wide spectrum of very advanced technology available, making measurements of any type easier, with more resolution, three‐dimensional, less invasive and more complex: computed tomography, magnetic resonance imaging, ultrasound, surface scanners and other three‐dimensional data‐collection devices, scanners. 2 An example of this “new technological age” is the estimation of body surface area (BSA). The estimation of BSA can be traced back to 1793, when Abernathy directly measured the surface area of the head, hand, and foot in humans using triangular‐shaped paper, estimating the remaining segments of the body using linear geometry [ 5 ]. Similarly in animals, initial BSA data were obtained by pasting strips of strong manila paper, gummed on one side, to the hair of the animals [ 6 ] or rolling a revolving metal cylinder of a known area, attached to a revolution counter [ 7 ]. Recently, however, complex techniques, such as computed tomography have been applied [ 8 ], and these have undoubtedly improved the quality (precision, ease) of data (and, frankly, I cannot imagine a live ferret being wrapped in a sheet of paper to estimate its BSA!).

A personal comment is in order here. These considerations have not been developed according to any deeper theoretical considerations. They are mainly based on personal experience of working with morphology in different contexts. Their aim is to provide an intuitive overview of how and for what purpose morphology can be applied, rather than attempting to formulate a strict thesis. Perhaps, needless to say, this is a text aimed at presenting certain personal ideas about morphometrics and morphology, not an attempt to give an exhaustive presentation of the literature on the topic. The bibliography presented is simply for things to make more sense and to demonstrate how I justify some assumptions on conceiving the ideas set forth.

Let us continue. Current software for morphometry can analyze data whatever their origin, and normally, it allows the construction of relevant images (the role of visual representations is very important in morphometrics, although algorithms sometimes cannot show completely accurate results, for instance, because they are not well adapted to a discrete framework).

Morphometrics was initially performed on organisms ( “Morphometrics is simply a quantitative way of addressing the shape comparisons that have always interested biologists” ) [ 9 ], extracting information by means of mathematical operations. Tools of morphometrical methods initially applied to study merely form (size + shape) 3 can be applied to other nonbiological fields. In this context, “morphometrical analysis” refers to the analysis of form within the particular scientific discipline where this term is used, including nonbiological forms. Many of the morphometrical concepts can, however, be generalized to encompass nonbiological hypotheses, and their applications are not currently restricted to biological uses. We now therefore have many branches of morphometrics which have emerged as a praxis of their own, such as “geomorphometry” [ 10 ] and “archaeometry” [ 3 ]. For a wider vision of morphology applications, it is recommended to read Zwicky’s publications, which are listed on the website of The Fritz Zwicky Foundation (FZF) at: http://www.zwicky‐stiftung.ch/index.php?p=6|8|8&url=/Links.htm . Furthermore, current morphological mathematical tools have similar advantages when applied to the study of “other‐than‐form” traits: color [ 11 ], pigmentation patterns, textures, etc. This is also the case when applied to meristic (countable) characters (for instance, fin rays in fish, cephalic foramina in skulls, etc.).

With this availability of many computational facilitations and so wide a spectrum of applications, current morphometric research cannot simply be applied to such a wide range of fields, but also requires the combination of many disciplines. All of these factors add up to a complex task, which should not be beyond our power as ordinary scientists. Morphometrics increasingly calls for an integrative research approach, in addition to a good understanding of the mathematical or logical basis of the approach considered.

In summary, we can give many answers based on any motivation of measurement, not only form, the morphé , on biological bodies. The important question in morphometrical analyses is frequently more related conceptually to how and what we measure than to how we should proceed mathematically. For instance, same samples measured by means of geometric morphometrics or lineal morphometrics show totally different results, although statistical multivariate analyses are similar (comparing, for instance, [ 12 , 13 ], it is clear how results can change according to a mere difference in how crude data were obtained (obviously I refer to technique, not quality)).

Morphology 4 “ refer (s) to the study of the structural relationships between different parts or aspects of the object of study ” [ 14 ]. It therefore includes aspects of outward appearance (shape, size, structure, color, pattern, i.e., external morphology or eidonomy), as well as the form and structure of the internal parts, like bones and organs, that is, internal morphology (or anatomy) 5 . Not only internal traits but also other external traits can therefore be mathematically analyzed with morphometric methods. We then have a huge cloud of research in a completely morphological—rather than merely morphometrical—field: biological or nonbiological specimens, on form or more structural traits, etc. For instance, in a study of mine of 322 eggs belonging to different Catalan hen breeds and varieties (data unpublished but available upon request from the author), the mere analysis of shape (using 3 classic descriptors “egg surface”, “egg volume” [ 15 ], and “shape index” [ 16 ]) allowed 3.7% of correct identifications. When the analysis included fresh weight (which could be interpreted as size), they increased to 18.0%; and when the traits studied included color (cream or tinted, white or brown), successful classification reached 20.8%. This is just an example of how results can be obtained by means of a production process—in some cases, a complex one—but which will be influenced by decisions on the hypothesis taken rather than by the mathematic algorithms concerned.

In conclusion, morphometrics, being a branch of statistics, must be viewed as a branch of morphology in the widest sense. 6 Also, on emphasizing the broad component of morphology, we do not rule out the significance of its mathematical component.

• 1. Reyment RA. Morphometrics: An historical essay, In: Elewa AMT, editor. Morphometrics for Nonmorphometricians, Lecture Notes in Earth. Vol. 124. Springer-Verlag Berlin Heidelberg
• 2. Richtsmeier JT, DeLeon VB, Lele SR. The promise of geometric morphometrics. American Journal of Physical Anthropology. 2002; 45 :63-91
• 3. Borel A, Cornette R, Baylac M. Stone tool forms and functions: A morphometric analysis of modern humans Stone tools from song terus cave (Java, Indonesia). Archaeometry. 2017; 59 (3):455-471
• 4. Adams DC, Rohlf FJ, Slice DE. A field comes of age: Geometric morphometrics in the 21st century. Hystrix. 2013; 24 (1):7-14
• 5. Daniell N, Olds T, Tomkinson G. Technical note: Criterion validity of whole body surface area equations: A comparison using 3D laser scanning. American Journal of Physical Anthropology. 2012; 148 (1):148-155
• 6. Hogan AG, Skouby CI. Determination of the surface area of cattle and swine. Journal of Agricultural Research. 1923; 25 (419):419-432
• 7. Elting EC. A formula for estimating surface area of dairy cattle. Journal of Agricultural Research. 1926; 33 (3):269-280
• 8. Jones KL, Abbigail Granger L, Kearney MT, da Cunha AF, Cutler DC, Shapiro ME, Tully TN, Shiomitsu K. Evaluation of a ferret‐specific formula for determining body surface area to improve chemotherapeutic dosing. American Journal of Veterinary Research. 2015; 76 (2):142-148
• 9. Zelditch ML, Swiderski DL, Sheets HD. Geometric Morphometrics for Biologists: A Primer. Boston, MA : Elsevier Academic Press; 2004
• 10. Guth PL. Drainage basin morphometry: A global snapshot from the shuttle radar topography mission. Hydrology and Earth System Sciences. 2011; 15 (7):2091-2099
• 11. Hall‐Spencer JM, Moore PG, Sneddon LU. Observations and possible function of the striking anterior coloration of Galathea intermedia (Crustacea: Decapoda: Anomura). Journal of the Marine Biological Association UK. 1999; 79 :371-372
• 12. Parés‐Casanova PM, Morros C. Molar asymmetry shows a chewing‐side preference in horses. Journal of Zoological and Bioscience Research. 2014; 1 (1):14-18
• 13. Parés‐Casanova PM, Reig E. Directional and fluctuating asymmetries in Cavall Pirinenc Català breed molars. Journal of Animal Ethnology. 2015; 1 :10-18
• 14. Álvarez A. Ritchey T. Applications of General Morphological Analysis. Acta Morphologica Generalis. 2015; 4 (1):1-40
• 15. Havlíček M, Nedomová Š, Simeonovová J, Severa L, Křivánek I. On the evaluation of chicken egg shape variability. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 2008; 56 (5):69-74
• 16. Altuntas E, Sekeroglu A. Effect of egg shape index on mechanical properties of chicken eggs. Journal of Food Engineering. 2008; 85 (4):606-612
• From the Greek μορϕή, morphe, meaning “form”, and –μετρία, metria, meaning “measurement.” The term “morphometrics” seems to have been coined in 1957 by Robert E. Blackith from Dublin University, who studied the subject in relation to locusts [1].
• No single type of imaging is always better; each has different potential advantages and disadvantages, and obviously their interpretation is subject to the hypothesis at hand.
• Shape contains the whole geometry (i.e., proportions) of objects, but it does not always take into account the overall complexity of the geometry of the specimens [3].
• From the Ancient Greek don, morphé, meaning “form,” and λόγος, lógos, meaning “word, study, research”.
• From the Ancient Greek ἀνατομή, anatomē, meaning “dissection”, and –τέμνω, témnō, meaning “I cut”.
• And with morphometric technique being dependent on images, would it be better defined as “morphography”? I leave it to the readers’ consideration.

© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Morphometric methods for the analysis and classification of gastropods: a comparison using Littorina littorea

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Darragh Doyle, Martin P Gammell, Róisín Nash, Morphometric methods for the analysis and classification of gastropods: a comparison using Littorina littorea , Journal of Molluscan Studies , Volume 84, Issue 2, May 2018, Pages 190–197, https://doi.org/10.1093/mollus/eyy010

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The study of morphology is a common means of biological grouping and classification. In recent years, morphometric studies have been dominated by quantitative geometric-morphometric methods of data extraction such as outline or landmark-based analysis. These methods are often used in conjunction with various classification methods such as linear discriminant analysis (LDA) and random forests (RF) in order to achieve inter- and intraspecific grouping based on environmental factors. Despite numerous studies incorporating these data-extraction and classification methods, comparisons of the effectiveness of these methods are largely lacking, especially for species which display low morphological variation. The aim of this study was to compare the effectiveness of two data-extraction methods, elliptic Fourier analysis (EFA) and generalized Procrustes analysis, and two classification methods, LDA and RF, using Littorina littorea as the study organism. The results show that the principal component scores derived from EFA, provided the optimal data input for classification while the greatest percentage of successfully classified individuals was achieved using LDA. However, based on this study RF is the recommended classification method as it is resistant to overfitting, makes no assumptions about the data, is well suited to morphometric data and produces similar rates of classification to LDA. The results are discussed in a biological context for L. littorea , based on the environmental factors of zonation and shore exposure.

The study of form and morphology has always been vital to taxonomic classification. Even with recent advances in genetics, morphological assessment is still the dominant means of species grouping and classification. As such, methodological comparisons for the extraction and analysis of morphological data are essential. In recent decades, the study of morphology has been advanced by the ‘geometric-morphometric revolution’, in reference to a family of methods that use landmark- or outline-based methods to capture morphology as a set of Cartesian coordinates or outline contours. This morphological information can then be compared between populations or species using multivariate statistics ( Rohlf & Marcus, 1993 ; Adams, Rohlf & Slice, 2004 ). These methods have been adopted for morphological analysis of numerous marine invertebrate groups, such as gastropods ( Primost, Bigatti & Márquez, 2016 ), bivalves ( Sherratt et al. , 2016 ) and polychaetes ( Glasby & Glasby, 2006 ).

Since the advent of this revolution, the literature has been dominated by landmark-based Procrustes methods ( Rohlf & Slice, 1990 ) such as generalized Procrustes analysis (GPA), which use biologically homologous points to describe shape differences between specimens. Even in the present day, far less attention is paid to outline-based methods such as Fourier analysis and its successor elliptic Fourier analysis (EFA). The fact that EFA has not attracted the same widespread use can be attributed to a number of reasons. First, the mathematical foundation underpinning EFA is believed to be quite complex in contrast to that of Procrustes-based methods ( Schmittbuhl et al. , 2003 ) and this perceived complexity may have deterred early practitioners ( Caple, Byrd & Stephan, 2017 ). Second, early critics of the method pointed to the fact that outline methods disregard biologically homologous points and instead give equal weight to the entire structure. However, both of these points have been tackled in recent years and with the advent of user-friendly proprietary software ( Iwata & Ukai, 2002 ), EFA has come into more widespread use, though still not to the same degree as Procrustes-based landmark methods. The early claim that EFA is an extremely powerful tool for morphological studies ( Rohlf & Archie, 1984 ) has never been disproven. A brief summary of both GPA and EFA is given below.

GPA uses points, or ‘landmarks’, which are biologically homologous between specimens, in order to capture shape ( Rohlf & Marcus, 1993 ). These points are then compared with their counterparts on each specimen in order to determine how shapes vary. As such, the derived shape depends entirely on the chosen landmark positions ( Webster & Sheets, 2010 ). Hence, there is a need to choose landmarks that are not only biologically meaningful, but which can be placed with precision from specimen to specimen. For some structures, this is extremely difficult. For example, most gastropod species tend to have few discernible landmarks on their shell surface. A solution to this has been to use ‘semilandmarks’, which are usually placed on a curve and optimally slid to achieve minimum bending energy. In this way, they capture a point or points which “acquire geometric homology or correspondence” from specimen to specimen ( Mitteroecker & Gunz, 2009 : 242). Another method is to superimpose a grid over each specimen and anchor the extremities of the grid to biologically homologous points, in order to provide consistent landmark positions. This grid should be rescaled from specimen to specimen to ensure that the same relative positions on each shell are being recorded ( Maddux & Franciscus, 2009 ; Vaux et al. , 2017 ).

In contrast to GPA, EFA entails the decomposition of an object outline into a sum of harmonically related ellipses (or harmonics; Tracey, Lyle & Duhamel, 2006 ). This provides a set of four coefficients for each harmonic: the trigonometric (sine and cosine) amplitudes of the X and Y increments ( Haines & Crampton, 2000 ; Van Bocxlaer & Schultheiß, 2010 ). The more harmonics that are used, the better the constructed outline adheres to that of the original object ( Kuhl & Giardina, 1982 ). As such, complex shapes require more harmonics to be reconstructed than do simple objects. Crampton (1995) recommended using the first eight harmonics to capture the shape of a bivalve shell. However, the author also cautioned that the number of harmonics to be used should be carefully considered and that the use of unnecessary or statistically insignificant harmonics may add ‘noise’ to the outline.

Once morphometric data have been extracted from the specimens and compiled, a central aim of most morphometric-based studies is to implement statistical procedures that find the greatest spatial differences between groups within the data. These spatial differences are then used to split the groups according to shape and to provide input for a classification or confusion matrix ( Conde-Padín, Grahame & Rolan-Alvarez, 2007 ; Van Bocxlaer & Schultheiß, 2010 ).

A method commonly employed to split groups based on shape differences is linear discriminant analysis (LDA; Fisher, 1936 ), producing axes that minimize the ratio of between-class and within-class variation ( Swets & Weng, 1996 ). Despite the vast amount of morphometric-based literature implementing LDA as a tool for classification (e.g. Valenzuela et al. , 2004 ; Urra, Oliva & Sepúlveda, 2007 ), very little attention has been given to the assumptions of the test itself ( Rexstad et al. , 1990 ). Certain assumptions, such as multivariate normality and equal covariance matrices for each class, are deemed difficult to achieve ( Van Bocxlaer & Schultheiß, 2010 ). Moreover, the number of samples within each group must exceed the dimensionality of each data vector (number of measurements of each sample) in order for the covariance estimates to have full rank and thus be inverted. This problem is difficult to avoid in smaller morphometric datasets (for example, restricting EFA to the first 10 harmonics results in 40 variables per specimen) and methods of ordination such as principal component analysis (PCA) or factor analysis are frequently used for dimensionality reduction. Despite these issues, LDA is generally regarded as being relatively robust to violations of certain assumptions of the test, namely multivariate normality and equal population-covariance matrices ( Lachenbruch & Goldstein, 1979 ; Li, Zhu & Ogihara, 2006 ).

