3.Gender:

	- a. Medical staff/security/police
	- b. Full time employed (except above)
	- c. Part time employed (except above)
	- d. Unemployed
	- e. Homemaker
	- f. Student
	- g. Others

5.Does anyone in your home belongs to medical staff/security/police?

	- a. Yes
	- b. No
	- c. Partially (need to go at least once a week)
	- d. Not applicable

Section B: Inferential Analysis Awareness:

	- a. 0 b. 1 c. 2 d. 3 e. 4


**Tuesday 10th December 2019**

**Scientific session 3:** Imaging diabetic foot infections

**Chairmen:** Cavallini - Pugliese

09:00-09:20 Radiological imaging of diabetic foot infection: X-ray, CT and NMR (Leone, Rome, Italy)

09:20-09:40 Nuclear Medicine imaging of diabetic foot infection: WBC and FDG (Lauri, Rome, Italy)

09:40-10:00 The diabetologist: how to use diagnostic information in clinical practice (Uccioli, Rome, Italy)

10:00-10:30 Round table discussion

10:30-11:00 Coffee break


Meet the editors

Lingwen Zeng received his PhD degree in Genetics from Mc-Master University, Canada, in 1993, and his postdoctoral training from the University of Chicago, USA. He has worked in three publicly traded companies (Quest Diagnostics Inc., Scios Inc., and Genetics Computer Group Inc.) in the USA as a research scientist and a project manager. Currently, he serves as a professor at the School of Food Science and Engineering, Foshan Univer-

sity, China, and a principal investigator at Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences. His research focuses on exploring novel diagnostic technologies for human diseases, food, and environmental safety.

Shilin Cao is an associate professor at the School of Food Science and Engineering, Foshan University, China. He received his PhD in Biochemical Engineering in 2016 from the South China University of Technology. From 2015 to 2016, he worked as a visiting researcher at the Department of Applied Biology and Chemical Technology, the Hong Kong Polytechnic University in Hong Kong. His research interests include enzyme catalysis in

novel solvent media, immobilization of enzymes onto novel supporting materials, and application of biobased nanomaterials.

Contents

**Section 1**

**Section 2**

*and Ling-Wen Zeng*

*by Mikhail Dolomatov*

Photosynthetic Cells

Spectrometers and Colorimetry

*and Luiz Antonio d'Avila*

Introductory Chapter: Color Detection

**Preface III**

Introduction **1**

**Chapter 1 3**

The Recent Progress of Color Detection **7**

**Chapter 2 9**

**Chapter 3 39**

**Chapter 4 65**

**Chapter 5 85**

*by Amanda Pereira Franco dos Santos, Kissya Kropf da Silva, Gisele Alves Borges*

Confocal Laser Scanning Microscopy for Spectroscopic Studies of Living

From Sophisticated Analysis to Colorimetric Determination: Smartphone

*by Shi-Lin Cao, Jin-Heng Hao, Wei-Dong Ou-Yang, Zi-Shi Chen, Yong-Si Lv*

New Results in the Theory and Practical Application of Color

*by Volkan Kılıç, Nesrin Horzum and Mehmet Ertugrul Solmaz*

*by Natalia Grigoryeva and Ludmila Chistyakova*

Fuel Quality Monitoring by Color Detection


*2.5. Confounding Factors*

We conducted surveys once during pregnancy and twice after birth. The questionnaire administered to the mothers included demographic data for mothers and children, maternal health and disease status, breastfeeding status, perinatal antibiotics use, and children's health and disease status. Data of potential confounders that would influence the infant gut microbiome, such as delivery mode, gestational age, and gestational weight gain, were also collected.


*3.3. Access Time*

Several tests were conducted to see what the access time was like to query the cells in each image and we found that the time for a random cell access took longer for a predictor compared to just using the *k*2-raster. This was expected but we should bear in mind that the bit rates are reduced when a predictor is used, thus decreasing storage size and transmission rate. Note that the last column also lists the time to decompress a gzip image file and it took at least 4 or 5 times longer than using a predictor to randomly access the data 105 times. Table 4 shows the results of access time in milliseconds for 100,000 iterations of random cell query done by getCell(), a function which was described in the paper from Ladra et al. [20] for accessing pixel values in a *k*2-raster.

