Kynurenic acid (KYNA) is an endogenous tryptophan metabolite exerting neuroprotective and anticonvulsant properties in the brain. However, its importance on the periphery is still not fully elucidated. KYNA is produced endogenously in various types of peripheral cells, tissues and by gastrointestinal microbiota. Furthermore, it was found in several products of daily human diet and its absorption in the digestive tract was evidenced. More recent studies were focused on the potential role of KYNA in carcinogenesis and cancer therapy; however, the results were ambiguous and the biological activity of KYNA in these processes has not been unequivocally established. This review aims to summarize the current views on the relationship between KYNA and cancer. The differences in KYNA concentration between physiological conditions and cancer, as well as KYNA production by both normal and cancer cells, will be discussed. The review also describes the effect of KYNA on cancer cell proliferation and the known potential molecular mechanisms of this activity. However, tryptophan is also metabolized along the serotonin pathway leading to the biosynthesis of neuroactive substances: serotonin and melatonin. Importantly, it is also a substrate for gut bacterial production of indoles which are potentially involved in signalling between microbiota and innate immune system  aPrevious studies have suggested that deregulation of the kynurenine pathway may lead to cancer progression through decreasing the effectiveness of the antitumour immune response . TheKYNA is produced enzymatically from the key intermediate in the tryptophan catabolic pathway—KYN , 8. InitThere are several studies focused on the KYNA content in various types of cancer Table , which rKYNA was detected in the serum of patients diagnosed with cancer in concentrations that ranged from 21.3 to 250 nM depending on the type of cancer , 40–44. Additionally, an increased KYNA concentration was observed in bone marrow plasma of MGUS and MM patients . Interes+ as a source of energy for their excessive metabolic processes.On the other hand, Fotopoulou et al.  observedl-tryptophan loading may be considered as a potential diagnostic marker for some types of cancer [l-tryptophan. These results were confirmed in the later studies by Fujinaga et al. [l-tryptophan loading was also elevated in urine of breast cancer patients, demonstrating increased level of two or more tryptophan metabolites in comparison to control [l-tryptophan administration [l-tryptophan. However, a similar increase in KYNA excretion was observed in only 1 of 18 patients with lymphosarcoma enrolled to the study [There are a few studies concerning the potential influence of cancer on KYNA urinary excretion, as this tryptophan metabolite is mainly excreted from the body with urine. Fujinaga et al.  reportedf cancer , 50–52. f cancer  reporteda et al. . KYNA lestration . AdditioOne of the recent studies carried by Gkotsos et al. revealed some changes in KYNA concentration in urine in the group of patients diagnosed with prostate cancer, but the authors finally concluded that KYNA cannot be considered a reliable marker for monitoring the progress of the disease . The medAdenoma tubulovillosum or Adenoma tubulare than in a healthy control group  and human glioblastoma U-343 MG cells. This effect was not observed in the mouse (J774) and human (U937) macrophage cell lines. Importantly, the mentioned study was conducted with the use of reduced serum culture medium (supplemented with 3% serum) [Taking into consideration KYNA production by cancer cells, the principal question is whether it may play any role in the process of carcinogenesis or cancer progression. There are only a few studies concerning the effect of KYNA on cancer cells’ proliferation, thus, this matter has not been fully elucidated Table . Di Seri% serum) , and the human U9 macropha% serum) , 63. It % serum) . However% serum) . It cann50 = 1.3 mM) [50 of 0.9, 0.2 and 1.2 mM, respectively [50 = 0.04 mM) [On the contrary, recent studies revealed an inhibitory effect of KYNA on the proliferation of human glioblastoma T98G cells (IC 1.3 mM) . It also 1.3 mM) , 38. KYNectively . Importaectively . In vitr50 = 8.9 mM) [50 = 4.4 mM) [50 = 2.1 mM) [50 = 0.6 mM) [Importantly, KYNA inhibited other cellular processes in cancer cells. KYNA in millimolar concentrations significantly decreased DNA synthesis in T98G (IC 8.9 mM) , HT-29 ( 4.4 mM)  and Caki 2.1 mM)  cells. I 0.6 mM)  and RCC  0.6 mM)  cells. AN-methyl-d-aspartate (NMDA) receptor [Despite the fact that KYNA was initially found to block all ionotropic glutamate receptors , later sreceptor  and as areceptor . Moreovereceptor , and an receptor , 69. ThuAlthough the molecular mechanism of antiproliferative activity of KYNA is not fully elucidated, an involvement of glutamate receptors should be considered. Previous studies indicated that glutamate antagonists inhibit proliferation and increased cell death of several cancer cell lines expressing this type of receptors on their surface, including HT-29 cells , 71. MorThe anticancer potential of KYNA may be also determined by interaction with alpha7 nAChR which is expressed in neuronal and various non-neuronal cells including endothelial and smooth muscle cells . alpha7  CYP1A1 and CYP1A-mediated metabolism in vitro.On the other hand, KYNA is an endogenous agonist of AhR which is involved in several processes including cell proliferation, apoptosis, adipose differentiation, tumour suppression and immune cell differentiation (reviewed in ) Fig. 33a. KYNA The potential role of AhR and its ligands in the process of carcinogenesis has been previously reviewed . It was 3) [Despite the fact, that GPR35 agonists have not been directly related to pro- or antiproliferative activity towards cancer cells to date, GPR35 signalling via ERK kinase is involved in several processes including proliferation, cell survival and metastasis   .SLC22A6) and hOAT3 (SLC22A8) in most cancer types except for brain and renal tumours (data not shown). Importantly, hOAT1 and hOAT3 expression was markedly decreased in renal cancer tissue in comparison to healthy renal tissue. On the other hand, KYNA concentration might be also increased in the cells due to its intracellular synthesis from kynurenine which is taken up by Na+-independent transporter of neutral amino acids as shown by Speciale et al. [The direct interaction of KYNA with receptor seems to be a critical step determining biological activity of the substance. However, the involvement of human organic anion transporters 1 (hOAT1) and 3 (hOAT3) in the uptake of KYNA has been also suggested . The mene et al.  in astroImportantly, KYNA has a modulatory effect on signalling pathways which are well-known targets of GPR signalling, including the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) and mitogen-activated protein kinase (MAPK) pathways , 99. KYNOn the other hand, KYNA interaction with GPR35 and mentioned signalling pathways may also affect immunological response to cancer cells. It was suggested that KYNA–GPR35 interaction might lead to suppression or limitation of inflammation . MoreoveWaf1/Cip1 , cancers with GPR35 overexpression  and cancers with constant expression of GPR35  Fig. a, which Waf1/Cip1 protein expression in colon adenocarcinoma cells [CDKN1A have also decreased expression of genes coding KATs, enzymes directly involved in KYNA synthesis  and markers of inflammation, which, as it was discussed previously, may influence the KYNA concentration in body fluids.Searching for correlation between KYNA content, type of cancer and the stage of cancer progression should include not only the changes in the physiological level, but also in the level of genetic alternations. The possible alternations in expression level of genes coding kynurenine pathway enzymes have not been excessively studied in cancer so far.Genetic modifications, including knockout animals, animals with KAT or KMO overexpression, may introduce significant changes in KYNA level in particular organs or the entire organism and may result in a better understanding of the role of KYNA in the cancer promotion and progression. Interestingly, due to fast life cycle, simplicity of genetic modifications and well-established cancer models, zebrafish may be one of the most valuable animal models , 117. AdIf KYNA promotes carcinogenesis and cancer progression, its supplementation and production should be decreased or inhibited. Decrease of KYNA supply in human daily diet seems not to be difficult, as only a few food products contain a significant amount of this compound, including chestnut honey and other honey, bee products, fresh broccoli and potato , 118. OnAssuming anticancer activity of KYNA, there are many theoretical possibilities to introduce KYNA as a supportive agent to anticancer therapy. Taking into consideration the knowledge about KYNA content in food products , 118, KY+ synthesis [+ and NADP+ play the crucial role in various essential cellular processes, including energy metabolism, cell proliferation, DNA repair and apoptosis [+ leads to inhibition of glycolysis, activity of citric acid cycle and oxidative phosphorylation, thus, the process of NAD+ synthesis is an attractive molecular target for anticancer therapy. Previous studies revealed that depletion of NAD+ in cancer cells results in dysfunction of antioxidant defense system, inhibition of proliferation and apoptosis interacting with MAPK and p53 signalling pathways [+ level without any impact on KYNA synthesis would clarify whether KYNA or disruption of NAD+ synthesis plays a crucial role in cancer promotion and progression. Taking those into consideration, the potential use of KYNA as a diagnostic/prognostic marker in carcinogenesis or cancer therapy should be excessively investigated. Unfortunately, there are no studies describing the dynamics of changes in the level of KYNA and other KYN metabolites during the cancer treatment so far.There is also a possibility that KYNA does not exert any activity during cancer promotion and progression and we should consider whether the previous results show specific effects of KYNA activity or non-specific effects of modified cancer cells’ metabolism. KYNA is only one of the elements of kynurenine pathway. Kynurenine pathway produces several biologically active metabolites and plays various functions in the organism including detoxification of excess tryptophan and control of its plasma availability, regulation of hepatic heme biosynthesis, regulatory role in organism via niacin and NADynthesis . Kynurenpoptosis , 125. DeSumming up, there is a need for further studies and new data which will help to solve the dilemma of the role of KYNA in cancer in an unambiguous manner.


