Policies that helped biodiesel in Brazil.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Noninvasive functional neuroimaging has been introduced as a promising approach, in combination with psychological tests, to clinical diagnosis. Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique which noninvasively monitors human brain activation patterns, utilizing the tight coupling between neural activity and regional cerebral hemodynamic responses, which has a high affinity with the study of developing brains (reviewed in, for example, [1, 2, 3, 4, 5]).
fNIRS has distinct advantages in its compactness, tolerance to body motion, affordable price, and accessibility [2, 6, 7, 8, 9, 10, 11]. These advantages allow fNIRS to be contrasted with conventional imaging modalities such as functional magnetic resonance imaging (fMRI), single photon emission computed tomography (SPECT), positron emission tomography (PET), and magnetoencephalography (MEG), which are susceptible to motion artifacts and are performed using large apparatuses. Conversely, we expect fNIRS to occupy a unique position among neuroimaging methods: to provide complementary usage in clinical settings, such as bedside situations, for the purposes of diagnosis and treatment [8].
Indeed, fNIRS has been applied in various clinical studies including assessment of the outcome of neurologic rehabilitation for pathological gait [12], monitoring of ischemia [13], monitoring of language dominance before neurosurgery [14], identification of epileptic focus [15, 16], making a diagnosis of various neurological and psychiatric diseases [8, 17], and so on. Furthermore, in Japan, the first clinical applications of fNIRS in neurosurgery, assessment of hemispheric dominance for language function [14], detection of epileptic focus [15], and aid for differential diagnosis of depressive symptoms, have been included under National Health Insurance coverage. There are, indeed, great expectations for the application of fNIRS in various clinical situations, such as the exploration of objective diagnoses for developmental disorders and dementia as well as treatment assessment of medication intervention and rehabilitation. Among these, one of the most promising clinical applications of fNIRS, for which its convenience and robustness would be highly appreciated, is the functional monitoring of ADHD children, who have difficulty performing active cognitive tasks in the enclosed environments of other imaging modalities such as fMRI, PECT, PET, and MEG. A growing body of fNIRS studies has started to investigate the cortical hemodynamics of ADHD patients [18, 19, 20, 21, 22, 23].
Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in children. It affects between 3 and 7% of early elementary school children with typical behavioral symptoms of inattention, impulsivity, and hyperactivity [24, 25]. While ADHD is often diagnosed between the ages of 4 and 6 [26], ADHD symptoms are not specific to childhood, and 75–85% of patients are estimated to continue experiencing symptoms through adolescence and adulthood [27]. Consequently, 4–5% of adults have recently been reported to have ADHD [28]. Therefore, early identification and appropriate treatment are considered important in order to increase the quality of life of ADHD patients [29]. Today, the diagnosis of ADHD depends mainly on interview-based evaluation of the degrees of the phenotypes according to diagnostic criteria listed in the DSM-5 as observed by a patient’s parents or teachers (http://www.dsm5.org). However, interview-based assessments often entail subjective evaluations by parents and teachers, which present the risk of under or overestimations of ADHD symptoms [30, 31].
ADHD clinical guidelines provide recommended medication treatment, behavioral therapy, and community therapy for ADHD children [32, 33, 34]. Furthermore, an American Academy of Pediatrics (AAP) and MTA study revealed that medication treatment was superior to behavioral therapy for school-aged children over 6 years old [35]. According to copious evidence accumulated over several decades, one of the most commonly recommended treatments has been the administration of psychostimulants and non-psychostimulants, such as methylphenidate (MPH) and atomoxetine (ATX) to improve ADHD symptoms [36]. An objective biomarker of the pharmacological effects is urgently required because current treatment evaluation of ADHD depends on evaluation of the degrees of the symptoms listed in the diagnostic criteria. Interview-based measurements need to be rated by parents or teachers of the children, and thus often tend to entail subjective evaluation. Because of the technical limitations of relying on interview-based clinical observation of ADHD patients, the identification of a biological marker is needed to help facilitate objective assessments of pharmacological responses [37, 38, 39].
This led us to postulate that fNIRS would be effective in monitoring the effects of the ADHD medications MPH and ATX, especially in younger children who are difficult to assess using other neuroimaging modalities. The lack of evidence associating a neuropharmacological mechanism to therapeutic improvement is tantamount to a missed opportunity for appreciating how MPH and ATX work in the central nervous system, and such understanding is a vital step toward developing an objective, evidence-based neuropharmacological treatment for ADHD children. Thus, we performed an fNIRS study in order to assess acute neuropharmacological effects of MPH and ATX on the inhibitory functions of ADHD children.
We selected a go/no-go task as the experimental task. Go/no-go task has emerged as a principal paradigm for involving the multidimensional construct of response inhibition that refers to the ability to suddenly and completely stop a planned course of action. It is an essential executive function required in daily life, and impaired response inhibition is a strong candidate for a biomarker for ADHD [40] Former fMRI studies successfully visualized decreased hemodynamic responses with ADHD using motor response inhibition tasks including go/no-go, stop signal, and Stroop tasks [41, 42, 43, 44, 45]. Among these tasks, go/no-go task performance matures at approximately 12 years [46], followed by stop signal task at 13–17 years, and lastly, Stroop task at around 17–19 years of age [47, 48]. Therefore, a go/no-go task would be the primary choice for a study of school-aged children. fNIRS studies that presented right VLPFC activation during go/no-go tasks have been replicated [9]. Furthermore, structural neuroimaging studies of ADHD have fairly consistently indicated gray matter density reductions in the striatum and right IFG [49].
Therefore, in two consecutive studies making the most of fNIRS’s merits, we have explored the neural substrate of inhibitory controls in school-aged ADHD children for the detection of a clinically-oriented biomarker for ADHD diagnosis and evaluation for ADHD medications. In Study 1, we explored differential neural substrates for ADHD and healthy control children during go/no-go task in group analyses using fNIRS measurement. In Study 2, we explored a method of individual differentiation between ADHD and healthy control children using multichannel fNIRS, emphasizing how the spatial distribution and amplitude of hemodynamic response associated with go/no-go task execution can be utilized.
