\r\n\tThis book will consist of chapters that are an elegant mix of reviews and current developments on the subject that will be useful both to an expert on the subject as well as a newcomer to this area of research.
",isbn:"978-1-83969-076-1",printIsbn:"978-1-83969-075-4",pdfIsbn:"978-1-83969-092-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"8a2fd9bbbbae283bf115881d9d5cc47a",bookSignature:"Dr. Ashim Kumar Dutta",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11857.jpg",keywords:"Frenkel Excitons, Wannier-Mott Excitons, Low Dimensional Solids, Molecular Crystals and Aggregates, Exciton Diffusion and Hopping, Exciton–Exciton Annihilation, Dynamics, Scaling Laws, Photoluminescence, Exciton Lifetime, Energy Harvesting, Semiconductors",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 19th 2022",dateEndSecondStepPublish:"May 17th 2022",dateEndThirdStepPublish:"July 16th 2022",dateEndFourthStepPublish:"October 4th 2022",dateEndFifthStepPublish:"December 3rd 2022",remainingDaysToSecondStep:"5 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Ashim Kumar Dutta received his Ph.D. in physical chemistry from the Indian Association for the Cultivation of Science (IACS). He has worked on various international post-doctoral fellowships in Japan, Canada, and USA. Dr. Dutta has worked as head of research and product development in several companies, and presently works as vice-president for India Glycols Limited. He has authored/co-authored 36 articles in international journals and 21 patents.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"277477",title:"Dr.",name:"Ashim",middleName:"Kumar",surname:"Dutta",slug:"ashim-dutta",fullName:"Ashim Dutta",profilePictureURL:"https://mts.intechopen.com/storage/users/277477/images/system/277477.jpg",biography:"Dr. Ashim Kumar Dutta presently works as the vice president (R&D) with India Glycols Limited, one of the largest manufacturers of Green Surfactants in South East Asia. Earlier, he had worked with Unilever as a senior researcher and product development manager in their Home and Personal Care Category, with United Phosphorus Limited and Indofil as their global head for agrochemical formulations. He has authored/co-authored 36 articles in international journals and 19 patents. He received his Ph.D in physical chemistry from Indian Association for the Cultivation of Science (IACS) – a premiere research institute in India in 1993. Dr. Dutta has worked on various international post-doctoral fellowships in Japan, Canada and USA. His research interests include supramolecular assemblies, ultrathin nanostructured films, nanoparticles, novel surfactants, surfactant-polymer interactions, bio-membranes and spectroscopy of Langmuir-Blodgett films, tribology and rheology of complex systems.",institutionString:"India Glycols Limited",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"440204",firstName:"Ana",lastName:"Cink",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/440204/images/20006_n.jpg",email:"ana.c@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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According to the United States Centers for Disease Control and Prevention (CDC), a symptomatic urinary tract infection must meet at least one of the following criteria:
Patients had/did not have an indwelling catheter in place at the time of specimen collection or onset of signs or symptoms
Patient has at least one of the following signs or symptoms with no other recognized cause: fever (>38oC), urgency, frequency, dysuria, suprapubic tenderness or costovetebral angle pain or tenderness
Patient has a positive urine culture of ≥105 with no more than 2 species of microorganisms [20].
UTI is considered to be the most common bacterial infection [107]. It is the second most common infection of any organ and is one of the most common infections in humans [157]. UTIs account for nearly 8 million physician visits and 1.5 million visits to emergency rooms annually in the United States [44, 87, 144]. Although every individual is susceptible to UTIs, certain specific subpopulations are more predisposed to the risk of UTIs. This includes infants, pregnant women, elderly, patients with spinal cord injuries and/or catheters, patients with diabetes, multiple sclerosis, or acquired immunodeficiency virus, and patients with underlying urologic abnormalities [13, 31, 43, 127, 130]. UTIs are usually localized to the bladder, kidneys or prostate. The etiology of UTIs has been regarded as well-established and consistent.
In addition to being the most common bacterial infection, UTIs are also the most common type of hospital acquired infections (HAI). HAIs can be defined as a localized or systemic condition resulting from an adverse reaction to the presence of an infectious agent or toxin, which occurs in a patient in a health care setting and was not present or incubating at the time of admission [64, 66]. UTIs account for 30% of all HAI [77]. Of these 30% infections, 80% of them are estimated to be catheter-associated [89]. According to the CDC, CAUTIs are defined as an UTI in a patient who had an indwelling urinary catheter in place at the time of or within 48 hours prior to infection onset. CAUTI can lead to complications such as cystitis, pyelonephritis, gram-negative bacteremia, prostatitis, epididymitis, endocarditis, vertebral osteomyelitis, septic arthritis, endophthalmitis and meningitis [20]. Additionally CAUTIs also result in prolonged hospital stay, increased cost and mortality [77]. An estimated 15-25% of hospitalized patients will have a urinary catheter at some point during their hospital stay [175]. Obstruction of indwelling catheters can lead to sepsis, even resulting in mortality [174]. Each year around 13,000 deaths are attributed to UTIs in the United States [77]. The cost associated with CAUTI episodes is about $750-$1000 per infection, and the estimated total cost in the United States ranges from $340-$450 million annually [132].
Millions of transurethral, suprapubic and nephrostomy catheters or urethral stents are used in patients every year. These devices overcome several host defenses and enable bacterial entry at a rate of 3 to 10% (cumulative rate) per day, which leads to bacteriuria in patients after a month [8]. In intubated patients, bacteria frequently ascend from the urethral meatus into the bladder between the mucosal and catheter surfaces. In certain cases, bacteria may ascend through the drainage system due to contamination of the drainage bag or disruption of the tubing junction. The presence of a device enables the persistence of the etiologic organism in the urinary tract. Several studies have demonstrated that bacteria exist as biofilms on these devices [53]. Formation of a biofilm and incrustation with calcium and magnesium struvites has a significant role in the pathogenesis and treatment of catheter-associated infections.
Biofilms have been around for billions of years. They have been identified in 3.2 – 3.4 billion year old South African Kornberg formation, and in deep-sea hydrothermal rocks [55]. Similar biofilms can be found in modern hot springs and deep-sea vents [124, 160]. The presence of biofilms in both ancient fossils and in similar modern environments indicates that biofilm formation is an ancient and integral characteristic of prokaryotes. It is likely that biofilms provided homeostasis during the harsh and fluctuating conditions of the primitive earth such as extreme temperatures, pH and exposure to UV light, thus enabling complex interactions between individual cells. It is, however, generally accepted that planktonic cells existed before the development of biofilm communities. The concomitant development of both planktonic and sessile bacteria in biofilm communities could be attributed to the conditions offered by life on surfaces [151]. The ability of bacteria to adhere to surfaces and form biofilms in different environments is due to the selective advantage that surface association offers the bacteria.
The definition of biofilm has evolved over the years. Marshal in 1976 [94] observed the presence of fine extracellular polymer fibrils that anchored bacteria to the surface. Costerton and coworkers [1978; 28] defined biofilms as communities of attached bacteria that were found to be encased in a glycocalyx matrix of polysaccharide that mediates adhesion [28]. They also stated that biofilms consist of single cells and microcolonies which are embedded in the matrix [26]. This definition was later modified to include the ability of biofilms to adhere to surfaces and to each other forming microbial aggregates and floccules [29]. The adhesion to a surface also triggers the expression of genes controlling production of bacterial components required for biofilm formation, thus including the role of gene modulation in the definition [29]. Consequently, a definition of biofilm must include the ability of cells to attach to a surface, extrapolymeric encasing, presence of noncellular and abiotic components in the matrix, physiological attributes of these organisms and the differential gene expression in biofilm cells versus planktonic cells. Taking all this into account, biofilms can be defined as a microbially derived sessile community consisting of cells that are attached to an interface or to each other, are embedded in an extracellular polymeric matrix that they have produced and demonstrate altered phenotype associated with differential gene expression [38]. This definition also applies to biofilm cells that have broken off from a biofilm on a colonized medical device and circulate in the body fluids with the ability to establish itself in another niche.