Background. This study aimed to investigate the endodontic debridement efficacy of different sodium hypochlorite (NaOCl) irrigation regimens with and without ultrasonic agitation, followed by ethylenediaminetetraacetic acid (EDTA) via scanning electron microscopy (SEM) after using a rotary instrumentation system.Methods. Mandibular premolars (n=50) were randomly divided into five experimental groups (n=10) for root canal instrumentation with ProTaper Universal rotary system up to F3. The root canal system was treated with intracanal-heated NaOCl (100°C) or preheated NaOCl (55°C), followed by ultrasonic agitation and EDTA treatment. Samples irrigated with conventional needle irrigation (CNI) using normal saline solution were used as controls. Debridement efficacy was analyzed by SEM. A five-point scale was used to estimate the presence/absence of debris for each canal segment . The results were analyzed using one-way ANOVA and post hoc Tukey tests (P<0.05).Results. The experimental groups exhibited less debris compared to CNI with saline (P<0.05). The amount of debris decreased significantly for the group with NaOCl intracanal heating compared to extraoral heating. Ultrasonic agitation further enhanced the root canal debridement efficacy of NaOCl.Conclusion. In summary, intracanal heating of NaOCl with and without ultrasonic agitation followed by EDTA appears to be a promising method to flush debris from the root canal system. CNI is not, however, fully effective in delivering irrigants into the intricate areas of the root canal, such as the apical third, dentinal tubules, isthmus, and lateral canals, nor are they universally accepted.5 This is because the extent of flushing activity achieved by the CNI technique is only 0‒2 mm from the needle tip depending on the depth of placement and the diameter of the needle, and canal cross-sectional shape and diameter.6Clinically, the success of root canal therapy’s success relies heavily on the chemomechanical instrumentation and disinfection of the root canal system.5 Various methods have been studied to increase the potency of NaOCl and enable the use of low concentrations of NaOCl with decreased risk for toxicity or side effects.8 For example, NaOCl heating enhances the disinfecting and debridement properties due to an increase in the irrigation flow and reaction rate.10 Increases in irrigant temperatures are accomplished by preheating the irrigant extraorally or heating the NaOCl within the canal by utilizing ultrasonic equipment,8 or lasers.9 The heating of NaOCl often decreases its viscosity, enabling greater penetration, dissolving, and disinfection properties.10 Iandolo et al11 showed that NaOCl intracanal heating at 180°C significantly decreased debris compared to extraoral heating at 50°C. Besides, the ultrasonic agitation of intracanal-heated NaOCl resulted in improved debris removal relative to CNI and passive ultrasonic irrigation.Research firmly supports that sodium hypochlorite (NaOCl) solution is an ideal endodontic irrigant to flush debris from the root canal system.14 However, the heating of NaOCl solution above boiling point can cause heat transfer through the dentin, jeopardizing the surrounding tissues, alveolar bone, and periodontal ligament.16 Very few studies have evaluated the efficacy of NaOCl at its boiling temperature (96‒120°C).13 Therefore, this study aimed to evaluate the efficacy of NaOCl at a lower temperature (100°C), thereby reducing the above-mentioned risk and increasing the NaOCl efficacy. Furthermore, removing the debris from the root canal is more significant when EDTA and NaOCl are used in conjunction rather than alone.17 Limited data are available on the ultrasonic agitation of intracanal heating of NaOCl solution, followed by EDTA cleaning. Thus, we compared the root canal debridement efficacy of heated NaOCl with and without ultrasonic agitation, followed by EDTA. The null hypotheses were that extraoral heating of this irrigant does not significantly reduce the amount of debris compared with intracanal heating and that there is a significant difference between the percentage of remaining debris with and without ultrasonic agitation.Similarly, other studies have evaluated the efficacy of intracanal heating of NaOCl.Fifty mandibular premolars extracted for periodontal or orthodontic purposes were collected at the Maharashtra Institute of Dental Sciences and Research Centre with the donors’ informed consent and Ethics Committee approval (Ref: MUHS/PH-T/E-1/2593/2019). The teeth with root resorption or open apex were excluded. The root segment was obtained by separating the crown of each tooth at the level of the cementoenamel junction. Two longitudinal grooves on the lingual or buccal surfaces of each root were created using a high-speed handpiece with a diamond bur to facilitate the vertical separation of the root segments.All the root canal instrumentation procedures were performed by a single trained operator. Root canal instrumentation was carried out with the ProTaper Universal system  in the sequence S1-S2-F1-F2-F3, at 200 rpm, and 3 N/cm torque. The root canal was irrigated during the procedure with 5 mL of 5% NaOCl. Intracanal heating of NaOCl was carried out at 100°C using Calamus Dual  with 30/04 carrier tips mounted 3 mm above the working length. Extraoral heating of NaOCl was carried out at 55°C using a kettle. The temperature was controlled using a thermometer. Ten activation cycles, each of 5 seconds, followed by 10 seconds of rest, were performed. After each activation cycle, the NaOCl was renewed, and the files were cleaned to remove debris to maintain effectiveness. The ultrasonic agitation was carried out using an ultrasonic activator . For control, CNI was carried out using normal saline solution with a 30-gauge side-vented needle. The five different protocols of agitation were as follows:Group A: Intracanal heating of 5% NaOCl to 100°C with ultrasonic agitation, followed by EDTA.Group B: Intracanal heating of 5% NaOCl to 100°C without ultrasonic agitation, followed by EDTA.Group C: Extraoral heating of 5% NaOCl to 55°C with ultrasonic agitation, followed by EDTA.Group D: Extraoral heating of 5% NaOCl to 55°C without ultrasonic agitation, followed by EDTA.Group E: Normal saline solution.18After root canal instrumentation, a stainless steel chisel was used to split the samples for scanning electron microscopy  imaging. Briefly, the samples were dried overnight, sputter-coated with gold , and photomicrographs  in threeHulsmann’s scoring criteria for debris removal:Score 1: Clean root canal walls, with or without small debris particlesScore 2: Few debris agglomerationsScore 3: Debris agglomerations covering <50% of the root canal wallsScore 4: Debris covering >50% of the root canal wallsScore 5: Debris covering the complete or nearly complete root canal wallsStatistical analyses were performed with SPSS , and descriptive statistics were calculated for mean scores and minimum and maximum scores. One-way ANOVA and post hoc tests were used to compare the mean differences between individual groups.The comparisons of debris removal effects and mean value scores of the five debridement regimes are presented in 5 The effect of temperature on enhancing the efficacy of NaOCl has been documented in numerous studies.15 Preheating of NaOCl solution from 22°C to 45°C is one simple way to enhance its debris dissolution ability and antibacterial action. However, the NaOCl will be rapidly buffered in the root canal, minimizing its benefits.11 Previous studies have shown that intracanal heating of NaOCl solution substantially increases root canal debridement compared to preheated and not heated NaOCl solution.11 However, there are major differences in the literature regarding the debridement efficacy of NaOCl solution, arising from the use of various experimental conditions and mitigating variables that have affected the outcomes of some in vitro and ex vivo studies.19 As the NaOCl solution boils at temperatures between 96°C and 120°C, it is futile to use the heat carrier above the boiling point.20 In the present study, the 100°C intracanal heating of the root canal was efficient for successful irrigation and cleaning the endodontic space. The lower temperature used in this study can potentially prevent the surrounding periodontal complex,3 thereby providing safe and effective root canal debridement.The efficacy of NaOCl depends on the mechanical flush operation and chemical dissolving ability of the solution.21 The typical features of fluid agitation attempts are increasing the fluid’s temperature, enhancing its chemical and biological activity. Presently, “ultrasonic agitation,” which consists of activating irrigants by ultrasonic tips, is the most commonly used technique to enhance NaOCl efficacy.13 This technique permits the intensified stirring of the irrigant and formation of submicroscopic voids that create shear stress to disrupt debris and damage biofilms physically, thus resulting in a superior cleansing action.22 Interestingly, in the present study, there were no significant variations in debris removal between the ultrasonic agitation and no agitation groups with intracanal heating of NaOCl solution, followed by EDTA. These findings are close to those reported in a study by Mayer et al,21 showing that NaOCl and EDTA ultrasonic agitation did not reduce the scores of debris in straight root canals compared to the non-activated group.Significant attempts have been made to improve the capacity of NaOCl to remove and dissolve debris through physical fluid agitation using mechanical vibration, ultrasonic agitation, or pulsed lasers.24 These studies are mostly carried out on straight root canals, and various effects can be attributed to disparities in apical preparation, volume, and working times of irrigation and agitation. However, the present study indicated that the use of the EDTA following NaOCl’s intracanal heating resulted in efficient cleaning of the root canal. Thus, the first hypothesis was confirmed: Intracanal heating of NaOCl solution enhanced debris removal. The second hypothesis, however, was not established as there was no significant difference in debris removal over the length of the root canal system, with and without the agitation of NaOCl solution. Although the results are satisfactory, further investigations are required to establish this strategy’s effectiveness in complex root canal anatomies and its impact on the surrounding tissues.Nonetheless, this result contradicts many earlier trials where ultrasound agitation led to more successful debris removal than irrigation with a syringe and sonic agitation.For complete debridement of the canals, intracanal-heated NaOCl, followed by EDTA, was more effective than extraoral preheating followed by EDTA over the length of the root canal system. NaOCl solution at a temperature of 100°C was as effective as higher temperatures, thereby eliminating the need for heating the irrigants to high temperatures and safeguarding the periodontal complex.YD was responsible for the concept and experimental design, performed the experiments, and wrote the manuscript. RK and SG conceived the idea, hypothesis, and experiment design, and carried out supervision. SD, SG, and SB were responsible for assisting in the experiment and contributed to the discussion. ND was responsible for supervision analysis, interpretation of data, and wring the manuscript. All authors have read and approved the final manuscript.The authors acknowledge the help of K S Nagaraja Rao from Physics Department, Osmania University, Hyderabad, India for SEM imaging.This research received no external funding.The authors assert no conflicting interests concerning the authorship and/or publishing of this article.The use of human mandibular premolars and dental pulp stem cells (DPSC) for research was approved by the Institutional Ethics Committee of MIDSR Dental College, Latur (India).


