Citation: Dominique G. Béroule. Autism-modifying therapy based on the promotion of a brain enzyme: An introductory case-report[J]. AIMS Molecular Science, 2019, 6(3): 52-72. doi: 10.3934/molsci.2019.3.52
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serotonin;
Autism Spectrum Disorders;
Catechol-O-MethylTransferase;
Dopamine;
gamma-aminobutyric acid;
Guided Propagation;
Educational and Medical Institute;
MonoAmine Oxidase A;
MonoAmine Oxidase B;
Methylphenidate;
NorEpinephrine;
Tryptophan hydroxylase;
Valproic Acid;
Sodium Valproate
A silicon carbide fiber-reinforced silicon carbide matrix (SiC/SiC) composite is the attractive candidate materials for advanced energy systems, aero-space system and etc. due to their potential excellent mechanical properties at high-temperature, chemical stability, radiation resistance and low induced radioactivity [1,2,3]. In generally, SiC/SiC composites has strength anisotropy depending on the reinforcement fiber architecture and their orientation. Therefore, in order to perform adequate fiber-architecture deign for each structural components, it is essential to understand the strength anisotropy due to fiber architecture.
On the other hands, there are many methods for SiC/SiC fabrication including chemical vapor infiltration (CVI), polymer impregnation and pyrolysis (PIP), reaction sintering (RS), liquid-phase sintering (LSP) and theirs hybrid process [4,5,6]. It is well known that the performance of SiC/SiC composites is different by fabrication process types. Therefore, properties evaluation of each SiC/SiC composites are necessary. The nano-infiltration and transient eutectic-phase (NITE) process is one of the most attractive processes for SiC/SiC fabrication because of its advantages in the formation of high density SiC matrix, size and shape flexibility and cost efficiency, which is the modified liquid phase sintering process [7,8,9]. The NITE process is improved to the industrialization grade process from the laboratory grade process by Organization of Advanced Sustainability Initiative for Energy System/Material (OASIS), Muroran Institute of Technology, Japan [2,10,11,12]. The feature of the industrialization grade NITE process is to utilize dry type inter-mediate materials such as green sheets and prepreg sheets. Since mechanical properties of SiC/SiC composites depend on fiber architecture, in order to ensure reliability of products by SiC/SiC composites, understanding of the strength anisotropy is important. However, strength anisotropy knowledges of NITE-SiC/SiC composites fabricated by the industrialization grade process are insufficient since this process is a new process.
This study aims to understand the strength anisotropy of NITE-SiC/SiC composites fabricated by the industrialization grade process with various fiber architecture. This paper is provided the basic strength anisotropy knowledges of Unidirectional (UD) type NITE-SiC/SiC composites with various fiber orientations by evaluation of microstructure and mechanical properties. The strength anisotropy prediction theories were also discussed to evaluate the anisotropic strength of UD NITE-SiC/SiC composites.
The SiC mixed slurry for SiC green sheet fabrication consisted of b-SiC nano-powder (IEST, Japan, mean grain size of 32 nm) and sintering additives with Al2O3 (Kojundo Chemical Laboratory Co. Ltd., Japan, mean grain size of 0.3 mm, 99.99%) and Y2O3 (Kojundo Chemical Laboratory Co. Ltd., Japan, mean grain size of 0.4 mm, 99.99%). SiC green sheet were produced by OASIS, Muroran Institute of Technology, Japan. UD prepreg sheets were prepared by a similar fabrication process with those of green sheets, where PyC-coated Cef-NITE fibers (IEST, Japan) were used as a reinforcing fiber. The Cef-NITE fiber is one of the highly crystallized SiC fibers. The PyC coating was formed by chemical vapor deposition (CVD) process, and the thickness of the coating was appropriately 0.5 mm. The reinforcements were dipped to mixed slurry in slurry bath before to fabricate the prepreg sheets. The prepreg sheets fabricated were stacked for preparation of UD preforms. The number of prepreg sheets stacked is 30 sheets. The fiber orientation angle variations of preforms are kinds of four types (UD 0°, 30°, 45°, 60°). The preforms prepared were hot-pressed at 1870 °C for 1.5 h in Ar under a pressure of 20 MPa. The bulk density and open porosity of the composites fabricated were measured by the Archimedes’ principle. Strength anisotropy evaluation was performed by axial/off-axial tensile test with the crosshead speed of 0.5 mm/min at room-temperature. The specimens were straight bar type, which measured 40L x 4W x 2.0T mm with a gauge length of 15 mm. Aluminum tabs were bonded at the gripping sections. Tensile strains were measured by a couple of strain gauges bonded on the both surface of a specimen. Fracture surface observation after axial/off-axial tensile test was performed by digital microscope and a scanning electron microscope (SEM).
