Nanopesticides can facilitate controlled release kinetics and efficiently enhance the permeability of active ingredients to reduce the dosage and loss of pesticides. To clarify the synergistic mechanism of graphene–insecticide nanocarriers against cotton bollworm, treatment groups, namely, control, graphene (G), insecticide  and cyfluthrin (Cyf)), and graphene-delivered insecticide groups were used to treat the third-instar larvae of cotton bollworm. The variations in phenotypes, namely, the body length, body weight, and mortality of the cotton bollworm, were analyzed. The results show that graphene enhances the insecticidal activity of lambda-cyhalothrin and cyfluthrin against cotton bollworm. The two graphene-delivered insecticides with optimal compositions (3:1) had the strongest inhibitory effects and the highest mortality rates, with the fatality rates for the 3/1 Cyh/G and Cyf/G mixture compositions being 62.91% and 38.89%, respectively. In addition, the 100 μg/mL Cyh/G mixture had the greatest inhibitory effect on cotton bollworm, and it decreased the body length by 1.40 mm, decreased the weight by 1.88 mg, and had a mortality rate of up to 61.85%. The 100 and 150 μg/mL Cyh/G mixtures achieved the same mortality rate as that of lambda-cyhalothrin, thus reducing the use of the insecticide by one-quarter. The graphene-delivered insecticides could effectively destroy the epicuticle spine cells of the cotton bollworm by increasing the permeability and, thus, the toxicity of the insecticides. An efficient and sustainable agricultural and forestry system is crucial to human survival . The gloNanomaterials have large specific surface areas and have been widely used in physics, electrochemistry, medicine, and agriculture ,10. The Helicoverpa armigera), one of the most destructive pests worldwide. Third-instar cotton bollworm larvae were treated with distilled water (defined as the CK group), graphene, two insecticides, or graphene–insecticide mixtures. By comparing the body length, weight, and mortality of the cotton bollworm, the optimal ratio of the graphene and insecticide mixture was examined. The synergistic insecticidal mechanisms of the graphene–insecticide mixtures were analyzed. The results show that graphene-based nanoinsecticides may have broad application prospects in the plant protection field.Lambda-cyhalothrin (Cyh) and cyfluthrin (Cyf) are common insecticides used to control a wide variety of agroforestry pests worldwide ,21,22. IDistilled water (CK), graphene, insecticides, and graphene-delivered insecticides were used to treat the third-instar larvae of cotton bollworm for 24 h. The changes in the indices of the body length, body weight, and mortality of the cotton bollworm were measured. No acute toxicity of graphene was found compared with the distilled water. However, compared to the insecticides, enhanced insecticidal activities were observed in the graphene–insecticide group, whose synergistic mechanism was related to the damage caused to the epicuticle spine cells of the cotton bollworm A.Graphene, insecticides (Cyh and Cyf), and graphene–insecticide mixtures were analyzed using SEM. As shown in The SEM and TEM images further reflect the surface morphology and internal structure of the graphene, respectively. In −1 and 3207 cm−1, which were derived from H2O and carboxyl groups (–COOH), respectively. The stretching vibration peak of C–O appeared at 1122 cm−1. The double peaks of 2919 cm−1 and 2848 cm−1 were caused by the stretching vibration of the –CH2– group, while the stretching vibration absorption peak of carbonyl C=O appeared at 1720 cm−1. At 1637 cm−1, there was a stretching vibration region of carbon–carbon double bonds (C=C). The out-of-plane bending vibration of C–H was observed at 655 cm−1. These results indicate that graphene had oxygen-containing groups, such as –COOH and –OH, on its edge, which can form hydrogen bonds with water. Raman spectroscopy is another method most commonly used to characterize graphene. −1), the G peak (1575 cm−1), and the 2D peak (2682 cm−1). The intensity ratio of ID/IG = 0.625 indicated that the defect density of the graphene was low. The intensity ratio of the G peak to the 2D peak was greater than 1, indicating that the graphene used in this experiment had multilayer sheets, which is consistent with the TEM results. The particle size analysis diagram shows the distribution of the graphene sheet size  of Cyh and Cyf for cotton bollworm control , seven CThe results of the treatments with Cyh/G mixtures are shown in H. armigera in the control group increased by 2.20 mm, while the body length of those treated with Cyf/G mixtures changed by −0.19~1.82 mm, among which the 3/1 composition caused a decrease of 0.19 mm  of graphene, Cyh, Cyf, Cyh/G, and Cyf/G mixtures (3/1) were prepared and used to treat third-instar H. armigera. The Cyh and Cyh/G mixtures (3/1) significantly inhibited the growth and development of H. armigera, and the higher the concentration, the more significant the inhibitory effect (when below 100 μg/mL concentrations) (As shown in rations) A.H. armigera in the control group increased by 1.74 mm, while the body length of those in the graphene groups increased by 1.56~2.94 mm. In contrast, the body length of H. armigera in the Cyh group decreased by 0.13~1.06 mm, and the body length of those in the Cyh/G mixture group decreased by 0.22~1.40 mm. The Cyh/G mixture (100 μg/mL) showed the optimal effect, causing a 1.40 mm decrease in body length , the mortality of the cotton bollworm in the graphene group ranged from 2.08% to 11.17%. Cyf caused high mortality, ranging from 14.96% to 41.67%, and the Cyf/G mixture led to a higher fatality rate with the four concentrations, ranging from 18.75% to 45.08% C. At theTo analyze the mechanism of the high insecticidal activity of Cyh/G against cotton bollworm, the changes in the epicuticular cell structure of the cotton bollworm treated with different concentrations of graphene, Cyh, and Cyh/G were observed using SEM . In the Nanomaterials (with diameters of less than 100 nm) can cross plant biological barriers  and enter vascular bundle tissue, providing a new path for the delivery of pesticides ,25, and Callosobruchus maculatus than a commercial formulation [Spodoptera litura and did not induce any cytotoxicity in a human fibroblast cell line [Nanopesticides or nano-plant protection products represent a hopeful scientific development that offers a variety of benefits, including increased effectiveness, durability, and a reduction in the amount of active ingredients (AIs) that are used to protect crops against diseases, insects, and weeds ,32. Succmulation . Nano-acell line .Myzus persicae after a 14-day treatment [Bemisia tabaci, a serious pest of many crops [In addition, chitosan, a biodegradable polymer obtained from the deacetylation of chitin, has attracted considerable interest for its role in achieving the effective and controlled release of pesticide activity. Nanostructured pyrifluquinazon prepared using chitosan as a carrier showed the best lethal efficiency against the green peach aphid reatment . Recentlny crops .Graphene has been widely used in nano-plant protection fields. Previous studies showed that graphene has multifunctional synergistic activity toward insecticides against spider mites and Asian corn borers ,19. In tGraphene materials were prepared by the Institute of Carbon Materials Science, Shanxi Datong University ,39. The Two insecticides (Cyh and Cyf) were purchased from the Zhongbao Green Agriculture Group, the Institute of Plant Protection, the Chinese Academy of Agricultural Sciences, and the second-instar larvae of cotton bollworms were purchased from KEYUN Biology Co., Ltd. .3 fodder in each cell for one day. Then, they were reared in a chamber at 28 °C under a relative humidity of 40% and a photoperiod of 14 h of darkness and 10 h of light, with a light intensity of 33%. Two days later, the third-instar larvae of the cotton bollworms were treated with either 1.5 mL of distilled water (CK), graphene, insecticides (Cyh and Cyf), or graphene–insecticide mixtures (Cyh/G and Cyf/G), at concentrations of 37.5, 75, 100, and 150 μg/mL. There were four replicates for each group, with 12 cotton bollworms in each treatment. After treatment, the cotton bollworms were reared under the above conditions again. The statistical indices of the body length, weight, and mortality of the treated cotton bollworms were measured at 0 h and 24 h. A tested cotton bollworm was considered dead if it could not wriggle when prodded with an insect needle.The second-instar larvae of the cotton bollworms were put into a 24-cell culture box with 1 cmThe morphology of cotton bollworm epicuticle cells was observed using SEM to illustrate the dynamic changes in the ultrastructure of the epicuticle cells. The third-instar larvae were treated with distilled water, graphene, Cyh, and Cyh/G. Then, the cotton bollworm larvae were fixed with 2.5% glutaraldehyde with a vacuum pump in an ice bath for 30 min, followed by 4 h of incubation at 4 °C and three washes with phosphate-buffered saline (PBS). After that, the larvae were postfixed 3 times with phosphoric acid buffer (PB)  for 15 min. The PB was fixed with 1% osmium at room temperature protected from light for 1~2 h, and then it was used to rinse the samples 3 times for 15 min. Subsequently, the larvae were dehydrated with an ethanol series  and put into a critical point drying instrument to dry. Finally, the samples were subjected to conductive processing and examined under SEM.Two insecticides were mixed with graphene to treat the third-instar larvae of cotton bollworm. The effects on the body length, body weight, and mortality of the cotton bollworm were analyzed among control, graphene, insecticide (Cyh and Cyf), and graphene-delivered insecticide groups. The results show that graphene–insecticide mixtures (mass composition: 3:1) greatly enhanced the insecticidal activities of Cyh and Cyf against cotton bollworm. The 3/1 Cyh/G treatment had the greatest inhibitory effect on cotton bollworm, with a 1.39 mm decrease in body length, a 6.17 mg decrease in weight, and a 62.91% mortality rate. In contrast, in the cotton bollworm, the 3/1 Cyf/G treatment led to a 0.19 mm decrease in body length, a 1.99 mg decrease in weight, and a 38.89% mortality rate. Compared to the use of the insecticides alone, the graphene–insecticide mixtures significantly inhibited the body length and weight gain of the cotton bollworm and achieved higher mortalities. The 100 μg/mL Cyh/G mixture decreased the body length by 1.40 mm, decreased the weight by 1.88 mg, and had a 61.85% mortality rate. In contrast, the 150 μg/mL Cyf/G mixture had a 45.08% mortality rate, which is higher than that of 100 μg/mL. The graphene-delivered Cyh could effectively destroy the epicuticle and spine cells of the cotton bollworm. This result proves that graphene has synergistic activity with insecticides, and this might have big implications in the plant protection field.


