Are there any chromosomal/molecular difference in following human reproductive- traits?

Are there any chromosomal/molecular difference in following human reproductive- traits?

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Are there any chromosomal/molecular difference in 5 following human traits?Klinefelter-syndrome, Transgender, Bisexual, intersex, hermaphrodite. Are all 5 terms synonymous?

Well… Strictly speaking… They are not the same. Each of them has actually a different meaning and some of them such as hermaphrodite is used for lower animals while bisexual for plants animals etc. Klinefelter's syndrome is totally different and has got nothing to do with the normal bisexual conditions.

Yeah… sometimes we can use these words interchangeably.

Molecular Development

This page is a link to many different resources related to molecular development. I am 2.85 billion nucleotides of DNA, but so is a chimpanzee, and all this DNA encodes only about 20,000-25,000 protein-coding genes. In development, I am not that different from a mouse or a fly and many of the signals that regulate development are used time and time again.

We have come a long way from just observing development to now wanting to understand how the complex program of development is controlled. Using new research tools and some excellent animal models researchers have discovered common themes and mechanisms that tie all embryonic development together.

What is remarkable, given our biological diversity, is the strong evolutionary conservation of developmental mechanisms. This has been a boon in allowing the use of many (easier) model systems such as the genetist's tool the fruitfly, and the worm, frog, chicken, zebrafish and mouse (see Animal Development page).

Gene Regulatory Network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. In the embryo, these are the molecular mechanisms/pathways used in the development and differentiation of specific cell types and tissues.

A continuing theme also seems to be the reuse of signals at different times and places within the embryo, for diiferent jobs. This has given rise to the concept of "switches" which by themselves may contain no "information" but to activate other genes or switches. Finally, you can imagine that of our 20,000-25,000 protein-coding genes, a large number of these may only be expressed during development or if reused, have a completely different role in the mature animal. The new field of epigenetics has also begun to florish with some recent important findings.

Molecular mechanisms of development is an exciting area and requires a variety of different skills. This page introduces only a few examples and should give you a feel for the topic. Note that each section of system notes has a page covering molecular development in that system. I have included information about the basic building blocks of proteins (amino acids).

Molecular Links: molecular | genetics | epigenetics | mitosis | meiosis | X Inactivation | Signaling | Factors | Mouse Knockout | microRNA | Mechanisms | Developmental Enhancers | Protein | Genetic Abnormal | Category:Molecular
Historic Embryology  
By Morgan, T. H. (1925). Evolution and genetics. Princeton: Princeton University Press.
Evolution and Genetics: 1 Different Kinds of Evolution | 2 Four Great Historical Speculations | 3 Evidence for Organic Evolution | 4 Materials of Evolution | 5 Mendel's Two Laws of Heredity | 6 Chromosomes and Mendel’s Two Laws | 7 Linkage Groups and the Chromosomes | 8 Sex-Linked Inheritance | 9 Crossing-over | 10 Natural Selection and Evolution | 11 Origin of Species by Natural Selection | 12 Non-Inheritance of Acquired Characters | 13 Human Inheritance | Figures

What are Autosomes

Non-sex chromosomes which determine the trait of an organism is identified as autosomes. They are also known as somatic chromosomes since they determine the somatic characters of an individual. A genome mainly consists of autosomes. For example, human body contains 46 chromosomes within its genome and 44 chromosomes of them are autosomes. Autosomes exist as homologous pairs and 22 autosome pairs can be identified in the human genome.

Both autosomal chromosomes contain the same genes, which are arranged in the same order. But an autosomal chromosome pair differs from other autosomal chromosome pairs within the same genome. These pairs are labeled from 1 to 22, according to the base pair sizes contained in each chromosome.

Autosomes also participate in sex determination. SOX9 gene is an autosomal gene on chromosome 17. It activates the function of TDF factor which is encoded by Y chromosome. TDF factor is critical in male sex determination. Hence, a mutation of SOX9 causes the development of Y chromosome, resulting in a female.

Autosomal genetic disorders occur due to either the non-disjunction in parent chromosomes (Aneuploidy) during gametogenesis or the Mendelian inheritance of deleterious alleles. An example for aneuploidy is Dawn’s Syndrome, which possesses three copies of chromosome 21 per cell. Disorders with Mendelian inheritance can either be dominant or recessive (Ex: Sickle cell anemia).