Density, fiber volume fraction and mechanical properties of SiC/SiC composites fabricated with various fiber orientation angles are summarized in Table 1.
ID | UD0 | UD30 | UD45 | UD60 |
Angle [°] | 0 | 30 | 45 | 60 |
Fiber volume fraction [%] | 46 | 45 | 46 | 46 |
Bulk density [g/cm3] | 2.9 | 2.9 | 2.9 | 2.8 |
Elastic modulus [GPa] | 289 ± 8 | 231 ± 15 | 194 ± 7 | 170 ± 7 |
Proportional limit strength [MPa] | 210 ± 1 | 88 ± 9 | 44 ± 7 | 27 ± 2 |
Ultimate tensile strength [MPa] | 210 ± 1 | 90 ± 10 | 66 ± 10 | 31 ± 3 |
Strain at fracture [%] | 0.073 ± 0.003 | 0.041 ± 0.003 | 0.036 ± 0.005 | 0.019 ± 0.001 |
The composites with fiber orientation angles of UD 0°, 30°, 45°, 60° is called UD0, UD30, UD45, UD60, respectively. PLS and UTS indicates the proportional limit strength (PLS) and ultimate tensile strength (UTS), respectively. The fiber volume fraction of SiC/SiC composites fabricated was about 45%. SiC/SiC composites fabricated have > 2.8 g/cm3 of the balk density regardless of fiber orientation angle. Figure 1 shows elastic modulus and proportional limit strength of SiC/SiC composites fabricated with various fiber orientation angles. Elastic modulus and PLS of UD0 was the most highest. Both of elastic modulus and PLS tend to decrease with increasing of fiber orientation angle. Figure 2 shows digital microscope images of SiC/SiC composites fabricated with various fiber orientation angles after tensile tests. Some fiber pull-outs were observed on UD0 (Figure 2(a)). UD30, UD45 and UD60 indicated fracture along the fiber reinforce direction (Figure 2(b)-(d)). Figure 3 shows SEM images of fracture surface of SiC/SiC composites fabricated with various fiber orientation angles after tensile tests. In the UD0, some fiber pull-outs in the fiber-bundle unit were observed (Figure 3(a)). On the other hands, inter-laminar detachment fracture along the fiber directions was observed in the UD30, UD45 and UD60 (Figure 3 (b)-(d)).