Upon deliberate tuning and evolution of the filament, 32 half integer quantized conductance states in the 16 G0 to 0.5 G0 range with enhanced distribution uniformity was achieved. Simulation results revealed that the numbers of the available QC states and fluctuation of the conductance at each state play an important role in determining the overall performance of the neural networks. The 32-state QC behavior of the hafnium oxide device shows improved recognition accuracy approaching 90% for handwritten digits, based on analog type operation of the multilayer perception (MLP) neural network.Conductance quantization (QC) phenomena occurring in metal oxide based memristors demonstrate great potential for high-density data storage through multilevel switching, and analog synaptic weight update for effective training of the artificial neural networks. Continuous, linear and symmetrical modulation of the device conductance is a critical issue in QC behavior of memristors. In this contribution, we employ the scanning probe microscope (SPM) assisted electrode engineering strategy to control the ion migration process to construct single conductive filaments in Pt/HfO Resembling the operating principle of human brains that transmit and process information through huge amounts of interconnected neurons and synapses ,2,3,4, nShrinking the dimension of the conductive filaments (CFs) into the atomic scale of quantum point contact allows memristor ballistic electron transport without scattering and quantized conductance (QC) characteristics in analog domains ,25,26. Ix memristor with homogenized, analog type conductance quantization for both handwritten digit patterns.In this contribution, we report an effective approach to regulate the multilevel quantized conductance characteristics through pre- and customized formation of the conductive filament in a controllable manner in metal oxide based memristor devices. By inducing the directional migration of oxygen anions under the stress of a concentrated electric field through a conductive scanning probe microscopic (SPM) tip, indentation and consequently protrusion of the metal electrode into the switching matrix can cause the construction of a single conductive filament at a fixed position with better controllability. Thirty-two continuous quantized conductance states can be obtained by deliberately manipulating the as-formed CF, which gives rise to ≈20% enhancement in the uniformity of conductance value distribution in each QC state. More importantly, enhanced recognition accuracy approaching 90% can be achieved by multi-layer perception, employing HfOx/Pt sandwich structured memristor devices were fabricated on commercial Pt/Ti/SiO2 wafers  by depositing 10 nm HfOx thin film through RF magnetron sputtering technique in a pure argon environment, with an environmental pressure of 1 Pa and using high purity HfO2 ceramic (99.995%) as the target. For samples with a flat Pt/HfOx interface, the top platinum electrodes, with a thickness of 50 nm, were directly deposited onto the HfOx nanofilm by electron beam evaporation at a pressure of ≈10−6 Pa and a deposition rate of ≈0.5 Å/s at room temperature. For samples with inward conical Pt electrode protrusion, a layer of photoresist  was first spin-coated onto the HfOx nanofilm, followed by photolithography patterning using a positive photomask and lift-off process to define an exposed HfOx area with a radii of 100 μm. Then a Pt-coated scanning probe microscope tip was placed onto the surface of the circular HfOx exposed area in contact mode, where local voltage sweeps with different amplitudes and loading cycles were applied onto the sample to drive the migration of oxygen anions/vacancies to form indentations  technique during large scale fabrications.Variation of the switching parameters, including the programming voltages and device resistances in different QC states, is the main cause of performance deterioration in terms of operating reliability and recognition accuracy of the memristor based neural networks. It can be ascribed to the random ion migration in the polycrystalline metal oxide switching matrix, and consequently the stochastic nature of the branch-shaped multiple conductive filament formation, disruption, and regeneration during cyclic operations ,36. In oechnique ,38. By sow range b. A highnetworks ,40. Herex/Pt device with increasing stopping voltages of −0.6 V to −1.6 V during direct current (DC) scanning can consecutively decrease the device currents. Replotting in the conductance vs. number of scanning curve, or the conductance vs. number of pulse stressing curve, reveals a continuous modulation of device conductance from 16 G0 to 0.5 G0 in a half-integer QC step  during cyclic modulation. For control sample B, without pre-treatment of the HfOx layer or top electrode protrusion, a linear but more discrete distribution of the device conductance was observed at each stage of the pulse-mode modulations  in a more homogenized manner. Increasing the sampling number of both kind of devices can reveal this feature more obviously. For instance, at the fifth pulse modulation state, the FWHM  of the Gaussian curve fitting of device A’s conductance distribution around the nominal value, 14 G0, was 4.4 (0) shows FWHMs of 1.2 for sample A, and a much larger value of 2.3 for sample B.Although the present electrode engineering strategy does not guarantee that each step of the modulation will strictly undergo a 0.5 Gulations b. The va was 4.4 c; wherea was 4.4 d, suggesG is the varying device conductance, n represents the number of the pulses applied, k, σ, and μ reflect the variation range, variability, and standard value of the device conductance at each modulation state, and f is a general number generator that obeys the NDF’s law. Upon setting k = 0.03, σ = 1, and μ = 16.5 − 0.5n for sample A, and k = 0.03, σ = 4, and μ = 16.5 − 0.5n for sample B, faithful reproduction of the device conductance for 12 continuous pulse modulation cycles can be obtained through the mathematical simulation shown in x/Pt memristor devices, with either microelectrode protrusion or flattening of electrode/oxide interfaces. As such, considering the deviation of the atomic point contact’s composition and geometry from pure metallic hafnium-based cones, which is probably the case during device operation by atomic exchange with the surrounding HfOx matrix under the concentration gradient, fractional conductance quantization can also be modelled accordingly. This may offer additional guidance for deliberate tuning of the device electrical performance to receive more  levels of linearly and symmetrically modulated conductance, which can lead to analog type operation and enhanced accuracy for pattern recognition with the memristive neural networks.Further analysis of the collected data for both samples A and B with the normal distribution function (NDF) allows us to receive an equation,For demonstration, simulation of supervised learning consisted of the offline training of the multilayer perception (MLP) neural network, synaptic weight updating in the memristor array, and tests of the handwritten digit pattern recognition, and was carried out using the experimental quantized conductance characteristics with back propagation (BP) algorithm a. Seven σ = 0) displays a recognition accuracy of ≈86.1% for handwritten digits. Minor deviation from the ideality does not affect the performance of the network significantly, and the red curve simulated with the memristive characteristics of sample A gives a comparable recognition rate of ≈86.0%. For the case of sample B without electrode engineering, although the accuracy rises rapidly as the training course continues (blue curve), there was still an obvious performance gap of ≈2% after 10 epochs, when in comparison with the network constructed from the ideal or sample A memristor devices. Therefore, the fluctuation occurring during device conductance modulation, beyond the promising linearity in synaptic weight updating, plays an important role in improving the overall performance of the memristive neural networks. Reduced fluctuation of the switching parameters can project the trained network onto the memristor array with higher learning accuracy, which in turn guarantees the fidelity of MAC operation and BP algorithm more reliably.To further confirm the influence of electrode engineering on the recognition accuracy of the MLP network, we use the 32-state QC characteristics of samples A and B for the supervised learning simulation. The normal distribution formula was adopted to simulate the conductance value fluctuation during the synaptic weight updating procedure. As shown in In this work, we demonstrate a reliable hafnium oxide based memristor device that displays homogenized conductance quantization characteristics with 32 half-integer QC states. Through deliberate design of the electrode/switching matrix interface with the assistance of a SPM tip, a single conductive filament can be generated inside the memristive layer, which suppresses the evolution randomness of the multiple CFs and enhances the uniformity of the device conductance effectively. Simulation results indicate that with the linearly and symmetrically modulated QC behavior with conductance states of 32 levels, improved pattern recognition accuracy approaching 90% can be achieved through analog type operation of the multilayer perception neural network with the present memristor devices.


