We demonstrate a simple and highly sensitive optical fiber relative humidity (RH) sensor based on a long-period fiber grating (LPFG) coated with polyethylene glycol (PEG)/polyvinyl alcohol (PVA) composite films. The resonance wavelength of the LPFG is sensitive to environmental humidity due to the change in effective refractive index caused by the strong surface absorption and desorption of the porous PEG/PVA coatings. The sensor is sensitive in a wide range from 50% to 95% RH, with a highest sensitivity of 2.485 nm/%RH in the range 50–75% RH. The proposed RH sensor has the advantages of compact size, good reversibility, and stability, which makes it attractive for high-humidity environments. Relative humidity (RH) is an important magnitude in many fields, such as agricultural production, food storage, pharmaceuticals, health monitoring, and meteorological services. Traditional RH sensors are electric humidity sensors which consist of two main sensing parts: Electric capacity and electric resistance. They usually show hysteresis when placed in a high-humidity environment for a long time and could be affected by electromagnetic interferences. Therefore, much attention has been paid on optical fiber humidity sensors ,2 due toVarious types of optical fiber RH sensors have been proposed with different approaches. Firstly, the amplitude-based techniques detect the change of the optical intensity ,4,5. Sec2O3 nanofilm on the LPFG and packaged it with the polymer material. A temperature sensitivity of up to 0.77 nm/°C was achieved. LPFG is usually insensitive to surrounding RH. Different coating methods have been studied to combine LPFG with RH sensitive films as an RH sensor. Alwis et al. [As an optical fiber sensor, LPFG has been widely used in many areas, for the measurements of SRI  and temps et al.  have pres et al.  have fab2 laser writing technique is a flexible technique for the fabrication of LPFGs thanks to its advantages such as low cost, high grating temperature stability, etc. The method can be applied to practically any fibers, and the writing process can be computer programmed to generate complicated grating profiles without using any masks [2-laser-written LPFGs can be attributed to the residual stress relaxation and glass structure changes due to laser heating. The LPFGs written in a commercial boron-doped fiber can give a clean transmission spectrum, just like UV-written LPFGs [There are different techniques for the fabrication of the LPFGs. The UV light writing technique is the common one adopted to fabricate LPFGs in photosensitive fibers by lots of groups . The CO2ny masks . The mecen LPFGs .2-laser-written LPFG coated with polyethylene glycol (PEG)/PVA composite films. The films are coated on the LPFG by spontaneous evaporation of the PEG/PVA water solution. The coated films improve the interaction between the LPFG and the external humidity environment based on the adsorption and desorption of the PEG/PVA thin films. The coated LPFG humidity sensor shows a high RH sensitivity in a wide range from 50% to 95% RH. The experimental results show good repeatability, reversibility, and stability. The sensor structure has the advantages of small size and simple fabrication, and the thickness of the film can be controlled by changing the concentration of the solution. The PEG and PVA materials have good hydrophilicity and high molecular stability. The polymer composite film has a refractive index close to that of fiber cladding, which makes the sensor highly sensitive to RH changes. Therefore, it is possible to accurately measure the external RH based on the wavelength shift of the LPFG.In this paper, we demonstrate a highly sensitive humidity sensor based on a COAccording to the phase-matching condition, the relationship between the fundamental core mode and the forward propagated cladding modes of LPFG can be expressed as follows :λm= with a period of 300 μm and a length of 1.8 cm. The CO2 laser pulse had a frequency of 5 kHz and a wavelength of 10.6 μm. The spot beam diameter of the CO2 laser focused on the fiber was approximately 30 μm. The output power of the laser was around 0.6 W. The LPFG had a resonance wavelength of ~1560 nm. The mode patterns of the LPFGs were observed using a Charge-coupled Device (CCD) camera  [0,8 cladding mode. The LPFG was fixed in the glass U-groove (50 mm × Φ2 mm × 1 mm) with the heat-curing adhesive. The U-groove was fixed on the slide with the heat-curing adhesive, as shown in The LPFG was written in boron–germanium co-doped single-mode fiber  by CO, Japan) ,21. The PEG : 25322-68-3) and PVA (CAS: 9002-89-5) solid powder reagents were used in the experiments. The organic solvents alcohol was of analytical grade, and deionized water was used throughout the experiments. In our experiment, relative humidity sensing is based on the wavelength shift measurement of the coated LPFG. Therefore, higher resolution can be achieved when the refractive index of the coating materials is near the refractive index of the fiber cladding. The refractive index of hydrophilic materials is generally higher than that of fiber cladding, it is very unusual for the material to have both higher hydrophilicity and lower refractive index. PEG is a polar material and was adopted to reduce the refractive index of the coating PVA films. The refractive index of PEG can be close to the index of the fiber cladding when the external humidity rises to a certain degree. The refractive index of the coating materials decreases due to absorption of water molecules. During the process of humidity change, the film swells and shrinks. As described in , as the According to the theoretical and experimental results, changes in the thickness of the nanofilms coated on the LPFGs could change the refractive index sensitivity of the LPFGs dramatically when the thickness of the nanofilms is a few hundreds of nanometers  ,25. In oBoth PVA and PEG materials are water-soluble polymers and have many OH and O groups bonded in the backbone chain. The 15% (wt./wt.) PEG solution was prepared by dissolving the PEG powder in deionized water. The solution was prepared at room temperature and stirred until it became clear and transparent. Then, the PVA and PEG solutions were mixed together. To obtain a uniform dispersive solution, the PEG/PVA solution required an hour of ultrasonic processing. The PEG/PVA films were coated on the LPFG by evaporation of the water molecules of the PEG/PVA solution in a dry oven. Before the coating process, the LPFG sample in the glass U-groove was cleaned repeatedly with deionized water and alcohol and dried with a vacuum oven. We used a high-precision pipette to inject the mixed solution into the glass U-groove, ensuring that the same volume of mixed solution was injected every time during the process. After the mixture solution was dropped into the glass U-groove, the coated LPFG was placed in the drying oven for about 12 h to ensure enough water evaporation. The PEG/PVA films were then coated on the LPFG surface. Since the glass U-groove filled with the PEG/PVA mixed solution was horizontally placed in the drying oven and the solute was uniformly dispersed in the solution, the evaporation of water molecules was a slowly and evenly changing process. Therefore, we believe that the film was uniformly coated on the surface of the LPFG after the water molecules were completely evaporated. The thickness of the polymer film was ultimately determined by the concentration of the mixed solution . Figure The scanning electron microscopy (SEM) image of the coated LPFG is shown in We measured the refractive index sensitivity of the LPFG before it was coated with polymer, as shown in During the experiment, the temperature of the RH chamber was fixed at 30 °C. The RH was changed from 50% to 95% with a step of 5% RH. When the humidity value is less than 50%, the number of water molecules absorbed by the materials is not large enough to decrease the refractive index of the coating materials to fall below the refractive index of the grating cladding, so the resonance dip of the LPFG will not shift with the change of the humidity. By finding coating materials with a lower refractive index, the dynamic range of the humidity measurements could be increased. The change of humidity was observed by measuring the wavelength shift of the resonance dip when the humidity reached a stable value at the set value. The refractive index change of the coating polymer could be estimated by comparing the wavelength shifts of the bare LPFG  and coatTo investigate the temperature cross-sensitivity of the proposed humidity sensor, the temperature sensitivity of the sensors was measured when the sensors were set at different temperatures from 20 to 50 °C with a step of 5 °C. The RH was fixed at a specific value when the temperature was changed. For the humidity of 66% and 70% RH, the bare LPFG showed a temperature sensitivity of −0.43 nm/°C. When the coated LPFGs were set under different relative humidity environments, the temperature sensitivity of the sensor was much lower than that of the bare LPFGs. Moreover, when the humidity was in the range of 66–70% RH, the temperature cross-sensitivity was almost negligible, as shown in We coated the LPFGs with films consisting of different compositions of PEG/PVA. The experimental results of the humidity sensing measurements suggested that the LPFGs coated with the composite polymers with different combination ratios have different humidity sensitivities.A comparison of wavelength-based fiber optic humidity sensors coated with different types of films is shown in The resolution of the humidity measurements is dependent on the wavelength resolution of the wavelength shift measurements. In our experiment, we used an OSA with a wavelength resolution of 0.02 nm to measure the wavelength shift of LPFGs. The resolutions of the sensors could be improved by using equipment with higher-wavelength resolution. Since the temperature and humidity test chamber  used in our experiment was quite large, it took about 5 min for the instrument itself to rise from one set RH value to another one. We did not have equipment to measure the response time of the RH sensor. The response time of the sensor is related to the film thickness and the coating materials. Our sensor has almost the same film thickness as the one reported in the reference . The senIn summary, a high-sensitivity optical fiber humidity sensor based on PEG/PVA film-coated LPFG was fabricated. Experimental results show that the sensor with a coating thickness of 906.3 μm has a high humidity sensitivity in a wide range of 50–95% RH. The measured results suggest that the proposed sensor has lower temperature cross-sensitivity. The sensor exhibits good linearity, high stability, and reversibility in the wide working range, which makes it a better choice for RH sensing measurement.