**Table 4.** A comparison of access time (in milliseconds) using *k*2-raster only and *k*2-raster with predictive and differential encoders.


**7. General discussion**

It is generally accepted that, in both *left- and right-handed* subjects, the preferred hand is controlled mainly by the hemisphere *contralateral* to that hand, whereas the *non-preferred* hand is controlled by *both hemispheres*. In relation to *left-to-right switches* in handedness, the switched individuals have been found to share features of both left-handers and right-handers regarding their motor control architectures. ([1], see Introduction).

encoding for that hand. There was no difficulty or delay, however, to successfully use thus acquired memory for guiding drawing with the right hand. It should be noted that, in the drawing phase, the hand under training receives multifaceted, Cognitive-Kinesthetic feedback, which affects the initial haptic memory, corrects and sharpens it, thus adding another layer of

Brain Reorganization in Late Adulthood: Rapid Left-to-Right Switch of Handedness…

http://dx.doi.org/10.5772/intechopen.76317

81

An important practical implication of these findings is that the effects of the Cognitive-Kinesthetic Training can generalize over the full "perception-cognition-action loop" involved throughout the process, which suggests its usefulness not only in the domains of spatial cog-

This study is the first to show results that contradict the models of the nondominant hand always being controlled by both hemispheres, as had been previously thought. It is particularly remarkable that this brief memory-guided drawing training was able to switch lifelong handedness, overturning almost six decades of dominance of the right hemisphere by inducing profound suppression in the previously dominant hemisphere. In terms of handedness research as a whole, the study suggests a critical role for functional mechanisms, such as inter-hemispheric competition, as opposed to an inherent structural predetermination in hand dominance. The results are consequently of high relevance to the long-standing debate about the sources of hemispheric asymmetry. The unprecedented effect on handedness of the rapid Cognitive-Kinesthetic Drawing Training also implies the powerful potential of this training for further rehabilitation domains, such as the rehabilitation of stroke or trauma

This research was supported by the National Eye Institute at the National Institute of Health (RO1EY024056) and National Science Foundation (NSF/SL-CN: 1640914) awarded to Lora Likova. The author would like to thank Christopher W. Tyler for helpful discussions on the manuscript, Kristyo Mineff for assisting with the drawing training, Spero Nicholas for helping with the functional Magnetic Resonance Imaging (fMRI) data collection, and preprocessing.

nition and memory rehabilitation but also in motor control rehabilitation as well.

enhancement and embodiment to the overall encoding.

**8. Conclusions**

affecting hand control.

**Acknowledgements**

**Author details**

Address all correspondence to<EMAIL_ADDRESS>
Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA

Lora T. Likova

Before training, the results for both the *preferred/*left and the *non-preferred/*right hand conformed to these preexisting models: the *preferred*/left hand produced predominantly *contralateral* activation, whereas the *non-preferred*/right hand produced a more balanced *bilateral* activation, indicating control by both hemispheres.

*After* training, however, the bilateral pattern expected in switchers was not observed any more. Instead, the non-preferred/right hand underwent a strong training-based reorganization of its motor control architecture, so as its bilateral activation pattern radically changed post-training into a contralateral one. Remarkably, this *contralaterality* (left hemisphere > right hemisphere) was caused not by increased contralateral (left) activation but by a massive suppression in the ipsilateral (right) hemisphere; it is particularly surprising that this happened despite the fact that the right hemisphere has been the dominant one since birth.

These findings show for the first time that the dominance of the preferred hemisphere can be rapidly overturned, and that this can happen even in late adulthood after decades of established dominance. Note that, until now, despite long-standing efforts across many disciplines to achieve a fully-fledged hand switching in left-handers, the best that has been achieved has been to engage the contralateral left hemisphere without being able to overturn the ipsilateral right hemisphere control [1, 12]. The fact that the Cognitive-Kinesthetic Drawing Training was able to transform the bilateral into a definitive contralateral pattern, and to do so effectively and efficiently, implies a serious deficiency in the current knowledge on motor control plasticity, and the need for enhanced investigation into this process. Moreover, the power of this memory-driven motor training to rapidly drive motor-control plasticity, in addition to the previously shown effects on memory and spatial cognition, for example [2–5, 39–41], suggests strong involvement of cognitive mechanisms in this process, as codified earlier by the introduction of the "perception-cognition-action loop" concept.

