The process of handling and bonding copper (Cu) and graphene inevitably creates defects. To use graphene/Cu composites as electronic devices with new physical properties, it is essential to evaluate the effect of such defects. Since graphene is an ultrathin anisotropic material having a hexagonal structure, an evaluation of graphene/Cu composites containing defects was conducted taking into account the inherent structural characteristics. The purpose of this study is to evaluate defects that may occur in the manufacturing process and to present a usable basic method for the stable design research and development of copper/graphene composites essential for commercialization of copper/graphene composites. In the future, when performing analytical calculations on various copper/graphene composites and defect shapes in addition to the defect conditions presented in this paper, it is considered that it can be used as a useful method considering defects that occur during application to products of desired thickness and size. Herein, density functional theory was used to evaluate the behavior of graphene/Cu composites containing defects. The density of states (DOS) values were also calculated. The analysis was implemented using three kinds of models comprising defect-free graphene and two- and four-layered graphene/Cu composites containing defects. DOS and Fermi energy levels were used to gage the effect of defects on electrical properties. PB. PB26]. The analysis model was constructed for a total of eight cases as a The laminate was modeled according to In the case of four layers, the graphene layer into which the defects were inserted was changed. We subdivided the case to compare the following designs: four layers with the defects inserted into the external graphene layer (first layer) a, four lx-axis corresponds to the energy level function value and the y-axis to DOS value. DOS value of point A (the maximum)   compared with graphene. In addition, in model 2, DOS value was negligible before point C  are presented in −1 and that of point B  and point B is lower by about 50.63% (17.44 e/eV−1). This implies that defects in graphene reduce DOS value and decrease electrical performance.Defects were inserted into the graphene layers of the two- and four-layered graphene/Cu composites to establish the effect of defects on DOS analysis. −1 and point B  and point B is higher by 2.74 e/eV−1 (about 6.61%). Although defects in the external graphene layer (first layer) in the four-layered graphene/Cu composites led to a slight decrease in DOS value of point A, defects are nevertheless considered to play a doping-like role and increase electrical performance at point B.To examine the effect of these defects in detail, DOS analysis of model 7 was performed by fixing the number of layers and changing only the defect location. Accordingly, the defects were inserted into the middle graphene layer (third layer) instead of the external graphene layer (first layer). The results are presented in −1 and point B (−1. Compared with −1 (about 13.95%), and point B by 6.31 e/eV−1 (about 15.22%). In the four-layered graphene/Cu composites, defects in the middle graphene layer (third layer) led to a slight decrease in DOS value of point A, similar to the effect of defects in the external graphene layer (first layer). However, DOS of point B indicates that defects play a doping-like role, facilitating electron movement and thereby improving electrical performance.DOS analysis results of model 7 are presented in The behavior of points A and B in DOS analyses was examined in detail using model 8 c. In thi−1 and point B  is −0.00053 e/bohr3. In model 8, the range of ρ(max) is 12.009 e/bohr3 and ρ(min) is −0.00056 e/bohr3. Therefore, it can be seen that the charge density decreases by about 3.167% based on ρ(max) due to defects. It is judged that this is because the existence of defects prevented the bonding force of electrons.Molecular weight(MW) of model 5 is 4906.88 g/mol. MW of model 8 is 4762.75 g/mol. In model 5, the range of ρ(max) is 12.402 e/bohrDOS analyses of graphene/Cu composites containing defects were conducted to estimate the effect of defects on electrical performance. The following conclusions were drawn:DOS behavior of the two-layered graphene/Cu composite (model 3) resembled that of a single graphene layer, i.e., a stable DOS value was observed across the entire section of Compared with the two-layered case, DOS value of the four-layered graphene/Cu composite was higher by ≥14.69% at point A, having the highest DOS value. Notably, DOS values over the sections In the two-layered graphene/Cu composite with graphene defects, point A was lower by about 22.85%, and point B by 50.63%, compared with the defect-free two-layered graphene/Cu composite.In the four-layered graphene/Cu composite containing defects in the external graphene layer (first layer), DOS value at point A was about 3.47% lower than that of the defect-free four-layered graphene/Cu composite, while DOS value of point B was higher by about 6.61%. In the four-layered graphene/Cu composite containing defects in the middle graphene layer (third layer), point A was lower by about 13.95% and B was higher by about 15.22%. Therefore, it is likely that the presence of graphene defects in four-layered graphene/Cu composites decreases the number of electrons at point A, and plays a doping-like role to increase electrical performance at point B. In addition, the external graphene defects performed better than the middle graphene defects at point A, but not at point B.In the four-layered graphene/Cu composites containing defects both in the external graphene layer (first layer) and middle graphene layer (third layer), DOS value of point A was only about 0.05% lower than that for the defect-free four-layered graphene/Cu composite, while the value of point B was about 25.42% higher. This means that the graphene defects decreased the number of electrons at point A and played a doping-like role, facilitating movement of electrons at point B and thereby improving electrical performance.This study presents basic data concerning the electrical behavior of graphene/Cu composites containing defects. Understanding the influence of such defects is essential for the commercialization of graphene, and to control the physical properties of graphene/Cu composites.


The incidence of complications in Gram negative bloodstream infection (GN-BSI) is not clearly defined. This retrospective cohort study evaluates the incidence of complications within 90 days of GN-BSI and examines the predictors for these complications.Hospitalized adult patients with monomicrobial GN-BSI at Prisma Health-Midlands hospitals in South Carolina between 1/1/2012 and 6/30/2015 were evaluated. Complications of GN-BSI were defined as endocarditis, septic arthritis, osteomyelitis, spinal infections, deep seated abscesses, and recurrent GN-BSI within 90 days of the initial episode. Clinical and microbiological variables were assessed as potential risk factors for complications, including initial response to antimicrobial therapy within the first 72-96 hours of GN-BSI using the early clinical failure criteria. Kaplan-Meier analysis and multivariate Cox proportional hazards regression were used to examine the incidence and risk factors of complicated GN-BSI, respectively.Escherichia coli was the most common bacteria . Overall, 13.9% developed complications within 90 days of GN-BSI. The median time to identification of these complications was 5.2 days from the index GN-BSI (interquartile range 1-28 days). The incidence of complications was notably higher in BSI due to Serratia species (39.7%), Proteus mirabilis (35.7%), and Pseudomonas aeruginosa (21.5%) than other bacteria . Independent risk factors for complications included early clinical failure criteria, non-urinary source, presence of indwelling prosthetic devices, BSI due to Serratia species, P. mirabilis or P. aeruginosa, and persistent GN-BSI (Table).A total 752 patients with GN-BSI were included in the study. The median age was 66 years and 380 (50.6%) were women. The urinary tract was the most common source of GN-BSI  and At least 13.9% of patients developed complications within 90 days of GN-BSI. Risk of development of complications may be predicted by specific host and microbiological factors. Stratification of patients based on these risk factors may aid in identifying patients requiring further diagnostic work up for early detection of complications.Julie Ann Justo, PharmD, MS, FIDSA, BCPS-AQ ID, bioMerieux: Honoraria|bioMerieux: Honoraria|Entasis Therapeutics: Advisor/Consultant|Entasis Therapeutics: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Merck & Co: Advisor/Consultant|Merck & Co: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi Inc.: Advisor/Consultant|Spero Therapeutics: Honoraria|Spero Therapeutics: Honoraria|Vaxart: Stocks/Bonds Julie Ann Justo, PharmD, MS, FIDSA, BCPS-AQ ID, bioMerieux: Honoraria|bioMerieux: Honoraria|Entasis Therapeutics: Advisor/Consultant|Entasis Therapeutics: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Merck & Co: Advisor/Consultant|Merck & Co: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi Inc.: Advisor/Consultant|Spero Therapeutics: Honoraria|Spero Therapeutics: Honoraria|Vaxart: Stocks/Bonds P. Brandon Bookstaver, PharmD, Spero Therapeutics: Advisor/Consultant.