To evaluate correlation of the experimental results and the prediction strength by strength anisotropy prediction theory, the maximum normal stress theory and the Tsai-Hill criterion were applied. The basic equations of the maximum normal stress theory can be described as:
Maximum normal stress theory;
${\sigma _\theta } < \frac{{{F_{T,PLS}}}}{{{{\cos }^2}\theta }}$ | (1) |
${\sigma _\theta } < \frac{{{F_{TL,PLS}}}}{{\sin \theta \cos \theta }}$ | (2) |
${\sigma _\theta } < \frac{{{F_{L,PLS}}}}{{{{\sin }^2}\theta }}$ | (3) |
where FT is tensile strength in the fiber orientation angle 0°, FTL is in-plan shear strength, and FL is inter-laminar detachment strength. Note that the axial T and L correspond to the directions parallel and perpendicular to the fiber longitudinal direction, respectively. The maximum normal stress has assumed to cause failure when any of stress of each equation (1), (2) or (3) reached failure limits. FTL, PLS and FL, PLS of general NITE-SiC/SiC composites by several experimental methods have been reported by T. Nozawa et al. [13]. T. Nozawa et al. reported that FTL, PLS by the Iosipescu method and FL, PLS by the trans-thickness tension method of UD NITE-SiC/SiC composites are 52±7 MPa and 19±2 MPa, respectively. The reinforcements and fiber/matrix interphase of reference materials are highly crystallized and near-stoichiometric SiC fibers and the pyrolytic carbon, respectively. The reference and the fabricated materials were based on the NITE process and a fiber-architecture of both materials was same in the UD type. The tensile strength of reference material in the fiber direction UD 0° was 160±24 MPa. The tensile strength of fabricated material is a little higher than reference material. Here, as a simple parameter study, FTL and FL of fabricated material were assumed to be determined by considering data scatter of FTL and FL of reference material. The strength anisotropy predictions by prediction theories were discussed by case 1 and case 2. The case 1 and the case 2 was defined as calculation results utilizing minimum scatter of reference material and maximum scatter of that, respectively. The case 1 was calculated as FT, PLS=210 MPa, FTL, PLS=45 MPa and FL, PLS=17 MPa. The case 2 was also calculated as FT, PLS=210 MPa, FTL, PLS=59 MPa and FL, PLS=21 MPa. Figure 4 shows an anisotropy map developed by maximum normal stress theory. The dotted line and solid line indicates the case 1 and the case 2, respectively. The experimental results in each fiber orientation angle were consistent with both of the case 1 and the case 2. UD30, UD45 and UD60 are estimated inter-laminar detachment failure from this anisotropy map. The failure mode of UD30, UD45 and UD60 was consistent with fracture surface observation results. The strength in the high fiber orientation angle indicates the relative low strength. In the case of fiber orientation angle 60°, the strength was about 40% strength comparing with the strength in the fiber orientation angle 0°. This reason is that failure in the high fiber orientation angle is dominated by inter-laminar detachment failure. Thus, if SiC/SiC composite products are fabricated, fiber architecture design for suppression of the failure by tensile stress in the inter-laminar detachment angle is very important.
Tsai-Hill criterion;
The basic equation of the Tsai-Hill criterion can be described as:
${\sigma _\theta } = {\left[ {\frac{{{{\cos }^4}\theta }}{{{F_{T,PLS}}^2}} + \left( {\frac{1}{{{F_{TL,PLS}}^2}} - \frac{1}{{{F_{L,PLS}}^2}}} \right){{\sin }^2}\theta {{\cos }^2}\theta + \frac{{{{\sin }^4}\theta }}{{{F_{L,PLS}}}}} \right]^{ - \frac{1}{2}}}$ | (4) |
The Tsai-Hill criterion is one of the criteria considering the mixed failure modes [14]. In generally, since structural materials are often used under the complex stress, it is important to consider application of the criteria by the mixed failure modes. It is well known that prediction values in the low fiber orientation angle side are different between the normal stress theory and the criteria by mixed failure modes such as Tasi-Hill criterion. It is also reported that the prediction values by the Tasi-Hill criterion were consistent with experiment results in the off-axial tensile test than that by the normal stress theory [15,16]. In order to more accurately understand strength anisotropy, it is necessary to evaluate strength anisotropy by the normal stress theory as well as the criteria by mixed failure modes. As a first step of the anisotropy evaluation for the industrialization grade NITE-SiC/SiC composites, the Tasi-Hill criterion were investigated in this study. Although the Tasi-Hill criterion doesnot consider the compression mode, this might be rationalized in this tensile mode only case. The anisotropy map developed by the Tsai-Hill criterion is shown as Figure 5. The dotted line and solid line indicates the case 1 and the case 2, respectively. In the case of case 1, although the experimental results of UD45 and UD60 were consistent with the prediction values, that of UD30 were a little different. On the other hand, the prediction values by case 2 were almost consistent with the experimental results. FTL and FL of fabricated materials are thought to close to case 2 than case 1 because the mechanical properties of fabricated materials are higher than that of reference materials. This result is suggested that the strength anisotropy of UD NITE-SiC/SiC composites is able to predict by Tsai-Hill criterion.
