The receipt situation of mediation center for neighborhood noise by conflict period and type of impact in Korea (2017–2018).
\r\n\tThe study of populations and plant communities in their different aspects; ecological, structural, functional and dynamic, it is essential to establish a posteriori models of forest and agricultural management.
\r\n\r\n\tFor this, the methodological approaches on the type of sampling are considered essential, since there are differences between the purely ecological and the phytosociological methods, despite the fact that both pursue the same objective.
\r\n\tAlthough the ecological method for the knowledge of the vegetation is widely extended, the phytosociological one is no less so, since in the European Union it has been developed as a consequence of policies on sustainability, through which regulations have been issued, such as the habitats directive.
\r\n\tOn the other hand, research on plant dynamics and knowledge of the landscape in an integral way, have multiplied in the last 30 years, which has favored a deep knowledge of the floristic and phytocenotic wealth, which is fundamental for agricultural management, livestock and forestry.
",isbn:"978-1-83969-386-1",printIsbn:"978-1-83969-385-4",pdfIsbn:"978-1-83969-387-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"0abf2a59ee63fc1ba4fb64d77c9b1be7",bookSignature:"Dr. Eusebio Cano Carmona, Dr. Ricardo Quinto Canas, Dr. Ana Cano Ortiz and Dr. Carmelo Maria Musarella",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9662.jpg",keywords:"Climatic Factors, Bioclimate, Thermotype, Flora, Conservation, Phytocenosis, Plant Dynamics, Landscape, Cartography, Vegetation Series, Crops, Reforestation",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 23rd 2020",dateEndSecondStepPublish:"January 25th 2021",dateEndThirdStepPublish:"March 26th 2021",dateEndFourthStepPublish:"June 14th 2021",dateEndFifthStepPublish:"August 13th 2021",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Cano Carmona and colleagues have directed 12 doctoral theses and more than 200 publications among articles, books, and book chapters. He has participated in national and international congresses with about 250 papers. He has held a number of different academic positions, including Dean of the Faculty of Experimental Sciences at the University of Jaen, Spain, and founder and director of the International Seminar on Management and Conservation of Biodiversity.",coeditorOneBiosketch:"Ricardo Jorge Quinto Canas is currently an Invited Assistant Professor in the Faculty of Sciences and Technology at the University of Algarve – Portugal, and a member of the Centre of Marine Sciences (CCMAR), University of Algarve. His current research projects focus on Botany, Vegetation Science (Geobotany), Biogeography, Plant Ecology, and Biology Conservation, aiming to support Nature Conservation.",coeditorTwoBiosketch:"Ana Cano Ortiz's fundamental line of research is related to botanical bioindicators. She has worked in Spain, Italy, Portugal, and Central America. It presents more than one hundred works published in various national and international journals, as well as books and book chapters; and has presented a hundred papers to national and international congresses.",coeditorThreeBiosketch:"Carmelo Maria Musarella is a biologist, specialized in Plant Biology. He is a member of the permanent scientific committee of the International Seminar on “Biodiversity Conservation and Management” guested by several European universities. He has participated in several international and national congresses, seminars, and workshops and presented oral communications and posters.",coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"87846",title:"Dr.",name:"Eusebio",middleName:null,surname:"Cano Carmona",slug:"eusebio-cano-carmona",fullName:"Eusebio Cano Carmona",profilePictureURL:"https://mts.intechopen.com/storage/users/87846/images/system/87846.png",biography:"Eusebio Cano Carmona obtained a PhD in Sciences from the\nUniversity of Granada, Spain. He is Professor of Botany at the\nUniversity of Jaén, Spain. His focus is flora and vegetation and he\nhas conducted research in Spain, Italy, Portugal, Palestine, the\nCaribbean islands and Mexico. As a result of these investigations,\nDr. Cano Carmona and colleagues have directed 12 doctoral theses\nand more than 200 publications among articles, books and book\nchapters. He has participated in national and international congresses with about\n250 papers/communications. He has held a number of different academic positions,\nincluding Dean of the Faculty of Experimental Sciences at the University of Jaen,\nSpain and founder and director of the International Seminar on Management and\nConservation of Biodiversity, a position he has held for 13 years. He is also a member of the Spanish, Portuguese and Italian societies of Geobotany.",institutionString:"University of Jaén",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Jaén",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"216982",title:"Dr.",name:"Ricardo Quinto",middleName:null,surname:"Canas",slug:"ricardo-quinto-canas",fullName:"Ricardo Quinto Canas",profilePictureURL:"https://mts.intechopen.com/storage/users/216982/images/system/216982.JPG",biography:"Ricardo Quinto Canas, Phd in Analysis and Management of Ecosystems, is currently an Invited Assistant Professor in the Faculty\nof Sciences and Technology at the University of Algarve, Portugal, and member of the Centre of Marine Sciences (CCMAR),\nUniversity of Algarve. He is also the Head of Division of Environmental Impact Assessment - Algarve Regional Coordination\nand Development Commission (CCDR - Algarve). His current\nresearch projects focus on Botany, Vegetation Science (Geobotany), Biogeography,\nPlant Ecology and Biology Conservation, aiming to support Nature Conservation.\nDr. Quinto Canas has co-authored many cited journal publication, conference articles and book chapters in above-mentioned topics.",institutionString:"University of Algarve",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"203697",title:"Dr.",name:"Ana",middleName:null,surname:"Cano Ortiz",slug:"ana-cano-ortiz",fullName:"Ana Cano Ortiz",profilePictureURL:"https://mts.intechopen.com/storage/users/203697/images/system/203697.png",biography:"Ana Cano Ortiz holds a PhD in Botany from the University of\nJaén, Spain. She has worked in private enterprise, in university\nand in secondary education. She is co-director of four doctoral\ntheses. Her research focus is related to botanical bioindicators.\nDr. Ortiz has worked in Spain, Italy, Portugal and Central America. She has published more than 100 works in various national\nand international journals, as well as books and book chapters.\nShe has also presented a great number of papers/communications to national and\ninternational congresses.",institutionString:"University of Jaén",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Jaén",institutionURL:null,country:{name:"Spain"}}},coeditorThree:{id:"276295",title:"Dr.",name:"Carmelo Maria",middleName:null,surname:"Musarella",slug:"carmelo-maria-musarella",fullName:"Carmelo Maria Musarella",profilePictureURL:"https://mts.intechopen.com/storage/users/276295/images/system/276295.jpg",biography:"Carmelo Maria Musarella, PhD (Reggio Calabria, Italy –\n23/01/1975) is a biologist, specializing in plant biology. He\nstudied and worked in several European Universities: Messina,\nCatania, Reggio Calabria, Rome (Italy), Valencia, Jaén, Almeria\n(Spain), and Evora (Portugal). He was the Adjunct Professor\nof Plant Biology at the “Mediterranea” University of Reggio\nCalabria (Italy). His research topics are: floristic, vegetation,\nhabitat, biogeography, taxonomy, ethnobotany, endemisms, alien species, and\nbiodiversity conservation. He has authored many research articles published in\nindexed journals and books. He has been the guest editor for Plant Biosystems and a\nreferee for this same journal and others. He is a member of the permanent scientific\ncommittee of International Seminar on “Biodiversity Conservation and Management”, which includes several European universities. He has participated in several\ninternational and national congresses, seminars, workshops, and presentations of\noral communications and posters.",institutionString:'"Mediterranea" University of Reggio Calabria',position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"1",institution:null},coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"72584",title:"Neighborhood Noise",doi:"10.5772/intechopen.92877",slug:"neighborhood-noise",body:'\nWith the improvement of living standards, urbanization, and industrialization, noise pollution has become an environmental factor that is most frequently encountered by anyone, anytime, and anywhere in everyday life. Unlike other environmental problems, noise pollution tends to increase continuously, and the sufferings of the residents exposed to noise also increase gradually. In particular, in a rapidly developing society, poor buildings’ quality, poor urban planning, and traffic noises generate more exposed to noise pollution.