In recent decades, chemiresistive gas sensors (CGS) have been widely studied due to their unique advantages of expedient miniaturization, simple fabrication, easy operation, and low cost. As one ubiquitous interference factor, humidity dramatically affects the performance of CGS, which has been neglected for a long time. With the rapid development of technologies based on gas sensors, including the internet of things (IoT), healthcare, environment monitoring, and food quality assessing, the humidity interference on gas sensors has been attracting increasing attention. Inspiringly, various anti-humidity strategies have been proposed to alleviate the humidity interference in this field; however, comprehensive summaries of these strategies are rarely reported. Therefore, this review aims to summarize the latest research advances on humidity-independent CGS. First, we discussed the humidity interference mechanism on gas sensors. Then, the anti-humidity strategies mainly including surface engineering, physical isolation, working parameters modulation, humidity compensation, and developing novel gas-sensing materials were successively introduced in detail. Finally, challenges and perspectives of improving the humidity tolerance of gas sensors were proposed for future research. In the last decades, gas-detection technologies have been widely employed in various application scenarios, such as the internet of things (IoT), healthcare, industrial and agricultural safety, environment monitoring, and food quality assessment ,2,3,4,5.2, In2O3, etc.), and two-dimensional layered nanomaterials , black phosphorus, etc.) /[Sn] was 0.05) sensor surpassed the pure SnO2 in humidity tolerance with a lower response drift (20% vs. 52%) value toward 20 ppm acetone at 450 °C when humidity changed from a dry condition to 80% RH  of pure WO3 counterpart. In addition to lanthanide elements, other metal elements were employed to reduce the water-poisoning effect of gas sensors. Suematsu et al.  = 1, 2 ppm in [RH] = 0%, 30%, 60% RH. The input training set was exhibited in 2O3/rGO sensors simultaneously for each group. 2 clusters, with 95.2% of variances explained. Moreover, the classification system realized the quantification of six classes of  conditions with five classes above 95% accuracy and an overall classification accuracy of 97.3%, as shown in 2O3 gas sensor array with high-humidity endurance. The sensor array (five sensors) was evaluated using PCA and neural network-based classification in terms of the gas sensor data type/amount, neural network algorithms, sensor combinations, and environmental factors. Furthermore, the discrimination of five types of VOCs under different humidity conditions  was realized by deep learning algorithms. The above-mentioned algorism compensations were based on a large amount of data in regard to the features of sensors. Therefore, sensors with high stability are very important to ensure constant sensing features for a long time.In regard to CGS, the interference caused by water vapor not only includes the variation of baseline resistance and response to target gas, but also the response/recovery time. Humidity compensation based on mathematical models and neural network could utilize these features to realize the accurate calculation for target gas concentration. Yan et al.  proposedon model b. Howeveetection d. The str (MSE)) e. Wu et 2Te nanowires for trace NO2 detection. When the Ag2Te sensor was exposed to 1 ppm NO2 with humidity changing from 50% to 90% RH, the sensor exhibited a small fluctuation of baseline resistance drift of 1.84% and response drift of 14.14%, suggesting a remarkable humidity tolerance. AgTe2 did not possess any hydrophilic functional groups, such as oxygen-containing functional groups anchored on the surface, which provided the operation stability in humid conditions. Our group initiatively proposed one humidity tolerant NO2 sensor based on nanoplate Bi2Se3 film at room temperature as shown in 2Se3 sensor exhibited excellent humidity resistance at a humidity range of 0.3–60% RH with a small baseline drift of 26.1% (2Se3 sensor toward NO2 (5 ppm) was enhanced under moist conditions  through spark plasma sintering (SPS). After the reduction process, oxygenate functional groups in GO were effectively removed to a minimal content of 8.8%, leading to the sharp decrease in the surface energy of GO materials. Therefore, the rough surface with unique hierarchical structures and reduced surface energy of the prepared rGO sample led to the transformation from hydrophilic GO to superhydrophobic rGO (water contact angle: 53° vs. 154°). Moreover, the response of rGO sensors to 1 ppm NO2 exhibited a small decline of 5% when the humidity changed from 0% to 70% RH, indicating excellent resistance to humidity. Furthermore, Li et al. [p-type metal oxide-based gas sensors, such as CuO, NiO, and Co3O4 have received significantly less attention compared with n-type metal oxides due to their lower sensitivity [p-type metal oxides may be more humidity-tolerant than n-type ones [2 detection. The NiO sensors exhibited stable response to NO2 at 150 °C under humid conditions (40% RH), indicating the less humidity-dependent characteristics of p-type metal oxides. Similarly, Wilson et al. [2 with staircase concentration (0.8–7 ppm) for two cyclic measurements at 50% RH. Miao et al. [2S gas sensor based on a monolayer film of CuO nanosheets, as shown in 2 and S during the reaction between hydroxyl groups and H2S on the surface of CuO under humid conditions served as the resistive surface layer to screen the humidity effect on gas sensing, as depicted in 2S in the shrinking pores and thus producing the CuxS/S resistive layer. These results suggest that p-type metal oxides are promising materials to design humidity-tolerant sensors, which warrants more extensive investigation.As traditional sensing materials could not well meet the demanding scenario requirements of future gas sensors, developing novel nanomaterials or new nanostructures is one promising strategy to improve the humidity tolerance of gas sensors. Yuan et al.  synthesiof 26.1% b. In connditions c, which u et al.  synthesihobicity d. The hyi et al.  utilizedsitivity . Inspiriype ones . For insype ones  proposedn et al.  preparedo et al.  reportedIn this review, recent progress in a series of anti-humidity strategies of CGS is summarized, including surface engineering, physical isolation, working parameters modulation, humidity compensation, and developing novel gas-sensing materials. Based on these anti-humidity strategies, recently reported studies have achieved inspiring humidity tolerance in gas detection. Despite the significant achievements, there are still some challenges to be overcome in the design of anti-humidity gas sensors in the future:2 molecules, and heterojunctions could promote sensitivity. Therefore, surface engineering with dual functions should be considered preferentially.(I) As presented in this review, surface engineering is the most widely studied anti-humidity strategy. In regard to traditional gas-sensing materials that have been intensively investigated, direct surface engineering is one simple method, which mainly includes five forms of functionalization of noble metals, element doping, modification with hydrophobic materials, composites with hydrophilic materials, and post-treatment. The basic principle of these five surface engineering methods is introducing additives to functionalize gas-sensing materials. Therefore, the adsorption sites on gas-sensing materials are inevitably occupied, leading to response degradation. This phenomenon is prevalent for surface engineering-type gas sensors as demonstrated in (II) Utilizing semipermeable membranes or molecular sieves to isolate sensing film from water vapor could effectively promote the humidity tolerance of gas sensors. Whereas, the diffusion of target gases would be hindered by these membrane or molecular sieves to some extent, leading to a decline in sensitivity . Further(III) In regard to CGS, sensitivity is closely related to the operating temperature. Although the elevation of operating temperature could improve the humidity tolerance of sensors, if the operating temperature does not match the optimal one, then the elevation of operating temperature for humidity tolerance probably would lead to a sensitivity recession. Light illumination has been intensively used to improve the sensitivity of gas sensors. Meanwhile, introducing light illumination to enhance humidity tolerance can be seen as one good way to kill two birds with one stone. However, in regard to working parameters modulation for anti-humidity performance, both the elevation of operating temperature and addition of illumination would increase the complexity of systems and energy consumption.(IV) An overwhelming majority of studies in regard to anti-humidity gas sensor focused on minimizing the attenuation of sensor response to target gas within humid conditions. In fact, adsorbed water molecules may promote the reaction between adsorption sites on the sensing film and target gas molecules, and thus improve the sensor sensitivity. Humidity compensation based on mathematical models and neural network is one promising way to obtain the real concentration of target gas regardless of the negative or positive interferences from humidity. Of note, only one compensation model probably cannot fit in a wide humidity range, especially considering the physisorption of water molecules at high humidity. In regard to compensation based on the neural network, abundant data with gas-sensing features under different humidity conditions are essential to train the neural network. Therefore, sensors with high stability are very important to ensure constant sensing features for a long time.p-type metal oxides in the past deserve more attention due to their humidity tolerance. Whereas, the process to develop new materials may be lengthy and uncertain. With the emerging of new technologies, machine learning and big data technology can theoretically design new materials before preparation, which can effectively shorten the time of developing new materials and propose various application-oriented solutions [(V) Developing novel gas-sensing materials with humidity tolerance renders the possibility to thoroughly eliminate humidity interference for gas sensors. The underappreciated olutions ,132.(VI) Almost all the previous reports in this review only utilized a single strategy to improve the humidity resistance of gas sensors. Resembling co-doping hydrophobic APTES and Ag nanoparticles to achieve the stable acetone sensing of Si nanowires , a combiIn the process of summarizing the latest research advances of anti-humidity gas sensors, we realized that the studies in this field are still insufficient. Moreover, in regard to the emerging subject, many endeavors need to be devoted to overcoming these challenges in the development of anti-humidity gas sensors. Therefore, we hope this review will enlighten readers and open new avenues to further explore novel gas sensors with excellent anti-humidity features without other performance cost indexes.