The resulting neural reorganization in this congenitally left-handed individual was correlated with similarly remarkable enhancement in the memory-drawing performance of the non-preferred hand, which post-training resembled the original much better than pre-training, and moreover, significantly better than the experienced preferred hand. This was particularly unexpected because the left hand had several additional advantages. First, the *haptic exploration* of the originals was *always* done with the *left* hand, thus providing a direct perception and encoding of this hand's movements along the lines of that image. In contrast, the non-preferred right hand *never* received any direct encoding of the trajectory but for planning and execution of the drawing trajectory it had to use the memory image developed through the other hand. Second, the nature of the left-hand exploration phase represents a strong form of *dual* memory encoding for that hand. There was no difficulty or delay, however, to successfully use thus acquired memory for guiding drawing with the right hand. It should be noted that, in the drawing phase, the hand under training receives multifaceted, Cognitive-Kinesthetic feedback, which affects the initial haptic memory, corrects and sharpens it, thus adding another layer of enhancement and embodiment to the overall encoding.

An important practical implication of these findings is that the effects of the Cognitive-Kinesthetic Training can generalize over the full "perception-cognition-action loop" involved throughout the process, which suggests its usefulness not only in the domains of spatial cognition and memory rehabilitation but also in motor control rehabilitation as well.


**Part 1**

**Compression**

VIII Preface

image chain. The chapter also presents both objective image metrics and analysts'

Chapter four presents an overview of H.264 motion estimation and its types and also the various estimation criterions that decides the complexity of the chosen algorithm.

Chapter five is a systematic review of the pixel domain based global motion estimation approaches. The chapter discusses shortcomings in noise filtering and computational cost, the improvement approaches including hierarchical global motion estimation, partial pixel set based global motion estimation and compressed domain based global motion estimation are provided. Four global motion based applications including GMC/LMC in MPEG-4 video coding standard, global motion based sport video shot classification, GM/LM based error concealment and text occluded region recovery are

Chapter six argues that exploiting saliency-based video compression is a challenging and exciting area of research and especially nowadays when saliency models include more and more top-down information and manages to better and better predict real human gaze. Multimedia applications are a continuously evolving domain and compression algorithms must also evolve and adapt to new applications. The explosion of portable devices with less bandwidth and smaller screens, but also the future semantic TV/web and its object-based description will lead in the future to a higher importance of saliency-

The large amount of studies developed for this purpose related to quality assessment gives a general idea about the importance of this theme in video compression. The evolution of metrics and techniques is constant, finding the best ways of evaluating

Chapter seven describes a state of the art in quality assessment and techniques of subjective and objective assessment, with the most common artefacts and impairments

I wish to thank all the authors who have contributed to this book. I hope that by reading this book you will get many useful ideas for your own research, which will help to bridge the gap between video compression technology and applications.

I also hope this book is enjoyable to read and will further contribute to video compression, which requires a further interest and attention in both research and

The School of Engineering and Advanced Technology, Massey University (Turitea),

**Dr Amal Punchihewa**

New Zealand

PhD, MEEng, BSC(Eng)Hons, CEng, FIET, MIPENZ,

MIEEE, MSLAAS , MCS, Leader - Multi-Media Research Group,

based algorithms for multimedia data repurposing and compression.

assessments of various compressed products.

described in this chapter.

the quality of video sequences.

application fields.

derived from compression and transmission.

**1**

*Massey University*

*Palmerston North New Zealand*

**Compressive Video Coding:**

*School of Engineering and Advanced Technology*

**A Review of the State-Of-The-Art**

Muhammad Yousuf Baig, Edmund M-K. Lai and Amal Punchihewa

Video coding and its related applications have advanced quite substantially in recent years. Major coding standards such as MPEG [1] and H.26x [2] are well developed and widely deployed. These standards are developed mainly for applications such as DVDs where the compressed video is played over many times by the consumer. Since compression only needs to be performed once while decompression (playback) is performed many times, it is desirable that the decoding/decompression process can be done as simply and quickly as possible. Therefore, essentially all current video compression schemes, such as the various MPEG standards as well as H.264 [1, 2] involve a complex encoder and a simple decoder. The exploitation of spatial and temporal redundancies for data compression at the encoder causes the encoding process to be typically 5 to 10 times more complex computationally than the decoder [3]. In order that video encoding can be performed in real time at frame rates of 30 frames per second or more, the encoding process has to be performed by