The axial/off-axial mechanical properties of UD NITE-SiC/SiC composites by the industrialization grade process were evaluated by axial/off-axial tensile test. Elastic modulus and proportional limit strength of NITE-SiC/SiC composites tended to decrease with increasing of fiber orientation angle. The experiment results by axial/off-axial tensile test were consistent with the strength anisotropy prediction theories by the maximum normal stress theory and the Tsai-Hill criterion. The failure modes of SiC/SiC composites fabricated with each fiber orientation angle were consistent with fracture surface observation results. Also, the strength anisotropy of UD NITE-SiC/SiC composites was suggested to be able to be predicted by Tsai-Hill criterion. The basic strength anisotropy of UD SiC/SiC composites was understood from correlation evaluation of mechanical properties, fracture surface observation and the strength anisotropy prediction theories.
The authors acknowledge helpful input from and discussion with Dr. Y. Kohno and Dr. J.S. Park. The authors’ appreciation is due members of OASIS for their continuing support and encouragement.
The authors declare that there is no conflict of interest regarding the publication of this manuscript.
[1] |
Kuo HY, Liu FC (2018) Molecular pathology and pharmacological treatment of autism spectrum disorder-like phenotypes using rodent models. Front Cell Neurosci 12: 422. doi: 10.3389/fncel.2018.00422
![]() |
[2] |
Uzunova1 G, Stefano PS, Hollander E (2016) Excitatory/inhibitory imbalance in autism spectrum disorders: Implications for interventions and therapeutics. World J Biol Psychiatry 17: 174-186. doi: 10.3109/15622975.2015.1085597
![]() |
[3] |
Béroule DG (2018) Offline encoding impaired by epigenetic regulations of monoamines in the guided propagation model of autism. BMC Neurosci 19: 80. doi: 10.1186/s12868-018-0477-1
![]() |
[4] | Essa MM, Al-Sharbati MM, Al-Farsi YM, et al. (2011) Altered activities of monoamine oxidase A in Omani Autistic children—a brief report. Int J Biolog Med Res 2: 811-813. |
[5] | Chauhan V, Gu F, Chauhan AImpaired activity of monoamine oxidase A in the brain of children with autism. Conference Abstract (2016) . |
[6] |
Hensler JG, Artigas F, Bortolozzi A, et al. (2013) Catecholamine/serotonin interactions: Systems thinking for brain function and disease. Adv Pharmacol 68: 167-197. doi: 10.1016/B978-0-12-411512-5.00009-9
![]() |
[7] | Béroule DGEncoding of memory across online/offline alternations, screencast of running computer simulation. (2016) .Available from: https://perso.limsi.fr/domi/Movie-S1_DGB_nov16.mov. |
[8] |
Alwinesh MTJ, Joseph RBJ, Daniel A, et al. (2012) Psychometrics and utility of psycho educational profile-revised as a developmental quotient measure among children with the dual disability of intellectual disability and autism. J Intellect Disab 16: 193-203. doi: 10.1177/1744629512455594
![]() |
[9] |
Baer DM, Wolf MM, Risley TR (1968) Some current dimensions of applied behavior analysis. J Appl Behav Anal 1: 91-97. doi: 10.1901/jaba.1968.1-91
![]() |
[10] |