Biofilms can form on abiotic surfaces such as minerals, air-water interfaces, and biotic surfaces such as plants, other microbes and animals. In the human body, bacteria reside as biofilms on skin, oropharynx and nose, intestine and indwelling medical devices. To form a biofilm, bacteria are attracted to the surface by environmental signals. On reaching the surface, the bacteria attach to it as single cells or as clusters. When single cells attach to a surface they form a monolayer biofilm. A monolayer biofilm can be defined as one in which the bacteria attach only to the surface [75]. When bacteria attach to a surface as a cluster, they form a multilayer biofilm. Multilayer biofilms can be defined as a microbial community, where the bacteria are attached both to the surface and the neighboring bacterial cells [75]. The type of biofilm formed depends on the environmental conditions and surfaces that favor their development, the genes that are activated, the architecture of the biofilm and the matrix composition [75].
Monolayer biofilms are composed of a single layer of cells attached to a surface. These biofilms are favored when cell-surface interactions predominate. Since monolayer biofilms offer bacteria more proximity to surfaces, they commonly occur during the interaction of the bacterial pathogen with the host. In flagellate motile bacteria, monolayer formation occurs in two steps, where bacteria first become attached to a surface when they come in close proximity to it. After attachment, the bacteria break the forces tethering them to the surface, resulting in transient attachment. However, a few bacteria that have transitioned from transient to permanent attachment remain attached to the surface. Multilayer biofilms form when bacteria adhere to the surface as well as to each other. Several adhesion factors are known to mediate this transition, including preformed adhesins, conditionally synthesized adhesins and specific adhesins.
Preformed adhesins include flagellum and pili. Motility is believed to increase the initial interaction between bacteria and the surface. Several studies have also demonstrated that flagellar motility promoted surface adhesion in bacteria [76, 85, 167]. However, under certain conditions, flagellar mutants that are defective in the synthesis of flagellar components have shown an increased synthesis of adhesive matrix that promotes bacterial attachment and multilayer biofilm formation [83, 176]. These observations indicate that flagellar impedence may be important in priming the bacteria for the formation of a multilayer biofilm. Nevertheless, mutants lacking the flagellum or the flagellar motor are completely defective in monolayer and multilayer biofilm formation [83], implying that flagellar motor plays a vital role in biofilm formation independent of flagellar motility. Retractable pili are critical for gram-negative bacteria to attach to surfaces [75]. It is hypothesized that these structures pull bacteria along surfaces by attaching to the surface and retracting, thus helping the bacteria approach the surface more closely [75].
Bacteria can also conditionally synthesize adhesins to promote surface attachment. In
Bacterial cells in the biofilm are surrounded by a variety of molecules that make up the matrix of the biofilm. The matrix is highly hydrated and can contain up to 97% water [154]. In addition, the matrix is composed of polysaccharides, proteins, DNA, surfactants, lipids, glycolipids, membrane vesicles and ions like calcium. This composition varies with different conditions or stages during biofilm maturation. The biofilm matrix is dynamic and interactive, and is essential to the integrity and function of the biofilm.
Exopolysaccharides are a major component of the biofilm matrix. The absence of polysaccharide synthesis and export leads to an inability to form multilayer biofilms in most bacteria. Bacteria capable of forming biofilms possess distinct genetic loci that encode for the synthesis of polysaccharides. One of the most common exopolysaccharides in the biofilm matrix is a polymer of β-1, 6-N-acteyl-D-glucosamine called PGA or PNAG. Several bacterial species, including
The biofilm matrix is also composed of proteins exported to the matrix by cells within the biofilm. Proteinaceous appendages such as fimbriae and pili confer adhesive properties in bacteria. In
Another major component of the biofilm matrix is eDNA (extracellular DNA). In
An important characteristic of bacterial cells within the biofilm is the chemical mediated cell-cell crosstalk known as quorum sensing. Quorum sensing allows bacteria to coordinate their gene expression in a density-dependent manner [75]. These circuits involve chemical mediators or autoinducers that are secreted by the bacteria and accrue in the extracellular environment. When the autoinducer concentration exceeds a certain threshold, quorum sensing is activated. In most gram negative bacteria, the prototype quorum sensing system is the LuxI/LuxR system [61]. LuxI proteins synthesize the autoinducer such as acylated homoserine lactone (AHL), which modulates the activity of LuxR to activate gene expression upon binding. In case of gram positive bacteria, oligopeptides serve as autoinducers which then activate gene expression in a two component system [61]. Activation of quorum sensing has been shown to stimulate biofilm formation in
Existence as a biofilm is advantageous to the bacterium since it enables its survival under a variety of conditions. However when the environmental conditions change or their microenvironment becomes unfavorable, bacteria can return to their planktonic state. This is referred to as dispersion of biofilms. Dispersion of biofilms can be brought about by degradation of the biofilm matrix, which will lead to disruption in cell to cell adhesion and escape from the biofilm. Several bacteria have been shown to produce enzymes that can degrade matrix components and result in biofilm dispersion [15, 69]. Another mechanism of dispersion is through the induction of motility. Onset of dispersal has been shown to coincide with a return in motility of the biofilm associated cells [72]. Certain bacterial biofilms also produce surfactants such as rhamnolipids. Biofilms formed by strains of
The biofilms on medical devices can be composed of gram-positive and gram-negative bacteria, or yeast. Commonly isolated bacteria include gram-positive organisms such as
CAUTIs account for around 80% of all nosocomial UTIs [89]. The risk of developing an UTI significantly increases with the use of indwelling devices. It has been reported that the risk of developing CAUTI increases 5% with each day of catheterization, and virtually all patients are colonized by day 30 [91]. Several studies also support the role of biofilm in the establishment of CAUTIs [161, 167]. The predominant pathogens associated with UTIs include
Prior to the initial attachment of bacteria to the device surface, it is critical that the surfaces are conditioned, where the attachment of proteins and polysaccharides from the fluid environment form a film on the exposed surface of the device [161, 167]. This conditioning film facilitates the initial bacterial attachment, which normally adhere poorly on uncoated surfaces [58]. Indwelling devices used in the urological settings include open and closed catheters, urethral stents and sphincters and penile prostheses. Biofilm formation has been documented from infection sites associated with all of these device types [24, 161]. Among all these devices, urinary catheters serve as the common substrate for the development of UTIs [166]. Numerous studies have demonstrated the presence of adherent biofilms on catheters removed from patients [104]. Additionally, scanning electron microscopy studies have documented extensive biofilm formation on urinary catheters [111]. Such catheters recovered from patients that failed antibiotic therapy were shown to contain
Foley catheters are commonly used to manage urinary incontinence in elderly patients and those with bladder dysfunction. These devices besides helping the patient also put them under high risk for the development of UTIs. Uropathogens such as
Uropathogenic
Type I pili are pertrichously present on the cell surface of many members of the Enterobacteriaceae, which includes both pathogenic and commensal strains of
In addition to their role in adherence, type I pili are also essential for the invasion of bladder epithelial cells by UPEC. TEM and SEM imaging have revealed that bladder cells internalize UPEC through interactions between FimH and UP1a [99]. Other studies have also demonstrated that type I pili carrying bacteria interact with plasma membrane micro domains knows as lipid rafts [39]. More specifically, caveolae, a subtype of the lipid rafts with a cave-like appearance have been shown to associate with intracellular bacteria during UPEC invasion. Besides the bladder cells, UPEC can also bind and invade macrophages [10] and mast cells [136], thereby serving as a source of chronic UTIs. The ability of UPEC to invade macrophages allows the bacteria to survive within them and evade phagocytosis. Besides tiding over phagocytosis, ability to survive inside bladder cells also helps to avoid host defenses, including urine flow, secretion of adhesion-binding competitors such as Tamm-Horsfall protein, IgA, chemokines, and exfoliation of superficial bladder cells [113, 155]. UPEC sequestered within the bladder cells are also protected from antibiotic treatments that sterilize the urine, and are provided a rich environment in which the bacteria replicate [100]. UPEC has the ability to form biofilms on abiotic surfaces such as polypropylene, polyvinylchloride, polycarbonate and borosilicate glass when grown statically [120]. Using transposon mutagenesis, Pratt and Kolter demonstrated that Fim mutants were defective in initial attachment and biofilm formation was severely impacted. This indicates that type I pili are essential for the initial attachment of UPEC to abiotic surfaces. Besides type I pili, motility also plays an important in biofilm formation. Non motile strains were severely defective in the initial attachment and consequently in biofilm formation [120].