Our initial effort [50] examined whether fNIRS-based monitoring for neuropharmacological effects could be visualized in actual clinical situations. To do so, we demonstrated that fNIRS could detect the cortical hemodynamic responses of ADHD children (7–14 years old) performing a go/no-go task before and 1.5 h after MPH administration, allowing the observation of the acute effect of MPH as a significant increase of hemodynamic (oxy-Hb) response in the right prefrontal cortex. As the monitoring takes about 6 minutes, we further demonstrated that the entire protocol can be implemented within a single-day hospital visit.
However, since that study was optimized for assessing the feasibility of fNIRS monitoring as an actual clinical tool that allows the pre- and post-medication comparison to be performed in a single-day hospital visit, a neuro-pharmacological assessment of the effects of ADHD medications has yet to be performed. Experimental designs should be optimized in a neuropharmacological assessment, including a randomized, double-blind design with comparison to healthy control subjects.
Thus, for the present study, to explore the neuropharmacological assessment of ADHD medications, we enrolled ADHD children and age- and sex-matched healthy control subjects, and examined the neuropharmacological effects of ADHD medications on inhibition control, utilizing a within-subject, double-blind, placebo-controlled design. Additionally, we desire to validate the feasibility of introducing fNIRS-based diagnosis of the effects of ADHD medications, MPH and ATX, for use with ADHD children as young as 6 years old, the earliest age at which the FDA recommends starting MPH and ATX administration. Figure 1 describes the experimental protocol. We examined the effects of MPH (OROS-methylphenidate commercially available as Concerta) and ATX (Strattera, Eli Lilly and Co., Indianapolis, IN, USA) in a randomized, double-blind, placebo-controlled, crossover study during a go/no-go task. All ADHD patients were pre-medicated with MPH (n = 16) or ATX (n = 16) as part of their regular medication regimen. We performed fNIRS measurements of ADHD subjects twice (the times of day for both measurements were scheduled to be as close as possible), at least 2 days apart, but within 30 days. Control subjects underwent a single, non-medicated session. On each examination procedure day, ADHD subjects underwent two sessions, one before drug (MPH/ATX or placebo) administration, and the other 1.5 h after drug administration. Before each pre-administration session, a washout period of 2–3 days was undertaken by all ADHD subjects. Each session involved 6 each of go (baseline) and go/no-go (target) blocks, which were alternated. Each block lasted 24 s and was preceded by trial instructions displayed on a PC monitor for 3 s, giving an overall block-set time of 54 s and a total session time of about 5.5–6.0 min. In the go blocks, we presented subjects with random sequences of two animal pictures and asked them to press a button for both pictures as quickly as possible. In the go/no-go blocks, we presented subjects with a no-go picture 50% of the time, thus requiring subjects to respond to half the trials (go trials) and inhibit their response to the other half (no-go trials). After ADHD subjects performed the first session, either MPH/ATX or a placebo was administered orally. We generated stimuli and collected responses using E-Prime 2.0 (Psychology Software Tools). Stimuli were presented to the subject on a 17″ desktop computer screen. The distance between the subject’s eyes and the screen was about 50 cm.
Experimental procedure to detect the differential neural activation pattern for ADHD and healthy control children during go/no-go task in group analyses, using fNIRS measurement. (a) A schematic showing the flow of pre- and post-medication administration sessions for ADHD subjects. (b and c) fNIRS measurements. Brain activity was measured using fNIRS, while ADHD and healthy control subjects performed the go/no-go task.
We used the multichannel fNIRS system ETG-4000 (Hitachi Medical Corporation, Kashiwa, Japan), which utilizes two wavelengths of near-infrared light (695 and 830 nm). We analyzed the optical data based on the modified Beer-Lambert Law [51] as previously described [52]. This method enabled us to calculate signals reflecting oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb), and total hemoglobin (total-Hb) signal changes, obtained in units of millimolar·millimeter (mM·mm) [52]. In order to perform statistical analyses, we treated the oxy-Hb signal as the primary outcome of hemodynamic responses because of its higher sensitivity to changes in cerebral blood flow compared with deoxy-Hb and total-Hb signals [53, 54, 55], its higher signal-to-noise ratio [53], and its higher retest reliability [56]. We used two sets of 3x5 multichannel probe holders, which resulted in 22 channels (CH) per set. Each probe holder consisted of eight illuminating and seven detecting probes arranged alternately at an inter-probe distance of 3 cm to cover the lateral prefrontal cortices and inferior parietal lobe, referring to previous studies [9, 57, 58, 59, 60]. The midpoint of a pair of illuminating and detecting probes was defined as a channel location. The bilateral probe holders were attached in the following manner: (1) their upper anterior corners, where we connected the right and left probe holders by a belt, were symmetrically placed across the sagittal midline, (2) the lower anterior corners of the probe holder were placed over the supraorbital prominence, and (3) the lower edges of the probe holders were attached at the upper part of the auricles. Virtual registration was adopted for spatial profiling of fNIRS data [61, 62] to register fNIRS data to MNI standard brain space [63]. This method enables us to place a virtual probe holder on the scalp based on a simulation of the holder’s deformation and the registration of probes and channels onto reference brains in an MRI database [64, 65]. Specifically, the positions of channels and reference points, consisting of the Nz (nasion), Cz (midline central), and left and right preauricular points, were measured with a 3D-digitizer in real-world (RW) space. The RW reference points were affine-transformed to the corresponding reference points in each entry in reference to the MRI database in MNI space. We adopted the same transformation parameters to obtain the MNI coordinates for the fNIRS channels and the most likely estimates of the locations of given channels for the group of subjects together with the spatial variability associated with the estimation [66]. Finally, macroanatomical labels were estimated using a Matlab function that reads labeling information coded in macroanatomical brain atlases, LBPA40 [67] and Brodmann’s atlas [68].