Graphene grown directly on germanium is a possible route for the integration of graphene into nanoelectronic devices as well as it is of great interest for materials science. The morphology of the interface between graphene and germanium influences the electronic properties and has not already been completely elucidated at atomic scale. In this work, we investigated the morphology of the single-layer graphene grown on Ge substrates with different crystallographic orientations. We determined the presence of sinusoidal ripples with a single propagation direction, zig-zag, and could arise due to compressive biaxial strain at the interface generated as a result of the opposite polarity of the thermal expansion coefficient of graphene and germanium. Local density of states measurements on the ripples showed a linear dispersion relation with the Dirac point slightly shifted with respect to the Fermi energy indicating that these out-of-plane deformations were n-doped, while the graphene regions between the highs were undoped. These properties include high carrier mobility, transparency, and thickness of only one-atom2. They are important for several applications: data communications3, high-performance light emitting diodes (LEDs)4, low-power photonics5 and ultrafast- and broad-band photo-detection from ultraviolet to the terahertz range6. However, all of these properties were obtained by using exfoliated samples, a process that cannot be scaled for large scale device production. For this purpose, graphene must be defectless and produced over a large area at low cost7.Since the pioneering work of the Manchester’s group8. There are many reports about the synthesis of large area single-layer graphene samples. They represent the state of the art of the synthesis by CVD processes with the production of highly crystallographic oriented single-layer graphene with sample area as large as one square meter with good electronic properties9, such growth techniques apparently overcome the challenges of large area graphene production. However, this method is still based on the use of conductive substrates which turn it non-viable to be used for the fabrication of electronic devices. In this way, to obtain graphene by CVD, the film must be transferred to a dielectric or semiconducting substrate. The most commonly used technique is the transfer of graphene by applying a sacrificial layer of polymer over the sample and removing the copper foil using a suitable acid solution10. The transfer process usually leaves some polymeric residues that are responsible for the deterioration of graphene’s transport properties11. Also, since it is a delicate procedure it can generate defects in the transferred layer. Besides, it was demonstrated that metallic contamination levels on the graphene layer are much higher than the accepted in the fabrication of electronic devices12. One alternative route is the direct synthesis of single-layer graphene on dielectric or semiconducting substrates14.Today, chemical vapour deposition (CVD) is the main growth technique used and is the biggest bet for achieving this goal15. The growth of single-layer graphene on Ge(100) substrate was successfully achieved by both atmospheric- and low-pressure CVD 24. Although this orientation (100) is really interesting for implementation into Si technology, it undergoes reconstructions into the [107] facet in both the graphene growth and/or after post-growth annealing23, which would limit its use in possible applications. Nevertheless, there are several works about other atomically flat orientations such as (110) and (111) that arise as models to future applications as these surface orientations are expected to facilitate the high electron and hole mobility for n- and p- field effect transistors (FETs), respectively25. In the last few years, the study of the graphene/Ge interface was focused on the surface reconstruction of germanium26, the interaction between graphene and germanium substrate27, the effect of doping of the substrate on graphene growth28, and graphene as a protective layer of germanium substrate29. However, a more complete investigation that also considers the role of crystallographic orientation and the morphology of graphene is still needed.Germanium is an intrinsic semiconductor with higher carrier mobility than silicon, and its integration with graphene sheets is of high interest for both fundamental materials science and electronic device applications32. Ripples in graphene films have been observed and studied on both freestanding and supported single-layer graphene35. These results revealed that the interaction between graphene and the substrate is an important factor to be taken into consideration for the formation of those surface corrugations. In particular, the opposite polarity of the thermal expansion coefficient - negative for graphene and positive for metals – was suggested to be responsible for the wrinkles formation by CVD36. Epitaxial graphene grown on silicon carbide (SiC) shows the presence of ridges at the surface due to the relaxation of compressive stress generated during the cooling step of the synthesis because of the negative thermal expansion coefficient of graphene37.One important aspect is the morphological and electronic characterizations at nanoscale of the ripples formed in the graphene grown by CVD on Ge substrates, since they can have a significant influence on the electronic and magnetic properties of the graphene filmIn this work, we have studied the morphology and electronic structure of graphene films on Ge substrates. In order to do that, we have synthesized single-layer graphene by CVD using methane as precursor on germanium (undoped-Ge(110) and p-type doped with Ga Ge(100)) substrates. The films were characterized by Raman spectroscopy and mapping, while the surface topology was investigated in atomic-scale by scanning tunneling microscopy (STM) in ultra-high vacuum at room temperature conditions as well as by atomic force microscopy (AFM). The local electronic changes were determined by scanning tunneling spectroscopy (STS). Our Raman results revealed that the graphene layer was submitted to a biaxial compressive strain. The morphology of the graphene films was characterized by the presence of sinusoidal ripples with a single propagation direction (zig-zag).22. More information about preparation and growth of the samples such as temperature, environment, CH4:H2 flow ratios and time are in the method section and Supplementary Information, Fig. To study the morphology and electronic structure of graphene films, two sets of samples were synthesized by CVD. The first one was obtained on Ge(110) surface and the second one was prepared on Ge(100) surface as described elsewhere−1), G- (~1600 cm−1) and D- (~1350 cm−1) bands. In these spectra the O2 (~1557 cm−1)- and N2 (~2329 cm−1) peaks due to atmospheric oxygen and nitrogen were fitted and removed  and (33 ± 4) cm−1 for the growth on Ge(100), and the 2D- and G-bands intensity ratio (I2D/IG ≥ 2.0), see Figs D/IG ratios are around 0.20.We could determine that the graphene films were single-layer, by taking into account both the symmetry and Full Width at Half Maximum (FWHM) of the 2D-band fitted with a single Lorentzian curve with FWHM (39 ± 6) cmThe STM images shown in Figs The deformations observed in the Figs D) shifted around 50 meV, standard deviation of 20 meV, to the left from the Fermi level (EF), indicating that, at the top of the ripples, graphene films are slightly n-type doped.Local density of states (LDOS) of graphene/Ge interface was studied by STS at room temperature on every sample. Figures −1graphene (~3.9 nm−1) which is the lattice constant of graphene and second one is λ−1ripples (~0.27 nm−1) which is the wavenumber of the ripples. The two-dimensional-FFT of the same image is shown in Fig. The morphology of the ripples was determined through STM images and by means of analysis of profiles of the Fig. 18. The roughness of this sample was determined over an area of 20 × 20 µm2  and its value was of (0.8 ± 0.2) nm, i.e., the graphene/germanium interface is flat throughout the scanned area. Similar roughness was obtained from the surface of Ge (110) submitted to the same thermal cycle used for graphene growth, as can be seen in the Supplementary Information, Fig. 23. The formation of the Ge(107) facets was determined by different microscopy techniques and the roughness values are in the order of ~70 nm for bare Ge(100) and ~30 nm for graphene/Ge(100)22. On the other hand, for graphene grown on Ge(110) there is no such rearrangement (facet) of the Ge surface, so the system is quite flat.Although the synthesis procedure was performed close to the melting point of germanium, it was possible to obtain uniform and high-quality graphene films. Figure 22, we can estimate the type of strain  of the sample. The neutrality point (zero point) was taken from literature38, and corresponds to the expected 2D and G positions for suspended freestanding single-layer graphene. The experimental data are positioned in the region of neutrality of charge between the black and blue lines. We can say that the strain (ε) was compressive biaxial in the range between −0.2% and −0.6% for graphene but centred at −0.4%. These results were similar to those reported in20 for Ge (110). The origin of this compressive strain in graphene can be attributed to the opposite polarity of the thermal expansion coefficients between graphene (−8 × 10−6 K−1) and germanium (6 × 10−6 K−1) in the cooling step of the synthesis by CVD.Raman spectroscopy may be used to provide an estimate of the strain in graphene by analysing the positions of the 2D and G peaks. Figure F and ED coincide in the same energetic point . This result was different of what was observed in the Fig. Knowing that out-of-plane deformations present in the graphene films can be enough to cause distortions of the atomic bonds on ripples and promote displacement in ‘π’ orbitals, we analysed the Fig. 39. Our STS results showed that the ripples on graphene layer are slightly n-doped, while the valleys between two of them were undoped, in good agreement with the theoretical conclusions. These results can be compared with STM/STS results obtained from CVD graphene on copper, where the authors suggested that the constraint imposed at the boundaries between the intrinsic and the n-doped regions plays a vital role in creating these 1D ripples40. In this case, the n-doped region is generated by the introduction of nitrogen atoms, creating a defective zone that relaxes the ripples while the undoped region remained strained and with ripples near the boundaries. Recently, it was suggested that applied strain along the zig-zag direction results in the band gap opening in graphene32. However, since our STS results were obtained at room temperature, in this experiment we were not able to determine the opening of the band gap if it is below 50 meV.Recently, theoretical calculations using a molecular dynamics approach to model the formation of ripples in graphene have shown that sinusoidal ripples appear to arise in the presence of compressive biaxial strain, as indicated by Raman results, and the presence of these ripples induces spontaneous n-dopingIn this work, we have performed surface characterization of single-layer graphene grown on Ge substrates with orientations (110) and (100) by CVD. STM images clearly show the presence of sinusoidal ripples with ~3.6 nm wavelength and ~0.3 nm in height, independently of the crystallographic orientation of the substrates. Those deformations have the same direction of propagation (wave front), the zigzag direction, and are aligned with the armchair direction independently of the randomness of its distribution. We suggested that these sinusoidal ripples appear to arise in the presence of compressive biaxial strain, as revealed by Raman results, due to thermal expansion coefficients with different polarities of graphene and germanium substrates.The STS measurement results on the ripples showed a linear dispersion relation with the Dirac point, slightly shifted with respect to the Fermi energy, indicating that these out-of-plane deformations were n-doped, while the graphene regions between the highs were undoped. These periodic alternances between undoped and n-doped regions throughout the entire surface of graphene films must be taken into account in the future development of electronic devices.2) from 2.5 cm and 5.1 cm wafers, respectively , and cleaned with acetone, isopropyl alcohol, deionized water, respectively, and finally dried under pure N2 flux. After that, the Ge substrates were placed into the reactor chamber that was evacuated down to a base pressure of approximately 1.0 mPa. The growth environment was a mixture of Argon  and Hydrogen  gases at 100 and 50 standard cubic centimetres per minute (sccm), respectively. Two sets of samples were prepared as summarized in the Supporting Information and following the step-by-step from22. Once the deposition temperature and pressure were reached, methane gas was introduced in the reactor, and its flux was of 1.5 and 0.5sccm for Ge(110) and Ge(100) respectively. The synthesis was carried out at 910 °C with 5 × 104 Pa for both substrates, but the times were 60 and 120 minutes for Ge(110) and Ge(100), respectively. After the graphene deposition, the system was cooled to room temperature at a rate of 50 °C/s, under the same atmosphere used during the synthesis.Graphene films were synthesized on undoped Ge (110) and p-doped Ge(100) substrates by CVD at semi-atmospheric pressures. The substrates were cut into small pieces  chamber. These measurements were simultaneously performed at room temperature using an electrochemically etched W tip.Raman spectroscopy and AFM were performed using the integrated system from NT-MDT. Raman measurements used a micro-Raman spectrometer (NTEGRA SPECTRA), equipped with a CCD detector and a solid-state laser, which produced excitation energy of 2.62 eV. The Raman maps were taken over a 30 × 30 µmSupplementary Information


