Classification of the male internal reproductive organs in various insects.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"3040",leadTitle:null,fullTitle:"Transfusion Medicine Made Easy For Students of Biomedical Science, Allied Medical Sciences and Medicine",title:"Transfusion Medicine Made Easy For Students of Biomedical Science, Allied Medical Sciences and Medicine",subtitle:null,reviewType:"peer-reviewed",abstract:"This basic text is intended to optimise the training and practice of transfusion medicine in developing countries particularly in sub- Saharan Africa. \nIt is aimed at improving the knowledge and skills of allied medical and medical students, and other healthcare professionals involved in blood transfusion, empowering them to offer the best possible blood transfusion services to their patients. \nThis book is suitable not only for allied medical and medical students preparing for their examination in transfusion medicine but also for postgraduates preparing for examination in general medicine, haematology and transfusion science.\nThe chapters have been presented in an annotated and easy to understand format.",isbn:null,printIsbn:"978-953-51-0523-7",pdfIsbn:"978-953-51-6968-0",doi:"10.5772/3342",price:139,priceEur:155,priceUsd:179,slug:"transfusion-medicine-made-easy-for-students-of-biomedical-science-allied-medical-sciences-and-medicine",numberOfPages:314,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"c0bebb1b09be83d922e4e6fc7c1086fe",bookSignature:"Osaro Erhabor and Teddy Charles Adias",publishedDate:"May 31st 2012",coverURL:"https://cdn.intechopen.com/books/images_new/3040.jpg",numberOfDownloads:1575,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfCrossrefCitationsByBook:null,numberOfDimensionsCitations:0,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 20th 2012",dateEndSecondStepPublish:"February 10th 2012",dateEndThirdStepPublish:"May 16th 2012",dateEndFourthStepPublish:"August 14th 2012",dateEndFifthStepPublish:"September 13th 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Authored by",kuFlag:!1,featuredMarkup:null,editors:[{id:"35140",title:"Dr.",name:"Osaro",middleName:null,surname:"Erhabor",slug:"osaro-erhabor",fullName:"Osaro Erhabor",profilePictureURL:"https://mts.intechopen.com/storage/users/35140/images/system/35140.png",biography:"Professor Erhabor Osaro is a Chartered Scientist and fellow of the Institute of Biomedical Science (FIBMS), British Blood Transfusion Society (BBTS), Medical Laboratory Science Council (FMLSCN) and West African Postgraduate College of Medical Laboratory Science (FWAPCMLS). 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The morphology, structure and size of organs have functional significance. Insects are the most abundant of all organisms in terms of species number, resulting in female and male reproductive organs being highly diverse in their structure. While the general pattern of spermatogenesis in insects is basically similar to that in mammals, the morphology, structure, and size of sperm in insects are highly variable [1, 2].
Because available resources are usually limited, the number of sperm produced should be inversely proportional to their size [3]. Although smaller testes do not necessarily produce many sperm, sperm size is closely related to testis size. Among
In general, sperm size in animals including insects is not proportional to body size.
Sperm produced in the testes are usually stored in the seminal vesicles, if they are present. In insects, when present within the male reproductive organs, sperm are either not motile or their motility is more suppressive than when they are retained in the female reproductive organs. Therefore, males need to both store sperm in the seminal vesicles, and limit the energy costs associated with those sperm until mating. Recent studies have revealed that sperm age or die in the spermatheca [11, 12] or male reproductive organs [13], and it is interesting to see how aging affects sperm quality and survival in the seminal vesicles because the morphology of the seminal vesicles may be associated with sperm aging, as mentioned below. Furthermore, a recent molecular biology study indicates that increased expression of seminal fluid protein incorporated in spermatophore genes is correlated with increased sperm viability in the ejaculates [14].
During mating, male insects pass a spermatophore or bolus of seminal fluid to females. In many species, a spermatophore is produced by male reproductive accessory glands, whereas in lepidopteran insects, spermatophores are produced mainly from parts of the simplex (ejaculatory duct). In a nymphalid butterfly,
Testes function is to produce sperm and in turn intake or excrete the various substances including nutrients and hormones for spermatogenesis. In species lacking seminal vesicles, mature sperm are stored in the testis near the vas deferens. The shape of the testis is circular, oval or elongated, perhaps corresponding to sperm length, probably because sperm develop greatly during spermiogenesis (from the spermatid to the sperm) [5]. The testes are usually paired and the numbers vary widely from species to species. The color of the testes also varies greatly depending on the species; for example, many lepidopteran insects have white, yellow, red, or purple testes. Although the pigments that are no longer needed in the body may be deposited in their testes, their function and evolutionary significance have not been clarified.