Individual timeline data for the oxy-Hb and deoxy-Hb signals of each channel were preprocessed with a first-degree polynomial fitting and high-pass filter using cut-off frequencies of 0.01 Hz in order to remove baseline drift, and a 0.8 Hz low-pass filter to remove heartbeat pulsations. In fNIRS measurements, note that the Hb signals analyzed do not directly represent cortical Hb concentration changes, but contain an unknown optical path length that cannot be measured. Direct comparison of Hb signals among different channels and regions should be avoided as optical path length is known to vary among cortical regions [69]. Hence, statistical analyses were performed in a channel-wise manner. We computed channel-wise and subject-wise contrasts by calculating the inter-trial mean of differences in Hb signals between peak (4–24 s after go/no-go block onset) and baseline (14–24 s after go block onset) periods from the preprocessed time series data. We visually inspected the movements of the subjects and removed the blocks with sudden, obvious, discontinuous noise for the six go/no-go blocks. We subjected the resulting contrasts to second-level, random-effects group analyses.
Figure 2 describes the experimental results. The oxy-Hb signals were statistically analyzed in a channel-wise manner. Specifically, for healthy control subjects, who were examined only once, a target (no-go block session) vs. baseline (go block session) contrast was generated (Figure 2(a)). For ADHD subjects, we generated the following contrasts: (1) pre-medication contrasts: target vs. baseline contrasts for pre-medication conditions (either placebo or MPH/ATX administration) for the first day exclusively (Figure 2(b)), (2) post-medication contrasts: the respective target vs. baseline contrasts for post-placebo and post-MPH/ATX conditions (Figure 2(c, d)), and (3) inter-medication contrasts: differences between MPH/ATXpost−pre and placebopost−pre contrasts (Figure 2(c, d)). Cortical activation patterns of healthy control subjects (a) and of ADHD subjects (b–d) are shown as t-maps of oxy-Hb signal, with significant
Differences of neuroactivation patterns between ADHD and healthy control during go/no-go task. The channel location of oxy-Hb signals for the right CH 10. (a) Healthy control (b) pre-medicated ADHD (c) post-MPH administration and (d) post-ATX administration.
Firstly, to screen the channels involved in go/no-go tasks in healthy control subjects, paired t-tests (two-tails) were performed on target vs. baseline contrasts. The statistical threshold was set at 0.05 with Bonferroni correction for family-wise errors. We found significant oxy-Hb increase in the right CH 10 (mean 0.075, SD 0.074,
Previous fMRI measurements for healthy control subjects have provided preliminary evidence for the neural correlates of go/no-go tasks [70], including the bilateral IFG, MFG and SFG (superior frontal gyrus), supplementary motor area, anterior cingulate gyrus, inferior parietal and temporal lobes, caudate nucleus, and cerebellum [60]. In addition, recent fMRI [41, 42, 43, 44, 45] and fNIRS [71, 72] studies on acute medication effects on ADHD have also shown that bilateral IFG and MFG were robustly normalized after ADHD medications. Taken together, the specificity of the implicated brain regions, such as MFG and IFG, in healthy subjects, as well as functional and structural changes to those regions in ADHD patients, suggests that response inhibition is a good neuro-functional biomarker candidate for ADHD [73].
Our current study found activation in the right MFG and IFG (BA9, 46, 45) during the go/no-go task period in the healthy control subjects, but not in the pre-medicated ADHD subjects. These results suggest that ADHD produces impairment of right prefrontal function associated with go/no-go task performance. The administration of MPH and ATX led to a degree of right prefrontal activation in ADHD children comparable to that of the healthy control subjects, but the placebo did not. These results suggest that as observed using fNIRS, normalized right IFG/MFG activation associated with inhibition control would serve as a robust neurobiological marker for evaluating both MPH and ATX effects. In summary, we explored the feasibility of introducing fNIRS-based neuropharmacological assessment of the effects of MPH/ATX administration to ADHD children, and concluded that the right IFG and MFG activation could serve as robust objective neurobiological markers to visualize the effects of MPH/ATX on ADHD children based on the following observations.
The purpose of Study 2 was to explore the possibility of fNIRS-based single-subject diagnosis with various technical approaches. The exploration of fNIRS-based individual classification methodology has been attracting increasing research interest with extremely promising results pertaining to its use for the clinical diagnosis of psychiatric and neurodevelopmental disorders. Recently, a multi-site large-scale fNIRS study involving over 600 adult subjects suffering from bipolar disorder, depressive disorder, and schizophrenia demonstrated high classification accuracy using disorder-specified hemodynamic response patterns: sensitivity of differentiation from healthy control subjects was 76.9% for bipolar disorder, 74.6% for major depressive disorder, and 90.0% for schizophrenia [74]. Furthermore, in a different study enrolling nine boys with medicated ADHD and eight boys with autism spectrum disorder (ASD), use of a support vector machine on hemodynamic response data during a task involving viewing the subject’s mother’s face allowed the discrimination of the two populations with an 84% accuracy of classification [75].
In our previous session, described above, we introduced fNIRS-based monitoring of the neuropharmacological effects of ADHD medications. Furthermore, with group analyses, we successfully visualized differential neural substrates for ADHD and healthy control children for inhibitory control. The inhibition task recruited the right IFG/MFG, and activation was significantly high during the go/no-go task (Cohen’s
We explored a method for individual classification between ADHD and healthy control subjects using fNIRS, emphasizing how spatial distribution and amplitude of the hemodynamic response associated with go/no-go task execution can be utilized. To do this, we needed to identify the cut-off amplitude of cortical activation of each ROI mentioned above in order to differentiate ADHD children from healthy control children. We focused on individual oxy-Hb signal change during target (go/no-go) sessions at multichannel locations for the right MFG and IFG, where a go/no-go-task-related activation in control subjects was conspicuously large at a single-channel location in group analyses from our previous studies [76, 77, 78]. In order to identify a robust classification parameter, we adopted the right MFG and IFG as ROIs (call optimized ROIs). Then, making the best use of multichannel analysis, we adapted well-formed formulae to identify the constituent CHs of the optimized ROIs, and assessed whether a specific logic could improve the efficacy of classification.