Self-consistent density functional tight binding (SCC-DFTB) approaches were used to study optimized structures, energy, differential charge density, and Mülliken populations for the  carbon nanotubes (CNTs) connected to the graphene having different topology defects. Based on the calculations, nine seamless CNT-graphenes were selected. For these connected systems, geometric configurations of the graphene and nanotubes were characterized, and the nearest neighbor length of C-C atoms and average length were obtained. The intrinsic energy, energy gap, and chemical potential were analyzed, and they presented apparent differences for different connection modes. Differential charge densities of these connection modes were analyzed to present covalent bonds between the atoms. We have also thoroughly analyzed the Mülliken charge transfer among the C atoms at the junctions. Low dimensional carbon nanostructured materials have shown outstanding performance in various fields. Carbon nanotube (CNT)-graphene composites are composed of two carbon allotropes, concerning environmentally friendly carbon elements, which are used as a remarkable enhancer to improve plasma frequency , microwaIn laboratory, Li. et al. detected a unique seamlessly bonded CNT-graphene hybrid nanostructure introduced in an interlayer for efficient and stable perovskite solar cells, and the power conversion efficiency improved significantly . MaaroufIn the two tubes, the zigzag  tube has a small chemical hardness, and the carbon atoms in the cross section of the  tube are axisymmetric when it is adjacent to the graphene atoms. Therefore, one interesting issue arises naturally to understand connection patterns in the CNT-graphene. The study of the low dimensional nanomaterials for CNT-graphene is looking forward to a matter worthy of expectation to build devices having good binding and stability at the nanoscale. Accounting for the fact that electrical information determined by experiment is hardly possible for these connectors, computer simulations based on semi-empirical quantum mechanics, such as density functional tight binding (DFTB), are particularly well-suited to characterize microscopic details at atomic scale ,35,36,37In the present work, the structures and electrical properties are investigated by DFTB simulations for these connecting modes of the graphenes, respectively, having 16 defects linked to the  CNTs. These values are examined including geometric configurations, energy analysis, electron difference density, and Mülliken charge. The atomic simulations contribute to identify the defect’s role in the connection between the carbon nanotube and graphene as well as the bonding between the dangling atoms. The structural and electronic related information of carbon nanotube-graphene heterojunctions will be helpful in experimental characterization of the interface at atomic scale for novel 3D nanostructures based on carbon .The present simulations used DFTB+ software, which is developed by Bremen University in Germany . Within  qα0−qα) . Erep isHere, atomN atoms  and N electrons, molecular orbital is given below:A, and c orbit coefficient. Then,Mülliken populations were calculated from Linear Combination of Atomic Orbitals (LCAO) coefficients ,43,44. Fin to get the following,Integrating both sides of this equation, and multiplied by This equation can be used to analyze the charge distribution. The charge distributed on the atomic orbital isi, and the charge on the atomic orbital We sum the A, the charge of atom A is obtained:By summing all the atomic orbitals of atom A atom and the B atom isThe total overlapping charge of the The constructed  SWNTs having 96 carbon atoms were performed geometry optimization, where the C-C bond length was 1.420 Å being parallel to the tube axis and 1.434 Å being non-parallel to the tube axis, and the diameter was 6.337 Å. Meanwhile, the initial graphene was 18 Å in length and width. We started by removing the carbon atoms from the graphene to form the 16 possible defect patterns as given in the literature . SixteenX carbon atoms in the i  interval . Therefore, we define Fx as follows: CNT-graphene positions are described as the following. The maximum x-coordinate of carbon atoms in the graphene is Xmax and the minimum x-coordinate is Xmin. Xmax–Xmin is divided equally into intervals with an increment of ∆x. There are ∆Ny carbon atoms in the i  interval . Therefore, we define Fy = ∆Ny/N. The maximum z-coordinate of carbon atoms in the graphene is Zmax and the minimum z-coordinate is Zmin. Zmax–Zmin is divided equally into intervals with an increment of ∆z. There are ∆Nz carbon atoms in the i  interval . Therefore, we define Fz = ∆Nz/N.Here, it should be noted that N is the number of carbon atoms in the graphene for the CNT-graphene. Similarly, the maximum y-coordinate of carbon atoms in the graphene is Ymax and the minimum y-coordinate is Ymin. Ymax–Ymin is divided equally into intervals with an increment of ∆y. There are ∆NTo characterize the interaction of these atoms, the formula for the intrinsic energy per atom of these modes was calculated as follows:insE(CNT-graphene) is intrinsic energy per atom, cn is the number of carbon atom, and Hn is the number of hydrogen atom, and N is the total number of atoms. E(C) and E(H) are the monatomic energies of carbon and hydrogen, respectively. totE (CNT-graphene) is the total energy of CNT-graphene.Here, HOMO and the lowest unoccupied molecular orbital (LUMO) energy ELUMO. The energy gap is given by LUMOEg = E − HOMOE.In general, there is an energy difference between the highest occupied molecular orbital (HOMO) energy EAs shown x-axis or y-axis is 90 degrees, while the angle between the carbon nanotube axis and z-axis is 0 degree. For the nine seamless connection modes, the angle between the CNT tube and coordinate axis minus that corresponding to the initial position is the change values as illustrated in x-axis, the maximum positive value of the mode #13 corresponds to the positive maximum deflection of the graphene carbon atom along the x-axis. The #13 also has a large positive value between the tube axis and z-axis. The large positive angle between the tube axis and y-axis or z-axis for the #8 suggests that the tube of this connection also shows obvious inclination. The small angles for the #9 indicate that the tube is vertical on the graphene. The change of the angle between the tube axis and the coordinate axis for these carbon nanotubes is related to the deflection of carbon atoms in the graphene. In the meantime, the asymmetry of bond-length in the graphene is amplified near the tube, thus increasing the deflection density in these regions.In order to quantitatively analyze the geometries of the CNT-graphenes, we calculated fraction of the carbon atoms for the CNT-graphenes along three directions as given by the Equation (11). As illustrated in z-axis and close to the positive direction of the y-axis compared with that in the #1, and the angles with the y-axis and z-axis increases. For the #6, the length of four atomic pairs is 1.432 Å, the other four 1.414 Å. For the other connection modes, the scatter distribution of bond lengths suggests that there are also inclination angles between the tube and graphene. Especially for the #13 having the largest inclination angle, there is a larger length of four pairs and smaller length of the other four pairs.In the following For inorganic crystals composed of carbon or silicon, our previous works ,35,36,37x-axis. If there are not the two defect positions marked as “7” and “8” in the graphene, the #8 would be symmetrical just like the #9. The amount of charge obtained and lost among the carbon atoms at the junction of 7-7 and 8-8 for the #8 are higher than those for the #9. The carbon atoms near the “2-2” and “3-3” connections in the #10 have more gain and loss of charge than those near the other connections. With an eye to the #13, the defect arrangement of the graphene is symmetrical, resulting in the symmetrical arrangements of the atoms getting and losing charge at the junctions. Three of the sixteen atoms in the carbon nanotube gain charge and thirteen lose. The atoms in the graphene are dominated by charge loss.As shown in z-axis, and carbon tubes take forms mainly including upright and inclined. The phenomena can be also found in 3D graphene-CNT-Ni heteronanostructures. Here, the heteronanostructures embedded in electrodes and the ionic membrane of the artificial muscles present significantly bending deformation. Some CNT-graphene systems with vertical tubes have relatively high intrinsic energy and chemical stability. Owing to the pulling and pressure from the connected carbon tubes, the morphologies of the graphene present apparent differences, which greatly affect the distribution of electrical densities of the graphene. The locally morphological differences are similar to the localized part of experimentally growing CNTs on graphene, where optimized interfacial bonding state play a great significance in the strength/toughness and EMI shielding effectiveness. From the Mülliken population differences, it can be found that the lost amount of the charge on the tube’ atoms are higher than that the graphene’s ones. The transfer of Mülliken charge obviously occurs among the atoms at the junction between the tube and graphene, implying that there are weak ionic bonds between these atoms in addition to strong covalent bonds. The present calculations provide us the possible structures and charge distributions as well as the energy information for coupling CNTs with grapheme, and improve our understanding for such systems on electrical level. These will be helpful in constructing novel carbon nanotube arrays on 2D materials with excellent thermal properties.The geometric structural optimization and electronic information of the CNT-graphene are investigated by using SCC-DFTB simulations at atomic scale. CNT-graphene is a suitable choice for the carbon nanotubes linked with grapheme having topological defects within the present DFTB formalism. Carbon nanotubes having a larger diameter need to be connected with graphene having larger size topological defects, and this cannot be performed through even the semi-empirical DFTB calculations. Geometrically, thinner nanotubes can hardly form a seamless connection with graphene having topological defects. In the present work, the hydrogenation for the top C atoms of the nanotubes is used to represent their infinite length. For these 16 topology defects constructed in the graphene, there are only nine seamless connection modes with the  CNTs. After connection, the graphene shows obvious fluctuation along the


Drosophila. The sensilla housing Or42b neurons show cuticular deformations upon moist air stimuli, indicating a conversion of humidity into mechanical force. Accordingly, we found Or42b neurons directly respond to humidity changes and rely on the mechanosensitive ion channel TMEM63 to mediate humidity sensing (hygrosensation). Expressing human TMEM63B in Tmem63 mutant flies rescued their defective phenotype in moisture attraction, demonstrating functional conservation. Thus, our results reveal a role of Tmem63 in hygrosensation and support the strategy to detect humidity by transforming it into a mechanical stimulus, which is unique in sensory transduction.Birds, reptiles and insects have the ability to discriminate humidity levels that influence their survival and geographic distribution. Insects are particularly susceptible to humidity changes due to high surface area to volume ratios, but it remains unclear how humidity sensors transduce humidity signals. Here we identified Or42b-expressing olfactory sensory neurons, which are required for moisture attraction in  Drosophila, Or42b neurons, located in sensilla with humidity-dependent shape act as humidity sensors via the mechanically gated channel TMEM63.Humidity detection is a feature of many species. Here the authors show that in  Notably, insects have the ability to extract information about their surroundings via humidity sensation. For instance, the hawkmoth Hyles lineata uses humidity levels to assess the nectar availability of blooming flowers that produce a higher level of humidity compared with their ambiance7. In mosquitoes, the disease vectors, moisture serves as a key attractant for host seeking9. Drosophila are equipped with sophisticated hygrosensory organs to detect moisture levels and possess various humidity-induced behaviors19. Water-sated flies are averse to moisture, while dehydrated flies show attraction to moisture as well as moisture searching behavior18. Besides, multiple Drosophila species show diverse humidity preferences and have different distributions, for example, rainforest flies and desert-dwelling flies prefer 85% and 20% relative humidity (RH), respectively12. Although recent studies have revealed the neural and molecular basis underlying humidity preference in Drosophila15, the mechanisms by which humidity stimuli are transduced into electrical signals and guide moisture attraction behavior remain largely obscure.Humidity provides a cue for terrestrial animals to migrate toward favorable environments, which ensure their survival and reproduction22. In the mechanical hygrometer model, change of humidity levels causes shape change of sensilla structures so that mechanosensitive molecules are activated to mediate hygrosensation. In the osmometer model, humidity-dependent osmolality change occurs in the sensilla lymph and evokes hygrosensation. Additionally, the psychrometer model posits that thermosensitive molecules function as humidity transducers, which detect the evaporation cooling from the sensilla lymph. These concepts are not mutually exclusive as sensory inputs from different pathways may act in concert to mediate hygrosensation by a multisensory integration