In
Testis development including spermatogenesis is affected by developmental stages, temperatures, nutritional conditions, and hormones [22, 23]. In the yellow dung fly
Sperm polymorphism is apparent in the various insect orders [1, 2]. Generally, one sperm is long whereas the other is short; giant sperm have been observed in some
The production and storage of sperm can be a lifelong event for males. There are four types of sperm migration within the male reproductive organs. First, sperm formed in the testis move to the vas deferens or the seminal vesicle. In lepidopteran insects, sperm migration occurs from the testis to the duplex via the vas deferens, with a circadian rhythm. Second, sperm move from the seminal vesicle through the ejaculatory duct to the female reproductive tract during mating. Third, there is a process called sperm reflux. In
Sperm migration from the testis to the duplex via the vas deferens has been well studied in lepidopteran insects. Conversely, many dipteran insects do not have the seminal vesicles, which are sometimes called sperm reservoirs, however the distal end of the testis stores sperm. Their sperm are functionally complete. However, in lepidopteran insects, sperm are not mature in the testis and change morphologically when passing through the vas efferens from the testis to the vas deferens. It has been demonstrated that eupyrene sperm migration occurs in a circadian rhythm even in cases of in vitro culture of the testis- vas deferens-duplex complex [43]. It has also been reported that there is a circadian rhythm in the secretory activity of the upper vas deferens [44]. Although the reasons for sperm migration in lepidopteran insects being rhythmical are unclear, sperm migration is a time-consuming and energy-intensive process and thus it is reasonable to expect to be time managed.
Sperm formed in the testes are stored in the seminal vesicles until mating. At the time of mating, sperm are ejaculated into the female along with spermatophore. Interestingly, when
In
Although the function of the seminal vesicles is to store and protect sperm, it is known that organs other than the seminal vesicles also store sperm. In lepidopteran insects, several species have a dilated part, also called a secondary seminal vesicle, between the lower vas deferens and the duplex [46, 51, 52, 53]. Even if all the sperm in the duplex are ejaculated during mating, these stored sperm can be replenished immediately from the secondary seminal vesicle. In addition, even species with a large swelling in the middle vas deferens may store sperm there to some extent temporarily [54], and may transfer sperm to the duplex after mating to prepare for subsequent matings. In species with these functions, sperm reflux would not be present.
In insects, a spermatophore is passed into the female reproductive organs during mating directly or indirectly. The system of insemination varies greatly from species to species, the main cause of which is species diversity and complexity of the female reproductive organs. Probably, the female complex of reproductive organs have co-evolved with the male complex of reproductive organs.
The timing of transfer of sperm to females during mating varies greatly from species to species. Copula duration also varies widely, from minutes in parasitoid wasp and mosquitoes [55] to days in stick insects [17], but in the latter the ejaculation duration could be shorter than the copula one. The post-mating guard guarantees female oviposition behavior and ovarian development. It has been suggested that sperm progression to the spermatheca is supported by the activity of the muscles and nerves of the male and female reproductive organs in addition to sperm motility [56].
Fertilization efficiency also affects the number of ejaculations. As is well known, ants and wasps can efficiently fertilize many eggs with a small amount of sperm. For example, in a parasitoid wasp
The arrangement of male internal reproductive organs of various species is listed in Table 1. Figure 1 shows the positional relationship between reproductive organs. Alphabetical order does not represent the phylogenetic relationship between species. Most probably other formats of organ positioning are yet to be discovered. Some insects lack the accessory glands. Of particular importance is the positional relationship between the testes, vas deferens, seminal vesicles, accessory glands, and ejaculatory duct, because the placement of these organs is important for ejaculation.