Permanent cosmetic procedures including tattooing are determined as risk factors that prompt the development of cutaneous granulomatous conditions. Scar sarcoidosis is an uncommon manifestation of a systemic granulomatous disease with a few cases reported in the literature worldwide. Although the incidence rates of sarcoid lesions at sites of pigment deposition are low, granuloma formation can provoke a severe systemic inflammatory response. We report a 48-year-old Hispanic female with a new onset of scar sarcoidosis that progressed to a systemic condition. Erythematous maculopapular eruptions arose on her left eyebrow area at the sites of scars from cosmetic tattooing, prior to exacerbation of the small airway disease. Histopathologic examination revealed typical findings of cutaneous sarcoidosis, including non-caseating epithelioid granulomas. This case highlights the importance of early detection of cutaneous sarcoidosis in long-standing scars due to the associated risks of systemic sarcoidosis. Sarcoidosis, also known as a great dermatologic masquerader, is an idiopathic and systemic granulomatous disease that has a wide range of clinical manifestations . The disSkin lesions are typically classified into sarcoidosis-specific and nonspecific lesions. Both types generally present with papules and plaques that greatly fluctuate in colors, ranging from erythematous, violaceous, yellow, brown, and hypopigmented to flesh or skin-colored . NeverthA 48-year-old Hispanic female presented with the chief complaint of multiple erythematous papules involving both eyebrows and the skin of the surrounding lower forehead area Figure . The lesions appeared as small red papules three months before her initial visit and continued to grow until she presented for consultation. The patient had three dark-grey permanent make-up tattooing procedures that were performed on her eyebrows several years prior to the disease's onset. However, the most recent procedure, known as microblading, was performed one year prior to disease onset. She had been experiencing associated symptoms of pulmonary sarcoidosis for six months, including dry cough, shortness of breath, and intermittent wheezing prior The patient denied having any environmental exposure to toxins or to possible risk factors for acquiring tuberculosis. Additionally, she denied having any recent history of travel prior to the onset of symptoms. Her past medical history was pertinent for controlled primary systemic arterial hypertension with the use of angiotensin-converting enzyme (ACE) inhibitors. No prior history of bronchial asthma or lung disease was noted. Moreover, the patient did not take medications that could be associated with the development of granulomatous diseases. Upon physical examination, there were multiple red to slightly hyper-pigmented pruritic papules and nodules on the eyebrows where permanent make-up was previously performed and the surrounding low forehead Figure .Histopathologic examination of one of the lesions, excised from the left central eyebrow, revealed non-caseating epithelioid cell granulomas, which was consistent with sarcoidosis Figures , 3. Additionally, several inclusion bodies were observed in the clusters of granulomas, such as asteroid bodies and Schaumann bodies. Special stains were applied to rule out infectious etiologies of the disease, including fungal agents along with mycobacteriosis. Periodic Acid Schiff for fungus (PAS-F), Acid-Fast Bacilli (AFB), Fites Acid Fast (FITE), and Grocott’s Methenamine Silver (GMS) stains were found to be negative, excluding an infectious etiology.To confirm the diagnosis, the patient had undergone pulmonary function testing that revealed minimal airway obstruction suggesting small airway disease, which was consistent with the clinical presentation of pulmonary sarcoidosis.Mycobacterium spp. and Propionibacterium spp. [The hallmark of sarcoidosis is the presence of non-caseating epithelioid granulomas, along with compatible clinical presentation. While there is no specific etiology, it is hypothesized that certain genetic, environmental, and infectious causes are the mainstay of triggering the disease. Numerous etiologic agents have been associated with the pathophysiology of sarcoidosis, most notably involving ium spp. . In a miium spp. . Moreoveium spp. .The diagnosis is established by relying on the exclusion factors and histopathological evidence of non-caseating granulomas in at least one organ. The predominant finding of non-caseating granulomas spanning across superficial and deep dermis presents in 90% of patients with sarcoidosis. Histologically, lesions revealing numerous clusters of epithelioid histiocytes with an insignificant quantity of inflammatory cells surrounding them are known as naked sarcoid granulomas . MoreoveScar sarcoidosis is an exceptionally rare cutaneous manifestation of sarcoidosis with a few case reports found worldwide, accounting for fourteen percent of total sarcoidosis cases . PredomiIn addition to scar sarcoidosis, there is an increasing incidence of sarcoid reactions developing after certain cosmetic procedures. Tattooing and microblading are known risk factors that evoke inflammatory and infectious reactions . PermaneThis case highlights a rare presentation of cutaneous sarcoidosis induced by permanent makeup procedures performed on the patient’s eyebrows that resulted in the scar formation. In the current case study, the patient had been also experiencing symptoms of a small airway disease since the onset of cutaneous lesions, suggesting the development of systemic disease. Recognition of possible risk factors, including recent tattoo and permanent makeup procedures, is crucial for the detection and early treatment of systemic sarcoidosis with cutaneous manifestations.


The capacitance of the BiFeO3 film increased from 25 to 1410 pF with the increase of RH from 30% to 90%. In particular, the impedance varied by more than two orders of magnitude as RH varied between 30% and 90% at 10 Hz, indicating a good hysteresis and response time. The mechanism underlying humidity sensitivity was analyzed by complex impedance spectroscopy. The adsorption of water molecules played key roles at low and high humidity, extending the potential application of ferroelectric BiFeO3 films in humidity-sensitive devices.In this work, a BiFeO Humid environments are essential in many fields, such as weather forecasting, agricultural production and personnel health ,2. In ad3, where A is a rare earth, alkali or alkaline earth metal and B is a transition metal) humidity-sensing film materials, including BaTiO3, K0.5Bi0.5TiO3, K0.5Na0.5NbO3 and LaFeO3, can behave with remarkable humidity-sensing properties [3 is a well-known lead-free ferroelectric material that has been regarded as a promising spintronic and information-storage receptor material in recent years due to its large remanent polarization and high Curie point [3 is a distorted perovskite ferroelectric material with a non-stoichiometric ratio, which makes it behave with p-type semiconductor behavior and makes it a promising material for high-performance humidity-sensing applications [3 films. In humidity sensors, morphology and cation distribution can be controlled by the synthesis method, which affects the surface reaction. The sol–gel technique is a simple, low-cost and promising method for the preparation of BiFeO3 films [For the convenience of microelectronics integration, film materials are often prepared for humidity sensors. Some ferroelectric perovskite 3·5H2O) and ferric nitrate (Fe(NO3)3·9H2O) were dissolved in C3H8O2 solution with a molar ratio of 1:1 and agitated at room temperature for 30 min. Afterwards, enough CH3COOH was added to the solution for dehydration. During continuous stirring, 5 mL of aminoethanol was added to BiFeO3 solution in order to control the viscosity. Finally, a 0.3 mol/L red-brown mixed solution with a volume of 30 mL was obtained. The mixture was stirred on a magnetic stirrer for 2 h and left at room temperature for 12 h. The obtained reddish-brown BiFeO3 solution was spin-coated on a Pt/Si(111) substrate and dried for 3 min at 180 °C. Then, films were calcined for 20 min at 490 °C. Finally, conductive silver glue was used to stick electrodes on the surface of the BiFeO3 film for the electrical measurement.The sol–gel method was used to successfully prepare a BiFeOThe simple structure was determined via XRD . The microstructure was characterized via SEM . A ferroelectric analyzer was used to explore the ferroelectric hysteresis loop . The capacitance and impedance were measured using a precision impedance analyzer . The current–voltage relationship was measured using a current–voltage meter . A humidifier in an enclosed space was employed to generate an environment with 30% to 90% relative humidity. The RH was measured using a hygrometer.3 film. The diffraction peaks of the pure BiFeO3 sample are consistent with the standard chart of BiFeO3 with rhombohedral R3c structure (JCPDS PDF # 86-1518), as shown in 3. The scanning electron microscope image of the BiFeO3 film is shown in the inset of 3 film had a porous structure, indicating its excellent ability to adsorb water molecules, which is essential for humidity sensing.3 film is shown in 3 film had a serious electrical leakage problem due to the multiple valence states of Fe [The ferroelectric hysteresis loop of the BiFeOes of Fe .3 film on the RH was measured at the frequencies of 10, 40, 100, 300, 600 and 1200 Hz, as shown in 3 film increased from 25 to 1410 pF as the RH increased from 30% to 90% at 10 Hz. This was due to the increase of physisorbed water molecules on the BiFeO3 film surface with the increase of RH, which made more water molecules polarized. At high frequency , the capacitance remained almost constant with increasing RH, implying that frequency is a crucial factor in the humidity response. At high frequency, the dipoles of the water molecules slow their reorientation. The dipole rotation of water molecules no longer resonates with the external field at high frequencies, which means that the polarizability of the water molecules lags behind the frequency of the change of the external electric field. Therefore, the capacitance of the BiFeO3 film had a high humidity response at frequencies range of 10–100 Hz, while RH is independent of the capacitance