A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such  GESs generation was described by Subramanian et al. .For this study, we only used Level 5 GESs meaning that each GES is represented by an instance, that is a combination of a perturbagen , cell line, concentration and time point, and is composed by the plate-normalized expression z-scores of the whole genome, inferred from 978 landmark genes (measured gene that can be used for whole transcriptome inference). We focused on landmark signatures of compound perturbagens, which comprises 333,273 GESs for 21,300 unique compounds. GESs obtained in the exact same condition were averaged, to have one signature per condition.Among all obtained GESs, the ones generated at a 10 μM and 24 h time point were selected  algorithm (using the sklearn implementation) using Dice distance as metric, to reduce the data to a two-dimensional output that can be plotted to represent the chemical space  space wherein each cell line was color-coded in the plot. For every cell line, a t-SNE using the cosine distance metric was performed using all GESs profiled in the cell line, generating 2D biological space.Targets for which we know at least 50 active compounds (representing between 1 and 63% of active per target) were selected for machine learning modeling in order to have a minimum number of actives in test sets to evaluate the model performances, and for computational time purposes. Complete information on the number of active and inactive compounds for these selected targets is listed in Subsequently, for each cell line GES dataset, we created a target—cell line GES dataset, restricting to compounds for which target activity was known as shown in The training was performed using a 4-fold cross-validation on training set to tune the maximum depth of tree, before assessing prediction performances on the test set. The number of trees per model was set to 200. Models were built in Python 2.7 using the sklearn package: to account for unbalanced dataset, the “class_weight” parameter was set to “balanced_subsample,” to increase the weight of the under-represented class samples when training the trees , true negative (TN), false positive (FP) and false negative (FN). These parameters were combined in the following metrics in order to compare model performances:Balanced accuracy allows for a fair evaluation of model performances when using unbalanced datasets, by averaging accuracy for each class (here active and inactives).Between each possible pair of compounds active on the same target and in each cell line, Dice distance between Morgan fingerprints, and cosine distance between GESs in given cell line were computed. These 2 distances were plotted in a 2D plot (referred as distance plot), Dice distance on X-axis and cosine distance on Y-axis. These plots were theoretically split in 4 quadrants.Quadrant I in the top-right corner contains active compound pairs having different structures (Morgan fingerprints Dice distance >0.5) and presenting different GESs (cosine distance >0.5); quadrant II in top-left corner contains active compound pairs having similar structures (Morgan fingerprints Dice distance <0.5) and presenting different GESs (cosine distance >0.5); quadrant III in bottom-left corner contains active compound pairs having similar structures (Morgan fingerprints Dice distance <0.5) and presenting similar GESs (cosine distance <0.5); quadrant IV in bottom-right corner contains active compound pairs having different structures (Morgan fingerprints Dice distance >0.5) and presenting similar GESs (cosine distance <0.5). Number of active compound pairs in each quadrant were counted for each distance plot. Similar calculations were made using not only active compounds, but all compounds having an annotation (active or inactive) for considered target and profiled in the same cell line.n = 9,035) and their corresponding induced biological responses captured by GESs  in human tumor cell lines and evaluated the potential of machine learning approaches to infer about molecular targets involved in the compound bioactivity. In addition, we compared these machine learning models using GESs with counterpart models using Morgan fingerprints.In the present work, we investigated the link between compound structure information , MCF7 (breast cancer) and PC3 (prostate cancer). In addition, we selected 3 representative molecular targets showing different chemical and biological space profiles: compounds active on the glucocorticoid receptor (NR3C1) have similar structures, and similar GESs in some cell lines ; tubulinn = 54; mean Dice distance = 0.67). Most of NR3C1 active GESs are also grouped in a cluster in the A549 biological space , visible in n = 54; mean cosine distance = 0.92). Overall, these results show that compounds that are known to be active on the NR3C1 target can show a similar response in only certain cellular contexts.NR3C1 actives compounds are mostly grouped together in the chemical space, as shown in n = 51; mean Dice distance = 0.76). Most importantly, in each cell lines used in this work, GESs induced by TUBB actives compounds were similar , associated with diverse GESs for the 3 cell lines presented (mean cosine distance between 0.88 and 0.92 depending on the cell line), as illustrated in Finally, DRD1 actives compounds, that are represented in the chemical space t-SNE, have diverse chemical structures . We obtained one dataset per cell line—target combination  yielded better performances for the target activity prediction than their counterpart GES models (mean BA = 0.58). On the 495 cell line—target combinations, BA of GES models was higher than BA of counterpart Morgan FP models for 124 combinations (25%).On the 990 models, 208 models reached a BA higher than 0.7 (21%) for 40 targets , and 33 models reached a BA higher than 0.8 (3%) for 10 targets . For all 138 Morgan FP models reaching BA higher than 0.7, BA was superior to counterpart GES models, and for the 59 GES models reaching BA higher than 0.7, only 6 had counterpart Morgan FP model with higher BA.For NR3C1 activity prediction, Morgan FP models yielded a BA between 0.93 and 0.98 depending on the cell line dataset. It is not surprising considering that that NR3C1 actives have similar structure as shown in For TUBB activity prediction, GES models yielded BA between 0.81 and 0.88 depending on the cell line dataset, which was among the 10 best GES models. Interestingly, their counterpart Morgan FP models were not significantly underperforming (BA ranging from 0.78 to 0.82). Even though the TUBB active structures are diverse, the models still managed to identify structural fragments that could produce such predictive performance.For DRD1 activity prediction, Morgan FP models yielded BA between 0.68 and 0.74 depending on the cell line dataset, and were always better than their counterpart GES model, with a BA ranging from 0.58 to 0.64.Overall, we conclude that it was possible to build GES models with acceptable performances, performing similarly or better than their counterpart Morgan FP models in 25% of the target prediction tasks. Moreover, we see an important advantage in the GES models: they are theoretically performing independently of the chemical space considered, allowing target identification of new compounds even if their corresponding structural diversity is not represented in the training set.To further describe and understand the reasons for the differences in performances between GES model and Morgan FP model, for every dataset used in each cell line—target combinations, Morgan fingerprints Dice distance was plotted against GES cosine distance between each pair of active compounds in the given dataset.Generated distance plots were split in 4 quadrants separated by a 0.5 threshold for Dice distance  and a 0.5 threshold for cosine distance . Data points in top right (Quadrant I) represent pairs of active compounds showing diverse structures and different GESs in the considered cell line and contains most of compound pairs (average of 95.1%). Data points in top left (Quadrant II) represent pairs of active compounds showing similar structures while showing diverse GESs in the considered cell line (average of 1.9%). Data points in bottom left (Quadrant III) represents pairs of active compounds showing similar structures and similar GESs in the considered cell line and contains least compound pairs (average of 0.5%). Data points in bottom right (Quadrant IV) represents pairs of active compounds showing similar GESs while having different structures (average of 2.5%). Intuitively, we think that sample similarity within the same class (here: actives) is a good indicator to know if a machine learning model will be able to properly predict samples from this class.Overall, the mean percentage of compound pairs (active and inactives) were 99.3, 0.3, 0.01 and 0.4% for quadrants I, II, III and IV respectively. Based on this dataset, compounds active toward a molecular target have on average more similar structures and GESs than the totality of the compounds.We expected to reach good Morgan FP model prediction for combinations having a high proportion of points in quadrants II and III (similar structures), and good GES model prediction for combinations having a high proportion of points in quadrants III and IV (similar GESs). We evaluated the use of distance plots on the three targets and three cell lines used in previous space plots , 5. SimiFor NR3C1 distance plots, there are approximately 10% of compound pairs in quadrants II and III of the 3 plots , coherenFor TUBB distance plots , betweenFor DRD1 distance plots , 98% of Among the 50 best GES models, the mean percentage of active pairs in quadrants III and IV was 5.2% (vs. 2.3% in quadrants II and III). For the 50 best Morgan FP models, the mean percentage of active pairs in quadrants II and III was 4.0% (vs. 2.2% in quadrants III and IV). This suggests a positive relationship between sample similarity between active compounds using a given set of descriptors for active compounds and performances of models using these descriptors.In the current work, GESs were shown to be effective descriptors to predict compound activity toward molecular targets. In 25% of target prediction tasks, GES models outperformed their counterpart Morgan FP models, especially when using GES produced in a cell line exhibiting similar GESs between compounds active toward the target of interest. Such GES models performs independently of the structural diversity of compounds that were used to produce GESs, offering a great opportunity to escape the classical chemical space limitations associated with QSAR models. In addition, t-SNE plots, along with 2D distance plots, can give insights to assess the predictive power of GESs and Morgan fingerprints for target prediction, based on a limited dataset depending on biological (GES and bioactivity assay) data availability.Our results show that compound-induced transcriptomic responses derived from cell lines have the potential to support target prediction of unknown compounds with large structural diversity. Interestingly, we observed that compound induced biological responses are mostly cell line specific even when cell lines are derived from the same tissue. Nevertheless, machine learning models using GESs were shown to perform well as long as the appropriate cell line was used. Exploring biological spaces can help to overcome the limitations derived from a restricted chemical space when using traditional QSAR. To improve the predictivity of GES models, we have identified several limitations, and discuss possible improvements.First limitations come from gene expression data preprocessing. Gene expression values were obtained through multiple preprocessing steps from the initial generated raw data. For instance, there is a first peak deconvolution step to determine the gene expression levels, that as well as the plate-normalized z-scoring to obtain the normalized (“Level 5”) can still be improved as already stated by Li et al. . Using GSecondly, the CMAP L1000 technology relies on the measurement of 978 landmark genes, representing about 5% of the human transcriptome Pertea, . The genAnother limitation is also coming with the activity dataset that was used. Since compound activity is a selective interaction, there is for each target a low number of active compounds compared to the number of inactive compounds. As a consequence, the training sets used for model building were highly unbalanced favorizing the prediction of the category inactive. Moreover, not every compound was tested for activity in all targets, leading to a sparse dataset .On top of this activity data limitation, not all available compounds were profiled in all the 8 cell lines used in this work. There were only about 600 compounds profiled in all the cell lines, which is too limited to build predictive models, with regard to available activity data. Consequently, one dataset per cell line was created, formed by compounds profiled in this cell line and resulting GESs. For each target prediction, the cell line datasets were restricted to compounds having a known label for the target of interest. Since each task used a different dataset, performances of models across cell lines or targets the comparison across GES models was not possible. The difference in dataset sizes is explaining at least partly the variation of performances of GES models across targets, ranging from models close to a random predictor (BA = 0.50) to good GES models (BA = 0.88), as well as the variation of performances of counterpart Morgan FP models (BA ranging from 0.50 to 0.98).Biologically, variation of GES model performances can also be caused by the difference in the pathway representation in the cell lines and consequently to compound induced signatures. Compounds active on a given target might show GESs with different degree of similarity or no similarity among the considered cell lines. as illustrated by the cases of NR3C1, TUBB and DRD1. Gene expression responses depend on the cellular context as shown in this work and elsewhere  are widely used, but they come with some disadvantages: they show limited physiological representativity and have been shown to drift along passages , biological systems such as The advantage of transcriptomic evaluations over single endpoint assays is that in theory they have the potential to capture integrative responses from compound treatments, ranging from on target activity at high potency to off-target activities at lower potencies, depending on the tested concentrations. GESs responses are also known to be variable depending on time exposition , capturing the effect of compounds. These data could be used to make inference about biology . Each cell line shows a unique biological space that can be explored. However, these biological experiment data are prone to technical and biological variability like discussed earlier. Gene expression can be measured in different dose and time conditions, adding dimensions to explore in order to find the conditions reaching best performances in GES models. Finally, the gene expression measurements are more and more cost effective, making the use of such data at a large scale possible.de novo chemical design fulfilling a specific GES .Publicly available datasets were analyzed in this study. This data can be found in the Gene Expression Omnibus BB, OM-L, and DR contributed to the conception of the work. BB performed the workflow with the help of OM-L. BB wrote the first draft of the manuscript. JW, OM-L, and DR provided guidance and helped with the manuscript preparation and contributed to manuscript revision, read and approved the submitted version. DR and OM-L wrote sections of the manuscript.JW is employee of Bayer AG. OM-L, BB, and DR work directly or indirectly for Bayer SAS.