Order | Family | Scientific name | Type | References |
---|---|---|---|---|
Thysanura | Lepismatidae | F | [59] | |
Ephemeroptera | Leptophlebiidae | H | [60] | |
Plecoptera | Taeniopterygidae | B | [61] | |
Perlidae | G | [62] | ||
Dermaptera | Labiduridae | H | [63] | |
Anisolabididae | H | [64] | ||
Isoptera | Termitidae | H | [27] | |
Blattodea | Blattidae | E | [65] | |
Mantodea | Liturgusidae | E | [66] | |
Orthoptera | Caelifera | E | [67] | |
Tetrigidae | E | [68] | ||
Pyrgomorphidae | E | [69] | ||
Psocoptera | Psyllipsocidae | H | [70] | |
Psoqvillidae | H | [71] | ||
Homoptera | Delphacidae | B | [72] | |
Cicadellidae | B | [73] | ||
B | [74] | |||
Cicadoidea | B | [75] | ||
Hemiptera | Reduviidae | B | [76] | |
Pentatomidae Aphididae | B C | [77] [78] | ||
Hymenoptera | Colletidae | F | [79] | |
Andrenidae | F | [79] | ||
Megachilidae | B | [79] | ||
Apidae | F | [79] | ||
Eulophidae | B | [80] | ||
Formicidae | F | [81] | ||
Neuroptera | Chrysopidae | B | [82] | |
Ithonidae | B | [83] | ||
Nemopteridae | B | [84] | ||
Megaloptera | Corydalidae | B | [85] | |
Coleoptera | Rhysodidae | B | [86] | |
B | [87] | |||
Carabidae | C | [88] | ||
C | [88] | |||
C | [88] | |||
Tenebrionidae | B | [89] | ||
Trogossitidae | B | [90] | ||
B | [90] | |||
Chrysomelidae | B | [91] | ||
B | [92] | |||
Elateridae | B | [90] | ||
Scarabaeidae | B | [90] | ||
Ciidae | B | [93] | ||
Curculionidae | B | [94] | ||
B | [95] | |||
Bruchidae | B | [96] | ||
Brentidae | D | [97] | ||
Siphonaptera | Pulicidae | C | [98] | |
Diptera | Culicidae | B | [99] | |
Psychodidae | B H | [100] [101] | ||
Tephritidae | C | [102] | ||
C | [103] | |||
Tachinidae | B | [104] | ||
Glossindae | C | [105] | ||
Drosophilidae | B | [106] | ||
Lepidoptera | Crytophasidae | A | [107] | |
Olethreutidae | A | [51] | ||
Pyralidae | A | [108] | ||
Tortricidae | A | [53] | ||
Noctuidae | A | [109] | ||
A | [110] | |||
A | [111] | |||
A | [112] | |||
A | [113] | |||
A | [114] | |||
Geometridae | A | [115] | ||
Bombycidae | A | [116] | ||
Saturnidae | A | [117] |
Classification of the male internal reproductive organs in various insects.
Simple illustration of each type of configuration of male internal reproductive organs. TS, VD, SV, AG, and ED indicate testis, vas deferens, seminal vesicle, accessory gland, and ejaculatory duct, respectively. Arrow indicates the direction of ejaculates (semen or sperm).
In type A, ejaculates reach the seminal vesicle or duplex form the testis via the vas deferens, to which the accessory glands are open. Thus far, all lepidopteran insects studied represent type A. As the accessory gland material passes through the duplex during mating, it flushes out all sperm and semen in the duplex. This relies on constant daily sperm replenishment form the testis to support multiple matings. Type B is similar to type A, but the seminal vesicles differ in that they connect to the ejaculatory duct along with the accessory glands. Type B is common in the homopteran, coleopteran and dipteran insects. In these insects, sperm production is typically completed in the testes, and in contrast to the Lepidoptera, the sperm do not require the vas deferens to mature. Type C lacks the seminal vesicles, but is basically the same as type B and more common in dipteran insects. Similarly, mature sperm can immediately be ejaculated from the testes during mating. Type C is an effective arrangement for multiple matings by adult males. Type D is found in some weevil species, where both the seminal vesicles and accessory glands are connected to the ejaculatory duct via the vas deferens. Type E is found in cockroaches and orthopteran insects, and through the testes the vas deferens leads to the tubular accessory glands and tubular seminal vesicles, both of which lead to the ejaculatory duct. In Type F, the testes open into the vas deferens or seminal vesicles and then the ejaculatory duct. Some hymenopteran insects belong to type F. Types G and H represent those insects that lack the accessory glands.
The position, as well as the morphology, structure and function of the male internal reproductive organs, is presumably adaptive, probably for successful reproduction. The complex folds of these organs, which are surrounded by the tracheae and fat bodies, suggests that they require large amounts of oxygen and nutrients. However, until now, research on the arrangement of the reproductive organs has not been prioritized. We hope that this chapter will serve as an opportunity and foundation for studying not only the morphology, structure and function of reproductive organs, but also their positional relationships.
This work is supported by USDA-ARS Research Project# 6066-22000-091-00D - Ecologically Sustainable Approaches to Insect Resistance Management in Bt Cotton.