In the past ten years, we have seen substantial research and development of large sensor networks where a large number of sensors are deployed. For some applications such as video surveillance and sports broadcasting, these sensors are in fact video cameras. For such systems, there is a need to re-evaluate conventional strategies for video coding. If the encoders are made simpler, then the cost of a system involving tens or hundreds of cameras can be substantially reduced in comparison with deploying current camera systems. Typically, data from these cameras can be sent to a single decoder and aggregated. Since some of the scenes captured may be correlated, computational gain can potentially be achieved by decoding these scenes together rather than separately. . Decoding can be simple reconstruction of the video frames or it can be combined with detection algorithms specific to the application at hand. Thus there are benefits in combing reduced complexity cameras with flexible decoding processes to deliver modern applications which are not anticipated

Recently, a new theory called Compressed Sensing (CS) [4, 5, 6] has been developed which provides us with a completely new approach to data acquisition. In essence, CS tells us that

specially designed hardware, thus increasing the cost of cameras.

when the various video coding standards are developed.

**1. Introduction**


**Alexander Sobolev, Alexey Kossenko and Konstantin Borodianskiy \***

Department of Chemical Engineering, Ariel University, Ariel 40700, Israel<EMAIL_ADDRESS>(A.S<EMAIL_ADDRESS>(A.K.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +972-3-9143085

Received: 6 November 2019; Accepted: 27 November 2019; Published: 1 December 2019

**Abstract:** The micro arc oxidation (MAO) process has been applied to produce ceramic oxide coating on Ti-6Al-4V alloy. The MAO process was carried out at the symmetric bipolar square pulse in electrolyte containing Na2CO3 and Na2SiO3. The effect of current frequency on the surface morphology, the chemical and the phase compositions as well as the corrosion resistance was examined. Morphology and cross-sectional investigation by electron microscopy evaluated more compacted and less porous coating produced by high current frequency (1000 Hz). This alloy also exhibited a high corrosion resistance in comparison with the untreated alloy. Additionally, the alloy subjected to MAO treatment by a current frequency of 1000 Hz showed a higher corrosion resistance in comparison with alloys obtained by lower current frequencies. This behavior was attributed to more compacted and less porous morphology of the coating.

**Keywords:** micro arc oxidation; current pulse frequency; Ti-6Al-4V alloy; titanium oxide; corrosion resistance


**About the Editors**

**Alberto Jes ´us Perea Moreno** Associate Professor at the Department of Applied Physics, Radiology and Physical Medicine in the University of Cordoba (Spain), received his M.S. as an Agricultural Engineer and Ph.D. in Geomatics at the University of Cordoba (Spain). He has published over 40 papers in JCR journals (http://orcid.org/0000-0002-3196-7033), H-index 13. His main interests are Renewable Energy, Energy Saving, Biomass, Sustainability, and Remote Sensing. He was the Secretary of the Applied Physics Department (2017-2019) at the University of Cordoba. Awards: 2019 Winners Sustainability Best Paper Awards.

**Francisco G. Montoya** Professor at the Engineering Department and the Electrical Engineering Section at the University of Almeria (Spain), received his M.S. from the University of Malaga and his Ph.D. from the University of Granada (Spain). He has published about 75 papers in JCR journals and is the author or co-author of books published by MDPI, RA-MA, and others. His main interests are power quality, smart metering, smart grids, and evolutionary optimization applied to power systems and renewable energy. Recently, he has become passionately interested in Geometric Algebra as applied to Power Theory.