at frequencies in the range of 100 Hz to 1.2 kHz. The effect of RH on capacitance can be expressed by Equation (1) [γε and γ are the permittivity and the electrical conductivity of the BiFeO3 film, respectively. C0 and ε0 denote the capacitance of an ideal capacitor and the vacuum permittivity, respectively. C and ω are the capacitance and the frequency, respectively. Equation (1) indicates that the capacitance of the BiFeO3 film is inversely related to ω and is proportional to the material’s γ. Both γ and C increase as RH increases [The dependence of the capacitance of the BiFeO3 film at 30–90% RH and frequencies of 10, 40, 100, 300, 600 and 1200 Hz, as shown in 3 film decreased from 1.7 × 105 to 1570 kΩ when RH increased from 30% to 90%. The impedance decreased significantly at 10 Hz, indicating that the optimum working frequency is 10 Hz. Over the entire frequency range, the impedance decreased with the increase of RH. At the same frequency, the impedance change was not obvious at low RH, while the impedance drop was more significant at high RH. This is because the main conduction mechanism for humidity sensing is caused by proton hopping between the sensitive layer of the film and water molecules. At low RH, a small amount of water molecules are chemisorbed on the cations (Bi3+ and Fe3+) on the film surface [In order to determine the optimal working frequency, the dependence of impedance on RH was measured using BiFeO surface . Due to  surface . This re3 film usually occurred during the desorption of samples. The humidity hysteresis is a critical characteristic for the application of humidity sensing, and is defined as the maximum difference between adsorption and desorption of the humidity sensor. The humidity hysteresis (γH) is expressed in Equation (2) as [MAXRH is the maximum difference in the output of adsorption and desorption processes. FSF is the impedance change over the entire humidity range. The humidity hysteresis characteristics of the BiFeO3 humidity sensor at 10 Hz are shown in 3 showed a narrow hysteresis loop. The BiFeO3 film had a small hysteresis during the entire humidity test with a maximum hysteresis of approximately 16%, mainly caused by residual moisture in the BiFeO3 film layer. With the decrease of RH, the number of water molecules between the layers of the BiFeO3 film gradually decreased, resulting in the gradual disappearance of the hysteresis phenomenon [Humidity hysteresis of the BiFeOn (2) as :(2)γH=±Δenomenon ,24.3 film to reach 90% of the total impedance change during adsorption and desorption, respectively. 3 film in the maximum humidity range (30–90% RH) were 60 s and 70 s at 10 Hz, respectively. The recovery time of the BiFeO3 film was higher than the response time due to the higher bonding energy between the adsorbed water molecules and the surface of the sensor material [3 film could rapidly adsorb and desorb water molecules, indicating its potential value for practical applications. Based on the conversion circuit of a humidity sensor, RH changes in the environment can be converted into an electrical signal that is easy to control and identify. The ideal humidity sensor needs to meet the following characteristics: fast response speed, strong recovery ability and small humidity hysteresis error. The response and recovery times are the times required for the BiFeOmaterial . This re3 film in the range of 30–90% RH and in the scanning frequency range of 10–1000 kHz. The complex impedance spectrum of the BiFeO3 film presented a circular arc shape when the RH was lower than 50%, as shown in 3 films in the low-humidity range is composed of parallel connections of resistors and capacitors, as shown in 3 film, and the H2O molecules on the surface dissociate into H+ and OH−. Then, OH− and H+ are chemically combined with metal ions and oxygen ions, respectively, to form hydroxyl groups that constitute the first layer of physical adsorption [2O → H3O+ + OH−, which has a weak influence on the capacitance of the BiFeO3 film. H3O+ spontaneously transfers H+ to the second water molecule according to H3O+ → H2O + H+ [The complex impedance curve is an effective method to study the properties of humidity sensing . In AC csorption . The chaH2O + H+ ,28, and H2O + H+ .3 film showed a straight line with a slope of approximately 1 at frequencies from 10 to 100 Hz, as shown in 3 film has a significant Warburg impedance due to ion diffusion, as shown in 3 films is covered by water, resulting in a rapid increase in the amount of H+, which further increases the conductivity [When RH increased to 70%, the complex impedance spectrum of the BiFeOuctivity ,32.I–V characteristics of the BiFeO3 film at different RH levels are presented in I–V with RH. At different RHs, BiFeO3 film exhibited linear I–V characteristics, which indicates an ohmic contact between the BiFeO3 film surface and electrodes. Since the resistance was constant over the range of supply voltage, the sensitivity was the same regardless of the operating bias, which allows operation at low power in practical application [3 film increased, resulting in a decrease in current. The excellent humidity response makes BiFeO3 films a potential candidate for practical humidity-sensing applications.The lication . As RH i3 film prepared in this study via a simple sol–gel method exhibited significant humidity sensitivity with capacitance and impedance changes of nearly 2–3 orders of magnitude as RH increased from 30% to 90%. In the whole humidity range, the experimental results of humidity hysteresis and humidity response recovery indicate that BiFeO3 film is an excellent material for application in humidity sensors.The BiFeO


ATAC-seq) is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis. Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data; however, key quality control (QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics for ATAC-seq data, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package (AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity (20%–60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and it can be executed by one command line to generate a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed qATACViewer for the user-friendly interaction with the QC report. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.Assay for transposase-accessible chromatin with high-throughput sequencing ( To regulate the transcription of a eukaryotic genome, chromatin must remain in an accessible state to allow binding of transcription factors and initiation of transcription activation The ATAC-seq analysis strategy is primarily adopted from ChIP-seq data analysis. After aligning sequencing reads to the genome, peak calling tools, such as MACS2 qATACViewer, a visualization tool included in AIAP, for user-friendly visualization of QC reports. AIAP is compiled into a Docker/Singularity image to allow maximized compatibility on different operating systems and computing platforms. The software, source code, and documentation are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.Here, we present ATAC-seq Integrative Analysis Package (AIAP), a software package containing an optimized ATAC-seq data QC and analysis pipeline. Along with general QC metrics, such as library insert fragment size distribution, we specifically introduced a series of QC metrics for ATAC-seq, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. By applying AIAP, we demonstrated a significant improvement in both peak calling and differential analysis by processing the paired-end sequencing data in single-end mode: more than 20% of ATAC-seq peaks can be identified using AIAP, and over 30% more differentially accessible regions (DARs) can be identified by AIAP in downstream analysis. We applied AIAP to reanalyze 70 mouse ENCODE https://github.com/Zhang-lab/ATAC-seq_QC_analysis/.Here, we describe AIAP for processing and analyzing ATAC-seq data. The AIAP workflow typically consists of four steps, as shown in Cutadapt and aligned to the reference genome by BWAmethylQAmethylQA first filters unmapped and low-quality mapped PE reads and then identifies the Tn5 insertion position at each read end by shifting + 4 bp/−5 bp on the positive/negative strands. methylQA further extends 75 bp in both directions around the Tn5 insertion position to create two pseudo single-end (SE) mapped reads with the length of 150 bp in PE as SE (PE-asSE) mode. Next, AIAP compiles different files for downstream analysis (.bed files) and normalized visualization (.bigWig files). The bed file is used to perform peak calling by MACS2 --keep-dup 1000 --nomodel --shift 0 --extsize 150.The data processing step first configures the working path. The ATAC-seq paired-end (PE) raw-read FASTQ files are trimmed by --keep-dup 1000 --nomodel --shift 0 --extsize 0.In PE-noShift mode, AIAP first filters unmapped and low-quality mapped PE reads and then identifies the Tn5 insertion position at each read end by shifting + 4 bp/−5 bp on the positive/negative strands, then isolates the whole fragment between two insertion positions to create one PE mapped fragment for downstream analysis (.bed files) and normalized visualization (.bigWig files). The bed file is used to perform peak calling by MACS2 FastQC to check the sequencing quality, duplication rate, and GC bias before alignment. After alignment, AIAP generates the mapping statistics summary, chromosome distribution of uniquely mapped reads, mitochondrial genome (chrM) contamination rate, library insert fragment size distribution, and library complexity. AIAP also performs a series of post-peak calling quality checks, including peak width distribution, RUPr, BG, ProEN, and SubEn. AIAP also provides saturation analysis, promoter peak distribution, and signal ranking analysis. AIAP reports the quality metrics in a JSON file, which can be visualized using qATACViewer. The default QC standard of AIAP is determined by the QC metrics of mouse ATAC-seq data generated by ENCODE consortium . We defined the key QC metrics below.AIAP performs a series of quality checking steps before and after alignment. AIAP calls preseq (https://github.com/smithlabcode/preseq). 5) To obtain the total number of useful single ends, each end of a non-redundant uniquely mapped read pair will be shifted + 4 bp/−5 bp on the positive/negative strands and then further extended 75 bp in both directions around the Tn5 insertion position.There are 5 QC metrics for alignment. 