\nKorea is successfully industrialized country. With the industrialization, noise complaints began to emerge. Noise and vibration make up 90% of the environmental disputes. Most metropolitan residents in Korea are suffering from noise pollution. And 88% of metropolitan residents expected that noise level would get worse.
\nThe data of the nationwide environmental noise through automatic measurement network in 2018, which included major cities in Korea, were as follows (Figure 1). The distribution of noise level was 84.5% in the case of over 55 dBA at night (23–7 hours) and 99.9% in the daytime (8–22 hours), and some cases exceeded 75 dBA (0.95% at night, 4.54% during the day). The national average noise level was 64.6 dBA (54.6–69.9 dBA) during the night and 69.6 dBA (55.1–74.3 dBA) during the day, 5 dBA higher than during the night. Most of results exceeded the domestic standard for residential areas, 50 dBA at night and 55 dBA during the day. Because such noise level is a result of outdoor measurement, the indoor noise level might be 10–15 dBA lower than outdoor level usually [1]. The nationwide environmental noise level and noise-related problems in Korea are not getting better than before.
\nDistribution and mean noise levels of nationwide automatic measurement system in 2018, in Korea: Data from http://www.noiseinfo.or.kr.
According to the International Program on Chemical Safety [2], an adverse effect of noise is defined as a change in the morphology and physiology of organism that results in an impairment of functional capacity, or an impairment of capacity to compensate for additional stress, or increases the susceptibility of organism to harmful effects of other environmental influences. This definition includes any temporary or long-term decrement of physical, psychological, or social function of humans or human organs.
\nEnvironmental noise exposure is responsible for range of health effects, including increased risk of ischemic heart disease as well as sleep disturbance, cognitive impairment among children, annoyance, stress-related mental health risks, and tinnitus. Taken together, these risks in high-income European countries account for a loss of 1–1.6 million disability adjusted life years (DALYs) – a standardized measure of healthy years of life lost to illness, disability, or early death.
\nThe health effects of noise depend on its complexity such as time variation, frequency content, loudness, ambient noise level, type of noise, and individual difference. The lack of sufficient knowledge about the direct and indirect effects of these noises on health is limiting the provision of reasonable regulatory standards for living noise.
\nA rapid increase in population in the city and urbanization in 1960s and 1970s prompted the need for residential construction. To cope with the demand, high-rise flats were built. Regulation on building at that time did not include the test on sound insulation in residential building, so standards were often not adequate to protect people from everyday sounds from their neighbors made [3]. Although there were some differences by country, neighborhood noise issues were mostly published after the 1980s. They revealed that economic growth and urbanization affected the neighborhood noise issues.
\nAccording to the special report of New York City in 2005, there were 410,000 noise complaints to 311. New Yorkers perceived much more from neighborhood noise and also suffered more behavioral and emotional consequences, such as difficulty sleeping and relaxing and feeling annoyed, angry, or upset compared to the nationwide population. New Yorkers were especially bothered by neighbor noises such as inadequate floor covering and slamming of doors. Of those, young children running around excessively were noise complaints that were best handled by clauses in apartment leases. These findings demonstrated that New Yorkers could not find the requisite peace and quiet in their homes that they deserve [4].
\nThe European quality-of-life surveys were carried out examining both the objective circumstances of lives of European citizens and how they felt about those circumstances and their lives in general. The last (fourth) survey in 2016–2017 involved nearly 37,000 citizens, and respondents were asked whether they had major, moderate, or no problems with noise from the immediate neighborhood of their home. Almost one third (32%) of them reported problems with noise (ranging from 14 to 51% in individual countries), mainly in cities or city suburbs (49%) [5]. The neighborhood noise problem accounted for a large proportion of complaints related to noise and its proportion increased despite the government efforts such as campaign and legislation.
\nNeighborhood noise may stem from various potential sources of noise (such as ventilation systems; church bells; animals; neighbors; commercial, recreational and occupational activities; or shooting/military). As the sources might be located in close proximity to where people live, they could cause considerable annoyance even at low levels.
\nThe main background factors of noise issues include overcrowding, developing urbanization, sprawling development, building of apartments and houses with inadequate sound insulation, increased use of electric equipment at home, increased number of recreation facilities, and lack of communication among neighbors. In addition, the calmness of a residential area depends on the noises outside the house. These main noise issues simply divided neighborhood noise into three categories: (1) noise produced by using loudspeakers, (2) noise produced during the nighttime operation of commercial facilities, and (3) daily life noise [6].
\nAccording to the report of Right to Peace and Quiet Campaign (RPQC) in 1994, at least five people a year died from noise-related conflicts between neighbors in the UK [3]. Also, 18 people had serious social problems in 2010–2020 such as arson and murder followed by conflicts related to neighborhood noise in Korea.
\nPeople could feel more annoyed if they believe the noise might harm our health or put us in danger. They could be particularly disturbed when their neighborhoods suddenly become noisy. When noises become really disturbing, it could dominate every aspects of our lives. The desire to get rid of the offending noises by almost any means possible could be overwhelming. Murder or suicide is just the end point of that process. Although only a small number of people resort to suicide or murder, many lives could get altered forever by noise problems [3].
\nAccording to mediation center report, of the 137,813 telephone consultations (2012–2018), there were severe conflicts among neighbors, and 39,950 cases (29.0%) were requested for onsite diagnosis and measurement. The mediation service demand has increased by 3.2 times from 8795 cases in 2012 to 28,231 cases in 2018. 12493 cases required on-site diagnosis and measurement. Even though the construction year varied among those cases, the slab thickness of the apartments estimated to be less than 120 mm. Of the 1271 noise measurements, 1177 (92.6%) were within the standard, and only 94 (7.4%) exceeded the regulatory standard in 2018. Of the number of onsite diagnoses and measurements received, the floor impact sound distribution was 82.8%, and in particular, “children’s running or footsteps” accounted for 70.6%, followed by hammering, furniture pulling, door closing, vibrating machines, and exercise equipment. The most common air transmitted noise was generated by household appliances, followed by musical instruments, argument, pets, toilet drains, and air conditioner outdoor units.
\nMost of the damages reported to the Mediation Center were sleep disturbance, followed by rest disturbance, excessive protest from the victim, emotional anxiety, and learning disturbance. In case of the conflict period between neighborhoods, less than 6 months was the most frequent, and it tended to decrease over time until 2 years but increased after that.