A solution to the problem of nonparametric classification is to use a machine-learning tool. Machine learning refers to computer-based methods that automate analytical modelling. One of the most popular ensemble learning tools, random forests (RF) ( Liaw & Wiener, 2002 ), utilizes an ensemble of classification or regression trees to predict the dependent variable as a result of majority vote or average assignment across trees ( Breiman, 2001 ; Strobl, Malley & Gerhard, 2009 ). Once appropriately tuned, RF allows correlated predictor variables to obtain unbiased predictions and estimates of variable importance, and to achieve group classification ( Dub et al. , 2013 ).

Numerous methods of data extraction and classification have been used to study a variety of marine molluscs (e.g. Monnet et al. , 2009 ; Sherratt, Serb & Adams, 2017 ). However, the extraction of morphological data is a more straightforward task for some taxa as opposed to others. For example, smooth-shelled caenogastropods have few identifiable and homologous points that can be compared across individuals, with the exception of the protoconch-teleoconch boundary and gerontic features, which leave a record on the shell ( Johnston, Tabachnick & Bookstein, 1991 ). This is a problem when attempting to compare morphological features across species or ecotypes. Even more difficult is the morphological comparison of species that display low levels of interspecific variation.

In this study, Littorina littorea (Linnaeus, 1758) is used as the subject, for two reasons. First, the species shows a low level of genetic and ecophenotypic variation ( Fevolden & Garner, 1987 ; Reid, 1996 ) and so the sensitivity of the method used to detect morphological differentiation can be determined by its ability to discriminate this species into groups reliably, based on shore exposure and vertical zonation. Second, despite the vast body of literature concerning L. littorea morphology ( Kemp & Bertness, 1984 ; Cummins et al. , 2002 ; Cotton, Rundle & Smith, 2004 ), a thorough morphometric study exploring the effects of shore exposure and vertical zonation is lacking for the species.

Littorina littorea is a dioecious, intertidal caenogastropod. The species has an almost ubiquitous presence on North Atlantic rocky shores, where it is often the dominant macroalgal grazer ( Lubchenco, 1983 ). Shell polymorphism between populations of L. littorea has been studied extensively in the past (review by Reid, 1996 ) and even over distances of hundreds of kilometres the species has been found to exhibit only very slight morphological variation ( Johannesson, 1992 ). This lack of clear differentiation between allopatric populations is attributed to the planktotrophic mode of reproduction and prolonged larval dispersal phase ( Johannesson, 1988 ). This results in high gene flow between populations ( Yamada, 1987 ). However, morphological differences are still achievable through the implementation of a plastic phenotype ( Hollander et al. , 2006 ). These morphological differences are known to depend on a variety of environmental factors such as shore exposure and zonation, both of which will be explored here.

The aims of this study are (1) to compare the traditional classification method of LDA with the ensemble-learning method of RF to determine which method has greater discrimination success, (2) to provide a comparison of morphometric data extraction for the analysis of a gastropod species with low levels of sympatric and allopatric morphological variation and (3) to provide the first thorough geometric morphometric study of L. littorea , taking into account the factors of shore exposure, zonation and sex.

Sample site and collection

Littorina littorea specimens were collected from Blackhead (53°9′5.0004′′N, 9°16′6.9996′′W) and Flaggy Shore (53°9′29.736′′N, 9°5′27.384′′W) on the west coast of Ireland. Blackhead and Flaggy Shore are exposed and sheltered shores, respectively. Fifty specimens were taken from the upper and lower intertidal zones of each site ( n = 200) using haphazard quadrat sampling. Samples were taken in February 2017. Individuals with damaged or eroded shells were discarded, and only adult specimens were used. Since shell growth in this species is indeterminate, adulthood was assessed by shell height, according to the method used by Williams (1964) , Saier (2000) and De Wolf, Blust and Backeljau (2001) . Individuals with shell height ≥12 mm were considered adults, as that is the length at which sexual maturity is generally reached ( Williams, 1964 ; Yamada, 1987 ). The specimens were killed by freezing (−20 °C) in order to preserve the reproductive organs and to avoid any damage to the shells. Soft parts were removed from shells by detaching the columellar muscle. Once removed, the specimens were sexed based on the presence/absence of a penis.

Landmark collection

Shells were digitized in 2D using a Canon EOS 1200D SLR camera mounted on a tripod. Each shell was photographed with the aperture facing directly upwards and with the columella along the vertical axis. Shells were placed on a bed of white cement powder, which provided support and contrast. In order to compensate for a lack of type I ( Bookstein, 1997 ) biologically homologous landmarks, a virtual grid (constructed using Adobe Illustrator) was superimposed over each image to aid in the identification of landmarks. This was adapted from the method used by Maddux & Franciscus (2009) and Vaux et al. (2017) . The position of the grid was defined by biologically homologous structures on each individual, i.e. the final suture on the right side of the body and the most extreme point of the lower basal lip. Anchoring the grid at these homologous points and rescaling the grid from sample to sample provided consistent locations for digitization along the outline of each individual. Landmarks were applied to the points where the grid lines intersected the shell. In addition, the apex of the shell (the protoconch is rarely preserved in this species) and the penultimate suture both on the right and left side of the body were also landmarked. The shells were landmarked using TPSDIG2 software ( Rohlf, 2010 ). This provided a set of X and Y Cartesian coordinates that contained the size and shape information for each specimen. To remove variation due to size, position and orientation, the coordinates were subjected to generalized least-squares Procrustes superimposition ( Rohlf & Slice, 1990 ). This removed all confounding information that was not directly related to shape and provided a set of Procrustes residuals for use in the analysis.

Outline digitization

Shells were digitized using the same method as above, with one exception. In order to extract just the outline, the shells were secured to a glass panel in a constant position and lit from beneath using an LED spotlight. This provided a silhouette of each shell. The chain code was extracted by binarizing the images and automatically tracing the curve of each specimen. This chain code was used to compute normalized elliptic Fourier descriptors based on the first harmonic. This provided a set of 20 harmonics, each containing four coefficients ( n = 80). By visual inspection, it was determined that the first ten harmonics were sufficient to capture accurately the relatively simple shell shape. The first harmonic (which contains size and rotation information) was removed. This left 36 Fourier coefficients (harmonics 2–10) which could be analysed using conventional multivariate methods. Removal of noninformative harmonics (noise) also greatly reduced the size of the overall dataset. This was important as the data were later analysed using LDA, which requires a matrix inversion of the pooled covariance matrix. Data reduction is particularly important for EFA, as the Fourier coefficients are composed of the trigonometric amplitudes of the X and Y increments, which generally results in large numbers of variables. Shell outlines were extracted and normalized elliptic Fourier descriptors were calculated using various packages within the software suite SHAPE v. 1.3 ( Iwata & Ukai, 2002 ; Tracey et al. , 2006 ).

Statistical analysis

To classify individuals into groups, a number of different methods were employed. We refer to the four populations sampled from different exposures and zonations as: exposed–lower (EL), exposed–upper (EU), sheltered–lower (SL) and sheltered–upper (SU). Individuals were also assessed to determine if sexual shape dimorphism was present.