Objective: To investigate the association between tumor PD-L1 expression and patient survival to determine whether PD-L1 represents an independent prognostic feature for patients with non-metastatic clear cell renal cell carcinoma (RCC).Patients and Methods: The tissue bank of the Fudan University Shanghai Cancer Center was queried to identity tissue samples of patients treated with radical nephrectomy, for non-metastatic sporadic clear cell RCC (ccRCC) between 2008 and 2015. Real-time polymerase chain reaction and immunohistochemistry staining was performed to detect the expression level of PD-L1 in paired cancer tissue and paracancerous tissue.Results: Three-hundred-and-thirty patients were enrolled in this study, with a mean age of 55.0 years at surgery and a mean tumor size of 5.2 cm. Two-hundred-and-forty-two (73.3%) and 88 (26.7%) patients showed a high and low expression of PD-L1 mRNA, respectively, while 254 patients had positive PD-L1 immunohistochemistry staining. Two-hundred-and-ninety-two patients had consistent results for mRNA and the PD-L1 protein based on these different detection methods. Patients with high PD-L1 expression were more likely to exhibit adverse pathologic features including an advanced T stage (P = 0.002) and lymph node metastasis (P = 0.044). The Kaplan–Meier curves of PFS and OS stratified by PD-L1 expression had a statistically significant difference. PD-L1 expression maintained a significant predictive role for PFS and OS in the multivariate cox model.Conclusions: Our data suggests that PD-L1 correlates with prognosis in RCC and targeting the PD-1/PD-L1 pathway should be considered in the treatment of RCC patients. Immunotherapy plays an important role in the treatment of renal cell carcinoma (RCC) , 2. CytoProgrammed death-1(PD-1) and its ligand programmed death-ligand 1 (PD-L1) have recently become research hotspots in immunotherapy. New drugs targeting the PD-1/PD-L1 axis were reported to give very promising clinical responses in patients with various types of cancer –6. EarliThe tissue bank of Fudan University Shanghai Cancer Center (FUSCC) was queried to identity tissue samples of patients treated with radical nephrectomy for sporadic non-metastatic ccRCC, between 2008 and 2015. The institutional ethical committee has approved the informed consent provide for collecting the tissue into tissue bank. Tumor tissue samples paired with adjacent normal tissues were collected at the time of surgery and preserved under appropriate conditions only after informed consent was signed by individual patients. Clinical and pathological information were collected, including age at surgery, gender, clinical manifestation, laterality, tumor size, and the International Society for Urologic Pathology (ISUP) grade and tumor-node-metastasis (TNM) stage groupings , 15. CliPD-L1 were 5′-TGGCATTTGCTGAACGCATTT-3′ and 5′-TGCAGCCAGGTCTAATTGTTTT-3′. For the β-actin gene, the sense and antisense primers were 5′-GCCGACAGGATGCAGAAGGAGATCA-3′ and 5′-AAGCATTTGCGGTGGACGATGGA-3′, respectively. For each assay, a total of 10 μl reaction mixture was prepared using SYBR Green PCR master mix (Applied Biosystems) according to the manufacturer's instructions. Specific cycling conditions for β-action and PD-L1 were carried out as follows: denaturation at 95°C for 3 min, followed by 45 cycles of denaturation at 95°C for 20 s, annealing at 60°C for 20 s, extension at 68°C for 20 s, and measurement at 80°C for 20 s, followed by a final extension at 72°C for 5 min. To confirm the specificity of amplification, melting curve analyses were performed and PCR products were sequenced and resolved in a 1% agarose gel. The PD-L1 mRNA expression was represented as ΔCt = Ct(PD-L1) – ΔCt(β-actin), and the relative expression of PD-L1 in ccRCC was measured using the ratio of PD-L1 expression in ccRCC/matched normal tissues. “Low PD-L1 expression” denotes the ratio of PD-L1 mRNA expression in ccRCC/matched normal tissues of <1.5; “high PD-L1 expression” denotes the ratio of PD-L1 mRNA expression in ccRCC/matched normal tissues of >1.5.Total RNA was isolated using the TRIzol reagent , followed by reverse-transcribed reaction using the SuperScript First Strand cDNA system . Real-time PCR was performed using the ABI Prism 7900 sequence detection system (Applied Biosystems) with β-actin as an internal reference. The forward and reverse primers for 2O2 for 15 min at 37°C, and non-specific binding was blocked by incubation in 10% normal goat serum for 60 min at room temperature. For antigen retrieval, sections were autoclaved in 0.01 M sodium citrate buffer, pH 6.0 (at 20 psi) for 10 min. Tissue sections were then incubated overnight with primary antibody at 4°C. Chromogenic antigen detection was carried out using a peroxidase-conjugated secondary antibody (60 min incubation) and DAB reagents . Tissue sections were counterstained with Meyer's hematoxylin . All sections were examined and scored by two experienced pathologists blinded to all clinical data in an open discussion. The percentages of tumor cells with positive PD-L1 staining were quantified. In accordance with previous studies, PD-L1 tumor positivity was defined as membrane staining of ≥5% tumor cells  and the Envision detection kit . Tissue sections (4 μm thick) obtained from archived formalin-fixed, paraffin-embedded tissue blocks were deparaffinized in a xylene series and rehydrated with a graded ethanol series. Thereafter, endogenous peroxidase was quenched by incubation in 0.3% Hor cells , 18.PD-L1 mRNA expression or IHC expression with clinical and pathological features were evaluated using the Student's t-test for continuous variables and the χ2-test for categorical variables. Progression-free survival (PFS) and over survival (OS) was measured as the time from the date of surgery to the corresponding endpoints or until the most recent follow-up visit for censored (living) patients. Kaplan–Meier method was used for estimating PFS and OS, and a Log-rank test was taken to compare different Kaplan–Meier curves. Clinical outcomes were evaluated using Cox proportional hazards regression models univariately and multivariately, between groups with different levels of PD-L1 mRNA or IHC expression. The level of significance was defined as p < 0.05. SPSS software V13.0  was used for all statistical analyses.The associations of In total, 330 patients were enrolled in this study, with a mean age of 55.0 years at surgery and a mean tumor size of 5.2 cm. Males (66.4%) were approximately twice as many as females (33.6%). The demographics and disease characteristics were summarized in PD-L1 mRNA, respectively, while 254 patients showed positive PD-L1 IHC staining. Different methods might have controversial results for detection of PD-L1. As shown in PD-L1 mRNA expression had positive PD-L1 IHC expression, however, there were still some cases with negative PD-L1 IHC expression. On the other hand, most cases with low PD-L1 mRNA expression had negative PD-L1 IHC expression, while a small percentage of cases had positive PD-L1 IHC expression. The subgroup of 292 cases was then established which had consistent results for PD-L1 mRNA and IHC expression.Among 330 ccRCC patients, 242 (73.3%) and 88 (26.7%) showed a high and low expression of PD-L1mRNA or IHC expression is also shown in PD-L1mRNA expression were more likely to exhibit advanced pathologic features including poorer tumor grade (P = 0.010), advanced T stage (P = 0.034), and lymph node metastasis (P = 0.005). There was no statistically significant difference in age and sex among patients with different PD-L1 mRNA expression . Patients with positive PD-L1 IHC expression had almost the same clinical and pathologic features as patients with high PD-L1 mRNA expression, except for tumor size.The comparison of the clinical and pathologic features correlating with P < 0.001). The median PFS and OS for patients with low PD-L1 mRNA expression was longer than the subgroups with high PD-L1 mRNA expression (The Kaplan–Meier curves of PFS and OS stratified by PD-L1 expression are shown in pression , while tpression .PD-L1 mRNA expression may be associated with lower PFS. In the multivariate analysis, most of these factors remained statistically significant except for the age at surgery. PD-L1 mRNA expression. Multivariate analysis revealed that most of these factors maintained a significant predictive role for shorter OS. Notably, our study indicates that the expression stratification of PD-L1 based upon qRT-PCT or IHC staining may yield a different prognostic value for OS, that is, high PD-L1 mRNA expression was an independent prognostic factor for OS, however, positive PD-L1 IHC expression nearly lost significance after adjusting for covariants.The results of univariate and multivariate Cox regression analyses for PFS were described in PD-L1 is a cell surface glycoprotein, which was found in 1999 and belonged to the B7 family of T cell costimulatory molecules , 21. OveRCC is an immunogenic tumor associated with high levels of infiltrating immune cells, mainly mononuclear cells. These mononuclear cells are predominantly composed of T cells. As previously reported, PD-L1 expressed in tumor cells of RCC, influencing the biological behavior and correlating independently with poor cancer-specific survival rates, while PD-1 expressed in infiltrating mononuclear immune cells within RCC , 11. MosThis study included 330 cases of non-metastatic Chinese ccRCC patients and explored both qRT-PCR, which is highly sensitive and objective, and IHC to detect the PD-L1 expression in paired tumor tissues and adjacent normal tissues. Our results revealed that although high PD-L1 mRNA expression was an independent prognostic factor for OS, positive PD-L1 IHC expression nearly lost significance after adjusting for covariants, which indicated that IHC staining alone was not accurate enough to define the PD-L1expression level. To overcome the challenge of normalizing the expression levels, we used absolute values of cancerous tissue normalized to the values of corresponding paracancerous tissue to establish a measurable method of PD-L1 expression. With this approach we defined a 2-tier system for expression evaluation. The results of qRT-PCR in the present study should be more quantitative and reliable. It may be more persuasive to provide results based on two detection methods used together.Our results showed that the high expression of PD-L1 in tumor cells correlated to poorer pathological features, including a greater tumor size, high nuclear grade, lymph node metastasis, and more advanced stages. In addition, patients with high PD-L1 expression had a significantly increased risk of disease-specific mortality and overall mortality. Our work provides further evidence in the Chinese population that the functional level of the PD-L1/PD-1 pathway weakens the body's immune surveillance, thereby contributing to tumor progression.However, the present study also has some limitations. First, the expression of PD-L1 is not uniform even in the primary tumor. Near the margin of the tumor, the PD-L1 expression was induced by external factors produced by juxtaposed T lymphocytes. The PD-L1 expression of other parts is more likely to be affected by intrinsic factors and the molecular mechanism. The expression pattern of PD-L1 in different tumor locations could not be distinguished by qRT-PCR in the present study. Second, due to the heterogeneity of the tumor, the expressions of PD-L1 in primary and metastatic tumor cells were not the same. In some cases, the expression of PD-L1 in the primary tumor is negative, while it is positive in the metastatic tumor , 26. ThaOur data thus suggests that PD-L1 correlated with the prognosis in ccRCC and targeting the PD-1/PD-L1 pathway may be therapeutically efficacious and should thus be considered in the treatment of RCC patients.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.This study was carried out in accordance with the recommendations of the local ethics committee with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Fudan University Shanghai Cancer Center ethics committee.WX, YQ, and JZ conceived the idea. YQ, HZ, GS, BD, YZ, and YS provided patient data and material. YQ, FX, YPZ, and XZ contributed to the sample-preparations. FX, XZ, and SZ carried out the laboratory analyses. WX and YQ performed the statistics. WX, FX, and JZ analyzed and interpreted the data. YQ, JZ, and DY were involved in the planning and supervising. WX, FX, and YQ drafted the manuscript and designed the figures. WX, FX, YQ, and DY wrote the manuscript, with contributions from the other authors. All authors read and approved the final manuscript.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.