\nThese results showed that the victim initially responds to the neighborhood noise sensitively due to unfamiliar state of the living environment, but eventually the pattern of response improved due to changes in behavior attitude, improvement of mutual relations, and habitualization of noise. However, it is estimated that if the period gets prolonged, the damage is re-recognized when the subjective tolerable limit is exceeded (Table 1).
\nType of impact | \nConflict period between neighborhood (years) | \n|||||||
---|---|---|---|---|---|---|---|---|
Total (%) | \n>0.5 | \n0.5–1 | \n1–1.5 | \n1.5–2 | \n2–3 | \n3< | \nOthers | \n|
Total (%) | \n4684 (100.0) | \n1281 (27.3) | \n1114 (23.8) | \n809 (17.3) | \n269 (5.7) | \n452 (9.6) | \n538 (11.5) | \n223 (4.8) | \n
Sleep disturbance | \n2865 (61.2) | \n838 | \n705 | \n489 | \n163 | \n275 | \n333 | \n64 | \n
Rest disturbance | \n662 (14.1) | \n160 | \n109 | \n118 | \n35 | \n63 | \n90 | \n87 | \n
Excessive protest | \n590 (12.6) | \n182 | \n136 | \n109 | \n32 | \n56 | \n48 | \n27 | \n
Emotional anxiety | \n315 (6.7) | \n55 | \n98 | \n56 | \n20 | \n36 | \n41 | \n9 | \n
Learning disturbance | \n94 (2.0) | \n16 | \n28 | \n22 | \n4 | \n9 | \n13 | \n2 | \n
Others | \n159 (3.4) | \n30 | \n39 | \n15 | \n15 | \n13 | \n13 | \n34 | \n
The receipt situation of mediation center for neighborhood noise by conflict period and type of impact in Korea (2017–2018).
If the noise exposure persists over an extended period of time, increasing evidence suggests that more severe health consequences, such as cardiovascular diseases, may emerge as a result of prolonged physiological stress [7, 8].
\nKorea’s standards of Environment Noise were first established in 1964 as “Pollution Prevention Act” and have gone through several revisions in the following order, Environmental Protection Act (1978), Noise and Vibration Control Act (1991), and still the revision is ongoing. The intent of the law is to preserve proper environment, which requires the establishment of various measures, such as setting environmental standards, designating an area requiring countermeasures against noise, and setting rational permissible emission standards necessary to protect the health, property, and pleasant natural environment of the people. The law determines the regulation area of living noise where control standards are needed to impose adjustment of working hours, suspension of noise producing activities, and installation of soundproofing facilities. In addition, for those who fail to fulfill the act, it enables to prohibit the use or closure of the industry. In 2010, the revised enforcement rules have stipulated the range of noise generated by human activities (Table 2).
\nTarget areas | \nNoise source | \nMorning (05–07) Evening (18–22) | \nDay (07–18) | \nNight (22–05) | \n|
---|---|---|---|---|---|
Living area*\n | \nLoudspeaker**\n | \nOutdoor | \n60 | \n65 | \n60 | \n
Transmitted to indoor | \n50 | \n55 | \n45 | \n||
Factory | \n\n | 50 | \n55 | \n45 | \n|
Industry | \nSame building\n†\n\n | \n45 | \n50 | \n40 | \n|
Others | \n50 | \n55 | \n45 | \n||
Construction | \n\n | 60 | \n65 | \n50 | \n|
Other area | \nLoudspeaker | \nOutdoor | \n65 | \n70 | \n60 | \n
\n | Transmitted to indoor | \n60 | \n65 | \n55 | \n|
Factory | \n\n | 60 | \n65 | \n55 | \n|
Industry | \nSame building | \n50 | \n55 | \n45 | \n|
\n | Others | \n60 | \n65 | \n55 | \n|
Construction | \n\n | 65 | \n70 | \n50 | \n
Noise and vibration control act and related standards (dBA SPL).
Area straightly within 50 m from boundary of a general hospital under the Medical Act, schools under the Elementary and Secondary Education Act and the Higher Education Act, and public libraries under the Library and Reading Promotion Act.
The loudspeaker installed outdoors should be used within 3 minutes at once with at least 15-minute interval.
The term “Same building” refers to a building in accordance with Article 2 of the Building Act, which has a roof, pillar, or wall as a whole.
As the problem of neighboring noise became more serious, the government prepared comprehensive plans to reduce living noise in 2010. The related contents are in the following paragraphs.
\nFirst, strengthening the precautious prevention: provision of regulations for surrounding noise sources for quiet facilities (schools, libraries, hospitals, elderly facilities, childcare facilities, apartment houses, etc.), recognition of the amount of the fine caused by the noise and vibration dispute; second, management of new noise sources and living noise: present management standards for the floor impact noise and noise rating system for home appliances, preparing low frequency noise management guidelines; and third, traffic noise management: expanding the supply of low-noise cars and low-noise pavement and designating traffic noise management areas.
\nAs a result of these efforts by government departments, the standard for neighborhood noise was more strengthened than the first one. The following shows the standards for interlayer noise implemented since 2014 (Table 3). The inter-floor noise-related policies of other countries are centered on lightweight impact noise, and the recommendation to the perpetrator (the UK) and fine imposition (the USA and Germany) is the main method. The allowable range varies from 65 dB in Spain to 53 dB in Finland. In Korea, the law related to interlayer noise, which is stricter than that of the other countries, was enacted considering heavyweight shock, lightweight impact noise, and maximum time-weighted noise level (L\nmax).
\nClassification of neighborhood noise | \nStandard for neighborhood noise | \n||
---|---|---|---|
Parameter | \nMeasuring unit (dBA) | \nDaytime (6 am to 22 pm) | \nNight (22 pm to 6 am) | \n
Direct impact noise | \nEquivalent noise level (L\neq) for 1 minute*\n | \n43 | \n38 | \n
Maximum noise level (L\nmax)**\n | \n57 | \n52 | \n|
Air transmission noise | \nEquivalent noise level (L\neq) for 5 minutes*\n | \n45 | \n40 | \n
Supplementary standards for neighborhood noise (Note 3).
The equivalent noise level (L\neq) for 1 minute and the equivalent noise level (Leq) for 5 minutes are the highest values measured in accordance with Note 3.
The maximum noise level (L\nmax) is considered to have exceeded the standard if the value exceeded three times per hour.
According to a 2013 National Human Rights Commission survey, 88% was stressed by interlayer noise. The response choices included were patience (46%), request to visit (25%), report to the guard (19%), protest after visit (7%), and report to police and neighboring centers (1%).
\nThe Neighborhood Mediation Center opened in March 2012 to prevent neighborhood noise from social issue of multiunit apartments and to settle disputes reasonably (Table 4).
\nFirst-step service | \n1. Telephone counseling | \nNational noise information system and nationwide call center After determining the cause of neighborhood noise, conflict resolution and mitigation measures are presented | \n
2. Confirm respondent onsite diagnosis and related measurement acceptance | \nAccept: Implementation of second-step service Refuse: Noise reduction, conflict mitigation measures postal notice (end) | \n|
Second-step service | \n1. Additional telephone counseling | \nIn-depth consulting on both sides If satisfied or self-solve the problem then finish | \n
2. Onsite diagnosis | \nConsultation of mitigation measures after understanding housing structure, causes of conflict, and degree of conflict Write the consultation report | \n|
3. Onsite measurement of noise | \nMeasurement of noise level with equipment 24 hours Completion of the consultation report | \n
Work processing flows of neighborhood noise mediation center.