Multivariate statistical analyses were carried out using the Procrustes residuals for the GPA data and Fourier coefficients for the EFA data. To ordinate and visually explore the data, PCA was used, as it is simply an ordination method that makes no assumptions about the data. Also, it subjects the data to a rigid transformation so that no information is lost. A broken-stick test ( Jackson, 1993 ) was used to determine which principal components (PCs) were statistically significant for each dataset. Warped outline-deformation grids along PC1 and PC2 were generated for the GPA data, while contour deformations were generated for the EFA data in order to visualize morphological changes along the axes of greatest variation. Warped outline-deformation grids were constructed in TPSRELW v. 1.67 ( Rohlf, 2007 ) and contour deformations in SHAPE v. 1.3 – PrinComp ( Iwata & Ukai, 2002 ).

Similarity between the two different methods was assessed through the correlation of pairwise distances. Euclidean distance matrices were constructed from both the GPA residuals and the EFA coefficients. Both distance matrices were assessed for similarity through the use of a Mantel test. This test randomly permutates columns and rows to provide matrix correlations for unrelated matrices ( Smouse, Long & Sokal, 1986 ). This correlation was used to infer how similar or dissimilar the two different methods were, based on the r -test statistic. The Mantel test was carried out with 9,999 permutations. The data were assessed for multivariate normality by computing Mardia’s skewness and kurtosis ( Mardia, 1970 ), in addition to a Doornik and Hansen omnibus test ( Doornik & Hansen, 2008 ). Orthogonal PCs were extracted from the Procrustes residuals and the Fourier coefficients.

LDA was carried out on the raw EFA coefficients/GPA residuals, maximum PCs and on a variable number of PCs. To prevent overfitting, the number of PCs was incrementally reduced until the highest jack-knifed cross-validation group assignment percentage was achieved for each group. This method was used based on the findings of Sheets et al. (2006) . All multivariate statistics were computed using PAST v. 3.15 ( Hammer, Harper & Ryan, 2008 ).

RF was adopted, because the method is extremely resistant to overfitting of the data and it requires very little tuning to produce the optimal classification algorithm, compared with methods such as Support Vector Machines which, despite their incorporation into a number of recent morphometric studies ( Santos, Guyomarc’h & Bruzek, 2014 ), are regarded as more difficult to train to produce reliable classification. RF classification rate also improves with larger datasets, which are often found in morphometric-based studies dealing with large numbers of variables per sample ( Díaz-Uriarte & De Andres, 2006 ). RF was carried out on the raw EFA coefficients and GPA residuals, the maximum number of PCs for both methods, and also a variable number of PCs for both methods. The number of PCs used as input was again incrementally reduced until the highest cross-validated classification percentage was achieved. Here, tenfold cross-validation was employed. This method splits the data into training and testing sets and then provides an average of the results for each split in order to give an indication of the effectiveness of a model. The model was run with 100 iterations (trees). RF analysis was conducted using WEKA v. 3.8 ( Hall et al. , 2009 ).

Visual inspection of the PCA scatter plots for both GPA (Fig. 1 ) and EFA (Fig. 2 ) indicated that both methods recorded similar levels of variation in the dataset. The Mantel test revealed that the distance matrices of the Procrustes residuals and the Fourier coefficients were positively correlated with the r statistic, indicating weak positive correlation ( r = 0.327, P = 0.0001). This suggests that the two datasets are relatively similar. The broken-stick test revealed the first four principal components to be statistically significant for both EFA and GPA. Individuals appeared to cluster most clearly based on zonation, rather than shore type. SU and EU individuals clustered together, as did SL and EL individuals. The clustering based on zonation was most apparent for the PCA based on EFA coefficients (Fig. 2 ). Shape deformations generated for GPA revealed the morphological variation along the first two principal components. Upper-shore specimens displayed a broader shell with more pointed apex, as opposed to a narrower shape with a flatter apex for lower-shore specimens. As the aperture was recorded using GPA, deformations in aperture shape could also be visualized. This was not possible for EFA, in which structures inside the shell contour (i.e. the aperture) were not recorded. For the EFA contour deformations, individuals displayed a slightly narrower shell with a taller spire, in moving from negative to positive along PC1. No evidence of sexual dimorphism was found through PCA group separation or LDA (results not shown).

Principal components analysis scatterplot of Procrustes residuals showing morphological variation of shells of Littorina littorea individuals based on zonation and shore exposure. Shape deformations are included to show morphological change along principal component 1 and principal component 2. Abbreviations: E–L, exposed lower shore; E–U, exposed upper shore; S–L, sheltered lower shore; S–U, sheltered upper shore.

Principal components analysis scatter plot of Elliptic Fourier coefficients showing morphological variation of shells of Littorina littorea individuals based on zonation and shore exposure. Closed-contour shape deformations are included to show morphological change along principal component 1 and principal component 2. Abbreviations: E–L, exposed lower shore; E–U, exposed upper shore; S–L, sheltered lower shore; S–U, sheltered upper shore.

Based on the Mardia multivariate normality and Hansen and Doornik omnibus tests, the data were found not to adhere to a normal probability distribution function, indicating that the data were significantly nonnormal. As such, the assumptions of the LDA were deemed to be violated. However, the test is known to be robust to violations of both multivariate normality and equal covariance matrices for each class ( Lachenbruch & Goldstein, 1979 ), performing well in both dimensionality reduction and classification despite these violations ( Li et al. , 2006 ). For this reason, and in the interest of comparing the method with RF, LDA was still carried out.

Classification percentages produced by linear discriminant analysis.

Raw coefficients from elliptic Fourier analysis (EFA) and residuals from generalized Procrustes analysis (GPA); maximum principal components (PCs) and variable numbers of PCs provided data input.

Classification percentages produced by random forests.

Two methods of data extraction were employed in this study: EFA and GPA. Visual inspection of the PCA scatter plots for both the EFA and GPA data indicate that both methods recorded similar variation in the dataset, both methods revealing broader shell shape in upper shore specimens. In addition, the Mantel test showed that the distance matrices for both datasets were positively correlated, indicating that the two data extraction methods, despite their theoretical differences, recorded similar morphological variation. The results of this study are consistent with the findings of Van Bocxlaer & Schultheiß (2010) , who found that EFA performed better than semilandmark analysis in providing input for classification. These authors also found that the optimal method to employ for the analysis of gastropod shells depends on the level of shell ornamentation, with semilandmark analysis performing better with ornamented shells. However, for species with a relatively simple shape and low levels of sculpture such as Littorina littorea , EFA is the optimal method for obtaining morphological data, as evidenced by the current study. Sheets et al. (2006) also compared a number of morphometric methods, including semilandmark-based methods and EFA, and found comparable rates of classification success in all of them. However, these authors were unable to use automatic outline detection, due to the irregular shape of the material (feathers) and instead had to rely on manual tracing of the curves. This may have introduced a source of error that could explain the discrepancy in findings between their study and that of Van Bocxlaer & Schultheiß (2010) .

In contrast to the comparative study by Sheets et al. (2006) , the method of data extraction employed in the current study had a significant impact on classification. Data obtained through EFA performed consistently better at a priori classification in comparison with data obtained through GPA. This held true regardless of whether the data used were raw residuals/coefficients or PC scores, and regardless of the classification method used. This supports the findings of Van Bocxlaer & Schultheiß (2010) , who found that outline data provided the optimal input for the classification of unornamented shells. Also, EFA was much quicker to carry out than GPA. For EFA, the method of extracting morphometric data from each of the shells was automated from binarized images, whereas for GPA each shell required time-consuming individual landmarking. This is not a major consideration for the relatively small number of specimens used here ( n = 200) but, for morphometric studies with a greater number of samples and replicates, speed becomes more important. Regardless of data collection method or classification method, the highest classification percentages were obtained when using a variable number of PCs, as also found by Sheets et al. (2006) .

As a classification method, LDA superficially performed better than RF analysis upon jack-knifed cross-validation. The highest classification percentages were obtained when using a variable number of PCs. The main purpose of carrying out LDA on the data here was to provide a comparison for the RF. That the LDA produced very similar classification rates to that of the RF suggests that LDA is in fact somewhat robust to certain violations of the test assumptions, as indicated by previous studies ( Lachenbruch & Goldstein, 1979 ; Li et al. , 2006 ).