Transparent conducting electrodes (TCEs) are the most important key component in photovoltaic and display technology. In particular, graphene has been considered as a viable substitute for indium tin oxide (ITO) due to its optical transparency, excellent electrical conductivity, and chemical stability. The outstanding mechanical strength of graphene also provides an opportunity to apply it as a flexible electrode in wearable electronic devices. At the early stage of the development, TCE films that were produced only with graphene or graphene oxide (GO) were mainly reported. However, since then, the hybrid structure of graphene or GO mixed with other TCE materials has been investigated to further improve TCE performance by complementing the shortcomings of each material. This review provides a summary of the fabrication technology and the performance of various TCE films prepared with graphene-related materials, including graphene that is grown by chemical vapor deposition (CVD) and GO or reduced GO (rGO) dispersed solution and their composite with other TCE materials, such as carbon nanotubes, metal nanowires, and other conductive organic/inorganic material. Finally, several representative applications of the graphene-based TCE films are introduced, including solar cells, organic light-emitting diodes (OLEDs), and electrochromic devices. Anotherf 550 nm . The sta−1, requiring further improvement by additional acid treatment or doping. The method of LB assembly was also employed to deposit GO and SWNT in a layer-by-layer manner a. Zheng −1 at 90% transmittance cyclohexane (TAPC) (30 nm)/1,4,5,8,9,11-hexaazatriphenylene hexacarbonitrile (HAT-CN)(10 nm)/TAPC(30 nm)/HAT-CN(10 nm)/TAPC(30nm)/4,40,400-tri(N-carbazolyl)triphenylamine:iridium(III)bis picolinate (TCTA:FIrpic)(5 nm)/2,6-bis[30-(N-carbazole)phenyl] pyridine:iridium (III) bis picolinate (DCzPPy:FIrpic) (5 nm)/1,3-bisbenzene(BmPyPB) (40 nm)/lithium fluoride (LiF) (1 nm)/aluminum (Al) (100 nm), showed an electron quantum efficiency (EQE) of 15.6% and a power efficiency (PE) of 24.1 lm/W, comparable to the EQE of 18.5% and PE of 28.5 lm/W obtained with an ITO-based OLED [At first, a basic OLED structure of anode/PEDOT:PSS/lectrode . The grased OLED . Flexible OLEDs that were fabricated with graphene-based anode shown in Electrochromic devices are widely used in display devices, including smart windows and mirrors, for improving indoor energy efficiency or personal visual comfort. Recently, rigid electrochromic devices have advanced, and they now offer flexibility, stretchability, and foldability for deformable devices  119,133,141. PolSmart windows that are based on electrochromic devices were demonstrated by Kim et al., who investigated the electrochemical and electrical characteristics of the PEDOT:PSS polymer-based electrochromic devices s of the different number of graphene layers used as electrodes . The fou3-CH3NO2 and HNO3 to obtain a low sheet resistance, was provided for flexible heaters, and it was mechanically stable after bending 1000 times with 1.1% strain [High performance, transparent, and flexible heaters have been fabricated using graphene-based electrothermal films that are suitable for automobile defogging or deicing systems and heatable smart windows, as shown in % strain . AdditioA new strategy of reduced large-size graphene oxide (rLGO)/silver nanowirehybrid film has been reported to design high-performance transparent film heaters. The thin rLGO provided a protective effect for the silver nanowire network against oxidation as well as the low sheet resistance of rLGO .New TCE materials, including carbon nanotubes, metal grids and nanowires, conductive polymers, and recently graphene, have appeared over the past decades in many fields of application. In particular, these new materials offer prospective advantages of flexibility, bendability, and even stretchability, enabling them to be applied as TCEs in wearable optoelectronic devices and displays. Among them, graphene has attracted special attention due to its superior electrical conductivity and optical transmission when compared to other TCE materials. Additionally, the excellent moisture barrier and mechanical flexibility of graphene allows it to be hybridized with other TCE materials to further improve the properties of TCE films.In the past decades, a number of methods for synthesizing high-quality graphene at low cost have been extensively studied, and the development of a large-area synthesis and transfer method using a roll-to-roll process has been a viable step in the practical application of graphene TCEs. Despite the tremendous advances in the graphene synthesis process and quality improvement, TCE films that are based on graphene still have issues that need to be improved, such as poor adhesion to substrates, low abrasion, and poor electrical conductivity as compared to conventional ITO.


The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates’ condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties. The fast and accurate determination of molecular properties is particularly crucial in drug discovery. Here, the authors employ supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten classes of drug candidates and predict several condensed-phase properties. However, differential mobility spectrometry (DMS) has recently shown promise in this regard4. Most commonly employed as a narrow-pass filter to separate ions from chemical noise6, DMS takes advantage of an oscillating asymmetric electric field to drive rapid cycles of microsolvation and evaporation in environments that are seeded with low partial pressures of solvent vapor8. In examining how ion microsolvation properties are affected by specific structural attributes, the influence of electronic and resonance effects9, the steric hindrance of charge sites11, and the influence of intramolecular hydrogen bonding have been observed12. Thus, a picture is now emerging which suggests that the dynamic DMS environment facilitates swift and statistical sampling of an analyte’s potential energy surface. Consequently, strong correlations may be observed between an analyte’s clustering behavior within the DMS instrument and molecular properties in the condensed phase, such as cell permeability, which are partially dependent on the interaction potential between the analyte and its condensed phase environment15.Although mass spectrometry-based techniques have long found utility for fast and sensitive compound detection and for gas-phase properties measurements , the efficacy of employing gas-phase measurements to predict condensed phase properties remains a contested subject17. Thus, from the perspective of rational drug design, a fast and sensitive technique that can aid in predicting molecular properties is highly desirable. At present, there are several benchtop in vitro techniques available to measure cellular permeability 18 and other physicochemical properties that serve as preliminary appraisals of in vivo behavior 19. However, it is often challenging to discriminate between closely related structural analogues using the current state-of-the-art techniques21. Moreover, while exceptional progress has been made in refining these methods24, they still require considerable resources and often experience issues with reproducibility26. As a result, efforts to develop complementary techniques for assaying molecular properties are ongoing. A recent example of such a development is the experimental polar surface area (EPSA) technique28, which employs supercritical CO2 fluid chromatography to assess the partitioning of an analyte between a polar stationary phase and a non-polar mobile phase. EPSA has been demonstrated as a highly efficient means for indirect detection of intramolecular hydrogen bonding27. Such intramolecular hydrogen bonds (IMHBs) are common features of organic molecules, playing a critical role in defining the conformations of small molecules29, as well as the secondary structures of peptides and proteins30. With regard to drug design, introducing IMHBs has been put forward as a strategy to mask regions of higher polarity that might otherwise reduce cell permeability28. However, prior to the advent of the EPSA technique, identification of IMHBs, by either computation or experimentation, was challenging27. Thus, recent EPSA determinations of IMHB-containing molecules are quickly becoming a gold standard by which to test the ability of DMS to discern such features32.During the early stages of drug development, it is the goal of medicinal chemists to relate the cellular availability of a drug candidate to its physicochemical properties and chemical structure33, making them good candidates for study via DMS. As added functional groups on drug molecules, CRGs react with specific nucleophilic residues in proteins, serving to silence enzymatic activity until protein re-synthesis can occur37. To date, dozens of CRG-modified drugs have been approved for treatment of hyperlipidemia, infectious diseases, and cancer37. These drugs contain electrophilic moieties such as carbamates, acetates, β-lactones, β-lactams, and acrylamides, and several reviews have described the efforts to develop covalent inhibitor therapeutics37, including attempts to address-negative side-effects of these functional groups40. Given this considerable interest, there is an impetus towards a more detailed understanding of physicochemical and pharmacological properties of molecules of this type.Unlike the majority of the molecules studied here, whose passive and fundamental physicochemical properties were our focus, the CRG species key attribute lies in understanding the reactivity trends of these molecules. These chemical reactivities are strongly influenced by their interaction potentials41. Specifically, the more strongly a molecule interacts with solvent molecules purposefully added inside the DMS cell, the more negative the CV value at a given SV. This CV/SV pairing encodes the molecule’s interaction potential with the solvent system on a microscopic scale. However, variations in dynamic clustering behavior  across different molecule types/chemistries can render such a comparison ineffective. By taking a global view of the complete DMS behavior using a dispersion plot10, one can qualitatively determine whether the molecule of interest exhibits strong-clustering (type A), weak-clustering (type B), or hard sphere collision (type C) interactions with the gaseous environment of the DMS cell10. Note, that in some cases the species of interest does not strictly follow one of these three common behavior types  and optimal compensation voltage (CV) for transmission of the molecule through the DMS cellvide infra)4. We have also undertaken a parallel study of 24 acrylamide-based covalent reactive groups (CRGs) and have likewise included these species in a global analysis for all 89 small molecule drug candidates. We demonstrate that, with the aid of supervised machine learning (ML), the full range of DMS dispersion plot SV/CV data can be used to provide a quantitative means of accurately assessing a variety of molecular properties which are related to an analyte’s interaction potential.To ensure that the DMS-based assessment of molecular physicochemical properties is applicable across different chemistries, it is necessary to assess the properties of several molecular topologies jointly. To this end, we have combined the experimental results of our previous studies on ten 2-methylquinoline derivatives and twenty-two 2-methylquinolin-8-ol derivatives with the results from thirty-three IMHB-containing drug candidates  are shown in Fig. 2210. The IMHB-containing molecules are classified based on the IMHB data mining study of the Cambridge Structural Database published by Kuhn et al.29. Here, four of the most common IMHB topologies were investigated, which we label as topologies A–D10. The numeric portion of the label  methanol vapor at DMS cell temperatures of 150 °C and 300 °C. The search for appropriate DMS conditions to effect isomer separation was a critical aspect of this study as isomer separation is indicative of differences in the isomers’ relative interaction potentials. DMS experiments conducted using a pure N2 environment, or a N2 environment seeded with water, yielded hard sphere (Type C) behavior for all species10, and hence no separation between isomers. A N2 environment