First, we screened for any fNIRS channels involved in the go/no-go task for control and ADHD subjects at the group level (Figure 4). We found significant oxy-Hb increase in three CHs in the right (R) hemisphere, including R CH 5 (mean 0.057, SD 0.077,
Second, we assessed the group difference in oxy-Hb signals among the ROIs (Figure 3). The comparison between ADHD and healthy control subjects revealed significant activation of oxy-Hb signal in the right CHs 6 and 10 in the control subjects than in ADHD subjects at the group level (independent two-sample
Cortical activation patterns for group-level comparison between the ADHD and healthy control groups during a go/no-go task. t-Maps of oxy-Hb signals are displayed, with significant
We applied CHs 6 and 10 as statistically robust ROIs to represent the most significant activation in healthy control compared with ADHD subjects during go/no-go task execution. In order to classify ADHD and healthy control children with higher accuracy, we explored setting a cut-off value for individual fNIRS-based oxy-Hb signal patterns. We set the initial cut-off value for the oxy-Hb signal at 0 mM·mm. From this start point, the cut-off value was incremented or diminished until specificity or sensitivity reached 0 or 1. For each cut-off value, we plotted sensitivity and 1-specificity to create a receiver operating characteristic (ROC). In addition, we calculated the area under the resultant ROC curve (AUC). Then the best cut-off value was identified as that with the highest sensitivity and specificity, which is the point nearest to the top left corner of the curve (Figure 4). In this and in the previous study, the PASW Statistics (version 18 for Windows) (SPSS Inc., Chicago, USA) software package was used for statistical analyses.
In order to predict ADHD diagnosis using channel-wised hemodynamic changes, we applied CHs 6 and 10 as statistically robust ROIs to represent the most significant activation in healthy control compared with ADHD subjects during go/no-go task execution. We explored setting a cut-off value for individual fNIRS-based oxy-Hb signal patterns: (a) CHs 6 and 10, respectively, (b) average oxy-Hb signal contrasts for the right CHs 6 and 10, (c) optimized values using well-formed formulae. For each cut-off value, sensitivity and 1-specificity to create a receiver operating characteristic (ROC) were plotted. Subsequently, the area under the resultant ROC curve (AUC) was calculated. Finally, the best cut-off value was identified as that with the highest sensitivity and specificity, which is the point nearest to the top left corner of the curve.
First, we examined each channel (CH 6 and 10) component. For CH 6, the AUC value was 81.20%. At the optimal cut-off value of 0.0000 mM·mm, differentiation between ADHD and healthy control subjects was achieved with a sensitivity of 66.7% and a specificity of 83.3% (Figure 4(a)). For CH 10, the AUC value was 74.4%. At the optimal cut-off value of 0.0320 mM·mm, differentiation between ADHD and healthy control subjects was achieved with a sensitivity of 63.3% and a specificity of 80.0% (Figure 4(a)). Second, the averages of the integral values for CHs 6 and 10 for 30 individual ADHD and healthy control subjects were calculated (Figure 4(b)). The resulting AUC value was 84.7%. At the optimal cut-off value of 0.0374 mM·mm, differentiation between ADHD and healthy control subjects was achieved with a sensitivity of 83.3% and a specificity of 73.3% (Figure 4(a)).
Third, for further optimization, we adapted well-formed formulae for CHs 6 and 10 in the most optimized ROI. With “AND” logic, a subject was classified as normal (not ADHD), when the subject’s oxy-Hb signals for CHs 6 “AND” 10 were above a given threshold. When “OR” logic was applied, a subject was classified as normal (not ADHD), when the subject’s oxy-Hb signal for CH 6 “OR” 10 was above a given threshold. For each classification using well-formed formulae, ROC analysis was performed as described above. We adapted well-formed formulae for CHs 6 and 10 to better classify ADHD and healthy control subjects. When “OR” logic was adopted, the area under the AUC was 78.2%. At the optimal cut-off value of 0.0650 mM·mm, differentiation between ADHD and healthy control subjects was achieved with a sensitivity of 76.7% and a specificity of 70.0% (Figure 4). Finally, when “AND” logic was adopted, the AUC value was 85.0%, which was the highest percentage among all classifications. At the optimal cut-off value of 0.0111 mM·mm, differentiation between ADHD and healthy control subjects was achieved with a sensitivity of 90.0% and a specificity of 70.0% (Figure 4).
Optimized ROIs in the right IFG and MFG to differentiate ADHD children from healthy control children were successfully identified through individual assessment of channel-wise oxy-Hb signal changes using fNIRS; adaptation of well-formed formulae to two CHs to form optimized ROIs achieved 90% sensitivity for diagnostic predictions at the individual level. Thus, we suggest the high possibility that this novel fNIRS-based measurement may serve as an efficient diagnostic method to enable differentiation between ADHD and healthy children at an individual level. Previous neuroimaging studies have reported on methods for diagnostic classification of ADHD and healthy control subjects at the individual level that adopt multifactorial methods (e.g., neuroanatomical pattern classification) to structural MRI data [43, 79] and to fMRI data [37, 80, 81, 82, 83, 84]. However, our protocol requires only a single variable (the simple “integral value” of fNIRS signals for only two ROIs) and produces high classification rates (sensitivity: 90%). Our classification rates were equivalent to those reported for previous MRI and fMRI studies using multivariate statistical methods, which ranged from 67 to 93% for ADHD groups compared with healthy control children.
Recently, a considerable number of studies have introduced neural correlates for go/no-go tasks, including the right IFG and MFG [60]. Furthermore, a recent activation likelihood estimation (ALE) meta-analysis of go/no-go tasks [85] revealed a mainly right-lateralized network associated with response inhibition, including the right MFG and IFG (BA46/44) [70]. The right IFG and MFG have been implicated in processes of response selection, stimulus recognition, and maintenance and manipulation of stimulus-response associations, including selecting not to respond, all of which are critical in the performance of go/no-go tasks.