UPEC are capable of attaching and invading uroepithelial cells, persisting and forming intracellular reservoirs that help them escape host defenses [100]. Anderson and coworkers [2003; 7] hypothesized that UPEC reservoirs are established by the formation of biofilm-like pods or intracellular bacterial communities (IBC) within the bladder cells. Replication of UPEC in the superficial bladder cells leads to the formation of tightly packed biofilm-like pods that protrude into the lumen. Bacteria inside these pods undergo continuous development leading to the maturation of the IBCs. The development of IBC can be divided into four phases. The first phase begins 1-3 h after infection. The type I pili bind and invade the superficial bladder epithelial cells [74]. At this stage the bacteria are non-motile and divide rapidly and by 8 h post infection, they form loosely organized colonies that resemble microcolonies of abiotic biofilms, known as early IBC. The next phase leads to the formation of middle IBCs, which is characterized by a reduction in cell proliferation and cell size. Each pod corresponds to a single epithelial cell tightly packed with bacteria forming an intracellular biofilm. Within the pods, a polysaccharide matrix surrounds the bacteria [7, 74]. At around 12 h post infection, late IBCs are formed, when UPEC regain their rod shape and motility and flux out of the bladder cells. Fluxing aids UPEC in infecting neighboring cells [74]. The last phase of IBC formation results in UPEC filamentation which occurs 24 to 48 h post infection, where filamentation helps UPEC evade host immune responses. The filamentous bacteria can also separate to form rod-shaped daughter cells. The appearance of filamentous cells also coincides with the appearance of small groups of UPEC on newly infected healthy cells [74].
The pathogenesis of CAUTI depends on the physicochemical properties of the catheter material and its susceptibility to bacterial colonization. Bacterial binding to the bladder mucosa triggers an inflammatory response that leads to neutrophil influx and sloughing of the infected epithelial cells [78]. This helps to clear the bacteria from the mucosal surface. In the case of a catheter, besides the absence of inherent defense mechanisms, they also provide a survival advantage to the bacteria which become difficult to eradicate. The advantages include resistance from being swept away by the urine flow, resistance to phagocytosis and antimicrobials [167]. In addition to the catheter providing an environment for biofilm formation, the presence of a catheter helps to weaken many normal defenses of the bladder. The catheter helps to connect the heavily colonized perineum with the sterile bladder, thus providing a route for bacterial entry into the bladder. Urine pools in the bladder or in the catheter and the resulting urinary stasis promote bacterial growth. Additionally, the catheter also damages the bladder mucosa by triggering inflammatory response and mechanical erosion [175]. Once bacteria gain entry into the urinary tract, low level bacteriuria progresses within 24 to 48 h in the absence of an antimicrobial therapy [145].
CAUTI is the most common hospital acquired infection and accounts for up to 40% of all health care associated infections in the United States [102, 156]. About 15-25% of hospitalized patients have an urethral catheter in place during some point of their stay. It is estimated that around 30 million bladder catheters are placed annually in the United States, resulting in several hundred thousand cases of CAUTI [156]. A systemic review of the proportion of health care associated infections that can be prevented revealed that CAUTI was the most preventable nosocomial infection [170]. An estimate of the number of avoidable cases ranged from 95,483 to 387,550 per year and associated lives saved ranged from 2225 to 9031 annually. This prevention could also avoid the annual cost of these illnesses which is estimated at $1.8 million to $115 million [170]. This underscores the need for control strategies to prevent CAUTI. Prevention of CAUTI is primarily based on reviewing the criteria for appropriate placement and early removal of catheters. The advances in our understanding of the pathogenesis and key factors that influence the onset of infection are also critical in the development of adequate and effective control strategies [137]. Several protective strategies have been suggested for CAUTI, some of which are already in place for patient care, whereas others are still in development. The control strategies include:
It is estimated that about 21-50% of catheters are placed without justified need and catheters are inappropriately retained for 33-50% of total device days [73, 101]. The most effective ways for the preventing CAUTI are by reducing the duration of catheterization and its early removal [51]. Use of interventions such as nurse prompted removal suggestions and computer based reminders to the patients have resulted in a decline in catheter retention and a concomitant reduction in bacteriuria [164]. Thus, it is important to refrain from using an indwelling catheter without an appropriate indication. A study conducted in an emergency department indicated that use of pre-insertion checklists have led to an improved adherence to indications for placement resulting in the increase in the number of appropriately placed catheters from 37% to 51% [50].
Since the catheter provides a connection between the highly colonized perineum and the sterile bladder, sterility during catheter handling and placement is of greatest importance. In this regard, hand hygiene plays a vital role in the prevention of CAUTI [16]. Insertion of a catheter in the emergency room rather than an operating room has been shown to be associated with higher rates of catheter associated bacteriuria (CAB; 158). Use of an aseptic insertion technique reduces the risk of acquiring resistant organisms in the hospital [63]. A randomized study conducted by Platt and others [1983; 118] demonstrated that hospitalized patients intubated with a catheter without a pre-sealed junction were 2.7 times more likely to develop CAB than patients with pre-connected catheter drainage bags and sealed junctions. Therefore, the use of closed catheter drainage systems universally is recommended [63]. Similarly, any breach in the closed drainage system would also increase the risk for CAB. Any manipulation of the indwelling catheter should be avoided so that breaches in the closed drainage and shear trauma can be minimized [25].
Catheter design has not changed significantly since the inception of the Foley catheter in the 1930s [97]. In addition to the catheter design, biocompatibility of the material is crucial. Catheter material can also impact the rate of biofilm formation. Scanning electron microscopy imaging of latex catheters revealed that presence of more uneven surfaces on it than other silicone counterparts which can promote bacterial adhesion [150]. Additionally latex has been associated with toxic effects
Cross linked insoluble polymers that are hydrophilic and trap water are known as hydrogels. Use of hydrophilic coating on catheters has been shown to improve patient comfort, reduce bacterial adherence and encrustation. The presence of hydrogels also increases lubrication and decreases bacterial adhesion to the interface of the tissue and the catheter [11]. However, conflicting data exist on the ability of hydrogel coated catheters to reduce CAUTI, which could be attributed to the type of hydrogel incorporated. Tunney and Gorman [2002; 169] used
Antimicrobial modification of catheters is achieved by coating, matrix loading and immersion in an antimicrobial solution. The primary objective behind the incorporation of antimicrobial on a catheter is to reduce bacterial attachment and biofilm formation. Additionally, release of antimicrobials from the catheters into the milieu is also another potential approach to control planktonic cells of uropathogens [56].
Nanoparticles by virtue of their small size have the ability to penetrate bacterial cells, disrupt cell membranes and bind to the chromosomal DNA. Lelouche and others [2009; 84] demonstrated that glass surfaces coated with magnesium fluoride nanoparticles inhibited biofilm formation by
The application of low intensity direct current (Ionotophoresis)
A variety of antimicrobials applied on urinary catheters have been investigated for their efficacy in controlling UTIs using
Plants are capable of synthesizing a large number of molecules [47], most of which are produced as a defense mechanism against predation by microorganisms and insects. A variety of plant-derived polyphenols are active components in traditional medicines [178]. A significant body of literature exists on the positive effects of dietary intake of berry fruits on human health, performance and disease [134]. Cranberry products such as its juice and tablets have been used as an alternative medicine to prevent UTIs in humans for decades. Clinical and epidemiological studies support the use of cranberry in maintaining a healthy urinary tract [117]. Although several studies have tested the antimicrobial effect of cranberries against multiple uropathogens, it was found to be most effective against UPEC.