The suitable indicators for policy making on the basis of the most frequently used average noise indicators in Europe are L\nden and L\nnight. These are used widely for exposure assessment in health effect studies and noise impact assessments.
\nThe L\nden indicator is day-evening-night-weighted sound pressure level as defined in Section 3.6.4 of ISO 1996-1:2016. It is calculated by the A-weighted average sound pressure level measured over a 24-hour period, with a 10 dB penalty, a 5 dB, and no penalty, each added to the average level at night, evening, and the daytime period, respectively [9]. The penalties considered people’s extra sensitivity to noise during the evening and night. The L\nnight indicator is the equivalent continuous sound pressure level when the reference time interval sets during the night.
\nIn general, environmental noise is composed of complexed component such as impact sounds and impulse sounds, which make the L\nden or L\nnight indicators hard to represent a particular noise effect. For single-event noise indicators, the maximum sound pressure level (L\nA,max) and its frequency distribution can be more appropriate in specific situations, such as in the context of night-time railway, aircraft noise events, and neighborhood noise that can clearly elicit awakenings and other physiological reactions that can be determined by L\nA,max. The L\nA,max indicator is maximum time-weighted and A-weighted sound pressure level within a stated time interval starting at t\n1 and ending at t\n2, expressed in dB [10]. Nevertheless, the assessment of the relationship between different types of single-event noise indicators and long-term health outcomes at the population level remains tentative.
\nAll noise exposure prediction models used today estimate free-field exposure levels outdoors, and most noise abatement regulations refer to outdoor levels as well. Nevertheless, in certain cases, it would be helpful to estimate indoor levels based on outdoor values. The differences between indoor and outdoor levels are usually estimated at around 10 dB for open, 15 dB for tilted or half-open, and about 25 dB for closed windows [1].
\nRegarding the night noise impacts on health, below the level of 30 dB L\nnight, no effects on sleep are observed except for a slight increase in the frequency of body movements during sleep due to the night noise. There is no sufficient evidence that the biological effects observed at the level below 40 dB L\nnight are harmful to health. However, adverse health effects are observed at the level above 40 dB L\nnight, such as self-reported sleep disturbance, environmental insomnia, and increased use of somnifacient drugs and sedatives. Therefore, 40 dB L\nnight is equivalent to the lowest observed adverse effect level (LOAEL) for night noise. Above 55 dB, the cardiovascular effects become the major public health concern, which are likely to be less dependent on the characteristics of the noise. Closer examination on the precise impact will be necessary in the range between 30 and 55 dB because most will depend on the detailed circumstances of each case. The causal link between immediate physiological reactions and long-term adverse health effects is complex and difficult to prove [11, 12].
\nSince most of the problems of neighborhood noise are generated during the evening or at night, it is reasonable to estimate physical health effects using the noise indicators presented above. However, the actual field noise measurement results showed a few cases that exceeded the regulatory standards, and collisions among neighbors occurred even at relatively low noise levels. This means that the problems related to neighborhood noise are largely responsible for mental health effects such as annoyance and sleep disorders. It also suggests that the effects of “effect modifiers,” such as differences in noise levels from the ambient noise, socioeconomic status, and personal susceptibility to noise, should be considered.
\nExposure to noise can lead to auditory and nonauditory effects on health. Through direct injury to the auditory system, noise exerts auditory effects such as hearing loss and tinnitus. Noise is also a nonspecific stressor that has been shown to have an adverse effect on human health, especially following long-term exposure. These effects are the result of psychological and physiological distress, as well as disturbing homeostasis of an organism and increasing allostatic load [13].
\nThe most common noise-related health effect is annoyance. Noise annoyance is caused by noise-related disturbances of the individual’s speech communication, concentration, and performance of tasks, and it is commonly associated with negative emotional reactions, such as feelings of displeasure, anger, and disappointment. Furthermore, annoyance may give rise to physiological symptoms, including tiredness, stomachache, and stress symptoms. In fact, noise annoyance is a symptom of stress building up inside as a consequence of signals transmitted from the auditory system to the nervous system, stimulating several subsequent reactions in our bodies [14].
\nSince endocrine changes manifesting physiological disorders come first in the chain of cause effect for perceived noise stress, noise effects on stress hormones may therefore be detected in populations after relatively short periods of noise exposure. This makes stress hormones a useful stress indicator but in terms of the risk assessment, usually the quantitative interpretation of endocrine noise effects is often a quantitative one rather than quantitative one. The most well-known mechanism mediating the response to stress is the hypothalamic-pituitary-adrenal (HPA) axis. When the HPA axis receives a signal of a stress response, corticotropin-releasing factor is secreted from the hypothalamus, which in response releases adrenocorticotropic hormone from the pituitary gland. Adrenocorticotropic hormone then promotes the secretion of cortisol from the adrenal cortex through the blood, which triggers responses to various kinds of stress. The secretion of cortisol in response to stress inhibits the function of the HPA axis to disrupt the secretion of neurohormones and neurotransmitters as well as influencing the endocrine system, thereby disturbing homeostasis of the body, which can induce the development of various stress-related diseases [15]. Recent studies have also reported that sleep quality and noise sensitivity are not related to vascular function, but rather that night noise increases the risk of cardiovascular disease due to the increased blood pressure [7].
\nThe associations between noise and health could be modified by several factors (effect modifiers), so individuals may therefore be more or less affected by the noise. These so-called “effect modifiers” can be demographic factors, for instance, age, sex, and socioeconomic position; personal or attitudinal factors, such as noise sensitivity and fear of the noise source; or related to the individual lifestyle and occupation, including physical activity, psychosocial health, and job strain. In addition, coping mechanisms, such as use of ear plugs or window opening behavior, and situational factors, including time of day and type of activity, may modify the effect of exposure (Figure 2) [16]. Identification of risk groups, individuals who are particularly vulnerable to noise, is important for assessments of public health impact and can serve as a basis for preventive measures. For each specific health outcome, one should consider not only the available factors that may modify the effect of noise but also the annoyance rating of noise sensitivity as the most important individual characteristic when predicting dissatisfaction with the noise [14, 17].
\nThe framework of health effects of noise according to environmental noise and health – current knowledge and research needs ISBN 978-91-620-6553-9.
The health outcomes influenced by possible nonacoustic factors may include gender, age, education, subjective noise sensitivity, extroversion/introversion, general stress score, comorbidity, length of residence, duration of stay at dwelling in the day, window orientation of a bedroom or living room toward the street, personal evaluation of the source, attitudes toward the noise source, coping capacity with respect to noise, perception of malfeasance by the authorities responsible, body mass index, and smoking habits. In noise annoyance studies, nonacoustic factors may explain up to 33% of the variance [18].