1) Non-redundant uniquely mapped reads refer to the reads that are uniquely mapped to the reference genome after removing redundancy. 2) The second is chromosome distribution/chrM contamination rate. The former describes the distribution of uniquely mapped reads across all chromosomes, while for the latter, the number of uniquely mapped reads on chrM is used as a QC metric to measure the quality of the ATAC-seq library. 3) Library insert fragment size distribution is measured as the length of DNA fragment defined by non-redundant uniquely mapped read-pairs. 4) Library complexity is estimated in both duplication rate and predicted yield of distinct reads generated by RUPr is defined as the percentage of all useful ends  are randomly selected from the genome outside of ATAC-seq peaks. The ATAC-seq signal in each region is calculated as reads per kilobase per million mapped reads (RPKM). The percentage of all such regions with the ATAC-seq signal over the theoretical threshold (RPKM = 0.377) is considered high-background and used as a QC metric to indicate the background noise.The promoters, the regions +/− 1 kb around transcription start sites (TSSs) of active genes, provide a positive control for OCRs. The ATAC-seq useful ends enriched on detected promoters (ATAC-seq peaks) are used as a QC metric to measure the signal enrichment calculated as follows.The ATAC-seq signal (useful ends) enriched on the detected ATAC-seq peaks is used as a QC metric to measure the signal enrichment at the genome-wide level. To avoid sequencing-depth bias, 10 million useful ends are sampled from the complete dataset, and peak calling is performed to identify the OCRs. SubEn is calculated after 10 million pseudo counts are added into the calculation as background, which can avoid calculation failure caused by the low sequencing depth of testing ATAC-seq library.MACS2 is used to call narrow peaks for a series of subsampling from complete useful ends with a step of 10% of total sequencing depth. The length of identified peaks covering genomic regions at each subsampling are used to calculate the recovery (percentage) of complete peaks covering genomic regions when using complete useful ends.The ATAC-seq peak signals are ranked, and the percentage of promoter peaks in each quantile is determined.R package DESeq2AIAP includes two downstream analysis components: qATACViewer , and listed in Table S1. The ATAC-seq FASTQ files were processed by AIAP as described above. The ChIP-seq FASTQ files were aligned to the mouse genome (mm10 assembly) and were further processed by methylQA. Methylation calling of whole-genome bisulfite sequencing (WGBS) data was downloaded from the ENCODE data portal. The averaged signals of ATAC-seq, ChIP-seq, and WGBS were calculated at 100-bp windows within 5 kb around the center of the ATAC-seq peaks and were plotted in R. The processed DNase I hypersensitive site (DHS) data were downloaded from the ENCODE data portal.The raw data FASTQ files for ATAC-seq and histone ChIP-seq were downloaded from ENCODE data portal  and postnatal day 0 (P0), to evaluate the performance of AIAP. The ATAC-seq peaks generated by AIAP were used as test regions, the read counts were calculated in both PE-asSE and PE-noShift modes, and the DARs were identified as described above with adjusted A). The ATAC-seq peaks are sharper and stronger when the ATAC-seq data have high RUPr and ProEn values, suggesting better signal enrichment and better quality in the ATAC-seq experiments.Conducting QC checks at different steps of data processing and correctly interpreting QC metrics are crucial to ensure a successful and meaningful analysis. Different QC metrics report important information regarding different aspects of genomic data; thus, it is essential to define the key QC metrics for ATAC-seq data before performing an analysis. In addition to the traditional QC metrics shown in Figure 2We further defined BG to directly measure the background noise level in the ATAC-seq experiments. We randomly selected 50,000 genomic regions (size: 500 bp) from regions of the genome that do not overlap with ATAC-seq peaks after peak calling. The ATAC-seq signals over each region are calculated as RPKM, and the regions with an ATAC-seq signal over a theoretical threshold (RPKM = 0.377) are considered high-background regions. The percentage of high-background regions within 50,000 randomly selected genomic regions is used as a QC metric to measure the background noise of the ATAC-seq data. We noticed that different background noise levels were directly reflected by the QC metric of background, especially in the heterochromatin regions, which are enriched with H3K9me3 signals B.To further explore the QC metrics, we used AIAP to process 70 ATAC-seq datasets generated by the ENCODE consortium (Table S2). We specifically checked the key metrics, including RUPr, ProEn, SubEn, and BG. We noticed that these metrics were not dependent on sequencing depth . RUPr waA).To define ATAC-seq peaks, peak calling strategies adopted from ChIP-seq analysis are widely used to analyze ATAC-seq data. However, unlike ChIP-seq data, an ATAC-seq experiment does not have input control and is usually sequenced with the PE sequencing method to profile the size of DNA fragments. The uniquely aligned PE reads have been used to call open chromatin peaks after alignment in many studies Figure 3To validate the sensitivity of our analysis strategy, we downloaded published ATAC-seq data of GM12878 cells generated by the Greenleaf laboratory with the Omni-ATAC-seq protocol ATAC-seq peaks are generally considered regulatory elements that are enriched for specific histone modifications. We downloaded ChIP-seq data of GM12878 histone modifications  that are associated with promoter and enhancer activities to validate the functionality of PE-asSE-specific ATAC-seq peaks. Compared to randomly selected genomic regions, the PE-asSE-specific ATAC-seq peaks were highly enriched in all active histone modifications G–I. ThesA). Correspondingly, only 107 E11.5-specific and 72 P0-specific DARs were found by using only PE-noShift mode. Compared to PE-noShift mode, PE-asSE mode resulted in an ∼ 35% increase in the number of DARs identified. We further tested AIAP on other tissues at two developmental stages and found that AIAP identified 32%–168% more DARs in different tissues (Table S5).Chromatin accessibility is dynamically associated with cellular responses to developmental cues, disease progression, and environmental stimuli. The identification of DARs has become an important approach to monitor the activity changes of regulatory elements Figure 4We examined the genomic distribution of DARs and noticed that the distribution of PE-asSE-specific DARs had a similar distribution as shared DARs, that is, most DARs were located in intergenic and intronic regions, which is consistent with their potential enhancer functionality B. BecausAIAP is a new tool to perform quality assurance and downstream analysis of ATAC-seq data. Comparing with other tools (Table S6), AIAP provides a rapid and reliable data processing solution for ATAC-seq data. Using public datasets, we systematically tested the QC metrics of ATAC-seq data and established key QC metrics of ATAC-seq data. We determined that RUPr, ProEn, and BG were important measurements to estimate the quality of ATAC-seq data. All three QC metrics directly reflect the quality of library preparation, and the failure of these QC metrics reflect the low-quality of the ATAC-seq data, which cannot be corrected by merely increasing sequencing depth. We found that the RUPr and ProEn reflect the ATAC-seq signal enrichment, and BG indicates the overall background noise of the data. By combining these QC metrics, we obtained an accurate estimation of the quality of ATAC-seq data. We used AIAP to process 54 mouse ATAC-seq datasets to test and evaluate the QC metrics and generate the range of the QC metrics (Table S2). These ranges of QC metrics can be used as a reference to evaluate the success of ATAC-seq experiments.We optimized the widely used classic analysis methodology and specifically used PE-asSE mode to process the PE sequenced ATAC-seq data. AIAP aligns the PE ATAC-seq data in PE mode to increase the alignment accuracy, and the BAM file is further processed in SE mode for downstream analysis. In PE-asSE mode, AIAP doubles the sequencing depth and dramatically increases the sensitivity of OCR identification. In our test, AIAP identified 20%–40% more ATAC-seq peaks than the widely used classic analysis methods. We further used corresponding DHS data and histone modification data to validate the specificity of newly identified ATAC-seq peaks by AIAP; most of the novel ATAC-seq peaks identified by AIAP were independently validated with DHSs and enriched for active histone modifications. Such a result indicates the high true positive rate resulting from the AIAP analysis strategy.We also suggest that the PE-asSE strategy can improve the sensitivity of discovering DARs, which are wildly used to measure chromatin dynamics Finally, we compiled AIAP into a Docker/Singularity image to facilitate the easy operation of AIAP on high-performance computing clusters. AIAP can complete QC checking and file processing in ∼ 2 h for one typical ATAC-seq dataset, of 37 million PE reads (Table S7). Besides the high performance, AIAP also has a much better sensitivity and higher specificity F when coCode availability.https://github.com/Zhang-lab/ATAC-seq_QC_analysis.The software, source code, and documentation of AIAP are freely available at Competing interests.The authors have declared no competing interests.Shaopeng Liu: Methodology, Software, Formal analysis, Resources, Writing – original draft. Daofeng Li: Methodology, Software, Visualization. Cheng Lyu: Methodology, Software, Formal analysis. Paul M. Gontarz: Formal analysis, Resources. Benpeng Miao: Investigation, Software, Formal analysis. Pamela A.F. Madden: Supervision, Project administration. Ting Wang: Supervision, Project administration, Writing – original draft, Writing – review & editing. Bo Zhang: Conceptualization, Methodology, Software, Formal analysis, Investigation, Supervision, Project administration, Writing – original draft, Writing – review & editing, Visualization.