Cranberries exert anti-adhesive effects on certain uropathogens [112] and this effect is specific to certain components of cranberry [110]. Cranberries contain three different flavonoids (flavonols, anthocyanins and PAC), catechins, hydroxycinnamic and other phenolic acids and triterpenoids. The anthocyanins are absorbed in the human circulatory system and transported without any chemical change to the urine [117]. Cranberry products do not inhibit bacterial growth, but reduced bacterial adherence to uroepithelial cells, thereby decreasing the development of UTI. The anti-adhesive effects of p-fimbriated UPEC to uroepithelial cells are related with A-linked PAC as compared with lack of anti-adhesion activities of B-linked PAC from grape, apple juice, green tea and chocolate [67]. The A-type PAC in cranberries enhances the anti-adhesive effects
Cranberry has undergone extensive evaluation in the management of UTIs. However, currently there is no evidence that cranberry can be used to treat UTIs. Hence, the focus has been on its use as a prophylactic agent in the prevention of UTIs [52]. The consumption of cranberry juice can help to prevent the adhesion of UPEC to the uroepithelium and thereby help reduce the incidence of UTIs. With rising concerns of antibiotic resistance among UPEC, cranberry could serve as an effective alternative in controlling UTIs.
Trans-cinnamaldehyde (TC) is a major component of the bark extract of cinnamon [1]. It is a generally recognized as safe (GRAS) molecule approved for use in foods by the Food and Drug Administration (FDA). The U. S. Flavoring Extract Manufacturers’ Association reported that TC has a wide margin of safety between conservative estimates of intake and no observed adverse effect levels, from sub-chronic and chronic studies [1]. The report also indicated no genotoxic or mutagenic effects due to TC. Although, cinnamon or cinnamon oil has been used for ages in the treatment of UTIs, no scientific study was undertaken to investigate its antimicrobial efficacy against uropathogens. Amalaradjou and group [2010; 4] investigated the efficacy of TC for controlling UPEC biofilm formation. They observed that TC as a catheter lock solution or as a coating significantly inactivated UPEC and prevented biofilm formation when compared to untreated catheters. In a follow up study, these researchers reported that TC decreased the attachment and invasion of UPEC in cultured urinary tract epithelial cells by down-regulating several virulence genes in the pathogen [5].
Besides the use of cranberry and TC, other plant derived natural antimicrobials have also been shown to be effective against uropathogens. Sosa and Zunino [2009; 141] demonstrated that
Silver is a well-known antimicrobial exerting its bactericidal action by inactivating bacterial enzymes and causing cell wall damage [96]. Silver alloy and silver oxide coatings on catheters were investigated for reducing CAB, where silver alloy coating was found to be more effective [131]. In addition to reducing CAB, other studies also demonstrated the ability of silver alloy to decrease CAUTI compared to silver oxide or latex catheters [143]. However other researchers have observed conflicting results with no difference in antibiofilm effect of silver alloy and silver oxide [122, 143].
Urease producing bacteria are known to produce crystalline biofilms and encrustation on catheters. Use of urease inhibitors such as acetohydroxamic acid and fluorofamide have been reported to reduce encrustation and thereby prevent CAB [98]. These urease inhibitors have been also shown to prevent urea break down and pH increase
Use of nonpathogenic microorganisms to counteract pathogenic bacteria is known as bacterial interference [137]. Colonization of catheter surfaces with nonpathogenic bacteria can prevent adhesion and colonization by pathogens. The nonpathogenic
Another potential approach investigated for controlling CAUTI is the use of bacteriophages. Catheters coated with T4 bacteriophage against
Liposomes are carrier or delivery vehicles that can carry both hydrophilic and hydrophobic molecules to their target site for delivery. This helps to increase the half life of the drugs besides protecting them from the environment. Liposomes containing ciprofloxacin embedded in a hydrogel coated catheter were evaluated in a rabbit model to investigate its antibiofilm effect against
Quorum sensing between bacterial cells in a biofilm have been shown to be essential for biofilm formation and maintenance. Inhibition of quorum sensing could therefore provide a potential route for the control of biofilms.
Catheters containing peizo elements can generate low energy acoustic waves that can lead to the formation of a vibrating coat along the catheter and prevent bacterial attachment and biofilm formation [60]. Scanning electron microscopy studies demonstrated that application of surface acoustic waves led to reduced biofilm formation by
Catheter associated urinary tract infections are the most common nosocomial infections and a vast majority of them are caused by biofilms formed on catheters. The complications caused by biofilms can undermine the patient’s quality of life and threaten their health. The high incidence of CAUTI and the consequent complications warrants the development and application of effective control strategies. Prevention is predominantly based on enforcing guidelines for appropriate catheter placement and early removal. However, a comprehensive understanding of bacterial biofilm formation, pathogenesis and other key factors essential for development of UTIs would help in the development of novel and effective control strategies.
A new intellectual movement in the field of cognitive science1 has been developed, above all, in the last two decades of the current century, starting from debates that took place, mainly, in the philosophy of science at the end of the twentieth century. This movement has been described more broadly by many authors as a “new mechanistic philosophy” [4, 5, 6, 7]. Strongly influenced by recent advances in computer science, neuroscience, and artificial intelligence, the theoretical framework developed by some of the movement’s most prominent authors offers a new physicalist (or materialist) and mechanistic view of human cognition2 [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21].
The theory formulated from the application of the neo-mechanistic philosophy to cognitive science and, specifically, to human cognition, can be called the
One of the most central elements present in the framework of MTHC is a “model of human cognitive computation” [9, 10, 11, 13, 15], which is also part of the attempt made by several influential authors to provide some type of unification or integration for the field of cognitive science [9, 10, 23, 24, 25]. However, some complex cognitive capacities and some particular aspects of human cognition still present a challenge for explanations constructed by using this theoretical structure [22].
My central goal in this chapter, therefore, is to present an argument to show that human cognition cannot yet be completely understood and explained in terms of mechanistic computation and that this view indeed presents many substantial limitations.
To develop my argument, I present, firstly, some of the central elements of this neo-mechanistic framework and its application to cognitive science. Secondly, I present the mechanistic model of human cognitive computation, as it is currently framed, and, based on the specialized literature, I show in what dimensions it helps our understanding of some aspects of human cognitive capacities, such as visual perception and memory consolidation. Thirdly, I show that to understand and explain some human cognitive capacities, such as self-consciousness and conscious informal reasoning and decision making, the neo-mechanistic framework shows substantial limitations. I conclude the chapter by suggesting that the notion of human artificial cognitive computation can be useful for several projects, but to fully understand natural human cognition we will most certainly have to consider theories that go beyond the current neo-mechanistic model of human cognitive computation.
The contemporary movement of neo-mechanistic philosophy has been historically associated with ideas already present in the period of Ancient Philosophy. Philosophers, such as Democritus, Leucippus, Aristotle, Epicurus, and Lucretius [9, 14, 26], for example, have been mentioned in the specialized literature as precursors. Although there is no unity of thought regarding this philosophical tradition, these thinkers would arguably have launched, in Western philosophical thought, the first notions linked to mechanistic reflections. In other words, these philosophers would have proposed the general idea that many phenomena in nature must be explained through their basic components, their forms of movement, their properties, and their interactions since these phenomena are also composed of these basic elements.
In Modern Philosophy, the history of what might be called “mechanistic philosophy” is quite complex, given the many debates over definitions of the term and the variety of positions that can be considered within a more general view of what the term means in this period. In any case, many authors consider that the movement of mechanistic philosophy in the seventeenth century is a reaction to Aristotelian natural philosophy and various natural philosophies of the Renaissance period [27]. The French philosopher René Descartes (1596–1650), for example, is considered one of the main figures who laid the foundations of modern mechanistic philosophy, especially with regard to explanations of biological natural phenomena [9, 27, 28, 29, 30, 31]. Des Chene [30] argues that Descartes united a mechanistic ontology, on the one hand, with a method of mechanistic explanation, on the other, applying these ideas to numerous biological phenomena, including the behavior of non-human animals and the human body.