\nAccording to WHO report, the key health outcomes associated with environmental noise exposure based on the seriousness and prevalence and the anticipated availability of evidence were in the following. The health outcomes were divided as either critical or important for developing recommendations on the health impacts of environmental noise. The selection of health outcomes was based on the available evidence for the association between the environmental noise and the specific outcome, as well as public concern about the health outcome resulting from noise exposure. The critical health outcomes associated with environmental noise included such as cardiovascular disease, annoyance, sleep disturbance, cognitive impairment, and hearing impairment and tinnitus. In addition, the important health outcomes were adverse birth outcomes, metabolic outcomes, quality of life, well-being, and mental health [19].
\nThe following health outcomes were based on the evidence-based association between the environmental noise and the specific outcome, as well as public concern about the health outcome resulting from noise exposure. These health outcomes can be measured in various ways, and their prioritization was based on the impact of the disease and the disability weights (DWs) associated with the health outcome measure. A disability weight is a weight factor that reflects the severity of the disease on a scale from 0 (perfect health) to 1 (equivalent to death). Years Lost due to Disability (YLD) are calculated by multiplying the incident cases by duration and disability weight for the health condition [8]. In case of cardiovascular disease, DW of IHD is 0.405, DW of hypertension is 0.117, and the severity of the disease itself is high in IHD. However, the incidence rate varies depending on the survey area or country, the results of YLD may be different. The critical health outcomes, identification of the priority outcome measures, and justifications for their selections are listed in Table 5 [19].
\nCritical health outcome | \nCritical health outcome measures | \nJustification for selection | \n
---|---|---|
Cardiovascular disease (L\nden) | \nSelf-reported or measured prevalence, incidence, hospital admission, or mortality due to: \n
| \nExcept for self-reports, these are objective measures of the outcome, affect a large proportion of the population, have important health consequences, and can lead to more severe diseases and/or mortality. DW for IHD: 0.405 DW for hypertension: 0.117 | \n
Effects on sleep (L\nnight) | \n\n
| \nThis is the most meaningful, policy-relevant measure of this health outcome. Self-reported sleep disturbances are a very common problem in the general population: they affect quality of life directly and may also lead to subsequent health impediments. Effects on sleep may be in the causal pathway to cardiovascular disease. This measure is not a proxy for physiological sleep quality parameters but is an important outcome in its own right. DW for %HSD: 0.07 | \n
Annoyance (L\nden) | \n\n
| \nThis is the most objective measure of this health outcome. Large proportions of the population are affected by noise annoyance, even at relatively low exposure levels. Annoyance may be in the causal pathway to cardiovascular disease. DW for %HA: 0.02 | \n
Cognitive impairment (L\nden) | \n\n
| \nThis outcome measure is the most meaningful: it can affect vulnerable individuals (children) and has a significant impact later in life. DW for impaired reading and oral comprehension: 0.006 | \n
Hearing impairment and tinnitus (L\nAeq and L\nAF,max) | \n\n
| \nThis outcome measure can affect vulnerable individuals (children) and has a significant impact later in life. It is the most objective measure for which there is an ISO standard (ISO, 2013), specifying how to estimate noise-induced hearing loss. DW for mild severity level (threshold at 25 dB) for childhood onset: 0.0150 | \n
Critical health outcomes, outcome measures identified, and justifications for selection according to the WHO Environmental Noise Guidelines for the European Region.
DW: A disability weight is a weight factor that reflects the severity of the disease on a scale from 0 (perfect health) to 1 (equivalent to death).
The disability weight (DW) is used to rank the priority critical health outcome measures. DWs are ratings that vary between 0 and 1, in which 0 indicates no disability and 1 indicates the maximum amount of disability. The DWs have been proven useful in calculating the burden of disease (Table 6).
\nPriority health outcome measure (associated with DW) | \nRelevant risk increase considered for setting of guideline level | \n
---|---|
Incidence of IHD (DW: 0.405) | \n5% RR increase | \n
Incidence of hypertension (DW: 0.117) | \n10% RR increase | \n
%HA (DW: 0.02) | \n10% absolute risk | \n
%HSD (DW: 0.07) | \n3% absolute risk | \n
Permanent hearing impairment (DW: 0.0150) | \nNo risk increase due to environmental noise | \n
Reading and oral comprehension (DW: 0.006) | \nOne-month delay in terms of reading age | \n
Priority health outcomes and relevant risk increases for setting guideline levels according to the WHO Environmental Noise Guidelines for the European Region.
For cardiovascular disease, the DW value (DW: 0.405) specifically applied to acute myocardial infarction in the publication outlining the data sources, 5% increase of relative risk in ischemic heart disease (IHD) and 10% in hypertension. The DWs for high sleep disturbance (DW: 0.07), high annoyance (DW: 0.02), and impaired reading and oral comprehension (DW: 0.006) were developed in the context of calculating the burden of disease from environmental noise. According to the WHO night noise guidelines, there were observed adverse health effects at levels starting from 40 dB L\nnight, and self-reported sleep disturbance (HSD) and annoyance should not exceed 3 and 10% to be health protective, receptively. The DW for hearing impairment was available from the technical paper on the burden of disease from environmental noise; a DW of 0.0150 for moderate severity level “has difficulty following a conversation in a noisy environment, but no other hearing problems.” For cognitive impairment, the DW was derived from a very conservative value (DW: 0.006) for noise-related impairment of children’s cognition, equivalent to a DW for contemporaneous cognitive deficit in the context of a range of cognitive impairments in children. This impact cannot be predicted accurately [9].
\nAlso, WHO provides a list of the important health outcomes along with the reviewed measures. There was no prioritization of health outcome measures leading to justification of selection, since important health outcomes had less impact on the development of recommendations. In Table 7, the health outcome-related noise indicator was L\nden, and the most common health outcomes were relevant to psychoacoustic problems such as quality of life, well-being, and mental health [20].
\nImportant health outcome | \nHealth outcome measures reviewed | \n
---|---|
Adverse birth outcomes (L\nden) | \n\n
| \n
Quality of life, well-being, and mental health (L\nden) | \n\n
| \n
Metabolic outcomes (L\nden) | \nPrevalence, incidence, hospital admission, or mortality due to: \n
| \n
Important health outcomes and health outcome measures reviewed according to the WHO Environmental Noise Guidelines for the European Region.
Environmental noise is not believed to be a direct cause of mental illness, but it is assumed that it accelerates and intensifies the development of latent mental disorder. Studies on the adverse effects of environmental noise on mental health cover a variety of symptoms, including anxiety, emotional stress, nervous complaints, nausea, headaches, instability, argumentativeness, sexual impotency, changes in mood, increase in social conflicts, and general psychiatric disorders such as neurosis, psychosis, and hysteria [17, 21].
\nThen, even when exposed to lower noise levels than outdoor, why the problems of neighbor noise are taken so sensitively and seriously? This is because it is contrary to the expectation that home is a place of rest and a comfortable, quiet place.