This study presents the functionalization and characterization of graphene and electromagnetic interference (EMI) attenuation capacity in epoxy-nanocomposites. The modification of graphene involved both small molecules and polymers for compatibilization with epoxy resin components to provide EMI shielding. The TGA and RAMAN analyses confirmed the synthesis of graphene with a different layer thickness of the graphene sheets. Graphene samples with different layer thicknesses  were selected and further employed for epoxy coating formulation. The obtained nanocomposites were characterized in terms of EMI shielding effectiveness, SEM, micro-CT, magnetic properties, and stress-strain resistance. The EMI shielding effectiveness results indicated that the unmodified graphene and hexamethylene diamine (HMDA) modified graphene displayed the best EMI shielding properties at 11 GHz. However, the epoxy nanocomposites based on HMDA modified graphene displayed better flexibility with an identical EMI shielding effectiveness compared to the unmodified graphene despite the formation of aggregates. The improved flexibility of the epoxy nanocomposites and EMI shielding characteristics of HMDA functionalized graphene offers a practical solution for textile coatings with microwave absorbing (MA) capacity. Mmagnetic .MA materials can be classified into the following two categories: magnetic and dielectric absorbing materials ,7. A perPolymer-based composites could meet all these requirements ,16,17. FIn recent years, graphene has attracted lots of attention for its potential application in MA; the attractive potential properties of graphene for MA performance are probably attributed to the following reasons: the ultrathin thickness and optical transmittance property of graphene can not only decrease the density of the MA materials but also weaken the skin effect effectively; the resonance effect resulted from the layered and high porous morphology as well as the high specific surface area could be very beneficial to the absorption and attenuation of the microwave; the interfacial polarization caused by the large interface can also be helpful for the absorption of the microwave ,27,28,29Epoxy resins are the most important thermosets, widely used as adhesives, coatings, and as a polymer matrix to obtain polymer nanocomposites ,30,31,32Our study deals with the synthesis of new EMI shielding materials based on epoxy polymers and chemically modified graphene. The graphene has been functionalized for better dispersion in the epoxy resin (polymer matrix). In our case, the functionalization takes place with small molecules, respectively with polymers. Samples of graphene with different layer thicknesses  were selected and further employed for epoxy coating formulation. Thus, the EMI shielding attenuation capacity of the nanocomposites containing modified graphene was evaluated. Additionally, the mechanical properties of the epoxy composites were assessed given the final objective to obtain an efficient EMI shielding solution capable to be used in textile coatings. Thus, modified graphene-based materials were demonstrated to confer both EMI shielding effectiveness and suitable mechanical properties, making them one of the most appropriate materials for improving MA capacity in the future.3 and precipitation in methanol.Graphene nanoplatelets  (G)  were used as received. Nitric acid , sulfuric acid , glycidyl methacrylate , N,N’-dimethylformamide , lauryl peroxide , acetone (Sigma-Aldrich), methanol (Sigma-Aldrich), hexamethylene diamine , and acrylamide (Sigma-Aldrich) were used as received. Methyl methacrylate  was purified through vacuum distillation. Hexyl methacrylate  was purified by passing through an alumina column. Benzoyl peroxide  was recrystallized by dissolution in CHCl(a) Functionalization of Graphene with small moleculesGraphene oxide was prepared according to a modified method. Briefly, 1 g of graphene was dispersed in 30 mL of H2SO4 (95–98%), then 10 mL of HNO3 (68%) was slowly added and the reaction mixture was stirred for an additional 2 h. After, 100 mL H2O was added dropwise, and the reaction mixture was let to cool down under stirring before the addition of another 200 mL H2O. The reaction mixture was then neutralized using NaOH until pH 7 was achieved and the obtained GO was separated by filtration and washed thoroughly with water.Synthesis of Graphene-G-BP. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 0.5 g benzoyl peroxide. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with methanol and acetone.Synthesis of G-HMDA. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 g HMDA. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with water, methanol, and acetone before being dried.(b) Functionalization of Graphene with polymer chainsSynthesis of G-PAM. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 g acrylamide and 0.1 g of LP. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with DMF, water, and acetone before being dried.Synthesis of G-PGMA. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 mL GMA and 0.1 g of LP. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with DMF, water, and acetone before being dried.Synthesis of G-PHexylMA. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 mL hexylMA and 0.1 g of LP. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with DMF, water, and acetone before being dried.(c) Epoxy coating formulationEPOTEC YD 011X75  (a bisphenol-A based epoxy resin), dioctyl adipate (BASF) (as plasticizer), EPAMINE PC13 (PO.INT.ER) (as hardener), BP-183-B  (organic bentonite as rheology modifier), and IOX B-03 (LANXESS) (synthetic iron oxide-Fe3O4). The resin: hardener ratio was set at 5:1; a 1:1 toluene: acetone solvent mixture was used to facilitate the dispersion of the graphene and modified graphene either in the resin  or in the hardener component (G-HDMA) and other fillers (BP-183-B and IOX B-03). The concentration of the EMI active fillers is presented in Error! Reference source not found. The coating was applied on polyester woven material (used for EMI shielding efficiency) and on polyethylene sheets from which the epoxy films were recovered and used for the characterization of the mechanical properties. The deposition was performed by doctor blade technique to ensure a reproducible film thickness.The epoxy coating formulation involved the use of The polymer nanocomposite films were investigated by optical microscopy using an Olympus BX41 light microscope  equipped with live view E330 7.5 MP Digital SLR Camera and QuickPhoto Micro 2.3 software . The images were collected in transmission mode.The morphology of the nanocomposite films was also investigated by SEM (scanning electron microscopy) using a Tescan Vega II LMU SEM instrument  at 10 keV acceleration voltage.® 2D/3D Micro-CT Slice Visualization .The distribution of the graphene and iron oxide particles inside the epoxy matrix was examined via micro-CT technique. The SkyScan micro-CT attachment allowed for converting the Tescan Vega II LMU SEM to an X-ray microtomograph for non-destructive imaging and for measuring of the object’s internal microstructure of specimens. Analysis parameters: Exposure time—4 s per projection at electron beam currents of 100 nA; accelerating voltage—30 KeV; step size −1°; scanning time—24 min. Reconstruction was performed by the NRecon program, which used float-point data values for internal calculations during reconstruction and afterward allowed the operator to define the density window as a range of the reconstructed values. The full set of reconstruction results was visualized by the program DataViewerRaman spectra of the functionalized graphene were recorded on a DXR Raman Microscope  by 473 nm laser line.−1; samples mass, ~3 mg; temperature range, room temperature −900 °C; and heating rate, 10 °C min−1 in an alumina crucible.The thermogravimetric analyses (TGA) were performed using Netzsch TG 209 F3 Tarsus  equipment considering the next parameters: nitrogen atmosphere flow rate, 20 mL minStress-strain curves were obtained using an Instron 3382 testing machine . The samples were prepared for the tensile tests by cutting the nanocomposite films (without woven polyester). The tests were carried out according to the international standard ASTM D3039/D3039M-08. For each specimen, the rate of the extension was set at 500 mm/min, and the separation of the initial jaws was set at 50 mm (plain jaw faces). For each type of nanocomposite film, five tensile tests were carried out and the average of the measured values and the standard deviation for each point was registered. A comparative multigraph containing all the true stress/true strain values characteristic for each synthesized material was plotted to evaluate the influence of the nanofiller on their mechanical properties. This multigraph was designed to show only the curves with the closest parameters to the mean values from each set of specimens. −7 emu. For magnetic measurements, the epoxy thin film strips used were closed in small sample holders.Hysteresis loops at 300 K were measured using a SQUID magnetometer  running under the RSO (Reciprocating Sample Option) mode. This apparatus allows performing measurements with a magnetic moment resolution up to 10The ability of the synthesized materials to attenuate radar waves in the domain of 8–12 GHz was investigated as follows: A full-anechoic chamber , consistSE) is the result of three mechanisms, reflection loss, absorption loss, and multiple reflections loss.The EMI shielding efficiency  was used where iE and tE are the incident and transmitted electric fields, respectively.To calculate the The most utilized method to modify G involves obtaining GO, which was then mixed with epoxy resin and deposited on the textile material. This first stage can be used to highlight the physical compatibility between the textile and the modified epoxy resin. Thus, the textile material prior to and after the application of the modified epoxy resin coating was subjected to optical microscopy analysis.From the comparative analysis of the images in SEM analysis was performed on the two samples for a more precise analysis.From the images in Addressing the filler capacity to disperse in the resin matrix is a viable solution to further improve the coating quality. For this reason, several methods for graphene functionalization by the introduction of functional groups or polymer grafting were explored. The efficient dispersion and filler compatibility with the epoxy resin are critical aspects that affect the mechanical and microwave absorbing characteristics of the coating. The introduction of functional groups involved the oxidation of graphene, grafting of radical species obtained by the degradation of benzoyl peroxide, and the addition of hexamethylenediamine units to the graphene backbone. The polymer grafting assumed the polymerization in the presence of graphene. The selected monomers were methyl methacrylate (PMMA), glycidyl methacrylate (PGMA), hexyl methacrylate (PhexylMA), and acrylamide (PAM).Thermogravimetric analyses (TGA) were performed to confirm the functionalization of the graphene .In RAMAN spectroscopy was performed on the modified graphene to confirm the chemical modification of the graphene .