Shortly thereafter, this reasoning would also be applied quite influentially to human beings and their mental capacities. One of the most prominent advocates of this view was the French philosopher and physician Julien Offray de La Mettrie (1709–1751), who published
At the beginning of the twentieth century, the debate about the best explanation for the complex phenomenon of “life” was still quite strong [32]. The controversy was over whether or not this phenomenon could be explained in mechanistic terms. In this context, a very influential work was that of the German-born American physiologist and biologist Jacques Loeb (1859–1924), published in 1912,
In the second half of the twentieth century, philosophers of science sought to analyze, in a more precise way, this mechanistic explanatory strategy. One of the most influential analyzes is present in the work of the American philosopher Ernest Nagel (1901–1985),
It was also during this period that some philosophers of science working in the field of biology began the task of elaborating, in an even more robust and systematic way, notions related to mechanistic explanations in science – mainly in biology. Along these lines, some pioneering works were the following: Herbert Simon,
Within this line of philosophical thinking, the work of William Bechtel and Robert Richardson,
It is also important to point out that in the development of the neo-mechanist movement, at the end of the twentieth century, we can distinguish, more generally, two main trends [5]. One of them focuses more on metaphysical and ontological directions. Authors who work in this line seek, above all, to answer what mechanisms are as real things in the world. The other strand followed in the direction of a greater elaboration of the philosophy of science with epistemological and methodological discussions about scientific explanations, mainly in the area of biology. They seek to explain how something works and not make claims about the ultimate reality of things. These two strands of the new mechanism have been elaborated in an enormous specialized literature that covers several scientific and philosophical areas, dominating a great part of the central debates. Despite being two dimensions that can be separated in the debate, ontological and epistemological discussions are deeply related in many works, both directly and indirectly.
The neo-mechanistic philosophy began to be applied with greater emphasis to cognitive science since the decade of 1990 – with this application becoming stronger in the first decade of the twenty-first century – and it has been better elaborated since then until the present days in central works of very influential authors [9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 35, 36, 37, 38, 39, 40, 41, 42, 43]. According to this view, human cognition, specifically, as well as biological cognition, in general, can be understood and explained through complex models of multilevel neurocognitive mechanisms. At these levels, there are causal processes related to cognitive information processing, cognitive representation, cognitive computing, as well as processes related to chemical and physical reactions that can be used to explain a given cognitive phenomenon. These are, in fact, autonomous processes of causation, which take place at all these different levels and are relevant to the explanation of the phenomenon of interest [44]. According to this theory of human cognition, namely, MTHC, all these causal levels and processes, although autonomous, can be related in a pluralistic mechanistic explanation, where the relevant scientific theories are integrated. As a result, MTHC includes not only a theory of human cognition but also a theory of the human neurocognitive relationship; that is, the theoretical framework suggests a possible solution to the problem of how we are to understand and explain the connection between human neural and cognitive phenomena, thus attempting to relate neuroscience and cognitive science.
The main objective of a mechanistic scientific explanation in scientific areas, such as biology, cognitive neuroscience, and cognitive science, is to identify the parts of a mechanism, its operations, its organization, and thus show how these elements constitute the system’s relationship with the phenomenon that must be explained [9, 10, 45]. Particularly, in cognitive science, the central idea present in the theory is that human neurocognitive processes are a type of information processing performed by neural systems (mechanisms). These processes and the components that carry them out can be decomposed into subparts, and these subparts are decomposed again, as far as necessary for the understanding of the investigated phenomenon. After that, these components and activities have to be located in the brain as spatiotemporal parts of a complex multilevel neurobiological mechanism. As a result, there may be multiple levels of mechanistic composition in a human neurocognitive mechanism.
Another important feature of MTHC is that it was developed within a broad physicalist context that is present in a vast amount of work in contemporary cognitive science, philosophy of cognitive science, and philosophy of mind. In this physicalist context, the theory tries to combine central ideas present in traditional cognitive science with the main ideas present in certain fields of neuroscience that investigate human cognition. In this sense, some authors argue that this mechanistic physicalist framework can provide a consistent way to build a unified science of cognition and integrate cognitive science and neuroscience [23, 24, 25, 40].
Indeed, integrating and unifying, from a physicalist background, traditional cognitive science and traditional neuroscience to understand and investigate human cognition is an old dream held by many authors. Patricia Churchland, in 1986, calls for the unification of cognitive research and neural research in her book
It is possible to argue that MTHC was articulated with the objective of providing this integration and unification in a more precise theoretical way and within a clear physicalist background. The influential version of MTHC by William Bechtel is a clear example. He considers the human phenomenon “mind-brain” as “a set of mechanisms for controlling behavior” [9], and he explains that cognitive phenomena (e.g., perception, attention, memory, problem solving, and language) can be characterized as “information-processing mechanisms” [9]. Bechtel [9] states that scientific disciplines that aim to explain cognitive activities recognize that “in some way, these activities depend upon our brain.” Or, to put it in another way: “Psychological phenomena are realized in brains comprised of neurons” [45]. This means that cognitive phenomena are physical and need to be explained in some physical (neural) way.
Craver and Tabery [47] describe the physicalist commitment quite clearly—“many mechanists opt for some form of explanatory anti-reductionism, emphasizing the importance of multilevel and upward-looking explanations, without rejecting the central ideas that motivate a broad physicalist world-picture.” Therefore, in this approach, there is no space for any form of dualism, pluralism, or non-physicalism of any kind in relation to the ontology of human cognition. There is, indeed, a clear commitment to a form of ontological monism, namely, physicalism, that underlies the neo-mechanistic theory of human cognition.
Neo-mechanistic ideas about human cognitive phenomena are becoming increasingly dominant in fields related to theoretical cognitive science and cognitive neuroscience [48]. Consequently, the neo-mechanistic framework is often presented as one of the main theories, or the main theory, to explain human cognition in the twenty-first century.
Formulations of the idea that human cognition can be considered in computational terms can already arguably be found in the works of Thomas Hobbes (1588–1679) and Gottfried Leibniz (1646–1716). However, it is in the first half of the twentieth century that new developments in this tradition made the thesis gain great strength [49]. Alan Turing (1912–1954), with his work on computation, made a solid mathematical contribution to advances in the attempt to build machines capable of thinking like humans. And with the development of the computer and the emergence of studies in computer science and artificial intelligence, there was an even greater push for the acceptance of these ideas in the period. Indeed, these were crucial factors in the development of cognitive psychology in the 1950s and cognitive science (in the specific sense) in the 1970s. In discussing the foundations of cognitive science, Gardner [3] states that “there is the faith that central to any understanding of the human mind is the electronic computer.” Furthermore, according to him: “Involvement with computers, and belief in their relevance as a model of human thought, is pervasive in cognitive science” [3].
The first formulations of the philosophical foundations and the most central bases of the “computational theory of cognition” were presented, above all, in central works by Hilary Putnam (1926–2016) and Jerry Fodor (1935–2017). It is mainly based on works like these that the “classical model of cognitive computation” was formulated [49]. According to this proposal, the human mind is a computational system similar in important respects to a “Turing machine,” which works through “Turing-style computations.” In this view, cognitive processes, such as problem solving, decision making, and formal reasoning, are performed through computations similar to those of a Turing machine.
Another line of work, however, developed an alternative notion of cognitive computation. Inspired by research in the field of neurophysiology, some authors in the 1980s proposed that cognitive computation was something very different from Turing-style computation [50]. The correct format of cognitive computation for them was that of neural networks, in which, very briefly, data nodes are connected in a particular way so that when the network is activated through an input, it can provide an output. This framework became known as connectionism, and it has been developed in numerous works since then. Many cognitive models of different phenomena were built based on this view, such as object recognition, speech perception, and sentence comprehension.
The notion of “cognitive mechanistic computation” is part of this tradition, and it is especially related to the model of neural networks. Craver [10], for example, writes about the “computational properties of brain regions” and “computational properties of neural systems,” without giving much detail about what exactly this means. In any case, it is clear that the supposed computation is much more related to concrete properties of neural systems than to abstract functional properties of psychological capacities considered in terms of Turing computation or something similar. Milkowski [11], in turn, presents a proposal that holds that neurocognitive processing occurs over states that contain information, but he does not elaborate much on the content and the semantic dimension of cognitive information or of putative cognitive computations.