Family planning is indispensable in facilitating the prosperity and autonomy of women, their families, and their communities. Contraceptive choices, maternal and newborn health care, sexually transmitted infections, and sexual health are the main concepts of reproductive health [1]. The states agreed in 2001 that among the Millennium Development Goals (MDGs), target 5b was called for by 2015 for universal access to reproductive health. Global contraceptive prevalence is 64% (41% in low-income countries) and the global unmet need for family planning is 12% (22% in low-income countries) as reported at the end of the MDGs period. Sustainable Development Goals (SDGs) targets 3.7 and 5.6 call for universal access to sexual and reproductive health care services and sexual and sexual and reproductive health and reproductive rights, respectively [2, 3].
It has been calculated that maternal mortality has been reduced globally by 30% by the increase in contraceptive use [4]. Unintended pregnancies, pregnancy spacing, and reducing high-risk pregnancies are the consequences of contraceptive use [5, 6, 7]. Current studies show that every year, contraceptive use could reduce nearly 230 million births by stopping unwanted pregnancies [8]. As a result, the use of contraception improves the health of women and their children [6, 9]. However, the prevalence of contraceptive practice varied between 11.3% and 72.1% in different countries, namely Mozambique, 11.3%, Ghana, 21.5%, Bangladesh (modern method), 54.0%, and Sweden, 72.1% [9, 10, 11, 12].
Previous research has shown that various variables are significantly associated with contraceptive use, such as maternal age, maternal and husband’s educational level, wealth status, maternal age at first marriage, and so on [11, 13]. Through the promotion of family planning, appropriate diagnostics, and interventions, the prevalence of contraceptive use is increasing. Popular statistical methods (binary logistic regression) have been applied to determine important indicators of contraceptive use among women. But the main goal is to predict contraceptive practice among women aged between 15 and 49 in Bangladesh. Machine learning is a scientific method that can build models for prediction purposes. According to the research, traditional statistical procedures were shown to be ineffective in this form of modeling. Machine learning approaches have long been shown to be more successful and promising in handling a variety of complicated and nonlinear issues [14, 15, 16].
However, not many studies have explored machine learning methods to develop predictive models for studying contraceptive methods. Therefore, various well-known machine learning algorithms were applied to predict contraceptive practices among 15–49-year-old women in Bangladesh in this study. Before prediction, we applied a Hierarchical Logistic Regression classifier in machine learning approaches that were used to select potential risk factors associated with the contraceptive practice of women. To our best knowledge, the originality of the study is that it is almost new in the field of machine learning classifier approach in the contraceptive practice of Bangladesh context, for the first time using such methods, which will assist future data scientists.
In this study, the necessary information has been extracted from a representative secondary national data set, the Bangladesh Demographic and Health Survey (BDHS), 2014. This survey was carried out through a joint effort of the National Institute of Population Research and Training (Bangladesh), Mitra Associates (Bangladesh), and ICF International (USA).
The entire list of enumeration areas (EAs) that encompasses the entire country, provided by the Bangladesh Bureau of Statistics (BBS) for the 2011 population and housing census of the People’s Republic of Bangladesh, served as the sampling frame for the 2014 BDHS. An EA was a geographical zone with an average of 120 households. The survey uses a two-stage stratified sampling process that includes information on the EA region, residence (urban or rural), and the number of residential households counted. Viable interviews were conducted in 98% of the selected households (out of 17,989 total). For this study, 17,863 ever-married women aged 15–49 years were included in the final analysis. Note that to learn more about the detailed sampling procedure of the 2014 BDHS, see the final published report of the survey [17].
Since the main purpose of this study was to predict contraception practice among women aged 15–49 years, the response variable was “current contraception use”, which was classified as “Yes or No”. If the respondent currently utilizes a contraceptive method, she falls into the “Yes” group, otherwise, she falls into the “No” group.
Besides the response variable, a set of 21 demographic and socioeconomic risk factors were included in the analysis, which was associated with contraceptive practice and considered predictor variables. Several studies found that demographic and socioeconomic characteristics such as current age, division, religion, residence, respondent’s working status, FP media exposure, age at first marriage, currently breastfeeding, wealth status, women’s education, husband’s education, child ever born, number of living children, ideal number of children, fertility preference, marital status, and decision making for using contraception are potential risk factors that determine contraception practice among women [10, 11, 18, 19, 20, 21, 22, 23, 24]. The list of independent variables and their measures are presented in Table 1.