RF proved to be a relatively successful means of nonparametric classification, producing comparable rates to LDA for the EFA data. In order to produce the optimal results, a number of factors within the model needed to be fine-tuned. A major factor influencing classification rate was the number of iterations. RF proved less effective in the classification of GPA data, despite efforts to produce classification rates comparable to those of EFA data. Based on this, RF is recommended for outline data which do not adhere to multivariate normality or which otherwise do not meet the assumptions of LDA. For landmark data, there may be preferable machine-learning methods with the capability to produce better classification results—such as SVMs ( Van Bocxlaer & Schultheiß, 2010 ) or logistic regression ( Navega et al. , 2015 ). Our method did not achieve the extremely high rates of classification (>90%) commonly reported in the literature (e.g. Calle et al. , 2002 ; Huber et al. , 2011 ). However, many of the previous studies have explored interspecific variation (e.g. Jaramillo‐O et al. , 2015 ), in which morphological differentiation is often more pronounced. Future morphometric studies exploring relatively slight morphological variation, as in the present case of L. littorea , will likely report lower rates of correct classification.

Visual inspection of PCA scatter plots for both EFA and GPA data revealed that individuals of L. littorea showed a high degree of morphological similarity. This is consistent with the low levels of genetic variation between populations of the species reported in previous studies (e.g. Fevolden & Garner, 1987 ; Hollander et al. , 2006 ). However, despite group separation being slight, differentiation could still be observed, especially based on tidal zonation. Upper-shore individuals displayed a broader shell with a more pointed apex. This contradicted expectations, because in Littorina species larger and broader individuals are usually more prevalent on the lower shore ( Cummins et al. , 2002 ), where greater shell girth acts as a defence against crushing predators such as Carcinus ( Johannesson, 1986 ). The protoconch and early whorls of the shell of L. littorea are always sharply pointed ( Reid, 1996 ), so the finding of a flatter apex low on the shore could imply a degree of erosion of the very tip, which is less pronounced at higher tidal levels. Only mature adults were examined here, and this suggestion would require testing with juvenile shells showing well-preserved apices. Morphological separation based on exposure was not clearly defined. This is consistent with the work of Janson (1987) , who found virtually no morphological variation between L. littorea from exposed and sheltered shores. However, Cummins et al. , (2002) reported a significant correlation between shell width and exposure. No sexual dimorphism was detected in the present study. Saur (1990) and De Wolf et al. (2001) also reported no difference in shell height between male and female L. littorea . However, other authors ( Moore, 1937 ; Van den Broeck et al. , 2007 ) have found evidence of sexual dimorphism. GPA warped outline grids provided detailed shape deformations that included shape information for structures within the shell contour, i.e. the aperture. This is perhaps the greatest advantage of landmark-based methods as opposed to outline-based methods. It is especially significant for caenogastropods, where aperture shape can be highly variable depending on environmental factors and often provides an important aid to classification. EFA deformations were also generated for the closed contours, but revealed less information of biological significance than those of the GPA.

Recommendations for future studies

Based on the results of this study, outline-based methods appear preferable to landmark-based methods for the extraction of morphological data when few unambiguously homologous points are present. The subsequent classification approach that can be used depends on whether the data meet or violate the assumptions of LDA. In either case, RF is recommended because the method makes no assumptions about the data, and is a straightforward and robust method for classification as compared with LDA. In addition, it may be more straightforward for most morphometric practitioners to tune a RF model than to determine whether the assumptions of LDA are violated ( Karels, Bryant & Hik, 2004 ). It is also recommended that, whether using LDA or RF, cross-validation should be employed to ensure that statistical overfitting does not occur. However, RF is well known to be resistant to overfitting ( Breiman, 2001 ), especially when higher iterations are used, and this is another reason to favour this method. Using an incrementally reduced variable number of PCs resulted in the highest classification rate regardless of the data extraction or classification method used. If the goal of the study is to observe the exact features which best distinguish between populations or groups of specimens, then GPA is preferable to EFA, because structures within the 2D shell contour can be digitized, landmarked and then visualized.

Our conclusions and recommendations are based on two data extraction methods and two classifications, and aim to provide a general guide for the analysis of gastropods with few clearly homologous landmarks. However, a wealth of data-extraction methods exist in addition to the methods used here, each with variants of their own. The same is true for classification methods—ensemble learning is simply one suite within a broad range of machine-learning methods. Future comparative studies to investigate the effectiveness of various machine-learning methods are recommended as this means of classification becomes increasingly popular in morphometric studies.

We thank Galway–Mayo Institute of Technology and the Marine and Freshwater Research Centre for the use of their facilities. We thank Otto Storan, Steve Barrett and Mary Veldon for their technical assistance with the field and laboratory work. We also thank Associate Editor John Grahame for his careful reading of the manuscript and for his many insightful comments and suggestions.

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Role of Morphometrics in Fish Diversity Assessment: Status, Challenges and Future Prospects

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• Arvind Kumar Dwivedi   ORCID: orcid.org/0000-0003-4407-5752 1 &
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Identification of species and its stocks within the natural populations is considered essential biodiversity variables (EBVs) for estimating variation in global fish diversity. Among all the tools, morphometrics is one of the most primitive and frequently employed methods to assess these EBVs. In the recent past, revolutionary developments were achieved in the field of morphometrics and with the gaining popularity and efficiency of genetic tools, various morphometric methods (traditional morphometrics, truss network system and geometric morphometrics) are now used in association with molecular markers for fish diversity assessment. This article briefly presents the applications and limitations of various morphometric methods and their prospects in quantifying fish diversity.

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Larochelle CR, Pickens FA, Burns MD, Sidlauskas BL (2016) Long-term isopropanol storage does not alter fish morphometrics. Copeia 104:411–420. https://doi.org/10.1643/CG-15-303

Takács P, Vitál Z, Ferincz Á, Staszny Á (2016) Repeatability, reproducibility, separative power and subjectivity of different fish morphometric analysis methods. PLoS ONE 11:e0157890. https://doi.org/10.1371/journal.pone.0157890

Dwivedi AK (2019) Morphometric variations between seasonal migrants of anadromous shad, Tenualosa ilisha (Hamilton, 1822) from Hooghly estuary, India. Mar Freshw Res 70:1427–1435. https://doi.org/10.1071/MF19004

Cadrin SX (2000) Advances in morphometric identification of fishery stocks. Rev Fish Biol Fish 10:91–112. https://doi.org/10.1023/A:1008939104413

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Arvind Kumar Dwivedi

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Significance Statement: Revolution in the field of morphometrics has changed the dynamics of fish identification and characterization of its stock within natural populations. An integrated approach involving morphometric methods and molecular markers will have congruous insights in quantifying changes in global fish diversity for proper conservation and successful management of the fishery resources.

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Dwivedi, A.K., De, K. Role of Morphometrics in Fish Diversity Assessment: Status, Challenges and Future Prospects. Natl. Acad. Sci. Lett. 47 , 123–126 (2024). https://doi.org/10.1007/s40009-023-01323-x

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DOI : https://doi.org/10.1007/s40009-023-01323-x

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Morphometric study of the lumbar vertebrae in dried anatomical collections

Sharad ashish.

1 Department of Anatomy, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India

P. Kalluraya

Mangala m. pai, b.v. murlimanju, latha v. prabhu, amit agrawal.