Bechtel [9, 19] considers mental mechanisms as information-processing mechanisms that operate through neural representations and neural computations about vehicles and content. In his view, the “control theory of dynamical systems” shows how content is placed in this context. And Thagard [14, 15] thinks that mental mechanisms operate through computations that take place on representations at the cognitive level and computations that take place at the neural and molecular levels. In Thagard’s work, there is also recourse to the “theory of dynamical systems” (as in Bechtel’s); however, just in his version of the mechanistic theory, there is a definite number of mechanistic levels and extensive discussion about the “semantic pointers theory” of Chris Eliasmith.
Finally, there is the work of Piccinini [12, 13, 51], which is one of the most theoretically sophisticated and detailed among neo-mechanists regarding such issues. The author defends a mechanistic neurocomputational theory of human cognition. In his view, the human nervous system is a functional mechanism that produces computations through the activation of neurons, while the processing occurs in vehicles according to rules. Cognitive capacities are explained then by multilevel neurocognitive mechanisms that perform neural computations over neural representations. Besides, he thinks that neural computation (i.e., computations defined on the functionally relevant elements of neural activity) is not purely digital, as classically understood, nor purely analog, as alternatively understood; in his view, neural computation is
One does not need to enter so deep into these individual theories to see that they differ significantly. Craver mentions computations but does not offer an elaborated account. Thagard is the only one mentioning semantic pointers as central to the account. Milkowski and Piccinini attempt to avoid the problems with content, by means of focusing on formal properties. And Bechtel uses control theory to deal with the issue of content. As a result, it is not possible to derive from those accounts a single theory, as each author develops his/her own point of view with its significant particularities. There is, therefore, no theoretical substantial unity among these proponents.
However, one can try to find common aspects to evaluate at least the most basic and important tenets. To do that, an analysis of two cases where this mechanistic view on human cognitive computation can be applied will be helpful.
One of the best examples found in the specialized literature of a concrete application of this view to particular cognitive phenomena is related to memory, which, indeed, has been traditionally an object of study in the field of psychology [9, 10]. Functional analyses of the human memory capacity reveal the existence of many sub-capacities, such as short-term memory, long-term memory, phonological memory, visuospatial memory, semantic memory, episodic memory, and memory consolidation. In mechanistic terms, one of the best-understood phenomena in this memory system is memory consolidation. Roughly put, this is the phenomenon of transforming short-term memories (which are liable and easy to disrupt) into long-term memories, which are robust and enduring, when consolidation takes place and permits the organism to remember important events for a longer period of time and modify its behavior accordingly [52]. To explain this phenomenon, all the relevant regions in the brain responsible for the functions that compose the neuro-cognitive mechanism of memory consolidation, including all relevant mechanistic levels of decomposition, must be identified, that is, all the particular component parts and component operations of the whole mechanism must be determined, as shown on Figure 1. Finally, the causal processes and causal interactions within the mechanism functions need also to be understood, that is, the general organization of the mechanism.
An example of a simple model of a neuro-cognitive biological mechanism (M1). In this model, M1 is composed, at the level L1, by its component parts C1, C2, and C3, which perform the functions (or activities) f1, f2, and f3. The component parts can be decomposed into smaller components, as it happens with C3, which is composed, at level L2, of the sub-components SC1, SC2, SC3, and SC4. The component SC3 can be further decomposed, at level L3, into its subcomponents ssc1, ssc2, and ssc3.
The explanation starts at the highest level of the whole mechanism. At this level, it is necessary to correctly identify all the large neural network that is responsible for memory consolidation. Secondly, it must be established whether this large neural system is indeed all that is relevant for the explanation of the phenomenon. The mechanistic explanation at this level also needs to clarify how the neural network process information about new memory episodes through
Once this has been clarified, the explanation turns to the second level of description in which the large neural system is decomposed into particular sub-neural systems localized in more specific regions. Here the goal is to understand the information processing and computational operations (e.g., spiking patterns in populations of neurons) of these smaller neural networks and how they contribute to the performance of the whole mechanism composed of such neural nets.
Moreover, a further stage of decomposition must be reached that concerns the processes underlying memory at an intercellular level. The explanation at this particular level aims at describing the components of a particular neural network and at understanding how a small number of neurons operate (e.g., how they depolarize and fire in the process of propagation of action potentials, or how they are responsible for synaptic processes, neurotransmitters being released, and so on). Here it is possible to measure spiking rates of neurons, or spiking frequency and record neural activity in general.
Finally, the explanation can go even to another lower mechanistic level—the intracellular and molecular level. At this level, the description is in terms of the activity of relevant proteins, molecules, and ions. As one can see, this kind of explanation “exhibits a progression from the behavioral-level characterization of memory consolidation to the identification of important components in the process at progressively lower levels.” [52]. All levels are equally important to achieve the complete multilevel mechanistic explanation of the particular phenomenon in the end.
Another example is related to human visual perception [9, 13, 40], which is roughly understood as the capacity to acquire and process visual information from objects and events in the environment. In the biological mechanism related to human visual perception, the occipital lobe is central, since many studies on humans show deficits in visual processing due to damage in the occipital lobe. The mechanism also includes a projection of the optic tract going from the eye, passing by the lateral geniculate nucleus (LGN), which is an area of the thalamus, and achieving the occipital lobe. Besides, it includes the eyes, optic nerves, and other brain areas responsible for visual perception. All these areas can be decomposed in working components and their operations, and each decomposition is considered to be a lower level in the entire constitution of the mechanism. The occipital lobe, for instance, can be itself decomposed in areas responsible for particular visual functions, such as the striate cortex, also known as Brodmann area 17, or V1 (primary visual cortex, or visual area 1).
The same procedure can be done for all the other areas in the brain that are also part of the mechanism responsible for visual perception; for instance, V2, V3, V4, and V5/MT. It is necessary to identify also the cells (including visual receptor cells in the retina of the eye, such as cones and rods), networks of cells, or larger neural systems in these areas that are responsible for
As one can observe by looking at these two examples, the notion of “computation” in the mechanistic framework stands for some causal interactions within the nervous system and this is how different brain regions “compute” different information. Each brain region “stands for” some kind of particular information—related to perception, sensation, memory, language, reasoning, emotion, etc. The substantial problems with such an account of human cognition will be analyzed in what follows.
A great deal of criticism has arisen in the specialized literature concerning the notion of human cognitive computation. It is nearly impossible to review all of the works, but I will make some considerations of some of the most influential critics.
Fodor [53, 54, 55], for instance, claims that many mental representations (e.g., beliefs) and mental processes (e.g., abductive reasoning) are sensitive to global properties (i.e., properties that beliefs, for instance, have so that they are determined by a set of other beliefs which they are members of). For example, a belief about a tennis racket being broken may complicate the plan of playing tennis on the weekend, but not the plan of playing soccer. This means that a mental representation, such as an intention to play tennis, will depend on the context at the moment—whether there is a racket available for the game or not. Fodor argues, though, that classical symbolic computing models are only sensitive to local properties, and neural network models cannot handle this feature of human cognition.
Dreyfus [56], in turn, claims that much human knowledge cannot be captured by symbolic manipulation and formal rules, since this knowledge is constructed through direct contact and practicing in the world. Nagel [57] brings attention to the problem of phenomenal consciousness—roughly, the issue of what it feels like to experience something subjectively. Following this line of thinking, we can also say that a computer cannot know (if it can know anything) what it feels like to taste the flavor of chocolate. It has no idea of what it is like to eat chocolate, something that is quite basic for any child that does it. More than that, computers do not feel pain or pleasure, which is quite basic for human beings. Furthermore, Searle [58] brings attention to the difficulties related to intentionality, understanding, and meaning, with his famous “Chinese room argument.” And, additionally, Putnam [59] develops the idea that mental states cannot be identified with computational states, consequently arguing vigorously against computational reductionism3.