that was seeded with isopropyl alcohol vapor, on the other hand, yielded only strong-clustering (type A) behavior for all isomers10. Consequently, methanol—a protic solvent having properties intermediate of the previous two solvents tested—was chosen as the chemical modifier. Even so, at a temperature of 150 °C three sets of isomers (including set A1) could not be unambiguously separated into their isomeric components. However, at elevated temperatures the Gibbs’ energy of binding for the ion-solvent clusters is reduced to the point where one can clearly distinguish between molecules exhibiting IMHBs and those not exhibiting IMHBs with DMS , the site of protonation was comparatively distal to the site of IMHB formation10. In these instances, as one might expect, the DMS behaviors of isomers were relatively similar and separation was more difficult to affect. We hypothesize that, as the site of protonation (and hence solvent interaction) is relatively distant from the site of IMHB formation in these species, it is necessary that the degree of solvent clustering for these molecules be relatively large to ensure that the hydrogen-bonding network of the protic solvent can interact with the site of IMHB formation. Experimentally, there are two ways to test this hypothesis; one can either vary the partial pressure of solvent vapor to influence the size of ion-solvent clusters formed during the low-field portion of the SV duty cycle, or one can vary the temperature of the gas to achieve this same outcome . Figure 2 environment that was modified with 1.5% methanol vapor. At 300 °C, where the size of the ion-solvent clusters is expected to be relatively small, both isomers exhibit nearly identical dispersion plots. However, as the temperature of the collision gas is reduced, and the average size of the solvent clusters increases, we observed improved separation between the two isomeric species. Note also that the isomer which exhibits the IMHB (C3a) displays a more weakly clustering behavior than the non-IMHB isomer (C3b).In all 16 IMHB sets studied, the isomer exhibiting an IMHB displayed DMS behavior indicating weaker ion-solvent interactions than did the non-IMHB-containing analogue(s). This is worth additional consideration as our study probes protonated forms of the targeted molecules, whereas the EPSA technique which identified the IMHB-containing molecules is presumed to probe the neutral compounds33. These molecules can be classified into four different molecular topologies: (I) unsubstituted aromatic derivatives, (II) substituted aromatic derivatives, (III) substituted derivatives which do not contain heteroatoms, and (IV) substituted derivatives which contain heteroatoms. A more detailed description of these four structural motifs and the DFT-optimized geometries of all 24 derivatives are available as Supplementary Data10. In the case of the acrylamide CRGs, a N2 environment  that was seeded with 1.5% isopropyl alcohol vapor provided the widest distribution of analyte behavior . The dispersion plots for these species and all others included in this study are provided as Supplementary Figures 4610.In addition to the 33 IMHB-containing molecules, 24 acrylamide CRGs were studied, which were selected from an earlier examination of the intrinsic reactivities of these species42. Model selection is based on finding a compromise between bias and variance while avoiding over-fitting. Briefly, bias in a ML model can result from preselecting a functional description for your data, while high variance in a ML model will yield very different results depending on how data are partitioned into training and test sets. Over-fitting a model leads to poor applicability outside the range of molecules used for training. Decision Tree43-based models are unbiased learners and in particular Random Forest Regression44 yields results with low variance and shows a low susceptibility to over-fitting. Further details of our ML study are provided in Supplementary Method10.Although it is satisfying that DMS measurements can, for example, discriminate between isomeric species that do or do not contain IMHBs, the ultimate goal of this work is to demonstrate the use of DMS as a tool for quantitatively determining condensed phase physicochemical properties of molecules. To this end, we have introduced supervised machine learning (ML) as a means of inferring the relationships/correlations between molecular properties and DMS behavior. Preliminary efforts involved a survey of a few ML models45. As other types of DMS behavior do not show such a turn-around point, generalizing these findings across all molecular types is not trivial. To remedy this shortcoming, machine learning was used to infer relationships between physicochemical properties and the entire DMS SV/CV datasets. (i.e. data corresponding to the entire dispersion plot are utilized). Specifically, we train our ML model using Random Forest with 500 random decision trees. In general, we were able to observe a relatively strong correlation between the observed DMS behavior of drug candidates and physicochemical properties. This is in line with expectation since many properties are to a large extent dependent on molecular size and on the solvent interactions, i.e., propensity for desolvation and transport across the hydrophobic lipid bilayer15. It therefore stands to reason that including the molecular collision cross section (CCS) as a descriptor in the ML database should improve the accuracy and precision of the ML model. To do this, CCSs were calculated with the updated MobCal code reported by Campuzano et al.46 using DFT-optimized molecular structures48. A comparison of ML fits for cell permeability that include only DMS data with analogous ML fits that include DMS data and calculated CCSs are provided in Supplementary Methods, Supplementary Figures 5010. Although the fit containing only the DMS data exhibit a strong correlation with the experimentally determined RRCK rates of cell permeability (R2 = 0.889), inclusion of the CCSs for the protonated drug molecules results in a significant improvement (R2 = 0.989). On the other hand, if only CCS values are used to predict permeability we observe no significant correlation10.In our previous work on methylquinoline-8-ol derivatives, we were able to identify a weak correlation between the turn-around point in the dispersion plot of specifically type B systems and cell permeabilitiesN-fold cross-validation procedure (where N is the number of molecules in the set of compounds). Figure 50, EPSA28, and cell permeability are provided in Supplementary Methods, Supplementary Figures 5610. The CCS fits 10 involve a smaller subset of the ML database for which these condensed phase properties are available. Regardless of this limitation, correlation with DMS data are relatively strong. Because of this, we are optimistic that DMS behavior can also be used to predict these properties once a more extensive ML training database is constructed.In the case of CCS fits, absolute errors tended to increase with increasing molecular size. However, the percent error in CCS was relatively constant across the dataset. For the 89 species studied, we find an average error of .5% Fig.  for deteviz. CCS), one can correlate dynamic gas-phase clustering behavior within the DMS environment with condensed phase physicochemical properties. This suggests that DMS behavior is affected by the molecular interaction potentials of the analyte and collision gas within the DMS cell. This is demonstrated by, e.g., the fact that DMS can separate isomeric species based on the presence or absence of an IMHB; species that exhibit IMHBs display weaker ion-solvent clustering behavior than do the non-IMHB-containing analogues. The strong correlations observed between the DMS data and molecular physicochemical properties is appealing as it suggests that DMS might find use as a fast and accurate means of simultaneously screening molecular properties such as polar surface area, distribution coefficients, solubility, and cell permeability. This, in conjunction with the fact that DMS measurements can be conducted in minutes using only nanograms of sample, makes properties determination via DMS an attractive possibility for drug discovery. To advance this technology, it is necessary to develop an extensive database of DMS behaviors and properties for a wide range of molecules. Our work is ongoing in this regard.In summary, through application of supervised ML and consideration of molecular size  system was mounted on a 5500 QTRAP® system (SCIEX), between a TurboV TM ESI source and the mass spectrometer’s sampling orifice. Nitrogen was used as the curtain gas (3.5 L/min), throttle gas (0 or 0.7 L/min), and target gas (~3 mTorr) for the MS/MS experiments.Drug candidates were provided by Pfizer Inc. and were used without further purification. HPLC-grade methanol  was also used without further purification. Distilled deionized water (18 MΩ) was produced in-house using a Millipore  Integral 10 water purification system. In each experiment, an analyte solution (10 ng/mL) was infused at a rate of 15 μL/min into DMS-MS instrument via an ESI source operating at 5.5 kV, with a source temperature of 300 °C, nebulizing gas pressure of 20 psi, and auxiliary gas pressure of 20 psi. The DMS cell  of protonated for each molecule by generating all possible tautomers/protomers and optimizing them individually at the B3LYP/LANL2DZ level of theory. For the most stable structures, atomic partial charges were calculated using the ChelpG partition scheme52. The cluster PES was then modeled using the Universal Force Field53. To search the PES, the dihedral angles associated with single bonds were randomly distorted by −5° ≥ θ ≥ + 5° at each iteration of the BH code. In total, ~20,000 geometries were sampled for each molecule. Unique structures were identified based on zero-point corrected energy and geometry. Unique structures were then carried forward for geometry optimization at the PM7 level of theory for all molecules. To verify that calculations at the PM7 level produced appropriate geometries, the following subset of molecules were also geometry optimized at the B3LYP/6-311++G level of theory: A1a, A1b, A4a, A4b, B1a, B1b, C4a, C4b, C4c, D1a, D1b.To determine the most stable molecular geometries, a custom-written basin hopping (BH) search algorithm was used to map the potential energy surface (PES) of each drug candidate. The BH algorithm has been described in detail elsewhereFor all molecules in the above subset, PM7 calculations produced the same global minimum structure and the same relative energy order of tautomers/protomers as did the B3LYP/6-311++G calculations. In addition to geometry optimizations, normal mode analyses were also conducted to ensure that each structure was a local minimum on the PES.54. The labeled molecules were entered into a database which also included DMS data and calculated collision cross-sections (CCSs) as determined using the updated MobCal code reported by Campuzano et al.46. The Random Forest Regression employed 500 random decision trees and determined molecular properties via leave-one-out. This procedure trains the ML model using all molecules in the data except one, and then determines the property for the omitted molecule. This procedure is repeated for every molecule in the set. A series of correlation plots showing how the random forest (RF) model improves with additional data are shown in Supplementary Figures Supervised machine learning was conducted using the Orange3 Python packageTo produce learning curves, the database is split in to training and test sets. A ML model is produced from the training set and the mean absolute errors for the training and test sets are determined by applying the model to both. The plots provided in Fig. Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2