mechanism. Previous studies have established several ionotropic receptors (IRs) including IR40a, IR68a, IR93a and IR25a as attractive candidates for humidity receptors15, but it remains unclear whether these molecules respond directly to mechanical or thermal stimulus. Thus, molecular evidence for those concepts regarding humidity transduction mechanisms has been lacking.Humidity signals are transduced and encoded by hygroreceptors; however, the identification of molecular receptors in hygrosensory neurons has been hampered by the elusive nature of humidity, which is the amount of water vapor in the atmosphere. Three concepts that explain how humidity changes activate a hygroreceptor have been proposedTmem63 mutant flies, demonstrating functional conservation in hygrosensation.In this study, we identified a mechanosensitive ion channel TMEM63, which functions in a group of olfactory receptor (OR)-expressing neurons to mediate hygrosensation. Using a newly developed assay, we first identified a group of olfactory sensory neurons (OSNs), the Or42b neurons, which work in synergy with IR68a-expressing moist cells to guide moisture attraction behavior. The sensilla that house the dendritic branches of Or42b neurons show shape change upon increasing humidity, which suggests humidity stimulus can be converted into mechanical force on the dendritic membrane. We further show that the humidity response of Or42b neurons depends on TMEM63, a member of the recently identified family of mechanosensitive channels, thus providing molecular evidence for the model in which a mechanosensory pathway contributes to humidity sensing. The physiological function of TMEM63 proteins in the animal kingdom is largely unknown, we found human TMEM63B rescues moisture attraction defects in 19. Briefly, flies were placed in a Petri dish that was covered with nylon net. After desiccation for 6 h, the dish was placed above a 24-well plate with 2 holes filled with super-saturated salt solutions23 immediately beneath the nylon net, creating a humidity gradient between 20% RH and 70% RH. The attraction index was then calculated as the percentage of flies in the region of higher humidity every 10 seconds. The chance level of attraction to the 70% RH region is estimated to be ~12% based on the proportion of the moist area in a random distribution. The humidity gradient was stable during the 120 s experimental period 31. This group of sensory neurons have been reported to be essential for innate attraction to vinegar and odor-induced food searching33. These studies together with our results indicate Or42b neurons play pivotal roles in detecting food and water, two essential factors for animals’ survival.Four antennal cell groups have been associated with hygrosensation, including dry cells and moist cells located near the sacculusoup Fig. . IR68a-G RH Fig. . Similarion Fig. . Or42b-G34. Although no temperature gradient was presented in our behavior assay , which suggests the curved sensilla are more responsive to humidity while straight sensilla may react poorly.We then investigated the mechanism by which Or42b neurons regulate animal behavior in the 20-70% RH gradient. Among existing models for the mechanism of hygrosensationels Fig. . When swels Fig. , which iels Fig. . Moreoveels Fig. . Consideels Fig. . A detainanchung has been reported to play essential roles in the thirst state sensor in central brain36, which can also regulate the moisture attraction behavior, we chose to perform RNAi-mediated knockdown of this gene in Or42b neurons. All of these tested lines behaved similarly to the control. Although inactive and nanchung were both required for the humidity choice between 0% and 100% RH in water sated flies11, our study excluded their roles in the 70% RH-induced attraction in desiccated flies. The differences in humidity settings and internal state of animals might lead to distinct results. In addition, neither a P-element insertion mutant for Or42b (Or42bEY) nor Orco2 mutants exhibited behavioral defects in humidity-guided attraction to 70% RH  in S2 cells transfected with DmTMEM63-GFP   located near the sacculus chamber II  to similar levels when tested in a 20% to 70% RH gradient  in these neurons 7094, LexAop-GCaMP6m, LexAop-Gal80, UAS-Kir2.1, Orco-RFP, UAS-RedStinger, UAS-CD4-tdGFP, UAS-GCaMP6m and UAS-tdTomato were obtained from the Bloomington Stock Center. UAS-Tmem63-RNAi and UAS-Dicer2 flies were obtained from the Vienna Drosophila Resource Center. UAS-ReaperHid was a gift from Hermann Steller lab. Ir68a-Gal4 was a gift from Paul Garrity lab. iav1, tmcGAL4, nompCf00914 and UAS-pzl-RNAi were gifts from Wei Zhang lab. UAS-ppk-RNAi and UAS-nan-RNAi were from Tsinghua Fly Center. ppk28Δ was from the Core Facility of Drosophila Resource and Technology in Shanghai Institutes of Biochemistry and Cell Biology. The flies were raised on standard medium at 25 °C and 60% humidity under a 12 h/ 12 h light-dark cycle.Drosophila S2 cells were cultured in Schneider’s medium supplemented with 10% FBS at 25 °C. Cells were plated into 35 mm petri dishes before transfection. TransIT-Insect (Mirus) was used to transfect cells according to the product instructions. All constructs with the pUAST backbone were cotransfected with pActin-Gal4.41 to obtain the Tmem63KO mutant allele with an attp site introduced to the first intron, then reporter constructs were integrated into the attP site through phiC31 mediated gene integration to generate the corresponding reporter lines.We used a previously described targeting strategyTmem63KO mutant lines were generated through homologous recombination in Drosophila embryos via the CRISPR/Cas9 system. The 5ʹ and 3ʹ homologous arms of Tmem63 were cloned from nos-Cas9 flies by PCR amplification.The Primers for 5ʹ homology arm amplification:5ʹ-GATGCCAGAACAAATACATCGAGA-3ʹ and 5ʹ-GATTGTTATCCGCTTTCATAGCAC-3ʹPrimers for 3ʹ homology arm amplification:5ʹ-ACTTTACACCGGGCACCTACC-3ʹ and 5ʹ-TTGCCTTGCTGTTATCCTCCTG-3ʹ.To generate the knock out targeting vector, the 5ʹ arm and 3ʹ arm flanking an attP-3P3-RFP-loxP cassette were cloned into pBSK+ vectors using a MultiS One Step Cloning Kit (Vazyme). The sgRNA sequences were as follows:sgRNA1: 5ʹ-AAATAAGTGAATGCGACGAA-3ʹ;sgRNA2: 5ʹ-GTTCTGGGCCTCCTGCACAG-3ʹ.Nos-Cas9 embryos. F1 flies with RFP-positive eyes were selected as mutant candidates and verified by genotyping. The mutant was confirmed by RT-PCR, with the Actin5C gene as a positive control. The primer pairs were as follows:The guide RNAs were expressed under U6b promoter control. A mixture of the targeting vector and two sgRNAs was injected into Tmem63:forward-primer, AGGGATACTCTCATGGTTAAACCAG;reverse-primer, TCCGAGGGAAGGCAGAATCATG.Actin5C:forward-primer, CTGGGACGATATGGAGAAGATC;reverse-primer, CAGCTCGTAGGACTTCTCCAAC.Tmem63 before the stop codon was amplified from w1118 flies and cloned into the pBSK-attB-EGFP-T2A-Gal4 and pBSK-attB-V5-T2A-LexA vectors41 using the ClonExpress II One Step Cloning Kit (Vazyme). These vectors were then separately injected into embryos from nos-phiC31 females crossed with Tmem63KO males. Knock-in Tmem63LexA and Tmem63EGFP-Gal4 lines were obtained from F1 flies with red eyes and verified by PCR. Finally, the knock-in lines were crossed to hs-Cre flies to remove screening markers in the genome.To generate the knock-in marker line, the replaced sequence and the rest genomic region of Drosophila Tmem63 (RE44586) was purchased from the Drosophila Genomics Resource Center, and the full length cDNA of human Tmem63a (NM_014698.3), Tmem63b (NM_001318792.1) and Tmem63c (NM_020431.4) were synthesized (Genewiz). The coding regions of these genes were obtained by PCR and subcloned into the pUAST vector using the ClonExpress II One Step Cloning Kit (Vazyme). UAS-DmTMEM63, UAS-HsTMEM63A, UAS-HsTMEM63B and UAS-HsTMEM63C transgenic flies were generated by conventional P-element-mediated germ-line transformation.The full length cDNA of Or42b gene promoter was PCR amplified from the genomic DNA of Or42b-Gal4 flies. The CD8-RFP coding region following the Or42b promoter were assembled to pBSK-attb vector via MultiS One Step Cloning Kit (Vazyme). Or42b-CD8-RFP flies were generated by phiC31 mediated gene integration to the attP2 site on the third chromosome.The 5 kb 19. In total, 20–40 male flies aged between 7 and 10 days that had been sorted into groups 2 days before testing were placed in a 60 mm Petri dish that was covered with 250 mesh nylon net. Petri dishes with flies were placed in a sealed chamber with desiccant and desiccated for 6 h prior to the test; no food was provided during desiccation. The dish with flies under desiccation stress was placed above a 24-well plate with 5 holes immediately beneath the dish. Different humidity levels were created with distilled water or saturated salt solutions23. The moist hole on one side provided high humidity, while the hole on the opposite side provided low humidity, which results in a humidity gradient between the two specific humidity levels. The room humidity was kept at 50% to 60% RH with household humidifier or dehumidifier during testing. The room temperature was set at 25 °C. Assays were performed during 17:00 and 22:00. The water-deprived flies were allowed to walk for 2 min, and the petri dishes were videotaped at 30 frames per second. The attraction index was then calculated for every 10 s time point as follows :The humidity-guided attraction behavior assay was modified from the hygrotaxis assayAttraction index = Number of flies above moist hole / Total number of flies56. Two male flies were introduced to a 60 mm petri dish for 6 h of desiccation and then videotaped at 30 frames per second for 2 min under the humidity conditions in which the flies were raised.The locomotor activity of the thirsty flies was assayed by using a previously established video-assisted tracking method57  to analyze the positions and movement trajectories of the flies during the assay. Then, the data were processed and plotted using custom programs and scripts in MATLAB based on generic codes from Plotly (https://plotly.com/matlab/2D-Histogram). The whole arena was divided into 20 ×20 pixels, and the total time that a fly spent on each pixel during 80–90 s after the onset of the assay was calculated as the cumulative time. Pixels with a cumulative time above 10 s suggest more than one fly staying in the same pixel.For quantification of the attraction index for distinct groups, an average index of the attraction indexes from the plateau stage was used. For spatial distribution analysis and locomotion analysis, flies were first tracked using Flytracker42. The room humidity was also kept at 50% to 60% RH with household humidifier or dehumidifier during experiments. Briefly, 3- to 5-day-old flies were mounted in a custom made stage using two component silica gel. The antennae were pushed toward with a thin sheet of plastic on top of the head. Then a small window was made in both the plastic sheet and the head capsule to allow access to the antennal lobes. A drop of Adult Hemolymph-Like Saline  was then added to cover the imaging window. For the humidity stimulation, airstreams from a 50 mL syringe were passed through either a gas washing bottle with desiccant or a gas washing bottle containing a water soaked sponge, generating a 24.66 ± 0.62% RH dry airstream or a 66.67 ± 0.19% RH humid airstream, respectively13. Humidity changes were achieved by alternating the two syringes producing the dry airstream or humid airstream. Odors were obtained from filter paper with the corresponding odor source in a gas bottle as previously described42. The required dilution of ACV in water or ethyl propionate in mineral oil was used as the odor stimulus. The airflow rate was ~500 mL/min. We used flies carrying Nan-Gal4 (ref. 36), an alternative driver for the Or42b neurons, to express GCaMP6 and RFP for calcium imaging. Images were acquired with FV10-ASW 4.2 software from an Olympus FV1200 confocal microscope equipped with a 10x water-immersion objective. GCaMP and red fluorescent proteins (as references) were excited by a 473-nm and a 559-nm laser, respectively. The average GCaMP signals from the first 3 s before the introduction of the odorant stimulus or a humidity change were taken as F0, and ΔF/F0 was calculated for each data point.Antennal lobe calcium imaging was performed at room temperature (25 °C) as described previouslyOr42b-Gal4 and UAS-CD4-tdGFP were used to perform confocal microscopy imaging. Acutely isolated antennae were mounted on a slide using double-sided tape (Scotch 3 M). A coverslip was placed onto the slide using spacers so that a thin tube (~0.2 mm diameter) can be inserted for the humidity stimulation. Humidity stimulation was applied as described in calcium imaging experiments. The room temperature was set at 25 °C and room humidity was set to ~50% RH. Images were obtained at the region of the GFP-positive sensillum with FV10-ASW 4.2 software on an Olympus FV1200 confocal microscope equipped with a 60x water-immersion lens at ~2 Hz.One day old adult flies carrying 58 according to the documentation on Github (https://github.com/brouhardlab/Kappa). We first aligned the image stack using the StackReg plugin of Fiji, only the image stacks in which the sensilla base displayed no movement were opened in Kappa. The time point in which the dry air was converted to moist air was defined as 0 s. We chose the images at time points −5 s, 2.5 s and 10 s for the next analysis. In Kappa plugin, five points that track the midline of the sensilla were first manually defined to make an initial curve. We then adjusted parameters such as the color channel, the brightness threshold, and the distance from the curve to make all GFP signals or bright pixels within the sensilla contour selected. Next, we fitted the initial curve to the chosen pixels by the least square algorithm in Kappa. After setting the scale factor (μm/pixel) of the images, we exported the average radius of curvature of the fitted curve for statistical analysis.The curvature analysis is conducted using the Kappa plugin of Fiji12. Male 2- to 5-day-old flies were dissected in Adult Hemolymph-Like Saline, and the 3rd antennal segments were fixed in 4% PFA at room temperature for 5 min. Brains of 5- to 7-day-old flies were dissected in 1x PBS and fixed in 4% PFA at room temperature for 20 min. After three washes, the samples were blocked in block buffer  for 30 min at room temperature. The samples were then incubated with primary antibody at 4 °C overnight. On the second day, tissues were washed three times and incubated in secondary antibodies at room temperature for 2 h. All samples were mounted in Rapiclear 1.47 (SunJin Lab) for confocal microscopy (Olympus FV1200). The following antibodies were used: rabbit anti-GFP , mouse anti-nc82 , donkey anti-rabbit Alexa488 , donkey anti-mouse Alexa647 .Whole-mount antennal staining was performed as previously describedDrosophila S2 cell staining, we used a PCR-based approach to introduce the in-frame fused myc-tag (EQKLISEEDL) behind the F21 or G724 site of DmTMEM63. The pUAST-21myc-Tmem63-mCherry or pUAST-724myc-Tmem63-mCherry construct was cotransfected with pActin-Gal4. 24-36 h after transfection, cells were plated onto ConA-coated coverslips for staining.For Drosophila Medium and incubated with transfected cells for 30 min at 25 °C. After fixation with 4% PFA for 30 min at 4 °C, the cells were blocked for 30 min at room temperature and then incubated with the secondary antibody  for 30 min.For nonpermeabilized staining, the primary antibody  was diluted 1:200 in Schneider’s For permeabilized staining, cells were fixed and incubated with PBST (PBS + 0.1% Triton) for 10 min. Then the cells were blocked and stained with the primary and secondary antibodies.DmTMEM63-GFP or GFP empty vectors and incubated for 24-36 h before recording. Outside-out patch recordings of S2 cells were carried out at 25 °C under an Olympus BX51WI microscope equipped with a 40x water-immersion lens as described before59. Recordings were performed using borosilicate glass pipettes with resistances of around 10 MΩ. The pipette solution contained 140 mM potassium gluconic acid and 10 mM HEPES. The bath solution contained 140 mM NaMES (sodium methanesulfonate) and 10 mM HEPES. All solutions were adjusted to 320 mOsm and pH 7.2. Negative pressure was applied to the excised membrane patches using a HSPC device (ALA-scientific). The sample rate was 20 kHz and filtered at 1 kHz (low-pass). A multiclamp 200B amplifier, DIGIDATA 1550 A and Clampex 10.5 software (Molecular Devices) were used to acquire and process the data.S2 cells were transfected with Humidity was measured using an custom 2.5 × 2.5 mm probe equipped with SHT31 sensor and recorded with the SHT31 Smart Gadget (Sensirion). To monitor the humidity gradients formed in the humidity-guided attraction behavior assay, the arena was divided into 10 × 10 measurement points to allow the insertion of humidity sensor for monitoring the humidity distribution over a 120 s time period.t test, one-way ANOVA followed by Holm-Sidak or Dunnett’s post hoc test, or the two-way ANOVA with Sidak’s multiple comparison test. Post hoc power analyses were performed in PASS 15 (https://www.ncss.com/software/pass/) to ensure that statistical power >0.8 for all the significant differences. All data in bar and line graphs are presented as means ± SEM and the exact P values are displayed in the Figures.Experimental animals and genetic controls were tested at the same condition, and data were collected from at least three independent experiments. Statistical analysis were carried out in Prism 7 (GraphPad). Statistical methods used include two-tailed Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Reporting Summary


The use of graphene in surface plasmon resonance sensors, covering a metallic (plasmonic) film, has a number of demonstrated advantages, such as protecting the film against corrosion/oxidation and facilitating the introduction of functional groups for selective sensing. Recently, a number of works have claimed that few-layer graphene can also increase the sensitivity of the sensor. However, graphene was treated as an isotropic thin film, with an out-of-plane refractive index that is identical to the in-plane index. Here, we critically examine the role of single and few layers of graphene in the sensitivity enhancement of surface plasmon resonance sensors. Graphene is introduced over the metallic film via three different descriptions: as an atomic-thick two-dimensional sheet, as a thin effective isotropic material (same conductivity in the three coordinate directions), and as an non-isotropic layer . We find that only the isotropic layer model, which is known to be incorrect for the optical modeling of graphene, provides sizable sensitivity increases, while the other, more accurate, models lead to a negligible contribution to the sensitivity. Lately, much attention has been paid to the development of new chemical and biological sensors based on low cost and fast diagnosis ,2,3,4. SThe primary design feature of a SPR sensor is the sensitivity, which, depending on the interrogation method, is defined as the ratio between the shift of the SPR resonance angle or wavelength and the change in the refractive index of the analyte. An increased sensitivity translates into the possibility of the detection of analytes with low molecular weight and low concentrations . In thisp to 50% . These pp to 50% ,14,15, gp to 50% ,17, as wp to 50% ,19,20,21In all of the previous reports, the sensitivity of the N layer SPR sensor was calculated through the transfer matrix method, where each layer of graphene is modeled by an effective thickness. Note that this means that after the multiplication of the matrices of the individual layers, we end up with a thicker homogeneous sample, in opposition to the experimental evidence that showed that, in few-layer graphene, each monolayer is minimally perturbed by the presence of the other layers ,23. Withp-polarized light impinging on the prism at a certain angle larger than the critical angle of the prism–analyte interface. Thus, the SPR condition is easily calculated through the reflectance of the multilayer structure r23=ke being the electron charge and ℏ the reduced Planck constant.In this expression, ≈σ0=e24ℏ ,27, withn). The relation among all of them is well established: The refractive index of graphene in the 3D isotropic model is obtained from the surface conductivity ( and n=ε . The surg=σ3Ddg) . Thus, bGraphene’s effective thickness can be assumed to be the interlayer distance in graphite: =0.34 nm ,30,31,32From the experimental perspective, the optical constants of graphene were obtained after fitting processes of reflectance, transmittance and ellipsometry measurements ,34,35,36s- and p-polarized waves is zero. This ensures that the reflectance of the four-layer system with the anisotropic graphene continues to be calculated by Equation , the contribution of graphene is included in the Fresnel reflection coefficient of the gold/analyte interface (Equation ) throughThe effect of the different approaches used to model the graphene monolayer is presented in At first sight, the four models produce similar reflectance curves, with graphene only slightly modifying the response of the sensor. However, the zoom in the reflectance curves provided in However, the most important result of the present work is to quantify the sensitivity of the sensor , which is shown in iscussed , yields Having established that the way a single sheet of graphene is modeled affects the obtained  sensor sensitivity, we now investigate the sensitivity as a function of the number of graphene layers. To model more than one layer, previous works treating graphene as a thin film ,13,30,39le range ). Figurentegrals  and consIn essence, the problem of the isotropic model is that it assumes the electronic response of graphene to light to be independent of light’s electric field orientation. However, graphene’s out-of-plane conductivity significantly differs from the in-plane conductivity. As a matter of fact, since the early days of graphene isolation, it has been well known that the optical response of few-layer graphene in the visible range can be taken to be that of a stack of non-interacting 2D electron gas sheets . For exaIn this paper, we theoretically studied the influence of graphene modeling to the sensitivity obtained in a refractometer based on gold’s SPR. When graphene is modeled as a surface (2D model) or an anisotropic thin film (anisotropic 3D model) the system’s sensitivity is practically unaffected by the presence and number of layers of graphene. However, when graphene is modeled as an isotropic thin film (isotropic 3D model), a significant non-physical sensitivity increase is observed. Therefore, we suggest that graphene modeling through the 2D or 3D anisotropic models are more accurate alternatives, which must be adopted for the study of graphene-assisted SPR sensors. It is important to highlight that by no means are we suggesting that graphene cannot effectively improve the sensitivity or the performance of SPR sensors by other means, in the visible range, as recently reported ,46,47. O