No. | Variables | Measures |
---|---|---|
1 | Women current age (years) | 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49 |
2 | Division | Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, Sylhet |
3 | Religion | Islam, other |
4 | Sex of household head | Male, female |
5 | Residence | Urban, rural |
6 | Respondent working status | No, yes |
7 | Family planning (FP) media exposure | No, yes |
8 | Age at first marriage | <18, 18+ |
9 | Currently breastfeeding | No, yes |
10 | Currently amenorrhoeic | No, yes |
11 | Currently abstaining | No, yes |
12 | Wealth status | Poor, middle, rich |
13 | Women education | No education, primary education, secondary+ |
14 | Husband education | No education, primary education, secondary+ |
15 | Sexually transmitted infection (STI) | No, yes |
16 | Children ever born | 0–1, 2–3, 4+ |
17 | Number of living children | None, 1–2, 3+ |
18 | Ideal number of children | 0–1, 2–3, 4+ |
19 | Fertility preference | No more, have another, undecided, declared infecund, sterilized |
20 | Marital Status | Married, others |
21 | Decision making for using contraception | Respondent, others |
Description of independent variables.
The frequency distribution was used to describe the background characteristics of the respondents. In this study, we developed a Hierarchical Logistic Regression classifier in machine learning approaches that were used to select potential risk factors related to the contraceptive practice of women in Bangladesh by using the largest value of AUC (
Flow diagram for hierarchical logistic regression classifier in the machine learning process.
To meet the objective of the study, we fitted numerous numbers of model where the full model is denoted by
After selecting the final model, we applied the 7 most popular machine learning classifiers to predict contraceptive practice among ever-married women aged 15–49 in Bangladesh. In this study, we used seven different popular ML algorithms (Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), Least Absolute Shrinkage and Selection Operation (LASSO), Classification Trees (CT), AdaBoost, and Neural Network (NN)). A detailed description of the algorithms used is available in the literature [27, 28, 29, 30, 31, 32].
The Statistical Package for Social Science (SPSS) version 25 and R version 4.0.0 software were used for data management and analysis.
Data from ever-married women aged 15–49 was used in this study. Only ever-married women aged 15–49 was considered for the final analysis based on this criterion. Then, apply data preparation methods; for example, first find out missing data from the overall dataset. It is well known that the main drawbacks of missing information in a dataset are the reduced statistical power (because it reduces the number of samples n, the estimates will have larger standard errors). The main disadvantages of missing data in a dataset are statistical power reductions, which are well-known (because it reduces the number of samples n, the estimates will have larger standard errors). There are numerous imputation methods for imputing missing values nowadays, including direct deletion, mode imputation, hot-deck imputation, and so on [33]. A lower threshold of 5% missingness has been suggested in the literature [34]. We utilized the direct deletion method because this study had a low rate of missing values, which means we removed all missing values from the data set and conducted the analysis using the entire data set. The next step after missing value processing is to normalize/standardize the variables, which is useful when the data distribution is unknown. As a result, normalization is not required for any machine learning approach, especially in categorical data. Finally, all machine learning classifiers included in this study were performed on 70% of the respondents in each group (training data set,
Flow chart of the development of the seven machine learning classifiers.
We used the following criteria to evaluate the ML algorithms’ performance: confusion matrix, receiver operating characteristic (ROC), and the area under that curve (AUC). Generally, a confusion matrix has four possible prediction outcomes, such as TR = true positives, TN = true negatives, FP = false positives, and FN = false negatives. Several performance measures, including accuracy, precision, recall, and the F1 score, are usually calculated using these four potential outcomes to assess the classifier. The ROC curves have been calculated by utilizing the predicted outcomes as well as the true outcomes. To examine the ML algorithms’ discriminating powers, the AUC of the ROC has been averaged for the test data sets [35]. Theoretically, the AUC should be between 0 and 1, with 1 being the most extreme value for an ideal classifier. Since the usual lower bound for random classification is 0.5, an AUC greater than 0.5 has at least some capacity to separate between cases and non-cases [36]. In addition to these measures, we also used Cohen’s kappa statistic, which is a better measure to examine the agreement between two raters. It is calculated by utilizing the predicted and the actual classifications in a data set. The value of Cohen’s kappa statistic is
Table 2 shows the percentage distribution of women according to the selected socio-demographic characteristics of Bangladesh. The majority of women (19%) are between the ages of 25 and 29. The majority of them (35%) are from the Dhaka division, Muslims (90%), living in male-headed households (89%), and in the rural areas (72%). In terms of working status, slightly more than two-thirds (67%) of women are not currently involved in any kind of income-generating activities, and 80% of them do not have any media exposure. The majority of women (77% of them) married before their 18th birthday, and 79% of them were not breastfeeding their children at the time of the survey. The findings also show that around 96 to 97% of women are not amenorrheic (96%) or abstaining (97%). In terms of wealth status, 42% of the women were from rich families. Approximately half of the women (46%) had secondary or higher education. The majority of the husbands (44%) had a secondary or higher level of education. The number of women who knew about sexually transmitted infections (STIs) was found to be 67%. The majority of women (46%) have had 2–3 children, while 53% have 1–2 living children. The ideal number of children was 2–3 (86%) and more than half (57%) of the women were not interested in having another child. The vast majority of women are currently married (94%), and only 9% can make the decision to use a contraception method on their own. Regarding contraception use, according to the 2014 BDHS, 58.9% of women used it.