Figure 1: Human male karyotype

Molecular Genetics of Sex Determination

In this era of accelerated discovery and prolific output, Molecular Genetics of Sex Determination keeps readers abreast of this fields fast-moving biology. Its chapters were completed by experts in eacharea only months before publication. The text is organized into two parts. First, it reviews the basic biology of sex determination and summarizes ground-breaking work in mouse, marsupial, and Drosophila systems. Second, it covers current human genetics, clinical studies, and the syndromes of abnormal sex differentiation.

With chapters by preeminent reproductive biologists, this is a capital work. Ohno's law is described by Ohno the Lyon hypothesis, by Lyon Sinclair tells how he cloned the testis-determining gene and so on. Molecular Genetics of Sex Determination is authoritative, comprehensive, and current. It is prime reading for geneticists, developmental biologists, graduate students in these and related fields, clinical researchers, physicians, and medical students.

In this era of accelerated discovery and prolific output, Molecular Genetics of Sex Determination keeps readers abreast of this fields fast-moving biology. Its chapters were completed by experts in eacharea only months before publication. The text is organized into two parts. First, it reviews the basic biology of sex determination and summarizes ground-breaking work in mouse, marsupial, and Drosophila systems. Second, it covers current human genetics, clinical studies, and the syndromes of abnormal sex differentiation.

With chapters by preeminent reproductive biologists, this is a capital work. Ohno's law is described by Ohno the Lyon hypothesis, by Lyon Sinclair tells how he cloned the testis-determining gene and so on. Molecular Genetics of Sex Determination is authoritative, comprehensive, and current. It is prime reading for geneticists, developmental biologists, graduate students in these and related fields, clinical researchers, physicians, and medical students.


2.1 Sampling and DNA extraction

Peripheral whole blood (male international studbook #20612) and liver tissue (female international studbook #42652) were collected from the captive herd at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, USA. The male oryx represents approximately 15% of founders to the global population documented in the international studbook. Whole blood was collected into EDTA blood tubes (BD Vacutainer Blood Tube, Becton, Dickinson and Company) and stored frozen until analysis. Total genomic DNA was isolated and used to generate the de novo reference genome assembly (see below for details). Additional blood and tissue samples were obtained for whole genome resequencing from six individuals representing three of the main captive populations: the EEP (n = 2, international studbook numbers #35552 and #34412), the SSP (n = 2, international studbook numbers #33556 and #36948) and the EAD (n = 2, for further details see Table S1). EEP blood samples were collected by qualified veterinarians during routine health procedures and protocols were approved by Marwell Wildlife Ethics Committee. Total genomic DNA was extracted between one and five times using either the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Cat. No. 69504) or the QuickGene DNA Whole Blood or Tissue Kit (Kurabo Industries). Elutions were pooled and concentrated in an Eppendorf Concentrator Plus at 45°C and 250 x g until roughly 50 µl remained.

2.2 10X Genomics sequencing and assembly

Two technologies were employed to sequence and assemble the SHO reference genome: 10X Genomics linked-read sequencing and chromosome conformation capture (Hi-C). For the 10X assembly, high molecular weight genomic DNA was isolated from

2 ml of whole blood from individual #20612 using Nanobind magnetic discs (Circulomics, Inc.). Genomic DNA concentration and purity were assessed with a Qubit 2.0 Fluorometer (ThermoFisher Scientific) and NanoDrop 2000 spectrophotometer (ThermoFisher Scientific). Capillary electrophoresis was carried out using a Fragment Analyzer (Agilent Technologies, CA, USA) to ensure that the isolated DNA had a minimum molecule length of 40 kb. Genomic DNA was diluted to

1.2 ng/µl and libraries were prepared using Chromium Genome Reagents Kits Version 2 and the 10X Genomics Chromium Controller instrument fitted with a microfluidic Genome Chip (10X Genomics). DNA molecules were captured in Gel Bead-In-Emulsions (GEMs) and nick-translated using bead-specific unique molecular identifiers (UMIs Chromium Genome Reagents Kit Version 2 User Guide). Size and concentration were determined using an Agilent 2100 Bioanalyzer DNA 1000 chip (Agilent Technologies). Libraries were then sequenced on an Illumina NovaSeq 6000 System following the manufacturer's protocols (Illumina, CA, USA) to produce >60X read depth using paired-end 150 bp reads. The reads were assembled into phased pseudohaplotypes using Supernova Version 2.0 (10X Genomics). This assembly will hereafter be referred to as the 10X assembly.