The case of Bruner’s critics is also very interesting. One of the names most frequently mentioned in influential works of historical reconstruction of the events and studies that contributed to the beginning and development of the cognitive movement in psychology is the American psychologist Jerome Bruner (1915–2016) [1, 2, 3, 60, 61]. He is recognized for having founded, together with George Miller (1920–2012), the Center for Cognitive Studies at Harvard University, in 1960. In addition, Bruner published, together with colleagues, in 1956,
One of the most interesting points in Bruner’s work, however, is his strong criticism of the very cognitive movement he helped to develop. He has presented this criticism in key works, such as
In
However, in Bruner’s view, this is not the way forward. In
One of the major problems pointed out by Bruner in the computationalist approach is that the production of meaning is often extremely complex, sensitive to the context, and involves the difficulty of clear and precise understanding [64]. This is not the same as establishing computational procedures for the processing of input and output information to the system, whether this is computational processing in digital format or the form of neural networks. For Bruner, meaning making is not merely information processing; it is something more profound and more complex. Culture, in his view, has a fundamental role in human life and it is only through it and in it that certain processes and mental structures are formed and used.
The human being, in Bruner’s view, was able to develop a way of life in which reality is represented by a symbolism shared by members of a cultural community, and human life is organized and built from this symbolism that is conserved, elaborated, and transmitted through successive generations [64]. Although meaning is in the mind and is produced by it, it also has its origins in culture and has its importance within the culture in which it was generated. And for the production of meanings, the human mind creates and makes use of symbolic cultural systems. Thus, in this view, thinking and learning are always situated in a cultural context [64]. Computer systems, however, are not capable of producing meanings. They only deal with a certain set of formalized and operationalized meanings, but they do not make interpretations of human and cultural phenomena.
Furthermore, there is no very clear reason to suppose that processes and relationships between all mental phenomena are literally computational in nature, nor that all mental representations have this same character. The application of the concept of computation to these phenomena investigated in the tradition of psychological research is based only on a working hypothesis present in a certain particular theoretical system. Nevertheless, there is as yet no concrete proof that all human cognition works according to a type of computational processing x, y, or z. In fact, finding out what kind of computational processing is related to the human mind has become an extremely debated issue internally by adherents of any computational model of human cognition [49]. It is no accident that comprehensive theoretical systems were developed precisely with the intention of questioning the computational model of cognition.
Now, to illustrate more concretely some of the difficulties mentioned with the notion of human cognitive computation, let us consider some cases involving conscious complex informal reasoning and conscious complex decision making where explanations for human behavior might be required [22].
Consider, firstly, a case where a person is dissatisfied with her marriage and is thinking about getting a divorce. To make such a decision, she has been consciously reflecting for months on the current state of the marriage, her beliefs about the relationship, her emotions about her partner, her desires and expectations in life, the beliefs of her family and closest friends about the issue and what are the reasons to take action in this regard. After thinking carefully for a very long time, being aware that she really does not feel comfortable and happy at all, she decides to go for a divorce.
Consider also a second example. A person needs to decide which candidate she will vote for as president of her country. To make this decision, she needs to use her conscious informal reasoning ability. Thus, she reflects on the arguments put forward by politicians running for the election, the arguments put forward by commentators, scientists, and political analysts, as well as journalists writing on the subject, and the arguments of friends and family she finds relevant and credible. After three months of thinking, she has not decided yet but is rather still in doubt concerning her vote in the major candidates A and B. When someone asks her which candidate she is going to vote, she says: “I still don’t know.” Then, some surprising news arises in a serious newspaper with charges of corruption against candidate A, and she is a frequent reader of this newspaper, so she becomes immediately aware of this. Upon reflection on the matter and related issues, she takes the new information seriously and she finally decides that voting for candidate B is the best option. The major reason is that there is no charge whatsoever of corruption against him. When she is asked now which candidate she is going to vote for, she answers immediately: “candidate B.” After she made up her mind, she finally goes to the appropriate place on the proper day and time to cast her vote.
A third example is the case of a college student who suffers from difficulties related to his excessive anxiety. Through a general psychological assessment, it can be seen that the factors related to student anxiety are financial difficulties, difficulties in family life where physical and psychological violence occurs, difficulties in finding leisure time to relax and have fun (since they need to work and study at the same time) and difficulties with excessive concerns about the uncertain future, as he believes that it will not be easy to find a job when he graduates. All of these factors seem to contribute to generate in the student’s mind distorted and dysfunctional negative thoughts about himself and his life, and it seems very plausible that these distorted thoughts are strongly associated with his excessive anxiety. This interpretation is, indeed, supported by numerous works in the specialized literature in clinical psychology. Thus, we observe that the most relevant causal factors to explain this psychological phenomenon are not merely computational, but psychological, social, and environmental.
Psychological scientific explanations, in these cases, need considerations that go beyond the investigation of computations being performed in nervous systems or even in any abstract functional system. What explains the psychological phenomenon of belief formation and decision making in the first example and the excessive anxiety in the third example is the meaning formation and interaction of beliefs, desires, and intentions to act (according to logical rules, practical rules, and interpretation of reality), which are strongly affected by emotions, physical environment, and social factors.
In the second example, evidently, an informative explanation would have to mention an important causal factor—the event of the corruption charges against candidate A, appearing in a serious newspaper. Moreover, the explanation would have to mention that the person becomes aware of this event, accepts it as reliable, accepts the charges as true and accurate, and now this content is present in one or some of her beliefs. In possession of this content, she can rationally justify herself when engaging in discussions about the topic with family, friends, and other people, providing reasons for her related beliefs and her related behaviors. Thus, the influence of the event on her is external and affects the internal logic and content of her systems of beliefs, emotions, desires, and intentions. This explanation involves then particular properties of human cognitive systems, present for instance in belief and intention systems. These properties are clearly different than those involved in merely describing supposed automatic computational activities in her neural networks or describing what is happening in terms of physical and chemical neural processes. The explanation for this phenomenon of belief formation, therefore, would also have to account for how this new information could change a particular belief given her system of beliefs about the topic.
In the examples above, there are cognitive processes that often necessitate consciousness and complex informal reasoning about belief systems that are often linked to particular perceptions, sensations, emotions, desires, intentions, attitudes, as well as related to each other and the external environment. Some of these beliefs have great value, such as some moral beliefs, which makes this whole dynamic even more complex. In these cases, blind computation might even occur at some level, but what is most relevant are environmental, social, cultural, historical, and psychological factors (such as beliefs, emotions, desires, and intentions) that acquire meaning in a given cognitive system.
The relevant explanation of the actions in such cases is made through considerations—(1) about the creation and alteration of the content of perceptions, beliefs, sensations, emotions, maxims, wills, desires, intentions, etc.; (2) about their internal relationships; and (3) about their external relationships with the physical, social, historical, and cultural context. Rigorous empirical scientific research can aid in discovering strong and systematic (stable) regularities in human behavior explained in such terms without the need for the notion of computation. Statistical tools and analysis, through the mathematical application, can bring greater objectivity, avoiding both an extremely subjective and confusing vocabulary, as well as unproductive speculation and mere common sense.
Moreover, self-consciousness here is crucial, since we humans have the ability to
Therefore, human beings have the ability to form original belief systems and relate them according to logical and interpretative rules, building arguments to support their point of view, which often influences their behavior. Human beings are also able to think about different types of relevant information for months or years to make an important and complex decision. To make a difficult decision, a human being can take into account information related to plans for the very distant future, in which many scenarios are considered. A human may wonder what happened in the very distant past, or what might have happened, even if he or she knew what really happened. And complex informal reasoning and complex decision making are things that humans do naturally and often in their daily lives.
Thus, in cognitive science, it is necessary to deal with extremely complex phenomena, given that human beings show great differences when compared to other animals in nature. Human beings have a cumulative, complex, dynamic, and elaborate culture that is passed on through generations. Humans are also involved in understanding and writing their own history. They have natural languages with enormous, complex, and refined expressive power and sophisticated grammar. Human beings practice and appreciate art, such as literature, painting, cinema, and music. They engage in purely formal or very abstract thoughts when they do mathematics, logic, and engage in certain religious thoughts. They create legal laws for their societies and think about morality, building moral systems. They build artificial intelligence machines that are able to learn with a certain level of autonomy and are able to explore other planets. Furthermore, humans are involved in politics, science, and philosophy.