**5. Could** *M. tuberculosis* **infections persist as biofilms?**

While the long-term persistence of *M. tuberculosis* against the host immune system and antibiotics has striking similarity with the chronic infections of biofilm forming pathogens, it remains unclear if the tubercle bacilli form biofilms in the host. It is, however, noteworthy that *in vitro* cultures of all mycobacterial species grow in complex structures that eventually develop as pellicles on the liquid-air interface, unless a detergent is added as dispersal agent in the medium. Interestingly, such growth pattern of mycobacteria have frequently been noted in the literature as aggregation of cells driven by their surface hydrophobicity, and largely been ignored ever since Dubos and colleagues reported a method to grow dispersed culture of tubercle bacilli without diminishing their virulence (Dubos *et al.*, 1946). However, the emerging concept of microbial persistence in biofilms have recently led several groups to investigate the detergent-free *in vitro* growth of mycobacterial species from the perspective of organized multicellular structures (Hall-Stoodley & Lappin-Scott, 1998, Carter *et al.*, 2003). In one of the first genetic studies of surface associated growth of mycobacteria, Kolter and colleagues observed that an *M. smemgatis* mutant deficient in biosynthesis of acetylated glycopeptidolipid was also unable to attach and grow on an abiotic surface, thus demonstrating a specific genetic requirement for surface-associate mycobacterial growth (Recht & Kolter, 2001). Ojha *et al.* subsequently reported that a

Biofilms of *Mycobacterium tuberculosis*: New Perspectives of an Old Pathogen 187

Fig. 2. A. Growth of *M. tuberculosis* biofilms on the liquid-air interface in detergent-free Sauton's medium. B. Insertion of a mariner transpososn (Himar) in Rv1013 abolishes the formation of biofilms, although the growth of the mutant in planktonic state remains

Fig. 3. Presence of rifampicin tolerant persisters is significantly higher in biofilm populations of *M. tuberculosis* than in their planktonic counterparts, as reported by Ojha et al. (68).

morphotypes in detail, with equal focus on both the tissue structures as well as bacterial growth (Canetti, 1955). The goal of the undertaking was to bridge the partition between immunopathology and bacteriology of tuberculosis that a student of the disease was always confronted with. This partition ironically continues to be a relevant issue in questions as to how and where bacilli persist in the both latent and active TB. In a recent attempt to locate the bacilli persisting after antibiotic treatment, Orme and colleagues observed the persisters as microcolonies in the acellular rim of granulomas (Lenaerts *et al.*, 2007). Although it was

unaltered (68).

mutation in one of the non-essential chaperone, of *M. smegmatis* specifically retarded the maturation stages of pellicle formation, observed at 4- and 5-day of incubation, without affecting early attachment and growth during first three days of incubation (Ojha *et al.*, 2005). The mutant was also indistinguishable from its wild-type parent in planktonic growth. The maturation defect of the mutant was linked to defective synthesis in mycolic acids as a consequence of the loss of a KasA (enzyme involved in mycolic acid biosynthesis) interaction, which is induced in this phase of wild-type culture (Ojha et al., 2005). The regulated synthesis of mycolic acids was surprising because it is highly abundant in the cell wall, although it is consistent with the subsequent observation of induced synthesis of extracellular free mycolic acids during the maturation stage of the pellicles (Ojha *et al.*, 2010). The free mycolic acids (FM) are released through regulated hydrolysis of mycolyl esters of Trehalose, Trehalose 6',6' dimycolate (TDM), and by cutinase-like serine esterase (Ojha et al., 2010), although other mycolyl esters could also contribute to the FM pool through similar mechanism. One possible candidate could be mycolyl diacyl glycerol (MDAG), a mycolyl ester of glycerol, which is also found in low abundance in impaired biofilms of an *lsr2* mutant of *M. smegmatis* (Chen *et al.*, 2006). The accumulation of FM is likely facilitated through a three-step mechanism: 1) mediated upregulation in *de novo* synthesis of the nascent mycolic acids, 2) processing of nascent mycolic acids into a subset of mycolyl esters through housekeeping mechanisms, and 3) hydrolysis of these mycolyl esters through substrate-specific esterases. The elevated levels of extracellular free mycolic acids during the maturation of *M. smegmatis* pellicles is also consistent with the obvious waxy appearance of the structures, and thus could likely constitute the structure component of the ECM. The significance of *M. smegmatis* growth in pellicles is further highlighted by distinct phenotypes of the bacilli in these structures that are absent in planktonic cell suspension. For example, increased intercellular transfer of genetic materials, and extraordinary tolerance to anti-TB drugs (Ojha et al., 2010, Nguyen *et al.*, 2010). Most importantly, the fundamental properties of mycobacterial pellicles are conserved in *M. tuberculosis* when grown in detergent-free media under defined condition (Ojha et al., 2008). The pathogenic species not only has specific genetic requirement for forming the pellicles (Fig. 2A and B), but also produces large abundance of free mycolic acids in the structure, which expectedly harbor large numbers of drug tolerant bacilli (Ojha et al., 2008) (Fig. 3). Taken together, these *in vitro* studies strongly support the possibility that surface-associated multicellular structures of mycobacteria have all the characteristics of biofilms, developing through distinct growth phases, having specific genetic requirements, and conferring high tolerance to antibiotics. Despite a recent surge in understanding the multicellular structures of mycobacteria, multicellular structures of *M. tuberculosis* in the host have been difficult to define. This in part can be attributed to the conventional image of *M. tuberculosis* as an intracellular pathogen living in phagosome, which is too restrictive for exuberant growth of the pathogen in multicellular structure. However, while *M. tuberculosis* might be restricted to the phagosome in early stages of infection, at later t imes, especially when lesions contain liquefied caseum and when patients are highly infective, it is likely that many of the bacilli experience an extracellular environment. In a comprehensive histopathological study of TB lesions from 1,500 autopsies, Geroges Canetti documented several lesions including open cavities that had numerous extracellular bacilli growing in multicellular structures (Canetti, 1955). Interestingly, in this 7-year study Canetti microscopically analyzed lesions of various