\nNew Yorkers, like citizens in the quietest towns of the country, expect less noise when they close the doors to their apartment and homes. They may willing to deal with the noisy street traffic, crowds, and subways, as they transverse the city but they are less tolerant of noisy intrusions into their homes (Why noise matter chap 2) [3].\n
\nIt turned out that psychiatric disorders are associated with noise sensitivity, rather than with noise exposure level, and the association was found to disappear after adjustment for the baseline trait anxiety. These and other results show the importance of taking vulnerable groups into account because they may not be able to cope sufficiently with unwanted environmental noise. This is particularly true of children, the elderly, and people with preexisting illnesses, especially depression. Despite the weaknesses of the various studies, the possibility that community noise has adverse effects on mental health is suggested by studies on the use of medical drugs, such as tranquilizers and sleeping pills, on psychiatric symptoms and on mental hospital admission rates. About 1 of 10 people are particularly noise sensitive. These people will become 10% more annoyed by noise than general population [22].
\nAfter adjustment of noise-related variables, sociodemographic factors, medical illness, and duration of residence, subjects in the high noise-sensitive (NS) group were more than 2 times more likely to experience depression and insomnia and 1.9 times more likely to have anxiety, compared to those in the low NS group. The levels of noise recognition and psychological discomfort are affected by various factors, including individual components (e.g., age and effects of traits) and environmental factors, including contextual aspects and noise parameters (e.g., source, attitude toward noise, and amplitude modulation). Not all people exposed to environmental noise suffer from a disease or health problem, and the effects of noise differ among individuals [23].
\nHearing noises above the perceived normal threshold, higher noise sensitivity, and continuous noises were associated with higher levels of displaced aggression (DA). It occurs when a person is provoked, is unwilling or unable to retaliate against the original provocateur, and subsequently aggresses against a seemingly innocent target. Low frequency and high intensity noises were also associated with higher DA scores. DA score was higher in women and in older people living in the neighborhood for a longer time, in people with better education, and in those reporting poorer health. Moreover, low frequency and continuous noises resulted in higher DA. The frequency of hearing noises above the normal threshold was positively correlated with DA as was noise annoyance [24].
\nThe laws related to noise are increasingly being strengthened, and the actual noise level is decreasing compared to the past with the improvement of building technology and the reinforcement of regulatory standards. However, social problems related to noise have become more serious and are now an important part of the psychoacoustic problems related to the environment. This is also related to the expectation of a better quality of life as a result of the improvement of the economic level and the desire for home comfort. Also, even when the actual noise level is not high compared to other external environments and problems occur between neighbors. Therefore, it means that the problem of neighborhood noise should be discussed not only at the physical level but also at the psychoacoustic aspects, and the individual’s sensitivity and cultural difference should be considered. In particular, it needs to be treated more seriously, considering that health problems (especially mental problems) related to neighborhood noise may occur at a lower level than actual measured noise levels. Also, neighborhood noises tend to provoke the existing mental health problems related to noise more easily. Identifying the physiological and psychological effect of environmental noise precisely and making it recognized broadly comprise the essential part of solving the environmental noise problem to create better living environment. The neighborhood noise issues were in part due to the lack of communication between and among neighbors. Lack of contact within the community affected people’s perception of the loudness of daily life sounds [6]. Furthermore, intense emotional conflicts between and among some neighbors were also found to be one of the major sources of daily life noise. Even though the neighborhood noise annoyance will always be an issue, we want to be good neighbors and also hope to have good relationship with neighbors. Consideration of neighborhood, social responsibility, and changes in social behavior are important factors in addressing neighborhood noise. In addition, it is necessary to understand a scientific basis for the health impact of noise and to provide national support for noise reduction.
\nThe author declares no conflict of interest.
I would like to thank the members of the Department of Occupational and Environmental Medicine, Ulsan University Hospital. A Ram Kim, Daeyun Kim, and Sunghee Lee have contributed directly or indirectly to this chapter. We have shared information and ideas. Moreover, they have made suggestions and comments.
\nIn recent years, RNA-seq based on Next generation Sequencing has become an attractive alternative for conducting quantitative analysis of gene expression. This approach offers a number of advantages compared to microarray analysis such as the discovery of novel RNA species (RNA-seq is not limited by prior knowledge of the genome of the organism, it can be used for the detection of novel transcripts), the higher sensitivity for genes expressed either at low or very high level and the unbiased approach compared to microarrays that are subject to cross-hybridization bias. Overall, RNA-seq is a better technique for many applications such as novel gene identification, differential gene expression, and splicing analysis.
\nThe principle of RNA-seq is based on high-throughput next generation sequencing (NGS) technologies. The first step in the technique involves converting the population of RNA to be sequenced into cDNA fragments with adaptors attached to one or both ends, each molecule is then sequenced to obtain either single end short sequence reads or paired end reads [1]. These reads are stored in fastq files formats and consist of raw data for many analysis pipelines (Figure 1).
\nRNA sequencing.
The primary objective of this chapter is to present algorithms for clustering gene expression data from RNA-seq. Therefore, in the first section, we will describe the different steps of the gene expression analysis workflow from preprocessing the raw reads to gene expression clustering and classification. In the second part of the chapter we will describe traditional, model-based and machine learning clustering methods for gene expression data, then we will conclude this chapter with a study for clustering samples of four public datasets from recount2, using different clustering methods and also evaluating the performance of each one using the adjusted rand index (RDI) and accuracy.
\nRNA-seq has become a common tool for scientists to study the transcriptome complexity, and a convenient method for the analysis of differential gene expression. A typical RNA-seq data analysis workflow starts by preprocessing raw reads for contamination removal and quality control checks. The following step is to align the reads to a reference genome, or to make a de novo assembly if there is not any. Following the alignment, the quantification step aims to quantify aligned reads to produce a count matrix to use as entry data for Differential Expression (DE) analysis. Normalization and DE analysis normally go together as most of the methods have built-in normalization and accept only raw count matrix. For this study, we are more interested in the clustering step, we will perform Normalization of the raw counts separately and do the clustering without going through differential gene expression analysis. In the following section we describe with more details each step of the pipeline (Figure 2).
\nRNA-seq data analysis workflow.
Preprocessing raw reads consist of checking the quality of the reads, adapters trimming, removal of short reads and filtering bad quality bases. Tools like FastQC can generate a report summarizing the overall quality of the sequence information [2]. Based on this report we can determine how the quality trimming should be set up. Trimmomatic is one of many tools used to clean up the raw data. It can be used to remove adapters from the reads, trim off any low-quality bases at the ends of reads, and filter short reads that can align to multiple locations on the reference genome. Once the trimming step is done, it is a good practice to recheck the quality of the reads by rerunning FastQC.
\nNow that we have explored the quality of our raw reads, we can move on to read alignment. Read alignment is one of the first steps required for many different types of analysis. It aims to map the huge number of short RNA sequences generated by NGS instruments (reads) to a reference genome in order to identify the correct genomic loci from which the read originated. In RNA-seq, alignment is a major step for the calculation of transcript or gene expression levels; several splice-aware alignment methods have been developed for RNA-seq experiments such as STAR, HISAT2 or TopHat. These aligners are designed to specifically address many of the challenges of RNA-seq data mapping using a strategy to account for spliced alignments [3, 4, 5].
\nQuantification of gene expression is to count the number of reads that map to each gene using methods such as HTSeq-count, FeatureCounts or kallisto [6, 7, 8]. This step is crucial if we want to do a gene differential expression analysis, which means to identify genes (or transcripts), if any, that have a statistically significant difference in abundance across the experimental groups or conditions.