Computers, by contrast, so far, do not form beliefs on their own, they do not have the capacity to evaluate and improve them by themselves, and they do not interact in the social environment neither using natural language with a huge degree of sophistication as humans do nor engaging in social and cultural practices. If we look at the problem from a very concrete and objective point of view, we observe that even the most advanced computer systems, the most advanced robots, and the most advanced artificial neural and cognitive architectures today are still very far from behaving like human beings in relation to language and actions that involve consciousness and informal rationality. Humans are capable of playing chess, cooking pizza, making coffee, having a conversation about politics, creating a new song on a guitar, and playing tennis on the same day. No computational artificial system is currently capable of this generality in cognition. So, as a matter of current fact, computational artificial cognition cannot be used to fully explain the major capacities of human cognition and intelligence.
It is no surprise, then, that mechanistic accounts of psychological capacities usually suggest only
Difficulties with the notion of cognitive computation are recognized by influential neo-mechanists themselves. Milkowski [21], for instance, concludes his work by admitting that we “still don’t know how to model consciousness mechanistically.” Additionally, there are several alternative models of cognitive computation in cognitive science nowadays—syntactic computation; algorithmic computation; causal computation; and semantic computation [65]. None of the models has gained significant prominence over the others concerning the understanding and explanation of human cognition. Finally, there is strong criticism even of the attempt by neo-mechanists to propose that good computational explanations in cognitive science must be also mechanistic explanations [66, 67].
Therefore, if we think about the issue from the point of view of current facts, we need to recognize that the neo-mechanistic proposal for human cognition is still far from being able to be considered the best or most plausible understanding and explanation of human cognition. It is just one view among many.
The mechanistic framework has been offering significant contributions to the field of cognitive science, on the one hand. One of its best contributions is the promotion of debates on the issue of human cognitive computation. In this sense, there is a search for a better understanding of what this notion actually means. All this effort is very worthwhile and welcome. More generally, the theoretical debate about fundamental questions in cognitive science promoted by new mechanists is also very important, as well as their effort to clarify what a “biological mechanism” and a “cognitive mechanism” are and what a “mechanistic explanation” in cognitive science is. Furthermore, another contribution of the new mechanistic philosophy is to encourage historical research and current debate, in cognitive science and beyond, about the relationship among “mechanism,” “materialism,” “reductionism” and “computationalism”, so that these concepts are not confused and that the positions adopted by the authors, as well as the different dimensions of the debate, are appreciated in a fair and correct way. Finally, the new mechanistic philosophy applied to cognitive science is also contributing to the important debate concerning the unity, integration, and plurality in the field.
On the other hand, however, many of the current promises of the new mechanism for cognitive science are quite difficult to fulfill. Firstly, neo-mechanistic philosophy is a philosophy of science built primarily from examples from the biological sciences and neuroscience that is serving as the basis for building a philosophy of the science of mind. We live in a period in which neuroscience and artificial intelligence research have gained great prestige and recognition. A great deal of economic investment has been made in these areas and this is very attractive. In part, this also influences “the new wave of mechanism,” and the necessity of some authors to expand the framework. However, numerous particularities related to psychology and human cognition are being neglected in this theoretical structure, as I tried to show.
Secondly, there is considerable disagreement among leading neo-mechanists over the most plausible formulation of MTHC regarding fundamental issues, such as the idea of human cognitive computation. Thus, there is a considerable difficulty related to the internal articulation and unification of the theory. Furthermore, many alternative major theories, and the research programs based on them, strongly threaten the neo-mechanistic framework in current cognitive science, since they are also seeking predominance in the field, or just for having more space and recognition.
Given this, we can conclude that the mechanistic model of human cognitive computation cannot provide substantial theoretical or explanatory unification or integration to the field of cognitive science today, since there is no unification between the proponents themselves. Moreover, their different proposals are often unclear on many important aspects concerning traditional problems of intentionality, consciousness, and self-consciousness. The accounts are sometimes internally not well-articulated; and, externally, there is serious criticism of them, with countless debates and controversies on several fundamental questions. In addition, there are several alternative models competing for predominance on this particular issue. And it is yet by no means clear whether the explanatory power of any of them is greater than the explanatory power of the others.
This analysis shows, therefore, that the neo-mechanistic proposal concerning human cognitive computation has serious weaknesses. But the problem is not to use the idea of cognitive computing to advance models of biological and artificial cognitive architectures, since many human cognitive abilities can already be simulated. Indeed, it is very interesting to see that our science has advanced to the point where a computer can win against the best chess and go game players in the world. In fact, advancements within computational artificial systems and robotics could well be applied to improve our educational and health systems. For example, inspired by scientific developments in the field of cognitive science, artificial cognitive systems could possibly be developed to help children with the learning process of mathematics, natural language, or history at schools, or even at the university level. Artificial systems could possibly be developed to help people with excessive anxiety symptoms, as well. This could be extremely worthwhile. Moreover, better and more advanced artificial cognitive systems and robotic systems can contribute to improving theories of human cognition, as much as better and more correct theories of human cognition can help in faster advancements of cognitive artificial systems and robotic systems. But there is good reason to keep these efforts separated and to consider human cognition as a very complex and particular phenomenon in nature.
The problem arises only with the untenable suggestion that we already have, or that we are very close to getting, the complete and definitive understanding and explanation of all the major capacities of human cognition in computational terms. This, yes, is a mistake.
The author declares no conflict of interest.
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In this chapter, we have presented a brief comprehensive survey of cultural heritage using augmented reality systems. This survey describes the main objectives and characteristics of marker-less augmented reality systems through presenting up-to-date research results in this area. 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Microorganisms penetrate the stone, causing extensive disaggregation of the materials. This chapter reveals comparative studies between the usual research approaches applied in biodegradation studies, especially optical microscopy, epifluorescence, and electron microscopy (SEM). These in situ microscopy techniques propose some complex analyses for the evaluation of the relationship between the microorganism’s cells and the stone surfaces (adherence, interactions), and also for the evaluation of the level of health or balance of the niche complex, from mesoscale to microscale. The stages of the exact monitorization and evaluation of lithotypes and deterioration phenomena are periodical sampling and monument mapping. The aim of this chapter is to identify microscopical methods used in biodegradation studies, especially the facilities provided by these methods. Our in situ analysis (light microscopy, epifluorescence, and scanning electron microscopy) performed for the first time on the painted Matia-Fresco Loggia (Corvin Castle, Romania) highlighted several aspects, such as mixtures of mineral elements with different chromatic appearance and porosity, shredding degradation, depigmented areas, cracked portions, and highly biota activity (bacterial and fungal) on painted surface.",book:{id:"7699",slug:"advanced-methods-and-new-materials-for-cultural-heritage-preservation",title:"Advanced Methods and New Materials for Cultural Heritage Preservation",fullTitle:"Advanced Methods and New Materials for Cultural Heritage Preservation"},signatures:"Verginica Schröder, Daniela Turcanu Carutiu, Adina Honcea\nand Rodica-Mariana Ion",authors:[{id:"171504",title:"Prof.",name:"Rodica-Mariana",middleName:null,surname:"Ion",slug:"rodica-mariana-ion",fullName:"Rodica-Mariana Ion"},{id:"176482",title:"Prof.",name:"Daniela",middleName:null,surname:"Turcanu-Carutiu",slug:"daniela-turcanu-carutiu",fullName:"Daniela Turcanu-Carutiu"},{id:"259028",title:"Dr.",name:"Verginica",middleName:null,surname:"Schroder",slug:"verginica-schroder",fullName:"Verginica Schroder"},{id:"262554",title:"Dr.",name:"Adina",middleName:null,surname:"Honcea",slug:"adina-honcea",fullName:"Adina Honcea"}]},{id:"36575",title:"Heritage Protection in Pécs/Sopianae",slug:"heritage-protection-in-p-cs-sopianae",totalDownloads:2774,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"1999",slug:"archaeology-new-approaches-in-theory-and-techniques",title:"Archaeology",fullTitle:"Archaeology, New Approaches in Theory and Techniques"},signatures:"Tamás Molnár",authors:[{id:"112700",title:"Dr.",name:"Tamás",middleName:null,surname:"Molnár",slug:"tamas-molnar",fullName:"Tamás Molnár"}]}],onlineFirstChaptersFilter:{topicId:"1313",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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