2.3 Hi-C sequencing and scaffolding

Using liver tissue from individual #42652, an in situ Hi-C library was prepared as previously described (Rao et al., 2014 ). The Hi-C library was sequenced on a HiSeq X Platform (Illumina) to a coverage of 60X. The Hi-C data were aligned to the 10X Genomics linked-read assembly using Juicer (Durand et al., 2016 ). Hi-C genome assembly was then performed using the 3D-DNA pipeline (Dudchenko et al., 2017 ) and the output was reviewed using juicebox assembly tools (Dudchenko et al., 2018 ). No bases were changed, added or removed during Hi-C scaffolding and therefore the genome assembly represents a single individual: #201612. Where alternative haplotypes were present due to allelic variation in the 10X assembly, hereafter referred to as under-collapsed heterozygosity, one variant was chosen at random and incorporated into the 29 chromosome-length scaffolds. Alternative haplotypes are reported as unanchored sequences so that the primary scaffolds were free from duplication. This assembly will hereafter be referred to as the 10X + HiC assembly.

2.4 Genome annotation and completeness

To identify and annotate interspersed repeat regions we used repeatmasker v4.0.7 to screen the 10X assembly against both the Dfam_consensus (release 20170127: Wheeler et al., 2013 ) and RepBase Update (release 20170127: Bao, Kojima, & Kohany, 2015 ) repeat databases. Sequence comparisons were performed using rmblastn v2.6.0 + with the -species option set to mammal. We next predicted protein-coding genes with augustus version 3.3.2 (Stanke et al., 2006 ) using the gene model trained in humans. Prediction of untranslated regions of the genes was disabled. Functional annotation of the predicted genes was then performed using eggnog-mapper v2.0 (Huerta-Cepas et al., 2017 ) against the eggNOG 5.0 orthology database (Huerta-Cepas et al., 2019 ). The alignment algorithm diamond was specified as the search tool (Buchfink, Xie, & Huson, 2015 ). A final set of protein-coding genes was obtained by filtering the genes predicted by augustus for those with gene names assigned by eggnog-mapper . We then transferred the repeat and gene annotations from the 10X assembly to the 10X + HiC assembly using custom scripts. Genome completeness of both the 10X and 10X + Hi-C assemblies was assessed using busco v2 with 4,104 genes from the Mammalia odb9 database (Simão, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015 ) and the gVolante web interface (Nishimura, Hara, & Kuraku, 2017 ).

2.5 Genome synteny

We aligned the SHO chromosomes from the 10X + HiC assembly to the cattle genome (Bos taurus assembly version 3.1.1, GenBank accession number GCA_000003055.5: Zimin et al., 2009 ) using last v746 (Kiełbasa, Wan, Sato, Horton, & Frith, 2011 ). The cattle assembly was first prepared for alignment using the command lastdb. Next, lastal and last-split commands in combination with parallel-fastq were used to align the SHO chromosomes to the cattle assembly. Coordinates for all alignments were extracted from the resulting multiple alignment format file and visualised using the R package rcircos v1.2.0 (Zhang, Meltzer, & Davis, 2013 ) and the JavaScript library D3.js.

2.6 Whole-genome resequencing and alignment

Library construction was carried out for whole genome resequencing of the six focal individuals using the Illumina TruSeq Nano High Throughout library preparation kit (Illumina). Paired-end sequencing was performed on an Illumina HiSeq X Ten platform at a depth of coverage of 15X. Sequencing reads were mapped to the SHO 10X + HiC chromosomes using bwa mem v0.7.17 (Li, 2013 ) with the default parameters. Any unmapped reads were removed from the alignment files using samtools v1.9 (Li, 2011 ). We then used picard tools to sort each bam file, add read groups and mark and remove duplicate reads. This resulted in a set of six filtered alignments, one for each of the resequenced individuals.