This paper fabricated a high-performance chitin nanofibers (ChNFs)-integrated bulk acoustic wave (BAW) humidity sensor with an asymmetric electrode configuration. The ChNFs were successfully prepared from crab shells and used as moisture-sensitive materials to compare the performance of quartz crystal microbalance (QCM) humidity sensors with symmetric and asymmetric electrode structures. The QCM humidity sensor with a smaller electrode area exhibited high sensitivity of 58.84 Hz/%RH, competitive response/recovery time of 30/3.5 s, and low humidity hysteresis of 2.5% RH. However, it is necessary to choose a suitable electrode diameter to balance the stability and sensitivity because the impedance analysis result showed that the reduction of the electrode diameter leads to a sharp decrease in the Q value (stability). Next, the possible humidity-sensitive mechanism of the ChNFs-integrated asymmetric n-m electrode QCM humidity sensor was discussed in detail. Finally, the reasons for the highest sensitivity of the asymmetric n-m electrode QCM humidity sensors having a smaller electrode diameter were analyzed in detail in terms of both mass sensitivity and fringing field effect. This work not only demonstrates that the chitin nanofiber is an excellent potential material for moisture detection, but also provides a new perspective for designing high-performance QCM humidity sensors. The growing demand for precise humidity detection in many fields such as agricultural farming, medical monitoring, weather forecasting, and industrial manufacturing has contributed to the further development of humidity sensors ,2,3. AmoIn general, a QCM humidity sensor consists of a quartz crystal resonator (QCR) and a humidity-sensing material deposited on its surface, as the bare QCR lacks selectivity for the target detection . It is wAsymmetric electrode QCM sensors are also sensitive to the changes in electrical properties, such as conductivity and relative permittivity, because the upper surface within its partially electroded region is not shielded by the metal electrode, resulting in an enhanced fringing field effect ,15,16,17For QCM humidity sensors, the electrical parameters of the humidity-sensitive materials also change when adsorbing or desorbing water molecules, and this change affects the resonance state of the asymmetric electrode QCM humidity sensors, which in turn enhances their sensitivity. In 2021, Chen et al. proposed an asymmetric QCM humidity sensor with a ring electrode configuration . Using lChitin is a rich natural polymeric polysaccharide, which is the main structural component of crustacean shells. Chitin nanofibers treated by hydrolysis and ultrasonication have excellent properties such as low bulk density, large surface area, good chemical reactivity, low toxicity, biodegradability, biocompatibility, antioxidant activity, antibacterial properties, and excellent mechanical properties, which is well suited as moisture-sensitive materials ,22,23,24The experimental setup is schematically shown in The AT-cut, 6 MHz QCMs with different electrode structures were fabricated at Wuhan Hitrusty Electronics Co., Ltd. . The diameter of the quartz wafer was 8 mm. The symmetric and asymmetric electrode structures were formed by depositing circular electrodes of different diameters on both sides of the quartz wafer using a thermal evaporation method. The silver electrode on one side has a diameter of 5 mm, and for comparison purposes, the silver electrodes on the other side have diameters of 3, 3.5, and 5 mm, respectively, and were also labeled 3-5 QCM, 3.5-5 QCM, and 5-5 QCM, respectively. The equivalent circuit parameters of QCM with symmetric and asymmetric electrode structures were tested before depositing the ChNF films, as shown in 2SO4 solution (97.3% RH), KCl solution (84.3% RH), NaCl solution (75.3% RH), NaBr solution (57.6% RH), MgCl2 solution (32.8% RH), and LiCl solution (11.3% RH) at 25 The experimental equipment mainly consisted of humidity generation devices, a phase-locked oscillator , a vector network analyzer , and a personal computer (PC). The PLO and VNA were each linked to the PC for recording and evaluating the humidity sensing performance and resonance state of the ChNFs integrated QCM sensors. The fixed humidity levels were produced by saturated K−1 and 1557 cm−1 in the FTIR spectrum are two typical characteristic peaks of chitin, mainly attributed to the C=O stretching of amide I, and the combination of N-H bending vibrations with C-N and N-H vibrations in amide II, respectively [−1 and 3253 cm−1 are attributed to the O-H and N-H stretching vibrations, respectively, which are typical hydrophilic groups [The TEM image of the chitin nanofiber is displayed in ectively . Overallc groups ,25. Genec groups . Figure The Butterworth–Van Dyke (BVD) model illustrated in ceptance , as showAs shown in The dynamic responses of ChNF-3, ChNF-3.5, and ChNF-5 are in 2) of 0.9796, 0.9623, and 0.9812, respectively, like most published humidity sensors [The sensitivity for frequency-response humidity sensors is usually defined as the ratio of the frequency shift to the change in humidity levels. As shown in  sensors ,32,33,34The maximum frequency differences of ChNF-5, ChNF-3.5, and ChNF-3 are 29, 59, and 125 Hz, thus the humidity hysteresis of the sensors can be calculated as 2.0%, 2.1%, and 2.5 % RH at the humidity environment of 57.6% RH, respectively e. As sho2O molecules can be adsorbed on hydrophilic groups through hydrogen bonds. ChNF’s mass and viscosity will change as a result of the adsorption or desorption of H2O molecules, altering the Q value and operating frequency of the QCM. However, the electrical parameters of the ChNF film also change, thus affecting the fringing field distribution, and thereby changing the resonant state of the QCM.+ ions in the hydrophobic functional groups of ChNF combine with a tiny number of H2O molecules to create H3O+. As the humidity increases, ChNF adsorbs more water molecules, resulting in the formation of multilayer water molecules. At this stage, the sensitive film’s mass and viscosity start to rise even more, which lowers the resonance frequency and Q value of the QCM humidity sensors. Additionally, the electric field will cause the water molecules to become polarized, creating a significant amount of H3O+ ions [+, which also greatly enhances the ionic conduction of ChNF films.Monolayer water molecules are adsorbed via chemical bonding on the surface of ChNF at low relative humidity. Additionally, the frequency and Q value of QCM decrease insignificantly at this stage. The H3O+ ions . At the Since the asymmetric n-m electrode QCM humidity sensor has no metal electrode shading on the upper surface of the partially electroded area enhances its fringing field effect. As a result, frequency shifts associated with changes in the electrical characteristics of moisture-sensitive materials are created in addition to the frequency variations caused by mass and viscosity. Thus, the frequency variation of the asymmetric n-m electrode QCM humidity sensor contains three components, i.e., At first, for the asymmetric n-m electrode configuration, the partially electroded region is not shielded by the metal electrode because of the different electrode diameters at the top and bottom, which enhances its fringing field effect. The fringing field distributions of symmetric and asymmetric n-m electrode configurations were simulated using the finite element analysis method, and their potential and electric field distributions are given in For simplicity, 4 mm diameter, 500 nm-thick cylinders were placed in the center of QCMs to simulate the moisture-sensitive layer. In another aspect, the mass sensitivities of QCM determining equation ,56:(3)Sfectively . The u˜, P , and U (non-electroded region); h region .The mass sensitivity distributions of the 3-5 QCM, 3.5-5 QCM, and 5-5 QCM were calculated through the parameters of the AT-cut, 6 MHz QCM, as shown in Breath detection is beneficial in monitoring human signs and early medical diagnosis. In this paper, we investigate the potential application of the ChNF integrated QCM humidity sensor for respiratory detection. The sensor was placed 7 cm away from the nose and mouth for testing, and the results are shown in In summary, three QCM humidity sensors were fabricated by spraying chitin nanofibers on the surface of AT-cut, 6 MHz QCM transducers with different upper electrode diameters. The QCM humidity sensor having a smaller electrode area exhibited high sensitivity of 58.84 Hz/%RH, competitive response/recovery time of 30/3.5 s, and low humidity hysteresis of 2.5% RH. However, it is necessary to choose a suitable electrode diameter to balance the stability and sensitivity because the impedance analysis result showed that the reduction of the electrode diameter leads to a sharp decrease in the Q value of the QCM. Next, the possible humidity sensing mechanism of the ChNF-integrated asymmetric n-m electrode QCM humidity sensor was discussed. Since the asymmetric n-m electrode QCM humidity sensor has no metal electrode shading on the upper surface of the partially electroded area enhances its fringing field effect. The mass sensitivities of the 3-5 QCM, 3.5-5 QCM, and 5-5 QCM were calculated from the mass displacement amplitude, and it was found that the 3-5 QCM has the highest mass sensitivity. Therefore, frequency shifts associated with changes in the electrical characteristics of moisture-sensitive materials are created in addition to the frequency variations caused by mass and viscosity. Finally, we demonstrated the potential application of the ChNF integrated QCM humidity sensor through breath detection experiments. This work not only demonstrates that chitin nanofibers are an excellent potential material for moisture detection, but also provides a new perspective for designing high-performance QCM humidity sensors.


Current studies in extractive question answering (EQA) have modeled the single-span extraction setting, where a single answer span is a label to predict for a given question-passage pair. This setting is natural for general domain EQA as the majority of the questions in the general domain can be answered with a single span. Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps.In this article, we investigate the question distribution across the general and biomedical domains and discover biomedical questions are more likely to require list-type answers (multiple answers) than factoid-type answers (single answer). This necessitates the models capable of producing multiple answers for a question. Based on this preliminary study, we propose a sequence tagging approach for BioEQA, which is a multi-span extraction setting. Our approach directly tackles questions with a variable number of phrases as their answer and can learn to decide the number of answers for a question from training data. Our experimental results on the BioASQ 7b and 8b list-type questions outperformed the best-performing existing models without requiring post-processing steps.https://github.com/dmis-lab/SeqTagQA.Source codes and resources are freely available for download at Bioinformatics online. Extractive question answering (EQA) is the process of finding answers to questions from given passages. Biomedical Question Answering (BioQA) is a branch of QA where both the subject of the question and the passage has a biomedical context . EQA haslist questions  are more abundant in biomedical questions than in general domain questions.Despite recently increased interests in the field of BioQA, research on the characteristics of biomedical questions and corresponding passages is rare. As The single-span extraction setting, or ‘start-end span prediction’ setting, is common in existing general domain EQA and Biomedical EQA (BioEQA) models . We empirically verify that the sequence tagging approach is beneficial for answering list-type questions, as models are able to learn to tag multiple answers at once rather than being restricted to extract one answer span at a time. Furthermore, our setting is an end-to-end approach for list-type QA task that the model can learn to output the ideal number of answers for a given question, without using the rather complicated external processes of previous works. Adopting our sequence tagging strategy to BioEQA tasks showed that our model can achieve state-of-the-art (SOTA) performance for list-type questions. Average performance improvements over baseline model are 3.80% and 6.22% for BioASQ 7b and 8b List questions, respectively (F1 score).In this section, we briefly summarize previous works related to the questions in QA datasets and various modeling approaches for QA tasks.A number of general domain QA datasets are available as of today. SQuAD v1 and v2  are compIn comparison to the general domain, only a small number of QA datasets are currently publicly available for Biomedical NLP. The QA dataset of the BioASQ competition   is a series of datasets (https://www.nlm.nih.gov/databases/download/CQC.html). We select Iowa IC Questions as our source of questions as it is collected from the most diverse set of physicians among the CQC datasets. The Iowa IC Questions dataset contains 1,062 questions that were collected by observing 48 physicians for a half-day.datasets  that arePubMed queries We use the PubMed Query Log Dataset collected by naturally posed questions by PubMed users collected over one day.  dataset as a question dataset in the general domain setting.Corresponding passages or answers are not provided for both biomedical question datasets as they are not originally built for QA tasks. We use the n = 100; 50 for each biomedical question dataset) manually. For general domain questions, we counted questions using the given answers of the NQ dataset. We classify a set of randomly chosen biomedical questions (Our study shows that the proportion of factoid questions (i.e. questions which can be answered with a single phrase) is lower in biomedical questions than in questions in the general domain. In other words, the number of list-type questions among EQA questions, specifically factoid and list-type questions, is significantly higher in the biomedical context than in the general context. This finding verifies our hypothesis that biomedical questions have a tendency to require multiple answers.While