This study provides an insight into the association between abnormal fruit development and carbohydrate signaling from the early developmental stages and could be served as useful information for understanding the regulatory mechanisms related to almond fruit drop.Almond is one of the most featured nut crops owing to its high nutritional value. However, due to three different waves of flower and fruitlet drop, fruit drop is a major concern for growers. In this study, we carried out a time-course transcriptome analysis to investigate gene expression differences between normal and abnormal fruitlet development. By de novo assembly analysis, we identified 33,577 unigenes and provided their functional annotations. In total, we identified 7,469 differentially expressed genes and observed the most apparent difference between normal and abnormal fruits at 12 and 17 days after flowering. Their biological functions were enriched in carbon metabolism, carbon fixation in photosynthetic organisms and plant hormone signal transduction. RT-qPCR validated the expression pattern of 14 representative genes, including  Prunus dulcis (Mill.) D.A. Webb. syn. Prunus amygdalus Batsch), is the most important temperate nut crop regards to commercial production, and several hypotheses indicated that it originated from Iran and specifically adapted to harsh climates2. Despite its wide adaption, almond is usually cultivated in areas with Mediterranean climate. The important commercial production regions include Asia, Mediterranean countries, American and Australia. Main growing regions in Asia are Iran and Turkey2. In China, almond is mainly cultivated in western China in Xinjiang province and becomes one of the most important economic nut crops in the area3. The almond production of China in 2017 was 51,953 tons4. In almond, fruit drop occurs at three distinct stages with different reasons2. The first drop takes place just after bloom or even in the late stages of bloom on malformed flowers, mainly caused by deficient pistil development. The second drop occurs after bloom mainly on unfertilized flowers. The flowers may become pollinated but not fertilized because the pollination stimulates the initial development until they are pea sized. Some fertilized flowers may also drop due to cytological defects. The third drop is physiological and often referred to as ‘June drop’, which is probably caused by nutrimental competition, and at this stage the fruits going to fall normally having a smaller size and yellowish epiderm2. In production, a number of flowers/fruit drop is a need for almond production since not all flowers could develop into ripe fruits5, however, fruit drop also takes place in pollinated fruits and results in very low fruit sets, whatever the blooming time and other conditions during the growing stage5. In other fruits, it is also a factor that affects fruit development6. The abscission is a developmental process that mediated by both internal and environmental cues7. Endogenous phytohormones are demonstrated to contribute to fruit abscission, among which auxin is an important regulatory factor8. In the meantime, the external nutrient supply is suggested to take a major regulatory role. A few studies have revealed that the nutrient competition between fruitlets and leaves result in fruit abscission10. Much efforts have been done on peach, apple, longan and other fruit trees, and it is implicated that the sucrose accumulation and its metabolism are the main factors that cause abnormal fruitlets14.Almond  has been widely exploited in almond to uncover the molecular bases in response to cold, drought and other stresses25, as well as in the understanding of its self-incompatibility, flower bud dormancy and domestication history28. However, the transcriptome analysis associated to almond abscission has not been reported yet.High throughput sequencing technologies have revolutionized the genomic and transcriptomic studies. In particular, improved algorithms are now available for de novo reassembling the transcriptome of a non-model plant species without a valid reference genome sequenceThe aims of this study were to profile the gene expression levels in normal and abnormal fruitlets and to identify differentially expressed genes (DEGs) at different developmental stages, and to understand the gene regulatory networks underlying fruit abscission in almond. To this aim, a genome-wide time-course transcriptome analysis of almond using RNA-seq and validated by RT-qPCR analysis on representative genes was carried out.Our field study shows that the full-bloom stage of almond starts at the end of March in Xinjiang and the shoot buds also sprout at this stage. The fruit setting normally occurs at the 7th day after flowering (DAF). We investigated the new shoot elongation and the fruitlet dropping rate that at the period from 7 to 57 DAF. As shown in Fig. Given that the period from 12 to 37 DAF was the main stage that accounted for the fruitlet dropping and the new shoot elongation, we collected samples at six stages, including 12, 17, 22, 27, 32, 37 DAF. At each stage, we sampled normally developed and diapause atrophy fruits  in the same branch. The abnormally developed fruits were the ones that most likely to shed in the following days. The characteristics of normal and abnormal fruitlets at 32 DAF are shown in Fig. 29 and obtained 70,500 contigs, from which 33,577 representative unigenes with an N50 of 1,698 nt were generated. The length of unigenes ranged from 201 to 28,660 nt with the average length 1,026 nt , Q30 and GC content of the clean data were calculated as shown in Table https://transdecoder.sourceforge.net/). In total, 26,996 unigenes could encode ORFs with at least 100 amino acids. Using BLASTP and BLASTX, we annotated the unigenes by aligning the assembled transcripts and predicted peptide sequences to NCBI NR and Swiss-Prot databases. In total, we identified 22,182 unigenes containing significant matches to the annotated genes/proteins in at least one database. Additionally, we also aligned the unigenes to Pfam, COG, eggNOG, GO and KEGG databases (Table Prunus persica (52.24%) and Prunus mume (32.09%)  and identified 7,469 DEGs in total al Table . Of themMoreover, there were 5,843 and 6,245 developmental stage specific genes in normal and abnormal fruits, respectively Figure . CompariK-means Clustering analysis based on the similarities in their expression profiles . And 7,469 DEGs were classified into 120 clusters  and transcription factors , as well as probable protein phosphatase 2C. In group B, the genes expression was firstly up-regulated and peaked at 27 DAF and then down-regulated in the normal fruits, while in the abnormal fruit these gene slightly increased from 12 to 32 DAF and decreased afterwards. These genes including the ones encoding auxin response factor 6, basic leucine zipper 61 and glycosyl hydrolase family 10. In group C, the expression of genes gradually decreased in normal fruits underlying the developmental stages, while they fell sharply at 17 DAF and went up at 22 DAF then gradually decreased in abnormal fruits. These genes including three UDP-glucosyl transferases, one Alpha-amylase 3 encoding genes, as well as several secondary metabolites biosynthesis associated genes. In group D, genes expression in normal fruits first peaked at 27 DAF and then fell at 32 DAF and later raised again, while in the abnormal fruit these genes presented three peaks at 17, 27 and 37 DAF, respectively. These genes including several transcription factors (TFs) . The abundance of these representative genes was also shown in the heatmaps displayed in Fig. Four representative clusters were presented in Fig. 40. Given the amount of stage-specific DEGs expressed at 17 DAF, we performed GO enrichment analysis of normal-abnormal differentially regulated genes at 17 DAF. As presented in Fig. P-value < 0.05). The top 20 enriched GO terms of normal and abnormal samples are shown in Fig. Differentially expressed genes might perform biological roles related to fruit drop. Therefore, we performed Gene Ontology (GO) enrichment analysis to investigate the functions of the differentially induced/repressed genes using agriGO, GOEAST and ggplot2, DN26918_c0_g1). While the majority of genes at 17 DAF was classified into pathways for “carbon metabolism”  and “plant hormone signal transduction” . The pathway for “peroxisome”  was also enriched at 17 DAF. In addition, several carbohydrates associated DEGs  were annotated to be involved in energy production and conversion at both 12 and 17 DAF. We also observed that carbohydrate-related pathways or metabolisms were enriched across all the six stages, including the fatty acid metabolism at 22 DAF, glycerolipid metabolism at 27 DAF, carbon metabolism at 32 DAF, as well as the term of glycolysis/gluconeogenesis at 37 DAF, confirming that the carbohydrate-related pathway or metabolism might respond to fruitlet development in almond, which is in line with the findings in litchi and other fruits20. Differently, the pathway of “cutin, suberine and wax biosynthesis” was classified at both 27 and 32 DAF. As we know, cutin and suberine are polymer matrices for cell wall barriers that function in protecting plants from stresses and controlling plant morphology41.To further dissect DEGs-involved pathways, normal-abnormal DEGs at different stages were subjected to KEGG enrichment analysis with KOBAS 3.0 . There were 192 and 203 unigenes at 12 and 17 DAF assigned to 13 and 10 KEGG pathways in the KEGG database, respectively, and 43 genes fell into 17 KEGG pathways at other periods Fig. . InteresR = 0.754, P-value < 0.001, Figure In order to validate the transcriptional expression levels of differentially expressed unigenes in normal and abnormal fruits determined by RNA-seq data, we employed quantitative real-time PCR (RT-qPCR) assay to assess 14 randomly selected DEGs Fig. A. The RT42, and plant cell walls are composed of complex polysaccharides that require many GTs for their synthesis43. Comparing to normal fruits, both glycosyltransferase like family 2 (DN23864_c0_g1) and PAP15 (DN21705_c0_g1) were down-regulated in abnormal fruits at 12 and 17 DAF. In addition, there was a sharp reduction at 17 DAF and following a re-increment at 22 DAF during the whole abnormal development, and the expression profiles of both genes in abnormal samples were mostly lower than those in normal fruits. Purple acid phosphatase (PAP) is an enzyme family that demonstrates a violet color in solution, it confers shoot growth stimulation, enhances osmotic stress tolerance and ABA insensitivity. PAPs also present peroxidase activity and function