2 Department of Neurosurgery, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, 462020, India

Associated Data

• Pai M: LUMBAR VERTEBRAE MORPHOMETRIC DATA.xlsx. figshare.Dataset.2022. 10.6084/m9.figshare.21307917.v1 [ CrossRef ]

Figshare. LUMBAR VERTEBRAE MORPHOMETRIC DATA.xlsx. DOI: https://doi.org/10.6084/m9.figshare.21307917.v1 16

This project contains the following data:

• - This descriptive anatomical study included 200 adult cadaveric dry lumbar vertebrae. The sample size was as per the previous study by Singh et. al. The age and sex of the specimens was not taken into consideration. Congenitally deformed lumbar vertebrae were excluded from the present study. Measurements of this study are performed by the digital Vernier calipers. The data are expressed in millimeters and tabulated as mean ± standard deviation. The details of the measurements performed in this investigation are represented in Fig. 1 and Table 1. The SPSS software (version 26) was utilized to perform the statistical analysis.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC BY 4.0).

Version Changes

Revised. amendments from version 3.

In this revised version, the changes are performed to the scientific content as per the learned reviewers opinion. Two more recently published literature are cited. The limitations of this study based on the reviewer comments about the statistical analysis, ethnic variations are added. The table 1 was revised and the clarity was given in correlation to Figure 1 with respect to the measurements. The discussion part is added with one more paragraph.

The objective of this anatomical study was to perform the morphometry of dried lumbar vertebrae in human cadavers.

This study utilized 200 adult human cadaveric dried lumbar vertebrae. The digital Vernier calipers was used to perform the measurements. The height, antero-posterior length, transverse length of the body of the vertebrae, interpedicular distance at the lateral ends, lamina length, height and thickness, superior and inferior articular facet height and width, mid sagittal and transverse diameter of vertebral foramen, height, width and thickness of the pars inter-articularis were measured.

The vertebral body’s anteroposterior length was more at the lower border than at the superior border ( p < 0.01). The length of lamina was higher over the right in comparison to the left (p < 0.001). The height of lamina, width of inferior articular facet, diameter of lateral recess and thickness of pars inter-articularis were greater for the left sided specimens ( p < 0.01). The statistical significance was not observed for the comparison of the remaining parameters ( p > 0.05).

This anatomical study offered several dimensions of lumbar vertebrae, which are essential in the surgical practice. The implants at the lumbar vertebrae need to be manufactured based on the anatomical dimensions of that particular sample population.

Introduction

In Latin language, ‘lumbus’ means ‘lion’, hence lumbar vertebrae are compared to a lion. They are very flexible and offer stability to the vertebral column. There are few studies available, which offer the morphometric data of the lumbar vertebrae, however there are not many studies available about the dimensions of pars inter-articularis in the anatomical collections. Pars interarticularis is located between the superior and inferior articular facets of the zygapophyseal joints of each vertebra. In the radiographs of the lumbar vertebrae, the pars inter-articularis resembles the neck of a Scottish dog. Since the surgical techniques of the vertebral column involve the utilization of bony anatomical landmarks, the morphometric data of the various parts of the vertebrae are essential. The accurate anatomical dimensional knowledge is important to understand the etiopathogenesis of the lower backache. The bony landmarks like the pars inter-articularis, transverse process, superior and inferior articular facets are particularly important during the internal fixation of the lumbar spine. The pars interarticularis are important parts of lumbar vertebrae, which help during the surgical instrumentation. 1 The anatomical studies help in understanding the detail complex morphometry of the vertebral column. 2 , 3 There is an assumption that the morphometry of the vertebrae play a role in degenerative diseases of the vertebral column. 4 The measurements are essential while choosing a suitable implant and this may avoid the intervertebral space exceeding and subsequent injury of the blood vessels. 5 In this context, the objective of this anatomical study was to perform the morphometry of dried lumbar vertebrae of the human cadavers in sample Indian population.

This descriptive anatomical study included 200 adult cadaveric dry lumbar vertebrae. The sample size was calculated by referring the article by Singh et al. 6

The formula applied was

Z 1-α/2 =Z value at ‘α’ level of significance

Z 1-β =Z value at (1-β) % power

σ=anticipated population standard deviation of the outcome variable (or) common assumed standard deviation between the two groups

d=clinically significant difference

The protocol of this present research is available online at https://www.protocols.io/view/morphometric-study-of-the-lumbar-vertebrae-in-drie-cjqhumt6 . The study duration was 6 months from 17.02.2022 to 17.08.2022. The age and sex of the specimens was not taken into consideration. The present study did not segregate the vertebrae into typical or atypical, because of the random collections of the dried vertebrae. We could not number the vertebrae with respect to the lumbar region. Congenitally deformed lumbar vertebrae were excluded from the present study. Measurements of this study are performed by the digital Vernier calipers. All the measurements were performed by one among the authors of this manuscript. The same author performed the measurements to prevent the inter-observer error. Three measurements were performed and the average of it was taken to prevent the intra-observer error.

The data are expressed in millimeters and tabulated as mean±standard deviation. The details of the measurements performed in this investigation are represented in Figure 1 and Table 1 . The SPSS software (version 26) was utilized to perform the statistical analysis. The paired ‘t’ test was applied to compare the parameters between the right and left sides.

The dimensions given here in this table, are to be correlated with Figure 1 .

The ethics committee of our institution has approved this research (Institutional Ethics Committee, Kasturba Medical College, Mangalore, IEC KMC MLR: 02/2022/60, dated 17.02.2022). Since this is a study from the cadaveric dried bones, the consent from the participants is not applicable. This was waived by our institutional ethics committee. This present research is following the guidelines of the international ethical standards. Since this is a cross sectional study from the dried lumbar vertebrae of the donated cadavers and did not reveal the identity of the body donor, the written informed consent was not taken from the body donor’s family for the use and publication of this research.

The anatomical data obtained in this study are given in Tables 2 and ​ and3. 3 . The vertebral body anteroposterior dimension was more at its lower border than at the upper ( p <0.01). The length of lamina was higher over the right side (p < 0.001). The height of lamina, width of inferior articular facet, diameter of lateral recess and thickness of pars inter-articularis were greater for the left side ( p <0.01). The remaining parameters, which were compared on the right and left sides did not reveal the difference with respect to the statistical significance ( p >0.05).

Measurements are in mm; paired ‘t’ test; statistical significance

If the significant part of vertebral body is involved in a disease, there will be neurological deficits and instability of the back. Internal fixation of vertebral column is the best management available for the traumatic spine injury, lumbar canal stenosis, spondylolisthesis and malignant tumors. The internal fixation offers better stabilization and decreases the duration of the morbidity. The spinal surgery is also performed in prolapsed intervertebral disc and conditions like scoliosis. It was reported that, this is among the hardest surgeries to perform as it is prone for the postoperative complications. 7 Krag et al. 8 performed the morphometry of the vertebrae in cadavers, both manually and radiologically. Characterizing the morphology of the spine among populations, would allow personalizing the conditions under which each individual should be exposed. The morphometric data of the vertebrae are not only useful in the field of neurosurgery, but are also essential to the specialties like neurology and orthopedics. Dimensions of the cervical and thoracic spine were already determined in our collections, few years ago. 9 This present study was the continuation of this and here we determined the parameters in the lumbar vertebrae. The morphometrical data of various parts of lumbar vertebrae, procured from this study can be considered as the reference data for our study population.