From a genetic perspective, the catechol-O-methyltransferase (COMT) gene [86], the dopamine active transporter 1 gene (DAT1, also known as SLC6A3), and the dopamine receptor D4 (DRD4) gene [39] are deeply associated with the pathophysiology of ADHD. These genes are thought to be involved in the monoamine system, and their dysfunction in the prefrontal cortex, including the IFG and MFG, is considered to be the core pathomechanism of ADHD.
As discussed above, the current study has demonstrated that our fNIRS-based experimental method of using inhibition-elicited cerebral functional to differentiate between ADHD and healthy control children allows the observation of a distinct biological marker in clinical situations. However, before establishing its utility in clinical practice, several issues need to be addressed.
First, the scope of the current study does not necessarily extend to screening for ADHD with comorbidity. Therefore, our next step is to explore the disorder-specificity of fNIRS-based individual classification relative to other developmental and psychiatric disorders, such as autism spectrum disorder, oppositional defiant disorder, conduct disorder, depression, and anxiety.
Second, although most ADHD subjects had temporally stopped medication (MPH or ATX) for more than 48 hours before fNIRS examination, the condition of the ADHD subjects in this study may not precisely reflect immune brain activation. Several other neuroimaging studies examining medication-naïve ADHD patients have been reported in a recent meta-analysis [87]. Brain function can be changed with long-term MPH and ATX administration; the recent meta-analysis of human studies using fMRI suggested that long-term MPH treatment is associated with more normal activation in the right DLPFC. Therefore, we need to explore medication-naïve ADHD patients as our next step.
The climate of our planet undergoes changes that mainly derive from the emission of greenhouse gases (GHGs), among which is carbon dioxide (CO2), considered by many as the most significant man-made GHG. When expelled by the exhaust of internal combustion-powered vehicles, CO2 accumulates in the atmosphere and causes an increase in the Earth’s average temperature. As CO2 has a very long atmospheric lifetime [1], usually longer than the other three main heat-trapping gases (methane, nitrous oxide, and CFCs—except for CFC-13 and CFC-115), it is seen as the chief culprit of global warming, which has worried the scientific community around the world in recent years.
Bearing in mind that it is essential to limit CO2 emissions in the air, dozens of heads of state and hundreds of scientists from all over the globe gathered at the end of 2015, in Paris, for the 21st Conference of Parts—also known as COP21 or Paris Agreement—with the main goal of engaging nations in an international agreement on climate change in which everyone pledged to collaborate so that the increase in the average temperature of our planet should not exceed the limit of 2°C by the end of this century.
At the COP21, Brazil voluntarily committed to: (1) cut down on GHG emission up to 37% by 2025 and 43% by 2030—considering 2005 as the base year for both scenarios [2]—which would represent approximately 1.2 million tons of carbon dioxide equivalent (CO2eq) by the year 2020 [3]; (2) increase the use of biofuels from sustainable bioenergy up to 18% of the total domestic energy mix by the year 2030 [4]; and (3) increase the share of renewables to an estimated 45% by the same year [5].
So, considering the scenario wherein CO2 needs to be banned from our environment for the sake of our own planet, it is reasonable for biofuels to present themselves as clean and renewable alternatives [6] for the gradual replacement of fossil fuels such as gasoline and mineral diesel, for example. Therefore, because biodiesel is biodegradable, less toxic, and almost 100% renewable [7, 8], it stands as an option.
After 2005, some public policies were created in Brazil as an attempt to definitely establish biodiesel as replacement for fossil diesel, mainly in the transport sector. Although successful to some extent, none of them compare in scope and reach to the recently created National Biofuels Policy (RenovaBio), whereto there is a hope it will make the production and use of biodiesel increase at a much faster rate.
Therefore, this work is primarily aimed at informing the reader about the past biodiesel policies that were implemented to boost its production and use, providing special emphasis to the most recent one: RenovaBio.
The first time Brazil began to flirt with biodiesel was during the 1920s, when the
Although the first real tests with biodiesel in Brazil began to appear 20 years later, in the early 1940s, during World War II [10], it was only during the 1970s, after continued increases in oil prices, that conventional biodiesel technology, using a catalyst and an alcohol, became well known in Brazil [11].
With the oil shock of 1973, the world awakened to the importance of finding different sources of energy that were not dirty like those produced by the fossil fuels. That year became a landmark in our planet’s energy history, whose focus would be on overcoming the energy crisis via two main groups of action: conservation of energy—or energy saving—and the use of alternative sources of energy [12].
Following that type of awareness, Expedito Parente, the leading Brazilian biodiesel scientist at that time, created the first patents of that biofuel in the country, which would represent the outcome of his studies in the late 1970s and early 1980s. As a result, he became the mainstay and principal proponent of the early developments of biodiesel in Brazil.
The first policies created to promote the production and consumption of biodiesel in Brazil (Table 1) are discussed ahead.
Year | Mechanism | Program name and acronym (in Portuguese) |
---|---|---|
2004 | Decree No. 5297 | Social Fuel Stamp (SCS) |
2005 | Law No. 11097 | National Program of Production and Use of Biodiesel (PNPB) |
2009 | Law No. 12187 | National Policy on Climate Change (PNMC) |
2014 | Law No. 13033 | Mandatory blend on diesel: increase to 6% and 7% |
2016 | Law No. 13263 | Mandatory blend on diesel: increase to 8%, 9% and 10% |
2017 | Law No. 13576 | National Biofuels Policy (RenovaBio) |
Policies that helped biodiesel in Brazil.
The Brazilian government launched the Social Fuel Stamp program—or
Plentiful in both regions, castor would be a nice contender if it were not for the fact that this oilseed presents a very high viscosity content of 14.1 mm2/s, which is way above the maximum determined by the Brazilian Petroleum Agency (ANP) of 6.0 mm2/s [16]. That technical constraint alone deems castor an unsuitable alternative feedstock for the production of biodiesel [10, 17, 18, 19, 20].