in the production of ROS in plant response to stresses44. They may act as multifunctional enzymes and play crucial roles in plant growth and development under normal or stressed environment45. Arabidopsis PAP15 exhibits phytase activity and could function in enhancing cell wall synthesis by supplying additional myoinositol from phytate45. The overall reduction of glycosyltransferase like family 2 and PAP15 at six stages illustrates that the fruit development including the cell wall modifying related processes were influenced after the initiation of the abnormal development signaling in early stages.Glycosyltransferases (GTs) are enzymes that creating a glycosidic bond between a sugar molecule and a specific acceptor, which can be other sugars, proteins, lipids and small moleculesMYB39 (DN26519_c2_g1) was highly increased in abnormal fruit at 12 DAF. Multiple members from MYB and bZIP families have been previously characterized to be critical regulators to developmental or environmental stimuli in apple46, strawberry47 and peach49, and MYB39 was indicated to regulate the flavonoid biosynthetic pathway in strawberry50. Here, the identification of this gene in almond indicates its possible regulatory role in regulating the almond fruit development via a flavonoid associated pathway.In addition to the decreased expression level of genes encoding enzymes, the activity of a transcription factor 51. Among this information, the annotation using NR database showed that the most matched species is Prunus persica (52.24%), which is a genetically and evolutionarily correlated species with almond. Hence, the unannotated unigenes could be almond specific with unknown functions, and the corresponding transcriptome information of almond could be served as useful datasets for almond further study in fruit developmental processes.In this study, we constructed a transcriptome of normal and abnormal fruitlets from 12 DAF till 37 DAF. A total of 33,577 unigenes were assembled with a N50 of 1,698 nt. Since there is no reference genome of almond, we further annotated the almond unigenes to public databases  with Trinotate pipeline. There were 22,182 (66.1%) unigenes annotated, indicating that one third of the assembled unigenes own no significant known homologs in close species, as suggested in Plum9. Carbohydrates like sugars play crucial roles in plant development by fueling cellular carbon and energy metabolism, as well as by being signaling molecules52. The assimilate movement of carbohydrates was tracked using 14C, which proved a competition of carbohydrates between the process of shoot elongation and fruit development after anthesis10. Artificial shading could lead to fruit abscission owing to the decreasing photosynthesis, which further aggravates the nutrient competition among organs53. Considering the importance of optimal fruit set55, relatively low fruit set ratio and high abscission percentage in almond call for more researches to address the related developmental mechanisms5. Many biological studies have been conducted underlying physiological and molecular mechanisms associated with fruit abscission on apple11, litchi12, Macadamia56 and other fruits, however, there is no molecular understanding of the abnormally developed fruitlets signaling at the transcriptional level in almond. Therefore, the main objective of our study was to identify genes that participate in almond fruit developmental process that are expected to be differentially expressed following anthesis.Fruit is an organ that depends mainly on nutrients supplied by other parts of the plant. In the early stage of fruit development, the growing shoots and fruits compete for limited nutrient and carbohydrate resources20. Hence, we propose that the carbohydrate-related pathways or metabolisms might also take dominant roles in the abnormal fruitlet development in almond. In the meantime, we observed DEGs involved in carbon fixation in photosynthetic organisms and plant hormone signal transduction were enriched at 12 and 17 DAF, while the number of these genes decreased in later stages. These results suggest that the energy consumption of carbohydrates for maintaining the fruit development occurred in the early stage, and the signal already initiated at 12 DAF. This observation is consistent with the fruit abscission study on litchi, where younger fruitlets were used and the energy consumption occurred at a relative late stage20.In this study, we found that a number of differentially expressed unigenes with known function fell into the categories associated with carbon metabolism, carbon fixation in photosynthetic organisms and plant hormone signal transduction. Furthermore, the carbohydrate-related pathways or metabolisms were found enriched across all the six stages. Consistently, in litchi, the GO and KEGG analyses revealed that diverse metabolisms and pathways including carbohydrate metabolism, phytohormone signals, transcription factor activity and cell wall modification were significantly enriched among the genes involved in the early fruit abscission process14. In this study, the expression of POD44 (DN24026_c1_g1) in abnormally developed samples was increased comparing with that in normal samples at 17 DAF. There was a peroxisome KEGG pathway enriched at 17 DAF, moreover, the clustering analysis and the phenotype stage transition present a major transition occurring at 17 DAF in the abnormally developed fruit.Cutting off carbohydrate supply by physical treatments induced 100% longan fruit abscission, and a burst of reactive oxygen species (ROS) occurred in the plasma membrane and cell walls following treatments. In the meantime, activities of antioxidant enzymes including superoxide dismutase, catalase, and POD were all enhancedIAA13 (DN26918_C0_g1) and gibberellin-regulated protein 11-like (DN26787_C1_g1) in the abnormal sample was strongly declined. However, at 12 DAF, the abscisic acid receptors PYL4 (DN23029_c0_g1), PYL8 (DN22391_c0_g1), PYR1 (DN24477_C0_g1) showed relatively higher expression levels in abnormal fruits than in normal fruits. Plant hormones have long been well studied for their importance in fruit development, and this involves auxin, gibberellin, cytokinin, abscisic acid (ABA) and ethylene that undergo major change58. In general, ethylene and jasmonic acid accelerate fruit abscission, while auxin and gibberellin inhibit abscission59. It is widely believed that the endogenous balance of auxin and ethylene plays an important role in fruit abscission. In litchi, the auxin responsive genes were repressed in the dropped fruit induced by girdling plus defoliation20. In addition, a previous study on the relationship between the “June drop” of almond fruits and the fruit IAA levels proposed that the decreased IAA levels lead to a higher percentage of fruit abscission60. Therefore, apart from the dominant role of carbohydrates in fruit development, the plant hormones of IAA and GA are also likely to participate in the regulation of abnormal development.Additionally, we identified a few auxin, ethylene and ABA responsive genes, as well as several gibberellins regulated genes. Compared with the expression in normal fruit at 12 DAF, the gene expression of both glycosyl hydrolase family 1, 3, 9, 14, 28), and further enhance the activities of antioxidant enzymes  and TFs . This process is also accompanied by other metabolisms and pathways, such as protein processing in endoplasmic reticulum and fatty acid metabolism as well as cell wall modification, and finally leads to the fruit abscission.Based on these results, we speculate that the developmental signals in almond fruitlet initiates at an earlier phase. Fruits may first perceive carbohydrate stress caused by the competition between fruit enlargement and rapid shoot growth. Then, the sugar starvation in fruits might stimulate the induction of plant hormone signal transduction  and sucrose metabolism  was counted, respectively. Meanwhile, the length of new shoot was measured for five replicates of each tree. The growth state of the fruits at 32 DAF in Fig. Total RNA was extracted from each fruit sample using RNAiso Plus (Takara). cDNA was generated in 25-μl reaction mixtures containing 2 μg DNAse I-treated RNA, 200 U M-MLV reverse transcriptase (Takara), 40 U Recombinant RNase Inhibitor (Takara) and 0.1 μΜ oligo (dT) 18 primer. Library construction and quality detection were completed by the Biomarker company . RNA quality and integrity were analyzed using an Agilent Bioanalyzer 2100 system . By using the TruSeq PE Cluster Kit v3-cBot-HS (Illumia), the index-coded samples were clustered following the manufacturer’s instructions. Finally, the libraries were loaded into an Hiseq 2500  for pair-end model sequencing (101 bp).q-value less than 25, to obtain clean reads. Next, we performed de novo assembly using Trinity program29. The longest sequences of the redundant transcripts were considered as representative unigenes and subjected to downstream functional annotation and coding sequence (CDS) prediction.We removed the RNA-seq reads that containing adaptors, with 5% sequencing errors and/or https://trinotate.github.io/) method as follows: the unigenes were aligned to NCBI GenBank Non-Redundant Protein Sequences (NR) and Swiss-Prot databases, Clusters of Orthologous Groups (COG), eukaryotic Orthologous Groups (KOG), Evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG v4.5.1), Pfam database v31.0 (https://pfam.xfam.org), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by BLAST with E-value cut-off of 1 × 10–5. The best matches were used to predict the putative coding sequence region and determine the 5–3′orientation.The CDS were predicted using Trinotate v3.1.1  according to the effective length of genes and the library size . Then we estimated the significance of differential expression with DESeq2. Differentially expressed genes were selected with following criteria: P-value < 0.05 and |log2FoldChange|> 1 between two samples. GO enrichment analysis was subsequently performed for the DEGs using agriGO based on hyper geometric test 62. KEGG enrichment analysis of the DEGs was carried out using KEGG Orthology63 and KOBAS 3.0 (Available online: https://kobas.cbi.pku.edu.cn) based on the hyper-geometric test .The pipeline of the RNA-seq analysis could be found in Figure Clean reads were aligned back to the assembled unigenes using Bowtie2. The read counts estimated by RSEM software−ΔΔCt method64. All RT-qPCR reactions were conducted in at least three biological replicates with an annealing temperature of 60 °C and a total of 40 cycles of amplification. The related primers used for real-time PCR are listed in Table In this study, to reduce the variations caused by field environments, the RNA-seq samples were retrieved from three independent trees, and we used three biological replicates for the RT-qPCR assay. Two micrograms of total RNA for each sample were subjected to DNA digestion, and the mRNA were reverse-transcribed into cDNA with EasyScript One-Step gDNA Removal and cDNA Synthesis SuperMix Kit . The synthesized cDNA was diluted tenfold and 2 μL of the dilution was used for real-time PCR. The actin gene was set as the internal control, and the relative gene expression was quantified using the 2t-test was performed.For the statistical analysis, R programming language (version 3.5.1) was used. Expression correlation was calculated with least squares linear regression in R. To test significance between the expression levels of normal and abnormal pairs, Student's Supplementary InformationSupplemental Table 1Supplemental Table 2Supplemental Table 3Supplemental Table 4Supplemental Table 5


Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions  and the other engaging two default brain states. We computed EEG spectral power over three frequency bands  where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments. Quantitative electroencephalography (QEEG) involves the complex numerical analysis of digitally recorded EEG signals that can provide significant insight into the functional relevance of bioelectric brain activity . TraditiHowever, a major drawback to recording EEG in controlled environments is that the external validity of any quantitative results is limited ,3. ThereFortunately, mobile EEG technologies have recently emerged that have opened up new possibilities for the measurement of EEG signals related to active behavior conducted inside or outside of the laboratory . This teIn exploring the external validity limits of laboratory-based QEEG findings, it is useful to take an incremental approach in which the main factors affecting these limits (listed earlier) are investigated separately. In the present study, we examined the effect of uncontrolled background influences in the physical environment because manipulating this factor is relatively straightforward and little study has been given to it to date. Previous mobile EEG studies have mainly focused on neurocognitive performance within a single physical or virtual environment  ,24,25,26Another important factor to consider when testing the external validity of laboratory QEEG findings is the particular perceptual, cognitive, and motor functions that an individual performs during mobile EEG recording. Previous mobile EEG studies have focused on several functions  as engaged by a variety of simple and complex tasks ,25,26,27A third factor to consider when testing the external validity of laboratory QEEG findings is the particular QEEG metric utilized to index brain functioning. The majority of previous studies using mobile EEG to study brain function have utilized event-related potential (ERP) measures of averaged stimulus-locked EEG activity ,25,27,40Finally, we expected that a moderate influence of background environment would likely be small and idiosyncratic. Thus, to ensure sufficient sensitivity to both real and null effects, we employed null hypothesis significance testing (NHST) augmented by Bayesian model selection via use of Bayes factors. We observed EEG power effects typical of the neurocognitive states engaged by the PASAT and resting state task, but did not observe major EEG power differences between the two background recording environments for all three frequency bands. These results support the external validity of the EEG spectral power metric for complex and default neurocognitive states. (Note: This manuscript is based in part on an unpublished Master’s thesis submitted by the first author to Texas State University .)n = 21 participants were retained for the resting task. EEG data for the PASAT was also lost for 3 of the 5 participants, with lost resting task data due to equipment malfunction; thus data from n = 23 participants were initially available for the PASAT task. However, analysis of PASAT EEG requires a control condition for comparison, which here was the resting state eyes open condition , 4 males and 17 females) for both the PASAT and the resting task. The participants were given course credit for participation in the study. All participants gave written informed consent in accordance with the Declaration of Helsinki. The Texas State University Institutional Review Board approved this study.All procedures were approved by the Institutional Review Board at Texas State University. For this study, 26 young adults were recruited through Texas State University’s SONA System recruiting pool. However, resting task EEG data from 5 participants were lost due to equipment malfunction; thus data from Participant task performance and EEG signals were recorded in two environments within a single experimental session on the same day: our lab on the Texas State University campus and an outdoor area just outside the lab building , with thThe laboratory was a brightly lit, quiet, and stable environment maintained at a comfortable room temperature (approximately 21–22 °C per Texas State University policy). Participants sat in an office chair during task performance and EEG recording a, with aIn the outdoor environment, participants sat on a metal bench during task performance and EEG recording b. In conIn addition, the outdoor and laboratory environments also differed in terms of ambient lighting and other weather-related variables . OutdoorIn the PASAT task, participants listened to a human voice speak a single digit once every 3 s and then mentally added the most recent pair of digits together. For example, if the first two digits were “4” and “8”, then the participant should have written “12”. If the next digit was “2”, then the participant should have written “10”, as that is the new sum of 8 and 2. The auditory stimuli were presented to participants at a comfortable listening level via headphones from prerecorded audio files. Open back headphones  were used to ensure that participants would still be able to hear the background sounds within the natural environment. The open back headphones offered no noise cancellation while still providing adequate sound of the task stimuli to the participants.Participants were given one of two prespecified versions of the task in each background recording environment, with the two versions of the task balanced between environments across participants  powered by a small 3.7 V 500 mAh, lithium ion polymer battery ; see The EEG recording cap was connected to the amplifier unit via a custom-made connector constructed in our lab see a,b. The We planned to record and analyze EEG data over the first 5 min of task performance for the resting task and first 3 min for the PASAT in order to eliminate any effects of mental fatigue due to extended time-on-task. The OpenBCI GUI records data for preset periods of time in 5 min steps. However, we experienced a recording limitation in which the Cyton unit recorded for a time length 1–3 min shorter than specified in the GUI. We overcame this limitation by recording EEG for approximately 10 min for each session and task. We then only retained the necessary number of minutes of EEG data for each task analysis.Upon arrival, participants were informed of the procedure for the experiment before consent and equipment setup. Depending on the order of experimental session balancing, participants began the experiment either in the lab or in the outdoor environment . ParticiThe equipment and participant were then moved to the other recording location and the process repeated. We note that how quickly the EEG recording started in each environment depended on the order of the recordings. The recording in the first environment of the session occurred after the EEG setup, which lasted approximately 30–40 min. The recording in the second environment of the session only lasted approximately 10–15 min, which also involved a basic setup of the recording computer, task instruction reminders, and a check of EEG setup/connection status. However, the balancing of the order of the environments across participants should have controlled for any differential adaptation effects due to this time difference.M = 467, 95% CI = (440–493) for the laboratory environment and M = 447, 95% CI = (417–479) for the outdoor environment. For the PASAT, the average number of epochs remaining after artifact removal was M = 229, 95% CI = (193–264) for the laboratory environment and M = 237, 95% CI = (206–268) for the outdoor environment. There were no statistical between-environment differences in retained trial numbers for either task . Resting state and PASAT continuous EEG data were each decomposed into 1-second baseline-corrected epochs with 50% overlap, yielding 600 epochs and 180 epochs for the 5 min (300 s) and 3 min (180 s) of retained data for each task, respectively  units. Following previous EEG studies of resting mental states, attention, working memory, and executive function ,47,49,50H1 of the presence of an experimental effect to the probability distribution described by the null hypothesis H0 of no effect. NHST was achieved using ANOVAs and Pearson correlation coefficients, where the associated F-values were used as an input to Bayes factors in order to compute direct probabilistic measures of the evidence for each hypothesis [We statistically analyzed behavioral and EEG power data using null hypothesis significance testing (NHST) augmented by Bayesian model selection via use of Bayes factors. NHST was used to compare the probability distribution of data described by an alternative hypothesis pothesis . This ispothesis .p < 0.05. ANOVAs were conducted separately for each EEG power frequency band.For the resting task, we applied 2 × 2 × 2 repeated measures analysis of variance (ANOVA) with factors of Environment , Resting State , and Electrode . Following standard convention, we used a statistical significance criterion of p < 0.05. ANOVAs were conducted separately for each EEG power frequency band. Additionally, we analyzed PASAT performance scores via one-way repeated measures ANOVA with a within-participant factor of Environment.In order to assess EEG power reflecting cognitive engagement during the PASAT, a comparison condition is needed in which cognition is relatively disengaged. We utilized the eyes open resting state as this comparison condition. Hence for the PASAT task, we applied 2 × 2 × 2 repeated measures ANOVAs with factors of Environment, Task , and Electrode with a statistical significance