Characteristics | Sample women | |
---|---|---|
No. | % | |
15–19 | 2029 | 11.4 |
20–24 | 3224 | 18.0 |
25–29 | 3390 | 19.0 |
30–34 | 3047 | 17.1 |
35–39 | 2315 | 13.0 |
40–44 | 2092 | 11.7 |
45–49 | 1766 | 9.9 |
Barisal | 1111 | 6.2 |
Chittagong | 3301 | 18.5 |
Dhaka | 6223 | 34.8 |
Khulna | 1838 | 10.3 |
Rajshahi | 2103 | 11.8 |
Rangpur | 2056 | 11.5 |
Sylhet | 1232 | 6.9 |
Islam | 16,096 | 90.1 |
Other | 1767 | 9.9 |
Male | 15,854 | 88.8 |
Female | 2009 | 11.2 |
Urban | 5047 | 28.3 |
Rural | 12,816 | 71.7 |
No | 11,947 | 66.9 |
Yes | 5912 | 33.1 |
No | 14,316 | 80.1 |
Yes | 3547 | 19.9 |
<18 | 13,657 | 76.5 |
18+ | 4206 | 23.5 |
No | 14,033 | 78.6 |
Yes | 3830 | 21.4 |
No | 17,054 | 95.5 |
Yes | 809 | 4.5 |
No | 17,341 | 97.1 |
Yes | 522 | 2.9 |
Poor | 6767 | 37.9 |
Middle | 3560 | 19.9 |
Rich | 7536 | 42.2 |
No education | 4455 | 24.9 |
Primary | 5209 | 29.2 |
Secondary + | 8199 | 45.9 |
No education | 5189 | 29.0 |
Primary | 4289 | 27.3 |
Secondary+ | 7795 | 43.6 |
No | 11,947 | 66.9 |
Yes | 5912 | 33.1 |
0–1 | 5670 | 31.7 |
2–3 | 8139 | 45.6 |
4+ | 4054 | 22.7 |
None | 1814 | 10.2 |
1–2 | 9478 | 53.1 |
3+ | 6571 | 36.8 |
0–1 | 1127 | 6.3 |
2–3 | 15,308 | 85.7 |
4+ | 1429 | 8.0 |
No more | 9555 | 56.7 |
Have another | 5293 | 31.4 |
Undecided | 462 | 2.7 |
Declared infecund | 561 | 3.3 |
Sterilized | 986 | 5.8 |
Married | 16,858 | 94.4 |
Others | 1005 | 5.6 |
Respondent | 1515 | 8.5 |
Others | 16,348 | 91.5.5 |
Using | 10,527 | 58.9 |
Not Using | 7336 | 41.1 |
Percentage distribution of ever- married women age between 15 and 49 by selected socio-demographic characteristics.
In the initial step of the analysis, we applied hierarchal logistic regression to select the final model. Here, each variable was considered as one model. We added a potential risk factor (variable) to the previous model that was considered a new model in this analysis (Table 3). For example, in the initial model
Model | Model |
---|---|
M1 = respondent age | M12 = M11 + wealth status |
M2 = M1 + division | M13 = M12 + women education |
M3 = M2 + religion | M14 = M13 + husband education |
M4 = M3 + Sex of household head | M15 = M14 + sexually transmitted infection (STI) |
M5 = M4 + residence | M16 = M15 + children ever born |
M6 = M5 + respondent working status | M17 = M16 + number of living children |
M7 = M6 + FP media exposure | M18 = M17 + ideal number of children |
M8 = M7 + age at first marriage | M19 = M18 + fertility preference |
M9 = M8 + currently breastfeeding | M20 = M19 + marital status |
M10 = M9 + currently amenorrhoeic | M21 = M20 + decision making for using contraception |
M11 = M10 + currently abstaining |
Create a model-based hierarchical approach.