The SCS is also a mechanism that serves to reduce government taxes and allow the Brazilian Development Bank (BNDES) to grant lower funding fees for biodiesel producers who acquire raw materials from—and provide technical assistance to—family farmers [15].
Through ANP, the Federal government promotes auctions for the purchase of biodiesel. During the first stage of the auction, 80% of the total volume is offered to biodiesel producers who already have the SCS. This phase, restricted to holders of the stamp, grants them the purchase and delivery of the product for specified periods. The remaining 20% of biodiesel is offered to any producer, with or without the stamp [21, 22].
Although the SCS program was conceived to bring about major social and economic changes in the North and Northeast regions, as initially intended by the government, except for some modest progress, many authors see it as a failure. The reasons vary from high logistic and harvesting costs to low economies of scale, low utilization of agricultural machinery, artificial irrigation and fertilizer, and lack of access to financing due to red tape [18]. Therefore, this program is currently under revision in order to correct the aforesaid problems, already realized by the government.
On January 13, 2005, the Brazilian government formally introduced The National Program of Production and Use of Biodiesel (PNPB, Brazilian acronym for
One of the main objectives of PNPB, besides the promotion of social inclusion of family farmers, primarily from the North and Northeast regions of Brazil, was also to reduce the import of mineral diesel [9, 24], thus positively affecting the Brazilian trade balance.
In addition, the program also meant to establish a minimum blending percentage of biodiesel into petroleum diesel. At first, the suggested blending percentage (2%) was optional to run from 2005 to 2007, then mandatory from January 2008 onward (Table 2).
Table 2 shows that the production percentage rates from 2005 to 2010 were (on average) very high when compared to the ones from 2011 to 2017. Although 2014 was a year in which the blend changed twice, the percentage increase was relatively low: only 17%. And since then, the percentage has declined, even negative in 2016—a year in which there was a decrease in the biodiesel production for the first time since the establishment of the PNPB program, in 2005.
Right from the beginning of the PNPB program, soybean has become the main and overwhelmingly dominant raw material for biodiesel production, accounting for an average of 75% and beef tallow, in second, with an average of 15%. Other raw materials, such as cotton seed, used cooking oil, and other fats would represent, altogether, the remaining 10% [25]. This high dominance of both feedstocks has driven the Brazilian government to create mechanisms to incentivize the use of alternative raw materials [15, 26].
Law No. 12187 was sanctioned on December 29, 2009, and amended by the Presidential Decree No. 7390 on December 9, 2010 [27]. Together, they establish principles, goals, instruments, and guidelines of the National Policy on Climate Change (PNMC) and elucidate terms such as adaptation, mitigation, emissions and sources of emissions, greenhouse gases, and their eminent impacts [27].
Both law and decree also provide that any measures taken to reduce any type of emissions must have a national range, besides being focused on the prevention or minimization of damages caused directly by anthropogenic activities. On the other hand, these measures should also consider the different socioeconomic contexts of their application, as well as allocate to the population and economic sectors the burdens and charges resulting from such impacts.
Furthermore, both policies also shed light on how to diminish natural and anthropogenic impacts, and how to promote the understanding about the consequences of existing and forthcoming climate change events [27].
One of PNMC’s guidelines is Brazil’s Nationally Determined Contributions (NDC) committed at the Paris Agreement.
On September 24, 2014, the Federal government enacted Law No. 13033 [28], turning mandatory the blending of biodiesel into petroleum diesel in the following percentages: 6% (B6) beginning on July 1, 2014, and 7% (B7) starting on November 1 of the same year.
This policy also allows the voluntary addition of biodiesel to diesel in amounts greater than the mandatory percentage in public transport, rail, inland navigation, equipment, or vehicles for mineral extraction and electric power generation, tractors, and other automotive vehicles intended to pull or tow agricultural machinery or to carry out agricultural work [28].
Law No. 13263, implemented on March 23, 2016, alters Law No. 13033 to establish new mandatory blending percentages of biodiesel into mineral diesel, as follows: 8, 9, and 10 (B8, B9, B10) to commence on March 1 of 2017, 2018, and 2019, respectively [29]. But the latter policy upholds the prerogative of voluntary addition of the former.
This law also authorizes the increment of up to 15% (B15) of biodiesel to fossil diesel sold to end users, in any part of the country, after completion (up to 36 months of its enactment) of specific tests and experiments on engines that validate the use of the mixture [29]. The specific tests (50 or so) shall be conducted by more than 20 different companies, spread out through more than 15 locations nationwide [21].
With the anticipation of the B10 blend to March 2018, there should be a progressive increase in the biodiesel-to-diesel mix aiming to achieve the B15 in 2025 in order to meet the country’s NDC, committed at the Paris Agreement [21].
The National Policy on Biofuels or
Besides helping the biodiesel market, RenovaBio will also boost the ethanol industry and Brazil’s foreign trade balance by diminishing the country’s reliance on imported corn ethanol from the United States.
With a nationwide scope, this program also outlines rules for marketing biofuels in the country under the flag of environmental sustainability, and fosters credibility and predictability of national fuel supply. By doing so, RenovaBio will not just help the environment, but it will lay the market conditions for domestic and foreign private investments [31, 32].
This fresh and innovative regulatory framework is supported by two main pillars: the encouragement of energy efficiency throughout the production and use of biofuels and the recognition of biofuels capacity to remove carbon from the environment [33], or mitigate its impacts.
When formulating the basic tenets of RenovaBio, policy-makers and researchers took into account successful international models of initiatives that were enacted for the areas of biofuels and renewable energy, such as the Low Carbon Fuel Standard (LCFS), the Renewable Fuel Standard (RFS), and the Renewable Energy Directive (RED) [34, 35].
Both LCFS and RFS laws, implemented in the United States in 2005 and 2007, respectively, are primarily aimed at reducing carbon intensity in the transport sector [36, 37], while the RED directive, sanctioned in the Europe Union, in 2009, demand that each EU country expressively increase their share of renewable energy to the energy mix in a way that the overall EU share be 20% by the year 2020 [38].