criterion of p < 0.05 after correction for multiple comparisons across the six separate tests.Finally, the across-participant relationships between EEG power and the six outdoor environment weather-related variables listed in F-values, degrees of freedom, and sample size) and Pearson correlation coefficients  into Bayes factors B01 in favor of the null hypothesis using an established formula based on the Bayesian Information Criterion (BIC) approximation of the Bayes factor [B10 = 1/B01. We interpreted the strength of support for a hypothesis as indicated by a Bayes factor according to the Jeffreys’ scale [B < 3.16; substantial, 3.16 ≤ B < 10; strong, 10 ≤ B < 31.62; very strong, 31.62 ≤ B < 100; decisive, B ≥ 100. Following [P(H0|Data) = B01/(1 + B01) and the probability of the alternative hypothesis given the data as P(H1|Data) = 1 − P(H0|Data).A Bayes factor is the marginal likelihood ratio of two hypotheses , each specified as a statistical model, which quantifies the degree to which the data have increased or decreased the odds of one hypothesis relative to the other . This ths factor ,57,58,59s’ scale : weak, 1ollowing ,57, we cAs an extra step to assess the external validity of the present data, we conducted an analysis in which the sizes of the main effects for each task were compared to the effect sizes obtained from a simple meta-analysis of several laboratory EEG studies that utilized these tasks. For simplicity, we focused our analysis on each task’s main effect that statistically quantified the activation of the specific neurocognitive states engaged by each task. For the resting task, this was the main effect of Resting State; for the PASAT, this was the main effect of Task. It was not our intent to perform an exhaustive meta-analysis of the previous literature, but only to identify enough studies to obtain a reasonable estimate of the general effect sizes observed in the laboratory. Moreover, we only utilized studies with design and analysis structure that could be easily compared to that of the present study. We chose studies to include in the meta-analysis according to the following criteria: (1) a study utilized a resting state task, an arithmetic task, and/or the PASAT, (2) a study used a repeated-measures contrast to compare EEG power across experimental variables similar to the present study ; and (3) a study explicitly reported the information necessary to compute effect size . Using these criteria, we identified 7 studies using the resting task, with 4 of these studies measuring theta and beta power ,47,61,62https://dataverse.tdl.org/dataverse/brainscieeg; accessed on 20 February 2021).All empirical data and MATLAB data analysis scripts are available online at the Texas State University Data Repository  = 0.82.Finally, we performed a correlational analysis of the across-participant relationships between the six outdoor environment weather-related variables listed in F = 24.32, p < 0.001, B01 = 927.41, P(H1|Data) = 1.00, but smaller (0.73 µV) and statistically weaker for the eyes open resting state, F = 4.96, p < 0.038, B10 = 2.23, P(H1|Data) = 0.69.Main effects of Electrode were significant for all three frequency bands with strong to decisive Bayesian evidence . These ep < 0.084) with weak Bayesian evidence; further decomposition of this interaction yielded no significant outcomes .Notably, although the PASAT spectrum displayed small qualitative differences between background recording environments , there wps = 0.12 to 1.00, B01 = 1.22 to 4.47, P(H0|Data) = 0.55 to 0.82.We also performed a correlational analysis of the across-participant relationships between the six outdoor environment weather-related variables listed in F = 1.22, p < 0.28, B01 = 2.47, P(H0|Data) = 0.71.Finally, no PASAT behavioral performance differences were observed between the laboratory ) and outdoor )) recording environments, Given the fact that the outdoor environment was more complex and less stable than the laboratory  for the PASAT, but the corresponding Bayesian evidence for these outcomes was weak. No other environmental effects of EEG power variability were significant for either task (with weak to strong Bayesian evidence in favor of the null hypothesis).The pattern of results from these analyses paralleled the EEG power findings; see ps = 0.53 to 1.00, B01 = 3.61 to 4.47, P(H0|Data) = 0.78 to 0.82.Finally, we performed a correlational analysis of the across-participant relationships between the six outdoor environment weather-related variables listed in In the present study, we explored the external validity limits of laboratory-based QEEG findings by examining the influence of physical environment background on complex and simple neurocognitive states as, respectively, engaged by the PASAT and resting state task. Each participant performed these tasks in a controlled laboratory environment  and a mildly uncontrolled outdoor environment . Brain activity associated with these neurocognitive states was quantitatively indexed via computation of EEG spectral power. We observed a weak between-environment beta band power effect for the resting task, with greater beta power in the outdoor versus laboratory environment, but no other reliable spectral power differences between the two background recording environments in all spectral power bands for either task.The resting state task engages a default mental state, characterized by internal cognitive processing , endogenously directed attention, and the monitoring of the body, external environment, and emotional states ,76,77,78One concern with this observation is the degree to which it reflects the typical transition from one default mode state to another, or if it reflects environmental factors as well. For example, eyes open alpha rhythms can also be can be blocked by changes in visual stimulation via eye movements as participants fixate attention on different visual elements in their environment . Given tHowever, the present observation of greater resting state beta-band EEG power for the outdoor versus indoor environments suggests that certain resting state processes can be affected by the environment. In general, the frequency of EEG oscillation reflects the size of the recurrent neural network mediating the oscillation ,83, withWe must note that the significant beta-band effect was weak according to Bayesian evidence criteria  to the effect sizes obtained from a simple meta-analysis of several laboratory EEG studies that utilized this task. We found the directions of the presently observed resting state primary effects were consistent with the directions of the meta-analysis effects. This was case for the EEG data recorded in the laboratory and outdoor environments, which supports the external validity of laboratory resting state EEG findings. However, we also found the presently observed effects sizes to be smaller than the meta-analytic effect sizes obtained from laboratory-recorded data. Given that smaller effect sizes were observed for the present resting state data recorded in both environments, it is likely that this reflects a performance difference between traditional laboratory and mobile EEG technology. Mobile EEG technology is known to exhibit lower signal-to-noise ratios than traditional EEG systems ,13,15,16The PASAT is an arithmetic task that requires numerical information to be represented, retained, and transformed within the mind. The task engages a complex combination of cognitive functions, including number sense, attention, short- and long-term memory, and executive control ,33,34. SIn the present study, we observed similar alpha power decreases during PASAT performance relative to the resting task eyes open control condition (see P(H0|Data) ranged from 0.72 to 0.82, save for an Environment × Task ANOVA interaction, P(H0|Data) = 0.48 , and the Bayesian evidence for these null effects were mostly well-above chance; P(H0|Data) ranged from 0.70 to 0.82). We suggest that this complex pattern of discrepant results for the PASAT beta-band power could be due to two factors: (1) neurocognitive differences in arithmetic versus PASAT performance  or (2) a true difference in arithmetical neurocognition across different environments that was too small to be fully detected by our current experimental design or was otherwise obscured by other factors . Determining which (if any) of these factors is responsible for the present observations is a topic for future research.We also did not observe clear between-task differences in beta-band power. We did find a significant Task × Electrode interaction for beta-band power, but further analysis showed that this interaction reflected a positive difference between posterior versus frontal power that was stronger for the PASAT than the eyes open resting state. However, there were no significant between-task differences at either scalp location  influenced PASAT performance and electrophysiological outcomes. Investigating the external validity of the present QEEG metrics for other populations performing these tasks is a topic for future research.In conclusion, the present study probed the validity limits of laboratory QEEG by using a mobile EEG system to record EEG signals from human participants while they performed two neurocognitive tasks  within a controlled laboratory environment and a moderately uncontrolled outdoor environment. Null hypothesis significance testing (NHST) showed significant EEG spectral power effects typical of the neurocognitive states engaged by these tasks , but only a beta-band EEG power difference between the two recording environments for the resting task. Bayesian analysis showed that the remaining null effects of environment were unlikely to reflect measurement insensitivities. The overall pattern of these results supports the external validity of laboratory EEG spectral power findings for the complex and default neurocognitive states engaged in moderately uncontrolled environments. They also serve to bolster the credibility of efforts to use mobile EEG systems to index neurocognitive performance in non-laboratory environments.Human beings operate in a multitude of environments each day, which range drastically regarding the type and concentration of stimuli that are present, yet differences in environmental influences on cognition have been understudied. The present study used QEEG methods to only evaluate the effects of two particular physical environments using two specific cognitive tasks. Future mobile EEG research should study additional physical environments and tasks in order to further examine the external validity of QEEG and to better understand human neurocognition in real-world environments.