All models were statistically significant (
Model | AUC | DeLong’s test for AUC ( | Decision | Model selection |
---|---|---|---|---|
M1 | 0.629 | −9.26 (0.000) | M2 has a significantly different AUC from M1 | M2 is selected |
M2 | 0.660 | |||
M3 | 0.662 | −2.16 (0.031) | M3 significantly different AUC from M2 | M3 is selected |
M4 | 0.713 | −16.21 (0.000) | M4 significantly different AUC from M3 | M4 is selected |
M5 | 0.714 | −2.03 (0.041) | M5 had significantly different AUC from M4 | M5 is selected |
M6 | 0.715 | −2.24 (0.025) | M6 had significantly different AUC from M5 | M6 is selected |
M7 | 0.716 | −0.61 (0.545) | M7 had not significantly different AUC from M6 | M7 is not selected |
M8 | 0.716 | −2.63 (0.008) | M8 had a significantly different AUC from M6 | M8 is selected |
M9 | 0.723 | −4.34 (0.000) | M9 had a significantly different AUC from M8 | M9 is selected |
M10 | 0.762 | −14.38 (0.000) | M10 had a significantly different AUC from M9 | M10 is selected |
M11 | 0.773 | −8.05 (0.000) | M11 had a significantly different AUC from M10 | M11 is selected |
M12 | 0.773 | −0.72 (0.472) | M12 had not a significantly different AUC from M11 | M12 is not selected |
M13 | 0.774 | −2.22 (0.029) | M13 had a significantly different AUC from M11 | M13 is selected |
M14 | 0.775 | −2.17 (0.030) | M14 had a significantly different AUC from M13 | M14 is selected |
M15 | 0.776 | −2.13 (0.033) | M15 had a significantly different AUC from M14 | M15 is selected |
M16 | 0.799 | −11.81 (0.000) | M16 had a significantly different AUC from M15 | M16 is selected |
M17 | 0.813 | −9.26 (0.000) | M17 had a significantly different AUC from M16 | M17 is selected |
M18 | 0.816 | −4.74 (0.000) | M18 had a significantly different AUC from M17 | M18 is selected |
M19 | 0.828 | −11.45 (0.000) | M19 had a significantly different AUC from M18 | M19 is selected |
M20 | 0.847 | −14.69 (0.000) | M20 had a significantly different AUC from M19 | M20 is selected |
M21 | 0.866 | −21.75 (0.000) | M21 had a significantly different AUC from M20 | M21 is selected |
Best model selection based on Delong’s test.
This study used seven different machine algorithms to classify contraceptive practices among married women both training and an experimental/test dataset. Performance parameters (such as accuracy, precision, recall, F1, specificity, and AUC value) were used to compare the predictive performance of these algorithms. In addition, Cohen Kappa’s statistical information was used to determine the discriminant accuracy of the algorithm. The prediction results with performance parameters for each algorithm are shown in Table 5 and Figure 3.
Model name | Accuracy (95% CI) | Cohen’s kappa | Precession | Recall | F1 | AUC | Specificity |
---|---|---|---|---|---|---|---|
LR | 78.52 (77.39, 79.61) | 0.5559 | 81.23 | 82.39 | 81.81 | 86.57 | 73.03 |
RF | 77.57 (76.43, 78.68) | 0.5288 | 78.32 | 85.35 | 81.69 | 84.07 | 66.53 |
NB | 76.56 (75.40, 77.69) | 0.4995 | 75.73 | 88.32 | 81.54 | 84.17 | 59.90 |
LASSO | 79.08 (77.96, 80.16) | 0.5601 | 79.39 | 86.85 | 82.96 | 86.59 | 68.06 |
CT | 78.57 (77.45, 79.67) | 0.5464 | 78.16 | 88.06 | 82.81 | 85.59 | 65.13 |
AdaBoost | 78.50 (77.37, 79.59) | 0.5523 | 80.20 | 84.08 | 82.10 | 86.15 | 70.59 |
NN | 79.34 (78.23, 80.42) | 0.5626 | 78.71 | 88.76 | 83.44 | 86.90 | 65.99 |
Performance evaluation for seven ML algorithms (test data set).
Area under curve of all seven machine learning classifiers.