A novel relative humidity sensor that is based on a linear piezoelectric micromachined ultrasonic transducer (pMUT) array was proposed and microfabricated for high sensitivity, fast response, and good stability. The humidity-sensitive graphene oxide (GO) film was deposited on the pMUT array with a facile drop-casting method and characterized by scanning electron microscope (SEM), atomic force microscope (AFM), and Fourier transform infrared spectrum (FTIR). With the humidity level ranging from 10% to 90% RH, the sensor exhibited a high sensitivity of 719 Hz/% RH and an extremely high relative sensitivity of 271.1 ppm/% RH. The humidity-sensing results also showed good short-term repeatability and long-term stability, fast response and recovery, and low hysteresis. Moreover, the temperature coefficient of frequency (TCF) of the present humidity sensor was investigated and it could be easily compensated owing to the pMUT array structure design. This work confirmed that the GO functionalized pMUT is an excellent candidate in humidity detection and it may enable many potential applications, such as ultrasensitive mass detection and simultaneous detection of multiple parameters. Miniaturized humidity sensors enabled through micro-/nano-fabrication technologies have gained extensive attention due to distinctive advantages of small size, low cost, fast response, high sensitivity, and high stability. These micromachined humidity sensors have great potential for portable electronic systems and they play crucial roles in a wide range of applications, such as environmental monitoring, industrial process control, agricultural production, and medical treatments ,2. In geIt is well known that humidity-sensing properties, such as sensitivity, response, and stability are largely affected by the selected sensitive thin film. Until now, various kinds of humidity-sensing materials have been employed, including metal oxides ,15, ceraIn this work, a linear pMUT array based humidity sensor functionalized with GO sheets were proposed and fabricated with the microelectromechanical system (MEMS) technology. The GO thin film was directly deposited on the microfabricated pMUT array with a simple and facile drop-casting method and was characterized by scanning electron microscope (SEM), atomic force microscope (AFM), and Fourier transform infrared spectrum (FTIR). The humidity sensing characteristics of developed sensors, such as response/recovery, hysteresis, stability, and temperature effect, were investigated and discussed. Impedance and phase curves at different RH levels were also used to characterize the GO coated pMUT humidity sensor. Last, the sensing mechanism of the present sensor was analysed. This research demonstrates the potential of the pMUT based humidity sensor for highly sensitive mass detection in a wide range of applications.2/200 nm Pt/1.9 μm MPB-PZT/200 nm Pt.The pMUT based humidity sensor contains a pMUT linear array that was fabricated on a released silicon-on-insulator (SOI) wafer and a GO thin film deposited on the pMUT array. The schematic structures of the pMUT array and pMUT based humidity sensor are illustrated in 2 layer was deposited on the SOI wafer for electrical insulation, and then 200 nm Pt/10 nm Ti thin films were deposited by direct current (DC) magnetron sputtering and patterned as the bottom electrodes by Ar ions. After that, a layer of 1.9 μm MPB-PZT was formed using the sol-gel process and patterned through wet-etching. Next, another 200 nm Pt/10 nm Ti thin films were deposited and patterned as top electrodes. Last, the Si substrate was etched by deep reaction-ion etching (DRIE) to release the membrane. The 100 nm Au was formed as wire bonding pads. The as-fabricated pMUT array was then functionalized by drop-casting of diluted GO dispersions with concentration of 1 mg/mL. The original GO dispersions was prepared by the modified Hummers’ method and supplied from Suzhou Tanfeng Graphene Technology Co., Ltd. . After deposition, the prepared pMUT humidity sensors were placed into the oven and heated at 60 °C for 48 h. Before the experiments, all pMUT humidity sensors under the test were packaged and wire-bonded in the double in-line package (DIP) holders. The polydimethylsiloxane (PDMS) was applied to protect the bonding wires from damage during chip picking up and seal the backside released cavity for eliminating the interferences of moistures and gases that are trapped in the cavity when exposed to humidity conditions.The fabrication process started with an n-type SOI wafer with 10 μm thick device layer. A 1 μm SiOt, L, W, E, ρ, and ν are the thickness, length, width, Young’s modulus, Poisson’s ratio, and density of the rectangular membrane. It is shown that the operating frequency of the pMUT is proportional to the Young’s modulus and inversely proportional to the density. Therefore, coating a selectively sensitive thin film on the pMUT surface could shift the operating frequency upward or downward, which is mainly dependent on the material properties and thickness of the deposited layer. However, the physisorption or chemisorption of the analyte molecules on the sensitive thin film would result in a frequency decrease due to the mass or density change, well-known as the mass-loading effect. The frequency shift Δf of the developed pMUT humidity sensor can be estimated by the following equation [m and Δm are the mass of the effective vibration membrane and mass change after absorption of water molecules, respectively. According to the mechanical resonant frequency Expression (1) and the dimensions, the mass sensitivity per unit area of the designed pMUT is estimated at 16 ag/Hz/μm2.The basic resonant structure of one rectangular pMUT element is a PZT/Si layered rectangular membrane with fully clamped boundaries. The resonant characteristics are mainly determined by the material properties and the dimensions of the vibrating membranes. The fundamental flexural-mode operating frequency for a rectangular membrane is derived by Ref. (1)f0=0.equation :(2)Δf=−1Rq and Ra of 32.4 nm and 27.2 nm, respectively.The surface morphologies of the pMUT humidity sensor before and after coating GO film were characterized by SEM operated at 15 kV, as shown in −1 obtained by a high-resolution FTIR microscope at the transmission mode. Two peaks appeared at 2357 cm−1 and 2333 cm−1 due to the formation of unique chemical species in the presence of CO2 [−1 suggests the presence of carboxyl groups (C=O stretching vibrations). The strong and broad valley at 3579 cm−1 is correlated to the –OH stretching vibrations.e of CO2 . A chara2O5 desiccant and humidifier filled with deionized (DI) water were used together to achieve humidity levels from 10% RH to 90% RH with a 10% RH interval. The relative humidity in the test chambers was manually changed by adjusting the flow rate of the evaporative moistures and it was recorded in real time by a precise relative humidity meter. A precision impedance analyser  was used to measure the resonant frequency of the pMUT humidity sensors and the data were transmitted to a personal computer (PC) for saving and off-line processing. The temperature in the chamber was kept constant at 24 ± 0.5 °C to eliminate the interference from temperature changes. A Xiaomi Mijia bluetooth humidity temperature sensor  was also applied to track both the humidity and temperature values per second. During the experiments, the sensors were placed in one chamber first to obtain the response at one humidity level, and then were rapidly transferred into the other chamber to obtain the response at another humidity level. In this way, the humidity-sensing response and hysteresis characteristics could be explored by changing the relative humidity in two chambers from 10% RH to 90% RH and then back to 10% RH.The experimental setup for humidity-sensing property measurement is illustrated in pp. Subsequently, both the impedance and phase curves of the pMUT humidity sensors were measured before and after coating the GO thin film, as plotted in The finite element method (FEM) simulation that was based on COMSOL Multiphysics v5.2a was first employed to study the resonant characteristics of the pMUT. f with respect to the relative humidity levels can be fitted and described by the following expression:a, b, c, and d are the fitting coefficients and they are estimated correspondingly as 0.0979, 2.551, 0.03379, and −4.5518.The pMUT humidity sensors were tested in the well-sealed plastic chamber with the relative humidity increasing from 10% to 90% RH. The frequency shift to different humidity levels is shown in S and relative humidity sensitivity RS are defined asf0 is the fundamental resonant frequency of the pMUT humidity sensor with the GO thin film at 10% RH. Therefore, the sensitivity and relative sensitivity of the pMUT humidity sensor in the RH range of 10% to 90% RH are calculated as 719 Hz/% RH and 271.17 ppm/% RH, respectively. When compared with other resonant humidity sensors, our present pMUT sensor tends to exhibit an extremely high relative sensitivity, as summarized in The humidity sensitivity The hysteresis loops versus RH levels of the pMUT humidity sensor is shown in To study the dynamic response and recovery behaviours of the pMUT humidity sensor, two test chambers were kept at 10% RH and 85% RH, respectively. The sensor was manually transferred from one to the other chamber and was then transferred back. It took about 2 s to transfer the pMUT sensor. The response and recovery curves are shown in f and the limit of detection (LOD) of the humidity sensor [2 gas. It should be mentioned that during the CO2 sensing experiment, the relative humidity and temperature in the chamber were kept at about 10% RH and 24 ± 1 °C, respectively, to eliminate the interference from humidity and temperature changes. The CO2 concentration was initially kept at 0 ppm by introducing dry N2 gas for 10 min. The measurement results demonstrate that this sensor has no obvious response, even when exposed to CO2 gas with the concentration up to 20,000 ppm.The frequency stability including short-term stability and long-term stability is an important parameter during the measurements. The short-term stability can be expressed as an Allan deviation ) to estimate the noise Δy sensor . The aveThe impedance and phase response spectra were also recorded at various RH levels, as shown in TCF), defined by:T is the change in temperature. The TCFs of the pMUTs with and without GO thin film are −36.3 ppm/°C and −77.9 ppm/°C, respectively. It is noted that such temperature impact on the resonant frequency can be easily compensated due to the perfect linear frequency-temperature relationship and the multi-pMUT array structure, which is much more advantageous than other humidity sensors. Benefiting from the linear pMUT array design, we can use some untreated pMUT elements as the reference sensor to detect the temperature change and then compensate the temperature effect of the functionalized pMUT sensors during the humidity detection. Moreover, the pMUT array also confers a potential advantage in the simultaneous detection of humidity and temperature by adopting two pMUT elements to sense them independently.In practical applications, the environment temperature change would inevitably affect the frequency shift of the sensor and lead to an inaccurate testing result, namely the temperature effect. In this work, the temperature effect of the pMUT before and after coating the GO thin film was measured in the temperature range of 20 °C to 50 °C, as shown in The present humidity-sensing results indicate that the GO film functionalized pMUT is highly sensitive to water molecule adsorption, exhibiting a nonlinear frequency response and a high sensitivity of 719 Hz/% RH, as well as a high relative sensitivity of 217 ppm/% RH. A speculative mechanism explanation for these behaviours is illustrated in In summary, a highly sensitive linear pMUT array based humidity sensor functionalized with a GO thin film was designed, fabricated, and characterized. The impedance and phase analysis was used to analyse the frequency characteristics of the pMUT before and after the deposition of the GO film and investigate the humidity-sensing properties of the pMUT humidity sensor. The developed sensor exhibited excellent humidity sensitivity (719 Hz/% RH) and relative sensitivity (271 ppm/% RH) in a wide sensing range of 10% to 90% RH. Meanwhile, rapid response and recovery, small hysteresis, good stability, and a linear frequency-temperature response were also achieved. A possible humidity-sensing mechanism of the GO film coated pMUT was proposed according to the tested experimental results. This work demonstrates that the GO thin film functionalized pMUT is a good candidate for humidity detection and holds potential in the simultaneous detection of multiple parameters, owing to its highly integrated array structure.