Wu JB, Shih JC (2011) Valproic acid induces monoamine oxidase A via Akt/Forkhead Box O1 activation. Mol Pharmacol 80: 714-723. doi: 10.1124/mol.111.072744
![]() |
[11] |
Whitton PS, Oreskovic D, Jernej B, et al. (1985) Effect of valproic acid on 5-hydroxytryptamine turnover in mouse brain. J Pharm Pharmacol 37: 199-200. doi: 10.1111/j.2042-7158.1985.tb05040.x
![]() |
[12] | Treatment of Children With Autism Spectrum Disorders and Epileptiform EEG With Divalproex Sodium.Available from: https://clinicaltrials.gov/ct2/show/NCT02094651. |
[13] |
Lord C, Rutter M, Goode S, et al. (1989) Autism diagnostic observation schedule: A standardized observation of communicative and social behaviour. J Autism Dev Disord 19: 185-212. doi: 10.1007/BF02211841
![]() |
[14] |
Lord C, Rutter M, Le Couteur A (1994) Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24: 659-685. doi: 10.1007/BF02172145
![]() |
[15] | Renault DCompte-rendu de suivi Neuro-visuel du 24/07/2015, Unité Fonctionnelle Vision et Cognition, Fondation Ophtalmologique Adolphe de Rothschild (Hôpital Rothschild, Paris), 3 pages in French. (2015) . |
[16] |
del Campo N, Chamberlain SR, Sahakian BJ, et al. (2011) The roles of dopamine and noradrenaline in the pathophysiology and treatment of attention-deficit/hyperactivity disorder. Biol Psychatry 69: 145-157. doi: 10.1016/j.biopsych.2011.02.036
![]() |
[17] |
Vanicek T, Spies M, Rami-Mark C, et al. (2014) The norepinephrine transporter in attention-deficit/hyperactivity disorder investigated with positron emission tomography. JAMA Psychiatry 71: 1340-1349. doi: 10.1001/jamapsychiatry.2014.1226
![]() |
[18] |
Santos K, Palmini A, Radziuk AL, et al. (2013) The impact of methylphenidate on seizure frequency and severity in children with attention-deficit-hyperactivity disorder and difficult-to-treat epilepsies. Dev Med Child Neurol 55: 654-660. doi: 10.1111/dmcn.12121
![]() |
[19] |
Gara L, Roberts W (2000) Adverse response to methylphenidate in combination with valproic acid. J Child Adol Psychop 10: 39-43. doi: 10.1089/cap.2000.10.39
![]() |
[20] |
Nicolini C, Fahnestock M (2018) The valproic acid-induced rodent model of autism. Exp Neurol 299: 217-227. doi: 10.1016/j.expneurol.2017.04.017
![]() |
[21] |
Christensen J, Grønborg TK, Sørensen MJ, et al. (2013) Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism. JAMA 309: 1696-1703. doi: 10.1001/jama.2013.2270
![]() |
[22] |
Chateauvieux S, Morceau F, Dicato M, et al. (2010) Molecular and therapeutic potential and toxicity of valproic acid. J Biomed Biotechnol 2010: 479364. doi: 10.1155/2010/479364
![]() |
[23] |
Gervain J, Vines BW, Chen LM, et al. (2013) Valproate reopens critical-period learning of absolute pitch. Front Syst Neurosci 7: 102. doi: 10.3389/fnsys.2013.00102
![]() |
[24] |
Ajram LA, Horder J, Mendez MA, et al. (2017) Shifting brain inhibitory balance and connectivity of the prefrontal cortex of adults with autism spectrum disorder. Transl Psychiatry 7: e1137. doi: 10.1038/tp.2017.104
![]() |
[25] |
Ganai SA, Ramadoss M, Mahadevan V (2016) Histone Deacetylase (HDAC) Inhibitors-emerging roles in neuronal memory, learning, synaptic plasticity and neural regeneration. Curr Neuropharmacol 14: 55-71. doi: 10.2174/1570159X13666151021111609