Biofuels have aroused the interest of the Brazilian government in the light of its mitigating potential against the harm caused by petroleum-derived fuels, mainly in the transport sector [22]. Therefore, by enacting RenovaBio, the Brazilian government has overtly shown to the international scientific community its desire to comply with the Paris Agreement, in which the country presented its voluntary goals for 2030 under the NDC.
To achieve these goals, Brazil will adopt actions to reduce GHG projected emissions and increase the share of renewable energy and biofuels into the country’s energy matrix [39], as aforementioned.
Furthermore, there is a possibility of voluntary use of biodiesel in mineral diesel to be greater than the mandatory blend in specific cases, such as: 20% in captive fleets or road users serviced by supply point; 30% in rail transport; 30% in agricultural and industrial use; 100% in experimental use, specific or in other applications [21].
Decree No. 9308, sanctioned on March 15, 2018, addresses the annual compulsory targets to reduce domestic emissions of GHGs and assigns to the National Energy Policy Council (CNPE) the definition of these goals, which are based on recommendations of the Interministerial Committee on Climate Change (CIM) [40].
The breaking down of the national mandatory targets—provided by CNPE—into an individual goal that must be assigned to each biofuel distributor is responsibility of ANP. The distributors that do not comply with their individual goals will be subject to a fine proportional to the noncompliance goal, which may not exceed 5% of their annual revenue, registered in the previous two years [40].
This decree has also established a RenovaBio Committee to provide technical support to CNPE in the process of defining—until June 2018—the annual national reduction targets and it is consisted of agents from seven Ministries, among them Mines and Energy, Environment, and Agriculture. Representatives of other federal, state, and municipal agencies, as well as public and private sector entities from the biofuels market, in addition to technicians and specialists from the sector, whose assistance will be considered provision of unpaid public services, may also be part of this committee as guests [40].
One of the new facets of RenovaBio, missing in previous biofuels policies, is the creation of two important market mechanisms:
Chapter 2 of the RenovaBio policy defines the CPEB as a document issued exclusively by inspection companies as a result of the biofuel certification process. These companies must be highly qualified as such (ISO-standard) and be approved by the government in order to inspect the biofuel companies—either producers or importers [30].
After the inspection and certification are over, ANP will audit the whole process in order to approve or disapprove the issuance of the certificate. In case of approval, the list of certified producers and/or importers is published in the official government gazette so they can take advantage of their certification status when marketing their biofuels.
In case of disapproval, ANP then assesses the whole process to see if there was incompleteness or any sort of fraud. If the latter is the case, then ANP establishes an administrative process to revoke the accreditation of the inspection company. If the former is the problem, then ANP sends the whole paperwork to the inspection company redo the process again [41], as shown in Figure 1.
CPEB issuance process flowchart. Source: Adapted from [
Article 28 of the RenovaBio law states that a bonus of up to 20% will be applied on the value of the energy and environmental efficiency grade of the producer or importer of biofuel whose CPEB proves a negative emission of GHGs in its life cycle in relation to its substitute of fossil origin.
It is up to the RenovaBio Committee, among other responsibilities, to monitor the market, supply, and development of the production of biofuels, particularly the installed capacity of companies that hold the certificate that deems their production efficient and environmentally friendly. Government Order No. 103 of March 22, 2018, has set the green light for this Committee to start its activities immediately [42].
Chapter 2 of the RenovaBio law states that Decarbonization Credits are instruments registered in the form of scripture for the purpose of attesting the individual target of fuel distributors whose proof of achievement shall be based on the amount of credits held by the fuel distributor on the date defined by the policy [30].
The values of the annual mandatory targets were established in units of CBios, with each CBio corresponding to 1 ton CO2eq, whose calculations consider the difference between the GHG emissions in the life cycle of a biofuel and the emissions in the life cycle of its fossil substitute [40].
These calculations will consider energy efficiency in MJ/ton or cbm and environmental impact in gCO2e/ton or cbm [33] and will be carried out by RenovaCalc, which is a tool that accounts for the carbon intensity of a biofuel in gCO2eq/MJ [43].
Besides carrying out analyses and studies for the definition of compulsory goals, plus the evaluation and suggestion of preventive measures to adapt them, other responsibilities of the RenovaBio Committee are the monitoring of supply, demand, and price of CBios issued and traded in the stock exchange from the commercialization of biofuels. Figure 2 shows the links of CBios with the biofuel producers and distributors and how they interplay with the government agencies and certifying companies.
CBios flowchart. Source: Adapted from [
Historically, Brazil has adopted soybean and beef tallow as the main feedstocks for the fulfillment of biodiesel demand in the country. As previously mentioned, soybean alone accounts for three-fourths of the domestic production, mostly done in the Mid-West and in the South, regions that house the vast majority of Brazil’s biodiesel plants, whose owners are also major soy producers and agribusiness companies, well established in both areas for a long time and, therefore, are better prepared to take advantage of the big soy market that was developed throughout the country [3].
Since the inception of the PNPB program, in 2005, biodiesel production has leap-frogged from 736 thousand to 4.3 billion liters in just 12 years (Figure 3). Such a growth turned Brazil into the second biodiesel producer in the world, trailing only the United States, as the number one producer with approximately 5.5 billion liters, in 2016 [44].
Biodiesel annual production (B100) and accumulated installed capacity. Source: Adapted from [
In footstep with the production growth, the installed capacity also jumped from zero to approximately 8 billion liters in about the same period (Figure 3). If on one hand, this growth leads to an idle capacity rate that is close to 50%; on the other hand, it makes biodiesel producers optimists to comfortably meet the production forecasts for at least 5 years after the enactment of the new biofuel policy.
The expected biodiesel production growth to 18 billion liters in 2030 suggests a major boost of the installed capacity to around 22 billion liters. In order to meet that forecast, it will be necessary not just to expand the capacity of current plants but also a twofold increase in the number of biodiesel plants in operation now (Table 3).