mutation in one of the non-essential chaperone, of *M. smegmatis* specifically retarded the maturation stages of pellicle formation, observed at 4- and 5-day of incubation, without affecting early attachment and growth during first three days of incubation (Ojha *et al.*, 2005). The mutant was also indistinguishable from its wild-type parent in planktonic growth. The maturation defect of the mutant was linked to defective synthesis in mycolic acids as a consequence of the loss of a KasA (enzyme involved in mycolic acid biosynthesis) interaction, which is induced in this phase of wild-type culture (Ojha et al., 2005). The regulated synthesis of mycolic acids was surprising because it is highly abundant in the cell wall, although it is consistent with the subsequent observation of induced synthesis of extracellular free mycolic acids during the maturation stage of the pellicles (Ojha *et al.*, 2010). The free mycolic acids (FM) are released through regulated hydrolysis of mycolyl esters of Trehalose, Trehalose 6',6' dimycolate (TDM), and by cutinase-like serine esterase (Ojha et al., 2010), although other mycolyl esters could also contribute to the FM pool through similar mechanism. One possible candidate could be mycolyl diacyl glycerol (MDAG), a mycolyl ester of glycerol, which is also found in low abundance in impaired biofilms of an *lsr2* mutant of *M. smegmatis* (Chen *et al.*, 2006). The accumulation of FM is likely facilitated through a three-step mechanism: 1) mediated upregulation in *de novo* synthesis of the nascent mycolic acids, 2) processing of nascent mycolic acids into a subset of mycolyl esters through housekeeping mechanisms, and 3) hydrolysis of these mycolyl esters through substrate-specific esterases. The elevated levels of extracellular free mycolic acids during the maturation of *M. smegmatis* pellicles is also consistent with the obvious waxy appearance of the structures, and thus could likely constitute the structure component of the ECM. The significance of *M. smegmatis* growth in pellicles is further highlighted by distinct phenotypes of the bacilli in these structures that are absent in planktonic cell suspension. For example, increased intercellular transfer of genetic materials, and extraordinary tolerance to anti-TB drugs (Ojha et al., 2010, Nguyen *et al.*, 2010). Most importantly, the fundamental properties of mycobacterial pellicles are conserved in *M. tuberculosis* when grown in detergent-free media under defined condition (Ojha et al., 2008). The pathogenic species not only has specific genetic requirement for forming the pellicles (Fig. 2A and B), but also produces large abundance of free mycolic acids in the structure, which expectedly harbor large numbers of drug tolerant bacilli (Ojha et al., 2008) (Fig. 3). Taken together, these *in vitro* studies strongly support the possibility that surface-associated multicellular structures of mycobacteria have all the characteristics of biofilms, developing through distinct growth phases, having specific genetic requirements, and conferring high tolerance to antibiotics. Despite a recent surge in understanding the multicellular structures of mycobacteria, multicellular structures of *M. tuberculosis* in the host have been difficult to define. This in part can be attributed to the conventional image of *M. tuberculosis* as an intracellular pathogen living in phagosome, which is too restrictive for exuberant growth of the pathogen in multicellular structure. However, while *M. tuberculosis* might be restricted to the phagosome in early stages of infection, at later t imes, especially when lesions contain liquefied caseum and when patients are highly infective, it is likely that many of the bacilli experience an extracellular environment. In a comprehensive histopathological study of TB lesions from 1,500 autopsies, Geroges Canetti documented several lesions including open cavities that had numerous extracellular bacilli growing in multicellular structures (Canetti, 1955). Interestingly, in this 7-year study Canetti microscopically analyzed lesions of various