![]() |
[26] |
Fujiki R, Sato A, Fujitani M, et al. (2013) A proapoptotic effect of valproic acid on progenitors of embryonic stem cell-derived glutamatergic neurons. Cell Death Dis 4: e677. doi: 10.1038/cddis.2013.205
![]() |
[27] |
Laeng P, Pitts RL, Lemire AL, et al. (2004) The mood stabilizer valproic acid stimulates GABA neurogenesis from rat forebrain stem cells. J Neurochem 91: 238-251. doi: 10.1111/j.1471-4159.2004.02725.x
![]() |
[28] |
Giorgio ED, Brancolini C (2016) Regulation of class IIa HDAC activities: It is not only matter of subcellular localization. Epigenomics 8: 251-269. doi: 10.2217/epi.15.106
![]() |
[29] |
Al-Asmary S, Kadasah S, Arfin M, et al. (2014) Genetic association of catechol-O-methyltransferase val (158) met polymorphism in Saudi schizophrenia patients. Genet Mol Res 13: 3079-3088. doi: 10.4238/2014.April.17.4
![]() |
[30] |
Le TV, Chu TTQ, Le BN, et al. (2019) Prevalence of autism spectrum disorders and their relation to selected socio-demographic factors among children aged 18–30 months in northern Vietnam, 2017. Int J Ment Health Syst 13: 29. doi: 10.1186/s13033-019-0285-8
![]() |
[31] |
Lai DC, Tseng YC, Hou YM, et al. (2012) Gender and geographic differences in the prevalence of autism spectrum disorders in children: analysis of data from the national disability registry of Taiwan. Res Dev Disabil 33: 909-915. doi: 10.1016/j.ridd.2011.12.015
![]() |
[32] |
Cohen IL, Liu X, Schutz C, et al. (2003) Association of autism severity with a monoamine oxidase: A functional polymorphism. Clin Genet 64: 190-197. doi: 10.1034/j.1399-0004.2003.00115.x
![]() |
[33] | Hranilović D, Novak R, Babić M, et al. (2008) Hyperserotonemia in autism: The potential role of 5HT-related gene variants. Coll Antropol 32: 75-80. |
[34] | Abeling NG, van Gennip AH, van Cruchten AG, et al. (1998) Monoamine oxidase A deficiency: Biogenic amine metabolites in random urine samples. J Neural Transm 52: 9-15. |
[35] |
Frye RE (2018) Social skills deficits in autism spectrum disorder: Potential biological origins and progress in developing therapeutic agents. CNS Drugs 32: 713-734. doi: 10.1007/s40263-018-0556-y
![]() |
[36] |
Hellings JA, Weckbaugh M, Nickel EJ, et al. (2005) A double-blind, placebo-controlled study of valproate for aggression in youth with pervasive developmental disorders. J Child Adol Psychop 15: 682-692. doi: 10.1089/cap.2005.15.682
![]() |
[37] |
Hollander E, Chaplin W, Soorya L, et al. (2010) Divalproex sodium vs placebo for the treatment of irritability in children and adolescents with autism spectrum disorders. Neuropsychopharmacology 35: 990-998. doi: 10.1038/npp.2009.202
![]() |
[38] |
Hirota T, Veenstra-Vanderweele J, Hollander E, et al. (2014) Antiepileptic medications in autism spectrum disorder: A systematic review and meta-analysis. J Autism Dev Disord 44: 948-957. doi: 10.1007/s10803-013-1952-2
![]() |
[39] |
Takuma K, Hara Y, Kataoka S, et al. (2014) Chronic treatment with valproic acid or sodium butyrate attenuates novel object recognition deficits and hippocampal dentritic spine loss in a mouse model of autism. Parmacol Biochem Behav 126: 43-49. doi: 10.1016/j.pbb.2014.08.013