−1) and G (1575 cm−1) bands. Thus, based on the ID/IG ratio  bands. Using the IG/I2D ratio . Similarly, for the G-PHexylMA derivative, a similar process is observed, despite the lower functionalization degree. This can be explained by the steric effect of the hexyl unit. In contrast, in G-PGMA and G-PMMA a higher thickness of the graphene stacks was observed. Thus, although G-PMMA and G-PHexylMA display a similar functionalization degree, the thickness of the graphene sheets is different. Another explanation for this aspect resides in the competition during the functionalization process between the radical trapping by the graphene and the homopolymerization of the monomer units. Thus, MMA is more susceptible to homopolymerization compared to hexyl methacrylate and the radical species are more stable than in the case of acrylamide. Surprisingly, in the case of the HDMA, although a high degree of functionalization was obtained, from the IG/I2D ratio a multilayer graphene structure was observed. This can be explained by the bifunctionality of the HMDA, which can be intercalated between the graphene sheets and react with both layers. A graphical representation of the obtained structures is presented in RAMAN spectra also offer information on the thickness of the graphene sheets and their variation induced by the functionalization technique can be ascertained using the ratio between the G and 2D  has both an electric and a magnetic component. Magnetism is defined as the physical phenomena produced by the moving electric charge. A magnetic field can also induce the movement of the charged particles producing an electrical current, which is the proof of the dependence between magnetism and electric conductivity. The magnetic properties of the samples were assessed to explain their electromagnetic shielding capacity . The valMB015 and MB016, respect the order for both magnetism and EMI attenuation. These low values could be explained by a higher amount of doping agent which leads to the formation of larger aggregates in the case of MB015, therefore larger “multilayer” structures by reaction of the amine group of HMDA with the oxirane of the epoxy component. In the case of MB016, although initially G-PGMA displays a “few layers” structure, the introduction in the epoxy resin could lead to aggregates by reaction of the oxirane present in PGMA with the amine component of the resin. The enhanced magnetic properties of MB014 can be explained by the increased amount of dopant presenting magnetic characteristics. Based on these explanations, a clear correlation about the determining factor for EMI shielding improvement cannot be made. Therefore, the next step consisted in the highlighting of aggregates formation by micro-CT analysis , while in the case of MB013 the good results good be attributed to an improvement of the electrical conductivity. Therefore, the electroconductive properties of the fillers counterbalance the lack of uniformity of the sample leading to a good EMI shielding efficiency. Thus, it can be concluded that G-HMDA presents an enhanced electrical conductivity compared with the unmodified graphene which is confirmed by similar examples in the literature data [The largest aggregates can be observed for sample ure data .MB013 is the most elastic sample, but it also presents the lowest mechanical resistance. This could be explained by an improved load transfer due to the presence of amino-functionalized graphene [MB014) probably due to a reinforcing effect of the filler. On the other hand, a minor influence on the improvement of the mechanical performances of these thin films was observed when doubling the concentration of G-HDMA (sample MB015). In contrast with these first three materials, the samples containing G-PGMA (MB016) displayed superior mechanical performances, reaching a maximum value of approximately 1.5 × 107 Pa tensile stress at 1.64% strain, probably due to the interactions established between the two polymers chain, the epoxy resin and the PGMA, grafted on the G surfaces, respectively. The samples containing neat G (MB017) resisted up to 1.4 × 107 Pa tensile stress, the value measured at 1% strain. The mechanical behavior of MB018 to tensile load is comparable to that of MB014. The analysis of the mechanical properties of the materials was also performed considering the application of the materials. graphene ,49. The This study highlights the synthesis and characterization of epoxy-nanocomposites containing nanofillers of unmodified and functionalized graphene. The graphene modification involved both functional groups or small molecules and polymers for compatibilization with the epoxy resin components to provide electromagnetic interference (EMI) shielding effectiveness. The first part of the study was dedicated to the modification of graphene and its characterization by RAMAN and TGA. The results confirmed the obtaining of modified graphene with different thicknesses of the graphene sheets. Samples of graphene with different thicknesses  were selected and further employed for epoxy coating formulation. The obtained nanocomposites were characterized in terms of EMI shielding effectiveness, SEM, micro-CT, magnetic properties, and stress-strain resistance. The interesting aspect is that the unmodified graphene and HMDA-modified graphene displayed the best EMI shielding properties, although the ununiformed characteristic of the nanocomposites was confirmed by the micro-CT. In addition, the epoxy nanocomposites based on HMDA-modified graphene displayed better flexibility with little difference in the EMI shielding effectiveness. The improved mechanical properties and EMI shielding characteristics make HMDA-graphene a good candidate for textile coatings with MA capacity.


The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development. Wearable sensors are an expanding field of research, with growing applications in telehealth , fitnessHeart rate signals from photoplethysmogram (PPG) measurements are known to contain noise, large amplitude variations and a broad variation of peak morphology . The HeaThe discovery of graphene  has spurHeartbeat signals contain the value of numerous physiological parameters that are important health indicators. The most commonly extracted value, heart rate, is defined as the number of heartbeats per minute. Heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats . AnotherThe paper is organized as follows. In LIG was produced by scanning a e, as in . The lasScanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) was done with a PhenomProX electron microscope . Reported elemental compositions are obtained as the average over several positions recorded with a magnification of ×1000, where EDS was performed over the entire field of view. The Raman spectra of the samples were recorded with a DXR Raman microscope . The samples were excited with a diode laser at a wavelength of 532 nm and a power of 10 mW focused on a 2.1 Graphene devices were attached by adhesion to the arm of a still subject at the position of the median cubital vein, as in The signals were analyzed with the HeartPy Python Toolkit . Two difThe obtained signals differ in quality from one measurement instance to another, depending on two factors—on the exact sensor positioning on the subject’s arm, and on the stillness of the subject. In the case of an extremely noisy signal, preprocessing was performed before applying the HeartPy process function. Noisy input signals were normalized to zero mean and unit variance. Filtering techniques such as low pass filtering and high pass filtering were applied to eliminate high frequency noise from the signal. Specifically, 8th order Butterworth band-pass filtering was performed .In order to identify peaks in the heartbeat with HeartPy, the process function starts by calculating the moving average using a window of 0.75 s on both sides of each data point. Next, regions of interest (ROI) are defined as regions between the two points of intersection where the amplitude of the signal is larger than the moving average. The process function uses two approaches to identify the location of a peak. In the first approach, the maximum value in the marked ROI is taken as the peak position, while in the second approach a univariate spline is used to upsample and interpolate the ROI, and solved for its maximum . A speciSEM imaging of LIG on PI reveals a highly developed porous network a,b. EDS −1, and a very well developed 2D region  is an aggregate measure, calculated as the average beat–beat interval across the entire analyzed signal (segment), resulting in a very robust algorithm. The BR by HeartPy is obtained by exploring the property of the heart that the frequency of the heartbeat is strongly influenced by breathing. There is a strong relationship between the breathing and the intervals between the heartbeats . HeartPy upsamples the list of detected R–R intervals by interpolation, followed by extraction of breathing peaks in the signal .The HeartPy process function also returns 11 more parameters—7 are in the time domain, and 4 are non-linear measurements. The time-domain parameters are computed on the detected and accepted peaks in the R–R intervals. These time-domain indices of HRV quantify the amount of variability in measurements of the inter-beat interval (IBI), which is the time period between successive heartbeats. SDNN is the standard deviation of IBI, measured over normal sinus beats , while SDSD is the related standard deviation of successive R–R interval differences, only representing short-term variability. RMSSD is the root mean square of successive differences between normal heartbeats. It is obtained by calculating and squaring each successive time difference between heartbeats and averaging the result before the square root of the total is produced. Parameters pNN50/pNN20 represent the percentage of adjacent normal sinus beats that differ from each other by more than 50/20 ms . The funBPM—the number of heartbeats per minute, the HR;Breathing rate in Hz, that multiplied by 60 gives the number of breaths per minute, the BR.In a nutshell, the used HeartPy process function produces the following parameters:Time-domain measuresIBI—inter-beat interval, the mean distance of intervals between heartbeats;SDNN—the standard deviation of intervals between heartbeats ;SDSD—the standard deviation of successive differences between adjacent R–R intervals;RMSSD—the root mean square of successive differences between adjacent R–R intervals;PNN50/PNN20—the proportion of differences greater than 50 ms/20 ms;MAD—median absolute deviation of R–R intervals;Non-linear measuresSD1, SD2, S, SD1/SD2—Poincare plot analysisThe experiments were conducted with a graphene sensor attached to the forearm of a still sitting subject. Reference values were measured with a smartphone app that uses the device camera to record HR, applying optical methods. In this study experiments were conducted with 3 different versions of the graphene patch. The presented results are representative examples of signals obtained from the three different patch versions. The results were reproducible in 90% of the cases. The reproducibility and the quality of processed signals mainly depend on the precise positioning and fixture of LIG-based sensors. The tighter the sensor is applied to the cubital vein, the higher the signal quality.First experiment—pure laser-induced graphene on PI;Second experiment—graphene on PI protected by a PDMS layer on top;Third experiment—graphene completely transferred to PDMS.The experiments are classified as follows:The measurements from the first experiment are depicted in The measurements and the analysis for the second experiment are presented in An overview of the 13 extracted parameters from HeartPy analysis toolkit, with the corresponding values for the all three experiments are presented in It is evident that the values of the HRV time-domain and non-linear measurements from the process function mostly have risen as the graphene is further treated. The standard deviations