Fig. 2. A. Growth of *M. tuberculosis* biofilms on the liquid-air interface in detergent-free Sauton's medium. B. Insertion of a mariner transpososn (Himar) in Rv1013 abolishes the formation of biofilms, although the growth of the mutant in planktonic state remains unaltered (68).

Fig. 3. Presence of rifampicin tolerant persisters is significantly higher in biofilm populations of *M. tuberculosis* than in their planktonic counterparts, as reported by Ojha et al. (68).

morphotypes in detail, with equal focus on both the tissue structures as well as bacterial growth (Canetti, 1955). The goal of the undertaking was to bridge the partition between immunopathology and bacteriology of tuberculosis that a student of the disease was always confronted with. This partition ironically continues to be a relevant issue in questions as to how and where bacilli persist in the both latent and active TB. In a recent attempt to locate the bacilli persisting after antibiotic treatment, Orme and colleagues observed the persisters as microcolonies in the acellular rim of granulomas (Lenaerts *et al.*, 2007). Although it was

Biofilms of *Mycobacterium tuberculosis*: New Perspectives of an Old Pathogen 189

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not clear in their study whether these bacilli were alive or dead, these are reminiscent of the extracellular multicellular structures of bacilli reported in Canetti's study.


*2.3. q-PCR*

To process fly tissues, whole frozen flies were vortexed over two superposed meshes of di fferent calibers. From bottom to top, tissue fractions successively consisted of the appendages (maxillary palps, antennal segments), heads (without appendages), and bodies (without heads or appendages). RNAs were extracted from *Drosophila*-fractionated tissues using RNAzol reagent (Euromedex, Sou ffelweyersheim, France) and treated with RNase-free DNase to avoid contamination by genomic DNA. Total RNA (1 μg) was reverse-transcribed using the iScript cDNA Synthesis Kit (BioRad, Hercules, USA). q-PCR reactions were carried out on a MyIQ (BioRad, Hercules, USA) using the IQ SYBR Green Supermix (BioRad, Hercules, USA). The q-PCR conditions were as follows: 98 ◦C for 5 min to activate the hot-start DNA polymerase, followed by 40 cycles of 95 ◦C for 30 s, 65 ◦C for 30 s and 72 ◦C for 30 s. Each reaction was performed in triplicate and the mean of the three independent biological replicates (corresponding to three extractions) was calculated. All results were normalized to the RP49 and Actine5C mRNA level and calculated using the ΔΔCt method [57].

#### *2.4. Expression Pattern of GAL4 Strains*

The expression pattern of all GAL4 strains was always observed in F1 males resulting of the cross between UAS-GFP females and GAL4 males. F1 males were dissected and analyzed using fluorescent microscopy (Leica DM5000B, Leica Microsystems, Wetzlar, Germany). Frozen sections of antennae were collected and fixed in 4% formaldehyde in PBS for 30 min and washed with PBS. Antennal

sections were then stained with goat anti-GFP (1:500; Rockland, Limerick, Ireland) and mouse anti-Elav (1:1000; Developmental Studies Hybridoma Bank). Detection of the di fferent primary antibodies was carried out using AlexaFluor488 anti-goat (1:800, Molecular Probes, Eugene, USA) and AlexaFluor594 anti-mouse (1:800, Molecular Probes, Eugene, USA). After mounting in Vectashield (Vector Labs, Burlingame, CA, United States) images were made with a Leica TCS-SP2 confocal microscope.