![]() |
[40] |
Qin L, Ma K, Wang ZJ, et al. (2018) Social deficits in Shank3-deficient mouse models of autism are rescued by histone deacetylase (HDAC) inhibition. Nat Neurosci 21: 564-575. doi: 10.1038/s41593-018-0110-8
![]() |
[41] |
McDougle CJ, Naylor ST, Goodman WK, et al. (1993) Acute tryptophan depletion in autistic disorder: A controlled case study. Biol Psychatry 33: 547-550. doi: 10.1016/0006-3223(93)90011-2
![]() |
[42] |
McDougle C, Naylor ST, Cohen DJ, et al. (1996) Effects of tryptophan depletion in drug-free adults with autistic disorder. Arch Gen Psychiatry 53: 993-1000. doi: 10.1001/archpsyc.1996.01830110029004
![]() |
[43] |
Boccuto L, Chen CF, Pittman AR, et al. (2013) Decreased tryptophan metabolism in patients with autism spectrum disorders. Mol Autism 4: 16. doi: 10.1186/2040-2392-4-16
![]() |
[44] |
Bird PD (2015) The treatment of autism with low-dose phenytoin: A case report. J Med Case Rep 9: 8. doi: 10.1186/1752-1947-9-8
![]() |
[45] |
Gupta V, Khan AA, Sasi BK, et al. (2015) Molecular Mechanism of monoamine oxidase A gene regulation under inflammation and ischemia-like conditions: Key roles of the transcriptions Factors GATA2, Sp1 and TBP. J Neurochem 134: 21-38. doi: 10.1111/jnc.13099
![]() |
[46] |
Minkiewicz P, Darewicz M, Iwaniak A, et al. (2016) Internet databases of the properties, enzymatic reactions, and metabolism of small molecules—search options and applications in food science. Int J Mol Sci Dec 17: 2039. doi: 10.3390/ijms17122039
![]() |
[47] | Koenraad PM, Braber AFUse of nonanoic acid as an antimicrobial agent, in particular an antifungal agent, patent A61Q17/005 (Antimicrobial preparations), 1999. (2016) .Available from: https://patents.google.com/patent/WO2001032020A2/en. |
[48] |
Meyer JH, Ginovart N, Boovariwala A, et al. (2006) Elevated monoamine oxidase A levels in the brain: An explanation for the monoamine imbalance of major depression. Arch Gen Psychiatry 63: 1209-1216. doi: 10.1001/archpsyc.63.11.1209
![]() |
[49] |
Bacher I, Houle S, Xu X, et al. (2011) Monoamine oxidase A binding in the prefrontal and anterior cingulate cortices during acute withdrawal from heavy cigarette smoking. Arch Gen Psychiatry 68: 817-826. doi: 10.1001/archgenpsychiatry.2011.82
![]() |
[50] | Cathcart MC, Bhattacharjee A (2014) Monoamine oxidase A (MAO-A): A signature marker of alternatively activated monocytes/macrophages. Inflamm Cell Signal 1: e161. |
[51] |
Hviid A, Hansen JV, Frisch M, et al. (2019) Measles, mumps, rubella vaccination and autism: A nationwide cohort study. Ann Intern Med 170: 513-520. doi: 10.7326/M18-2101
![]() |
[52] |
von Ehrenstein OS, Ling C, Cui X, et al. (2019) Prenatal and infant exposure to ambient pesticides and autism spectrum disorder in children: population based case-control study. BMJ 364: l962. doi: 10.1136/bmj.l962
![]() |
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3. | Ye B Shapovalov, I L Yakymenko, O M Salavor, K Šebková, The state of the European Union – Ukraine Association Agreement implementation on the air quality, 2022, 1049, 1755-1307, 012044, 10.1088/1755-1315/1049/1/012044 | |