annotating the types of questions, we observed that factoid questions often have alternative valid answer spans. In most cases, they are varied forms of the originally annotated answer, such as synonyms or abbreviations. The prevalence of synonyms and abbreviations for biomedical entities  are language representation models that are pre-trained on biomedical literature such as articles from PubMed and PubMed Central. They are the primary building blocks of multiple BioNLP tasks, such as NER, RE and QA tasks, and provide contextualized representations of the input sequences.In our experiments, BioBERT   and BiLSFollowing NER tasks, we used the BIO tag schema to denote answer spans, where B stands for the ‘beginning’ of an answer span, I for ‘inside’ and O for ‘outside’.broken sub-word tokens.When calculating loss and gradients, we exclude tags predicted at the positions of special tokens and question tokens: this means that the model will not be penalized for predicting wrong tags for these tokens. Tokens that we ignored predictions are as follow: [SEP], [CLS], tokens that compose questions and 4.2.3detokenize the output of the final layer to restore tokenized strings into their original forms, including reconstruction of sub-word tokens and proper removal of whitespace around punctuation marks. Other essential task-specific post-processing steps are taken to fit our output to the official evaluation scripts for the datasets. For example, answer candidates of list questions of BioASQ can be prepared by simple concatenation of predictions of the passages that belong to a question. For the approach where BiLSTM-CRF layer is used as the Sequence Tagging Layer, we choose answer candidates by using the Viterbi algorithm, and rank candidates based on the probability scores of the LSTM network.We apply minimal post-processing steps to the output of a tagged sequence. The output of the final layer (i.e. the sequence tagging layer) is a sequence of tokenized words and their corresponding tags. The main purpose of the post-processing step is to In this section, we first elaborate on our experimental settings. For the rest of the section, we report our experimental results and comparisons between the models with different settings.The BioASQ datasets are directly derived from the BioASQ Challenge which is an annual competition for biomedical semantic indexing and QA . Following A single sample contained in the BioASQ datasets contains the question and URL of relevant articles and snippets  as a sample for training and testing. A triplet contains only one item for a question and a passage element. The number of items for an answer element in a triplet is only one for single-span extraction configuration, while for a sequence-tagging configuration, an answer element can contain multiple items. Specifically, for a list-type question Q with a set of relevant passages i for a sequence-tagging configuration. For the training dataset, we excluded passages from which we could not find any matching answers. We defer details regarding pre-processing to the Statistics of list-type questions and data points for the BioASQ datasets are described in http://participants-area.bioasq.org/Tasks/b/eval_meas_2018; https://github.com/BioASQ/Evaluation-Measures). Each example of list-type questions is evaluated based on the precision, recall and F-1 measures. The scores are averaged over the list-type questions and reported as the final score for the system.The official evaluation metrics for BioASQ datasets differ according to the question type . We used a single NVIDIA Titan RTX (24GB) GPU for fine-tuning (single run) and the training process took less than 24 h for an experiment.We used In this section, we report the experimental results and verify that our approach to reformulate QA as sequence tagging improves the performance of existing QA models on list-type questions.5.4.1number of answers detection from question strings for such questions containing a number. On top of the previous model, Our baseline model from The original models of 5.4.2Our experimental results using the fully connected linear layer as Sequence Tagging Layers are displayed in 5.5Previous research papers have shown that the application of Recurrent Neural Networks (RNN), such as LSTM, and CRF layer can boost the accuracy of popular sequence tagging tasks, such as NER or part-of-speech tagging . The number of answers is most frequent in the first bin  for the baseline, but in our model and the testing dataset, the second bin  was the most frequent, providing circumstantial evidence that our model can learn the proper number of answers for a given question.number of answers detection, to handle list-type questions. For example, in order to answer the question ‘What 2 biological processes are regulated by STAMP2 in adipocytes?’, the model detects any number in the question, two (2), and outputs answers as many as the detected number. However, models with number of answers detection are susceptible to the proportion of questions with answers that have numbers. For the BioASQ 8b testing dataset, the proportion is nearly half than that of BioASQ 7b . Average performance improvements over baseline model (btac397_Supplementary_DataClick here for additional data file.


The difficulties in social interaction present in individuals with autism spectrum conditions may are related with the abnormal attentional processing of emotional information. Specifically, it has been hypothesized that the hypersensibility to threat shown by individuals with autism may explain an avoidance behaviour. However, this hypothesis is not supported by research and the underlying psychological mechanisms of social interaction in autism still unclear.The aim of the present study was to examine attentional processing biases by administering a computer-based attentional task in a sample of 27 children with autism spectrum conditions and 25 typically developed participants (age 11-15 years).The initial orienting of attention, the attention al engagement, and the attentional maintenance to different emotional scenes in competition  were measured by recording the eye movements during a 20 seconds free-viewingtask.The main findings were: i) children with autism spectrum conditions showed an initial orientating bias towards threatening stimuli; and ii) while typically developed children revealed an attentional engagement and attentional maintenance bias towards threatening stimuli, children with autism spectrum conditions did not.The findings of the present study are consistent with the affective information processing theories and shed light on the underlying mechanisms of social disturbances in autism spectrum conditions.


Hydrollbot, harnesses energy from evaporation for spontaneous and continuous fast self-rolling locomotion with a programmable trajectory in a constant-humidity environment. Moreover, the geometric parameters of the film were fine-tuned to maximize the rolling speed, and the optimised hydrollbot is capable of carrying a payload up to 100% of its own weight. The ability to self-propel fast under constant conditions with programmable trajectories will confer practical advantages to this robot in the applications for sensors, medical robots, actuation, etc.A range of soft robotic systems have recently been developed that use soft, flexible materials and respond to environmental stimulus. The greatest challenge in their design is the integration of the actuator, energy sources, and body of robots while achieving fast locomotion and well-defined programmable trajectories. This work presents such a design that operates under constant conditions without the need for an externally modulated stimulus. By using a humidity-sensitive agarose film and overcoming the isotropic and random bending of the film, the robot, which we call the  Many kinds of artificial materials have been developed to respond to light –7, heat Hydrollbot. Meanwhile, we experimentally optimize the geometric design and adjust humidity to achieve the maximum rolling speed with programmed trajectories.To address this challenge, we focus on humidity-responsive materials, which can convert the energy from ambient evaporation into mechanical energy and thus have attracted increasing attentions to be used in humidity-driven actuators. Such materials include polymer gels –37, elasAgarose film contains abundant hydroxyl groups and is therefore very sensitive to moisture. It can rapidly absorb water under high humidity and desorb water under low humidity. As a result, it undergoes deformation and can aid in motion in very small humidity gradients . MeanwhiL) of 18 mm , width (w) of 5 mm, and thickness (t) of 10 μm, the total weight of the robot is 6.8 mg  · lQ > (mBP g) · lP, which causes the robot to roll to the end Q , which the film length uncovered by the tapes. For t = 10 μm and RH ≈ 60%, when L ≤ 18 mm with Lt = 3 mm, we observed no reverse rolling. When L = 21 mm still with Lt = 3 mm, the probability of reverse rolling increased to 25% , the substrate humidity, and the material properties.L = 18 mm, Lt = 3 mm, and RH ≈ 60% and varied t from 10 to 30 μm. The cycle duration, T, and robot speed, v ( = L/T), were measured for continuous 4-8 cycles, which was decided by the robot length and substrate length. The duration of the rolling cycle increased with film thickness  by adjusting the water temperature between 23 and 61°C in the vessel. Note that the humidity could only be controlled within a range of ±2%. The effect of humidity on the rolling locomotion is shown in t, L, and/or Lt are changed, the humidity limits will change accordingly. For example, when t is 30 μm, L = 35 mm, and Lt = 7 mm, the ideal RH is in the range of 33% to 70% with 70% humidity offering the highest speed. All the experiment data for Figures Third, apart from the dimensional parameters of the Hydrollbot, humidity strongly influences the locomotion. To investigate the effect of humidity, we set Fourth, to find out the effect of material property on the locomotion speed, we built a simplified analytical model to estimate the locomotion speed of the robot using energy method based on experimental observation. It was assumed in the model that the robot absorbed energy from the humid environment at a constant rate, which was used to bend the robot and overcome its gravity. Then, by equating the humidity energy absorption of the robot to the summation of bending energy gain and gravitational potential energy variation, the locomotion cycle was derived Note . It shout = 10 μm, L = 18 mm, Lt = 3 mm, and RH = 70%, the maximum speed reaches 0.714 BL/s. The locomotion comparison between this optimized robot and the one with randomly selected parameters  is presented in The analytical results drawn as black lines in Figures Meanwhile, the loading-carrying capability of Hydrollbot has been demonstrated in Movie A unique feature of Hydrollbot is that its trajectory can be programmed simply by changing the robot geometry. Because the agarose film is isotropic, we can change the rolling direction by designing the shape of the film and the positions of the feet attached to its short sides; additionally, we can control the bending direction of the film by adjusting the relative positions of the PET strips. Four representative example geometries and their corresponding trajectories are shown in Figures Our humidity-responsive soft robot can roll on a humid surface without any additional energy input. Unlike existing robots of this kind, which require an environment with actively controlled variable humidity to move, the rolling locomotion of the Hydrollbot is powered by the inherent difference in humidity between two sides of the robot body when operating on a constant-humidity substrate. The optimized rolling speed is 0.714 BL/s, which is two magnitudes faster than current reported state-of-the-art humidity-responsive soft robots that move in constant environments. Another advantage of the Hydrollbot is that in addition to performing straight-line locomotion, it can be programmed to move in curved, zigzag, or other predefined trajectories by simply altering its geometry. We expect that these features will greatly advance the applications of humidity-driven soft robots in medical treatment, sensing, actuation, and so on. Future research will focus on the design of more advanced Hydrollbot structures that can autonomously perform complex locomotion paths. Moreover, incorporation of new materials may enable the Hydrollbot responsive to multiple stimuli and with more intelligent and reliable performance.Agarose films were prepared according to the following procedure . 6.0 g ot ≤ 20 μm), it is not necessary to attach the PET strips on the area with tapes, which is P1 and Pn marked as dash line on Firstly, agarose film was cut into rectangle by a laser cutter. The width of the rectangle was fixed to 5 mm to avoid sideway rolling, while the length was tuned to achieve optimized performance. Then, tapes with specific size were attached to the two ends of the film. As shown in t = 10 μm, the PET strips allocated on the area of tapes were not attached, which is demonstrated as dash line in To prepare robot with zigzag trajectory, the film was cut into parallelogram and only 3 PET strips were attached parallel to the two short sides. To prepare robot with polygonal trajectory, the film was cut into trapezoid and 3 PET strips equally divided the angle between the two oblique sides. To prepare robot with circular trajectory, the film shape was changed into sector and 4 PET strips equally divided the sector angle. The robot with flower-shaped trajectory was developed by changing the film formed by two arches with different centers and radii. 4 PET strips equally divided the angle between the two short sides. Because the film thickness φ15, GB/T1914-2007) above a vessel (1 liter) in an oil bath (Figure To evaluate the rolling properties of the robot, Hydrollbots of different sizes were placed on a filter paper (h Figure . The wat