Researchers combine moiré patterns to break the geometric limitations in graphene superlattices, and access new energy scales. When two-dimensional (2D) atomic crystals are brought into close proximity to form a van der Waals heterostructure, neighbouring crystals may influence each other’s properties. Of particular interest is when the two crystals closely match and a moiré pattern forms, resulting in modified electronic and excitonic spectra, crystal reconstruction, and more. Thus, moiré patterns are a viable tool for controlling the properties of 2D materials. However, the difference in periodicity of the two crystals limits the reconstruction and, thus, is a barrier to the low-energy regime. Here, we present a route to spectrum reconstruction at all energies. By using graphene which is aligned to two hexagonal boron nitride layers, one can make electrons scatter in the differential moiré pattern which results in spectral changes at arbitrarily low energies. Further, we demonstrate that the strength of this potential relies crucially on the atomic reconstruction of graphene within the differential moiré super cell. Van der Waals heterostructures allow combining different two-dimensional (2D) materials into functional stacks . Because the lattice constants of graphene and hBN are relatively close to each other, the alignment between the two crystals leads to the formation of a moiré pattern  created by top and bottom hBN in hBN/graphene/hBN heterostructures. Such super-moiré patterns are not related to the difference in the lattice constants between the two crystals and thus can be of any arbitrary wave number, which makes it possible to arrange the spectrum reconstruction at arbitrary low energies.α and θβ). The fabrication and transfer procedures have been previously described in (2. We then use a thin film of polypropylene carbonate (PPC) on Polydimethylsiloxane (PDMS) to lift the hBN from its substrate. This film then facilitates bringing the top hBN into contact with a graphene crystal  as a function of carrier concentration is shown in xyR) measured in nonquantized magnetic field , which should result in two secondary Dirac points for electrons and two for holes. However, if electrons can feel potential from both moiré patterns simultaneously, then second-order processes can be allowed, which would result in the reconstruction of the electronic spectrum at many other wave vectors.The longitudinal resistance  are the moiré reciprocal lattice vector between the α or β hBN layer , δ is the graphene-hBN lattice constant mismatch, θα, β is the misalignment angle for α or β, and a is graphene’s lattice constant. One of the observed periods is larger than that could be expected for graphene aligned with hBN . However, it is clear that when a second aligned hBN layer is added, the width of the 2D peak increases by a factor of ~2  should have periodicities described by To evaluate the degree of strain within our super-moiré samples, we have used Raman spectroscopy. In or of ~2 . We attrTo conclude, graphene’s electronic spectrum is substantially altered by scattering from super-moiré structures described by the preexisting moiré between graphene and its substrate and encapsulating hBN layers. These alterations may be considered in two ways: as double-scattering events from both graphene-hBN moiré patterns or as single-scattering events from a reconstructed graphene layer. Such super-moiré potential can be of an arbitrarily small wave vector , which allows modification of the graphene band structure at arbitrarily low energies.2 wafer.In addition to the sample preparation described previously, the general process is as follows. We used PPC, or sometimes polymethyl methacrylate, spun on a thick PDMS membrane to facilitate moving and orientating the crystals. This membrane was used to pick up the first hBN layer. The crystal was then positioned and aligned to graphene before the two were brought into contact. We removed the membrane quickly which lifted the graphene off from its substrate. At this point, we inverted the membrane and perform various characterization techniques on the half-assembled heterostructure. We then picked up a further thin hBN crystal, before repeating our characterization. These previous steps could have been repeated multiple times to produce increasingly complex heterostructures with a variety of crystals . Last, the stack of crystals was positioned and brought into contact with a final “substrate” hBN. The membrane was removed slowly so that all the crystals were left on a SiOSL-resolution AFM images of the moiré patterns were taken in PeakForce Quantitative Nanomechanical Mapping (PF-QNM) mode on a Bruker Icon AFM. PF-QNM allows the capture of individual tip-sample force curves for each pixel in the image. These force curves were used to extract additional elastic information about the sample as well as topographic information. We used “ScanAsyst Fluid+” probes whose resolution is regularly better than 2 nm.Raman spectroscopy measurements were performed on a HORIBA XploRA PLUS Raman spectrometer. The laser wavelength was 532 nm with a power of 0.5 mW through a ×100 objective.The analysis of moiré patterns in double-aligned heterostructures was performed following an approach described in (−6 eV/Å.Molecular dynamics simulations were performed for the single-aligned hBN/graphene and the double-aligned hBN/graphene/hBN by allowing the relaxations of both hBN and graphene layers. We used the bond-order Brenner potentials for the graphene layer, Tersoff potentials for the B-N interaction in the hBN layers, and the Morse potential developed in (Using the relaxed structures, we calculated the shift of the 2D peak and the entire Raman spectrum of the graphene layer. Calculations were performed using the prescriptions given in (


TF4 (transcription factor–like protein 4) gene. A comparison of attributes between model-generated sequences and chordate introns showed that the MD model with proper parameter settings could generate sequences that have attributes matchable to chordate introns, whereas the MI model with any parameter settings failed in doing so. These data suggest that the surveyed chordate introns have evolved from a long ancestral sequence through gradual reduction in length. The established methodology provides an effective measure to study the evolutionary pattern of intron sequences from organisms of various taxonomic groups. (C++ scripts of MD and MI models are available upon request.)Introns are well known for their high variation not only in length but also in base sequence. The evolution of intron sequences has aroused broad interest in the past decades. However, very little is known about the evolutionary pattern of introns due to the lack of efficient analytical method. In this study, we designed 2 evolutionary models, that is, mutation-and-deletion (MD) and mutation-and-insertion (MI), to simulate intron evolution using randomly generated and mutated bases by referencing to the phylogenetic tree constructed using 14 chordate introns from  The evolution of intron sequences has aroused broad interest in the past decades. However, 2 contrary hypotheses, namely, the “introns-early” versus the “introns-late” theories, put forward to explain the evolutionary mechanisms of introns are still under debate. Introns-late theory proposes that introns are an innovation of eukaryotes and intron gain has been a continuous process during the evolution of eukaryotes.813 However, the proponents of introns-late theory have still not been persuaded. The main reasons, as we understand, come from 2 aspects. One is that no evidence has been obtained to prove the existence of introns in ancestral bacterial protein-coding genes. The other is that introns-early theory cannot explain why intron number and length increase with the complexity of eukaryotic organisms. It must be confessed that addressing these 2 concerns is confronted with great difficulty. First, all ancestral bacteria are not available today. Thus, no ancient bacterial gene samples are available for examination about intron existence. Yet, this problem could be circumvented to some extent by searching and examining horizontally transferred bacterial genes harbored in eukaryotes. Using this approach, we have found an intron-containing bacterial gene harbored in sea anemone, which suggests possible existence of introns in ancestral bacterial genes.15 Second, the lengths and base sequences of introns vary greatly across various organisms. With a group of introns having different lengths, currently there is no measure to find out whether they are evolved from a longer ancestral sequence through gradual reduction in length or from a shorter ancestral sequence through gradual increase in length. Phylogenetic analysis has been widely used to infer evolutionary patterns of gene and protein sequences. However, it is inefficient for studying the evolutionary pattern of introns because the phylogenetic tree formed by intron sequences generally has very poor statistical support, based on which no evolutionary pattern can be inferred to explain how intron sequences have evolved.In recent years, more and more data have been obtained to favor the introns-early theory.1618 The established models are effective in simulating evolution of real sequences,20 in establishing databases of simulated protein alignments,21 and in exploring early events in the ecological differentiation of bacterial genomes.22 However, these methods were mostly developed for simulation of gene or protein sequences which possess high conservatism. They are not suitable for simulation of intron sequences that have undergone high number of nucleotide mutation and large pieces of nucleotide deletion or insertion. Therefore, in this study, we designed and constructed 2 types of evolutionary models, that is, mutation-and-deletion (MD) and mutation-and-insertion (MI), to simulate the evolution of introns from a chordate gene using randomly generated and mutated sequences. Then, we compared attributes of model-generated sequences with real chordate introns. The results show that the MD model with proper parameter settings could generate sequences that have attributes matchable to chordate introns, whereas the MI model with any parameter settings failed in doing so. These results suggest that the surveyed chordate introns should have evolved from a longer ancestral sequence through gradual reduction in length. The established methodology provides an effective measure to study the evolutionary pattern of introns from organisms of specific taxonomic groups.Sequence simulation is an important measure for evolutionary studies. A considerable number of statistical models and methods have been developed and tested for inferring