2.7 SNP calling and filtering

HaplotypeCaller in gatk v3.8 (Van der Auwera et al., 2013 ) was first used to call variants separately for each filtered bam file. GenomicVCF files for each individual were then used as input to GenotypeGVCFs for joint genotyping. The resulting SNP data set was filtered to include only biallelic SNPs using bcftools v1.9 (Li, 2011 ). We then applied a set of filters to obtain a high-quality data set of variants using vcftools v0.1.13 (Danecek et al., 2011 ). First, loci with Phred-scaled quality scores of <50 and genotypes with a depth of coverage <5 or >38 (twice the mean sequence read depth) were removed. Second, loci with any missing data were discarded. Finally, we removed loci with a minor allele frequency of less than 0.16 to ensure the minor allele was observed at least twice.

2.8 Mitochondrial genome assembly

Sequencing reads for the six resequenced individuals were mapped using bwa mem v0.7.17 (Li, 2013 ) to a published mitochondrial reference genome of an SHO originating from the Paris Zoological Park (NCBI accession number: JN632677, Hassanin et al., 2012 ). Alignment files were filtered to contain only reads that mapped with their proper pair. Variants were called using samtools mpileup and bcftools (Li, 2011 ) call commands and filtered to include only those with Phred quality scores over 200 using vcftools (Danecek et al., 2011 ). The resulting VCF file was manually checked and sites where the called allele was supported by fewer reads than the alternative allele were corrected. Consensus sequences for each individual were extracted using the bcftools consensus command. We next used geneious prime v2019.2.1 ( to annotate the mitochondrial consensus sequences and extract the cytochrome b, 16S and control region from each individual. Sequence similarity and haplotype frequencies were calculated using the R package pegas (Paradis, 2010 ). To place the mitochondrial data into a broader geographic context, the six control region sequences were aligned to 43 previously described haplotypes (NCBI accession numbers DQ159406–DQ159445 and MN689133–MN689138, Iyengar et al., 2007 Ogden et al., 2020 ) using Geneious Prime . A median-joining haplotype network was generated using popart v1.7 (Leigh & Bryant, 2015 ).

2.9 Genetic diversity

We assessed genetic diversity of SHO using two genome-wide measures. First, we used vcftools to estimate nucleotide diversity (π) across all six resequenced individuals based on high-quality variants called by gatk . Second, we estimated individual genome-wide heterozygosity as the proportion of polymorphic sites over the total number of sites using the site-frequency spectrum of each individual sample. For this, filtered bam files were used as input to estimate the observed folded site-frequency spectrum (SFS) using the -doSaf and -realSFS functions in the program angsd (Korneliussen, Albrechtsen, & Nielsen, 2014 ). We excluded the X chromosome and skipped any bases and reads with quality scores below 20. Genome-wide heterozygosity was then calculated as the second value of the SFS (number of heterozygous genotypes) over the total number of sites, for each chromosome separately. To compare the level of diversity in SHO with other species, we visualised genome-wide heterozygosity values for a number of mammalian species collected from the literature (Table S2) against census population size and International Union for Conservation of Nature (IUCN) status. Finally, assuming a per site/per generation mutation rate (μ) of 1.1 × 10 –08 , we used our estimate of nucleotide diversity (π) as a proxy for θ to infer long-term Ne, given that θ = 4Ne μ.

2.10 Demographic history

To reconstruct the historical demography of the SHO, we used PSMC. (Li & Durbin, 2011 ). This method uses the presence of heterozygous sites across a diploid genome to infer the time to the most recent common ancestor between two alleles. The inverse distribution of coalescence events is referred to as the instantaneous inverse coalescence rate (IICR) and for an unstructured and panmictic population, can be interpreted as the trajectory of Ne over time (Chikhi et al., 2018 ). To estimate the PSMC trajectory, we first generated consensus sequences for all autosomes in each of the filtered bam files from the six resequenced individuals using samtools mpileup, bcftools call and vcf2fq. Sites with a root-mean-squared mapping quality less than 30, and a depth of coverage below four or above 40, were masked as missing data. PSMC inference was then carried out using the default input parameters to generate a distribution of IICR through time for each individual. To generate a measure of uncertainty around our PSMC estimates, we ran 100 bootstrap replicates per individual. For this, consensus sequences were first split into 47 nonoverlapping segments using the splitfa function in PSMC. We then randomly sampled from these, 100 times with replacement, and re-ran PSMC on each of the bootstrapped data sets.