**2.2 Analyzing gene expression profiles**

With the increased availability of genome-wide gene expression assays in public databases, there is increasing demand for more efficient computational models for data interpretation. The use of artificial neural networks in biomedical research is currently taking precedence over traditional analysis methods, as they have been proven to be better classifiers. Deep neural networks, using data from RNA-seq as inputs, are being used for prediction modeling. Classic models in applications like predicting patient outcomes using gene expression data are still not effective to the expected level, thus creating a need for more efficient robust algorithms. Recent studies that use deep learning models on gene expression data have indicated better performance. Urda et al. [17] illustrated the use of a multi-layer feed-forward artificial neural network, shown in **Figure 2**, in analyzing the RNA-seq gene expression data.

Dincer et al. [18] present a model that uses variational auto-encoders (VAEs) to extract latent variables from publicly available expression datasets and use them as features for predicting phenotypes. Their system, called DeepProfile, uses deep learning to learn a feature representation from large unlabeled expression data samples that are not incorporated in the prediction problem. This system was successfully used for the prediction of response to cancer drugs based on gene expression data. It also helped determine the effects of given drugs on specific patients

#### **Figure 2.**

*Example neural network for binary classification. Input layer of P gene expression levels connected to k-hidden layers through synaptic weights w.*

**7**

different levels of biology.

*Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic…*

and thus provides a tool for precision medicine. The model was trained on gene expression data of acute myeloid leukemia, from GEO. Results indicated that lowdimensional representation (latent variables) generated using VAEs significantly outperformed the original input feature representation (gene expression levels) in the drug response prediction problem. Therefore, variational auto-encoders were shown to be effective in extracting a low-dimensional feature representation from unlabeled gene expression datasets and these learned features were found to

It is worth noting that detecting certain differentially expressed genes (DEGs) from RNA-seq results still faces challenges despite the quality control measures applied during sample preparation and data analysis. Data processing methods can lead to a certain number of false-positives and false-negatives that affect the accuracy and sensitivity of DEGs analysis. The combination of machine learning techniques with RNA-seq has been shown to significantly improve the sensitivity of DEGs [18] and thus help increase the identification of DEGs that are missed by traditional RNA-seq techniques. The study by Wang et al. [19] used a differential network analysis, based on machine learning, to predict stress-responsive genes by learning the patterns of 32 expression characteristics of known stress-related genes. For analysis using machine learning, the WEKA 3 data mining software was used for feature selection, classifier training, and evaluation. Three feature selection algorithms, correlation feature selection (CFS), information gain (InfoGain), and RELIEF [20], were used to identify features and five classifiers, logistic regression, random forest, LMT, classification via regression, and random subspace, that exhibited better performance than other machine learning algorithms, were deployed to predict up- and down-regulated genes. With this approach, the authors were able to identify the

**2.3 Inferring protein-protein interaction and biological networks for knowledge**

In the context of this chapter, we only focus on protein-protein interaction (PPI) network, which is defined as a set of nodes (or vertices), representing proteins connected by undirected edges (or links), which are the interactions or relationships between them (either *direct physical* or *functional* interactions). A physical interaction is an interaction that involves physical contact between proteins, and on the other hand, functional interaction, which is broad, does not necessarily involve direct physical contact, but rather refers to a mechanism through which a protein participates in cell functions [21]. Several learning algorithms have been used to infer human and human-pathogen PPIs [22], including

There exist several types of PPI networks based on the type of interactions and when integrated in a single network, the relationships between proteins in a unified network are referred to as functional interactions. Here, we only refer to functional interactions, which include physical and genetic interactions, and those inferred from knowledge about co-expression and shared evolutionary history or biological pathways. Other types of biological networks include signaling networks, gene regulatory or DNA-protein interaction networks [24, 25], disease-gene networks linking diseases to genes causing the disease, and drug interaction networks connecting drugs to their targets [26]. These biological networks have been used in several applications and analyzing individual, collective, and sub-network behaviors of these biological networks has enabled effective knowledge discovery at

capture important processes relevant to the prediction problem.

*DOI: http://dx.doi.org/10.5772/intechopen.84148*

top 23 most informative features.

**discovery**

ANN [23].


**Author details**

Stephan Leible\*, Dejan Simic, Gian-Luca Gücük, Tom Lewandowski and Emir Kučević University of Hamburg, Hamburg, Germany

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.