There are not many studies being performed about the morphometry of pars inter-articularis. It offers structural support to the vertebral column and considered as the main support. Pars inter-articularis is a dense cortical bone and is exposed in the posterior approaches. There are morphometrical studies, which are performed by using the radiological methods like utilizing the radiographs and computed tomogram scans. 10 The vertebral column robusticity increased significantly over the time affecting the dimensions of the vertebral body as well. 10 According to Kapoor et al. , 11 the inter-pedicular distance was 18.5 mm at the first lumbar vertebra, 21.5 mm at the lower lumbar vertebrae. Aly and Amin 12 reported that the interpedicular distance in the lumbar vertebrae varies from 17 to 43.4 mm and this increases towards inferior region. Nayak et al. 13 opined that the dimensions of vertebral foramen are higher in the atypical lumbar vertebrae than in the typical. The height of body of vertebrae was 171 cm in males and 158.2 cm in females. 10

During the neurosurgical procedures like pedicle screw insertion, improper design of the implant, the depth of insertion, morphometry, density of bone and iatrogenic mistakes can lead to complications like injury to the meninges, CSF leakage and injury to the neurovascular structures. 14 Best knowledge of human anatomy about the morphometry and angulations of the pedicles in relation to the clinically oriented anatomical landmarks can prevent these complications. 14 A South African research studied the ethnic variation in the osseous morphology of the dried lumbar vertebrae. 15 In this present study, the ethnic variations were not studied and also the comparison with the previously published data was not possible as there were differences in the method of measurements performed.

In the present study, we could not segregate the vertebrae with respect to their number, age and sex. The segregation of the lumbar vertebrae into typical and atypical groups might have added more clarity and specificity. This can be considered as a limitation of this anatomical research. In this study, repeatability of the measurements by asking a secondary observer was not performed. It would have been better if a subset of the sample was measured by a secondary observer and intraclass correlation was applied statistically. If the measurements are consistently taken by one observer incorrectly and then averaged to one value it still might not be representative of the ‘true’ dimensions of the vertebrae. Statistically confirming the agreement between these measurements would strengthen the quality of the research. If the intra-observer error is high, it might suggest the requirement for better measurement definitions or suggest using different tools for them in the field of vertebral morphometry in general.

Since it was just a cross sectional anatomical investigation from the dried vertebrae, the specimens from the same cadaver could not be determined as these are random collections. More studies with larger cohort and validated methods of accurate geometric measurements will be helpful in studying this complex anatomy. However, this study offers data about the pars interarticularis of the lumbar vertebrae, which are scarcely reported in the scientific literature. This makes this study interesting as this is novel in the anatomical literature and different from the previous publications.

We report the measurements of parts of the vertebrae of the lumbar region in sample Indian population. It is believed that, these data will help the operating neurosurgeons and spine surgeons during the surgeries like laminectomy and decompression. They are also essential in planning the accurate sizes of the plates and screws in the internal fixation. The implants have to be manufactured depending on the anatomical dimensions of that particular sample population.

[version 4; peer review: 4 approved]

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

Data availability

• Version 4. F1000Res. 2022; 11: 1408.

Reviewer response for version 4

Ravi kant narayan.

1 ESIC Medical College & Hospital, Bihar, India

No further comments.

Is the work clearly and accurately presented and does it cite the current literature?

If applicable, is the statistical analysis and its interpretation appropriate?

Are all the source data underlying the results available to ensure full reproducibility?

Is the study design appropriate and is the work technically sound?

Are the conclusions drawn adequately supported by the results?

Are sufficient details of methods and analysis provided to allow replication by others?

Reviewer Expertise:

Morphometrical study of bones, Bioinformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Reviewer response for version 3

Nicholas bacci.

1 University of the Witwatersrand, Johannesburg, South Africa

This paper presents a morphometric investigation of the lumbar vertebrae of the Indian population with relevance and application to spinal orthopedic clinical practice. The authors put forward a well-structured, detailed manuscript of high scientific quality, including a good ethical standard, and a high level of transparency with the protocol, and data files.

I have some minor comments and suggestions to improve the manuscript:

Introduction:

1. When referring to the shape of the pars inter-articularis as resembling the neck of a Scottish dog, please can a clear morphological description be provided instead?

1. Please change the reference made to 'gender' to 'sex' instead as this is a biological study, gender is a social denomination and may not always be applicable in the biological context.

2. This study is commendable for including repeatability considerations as a lot of studies in anatomy do not do that, while it is a core premise of reproducibility. However, inter-observer error cannot be prevented entirely by simply having only one observer take the measurements. It would be better practice to test the repeatability of the measurements in a subset of the sample (usually 10-15% of the overall sample) by asking a secondary observer to measure the subset of the sample and using a statistical test (e.g., Intraclass Correlation also known as ICC). While this is not common practice in anatomy, if the physical lumbar vertebrae are still accessible it would improve the reproducibility of the overall study conducting these measurements and statistically testing them.

3. While the majority of morphometric studies tend to follow the same procedure the authors do regarding intra-observer error (i.e, measure 3 times and take the average), it would also be better to confirm this statistically by conducting an ICC on the same data (or a subset of them). If the measurements are consistently taken by one observer incorrectly and then averaged to one value it still might not be representative of the 'true' dimensions of the vertebrae. Statistically confirming the agreement between these measurements would strengthen the quality of the paper. If the intra-observer error is high, it might suggest the requirement for better measurement definitions or suggest using different tools for them in the field of vertebral morphometry in general.

4. In Table 1, please add dashes between the letters of the landmarks referring to Figure 1 (i.e., Vertebral body height (AB) should be: Vertebral body height (A-B)) and clarify in the table these letters refer to Figure 1, as it may be confusing to a reader without that context.

Discussion:

1. The study could benefit from direct comparison to other populations to contextualize the differences between the Indian population and other populations to manufacturers of vertebral implants. An example of a "recent" study from South Africa that may be worth including is:

Sani, H.Y., 2018. The morphometric description of the thoracic and lumbar vertebral pedicles in European, African, and mixed populations of South Africa. The Faculty of Health Sciences, University of the Witwatersrand, Johannesburg . Available at: https://wiredspace.wits.ac.za/server/api/core/bitstreams/bcb279ea-d4d3-4301-ad99-062294c1aa12/content

However, this is a master dissertation/thesis (grey literature) and not an academic journal publication and may not fit the requirements of the authors or publishing platform for inclusion. As such this suggestion is by no means a requirement.

Morphological anatomy, biological anthropology, bone histology, forensic anthropology, facial comparison, facial identification.

• Sub-article

Mangala Pai

Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India

We thank the learned reviewer for the time and expert comments. We have revised the manuscript as per the suggestion.

1. The accurate definition of the pars interarticularis is given in the introduction section of this revised version.

2. The word 'gender' is replaced by 'sex' in the whole manuscript.

3.  In this study, repeatability of the measurements by asking a secondary observer was not performed. It would have been better if a subset of the sample was measured by a secondary observer and intraclass correlation was applied statistically. This information suggested by the reviewer is added as the limitation of our study.

4. If the measurements are consistently taken by one observer incorrectly and then averaged to one value it still might not be representative of the 'true' dimensions of the vertebrae. Statistically confirming the agreement between these measurements would strengthen the quality of the research. If the intra-observer error is high, it might suggest the requirement for better measurement definitions or suggest using different tools for them in the field of vertebral morphometry in general.

This is also added as the limitation.

5. The table 1 is revised as per the suggestion,  like AB is revised as A-B

6. The given dissertation was referred and since it is a very good scientific literature, it is cited in the revised manuscript with respect to the ethnic variations. 

Gyanaranjan Nayak

1 Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

First of all, I am privileged to review this article. This seems like a well-conducted research. This is approved by the Institution Ethics Committee which is laudable. Despite working on only dried bones, the authors have ventured to get clearance from the said committee. The collection and analysis of the data seem adequate.

• However, I would like to comment that the segregation of the lumbar vertebrae into typical and atypical groups might have been done by the authors.
• I may suggest that segregating the vertebrae in the above manner will add more clarity and specificity to this well-conducted research and aptly written article.
• The authors also highlight that they have not done the above-mentioned segregation. But as for me, the criteria for segregating the vertebrae into typical atypical classes are rather lucid.
• The criteria can be easily looked up in any standard anatomy text. Apart from this obvious point, the research appears adequate.

Human Anatomy, Clinical Anthropology, Cadaveric Anatomy, Histology

We accept the reviewer opinion and unfortunately, this segregation was not performed. This is added in the revised version as the limitation of the present study.