To determine the extent to which the PSMC trajectory could vary, we scaled the coalescence rates and time intervals to population size and years based on three categories of neutral mutation rate and generation time. Our middle scaling values corresponded to a mutation rate of 1.1 × 10 –08 and a generation time of 6.2 years. These were based on the per site/per generation mutation rate recently estimated for gemsbok (Oryx gazella, Chen et al., 2019 ) and the generation time reported in the International Studbook for the SHO (Gilbert, 2019 ) and are therefore considered the most appropriate estimates. Low scaling values corresponded to a mutation rate of 0.8 × 10 –08 and a generation time of three, and high scaling values corresponded to a mutation rate of 1.3 × 10 –08 and a generation time of 10. Finally, to test the reliability of our IICR trajectories, we simulated sequence data under the inferred PSMC models and compared estimates of genome-wide heterozygosity with empirical values (Beichman, Phung, & Lohmueller, 2017 ). To do this, we used the program macs (Chen, Marjoram, & Wall, 2009 ) to simulate 1,000 × 25 Mb sequence blocks under the full demographic model of each individual, assuming a recombination rate of 1.0 × 10 –08 base pair per generation and a mutation rate of 1.1 × 10 –08 . Simulated heterozygosity was then calculated as the number of segregating sites over the total number of sites for each 25 Mb sequence. Empirical heterozygosity was calculated for each individual as the number of variable sites over the total number of sites in 25 Mb nonoverlapping sliding windows along the genome. This was carried out using the filtered SNP data set and the R package windowscanr .

Culex species

C. pipiens is common in temperate regions and is subdivided into two subspecies, Culex pipiens pipiens (Europe and North and South Africa) and Culex pipiens pallens (East Asia) (Dumas et al., 2016 ). In addition, two recognized forms, 'pipiens' and 'molestus', also appeared in C. pipiens pipiens in the Northern Hemisphere (Dumas et al., 2016 ). A second species, C. quinquefasciatus, is found across the tropics and the lower latitudes of temperate regions (Dumas et al., 2016 ). Rasgon et al. reported that a cryptic species is present within the C. pipiens complex in South Africa (Rasgon and Scott, 2003 Rasgon et al. 2006 Dumas et al., 2016 ). The authors observed the presence of Wolbachia-uninfected C. pipiens specimens in several breeding sites in Europe and North Africa. Using a multilocus typing scheme, they further confirmed that these uninfected specimens unambiguously belonged to the C. pipiens complex. On the basis of ace-2 DNA sequences, they were included within the C. pipiens pipiens clade (Dumas et al., 2016 ). Remarkably, novel mitochondrial(mt)DNA haplotypes were found in samples from Europe and North Africa that were related, but different to the mtDNA haplotypes found in Wolbachia-infected C. pipiens complex members (Dumas et al., 2016 ). This genetic pattern demonstrated that uninfected specimens are not a result of imperfect maternal transmission from Wolbachia-infected specimens, but rather belong to a specific lineage (Dumas et al., 2016 ). Compelling evidence suggested that specimens of the cryptic species do not readily hybridize with Wolbachia-infected C. pipiens and C. quinquefasciatus specimens. However, Dumas et al. suggest that a Wolbachia-uninfected population of C. pipiens was present in South Africa and was recently proposed as a cryptic species (Dumas et al., 2016 ).

Key vectors of Japanese encephalitis are Cx. tritaeniorhynchu, Cx. vishnui, Cx. pseudovishnui, Cx. gelidus, Cx. fuscocephala, Cx. quinquefasciatus, Culex pipiens pallens (Coquillett), Culex bitaeniorhynchus Giles and Culex annulirostris Skuse. Cx. tritaeniorhynchus and Cx. vishnui were considered under the Cx. vishnui subgroup. Karthikaa et al. using DNA barcoding analysis of Culex fuscocephala, Culex gelidus, Culex tritaeniorhynchus, Culex pseudovishnui and Culex vishnui, showed that C. tritaeniorhynchus exhibited the highest variation in all the ranges. C. tritaeniorhynchus exhibited high numbers of polymorphic sites and mutations, suggesting high nucleotide diversity. By contrast, the sister species C. vishnui and C. pseudovishnui showed a moderate rise. The results suggested that one or more new cryptic subspecies may exist in Culex mosquitoes (Karthika et al., 2018 ).