In the brain, primary cilia are found on most, if not all, central neurons. The importance of neuronal cilia is underscored by the fact that human diseases caused by primary cilia dysfunction, which are known as ciliopathies, are associated with neuropathologies, including neuropsychiatric disorders and learning and memory deficits. Neuronal cilia are enriched for certain G protein-coupled receptors and their downstream effectors, suggesting they sense and respond to neuromodulators in the extracellular milieu. GPCR ciliary localization is disrupted in neurons from mouse models of the ciliopathy Bardet-Biedl syndrome, with GPCRs failing to localize to cilia, indicating the Bardet-Biedl syndrome proteins are required for trafficking of G protein-coupled receptors into neuronal cilia. Yet, dopamine receptor 1 accumulates in cilia in the absence of Bardet-Biedl syndrome proteins, suggesting Bardet-Biedl syndrome proteins are required for normal ciliary import and export. To further explore the roles of the Bardet-Biedl syndrome proteins in neuronal cilia, we examined localization of ciliary signaling proteins in a new constitutive Bbs1 knockout mouse model. Interestingly, we find that two additional ciliary G protein-coupled receptors (Gpr161 and Gpr19) abnormally accumulate in cilia on Bardet-Biedl syndrome neurons. In addition, we find that the GPCR signaling protein β-arrestin accumulates in a subset of cilia in the brain, suggesting the presence of additional unidentified ciliary G protein-coupled receptors. These results confirm the importance of the Bardet-Biedl syndrome proteins in establishing ciliary GPCR pathways and indicate that loss of Bbs1 leads to complex changes in the localization of signaling proteins in the brain. Primary cilia are signaling organelles that mediate specialized signal transduction pathways critical for normal development and cellular homeostasis . These pCiliopathies are associated with neuropathologies, such as hyperphagia-induced obesity, neuropsychiatric disorders, and learning and memory deficits . CertainGPCR localization to neuronal cilia is dynamic and regulated by environmental cues. For example, D1 ciliary localization is increased in response to an increase in cyclic AMP levels and decreased in response to agonist treatment . NPY2 anPrevious studies characterizing the impact of Bbs1 loss in mice utilized a model where the most common human BBS mutation (M390R) was knocked into the mouse gene . AlthougBbs1 mRNA is lost following recombination (Bbs1+/−) to generate wildtype (Bbs1+/+) and constitutive knockout (Bbs1−/−) animals. To test whether loss of Bbs1 impacts ciliary GPCR localization in the brain, we colabeled brain sections from adult Bbs1+/+ and Bbs1−/− mice with antibodies against Sstr3, Mchr1, or D1 and an antibody against AC3, which serves as a marker for neuronal cilia   . The len < 0.05) , indicat < 0.05) . NeverthBbs1+/+ and Bbs1−/− mice with antibodies against Gpr161 and AC3. In Bbs1+/+ sections Gpr161-positive cilia were barely detectable    . Similar < 0.01) . Interessections , which min vivo findings, we colabeled cultured Bbs1+/+ and Bbs1−/− hippocampal neurons with antibodies against Gpr161 and AC3. In Bbs1+/+ cultures, we rarely observed Gpr161-positive cilia. The proportion of Gpr161-positive cilia we observed was significantly lower than what has been previously published    . However 0.0001) , providi1 allele . Gpr161 1 allele .Bbs1+/+ and Bbs1−/− mice using our labeling procedures, we tested the impact of Bbs1 loss on Gpr19 ciliary localization by colabeling cultured Bbs1+/+ and Bbs1−/− hippocampal neurons with antibodies against Gpr19 and AC3. Similar to Gpr161, we rarely observed Gpr19-positive cilia in Bbs1+/+ cultures but observed abundant Gpr19-positive cilia in Bbs1−/− cultures (p = 0.1)    but a si 0.0001) . Thus, tBbs1+/+ and Bbs1−/− mice with antibodies against β-arrestin and AC3. We did not observe β-arrestin-positive cilia in Bbs1+/+ sections but observed abundant β-arrestin-positive cilia in Bbs1−/− sections  of cilia  allele has been previously described (Bbs1wt/null mice on a congenic background (C57BL/6J) were crossed with FVB/NJ mice from the Jackson Laboratory (#001800). Constitutive Bbs1null/null mice were generated by intercrossing Bbs1wt/null male and female mice on a mixed background. Fixed mouse brains were generated and processed as previously described . For each section, six to nine fields were imaged and the number of Sstr3-, Mchr1-, D1-, Gpr161-, arrestin-, and AC3-positive cilia was counted by an individual blind to the genotype. The results were expressed as the proportion of AC3-positive cilia that were positive for each GPCR or arrestin. To quantify relative ciliary AC3 levels, fluorescence intensity of ciliary AC3 in D1-positive and -negative cilia and Gpr161-positive and -negative cilia were measured in ImageJ. ROIs were drawn around individual cilia and the mean fluorescence intensity of the cilium compared to background was calculated. The fluorescence intensity of AC3 in D1-and Gpr161-positive cilia were averaged and compared to the average fluorescence intensity of AC3 in D1-and Gpr161-negative cilia, respectively. Quantification of ciliary lengths was measured in ImageJ by drawing lines calibrated to the scale bar along the length of AC3-positive cilia. Quantification of Gpr161-and Gpr19-positive cilia in cultured neurons was performed on three coverslips from three animals for each condition and genotype. For each coverslip at least six fields were imaged. The number of nuclei, Gpr161-or Gpr19-positive cilia, and AC3-positive cilia was counted by an individual blind to the genotype. The results were expressed as the proportion of AC3-positive cilia to nuclei and the proportion of Gpr161-or Gpr19-positive cilia to AC3-positive cilia. Data were analyzed using Prism nine software (GraphPad Software) and results were expressed as mean ± SEM. Statistical significance was tested using Student’s t-test.Quantification of cilia in brain slices was performed using coronal sections from 5–7-week-old


R between 0.896 and 0.998). Used together, these reverse descriptors are graphed in relation to their response to graphene. Based on this study’s findings, it is possible to predict the exchange correlation energy as well as the geometric structures of graphene sheets with very little computational cost.Over the past few years, the popularity of graphene as a potential 2D material has increased since graphene-based materials have applications in a variety of fields, including medicine, engineering, energy, and the environment. A large number of graphene sheets as well as an understanding of graphene’s structural hierarchy are critical to the development of graphene-based materials. For a variety of purposes, it is essential to understand the fundamental structural properties of graphene. Molecular descriptors were used in this study to investigate graphene sheets’ structural behaviour. Based on our findings, reverse degree-based molecular descriptors can significantly affect the exchange-correlation energy prediction. For the exchange-correlation energy of graphene sheets, a linear regression analysis was conducted using the reverse general inverse sum indeg descriptor,  Carbon is a widely studied and influential element across many scientific disciplines. Many allotropes of carbon exist, each with special properties, such as graphite, diamond, and amorphous carbon as well as fullerenes, carbon nanotubes (CNTs), and graphene ,3,4,5,6.A nanostructure is composed of distinct and measurable elements, known as nano-patterns. In contrast to random patterns, these patterns follow the order of chemical and physical laws. Physical and chemical laws determine how atoms and molecules form discrete and measurable geometric structures, ranging from repeating lattices to complex shapes. Rules from the chemical graph theory can be used to analyze and predict the properties of these well-defined structures . In the In this article, molecular graphs are represented by e degree  of a verTo derive a set of reverse degree-based topological descriptors, we first define the reverse general inverse sum indeg descriptor, denoted by Our main objective is to offer an alternate method, with high accuracy, for computing the exchange-correlation energies of graphene sheets. The DFT calculations of the exchange-correlation energies of graphene sheets have the advantage of being accurate, but they also have the disadvantage of being computationally expensive. Therefore, A wide range of molecular descriptors have been proposed in the current literature, but many of them show little evidence that they correlate with any of the physical or chemical properties of the chemical structure. This section highlights the inquiry that was undertaken to determine whether reverse general inverse sum indeg descriptors possess any predictive power and whether or not they should be used in any chemical applications. In order to achieve this, we selected ten graphene sheets from one cycle to ten cycles. The molecular structures of these graphene sheets are provided in C24 (ii)(iii)(iv)(v)(vi)(vii)(viii)(ix)(x)(xi)Case 2. When Applying the definition of the reverse general inverse sum indeg descriptor, Using Remark 2.(i)(ii)(iii)(iv)(v)(vi)(vii)(viii)(ix)(x)(xi)Case 3. Forthe reverse edge partition of the graphene containsedges, edges, andedges.Using the definition of the reverse general inverse sum indeg descriptor, Using Remark 3.(i)(ii)(iii)(iv)(v)(vi)(vii)(viii)(ix)(x)(xi)Case 4. Whenthe reverse edge partition of the graphene contained onlyedges, and by the definition of reverse general inverse sum indeg descriptor, Remark 4.(i)(ii)(iii)(iv)(v)(vi)In this case, we noted the following 11 reverse topological descriptors for the graphene as follows:l and k. As these values increase, the magnitude of all reverse descriptors also increases, and this can be visualized by the 3D graphical representation in In The regression models  derived The reverse sum−connectivity descriptor with The density functional theory (DFT) calculations of the electronic structure, such as the exchange-correlation energies of the graphene sheets, were precise; however, they were computationally expensive while the reverse topological descriptors models presented herein required minimal computations and provided high levels of accuracy.Analytical expressions of the reverse first and second Zagreb descriptor, reverse Randić descriptor, reverse sum−connectivity descriptor, reverse harmonic descriptor, reverse hyper Zagreb descriptor, reverse geometric−arithmetic descriptor, reverse inverse sum indeg descriptor, and reverse redefined first and third Zagreb descriptors have been obtained for graphene structures.Researchers who are trying to better understand the behaviour of graphene are likely to find the numerical values and graphical representations presented in this article helpful.In this paper, we presented a reverse general inverse sum inverse degree descriptor