4. | Roman A. Tarasenko, Viktor B. Shapovalov, Stanislav A. Usenko, Yevhenii B. Shapovalov, Iryna M. Savchenko, Yevhen Yu. Pashchenko, Adrian Paschke, Comparison of ontology with non-ontology tools for educational research, 2021, 8, 2833-5473, 82, 10.55056/cte.208 | |
5. | Patrizio Tratzi, Doan Thanh Ta, Zhiping Zhang, Marco Torre, Francesca Battistelli, Eros Manzo, Valerio Paolini, Quanguo Zhang, Chenyeon Chu, Francesco Petracchini, Sustainable additives for the regulation of NH3 concentration and emissions during the production of biomethane and biohydrogen: A review, 2022, 346, 09608524, 126596, 10.1016/j.biortech.2021.126596 | |
6. | Yevhenii B. Shapovalov, Viktor B. Shapovalov, Roman A. Tarasenko, Stanislav A. Usenko, Adrian Paschke, A semantic structuring of educational research using ontologies, 2021, 8, 2833-5473, 105, 10.55056/cte.219 | |
7. | Pramod Jadhav, Zaied Bin Khalid, Santhana Krishnan, Prakash Bhuyar, A. W. Zularisam, Abdul Syukor Abd Razak, Mohd Nasrullah, Application of iron-cobalt-copper (Fe-Co–Cu) trimetallic nanoparticles on anaerobic digestion (AD) for biogas production, 2022, 2190-6815, 10.1007/s13399-022-02825-2 | |
8. | Yevhenii B. Shapovalov, Viktor B. Shapovalov, Roman A. Tarasenko, Stanislav A. Usenko, Adrian Paschke, 2021, 10.31812/123456789/4433 | |
9. | Roman A. Tarasenko, Viktor B. Shapovalov, Stanislav A. Usenko, Yevhenii B. Shapovalov, Iryna M. Savchenko, Yevhen Yu. Pashchenko, Adrian Paschke, 2021, 10.31812/123456789/4432 | |
10. | Pramod Jadhav, Zaied Bin Khalid, A.W. Zularisam, Santhana Krishnan, Mohd Nasrullah, The role of iron-based nanoparticles (Fe-NPs) on methanogenesis in anaerobic digestion (AD) performance, 2022, 204, 00139351, 112043, 10.1016/j.envres.2021.112043 | |
11. | Ye B Shapovalov, S A Usenko, A I Salyuk, R A Tarasenko, V B Shapovalov, Sustainability of biogas production: using of Shelford’s law, 2022, 1049, 1755-1307, 012023, 10.1088/1755-1315/1049/1/012023 | |
12. | Xuna Liu, Luqing Qi, Efthalia Chatzisymeon, Ping Yang, Weiyi Sun, Lina Pang, Inorganic additives to increase methane generation during anaerobic digestion of livestock manure: a review, 2021, 19, 1610-3653, 4165, 10.1007/s10311-021-01282-z |
ID | UD0 | UD30 | UD45 | UD60 |
Angle [°] | 0 | 30 | 45 | 60 |
Fiber volume fraction [%] | 46 | 45 | 46 | 46 |
Bulk density [g/cm3] | 2.9 | 2.9 | 2.9 | 2.8 |
Elastic modulus [GPa] | 289 ± 8 | 231 ± 15 | 194 ± 7 | 170 ± 7 |
Proportional limit strength [MPa] | 210 ± 1 | 88 ± 9 | 44 ± 7 | 27 ± 2 |
Ultimate tensile strength [MPa] | 210 ± 1 | 90 ± 10 | 66 ± 10 | 31 ± 3 |
Strain at fracture [%] | 0.073 ± 0.003 | 0.041 ± 0.003 | 0.036 ± 0.005 | 0.019 ± 0.001 |
ID | UD0 | UD30 | UD45 | UD60 |
Angle [°] | 0 | 30 | 45 | 60 |
Fiber volume fraction [%] | 46 | 45 | 46 | 46 |
Bulk density [g/cm3] | 2.9 | 2.9 | 2.9 | 2.8 |
Elastic modulus [GPa] | 289 ± 8 | 231 ± 15 | 194 ± 7 | 170 ± 7 |
Proportional limit strength [MPa] | 210 ± 1 | 88 ± 9 | 44 ± 7 | 27 ± 2 |
Ultimate tensile strength [MPa] | 210 ± 1 | 90 ± 10 | 66 ± 10 | 31 ± 3 |
Strain at fracture [%] | 0.073 ± 0.003 | 0.041 ± 0.003 | 0.036 ± 0.005 | 0.019 ± 0.001 |