18.2 | Mendel’s Principles of Inheritance

By the end of this section, you will be able to:

  • Describe the three principles of inheritance.
  • Explain the relationship between phenotype and genotype.
  • Develop a Punnett square to calculate the expected proportions of genotypes and phenotypes in a monohybrid cross.
  • Explain the purpose and methods of a test cross.
  • Draw and interpret a pedigree.

Mendel generalized the results of his pea-plant experiments into three principles that describe the basis of inheritance in diploid organisms. They are: the principle of segregation, the principle of dominance, and the principle of independent assortment. Together, these principles summarize the basics of classical, or Mendelian, genetics.


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Male Gametes: A male gamete is the male reproductive cell, which unites with the female gamete to produce the zygote.

Female Gametes: A female gamete is the female reproductive cell, which unites with the male gamete to produce the zygote.

Formed by

Male Gametes: Male gametes are produced by spermatogenesis.

Female Gametes: Female gamates are produced by oogenesis.

In Seed-bearing Plants

Male Gametes: Male gametes can be found inside the pollen grains of the seed-bearing plants.

Female Gametes: Female gametes can be found inside the ovary of seed-bearing plants.

In Animals

Male Gametes: Male gametes are produced in the testes.

Female Gametes: Female gametes are produced in the ovaries.

Male Gametes: Male gametes are smaller than female gametes.

Female Gametes: Female gametes of humans are 100 000 times larger than male gametes of humans.


Male Gametes: Male gametes are corn-shaped cells.

Female Gametes: Female gametes are spherical-shaped cells.

Size of Cytoplasm

Male Gametes: Male gametes contains a small cytoplasm. Therefore, male gametes have a lower weight, enabling the swimming.

Female Gametes: Female gametes contains a larger cytoplasm to nourish the embryo.


Male Gametes: Male gametes are mobile.

Female Gametes: female gametes are immobile.


Male Gametes: Male gametes contain a tail or flagella, which helps in swimming.

Female Gametes: Female gametes do not contain tails or flagella.


Male Gametes: Male gametes are produced in large numbers to ensure a successful fertilization.

Female Gametes: Only one female gamete is released per month in humans.

Zona Pellucida

Male Gametes: male gametes lack a zona pellucida.

Female Gametes: Female gametes comprise a jelly coat called zona pellucida to which the male gametes bind to.


Male Gametes: Male gametes comprise an acrosome, which contains the enzymes to degrade the layers surrounding the female gamete.

Female Gametes: Female gametes lack acrosomes.

Mitochondrial Level

Male Gametes: Male gametes comprise a lot of mitochondria to produce energy to swim.

Female Gametes: Female gametes comprise a few mitochondria since they are immobile.


Male and female gametes are the two types of haploid reproductive cells produced by plants and animals. Male gametes are called sperms. Female gametes are called egg cells. Both male and female gametes are formed by the meiosis. Therefore, both types of gametes comprise a single set of chromosomes of the species. One male gamete unites with one female gamete to form the zygote, which develops into a new organism.


1. “Sperm.” Encyclopædia Britannica. Encyclopædia Britannica, inc., n.d. Web. Available here. 14 Aug. 2017.
2. Battista, Jeremy. “Male Gamete in Plants: Definition & Concept.” N.p., n.d. Web. Available here. 14 Aug. 2017.
3. Alberts, Bruce. “Eggs.” Molecular Biology of the Cell. 4th edition.U.S. National Library of Medicine, 01 Jan. 1970. Web. Available here. 14 Aug. 2017.

Image Courtesy:

1. “Image 1” (Public Domain) via Pixino
2. “Sperms (urine) – Spermler (idrar) – 02” By Doruk Salancı – Own work (CC BY-SA 3.0) via Commons Wikimedia
3. “Rose hip 02 ies” By Frank Vincentz – Own work (CC BY-SA 3.0) via Commons Wikimedia
4. “Gray3” Von Henry Vandyke Carter – Henry Gray (1918) Anatomy of the Human Body (See “Buch” section below) Gray’s Anatomy, Tafel 3 (Gemeinfrei) via Commons Wikimedia

About the Author: Lakna

Lakna, a graduate in Molecular Biology & Biochemistry, is a Molecular Biologist and has a broad and keen interest in the discovery of nature related things


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