Year | B100 (m3) | % | Blend |
---|---|---|---|
2005 | 736 | — | B2 |
2006 | 69,002 | 9275 | B2 |
2007 | 404,329 | 486 | B2 |
2008 | 1,167,128 | 189 | B2/B3 |
2009 | 1,608,448 | 38 | B3/B4 |
2010 | 2,386,399 | 48 | B5 |
2011 | 2,672,760 | 12 | B5 |
2012 | 2,717,483 | 2 | B5 |
2013 | 2,917,488 | 7 | B5 |
2014 | 3,422,210 | 17 | B5/B6/B7 |
2015 | 3,937,269 | 15 | B7 |
2016 | 3,801,339 | −3 | B7 |
2017 | 4,291,294 | 13 | B8 |
2018 | 5,590,000* | 30 | B10 |
Status | Accomplished | Forecasted | Unit (million) | ||
---|---|---|---|---|---|
Year | 2016 | 2020 | 2025 | 2030 | |
Soybean | |||||
Processed | 40.7 | 55.1 | 77.3 | 107.2 | t/year |
Installed capacity | 65.0 | 68.8 | 96.6 | 134.0 | t/year |
Number of plants | 117 | 120 | 139 | 165 | — |
Biodiesel | |||||
Production | 3.8 | 6.4 | 11.4 | 18.0 | m3/year |
Installed capacity | 7.3 | 8.0 | 14.3 | 22.5 | m3/year |
Number of plants | 50 | 51 | 76 | 109 | — |
Future scenario for soybean processing units and biodiesel refining.
Source: Adapted from [45].
That kind of growth will require an investment volume of R$ 21.7 billion [45], equivalent to approximately US$ 7 billion, from the government and the private sector, which will represent a major boost on the local economy where the current biodiesel plants are already installed, as well as where the new ones should be built.
Such expansion in the production and use of biodiesel should represent some positive externalities like the creation of new jobs along the entire biodiesel chain, and the reduction of CO2 emissions in the atmosphere as a replacement for part of the petroleum diesel Brazil still needs to import, which should represent an economy of US$ 1 billion per year for an equivalent amount of nearly 1.2 billion liters of diesel not imported [3].
The complexity of this policy requires the government to pay special attention to questions on how the process steps will be supervised and how to allocate the individual targets of the biofuel distributors which, in turn, can acquire the decarbonization certificates whose prices still need to be defined, as well as the costs for the certification of production [46].
The technology mostly used to produce biodiesel in Brazil is the transesterification, which is inefficient. Besides, the process uses methanol, which is from a fossil source, instead of ethanol, which is cleaner, renewable, and produced locally from sugarcane. And the country still imports a good chunk of the alcohol that is used to produce biodiesel, therefore affecting the trade balance negatively.
Furthermore, there needs to be greater incentives for the diversification of the mix of raw materials used in the production of biodiesel, since soybeans and beef tallow together represent about 90% of the total and they present environmental problems due to the use of pesticides and herbicides [47], as well as GHG from land use and land use change [48].
Perhaps alternative sources, such as palm—whose yield per hectare is approximately six times greater than that of soybeans [22]—may be an option. However, this feedstock still needs investments in R&D so that its production increases in such a way that the amount of available oil would be enough not only to meet its main market—culinary—but also the production of biodiesel.
Although at this moment in time palm does not have enough scale to meet the market demand for biodiesel, Embrapa’s research with this oilseed has had positive results in adapting it to other environments that are different from the traditional ones in the legal Amazon, where most of palm is harvested [21].
Thus, it is hoped that palm oil will have a much larger penetration in the mix of raw materials for the production of biodiesel and, therefore, provide a greater competitiveness of the sector and increase the income of family farmers, especially those from the north and northeast. But that will depend as well on the success of policies such as
On the overseas front, the European Union has recently strengthened its position to disfavoring traditional biofuels, such as ethanol from sugarcane and corn, and biodiesel from oilseeds. The maximum demand for these biofuels in energy demand is forecasted to decrease from 7% in 2020 to 3.8% in 2030. This proposal has received harsh criticisms from various sectors of the industry [21], as well as from the scientific community.
At a moment in which the demand for food and bioenergy will continue to grow for the next years as a result of populational growth, increased world income, and the evolution of energy policies toward cleaner and more sustainable sources [21], the European Union position, along with the United States decision to withdraw from the Paris Agreement, raises concerns regarding the immediate growth of biofuels worldwide. On the other hand, Brazil has now a great opportunity to set the stage to confirm its world leadership in the field of biofuels [33].
Over the last years, we have noticed that a clear global movement is underway to reduce the use of fossil fuels in order to decrease the GHG emission in the atmosphere and, therefore, limit the rise of the temperature of our planet to 2°C, preferably to 1.5. One of the ways to achieve this reduction is by replacing fossil fuels used in motor vehicles with renewable ones such as biodiesel.
The enactment of the latest and most innovative domestic biofuels policy to date, RenovaBio, establishes a legal framework for its production and use in the country by promoting energy efficiency and competitiveness of the sector through meritocracy.
By allowing greater market predictability for the entire biofuels chain, RenovaBio poises to take this industry in Brazil to new heights by creating more jobs and boosting the economy with major investments from the government and private sectors.
However, there are some problems that may undermine the whole program if not addressed accordingly. For example, at the time of the creation of the Social Fuel Stamp, the focus was put on the social side of the program, neglecting the technical aspects that deemed that initiative a failure, as in the case of castor and palm feedstocks.
Another major problem lies in the technology used to produce biodiesel in Brazil: the transesterification, which is inefficient. Also, the process uses methanol—which besides from being a fossil source, the country still imports a good chunk of it—instead of ethanol, which is cleaner, renewable, and produced from the local sugarcane.
Furthermore, the main raw materials used to produce biodiesel in Brazil (soy and beef tallow) present some environmental problems that are already known by the government and industry experts, largely due to the use of pesticides and herbicides, in addition to GHG from land use and land use change.
This work was supported by grants from CAPES and FAPESP through grant number 2012/51466-7 and grant number 2014/50279-4.
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