• The study is a repetition of numerous previous published literature.
• There is nothing novel in the manuscript except for the data from the unnamed population.
• There is a lack of citations for multiple recently published articles on the same topic, such as Priya et al. (2023 1 ).
• Further, the discussion part is too thin and could have had much more clinically correlated data.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

We thank the reviewer for the expert opinion.

As per the suggestion, recently published literature is cited in this revised version.

Priya A, Narayan RK, Ghosh SK, Sarangi PK. Analysing lumbar pedicle morphometry observed via traditional and recent modalities. J Orthop. 2023 Jul 22;43:17-24.

The discussion is added with one more paragraph.

The references are updated.

Reviewer response for version 2

Srinivasa rao sirasanagandla.

1 Department of Human & Clinical Anatomy, College of Medicine & Health Sciences, Sultan Qaboos University, Muscat, Oman

The authors have responded to all queries raised for version 1 of the manuscript. I approve all the changes made by the authors in version 2.

Natural products, osteoporosis, cardiovascular research and Bisphenol A associated toxicity and morphological variations.

Reviewer response for version 1

The present study provides the morphometric data of the lumbar vertebrae in dried anatomical collections in the Indian population. The authors presented the manuscript in a detailed manner, along with protocol, and data availability files. However, I have the following comments for the authors to make the article more effective and scientifically sound.

• Authors should cite some of the recent articles dealing with the morphometry of lumbar vertebrae. 
• It should be stated how many observers were involved in the data collection.
• This section should explain how the authors avoided observational bias.
• The time of the study's conduct needs to be mentioned. 
• If possible, provide more information about the lumbar vertebrae, such as whether they are typical or atypical.
• The statistical test used in the study is missing, though it is mentioned in the data availability file.
• In Tables 2 and 3, the statistical tests used need to be mentioned in the caption. 
• In Table 2,  p value representation for AP length needs to be double-checked. 
• The heading "Implications" in this section may not be appropriate as it deals only with the review of the literature.
• Including information about how the current study's findings differ from those of previous studies will make the paper more interesting. 

Reviewer comment - Introduction - Authors should cite some of the recent articles dealing with the morphometry of lumbar vertebrae. 

Author Reply: Two more recent references are added in this revised version.

• Julin M, Saukkonen J, Oura P,  et al. : Association between vertebral dimensions and lumbar modic changes.  Spine (Phila Pa 1976).  2021;  46 (46): E415–E425.  PubMed Abstract  |  Publisher Full Text
• Kot A, Polak J, Klepinowski T,  et al. : Morphometric analysis of the lumbar vertebrae and intervertebral discs in relation to abdominal aorta: CT-based study.  Surg Radiol Anat.  2022;  44 : 431–441.  PubMed Abstract  |  Publisher Full Text

Reviewer comment -  Methods:  It should be stated how many observers were involved in the data collection.

Author Reply: All the measurements were performed by one among the authors of this manuscript.

Reviewer comment -  Methods: This section should explain how the authors avoided observational bias.

Author Reply: The same author performed the measurements to prevent the inter-observer error. Three measurements were performed and the average of it was taken to prevent the intra-observer error.

Reviewer comment -  Methods: The time of the study's conduct needs to be mentioned. 

Author Reply: The study duration was 6 months from 16.02.2022 to 16.08.2022.

Reviewer comment -  Methods: If possible, provide more information about the lumbar vertebrae, such as whether they are typical or atypical.

Author Reply: The present study did not segregate the vertebrae into typical or atypical, because of the random collections of the dried vertebrae. We could not number the vertebrae with respect to the lumbar region.

Reviewer comment -  Methods: The statistical test used in the study is missing, though it is mentioned in the data availability file.

Author Reply: The paired ‘t’ test was applied to compare the parameters between the right and left sides.

Reviewer comment -  Methods: In Tables 2 and 3, the statistical tests used need to be mentioned in the caption. 

Author Reply: It was already given in the footnote of the table; now, in this revised version, this is also added in the caption.

Reviewer comment -  Methods: In Table 2,  p  value representation for AP length needs to be double-checked.

Author Reply: The AP length was rechecked and it is correct as the dimension was statistically significant and more for the inferior border.

• Sub-article 2

In this revised version, we have removed the headings ‘implications’ and ‘limitations’. The sentence about difference of this study from the previous report is added.

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Tracing Largely Forgotten History of Major Community

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Julia Tellides discovered the rich Jewish heritage of Thessaloniki two years ago on a Harvard Summer School Study Abroad program.

“It was the first time I heard about there being a large Jewish community anywhere in Greece,” said the graduating senior, a joint history and classics concentrator. “I thought, why have I never heard about this before? If anyone should know about this history, it’s me.”

Tellides, who grew up in New Haven, Connecticut, with a Greek father and Jewish mother, went on to devote her senior thesis to the city’s politically active Jewish residents during a period of upheaval in the early 20th century. Once home to the largest Sephardic Jewish population in Europe, Thessaloniki (traditionally known as Salonica or Salonika) proved a gold mine of Jewish culture and resistance, with Tellides surfacing new insights on the community’s struggle for survival.

“For an undergraduate to have gone into such depth, and with such originality, is remarkable,” said Tellides’ thesis adviser Derek Penslar , the William Lee Frost Professor of Jewish History and director of Harvard’s Center for Jewish Studies .

Greece’s second-largest city, situated 300 miles north of Athens on the Aegean Sea, once served as an economic and cultural crossroads. “It was one of the most important ports in the Ottoman Empire,” Tellides explained. It was also a melting pot where Jews, Muslims, and Christians coexisted in relative peace.

Prospective neuroimaging and neuropsychological evaluation in adults with newly diagnosed focal epilepsy

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Objective: There are few prospective longitudinal studies in patients with newly diagnosed epilepsy (NDE) despite that this is a key time point to understand the underlying biology of epilepsy and to identify potential interventions and biomarkers for seizure and cognitive outcomes. Here we have performed a prospective combined neuroimaging and neuropsychological study in a cohort of patients with focal NDE and healthy controls. Methods: We recruited 104 patients with NDE and 45 healthy controls for research-grade 3 Tesla MRI (diagnostic and structural imaging, diffusional kurtosis imaging, resting-state functional MRI, task-based functional MRI), EEG, comprehensive neuropsychological, and blood biomarker investigations. We report here on the baseline clinical, neuroradiological, MRI morphometric, and neuropsychological findings in this cohort. Results: 38% of patients had unremarkable MRI features, 12% had lesions of known significance in epilepsy, 49% with abnormalities of unknown significance in epilepsy, and 23% with incidental findings. In comparison, 56% of controls had unremarkable MRI features, 7% had lesions of known significance in epilepsy, 33% with abnormalities of unknown significance in epilepsy, and 16% had incidental findings. Patients had a higher incidence of white matter hyperintensities compared to controls. Reduced bihemispheric frontal lobe cortical thickness and thalamic volumes were observed in patients with moderate effect sizes. Patients scored significantly lower on tasks of executive function, processing speed, and visual, delayed, and immediate memory, and significantly higher on depression and anxiety assessments compared to controls. Patient neuropsychological performance was related to various brain morphometric features. Significance: People with adult focal NDE have a greater proportion of MRI-positive findings than previously reported. Subtle white matter lesions may represent an important diagnostic criterion and have a pathophysiological basis in focal epilepsy. Morphometric and neuropsychological alterations are present at the point of diagnosis of epilepsy, which suggests that brain and cognitive changes are not exclusively due to the deleterious impact of chronic epilepsy.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was funded by a UK Medical Research Council (MRC) research grant (MR/S00355X/1).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Northwest Liverpool East Research Ethics Committee (19/NW/0384) through the Integrated Research Application System (Project ID 260623). The research is sponsored by the University of Liverpool (UoL001449) and UK Health Research Authority approval was provided on 22nd August 2019.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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