the evolutionary relationship of nucleotide and protein sequences.TF4 (transcription factor–like protein 4) has only 1 phase zero intron in chordates. Exon sequences flanking this intron are highly conserved. This intron was selected as the research subject to ensure that the introns of different chordates come from the common ancestor. This intron is 112 to 1975 bases long in the 14 species chosen to represent various classes of chordates , RK2+I (ratio of transition to transversion under K2+I parameter model25), TSML (topology score of the constructed ML tree), which were found to be 2139 bases, 1.53, 1.084, 0.089, and 28, respectively. Detailed steps for determining these attributes are described in Supplementary File 1.The gene segment coding for bHLH (basic helix-loop-helix) motif of hordates . These 1tic tree  using ma1) through gradual reduction/increase in length accompanied by base mutation  were generated at random and were referred to bases A, G, T, and C, respectively. After a sequence of demanded length was obtained, its first and last 2 bases were replaced by GT and AG to mimic the structure of an intron. Starting from the AS1 sequence, the formation of each chordate intron can be divided into separate stages. For instance, intron Cm376  was evolved from AS2, and AS2 itself was evolved from AS1. The evolutionary models we constructed have 5 adjustable parameters, that is, LAS1 , LAS12 , M1 (mutated bases per 1 branch length), LI/D (length of bases inserted or deleted each time), and MI/D (number of bases mutated each time). Parameters LAS1, LAS12, and M1 define how many bases should be deleted/inserted and mutated at each stage, whereas LI/D and MI/D define how many bases should be deleted/inserted and mutated each time within a stage. Once the values of these 5 parameters are set, an evolutionary event for a specific intron sequence can be simulated. As an example, Supplementary File 2 describes detailed steps of how to generate a 376-base sequence for simulating evolution of intron Cm376 using the MD model. The evolution of all other ancestral sequences and intron sequences can be simulated in the similar way.To simulate intron evolution, we assume that the 14 chordate introns are evolved from 1 common ancestral sequence  should have a valid length to ensure simulation of intron evolution. That is to say, in the MD model, AS2 to AS4, AS6, and AS8 to AS12 must be longer than the correspondent introns evolved from them. As such, the minimum value of LAS12 was set to 2000 bases .As shown in 00 bases  to ensur40 bases  to ensurR) is adjustable in MD and MI models. However, a fixed value is given to it prior to model running in accordance with the attributes of intron sequences for study. This value is set to 1.5 in both MD and MI models because the R value of the 14 chordate introns is 1.53 as revealed by model testing .The transition to transversion ratio . For data from orthogonally designed models, “univariate” analysis was conducted to evaluate the effects of various factors on attributes of model-generated sequences. Significance of variance was analyzed through comparing the main effect of each factor without investigating the interaction between factors. Duncan post hoc test was used to conduct multiple comparisons for observed means, which were then plotted to view the optimal level of each factor. For data from optimized MD and MI models, “independent samples 1) was evolved to form AS2 to AS12, which were then evolved to form the 14 chordate introns. The formation of each ancestral sequence or chordate intron sequence was considered as the result of both base mutation and base deletion/insertion, which are related to evolutionary distance shown above or below each branch of the tree. In other words, each sequence in the tree (except AS1) was evolved from its specific AS after a target number of bases was mutated and a target length of bases was deleted or inserted. Nevertheless, base mutation and base deletion/insertion are a continuous process, meaning that a target number of bases to be mutated and a target length of base deletion/insertion should be completed at consecutive stages. Therefore, we have designed and constructed evolutionary models to simulate base mutation and base deletion/insertion alternately before the target number of bases is mutated and the target length of bases is deleted/inserted. In addition, the evolutionary models were designed to receive parameters in batch so that a set of 14 sequences could be generated at one time. Detailed flow charts for the construction of MD and MI models are shown in Figures S1 and S2, respectively. The constructed MD model assumes that the 14 chordate introns were evolved from a fairly long AS1 sequence through a gradual reduction in length accompanied by base mutation, whereas the constructed MI model assumes that the 14 chordate introns were evolved from a fairly short AS1 sequence through a gradual increase in length accompanied by base mutation. Both programs were compiled with C++ language.The ML tree obtained from previous step  was refe16(4*5) orthogonal table  uses 6000 bases as LAS1, 2000 bases as LAS12, 600 bases as M1, 31 to 50 bases as LI/D, and 21 to 30 bases as MI/D , which are obtained from allowing each of the 14 chordate intron sequences to mutate by only 1 base  for 10 times and thus obtained their attributes in the “M ± SD” form.In  as MI/D . Among ty 1 base . This waM1 and LI/D. So, in test MD18, we used 2500 bases as LAS12 and 800 bases as M1 while keeping other parameters unchanged. But sequences generated by MD18 still have significant difference in P < .1 level. After this, we have tried various values for parameters LAS1, LAS12, and MI/D to run the MD model (tests MD19-MD24). It was found that 1 or 2 of the 5 attributes of sequences generated by tests MD19 to MD22 are still significantly different from those of the 14 chordate introns. However, all 5 attributes of sequences generated in tests MD23 and MD24 have no significant difference with chordate introns  uses 10 bases as LAS1, 120 bases as LAS12, 200 bases as M1, 31 to 50 bases as LI/D, and 31 to 40 bases as MI/D  of the model-generated sequences are significantly different from those of chordate introns. So, in test MI18, we changed MI/D to 11 to 20 bases while keeping other parameters unchanged. The model-generated sequences have no significant difference in LMSA but have significantly higher TSML value. After this, we have tried various values for all 5 parameters to run the MI model (tests MI19-MI24). It was found that 2 or 3 of the 5 attributes of sequences generated by tests MI19-MI24 are always significantly different from those of the 14 chordate introns (By referencing to  as MI/D . However introns . Thus, i27 and microRNAs.28 The unavailability of common sequence features makes it very difficult to study the evolutionary pattern of introns through phylogenetic analysis because intron sequences generally yield a phylogenetic tree with very poor statistical support. This is true even when the introns are from the same location of a gene in closely related organisms. For example, the bootstrap values in ML tree shown in Introns are well known for their high variation not only in length but also in base sequence. So far, no common sequence structures/features have been found in introns except those containing transposable elements1114 While previous studies have made it clear that intron number has been reduced, it remains unclear whether intron length has been increased or decreased during intron evolution. Our present work provides an example of intron length reduction during the evolution of chordate TF4 gene. Theoretically, the established methodology can be used to study the evolution of introns at different numbers and lengths and from different organisms because the intron sequences for study are only used to construct the original ML tree (eg, So far, studies about evolution of intron sequences have mainly focused on the presence or absence of introns in certain genes across various organisms. Through investigating a large number of intron-gain and intron-loss events, it has been revealed that the evolution of eukaryotic genes primarily involves intron loss.tree eg, , which i29 higher intron retention rate in human than in fruit fly and nematode,30 reductive evolution of genomes in complex archaeal ancestor,31 and intron-rich ancestor of eukaryotes.32 This higher efficiency in lower organisms could be due to higher reproduction rates of lower organisms than higher organisms because frequent genome replication provides more chances for genome streamlining.34 Therefore, more and longer introns existing in higher organisms are not the result of continuous intron gain, but are the result of low efficiency in reducing intron number and length. This may be considered new evidence to support the introns-early theory and to persuade the proponents of introns-late theory.Then, if intron number and length have been substantially reduced during evolution, why do intron number and length increase with the complexity of eukaryotic organisms? This is the main supportive fact of introns-late theory that has not been given a rational explanation from introns-early theory. Here, we propose our explanation to this question: higher organisms are not as efficient as lower organisms in reducing number and length of introns. This explanation is consistent with frequent intron loss in yeast,In this study, through designing and constructing MD and MI evolutionary models to simulate the evolution of 14 chordate introns, we found that these chordate introns should have evolved from a longer sequence through gradual reduction in length accompanied by random base mutation. Although successful simulation seems to be highly dependent on parameter settings for the constructed models, it does provide an effective measure to infer the evolutionary pattern of introns, especially in view of intron length variation. The established methodology is expected to facilitate more studies on evolutionary pattern of intron sequences from organisms of various taxonomic groups.Click here for additional data file.Supplemental material, Supplementary_file_1_xyz299583cdc1c74 for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary BioinformaticsClick here for additional data file.Supplemental material, Supplementary_file_2_xyz299586f3629a1 for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary BioinformaticsClick here for additional data file.Supplemental material, Table_S1_xyz29958520c37c8 for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary BioinformaticsClick here for additional data file.Supplemental material, Table_S2_xyz299588fc8496b for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary Bioinformatics