\nThe read counts generated in the quantification step need to be normalized to make accurate comparisons of gene expression between samples or when doing an exploratory data analysis. Several normalization methods are used for this purpose such as CPM (counts per million), TPM (transcripts per kilobase million), RPKM/FPKM (reads/fragments per kilobase of exon per million reads/fragments mapped), DESeq2’s median of ratios and EdgeR’s trimmed mean of M values (TMM) [9].
\nCluster analysis techniques have proven to be helpful to understand gene expression data by uncovering unknown relationships among genes and unveiling different subtypes of diseases when it comes to clustering biological samples [10]. In the following section, we present methods for sample-based and gene-based clustering, starting with traditional methods used after data transformation then model-based clustering for data generated using a combination of probability distributions (Figure 3).
\nCluster analysis of RNA-sequencing data.
Traditional clustering algorithms like hierarchical clustering and k-means cannot be directly applied to RNA-seq count data, to apply these methods for cluster analysis of RNA-seq data, that tend to follow an over-dispersed Poisson or negative binomial distribution, we need to transform the data in order to have a distribution closer to the normal distribution. In the following section, we present popular methods for data transformation:
Logarithmic, widely used method to deal with skewed data in many research domains, often used to reduce the variability of the data and make the data conform more closely to the normal distribution. However, it was demonstrated in [11], that in most circumstances the log transformation does not help make data less variable or more normal and may, in some circumstances, make data more variable and more skewed.
Variance stabilizing transformation: This method was used to transform microarray data to stabilize the asymptotic variance over the full range of the data [12].
Eight data transformations (r, r2, rv, rv2, l, l2, lv, and lv2) for RNA-seq data analysis were proposed in [13], these methods deal with the two common properties when it come to the count matrix generated in the quantification step, Sparsity and Skewness; Sparsity means that many counts in the count matrix are zero. Skewness means that the histogram of all counts in the count matrix is usually skewed.
Hierarchical clustering method is the most popular method for gene expression data analysis. In hierarchical clustering, genes with similar expression patterns are grouped together and are connected by a series of branches (clustering tree or dendrogram). Experiments with similar expression profiles can also be grouped together using the same method. This clustering technique is divided into two types: agglomerative and divisive. In an agglomerative or bottom-up clustering method each observation is assigned to its own cluster. In a comparative study on Cancer data [14], three variants of Hierarchical Clustering Algorithms (HCAs): Single-Linkage (SL), Average-Linkage (AL) and Complete-Linkage (CL) with 12 distance measure have been used to cluster RNA-seq Samples. The same methods will be used in our study along with hierarchical clustering with Poisson distribution [15].
\nK-medoids is a partitional clustering algorithm proposed in 1987 by Kaufman and Rousseeuw. It is a variant of the K-means algorithm that is less sensitive to noise and outliers because it uses medoids as cluster centers instead of means that are easily influenced by extreme values. Medoids are the most centrally located objects of the clusters, with a minimum sum of distances to other points. After searching for k representative objects in a data set, the algorithm which is called Partitioning Around Medoids (PAM) assigns each object to the closest medoid in order to create clusters. Like in k-means the number of classes to be generated needs to be specified.
\nYaqing Si et al. described a number of Model based clustering methods for RNA-seq data in their paper [16], these methods assume that data are generated by a mixture of probability distributions: Poisson distribution when only technical replicates are used and Negative binomial distribution when working with biological replicates. The first method they proposed is a model-based clustering method with the expectation-maximization algorithm (MB-EM) for clustering RNA-seq gene expression profile. The expectation-maximization algorithm is widely used in many computational biology applications, the authors in [17] explain how this algorithm works and when it is used. The second method is an initialization algorithm for cluster centers, the idea behind this method is to randomly choose one cluster center and then gradually add centers by selecting genes based on the distance between each gene and each of the selected centers. Two other stochastic algorithms have been proposed in this paper, a stochastic version of the expectation-maximization algorithm and a classification expectation maximization algorithm with simulated annealing. The last method in this paper is a model-Based Hybrid-Hierarchical Clustering Algorithm, it does not require to pre-specify the number of clusters to be generated as it is required by the previous methods. The authors propose to use agglomerative clustering starting with k0 clusters to speed up the calculation, then, it repeatedly identifies the two clusters that are closest together and merges the two most similar clusters. This method was called hybrid because it combines two steps: Obtaining the initial K0 clusters using one of the previous described algorithms then agglomerative clustering to build the hierarchical tree.
\nClassification in machine learning is a supervised learning approach in which the algorithm learns from the data given to it and makes new observations, then applies the conclusions to new data. Clustering on the other hand is an unsupervised learning problem for grouping unlabeled features. The learning algorithm that learns the model from the training data and maps the input data to a specific class is called classifier, in the following section, we briefly present three widely used classifiers for grouping RNA-seq data.
Random forests (RF): an ensemble method that trains a large number of individual decision trees, each tree gives a class prediction, the category that wins the majority votes is used as the final decision of the random forest model. The algorithm can perform both classification and regression tasks and has better accuracy among current algorithms.
Support Vector Machine (SVM): one of the most popular supervised learning models, used for both classification and regression, the data points are separated using an optimal hyperplane or a set of hyperplanes in a multidimensional space with the maximum possible margin between support vectors.
Poisson linear discriminant analysis: an approach used for the classification and clustering of RNA-seq data using a Poisson log linear model [15].
To test these algorithms, we used MLSeq (Machine learning interface for RNA-sequencing data) which is an R package including more than 80 machine learning algorithms and a pipeline to classify RNA-seq data including normalization, filtering and transformation steps [18].
\nDeep learning is also a technique that can be used to learn better data representation of high-dimensional data. The two recently published surveys [19, 20] present a taxonomy of existing deep clustering algorithms, by describing the different Neural Network Architecture that exists for feature representation, clustering loss function and Performance Evaluation Metrics for Deep Clustering. In [20], the authors categorize current deep clustering models into following three categories:
Auto-Encoders Based Deep Clustering
CDNN-Based Deep Clustering (feed-forward networks trained only by specific clustering)
Generative Adversarial Network (GAN)
These approaches are already used in the analysis of RNA-seq data, for example, an unsupervised deep embedding algorithm that clusters single cell (scRNA-seq) data was proposed in [21], another paper use a Lasso model and a multilayer feed-forward artificial neural network to analyze RNA-Seq gene expression data [22]. In [23], the authors used a Deep Neural Network model from the R package h2o for cancer data classification and in [24], ladder networks were used for gene expression classification.
\nClustering algorithms and software packages corresponding to the algorithms are shown in Table 1.
\nMethods | \nImplementation in R | \n
---|---|
Hierarchical clustering | \nhclust() function in “stats” | \n
k-means | \n“cluster”, “factoextra” | \n
k-medoids | \n“cluster”, “factoextra” | \n
SOM | \n“kohonen” | \n
Model-based clustering with the expectation-maximization algorithm (MB-EM). Stochastic version of the expectation-maximization algorithms (Deterministic annealing (DA) algorithm). Classification expectation maximization (CEM) algorithm with simulated annealing (SA). | \n“MBCluster.Seq” | \n
Machine learning algorithms | \n“MLSeq” | \n
Clustering algorithms and software packages corresponding to the algorithms.