SDNN, SDSD, and the median absolute deviation (MAD), show less reliable data in the second and the third experiment, compared to the first one. Nevertheless, the obtained values for HR and BR are within the acceptable range, as presented in The as-made LIG devices (first experiment) yield signals of the best quality of the three experiments, whereas the LIG devices completely transferred to PDMS (third experiment) yield signals with highest indicators related to noise. Since the Raman spectra measurements have indicated that the quality of the graphene does not decrease with treatment, the origin of the decrease of signal quality in the composite sensor is likely due to imperfect conformity of graphene to the PDMS layer and the increased mechanical stiffness of the samples that include PDMS.The obtained HRV time-domain measures and the Poincare analysis measurements are within the range expected from heartbeat analysis .In the above sections, we have shown that LIG wearable sensors can be used to reliably collect physiological data that can be processed with an easily accessible HeartPy open source toolkit. Wearables can be used to perform several basic functions: sense, analyze, store, transmit, and utilize data . ProcessOn their part, the graphene sensors have shown to be easy to make, inexpensive, and operable on various substrates, including PDMS which is often used as a platform for wearable electronic health patches. LIG heartbeat sensors are very sensitive, and can suffer from noise that is induced when the subject moves. This high-frequency noise needs to be removed in post-processing with functions such as filtering, which introduces extra processing steps.The main contribution of our work is that it demonstrates that a free, open-source toolkit for heartbeat analysis such as HeartPy can be used in conjunction with any heartbeat sensor, including ones in development, such as the one we developed from graphene. This will allow laymen to delve into developing sensor applications, by using, e.g., commercial off-the-shelf piezoelectric thin film sensors in conjunction with HeartPy. The combination of easy to make, inexpensive graphene heartbeat sensors and a widely available open access HeartPy toolkit for data analysis has potential for use by medical researchers or in finalized devices.To conclude, we have demonstrated effective heartbeat sensors that consist of graphene-based sensing elements on several flexible and biocompatible substrates, in conjunction with analysis with the open-source package HeartPy toolkit for Python. The LIG signals were processed with two HeartPy modules—heartpy and heartpy.filtering. The HeartPy package is an excellent tool that was designed mainly for evaluating heart rate signals from PPG data. Nevertheless, we have shown that HeartPy analysis can be conducted on the LIG signals as well, especially in obtaining the HR and BR parameters.This combination of hardware and software presents a relatively low barrier of entry for novice developers of heartbeat sensors, opening a path for widespread experimentation and application. Future work may address the biocompatibility, reusability, and price of the substrates and materials used, in order to bring solutions closer to the market. In addition, future research may exploit the advantages of big data to optimize the use of HeartPy with graphene-based sensors.


Automated question classification worked well for classifying the interviewers’ questions allowing interventions to improve interview quality.Previous research has shown that simulated child sexual abuse (CSA) interview training using avatars paired with feedback and modeling improves interview quality. However, to make this approach scalable, the classification of interviewer questions needs to be automated. We tested an automated question classification system for these avatar interviews while also providing automated interventions (feedback and modeling) to improve interview quality. Forty-two professionals conducted two simulated CSA interviews online and were randomly provided with no intervention, feedback, or modeling after the first interview. Feedback consisted of the outcome of the alleged case and comments on the quality of the interviewer’s questions. Modeling consisted of learning points and videos illustrating good and bad questioning methods. The total percentage of agreement in question coding between human operators and the automated classification was 72% for the main categories (recommended vs. not recommended) and 52% when 11 subcategories were considered. The intervention groups improved from first to second interview while this was not the case in the no intervention group (intervention x time:  Given tIn order to support children’s reporting accuracy, it is important to avoid leading questions as these can lead to inaccurate statements . A studyUnfortunately, most attempts at training interviewers have not been effective. An exception is the work of Michael E. Lamb and colleagues e.g., , showingvia computers or smartphones. This allows both remote and face-to-face training. In addition, so far, Avatar Training is the only training with evidence of the changes in interviewer behavior obtained within the simulations transferring to interviews with actual children both in a mock-victim  and has convincing support for its efficacy, a problem remains. In all of the studies described above, an operator has coded the questions asked by the participants and input this information into the software after which the response algorithms have taken over. In order for the training approach to be truly scalable, it is necessary to automate this process, that is, the operator needs to be replaced by an automated AI classification of the questions asked. Given that the approach to classify questions developed by Most research on training with avatars has so far used university student samples. However, given that professional groups may be expected to perform better given their education and experience  while using automated question classification.We had two hypotheses:We expected it to be possible to classify interviewer questions at a higher than chance level using an automated classifier developed based on training data taken from previous experiments.We expected there to be an interaction between time and intervention as a result of participants in the intervention groups improving from first to second interview while this was not expected to happen in the control group. Planned comparisons were used to investigate in which groups’ improvements occurred and which of the groups differed significantly during the second interview.2.2.1.Mage = 37.1, SD = 7.1) who completed the experiment for a compensation of 2,000 JPY (13 participants were not compensated due to their employer’s regulations prohibiting this). They consisted of clinical psychologists (n = 16), police personnel (n = 8), child guidance office staff (n = 4), hospital workers (n = 4), educational facility workers (n = 3), and others who preferred not to specify their affiliations (n = 7). Twenty-four (57%) had taken training course(s) in child interviewing, and 30 (71%) had experience of interviewing children. They were randomly allocated into either the no intervention (n = 14), the feedback (n = 15) or the modeling group (n = 13). The Research Ethics Committee of the Faculty of Psychology of Meiji Gakuin University (Japan) approved the study (20210031) before the data collection commenced.The participants were 42  professionals (2.2.n = 14), the feedback (n = 15), or the modeling (n = 13) group.The present study employed a 2  x 3  mixed design. Participants were randomly assigned to either the no intervention (2.3.2.3.1.https://youtu.be/hb9knAMzrds). The simulated interview was conducted in a question-and-answer format between the interviewer and the AI Avatar. The interviewer started recording a question by clicking the recording button presented below the avatar and ended the recording by clicking the end recording button. The recorded question was then transcribed using the Google Cloud Speech-to-Text API and sent to the N-gram extraction step. Next, the extracted N-gram was processed by a question classification algorithm based on a machine learning model and the answer selection algorithm then chose and played an avatar answer to the interviewer.To create an avatar that automatically responds to the interviewer, we developed an AI model (hereafter referred to as AI Avatar) that classifies the interviewer’s questions and selects appropriate answers without human assistance. The interview simulation application was built on the Microsoft Azure server and it was available 24/7 during the study period. 2.3.2.Of the total of 16 avatars available in the AI Avatar system, two avatars were allocated to each of the eight patterns of features with different ages (4 or 6 years old), gender (girl or boy), and abuse status (abused or non-abused). Two avatars (a 4-year old non-abused boy and a 6-year old abused girl) were used in the modeling intervention as examples of good and bad interviews and were not used in the simulated interviews.2.3.3.The question classification algorithm of the AI Avatar categorized the transcribed questions into one of 11 possible types as described in previous studies see . The algIn addition to machine learning, a series of additional rules were implemented in the question classification algorithm to improve the accuracy of question classification. For example, questions that could be classified with high accuracy by a specific character pattern, such as a greeting  at the beginning of an interview were processed by a specifically set if-then rule instead of a machine learning model. In addition, there are questions that should have different classification results depending on keywords that suggest abusive experience even if the question otherwise resembles a recommended question type. For example, the question “Tell me about someone hurting you” should be classified as suggestive-open rather than as an invitation-focus question in case the child has not mentioned anything about a painful experience. To obtain an accurate classification for these questions, a list of suggestive keywords was created based on the training data and was used to determine the presence of suggestive keywords in a question. Furthermore, to detect the repetition of questions, a Jaccard coefficient was calculated as the similarity of the N-gram patterns between consecutive questions for determining whether a question was a repetition of the immediately preceding question. The relationship between the two questions can be represented by a 2×2 contingency table. The Jaccard coefficient is a measure of similarity that does not consider cases where attributes (N-gram patterns in the present study) were not present in either of the two sample sets (questions in the present study) as similarity.This characteristic of the Jaccard coefficient fits the data structure of the present study, in which the frequency of each N-gram pattern is 0 for most of the questions contained in the training data. The threshold value of the Jaccard coefficients was set considering the classification accuracy of the repetition of questions in the training data. In addition, to make the avatar’s responses resemble those of a real child, topic keywords  corresponding to predefined responses  of each avatar were tagged on the input question  when contained in a recommended question. AI Avatar had lists of avatar responses with their presentation order number corresponding to each keyword tag. An avatar response with the lowest number among