Table 5 shows that the logistic regression classifier has an accuracy of 78.52%. The precision and recall of the fitted model were 81.23% and 82.39%, respectively, while the F1 score was 81.81%. The area under the curve (AUC) was calculated to be 86.57%. The prediction performance result of a random forest was displayed with an accuracy of 77.57%. Here, the precision, recall, and F1 score of the random forest classifier were 73.82%, 85.35%, and 81.99%, respectively. The AUC, in this case, was 84.07%. The final accuracy of the naïve Bayes classifier was 76.56%, with a precision of 75.73% and a recall of 88.32%. The F1 score and the AUC value, in this case, were 81.54% and 84.17%, respectively. Using Least Absolute Shrinkage and Selection Operator (LASSO) analysis, the accuracy in the test data set was seen as 79.08% with precession and recall of 79.39% and 86.85% respectively, and the F1 score was 82.96%. According to the test observation results, the classification tree method showed 78.57% accuracy in predicting contraceptive practice among married women, with a precession of 78.16%, a recall of 88.06%, an F1 score of 82.81%, and an AUC value of 85.59%. For AdaBoost, these values are 78.05% (accuracy), 80.20% (precession), 84.88% (recall), 82.10% (F1 score) and 86.15% (AUC). Finally, we used an artificial neural network and obtained an accuracy of 79.34%. Here, other parameters such as precession, recall, F1 score, and AUC are 78.71%, 88.76%, 83.44%, and 86.90% respectively. Among the seven classifiers, we obtained the best performance from NN in terms of both accuracy and AUC. Cohen’s kappa value is 0.5626.
This violin plot shows the relationship of seven classifiers to accuracy. The shaded areas detail the distribution of the data in each classifier. Figure 4 shows that NN provided the highest mean accuracy, followed by LASSO and AdaBoost. Unlike the boxplot, the entire distribution of the 10-fold accuracy can be visualized in this violin plot (Figure 4).
Violin plots of the 10-fold cross-validation.
This is the very first study that uses a hierarchical logistic classifier in a machine learning approach. Then the predictive performance of the hierarchical logistic classifier was compared with the other six machine learning algorithms’ predictive power. In this study, the use of contraception among ever-married women in Bangladesh has been predicted using sociodemographic factors. This study can provide policymakers and academics with a starting point to examine key outlines in a larger framework and raise noteworthy interventions.
The study found that the prevalence of contraception was almost 59% in Bangladesh. The prevalence rate of contraceptives in India is 54%, while the rates were 47%, 34%, and 65%, respectively, for Nepal, Pakistan, and Sri Lanka [37, 38]. As the government of Bangladesh is committed to the London Summit on Family Planning to improve contraceptive access and use among impoverished people in both urban and rural areas [39], the findings of this study will provide grounding direction for the increase in the prevalence of contraception.
In this study, we used hierarchical LR, RF, NB, LASSO, CT, AdaBoost, and NN machine learning techniques to predict contraceptive practice among ever-married women in Bangladesh. The current analysis was to evaluate which performed better based on the accurate prediction rate of contraceptive use for 2014, BDHS data sets. Moreover, there was no evidence of scientific study that used a hierarchical logistic classifier and several supervised learning. In this study, 70% of the respondents were used for model tuning purposes, and the remaining 30% were used to check model performance, for the model tuning was performed using 10-fold cross-validation on the training dataset. The researcher observed that cross-validation is most commonly used to evaluate model performance [40]. The prediction of contraceptive use was measured by performance parameters (such as accuracy, precision, recall, F1, and AUC value) compared to the performance of seven different machine learning classifiers in this analysis. Cohen’s kappa, the proportion of predicted to actual classification in the dataset, is used to assimilate model perfection. Among the used models, the Neural Network outperformed other models with an accuracy of 79.34%. Additionally, in terms of Cohen kappa, the result of this analysis also highlighted that the Neural Network provides the best predictive performance (Cohen’s
In this paper, we investigate the hierarchical logistic regression classifier in machine learning approaches to identify potential risk factors related to contraceptive practices of women in Bangladesh. In summary, we conclude that all of the selected covariates were significant determinants for contraceptive practice except FP Mass media exposure and wealth status according to the hierarchical logistic regression classifier in machine learning approaches based on the Delong test. Here, we compared seven supervised machine learning algorithms to predict contraceptive practice among ever-married women aged between 15 and 49 years in Bangladesh. The NN model has exhibited the best results based on the performance parameters, having demonstrated an accuracy of 79.34%, a precision of 78.71%, a recall of 88.76%, an F1 score of 83.44%, and an AUC value of 86.90. Among the seven algorithms, the NN model performs the best in terms of accuracy, Cohen’s kappa statistic, and area under the curve (AUC). This study recommends the use of the NN model and policymakers should pay attention to continuing this study in the future.
A special thank goes to the Demographic Health Surveys for enabling us to use Bangladesh Demographic Health Survey data for our study from https://dhsprogram.com/data/.
The authors declare that they are not competing of interest.
This study did not receive funding.
This study was analyzed using secondary data, which were available at “https://dhsprogram.com/data/”.
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A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/77407",hash:"",query:{},params:{id:"77407"},fullPath:"/chapters/77407",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()