Recount2 is a multi-experiment resource of analysis-ready RNA-seq gene and exon count datasets. It contains 2041 different studies and over 70,000 human RNA-seq [25]. We selected for our study four different datasets based on the number of samples and the number of classes. We then performed sample-based clustering on each dataset and compared the results to the classes in the phenotype table in recount2 to evaluate the performance of each method. The methods used to classify the data are 3 subtypes of the hierarchical clustering with the Euclidean distance, hierarchical clustering with a Poisson model and k-medoids.
\nDescription of the four datasets from recount2 is shown in Table 2.
\nDataset (accession) | \nNumber of samples | \nNumber of classes | \nClasses | \n
---|---|---|---|
SRP032789 | \n20 | \n4 | \n17 breast tumor samples of three different subtypes: \n
| \n
SRP049097 | \n54 | \n4 | \n3 subtypes of Leiomyosarcoma: \n
| \n
SRP042620 | \n168 | \n6 | \n\n
| \n
SRP044668 | \n94 | \n3 | \n\n
| \n
Description of the four datasets from recount2.
There are several similarity measures for cluster evaluation, we chose to work with the adjusted Rand index which is the corrected-for-chance version of the Rand index. It is a measure used in data clustering to evaluate the performance of a clustering method, by comparing the results of a clustering algorithm against known classes from external criteria [26]. In our study, we performed different sample-based classification method on four different datasets, after that, we compared the results to the class labels we associated to each sample based on the field “characterization of the samples” in the phenotype table in recount2, then we used the ARI for cluster validation.
\n\nFigures 4–6 are examples to show the standard deviation of the transformed data, across samples, against the mean, using the shifted logarithm transformation, the regularized log transformation and the variance stabilizing transformation.
\nStandard deviation of the transformed data using the shifted logarithm transformation.
Standard deviation of the transformed data using the regularized log transformation.
Standard deviation of the transformed data using the variance stabilizing transformation.
Three widely used machine learning algorithms were used for the classification of the four datasets, Random forests, support vector machine and Poisson linear discriminant analysis. To perform this analysis, we first split the data into two parts as training and test sets, with 70% of samples for the training dataset, and the remaining 30% samples for the testing dataset, the training set is used to fit the parameters of the model, that is used thereafter to predict the responses for the observations in the test dataset. Normalization was applied with Deseq median ratio method and the variance stabilizing transformation was applied for the normalization of the dataset. The model was trained using 5-fold cross validation repeated 2 times. The number of levels for tuning parameters is set to 10.
\nAll experiments are performed on a machine with 16 GB RAM, 1024 GB hard disk running with a windows operating system and MLSeq R package.
\nClustering the samples of the three datasets to the sub-classes defined in the phenotype table of recounts2 was not easy. We first tried to visualize the separation between the subtypes using principal component analysis (Figures 7 and 8–10), then using 4 variants of the hierarchical clustering and k-medoids we classified the samples of each dataset (Figures 11 and 12 show the hierarchical clustering plots of the dataset SRP032789). The performance of the 5 methods was different depending on the dataset (Tables 3–5), making it impossible to make a general system of recommendation. However, we can see that the k-medoid method has relatively better performance than the other methods for all the datasets. In the second part of the study, we compared a few machine learning methods used for the classification of RNA-seq data. The performance of the models surpasses the classical methods used before, also RF and PLDA performed better than SVM which does not perform very well when the data set is large and has noise. Note that the model accuracies given in this study should not be considered as a generalization. The results can depend on several criteria: normalization and transformation methods, gene-wise overdispersions, outliers, number of classes etc. (Tables 6–11).
\nPCA of data from the study SRP032789.
PCA of the data from the study SRP049097.
PCA of the data from the study SRP042620.
PCA of the data from the study SRP044668.
Dendrograms obtained for the dataset from SRP032789 study using three variants of the hierarchical clustering method with the Euclidean distance.
Dendrograms obtained for the dataset from the study SRP032789 using the hierarchical clustering method with the Poisson distance.
hclus (complete) | \nhclust (single) | \nhclust (average) | \nhclust (complete) | \nk-medoids | \n
---|---|---|---|---|
Euclidean | \nEuclidean | \nEuclidean | \nPoisson distance | \nEuclidean | \n
0.4146015 | \n0.3818763 | \n0.4146015 | \n0.4146015 | \n0.6798897 | \n
Performance of clustering methods (SRP032789).
hclus (complete) | \nhclust (single) | \nhclust (average) | \nhclust (complete) | \nk-medoids | \n
---|---|---|---|---|
Euclidean | \nEuclidean | \nEuclidean | \nPoisson distance | \nEuclidean | \n
0.02880412 | \n−0.003409256 | \n0.0005777741 | \n0.1874828 | \n0.2791547 | \n
Performance of clustering methods (SRP049097).
hclus (complete) | \nhclust (single) | \nhclust (average) | \nhclust (complete) | \nk-medoids | \n
---|---|---|---|---|
Euclidean | \nEuclidean | \nEuclidean | \nPoisson distance | \nEuclidean | \n
0.1944569 | \n0.005551586 | \n0.1285448 | \n0.1468464 | \n0.2579758 | \n
Performance of clustering methods (SRP042620).
hclus (complete) | \nhclust (single) | \nhclust (average) | \nhclust (complete) | \nk-medoids | \n
---|---|---|---|---|
Euclidean | \nEuclidean | \nEuclidean | \nPoisson distance | \nEuclidean | \n
0.2379903 | \n−0.007755123 | \n0.399417 | \n0.2657942 | \n0.3771837 | \n
Performance of clustering methods (SRP044668).
Classifier | \nAccuracy | \nKappa | \n
---|---|---|
rf | \n1 | \n1 | \n
SVM | \n0.6667 | \n0.5 | \n
PLDA | \n1 | \n1 | \n
Classification results for SRP032789 data.
Classifier | \nAccuracy | \nKappa | \n
---|---|---|
rf | \n0.8235 | \n0.765 | \n
SVM | \n0.7647 | \n0.6909 | \n
PLDA | \n0.7647 | \n0.6866 | \n
Classification results for SRP049097 data.
Classifier | \nAccuracy | \nKappa | \n
---|---|---|
rf | \n0.9412 | \n0.9249 | \n
SVM | \n0.5882 | \n0.4685 | \n
PLDA | \n0.7843 | \n0.7267 | \n
Classification results for SRP042620 data.
Classifier | \nAccuracy | \nKappa | \n
---|---|---|
rf | \n0.8214 | \n0.7271 | \n
SVM | \n0.6786 | \n0.5218 | \n
PLDA | \n0.7143 | \n0.5573 | \n
Classification results for SRP044668 data.
\n | SRP032789 | \nSRP049097 | \nSRP042620 | \nSRP044668 | \n
---|---|---|---|---|
rf | \n176.67 | \n781.31 | \n4234.89 | \n1412.19 | \n
SVM | \n1080.92 | \n2333.52 | \n6645.21 | \n1597.89 | \n
PLDA | \n31.45 | \n60.93 | \n234.98 | \n72.66 | \n
Computational time in seconds.
The authors declare no conflict of interest.
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