unplayed ones on the response list of a tagged keyword was played to give the interviewer an answer that met the context of the question.2.3.4.The answer selection algorithm implemented in the AI Avatar adopted the method used in previous research on Avatar Training , 2021. IEach avatar had nine relevant details in their memory, and these were provided one at a time. The relevant details were provided in a fixed order with the last four containing the information necessary to reach the correct conclusion about both abused and non-abused cases. Therefore, the difficulty of reaching the correct conclusion was comparable between abused and non-abused cases. Incorrect details (details that contradicted the information held by the avatar) could be created by the interviewer by asking not-recommended questions. The information held by the avatar is predefined as a set of responses, and inconsistencies with them were defined as erroneous information. For example, if the interviewer asked an avatar that does not have information about any abusive experience “Did your dad hurt you?,” and the response algorithm returns “Yes,” an incorrect detail has been created. The answer selection algorithms were developed based on findings of experimental research on children, analyses of records of child interviews in legal settings, and theoretical research on children’s memory. Previous research using these algorithms , 2021 ha2.4.The mean length of experimental sessions was 52.1 min (SD = 14.4). After completing the informed consent and demographic information forms, the participants read short instructions see  on best Participants conducted simulated interviews with randomly selected two avatars out of the 14 available. Before each of the interviews, the participants first read a background scenario  the presence of the abuse (“present” or “absent”), (2) confidence in their assessment on a 6 point scale (“50%: guessing” to “100%: completely sure”), and (3) a description of what, according to them, had happened to the avatar. Unfortunately, the answers to these questions were missing for 27 (64%) participants due to a system error. Therefore, the correctness of conclusions was not included in the statistical analyses.Between the two interviews, participants received either no intervention or either feedback or modeling as a training intervention. As the feedback intervention, the participants were provided two types of feedback after the first interview: (1) feedback consisting of the outcome of the case and (2) feedback on the questions (two recommended questions and two not recommended questions) asked in the interview. For feedback concerning questions, the AI avatar chose questions randomly from the questions recorded during the interview and then provided automated feedback regarding them. The modeling intervention included (1) reading a series of learning points of good and bad questioning methods and (2) watching a total of four 2.5-min videos of good and bad interviews with both an abused and a non-abused avatar. The contents of the modeling intervention were the same as those in 2.5.The fourth and fifth authors of this paper recoded 48%  of completed interviews in the present research. They were also involved in coding questions in all three data sets  x 3  mixed design two-way MANOVA was performed on the number of recommended questions, the number of not recommended questions, the proportion of recommended questions, number of relevant details, and number of incorrect details. Planned comparisons were conducted to identify where exactly the significant differences appeared.3.3.1.p < 0.001, 95% CI . This can be considered moderate agreement , with a Cohen’s kappa (κ) of.42, greement .p < 0.001, 95% CI . This can also be considered moderate agreement  of questions between operators and the AI Avatar was 72% (chance level = 33%), with a Cohen’s kappa (κ) of.49, greement . These r3.2.For the first interview (before the interventions), the overall means were 9.8 (SD = 5.4) for the number of recommended questions, 19.9 (SD = 8.1) for the number of not recommended questions, 34.4 (SD = 17.6) for the proportion of recommended questions, 1.2 (SD = 1.5) for the number of relevant details, and 5.2 (SD = 2.8) for the number of incorrect details.3.3.r = −0.08, p = 0.482.3.4.F  = 9.90, p < 0.001, pη2 = 0.59, 1−β = 1.00, and for the interaction between intervention and time, F  = 2.72, p = 0.007, pη2 = 0.28, 1−β = 0.95, while there was no significant main effect for intervention, F  = 1.01, p = 0.444, pη2 = 0.13, 1−β = 0.49. The significant interaction supports our second hypothesis.Significant effects on the combined dependent variables were found for time, F  = 46.95, p < 0.001, pη2 = 0.55, 1−β = 1.00, the proportion of recommended questions, F  = 24.45, p < 0.001, pη2 = 0.39, 1−β = 1.00, and the number of relevant details, F  = 6.65, p = 0.014, pη2 = 0.15, 1−β = 0.71, but not for the number of not recommended questions, F  = 2.36, p = 0.132, pη2 = 0.06, 1−β = 0.32, and the number of incorrect details, F  = 0.51, p = 0.479, pη2 = 0.01, 1−β = 0.11.Univariate level significant effects of time emerged for the number of recommended questions, F  = 3.50, p = 0.040, pη2 = 0.15, 1−β = 0.62, but not for the number of recommended questions, F  = 1.38, p = 0.264, pη2 = 0.07, 1−β = 0.28, the number of not recommended questions, F  = 0.40, p = 0.674, pη2 = 0.02, 1−β = 0.11, the proportion of recommended questions, F  = 0.71, p = 0.499, pη2 = 0.04, 1−β = 0.16, and the number of incorrect details, F  = 0.30, p = 0.742, pη2 = 0.02, 1−β = 0.09.At a univariate level, significant effect of intervention emerged for the number of relevant details, F  = 9.62, p < 0.001, pη2 = 0.33, 1−β = 0.97, the number of not recommended questions, F  = 12.25, p < 0.001, pη2 = 0.39, 1−β = 0.99, the proportion of recommended questions, F  = 12.10, p < 0.001, pη2 = 0.38, 1−β = 0.99, and the number of incorrect details, F  = 3.97, p = 0.027, pη2 = 0.17, 1−β = 0.68, but not for the number of relevant details, F  = 1.43, p = 0.251, pη2 = 0.07, 1−β = 0.29. These interactions partly support our second hypothesis.Univariate level significant interaction between time and intervention emerged for the number of recommended questions, Significant statistical differences in the planned comparisons between groups for the first and second interviews are also shown in 4.4.1.The present study examined the accuracy of question classification by AI and the effectiveness of CSA interview simulations using AI-based question classification combined with two interventions (feedback and modeling) shown to be effective in previous research. Regarding the question classification, higher-than-chance-level classification accuracy was obtained both for 11 question types and for their two supra-categories (recommended and not recommended), indicating that AI worked well for classifying the interviewers’ questions. Question classification accuracy is an important factor in mimicking the experience of interviewing a real child, where recommended questions elicit informative utterances from a child, while not recommended questions elicit shorter responses sometimes even containing erroneous details. In the AI Avatar interviews, randomly chosen questions were used for the feedback intervention. Therefore, the accuracy of question classification was also required to provide accurate feedback. Importantly, the number of recommended questions significantly increased in the feedback group, suggesting that feedback improved participants’ questioning skills. Given that the discrimination between recommended and not recommended questions is essential for feedback to be effective in enhancing use of recommended questions, the classification accuracy of over 70% for these two categories is an important result of the present study.4.2.Importantly, the simulated interviews with AI Avatars could be used to improve interviewing skills and quality of the information elicited from the avatars when combined with feedback and modeling interventions. The increase in the number of recommended questions shown in the feedback group has also been demonstrated robustly in a mega-analysis  that comModeling resulted in more robust improvements than feedback, including a decrease of not recommended questions and increase of relevant details, with many significant differences compared to the no intervention group. These improvements are consistent with a previous study that examined modeling , which svia voice call  found here is also consistent with ice call , 2021 inice call , additiovia texts. However, in the current study, the AI Avatar chose the questions and provided feedback in text format. A systematic review of the effects of online learning from automated feedback , future improvement is desirable. As a potential approach to improve the question classification accuracy, examining more advanced models such as the Bidirectional Encoder Representations from Transformers BERT;  and compIt should also be noted that only the results of a single interview  could show intervention effects. This may not have been a problem for the modeling intervention as previous research supports the efficacy of a single administration . HoweverAlthough the present research showed robust improvements using modeling, a need to test the transfer of this learning effect is necessary separately for modeling interventions. As described above, the interviewer was free to ask questions without restrictions and avatar responses were chosen from a set of responses with predefined selection probabilities following each question type. This environment, which provides a variety of contexts, encourages interviewers to customize the questions learned from the model flexibly and may develop questioning skills that can be applied to interviews with real children. Given that several factors  were pointed out to maximize transfer regarding behavioral modeling , the traUnfortunately, we could not use conclusion correctness in our analyses. However, this is less of a problem than it may seem given that a correct conclusion is made possible by uncovering the memories of the avatar while not eliciting incorrect information from it. These latter variables were measured and shown to be impacted by the interventions.Moreover, automated and scalable training needs to be investigated using a larger sample. Previous research on interview training has generally only used up to a hundred participants in total due to the need to conduct individual training sessions for each participant. However, the automated training protocol used in the present research can train several participants in parallel by adjusting the available number of accesses and processing capacity of the server accessed by the participants. To provide potential trainees with more training opportunities regardless of time and place, future research is needed to further improve the AI Avatars.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee of the Faculty of Psychology of Meiji Gakuin University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.SH and PS contributed to the conception, design of the study, and wrote the first draft of the manuscript. SH and TI collected the data. SH performed the statistical analyses. All authors contributed to the interpretation of the analyses, revision of the manuscript, edited and gave final approval for publication, and were accountable for this work.This research was supported by grants from HAYAO NAKAYAMA Foundation for Science & Technology and Culture (grant number: R1-A1-29) and the NYU Shanghai Start Up Fund.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.